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| author | Chunseok Lee <chunseok.lee@samsung.com> | 2021-08-23 13:25:15 +0900 |
|---|---|---|
| committer | Chunseok Lee <chunseok.lee@samsung.com> | 2021-08-23 13:25:15 +0900 |
| commit | f4cf19e579a19c5346ccb2aad55bfd251065e447 (patch) | |
| tree | 5d436b11f89be0e8a8289ea82b773da6402c1add | |
| parent | 589bb1db6db6784efe21b3fbbfbfdb79aaa5f14e (diff) | |
| download | nnfw-f4cf19e579a19c5346ccb2aad55bfd251065e447.tar.gz nnfw-f4cf19e579a19c5346ccb2aad55bfd251065e447.tar.bz2 nnfw-f4cf19e579a19c5346ccb2aad55bfd251065e447.zip | |
Imported Upstream version 1.17.0upstream/1.17.0submit/tizen/20210823.054833submit/tizen/20210823.045832submit/tizen/20210823.044411accepted/tizen/unified/20210823.124210
1130 files changed, 68667 insertions, 5446 deletions
diff --git a/.ahub/sam/advanced.cfg b/.ahub/sam/advanced.cfg new file mode 100644 index 000000000..4f8bf6420 --- /dev/null +++ b/.ahub/sam/advanced.cfg @@ -0,0 +1,2 @@ +[SamPolicy] +preset=CCD_FOR_OO diff --git a/.ahub/sam/exclude.txt b/.ahub/sam/exclude.txt new file mode 100644 index 000000000..c9ba5e084 --- /dev/null +++ b/.ahub/sam/exclude.txt @@ -0,0 +1,33 @@ +# External code: Android NN API +/ONE/compiler/ann-api/include/NeuralNetworks.h +/ONE/compiler/ann-ref + +# Eigen +/ONE/compiler/nnc/backends/soft_backend/code_snippets/eigen.def + +# Test codes +/ONE/tests + +# Flatbuffers generated +/ONE/runtime/onert/frontend/circle_schema/include/circle_schema_generated.h +/ONE/runtime/onert/frontend/tflite/src/tflite_schema_generated.h + +# External code: Android NN API +/ONE/runtime/nnapi-header/include/NeuralNetworks.h +/ONE/runtime/nnapi-header/include/NeuralNetworksExtensions.h + +# External code: Tensorflow lite +/ONE/runtime/libs/nnapi +/ONE/runtime/libs/profiling + +# External code: 3rd party +/ONE/runtime/3rdparty + +# External code: compute libraries +/ONE/compute + +# Experimental subprojects not for release +/ONE/runtime/contrib + +# Downloaded externals +/ONE/externals diff --git a/.ahub/tcchecker-tca/config.yaml b/.ahub/tcchecker-tca/config.yaml index a42e23645..9c0a8d881 100644 --- a/.ahub/tcchecker-tca/config.yaml +++ b/.ahub/tcchecker-tca/config.yaml @@ -1,36 +1,107 @@ version: 2 test: - - name: NN Runtime + - name: NN_Runtime testCaseLanguage: CPP testFW: GTEST testCaseFolder: - - ./compute/test/cker - - ./runtime/onert/core/src/backend/basic - - ./runtime/onert/frontend/nnapi - - ./runtime/onert/test/core/compiler - - ./runtime/onert/test/core/exec - - ./runtime/onert/test/core/interp - - ./runtime/onert/test/graph - - ./runtime/onert/test/graph/operand - - ./runtime/onert/test/graph/operation - - ./runtime/onert/test/graph/verifier - - ./runtime/onert/test/ir - - ./runtime/onert/test/util - - ./tests/nnapi/src - - ./tests/nnfw_api/src - - ./tests/tools/tflite_run/src + - /compute/test/cker + - /runtime/onert/core/src/backend/basic + - /runtime/onert/frontend/nnapi + - /runtime/onert/test/core/compiler + - /runtime/onert/test/core/exec + - /runtime/onert/test/core/interp + - /runtime/onert/test/graph + - /runtime/onert/test/graph/operand + - /runtime/onert/test/graph/operation + - /runtime/onert/test/graph/verifier + - /runtime/onert/test/ir + - /runtime/onert/test/util + - /tests/nnapi/src + - /tests/nnfw_api/src + - /tests/tools/tflite_run/src testFile: - extension: cpp any: true - extension: cc any: true - + - excludes : + - DepthwiseConv2D.cc + - ArgMinMax.cc + - AveragePool2D.cc + - Concat.cc + - DepthToSpace.cc + - DepthwiseConv2D.cc + - Fill.cc + - If.cc + - Pad.cc + - Reduce.cc + - ResizeBilinear.c + - Slice.cc + - Softmax.cc + - While.cc testCase: - condition: - functionName: starts: - TEST + - excludes : + - Verifier.dag_checker + - graph_operand_LayoutSet.layout_set_operators + - InterpExecutorTest.executeTwoStep + - InterpExecutorTest.execute + - InterpExecutorTest.setOutput + - InterpExecutorTest.create_empty + - InterpExecutorTest.setOutputForUnspecifiedDimensions + - InterpExecutorTest.setInputForUnspecifiedDimensions + - InterpExecutorTest.setInput + - InterpExecutorTest.create_simple + - ExecTime.structure + - ExecTime.roundtrip_ok + - SchedulerTest.branched_graph_profiling_mode + - SchedulerTestWithExecutorParam.straight_graph_known_exec_time + - SchedulerTestWithExecutorParam.branched_graph_known_exec_time + - TFLite_test_case.simple_test + - ExecInstance.simple + - ExecInstance.twoExecution + - ExecInstance.twoCompile + - ExecInstance.async + - ExecInstance.twoThreads + - graph_operand_usedef.usedef_test + - Graph.inputs_and_outputs + - nnfw_create_session.Test_001 + - nnfw_create_session.Negative_001 + - WICPlanner.claim_release_test + - BumpPlanner.claim_test + - Allocator.allocate_test + - FirstFitPlanner.claim_release_test + - graph_operation_setIO.operation_setIO_concat + - graph_operation_setIO.operation_setIO_conv + - ValidationTest.neg_prepare_001 + - ValidationTestOneOpModelLoaded.prepare_001 + - graph_OperandIndexSequence.replace + - graph_OperandIndexSequence.append + - MODEL.model_build + - graph_operation_Set.operation_test + - graph_operand_Set.set_test + - ValidationTestSessionCreated.neg_load_session_001 + - ValidationTestSessionCreated.load_session_001 + - ShapeInference.Pool2DNodeExplicit + - ShapeInference.Elementwise + - ShapeInference.Concat + - ShapeInference.Pool2DNodeSame + - ShapeInference.IncorrectElementwise + - ShapeInference.Conv2D + - ShapeInference.Pool2DNodeValid + - ShapeInference.FullyConnected + - ShapeInference.DepthwiseConv2D + - ObjectManager.non_const_iterate + - ObjectManager.const_iterate + - ObjectManager.emplace + - ObjectManager.remove_2 + - ObjectManager.remove_1 + - ObjectManager.push + - Index.index_test negativeTestCase: - condition: @@ -41,3 +112,455 @@ test: positiveTestCase: - condition: - inverse: negativeTestCase + + - name: NN_Compiler + testCaseLanguage: CPP + testFW: GTEST + testCaseFolder: + - /compiler/angkor + - /compiler/arser + - /compiler/circle2circle + - /compiler/circle-quantizer + - /compiler/circle-tensordump + - /compiler/circlechef + - /compiler/circledump + - /compiler/crew + - /compiler/cwrap + - /compiler/foder + - /compiler/hermes + - /compiler/hermes-std + - /compiler/loco + - /compiler/locomotiv + - /compiler/locop + - /compiler/logo + - /compiler/logo-core + - /compiler/luci + - /compiler/luci-interpreter + - /compiler/luci-eval-driver + - /compiler/luci-pass-value-test + - /compiler/luci-value-test + - /compiler/mio-circle + - /compiler/mio-tflite + - /compiler/oops + - /compiler/pepper-assert + - /compiler/pepper-str + - /compiler/pepper-strcast + - /compiler/pp + - /compiler/record-minmax + - /compiler/safemain + - /compiler/souschef + - /compiler/tflchef + - /compiler/tflite2circle + - /compiler/vconone + + testFile: + - extension: .test.cpp + any: true + + testCase: + - condition: + - functionName: + starts: + - TEST + - excludes : + - ConstantFolding.const_relu_to_const + - ConstantFolding.const_relu_to_concat + - ADT_TENSOR_OVERLAY.access + - ADT_TENSOR_OVERLAY.ctor + - ADT_TENSOR_OVERLAY.read + - NodeExecution_BiasEncode.f32 + - NodeExecution_BiasEncode.s32 + - NodeExecution_EltwiseDiv.f32 + - CircleLogicalOrTest.constructor_P + - NodeExecution_TensorConcat.f32_2 + - NodeExecution_TensorConcat.f32 + - CircleShapeInferenceRuleTest.avgpool2d_valid + - CircleShapeInferenceRuleTest.TFAdd_shapeinf_different + - CircleShapeInferenceRuleTest.minimal_with_CircleRelu + - CircleShapeInferenceRuleTest.CircleTranspose_simple + - CircleShapeInferenceRuleTest.avgpool2d_same + - CircleConv2Dest.constructor_P + - ADT_TENSOR_BUFFER.access + - ADT_TENSOR_BUFFER.ctor + - CircleRelu6Test.constructor_P + - Circle2CircleTest.NoArg_NEG + - CircleInstanceNormTest.constructor + - ADT_KERNEL_INDEX_ENUMERATOR.iterate_full_range + - ADT_TENSOR_INDEX_ENUMERATOR.iterate_full_range + - CirclePadTest.constructor_P + - ADT_KERNEL_KERNEL_NHWC_LAYOUT.n_increment + - ADT_KERNEL_KERNEL_NHWC_LAYOUT.col_increment + - ADT_KERNEL_KERNEL_NHWC_LAYOUT.ch_increment + - ADT_KERNEL_KERNEL_NHWC_LAYOUT.row_increment + - ADT_TENSOR_LEXICAL_LAYOUT.last + - ADT_TENSOR_LEXICAL_LAYOUT.lexical_first + - ADT_TENSOR_LEXICAL_LAYOUT.lexical_middle + - FeatureShapeTest.settet_and_getter + - FeatureShapeTest.default_constructor + - INDENTED_STRING_BUILDER.usage + - NodeExecution_Fixed_Reduce_Mean.f32_1 + - NodeExecution_Fixed_Reduce_Mean.f32_0 + - CircleAbsTest.constructor + - CircleMaximumTest.constructor_P + - FORMAT.simple_string + - FORMAT.concat_rvalue + - FORMAT.concat_lvalue + - FORMAT.simple_number + - ADT_KERNEL_BUFFER.ctor + - ADT_KERNEL_BUFFER.access + - ADT_TENSOR_SHAPE.num_elements_rank_0 + - ADT_TENSOR_SHAPE.squeeze_neg_0 + - ADT_TENSOR_SHAPE.num_elements_zero + - ADT_TENSOR_SHAPE.copy + - ADT_TENSOR_SHAPE.eq_negative_on_unmatched_dim + - ADT_TENSOR_SHAPE.num_elements_nulldim + - ADT_TENSOR_SHAPE.eq_positive + - ADT_TENSOR_SHAPE.squeeze_pos + - ADT_TENSOR_SHAPE.resize + - ADT_TENSOR_SHAPE.ctor_initializer_list + - ADT_TENSOR_SHAPE.squeeze_neg + - ADT_TENSOR_SHAPE.squeeze_nested + - ADT_TENSOR_SHAPE.num_elements_nonzero + - ADT_TENSOR_SHAPE.eq_negative_on_unmatched_rank + - ADT_TENSOR_SHAPE.dim + - ADT_TENSOR_SHAPE.ctor + - GraphBuilderTest.Usecase_000 + - QueueTest.take + - MultiDialectShapeInferenceRuleTest.test1 + - AlgorithmTest.postorder_traversal_incomplte_graph + - AlgorithmTest.active_nodes + - AlgorithmTest.postorder_traversal_visit_once + - AlgorithmTest.postorder_traversal + - CircleSquaredDifferenceTest.constructor_P + - NodeShapeTest.feature_shape_constructor + - NodeShapeTest.filter_shape_constructor + - NodeShapeTest.default_constructor + - NodeShapeTest.copy_constructible + - NodeShapeTest.tensor_shape_constructor + - NodeShapeTest.dwfilter_shape_constructor + - NodeShapeTest.bias_shape_constructor + - ADT_KERNEL_KERNEL_NCHW_LAYOUT.n_increment + - ADT_KERNEL_KERNEL_NCHW_LAYOUT.col_increment + - ADT_KERNEL_KERNEL_NCHW_LAYOUT.row_increment + - ADT_KERNEL_KERNEL_NCHW_LAYOUT.ch_increment + - CircleEqualTest.constructor_P + - VerifierTest.valid_error_reporter + - VerifierTest.valid_minimal + - DataTypeTraitsTest.FLOAT32 + - NodeExecution_EltwiseSub.f32 + - NodeExecution_FeatureCodec.s32 + - NodeExecution_FeatureCodec.f32 + - ADT_TENSOR_INDEX.operator_add + - ADT_TENSOR_INDEX.ctor_initializer_list + - ADT_TENSOR_INDEX.fill + - ADT_TENSOR_INDEX.operator_eqaul + - ADT_TENSOR_INDEX.resize + - ADT_TENSOR_INDEX.operator_add_different_size + - ADT_TENSOR_INDEX.at + - ADT_TENSOR_INDEX.ctor + - ADT_TENSOR_INDEX.copy + - ADT_KERNEL_OVERLAY.access + - ADT_KERNEL_OVERLAY.read + - ADT_KERNEL_OVERLAY.ctor + - BiasShapeTest.default_constructor + - FildesTest.destructor + - FildesTest.value_constructor + - FildesTest.move_constructor + - FildesTest.default_constructor + - CircleGatherTest.constructor + - LinearV1FormatterTest.node_summary_builder_composition + - LinearV1FormatterTest.user_defined_node_summary_builder + - LinearV1FormatterTest.simple + - SourceTest.construct + - SourceTest.macro + - CircleFullyConnectedTest.constructor + - ADT_FEATURE_OVERLAY.read + - ADT_FEATURE_OVERLAY.access + - ADT_FEATURE_OVERLAY.ctor + - ContextTest.constructor + - CircleDivTest.constructor_P + - NodeExecution_Reshape.f32 + - MultiDialectTypeInferenceRuleTest.test1 + - CanonicalTypeInferenceRuleTest.relu6 + - TypeInferenceTest.framework + - CanonicalTypeInferenceRuleTest.tensor_broadcast + - CanonicalTypeInferenceRuleTest.minimal + - PermutingDecoderTest.feature + - PemutationTest.feature + - PermutingEncoderTest.depthwisefilter_init + - PermutingDecoderTest.filter + - PermutingEncoderTest.depthwise_filter + - PermutingEncoderTest.filter + - PermutingEncoderTest.feature_clone + - PermutingEncoderTest.feature + - PermutingDecoderTest.depthwise_filter + - PemutationTest.depthwise_filter + - PermutingDecoderTest.feature_clone + - PemutationTest.filter + - PadTest.default_constructor_2D + - NodeDomain.as_annotation + - CirclePackTest.constructor + - ADT_TENSOR_LAYOUT.move + - ADT_TENSOR_LAYOUT.ctor + - ADT_TENSOR_LAYOUT.copy + - DepthwiseFilterShapeTest.settet_and_getter + - DepthwiseFilterShapeTest.default_constructor + - CircleTypeInferenceRuleTest.minimal_with_CircleRelu + - GenericNodeSummaryBuilderTest.simple + - LogoPassTests.pass_name_over_unnamed_pass + - LogoPassTests.pass_name_over_named_pass + - CircleReluTest.constructor_P + - PaddingNDTest.default_constructor_ND + - TensorShapeTest.copy + - TensorShapeTest.rank + - TensorShapeTest.element_count + - TensorShapeTest.dim + - TensorShapeTest.rank_update + - TensorShapeTest.default_constructor + - TensorShapeTest.initializer_list_constructor + - DepthwiseFilterIndexTest.settet_and_getter + - DepthwiseFilterIndexTest.default_constructor + - MemoryTest.make_unique + - AnnotatedItemTest.annotation + - NodeExecution_DepthwiseFilterEncode.f32 + - CircleBatchToSpaceNDTest.constructor + - WindowTest.setter_and_getter_2D + - WindowTest.default_constructor_2D + - NodeExecution_Tanh.f32 + - MessageBufferTest.pass_constructed_message_on_descturction + - NodeExecution_TensorBroadcast.f32 + - CircleSubTest.constructor_P + - NodeExecution_AvgPool2D.f32_1x3x3x1_calculation + - NodeExecution_AvgPool2D.f32_1x4x4x1_calculation + - NodeExecution_Conv2D.f32_multiple_channel + - NodeExecution_Conv2D.f32_1x5x5x1_calculation + - NodeExecution_Conv2D.with_padding + - ADT_FEATURE_HWC_LAYOUT.W_increase + - ADT_FEATURE_HWC_LAYOUT.C_increase + - ADT_FEATURE_HWC_LAYOUT.H_increase + - SimplifyDomainConversionPass.FilterEncode_FilterDecode_equal_perms + - SimplifyDomainConversionPass.FilterEncode_FilterDecode_different_perms + - CircleDialectTest.get_N + - CircleDialectTest.get_P + - LINEAR_DOCUMENT.line + - LINEAR_DOCUMENT.lines + - NodeExecution_Push.f32 + - NodeExecution_Push.s32 + - NodeExecution_DepthwiseConv2D.f32_random_valid + - NodeExecution_Pad.tensor_constant_pad_6_dim + - NodeExecution_Pad.tensor_constant_pad_1_dim + - NodeExecution_Pad.tensor_constant_pad_4_dim + - DepthwiseConv2DTest.constructor + - ConstGenTest.constructor_s32 + - TransposedConv2DTest.constructor + - PullTest.shape + - MatrixDecodeTest.constructor + - FilterEncodeTest.constructor + - AvgPool2DTest.constructor + - Reshape_Fixed_Test.shape + - TensorConcatTest.constructor + - EltwiseSqrtTest.constructor + - TensorBiasAddTest.alias + - EltwiseSubTest.constructor + - TensorBroadcastTest.mapping + - PullTest.constructor + - PushTest.shape + - MaxPool2DTest.pad + - EltwiseMulTest.constructor + - DepthwiseFilterEncodeTest.constructor + - ForwardTest.constructor + - MaxPool2DTest.constructor + - TransposeTest.perm + - MatMulTest.constructor + - FeatureBiasAddTest.constructor + - TensorBroadcastTest.constructor + - FeatureEncodeTest.constructor + - MatrixEncodeTest.constructor + - ReLUTest.constructor + - BiasEncodeTest.constructor + - FilterDecodeTest.constructor + - EltwiseDivTest.constructor + - PushTest.constructor + - EltwiseAddTest.constructor + - Conv2DTest.constructor + - EltwiseMaxTest.constructor + - Reshape_Fixed_Test.constructor + - TransposeTest.constructor + - ConstGenTest.constructor + - FeatureBiasAddTest.alias + - DepthwiseFilterDecodeTest.constructor + - ReLU6Test.constructor + - FeatureDecodeTest.constructor + - TensorBiasAddTest.constructor + - NodeExecution_ReLU6.f32 + - CircleSqrtTest.constructor_P + - CircleRsqrtTest.constructor + - LINEAR_DOCUMENT.append_empty_string + - LINEAR_DOCUMENT.indent + - LINEAR_DOCUMENT.append_multi_line_text + - LINEAR_DOCUMENT.append_void + - LINEAR_DOCUMENT.document_append + - LINEAR_DOCUMENT.formatted_append + - LINEAR_DOCUMENT.forward_append + - LINEAR_DOCUMENT.reverse_append + - NodeData.as_s32_buffer_wrapper + - NodeData.as_f32_buffer_wrapper + - ConsoleReporterTest.constructor + - ConsoleReporterTest.notify + - NodeExecution_TensorBiasAdd.f32 + - NodeExecution_FeatureBiasAdd.f32 + - ADT_KERNEL_SHAPE.num_elements + - ADT_KERNEL_SHAPE.operator_eq + - ADT_KERNEL_SHAPE.ctor + - CircleLogicalNotTest.constructor_P + - CircleConcatenationTest.constructor_P + - ModuleTest.add_more + - ModuleTest.consturctor + - ModuleTest.add + - ModuleTest.add_nullptr_NEG + - ModuleTest.graph_index_overflow_NEG + - CircleArgMaxTest.constructor_P + - CircleReshapeTest.alloc_new_shape_P + - CircleReshapeTest.constructor_P + - CircleAddTest.constructor_P + - CanonicalShapeInferenceRuleTest.tensor_concat + - CanonicalShapeInferenceRuleTest.feature_codec + - CanonicalShapeInferenceRuleTest.maxpool2d + - CanonicalShapeInferenceRuleTest.minimal + - CanonicalShapeInferenceRuleTest.const_gen + - CanonicalShapeInferenceRuleTest.depthwiseconv2d + - CanonicalShapeInferenceRuleTest.infer_v2 + - CanonicalShapeInferenceRuleTest.avgpool2d + - CanonicalShapeInferenceRuleTest.tensor_broadcast + - CanonicalShapeInferenceRuleTest.transposedconv2d + - CanonicalShapeInferenceRuleTest.fixed_reshape + - CanonicalShapeInferenceRuleTest.relu + - CanonicalShapeInferenceRuleTest.tensor_transpose + - NodeExecution_Softmax.f32 + - CircleCosTest.constructor_P + - HermesTest.simple_usecase + - CircleMaxPool2DTest.constructor_P + - GraphTest.graph_node_enumeration + - GraphTest.graph_name + - GraphTest.create_input + - NamedTest.constructor + - NamedTest.setter_and_getter + - GraphTest.create_and_destroy_node + - GraphTest.graph_name_nullptr_NEG + - DataTypedMixinTest.constructor + - DataTypedMixinTest.setter_and_getter + - GraphTest.consturctor_with_param_node + - TensorShapedMixinTest.setter_and_getter + - GraphTest.getters_over_const_instance + - GraphTest.create_output + - GraphTest.graph_inout_enumeration + - StrideTest.default_constructor_2D + - StrideTest.setter_and_getter_2D + - ADT_FEATURE_CHW_LAYOUT.col_increase + - ADT_FEATURE_CHW_LAYOUT.ch_increase + - ADT_FEATURE_CHW_LAYOUT.row_increase + - TensorIndexTest.copy + - TensorIndexTest.fill + - TensorIndexTest.at + - TensorIndexTest.ctor_initializer_list + - TensorIndexTest.resize + - TensorIndexTest.ctor + - NodeDataImpl.as_annotation + - MUILTI_LINE_TEXT_UTILS.operator_shift + - SeverityTest.fatal + - SeverityTest.warn + - SeverityTest.error + - SeverityTest.info + - SeverityTest.verbose + - MessageTest.ctor + - MessageTextTest.multiline + - NodeExecution_TransposedConv2D.f32 + - ADT_FEATURE_BUFFER.ctor + - ADT_FEATURE_BUFFER.access + - UseTest.constructor + - UseTest.link_node + - NodeExecution_FilterEncode.f32 + - NodeExecution_FilterEncode.s32 + - CircleTransposeTest.constructor_P + - DimensionTest.value_constructor + - DimensionTest.default_constructor + - DimensionTest.operator_eq + - DimensionTest.make_unknown_dimension + - DimensionTest.unset + - DimensionTest.set + - TensorShapeTest.ctor_initializer_list + - TensorShapeTest.eq_negative_on_unmatched_dim + - TensorShapeTest.copy + - TensorShapeTest.eq_negative_on_unmatched_rank + - TensorShapeTest.dim + - TensorShapeTest.resize + - TensorShapeTest.eq_positive + - TensorShapeTest.ctor + - TensorFlowLiteImport.Dummy + - CircleTransposeConvTest.constructor_P + - LOCO.identity_network + - CanonicalDialectTest.get + - FeatureIndexTest.default_constructor + - FeatureIndexTest.settet_and_getter + - ADT_FEATURE_LAYOUT.move + - ADT_FEATURE_LAYOUT.ctor + - ADT_FEATURE_LAYOUT.copy + - CircleSoftmaxTest.constructor_P + - CanonicalNodeTest.mutable_visitor + - CanonicalNodeTest.visitor + - CanonicalNodeTest.visitor_with_user_default_impl + - NodeExecution_ReLU.f32 + - ShapeInferenceTest.framework + - NodeExecution_EltwiseSqrt.f32 + - NodeExecution_MatrixCodec.WH_f32 + - NodeExecution_MatrixCodec.HW_s32 + - ADT_FEATURE_SHAPE.operator_eq + - ADT_FEATURE_SHAPE.ctor + - ADT_FEATURE_SHAPE.num_elements + - SET.operator_diff + - SET.operator_eq + - NodeExecution_ConstGen.s32 + - NodeExecution_ConstGen.f32 + - CircleMulTest.constructor_P + - StrCastTests.safe_strcast_int + - NodeExecution_EltwiseMax.f32 + - NodeExecution_Pull.check_data_ready + - FormattedTensorShapeTest.BracketFormat + - FilterShapeTest.settet_and_getter + - FilterShapeTest.default_constructor + - NodeExecution_MaxPool2D.with_padding + - NodeExecution_MaxPool2D.f32_1x3x3x1_calculation + - NodeExecution_EltwiseAdd.f32 + - ADT_KERNEL_LAYOUT.move + - ADT_KERNEL_LAYOUT.ctor + - ADT_KERNEL_LAYOUT.copy + - NodeExecution_MatMul.s32_4x2_2x6 + - NodeExecution_MatMul.f32_2x3_3x3 + - CircleDepthwiseConv2DTest.constructor_P + - NodeExecution_Forward.s32 + - NodeExecution_Forward.f32 + - NodeExecution_EltwiseMul.f32 + - FilterIndexTest.default_constructor + - FilterIndexTest.settet_and_getter + - DialectTest.service + - Session.inference_identity + - Session.dtor + - Session.session_for_subgraph + - Session.set_input + - Session.ctor_by_range + - Session.graph_IO_size + - NodeTest.constructor + - NodeTest.replace_with + - NodeTest.succs + - NodeTest.preds + - FixedArityNodeTest.constructor + + negativeTestCase: + - condition: + - testName: + ends: + - _NEG + + positiveTestCase: + - condition: + - inverse: negativeTestCase diff --git a/Makefile.template b/Makefile.template index 0d601ded1..1f194fa21 100644 --- a/Makefile.template +++ b/Makefile.template @@ -133,6 +133,11 @@ ifneq ($(EXT_ACL_FOLDER),) printf "21.02" > $(OVERLAY_FOLDER)/ARMCOMPUTE.stamp endif +ifneq ($(DEBIAN_BUILD),) + test -d externals || mkdir -p externals + find packaging/ -type f -name "*.tar.gz" | xargs -i tar xf {} -C externals +endif + NNFW_WORKSPACE="$(WORKSPACE)" NNFW_INSTALL_PREFIX=$(INSTALL_PATH) ./nnfw configure \ -DCMAKE_BUILD_TYPE=$(BUILD_TYPE_LC) \ -DNNFW_OVERLAY_DIR=$(OVERLAY_FOLDER) \ diff --git a/compiler/.ahub/tcchecker-tca/config.yaml b/compiler/.ahub/tcchecker-tca/config.yaml deleted file mode 100644 index 9d3e6b8eb..000000000 --- a/compiler/.ahub/tcchecker-tca/config.yaml +++ /dev/null @@ -1,56 +0,0 @@ -version: 2 -test: - - name: NN Compiler - testCaseLanguage: CPP - testFW: GTEST - testCaseFolder: - - ./angkor - - ./arser - - ./circle2circle - - ./circle-quantizer - - ./crew - - ./cwrap - - ./foder - - ./hermes - - ./hermes-std - - ./loco - - ./locomotiv - - ./locop - - ./logo - - ./logo-core - - ./luci - - ./luci-interpreter - - ./luci-eval-driver - - ./luci-pass-value-test - - ./luci-value-test - - ./mio-circle - - ./mio-tflite - - ./oops - - ./pepper-assert - - ./pepper-str - - ./pepper-strcast - - ./pp - - ./record-minmax - - ./safemain - - ./souschef - - ./tflite2circle - - testFile: - - extension: .test.cpp - any: true - - testCase: - - condition: - - functionName: - starts: - - TEST - - negativeTestCase: - - condition: - - testName: - ends: - - _NEG - - positiveTestCase: - - condition: - - inverse: negativeTestCase diff --git a/compiler/bcq-tools/CMakeLists.txt b/compiler/bcq-tools/CMakeLists.txt index 463f9a2e6..475ee04f5 100644 --- a/compiler/bcq-tools/CMakeLists.txt +++ b/compiler/bcq-tools/CMakeLists.txt @@ -1,6 +1,4 @@ set(BCQ_TOOLS_FILES - generate_bcq_metadata - generate_bcq_output_arrays generate_bcq_metadata.py generate_bcq_output_arrays.py ) diff --git a/compiler/bcq-tools/generate_bcq_metadata b/compiler/bcq-tools/generate_bcq_metadata deleted file mode 100644 index 8405556aa..000000000 --- a/compiler/bcq-tools/generate_bcq_metadata +++ /dev/null @@ -1,219 +0,0 @@ -#!/usr/bin/env python3 - -# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import numpy as np -import tensorflow as tf - -import argparse -import sys - -ONE_START_MAGICNUM = int(-2e9 + 27) -ONE_END_MAGICNUM = int(2e9 - 27) - - -def _get_parser(): - """ - Returns an ArgumentParser for generating BCQ metadata. - """ - parser = argparse.ArgumentParser( - description=("Command line tool to generate metadata of BCQ nodes")) - - # Input and output path. - parser.add_argument( - "-i", - "--input_path", - type=str, - help="Full filepath of the input file.", - required=True) - parser.add_argument( - "-o", - "--output_path", - type=str, - help="Full filepath of the output file.", - required=True) - parser.add_argument( - "-O", - "--output_arrays", - type=str, - help="Original model output arrays", - required=True) - - return parser - - -# This function is copied from -# https://github.com/tensorflow/tensorflow/blob/r2.3/tensorflow/examples/label_image/label_image.py#L26 -def load_graph(model_file): - graph = tf.Graph() - graph_def = tf.compat.v1.GraphDef() - - with open(model_file, "rb") as f: - graph_def.ParseFromString(f.read()) - with graph.as_default(): - tf.import_graph_def(graph_def, name="") - - return graph - - -def generate_metadata_header(original_graph, bcq_version, output_arrays): - # Generating metadata starts - metadata_values = np.array([ONE_START_MAGICNUM]) - - # Append BCQ version - metadata_values = np.append(metadata_values, bcq_version) - - # Append original output count - output_cnt = output_arrays.count(',') + 1 - metadata_values = np.append(metadata_values, output_cnt) - - return metadata_values - - -def generate_bcq_metadata_v1(flags): - """ - BCQv1 contains following metadata. - - The number of each BCQ information set - """ - - is_valid = True - allowed_info_names = [ - "bcqinfo_do_w_x", "bcqinfo_alpha", "bcqinfo_packed_binary_code", - "bcqinfo_number_of_clusters", "bcqinfo_size_of_clusters", - "bcqinfo_qbits_of_clusters", "bcqinfo_dequant_weight" - ] - - original_graph = load_graph(flags.input_path) - original_graph_def = original_graph.as_graph_def() - - prefix_infonames_dict = {} - - for node in original_graph_def.node: - if node.op == "Const" and "/bcqinfo_" in node.name: - prefix_index = node.name.index("/bcqinfo_") - prefix = node.name[:prefix_index] - infoname = node.name[prefix_index + 1:] - - if infoname not in allowed_info_names: - is_valid = False - break - - if prefix not in prefix_infonames_dict: - prefix_infonames_dict[prefix] = set() - - prefix_infonames_dict[prefix].add(infoname) - - # All the number of BCQ information should be same - num_of_bcqinfo = -1 - for key in prefix_infonames_dict: - infonames = prefix_infonames_dict[key] - if num_of_bcqinfo == -1: - num_of_bcqinfo = len(infonames) - elif num_of_bcqinfo != len(infonames): - is_valid = False - - # The number of BCQv1 information should be 6 or 7 - if num_of_bcqinfo != 6 and num_of_bcqinfo != 7: - is_valid = False - - # If BCQ information is invalid, return original model - if is_valid == False: - return original_graph_def - - new_graph_def = tf.compat.v1.GraphDef() - for node in original_graph_def.node: - new_node = new_graph_def.node.add() - new_node.CopyFrom(node) - - # Generate metadata header - metadata_values = generate_metadata_header(original_graph, 1, flags.output_arrays) - - # Append metadata of BCQv1 - metadata_values = np.append(metadata_values, num_of_bcqinfo + 1) - - # Finish generating metadata - metadata_values = np.append(metadata_values, ONE_END_MAGICNUM) - - # Generate metadata tensor - metadata_tensor = tf.make_tensor_proto(metadata_values, tf.int32) - - new_node = new_graph_def.node.add() - new_node.op = "Const" - new_node.name = "one_compiler/bcqinfo_one_metadata" - new_node.attr["dtype"].CopyFrom( - tf.core.framework.attr_value_pb2.AttrValue(type=tf.int32.as_datatype_enum)) - new_node.attr["value"].tensor.CopyFrom(metadata_tensor) - return new_graph_def - - -def determine_bcq_version(flags): - """ - CAUTION : For now, BCQ has only one version and thus always returns 1 when BCQ - information nodes are included. If new BCQ version is introduced, - this function must be updated accordingly. - - When BCQ information does not exist, -1 is returned. - """ - bcq_version = -1 - - original_graph = load_graph(flags.input_path) - original_graph_def = original_graph.as_graph_def() - - for node in original_graph_def.node: - if node.op == "Const" and "/bcqinfo_" in node.name: - bcq_version = 1 - break - - return bcq_version - - -def generate_bcq_metadata(flags): - """ - Basic format of metadata is as following. - - Magic number indicating start - - Version of BCQ Format - - The number of original outputs - - Metadata based on each BCQ format - - Magic number indicating end - """ - program_version = 1 - model_version = determine_bcq_version(flags) - - if model_version == 1: - result_graph_def = generate_bcq_metadata_v1(flags) - elif model_version == -1: - # When there is no BCQ information, do nothing - result_graph_def = load_graph(flags.input_path) - else: - err_msg = "BCQ version of the model(v{}) ".format(model_version) - err_msg += "is higher than " - err_msg += "the version supported by this program(v{})".format(program_version) - raise SystemExit(err_msg) - - tf.io.write_graph(result_graph_def, '.', flags.output_path, False) - - -def main(): - # Parse argument. - parser = _get_parser() - flags = parser.parse_known_args(args=sys.argv[1:]) - - # Generate a new pb file, which BCQ metadata is included. - generate_bcq_metadata(flags[0]) - - -if __name__ == "__main__": - main() diff --git a/compiler/bcq-tools/generate_bcq_metadata.py b/compiler/bcq-tools/generate_bcq_metadata.py index 8405556aa..9281647e2 100644 --- a/compiler/bcq-tools/generate_bcq_metadata.py +++ b/compiler/bcq-tools/generate_bcq_metadata.py @@ -19,8 +19,12 @@ import numpy as np import tensorflow as tf import argparse +import os import sys +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + ONE_START_MAGICNUM = int(-2e9 + 27) ONE_END_MAGICNUM = int(2e9 - 27) @@ -154,7 +158,7 @@ def generate_bcq_metadata_v1(flags): new_node.op = "Const" new_node.name = "one_compiler/bcqinfo_one_metadata" new_node.attr["dtype"].CopyFrom( - tf.core.framework.attr_value_pb2.AttrValue(type=tf.int32.as_datatype_enum)) + tf.compat.v1.AttrValue(type=tf.int32.as_datatype_enum)) new_node.attr["value"].tensor.CopyFrom(metadata_tensor) return new_graph_def @@ -216,4 +220,9 @@ def main(): if __name__ == "__main__": - main() + try: + main() + except Exception as e: + prog_name = os.path.basename(__file__) + print(f"{prog_name}: {type(e).__name__}: " + str(e)) + sys.exit(255) diff --git a/compiler/bcq-tools/generate_bcq_output_arrays b/compiler/bcq-tools/generate_bcq_output_arrays deleted file mode 100644 index 8544bbd2a..000000000 --- a/compiler/bcq-tools/generate_bcq_output_arrays +++ /dev/null @@ -1,161 +0,0 @@ -#!/usr/bin/env python3 - -# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import tensorflow as tf - -import argparse -import sys - - -def _get_parser(): - """ - Returns an ArgumentParser for generating output_arrays. - """ - parser = argparse.ArgumentParser( - description=("Command line tool to generated output_arrays of BCQ nodes")) - - # Input and output path. - parser.add_argument( - "-i", - "--input_path", - type=str, - help="Full filepath of the input file.", - required=True) - parser.add_argument( - "-m", - "--metadata_path", - type=str, - help="Full filepath for the file that provides metadata.", - required=True) - parser.add_argument( - "-A", - "--output_arrays_path", - type=str, - help="Full filepath for the file that provides output arrays", - required=True) - - return parser - - -# This function is copied from -# https://github.com/tensorflow/tensorflow/blob/r2.3/tensorflow/examples/label_image/label_image.py#L26 -def load_graph(model_file): - graph = tf.Graph() - graph_def = tf.compat.v1.GraphDef() - - with open(model_file, "rb") as f: - graph_def.ParseFromString(f.read()) - with graph.as_default(): - tf.import_graph_def(graph_def, name="") - - return graph - - -def find_bcq_version(flags): - """ - If BCQ metadata exists, BCQ version is in the second element. - Return -1 when the metadata is not found. - """ - graph = load_graph(flags.input_path) - graph_def = graph.as_graph_def() - for node in graph_def.node: - if node.op == "Const" and "one_compiler/bcqinfo_one_metadata" in node.name: - metadata_tensor = tf.make_ndarray(node.attr["value"].tensor) - return metadata_tensor[1] - return -1 - - -def print_bcqinfo_output_arrays_v1(flags): - """ - This function generates a file which includes output arrays of BCQ v1 - information bundles. Each bundle is consisted with one of candidate - operations (BCQ may be applied) and BCQ constant nodes related with - the operation. - """ - graph = load_graph(flags.input_path) - graph_def = graph.as_graph_def() - ops = graph.get_operations() - - # If there is a constant node named PREFIX_1/bcqinfo_alpha, - # it is used for applying BCQ to constant node named PREFIX_1. - # Collected prefixes will be used for connecting - # bcqinfo nodes and user operations of prefix nodes. - prefix_set = set() - has_dequant_weight = False - for op in ops: - if op.type == "Const" and "/bcqinfo_" in op.outputs[0].name: - # Metadata do not have prefix - if "one_compiler/bcqinfo_one_metadata" in op.outputs[0].name: - continue - - prefix_index = op.outputs[0].name.index("/bcqinfo_") - prefix = op.outputs[0].name[:prefix_index] - prefix_set.add(prefix) - - # Usually, output name of op is like "outputname:0" - # -2 is for removing ":0" - infoname = op.outputs[0].name[prefix_index + 1:-2] - if infoname == "bcqinfo_dequant_weight": - has_dequant_weight = True - - # Write the name of metadata node - with open(flags.metadata_path, 'w') as f_metadata: - f_metadata.write("one_compiler/bcqinfo_one_metadata,") - - # Write all pairs of a constant node and related BCQ information nodes. - with open(flags.output_arrays_path, 'w') as f_arrays: - for prefix in prefix_set: - f_arrays.write("," + prefix + "/bcqinfo_do_w_x") - f_arrays.write("," + prefix + "/bcqinfo_alpha") - f_arrays.write("," + prefix + "/bcqinfo_packed_binary_code") - f_arrays.write("," + prefix + "/bcqinfo_number_of_clusters") - f_arrays.write("," + prefix + "/bcqinfo_size_of_clusters") - f_arrays.write("," + prefix + "/bcqinfo_qbits_of_clusters") - f_arrays.write("," + prefix) - if has_dequant_weight: - f_arrays.write("," + prefix + "/bcqinfo_dequant_weight") - - -def print_bcq_output_arrays(flags): - program_version = 1 - model_version = find_bcq_version(flags) - - if model_version == 1: - print_bcqinfo_output_arrays_v1(flags) - elif model_version == -1: - # When BCQ information not found, print nothing. - f_metadata = open(flags.metadata_path, 'w') - f_arrays = open(flags.output_arrays_path, 'w') - f_metadata.close() - f_arrays.close() - else: - err_msg = "BCQ version of the model(v{}) ".format(model_version) - err_msg += "is higher than " - err_msg += "the version supported by this program(v{})".format(program_version) - raise SystemExit(err_msg) - - -def main(): - # Parse argument. - parser = _get_parser() - flags = parser.parse_known_args(args=sys.argv[1:]) - - print_bcq_output_arrays(flags[0]) - - -if __name__ == "__main__": - main() diff --git a/compiler/circle-part-driver/src/PModelsRunner.cpp b/compiler/circle-part-driver/src/PModelsRunner.cpp index 453ce9b5f..dd2ffe22d 100644 --- a/compiler/circle-part-driver/src/PModelsRunner.cpp +++ b/compiler/circle-part-driver/src/PModelsRunner.cpp @@ -171,8 +171,6 @@ bool PModelsRunner::run(void) luci_interpreter::Interpreter interpreter(module.get()); // Set input - // TODO support multiple subgraphs - assert(module->size() == 1); const auto input_nodes = loco::input_nodes(module->graph()); int32_t num_inputs = static_cast<int32_t>(input_nodes.size()); for (int32_t i = 0; i < num_inputs; i++) @@ -226,10 +224,13 @@ bool PModelsRunner::run(void) void PModelsRunner::save_outputs(const std::string &output_file) { + LOGGER(l); + // load source model as we need to get both shape and node name // TODO check for unknown shape auto source_fname = _pconfig.source.model_file; + INFO(l) << "save_outputs() loading file: " << source_fname << std::endl; auto module = import_circle(source_fname); const auto output_nodes = loco::output_nodes(module->graph()); @@ -238,6 +239,7 @@ void PModelsRunner::save_outputs(const std::string &output_file) const auto *output_node = loco::must_cast<const luci::CircleOutput *>(output_nodes[i]); auto output_name = output_node->name(); + INFO(l) << "save_outputs() save output node: " << output_name << std::endl; assert(_data_stage.find(output_name) != _data_stage.end()); auto tensor_data = _data_stage[output_name]; diff --git a/compiler/circle-part-value-test/CMakeLists.txt b/compiler/circle-part-value-test/CMakeLists.txt index d75b17d1f..b4b1b19db 100644 --- a/compiler/circle-part-value-test/CMakeLists.txt +++ b/compiler/circle-part-value-test/CMakeLists.txt @@ -11,11 +11,13 @@ get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) unset(RECIPE_LIST) unset(PARTITION_LIST) +unset(OUTPUT_COUNT_LIST) unset(TEST_DEPS) -macro(add RECIPE_NAME PARTITION_NAME) +macro(add RECIPE_NAME PARTITION_NAME OUTPUT_COUNT) list(APPEND RECIPE_LIST ${RECIPE_NAME}) list(APPEND PARTITION_LIST ${PARTITION_NAME}) + list(APPEND OUTPUT_COUNT_LIST ${OUTPUT_COUNT}) endmacro(add) # Read "test.lst" @@ -27,6 +29,7 @@ math(EXPR RECIPE_LENGTH_M1 "${RECIPE_LENGTH} - 1") foreach(IDX RANGE ${RECIPE_LENGTH_M1}) list(GET RECIPE_LIST ${IDX} RECIPE_NAME) list(GET PARTITION_LIST ${IDX} PARTITION_NAME) + list(GET OUTPUT_COUNT_LIST ${IDX} OUTPUT_COUNT) # NOTE about the name: # Use '.recipe' name for source tflite and circle files @@ -46,7 +49,7 @@ foreach(IDX RANGE ${RECIPE_LENGTH_M1}) add_custom_command(OUTPUT ${TFLITE_DST_PATH} COMMAND ${CMAKE_COMMAND} -E copy "${TFLITE_SRC_PATH}" "${TFLITE_DST_PATH}" - DEPENDS ${TFLITE_SRC_PATH} + DEPENDS ${TFLITE_SRC_PATH} ${PARTITIONER_OUTPUT_PATH} COMMENT "Copy ${RECIPE_NAME}.tflite" ) list(APPEND TEST_DEPS ${TFLITE_DST_PATH}) @@ -57,7 +60,7 @@ foreach(IDX RANGE ${RECIPE_LENGTH_M1}) add_custom_command(OUTPUT ${CIRCLE_DST_PATH} COMMAND ${CMAKE_COMMAND} -E copy "${CIRCLE_SRC_PATH}" "${CIRCLE_DST_PATH}" - DEPENDS ${CIRCLE_SRC_PATH} + DEPENDS ${CIRCLE_SRC_PATH} ${PARTITIONER_OUTPUT_PATH} COMMENT "Copy ${RECIPE_NAME}.circle" ) list(APPEND TEST_DEPS ${CIRCLE_DST_PATH}) @@ -69,7 +72,7 @@ foreach(IDX RANGE ${RECIPE_LENGTH_M1}) add_custom_command(OUTPUT ${PART_DST_PATH} COMMAND ${CMAKE_COMMAND} -E copy "${PART_SRC_PATH}" "${PART_DST_PATH}" - DEPENDS ${PART_SRC_PATH} + DEPENDS ${PART_SRC_PATH} ${PARTITIONER_OUTPUT_PATH} COMMENT "Copy ${PART_FILE}" ) list(APPEND TEST_DEPS ${PART_DST_PATH}) @@ -84,6 +87,16 @@ foreach(IDX RANGE ${RECIPE_LENGTH_M1}) COMMENT "Parition ${RECIPE_NAME}.circle with ${PART_FILE}" ) list(APPEND TEST_DEPS ${PARTITIONER_CONN_JSON}) + + # Write .excnt file; expected count of output models + set(COUNT_FILE "${PARTITION_NAME}.excnt") + set(COUNT_FILE_PATH "${PARTITIONER_OUTPUT_PATH}/${COUNT_FILE}") + add_custom_command(OUTPUT ${COUNT_FILE_PATH} + COMMAND echo ${OUTPUT_COUNT} > ${COUNT_FILE_PATH} + DEPENDS ${PART_SRC_PATH} ${PARTITIONER_OUTPUT_PATH} + COMMENT "Write ${COUNT_FILE} with ${OUTPUT_COUNT}" + ) + list(APPEND TEST_DEPS ${COUNT_FILE_PATH}) endforeach(IDX) add_custom_target(circle_part_value_test_prepare ALL DEPENDS ${TEST_DEPS}) diff --git a/compiler/circle-part-value-test/part_eval_one.py b/compiler/circle-part-value-test/part_eval_one.py index b0b65fd74..91e32d78f 100755 --- a/compiler/circle-part-value-test/part_eval_one.py +++ b/compiler/circle-part-value-test/part_eval_one.py @@ -4,6 +4,7 @@ import tensorflow as tf import subprocess import argparse import traceback +import json # # This script compares the execution result of TFLite interpreter and @@ -23,6 +24,30 @@ driver = args.driver tflite_model = args.name + ".tflite" circle_model = args.name + ".circle" partition_conn_ini = args.name + ".conn.ini" +partition_conn_json = args.name + ".conn.json" +expected_count = args.name + ".excnt" + +# Check expected count of models from partitioning +try: + with open(expected_count, "r") as expected_count_file: + expected_count_line = expected_count_file.readline() + + expected_count_line = int(expected_count_line) + if expected_count_line: + with open(partition_conn_json) as json_file: + json_data = json.load(json_file) + parts_value = json_data["parts"] + if len(parts_value) != expected_count_line: + print("Partitioned model count differs from expected:", + expected_count_line) + quit(255) + + print("Partitioned model count expected: ", expected_count_line) + else: + print("Skip expected partitioned model count check: 0") + +except: + print("Skip expected partitioned model count check: error") # Build TFLite interpreter. interpreter = tf.lite.Interpreter(tflite_model) diff --git a/compiler/circle-part-value-test/parts/Net_InstanceNorm_003.003.part b/compiler/circle-part-value-test/parts/Net_InstanceNorm_003.003.part new file mode 100644 index 000000000..eee3fd1d1 --- /dev/null +++ b/compiler/circle-part-value-test/parts/Net_InstanceNorm_003.003.part @@ -0,0 +1,9 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opname + +[OPNAME] +Mean_as_variance=acl_cl +Add_as_variance=acl_cl +Pow=acl_cl diff --git a/compiler/circle-part-value-test/parts/Part_Add_Sub_000.001.part b/compiler/circle-part-value-test/parts/Part_Add_Sub_000.001.part new file mode 100644 index 000000000..179cad191 --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_Add_Sub_000.001.part @@ -0,0 +1,7 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opname + +[OPNAME] +add1=acl_cl diff --git a/compiler/circle-part-value-test/parts/Part_Add_Sub_001.part b/compiler/circle-part-value-test/parts/Part_Add_Sub_001.part new file mode 100644 index 000000000..41ce4b23d --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_Add_Sub_001.part @@ -0,0 +1,7 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opname + +[OPNAME] +some/node/add2;and/another=acl_cl diff --git a/compiler/circle-part-value-test/parts/Part_Add_Sub_002.001.part b/compiler/circle-part-value-test/parts/Part_Add_Sub_002.001.part new file mode 100644 index 000000000..030653e8a --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_Add_Sub_002.001.part @@ -0,0 +1,9 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opname + +[OPNAME] +add1=cpu +add2=acl_cl +ofm=acl_cl diff --git a/compiler/circle-part-value-test/parts/Part_Add_Sub_002.002.part b/compiler/circle-part-value-test/parts/Part_Add_Sub_002.002.part new file mode 100644 index 000000000..837b36269 --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_Add_Sub_002.002.part @@ -0,0 +1,9 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opname + +[OPNAME] +add1=acl_cl +add2=acl_cl +ofm=cpu diff --git a/compiler/circle-part-value-test/parts/Part_If_Add_Sub_000.001.part b/compiler/circle-part-value-test/parts/Part_If_Add_Sub_000.001.part new file mode 100644 index 000000000..01b8c704e --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_If_Add_Sub_000.001.part @@ -0,0 +1,7 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opcode + +[OPCODE] +ADD=acl_cl diff --git a/compiler/circle-part-value-test/parts/Part_If_Add_Sub_001.001.part b/compiler/circle-part-value-test/parts/Part_If_Add_Sub_001.001.part new file mode 100644 index 000000000..01b8c704e --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_If_Add_Sub_001.001.part @@ -0,0 +1,7 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opcode + +[OPCODE] +ADD=acl_cl diff --git a/compiler/circle-part-value-test/parts/Part_While_000.part b/compiler/circle-part-value-test/parts/Part_While_000.part new file mode 100644 index 000000000..e469eeb26 --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_While_000.part @@ -0,0 +1,7 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opcode + +[OPCODE] +MAXIMUM=acl_cl diff --git a/compiler/circle-part-value-test/parts/Part_While_001.part b/compiler/circle-part-value-test/parts/Part_While_001.part new file mode 100644 index 000000000..e469eeb26 --- /dev/null +++ b/compiler/circle-part-value-test/parts/Part_While_001.part @@ -0,0 +1,7 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opcode + +[OPCODE] +MAXIMUM=acl_cl diff --git a/compiler/circle-part-value-test/test.lst b/compiler/circle-part-value-test/test.lst index 8316560f0..af2f5ba5c 100644 --- a/compiler/circle-part-value-test/test.lst +++ b/compiler/circle-part-value-test/test.lst @@ -1,20 +1,37 @@ # Add recipe names from /res/TensorFlowLiteRecipes to test. # Only add items exist in common-artifacts test: tflite/circle files are copied as source. # -# add(RECIPE_NAME PARTITION_NAME) +# add(RECIPE_NAME PARTITION_NAME EXPECTED_OUTPUT_COUNT) +# EXPECTED_OUTPUT_COUNT: 0 for skip expected count test -add(Part_Add_Sub_000 Part_Add_Sub_000) -add(Part_Sqrt_Rsqrt_000 Part_Sqrt_Rsqrt_000) -add(Part_Sqrt_Rsqrt_001 Part_Sqrt_Rsqrt_001) -add(Part_Sqrt_Rsqrt_002 Part_Sqrt_Rsqrt_002) -add(Part_Sqrt_Rsqrt_003 Part_Sqrt_Rsqrt_003) -add(Part_Sqrt_Rsqrt_Add_000 Part_Sqrt_Rsqrt_Add_000) -add(Part_Sqrt_Rsqrt_Add_001 Part_Sqrt_Rsqrt_Add_001) -add(Part_Sqrt_Rsqrt_Add_002 Part_Sqrt_Rsqrt_Add_002) -add(Part_Sqrt_Rsqrt_Add_003 Part_Sqrt_Rsqrt_Add_003) -add(Part_Sqrt_Rsqrt_Add_004 Part_Sqrt_Rsqrt_Add_004) -add(Part_Add_Sqrt_000 Part_Add_Sqrt_000) -add(Part_Add_Sqrt_Rsqrt_000 Part_Add_Sqrt_Rsqrt_000) -add(Net_InstanceNorm_003 Net_InstanceNorm_003) -add(Net_InstanceNorm_003 Net_InstanceNorm_003.001) -add(Net_InstanceNorm_003 Net_InstanceNorm_003.002) +add(Part_Add_Sub_000 Part_Add_Sub_000 2) +add(Part_Sqrt_Rsqrt_000 Part_Sqrt_Rsqrt_000 2) +add(Part_Sqrt_Rsqrt_001 Part_Sqrt_Rsqrt_001 2) +add(Part_Sqrt_Rsqrt_002 Part_Sqrt_Rsqrt_002 4) +add(Part_Sqrt_Rsqrt_003 Part_Sqrt_Rsqrt_003 3) +add(Part_Sqrt_Rsqrt_Add_000 Part_Sqrt_Rsqrt_Add_000 3) +add(Part_Sqrt_Rsqrt_Add_001 Part_Sqrt_Rsqrt_Add_001 3) +add(Part_Sqrt_Rsqrt_Add_002 Part_Sqrt_Rsqrt_Add_002 4) +add(Part_Sqrt_Rsqrt_Add_003 Part_Sqrt_Rsqrt_Add_003 1) +add(Part_Sqrt_Rsqrt_Add_004 Part_Sqrt_Rsqrt_Add_004 1) +add(Part_Add_Sqrt_000 Part_Add_Sqrt_000 3) +add(Part_Add_Sqrt_Rsqrt_000 Part_Add_Sqrt_Rsqrt_000 3) +add(Net_InstanceNorm_003 Net_InstanceNorm_003 3) +add(Net_InstanceNorm_003 Net_InstanceNorm_003.001 5) +# skip expected count for now +add(Net_InstanceNorm_003 Net_InstanceNorm_003.002 0) + +# comply=opname +add(Part_Add_Sub_000 Part_Add_Sub_000.001 3) +add(Part_Add_Sub_001 Part_Add_Sub_001 3) +add(Part_Add_Sub_002 Part_Add_Sub_002.001 2) +add(Part_Add_Sub_002 Part_Add_Sub_002.002 2) +add(Net_InstanceNorm_003 Net_InstanceNorm_003.003 3) + +# IF with subgraphs +add(Part_If_Add_Sub_000 Part_If_Add_Sub_000.001 3) +add(Part_If_Add_Sub_001 Part_If_Add_Sub_001.001 3) + +# WHILE with subgraphs +add(Part_While_000 Part_While_000 3) +add(Part_While_001 Part_While_001 3) diff --git a/compiler/circle-partitioner-test/CMakeLists.txt b/compiler/circle-partitioner-test/CMakeLists.txt new file mode 100644 index 000000000..ed8c97948 --- /dev/null +++ b/compiler/circle-partitioner-test/CMakeLists.txt @@ -0,0 +1,71 @@ +# NOTE Test below are for circle-partitioner is partitioning itself. +# Once this test passes, add partition to 'circle-part-value-test' for +# full test. +if(NOT ENABLE_TEST) + return() +endif(NOT ENABLE_TEST) + +get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) + +unset(RECIPE_LIST) +unset(PART_LIST) +unset(TEST_DEPS) + +macro(add RECIPE_NAME PART_NAME) + list(APPEND RECIPE_LIST ${RECIPE_NAME}) + list(APPEND PART_LIST ${PART_NAME}) +endmacro(add) + +# Read "test.lst" +include("test.lst") + +list(LENGTH RECIPE_LIST RECIPE_LENGTH) +math(EXPR RECIPE_LENGTH_M1 "${RECIPE_LENGTH} - 1") + +foreach(IDX RANGE ${RECIPE_LENGTH_M1}) + list(GET RECIPE_LIST ${IDX} RECIPE_NAME) + list(GET PART_LIST ${IDX} PART_NAME) + + set(PART_OUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${PART_NAME}") + + add_custom_command(OUTPUT ${PART_OUT_PATH} + COMMAND ${CMAKE_COMMAND} -E make_directory "${PART_OUT_PATH}" + COMMENT "Make directory ${PART_OUT_PATH}" + ) + + set(CIRCLE_SRC_PATH "${ARTIFACTS_BIN_PATH}/${RECIPE_NAME}.circle") + set(CIRCLE_DST_PATH "${PART_OUT_PATH}/${PART_NAME}.circle") + + # Copy circle + add_custom_command(OUTPUT ${CIRCLE_DST_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${CIRCLE_SRC_PATH}" "${CIRCLE_DST_PATH}" + DEPENDS ${CIRCLE_SRC_PATH} + COMMENT "Copy ${RECIPE_NAME}.circle" + ) + + set(PART_FILE "${PART_NAME}.part") + set(PART_SRC_PATH "${CMAKE_CURRENT_SOURCE_DIR}/parts/${PART_FILE}") + set(PART_DST_PATH "${PART_OUT_PATH}/${PART_FILE}") + + # Copy .part + add_custom_command(OUTPUT ${PART_DST_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${PART_SRC_PATH}" "${PART_DST_PATH}" + DEPENDS ${PART_SRC_PATH} + COMMENT "Copy ${PART_FILE}" + ) + + # Run partitioner + set(PART_CONN_JSON "${PART_OUT_PATH}/${PART_NAME}.conn.json") + add_custom_command(OUTPUT ${PART_CONN_JSON} + COMMAND circle_partitioner "${PART_FILE}" "${PART_NAME}.circle" "${PART_OUT_PATH}" + DEPENDS circle_partitioner ${CIRCLE_DST_PATH} ${PART_DST_PATH} + COMMENT "Parition ${RECIPE_NAME}.circle with ${PART_FILE}" + ) + # NOTE this is checked in build time and not added with 'add_test' command + # to reduce scripts to run testing. actual testing is done in 'circle-part-evel' + + list(APPEND TEST_DEPS ${CIRCLE_DST_PATH} ${PART_DST_PATH} ${PART_CONN_JSON}) +endforeach(IDX) + +add_custom_target(circle_partitioner_test ALL DEPENDS ${TEST_DEPS}) +add_dependencies(circle_partitioner_test common_artifacts_deps) diff --git a/compiler/circle-partitioner-test/README.md b/compiler/circle-partitioner-test/README.md new file mode 100644 index 000000000..46da98651 --- /dev/null +++ b/compiler/circle-partitioner-test/README.md @@ -0,0 +1,11 @@ +# circle-partitioner-test + +_circle-partitioner-test_ provides test of circle-partitioner; +to test partitioning is working correctly, without value testing. +- full value testing is done with _circle-part-value-test_. + +Purpose of this test is to check how the partitioning itself is done +before value testing, in local. After you've checked model partitioning is +working as you expect, you can add test to _circle-part-value-test_. + +It is not necessary to commit to test of this module to upstream. diff --git a/compiler/circle-partitioner-test/parts/Net_InstanceNorm_003.part b/compiler/circle-partitioner-test/parts/Net_InstanceNorm_003.part new file mode 100644 index 000000000..d4d439d27 --- /dev/null +++ b/compiler/circle-partitioner-test/parts/Net_InstanceNorm_003.part @@ -0,0 +1,7 @@ +[partition] +backends=cpu,acl_cl +default=cpu +comply=opcode + +[OPCODE] +DIV=acl_cl diff --git a/compiler/circle-partitioner-test/requires.cmake b/compiler/circle-partitioner-test/requires.cmake new file mode 100644 index 000000000..52685cf67 --- /dev/null +++ b/compiler/circle-partitioner-test/requires.cmake @@ -0,0 +1,2 @@ +require("circle-partitioner") +require("common-artifacts") diff --git a/compiler/circle-partitioner-test/test.lst b/compiler/circle-partitioner-test/test.lst new file mode 100644 index 000000000..b731f8d0e --- /dev/null +++ b/compiler/circle-partitioner-test/test.lst @@ -0,0 +1,7 @@ +# Add recipes in /res/TensorFlowLiteRecipes to test. +# NOTE: only add items exist in common-artifacts test: circle files are copied +# from common-artifacts. +# Use this list file before end-to-end test in 'circle-part-value-test'. +# add(RECIPE_NAME PART_NAME) + +add(Net_InstanceNorm_003 Net_InstanceNorm_003) diff --git a/compiler/circle-partitioner/CMakeLists.txt b/compiler/circle-partitioner/CMakeLists.txt index 573e6ec12..28a16c9fc 100644 --- a/compiler/circle-partitioner/CMakeLists.txt +++ b/compiler/circle-partitioner/CMakeLists.txt @@ -8,9 +8,11 @@ target_link_libraries(circle_partitioner luci_lang) target_link_libraries(circle_partitioner luci_log) target_link_libraries(circle_partitioner luci_import) target_link_libraries(circle_partitioner luci_service) +target_link_libraries(circle_partitioner luci_pass) target_link_libraries(circle_partitioner luci_export) target_link_libraries(circle_partitioner luci_partition) target_link_libraries(circle_partitioner arser) +target_link_libraries(circle_partitioner pepper_csv2vec) target_link_libraries(circle_partitioner vconone) target_link_libraries(circle_partitioner nncc_common) diff --git a/compiler/circle-partitioner/README.md b/compiler/circle-partitioner/README.md index 7c387cf76..e1a0258dc 100644 --- a/compiler/circle-partitioner/README.md +++ b/compiler/circle-partitioner/README.md @@ -1,3 +1,165 @@ # circle-partitioner _circle-partitioner_ provides model partitioning of circle model to two or more circle models. + +## How circle-partitioner work + +_circle-partitioner_ requires 3 positional arguments +- first: `partition` file +- second: `input` circle model file +- third: `work` folder + +And options to override `partition` file as a helper to try out without editing `partition` file. +- `--backends`: override `backends` of `[partition]` section +- `--default`: override `default` of `[partition]` section + +_circle-partitoner_ will read the `partition` and `input` files and group nodes with same backend +and store them into new circle models in `work` folder, where the `partition` and `input` files +are read from `work` folder. + +Outputs are (1) one or more partitioned circle models and (2) connection file that gives how +the partitioned models should be connected to act like the source `input` model. + +Why does input files be placed in `work` folder too? +- this is still work in progress condition +- use cases are still ambigious +- original `input` model file can be used by the backend, so `.conn` file links it as `source` +- to make things simple for the backend, it would be better not to use relative path for the files + +### `partition` file + +`partition` follows INI format of _crew_ project. + +Several example files exist in _circle-part-value-test_ `parts` folder. + +This section will explain with `Net_InstanceNorm_003.part` file as example. +```ini +[partition] +backends=cpu,acl_cl +default=cpu +comply=opcode + +[OPCODE] +DIV=acl_cl +``` + +##### `[partition]` section + +`[partition]` section is the main section first to read. +- `backends`: Existing partition group names which nodes should be placed, in CSV format. +- `default`: Default group name which should be one of `backends` item. +- `comply`: How to group nodes of the model. + - currently `opcode` is supported + - future work: set group by node name or sequence number. + +##### `[OPCODE`] section + +This section provides how to group nodes in OPCODE types. +Nodes with same OPCODE will be grouped to that type. +This does not mean number of output circle files will be same as number of backends. +Number of output circle files will depend also on the network structure. + +For above example, all `DIV` OPCODE nodes will be grouped to `acl_cl` backend. + +`[OPCODE]` can override `default` backend set from `[partition]` section by using `_`. + +For example, we can change default to `cpu`. +``` +[OPCODE] +_=cpu +DIV=acl_cl +``` + +### `circle` file + +Just normal `circle` file. Currently partition is supported in limited properties and +models with these properties are not support yet; +- Have multiple subgraph models +- Operators with multiple output nodes such as IF or WHILE. + +### `work` folder + +`partition` and `circle` file should reside in `work` folder. Output files will be +generated inside this folder. + +### Example + +Typical source of paritioning +``` +$ tree Net_InstanceNorm_003/ +Net_InstanceNorm_003/ +├── Net_InstanceNorm_003.circle +└── Net_InstanceNorm_003.part +``` + +Command example +``` +./circle_partitioner Net_InstanceNorm_003.part Net_InstanceNorm_003.circle Net_InstanceNorm_003 +``` + +Result of _circle-partitioner_ +``` +$ tree Net_InstanceNorm_003/ +Net_InstanceNorm_003/ +├── Net_InstanceNorm_003.00001_cpu.circle +├── Net_InstanceNorm_003.00002_acl_cl.circle +├── Net_InstanceNorm_003.00003_cpu.circle +├── Net_InstanceNorm_003.circle +├── Net_InstanceNorm_003.conn.ini +├── Net_InstanceNorm_003.conn.json +└── Net_InstanceNorm_003.part +``` + +### `Net_InstanceNorm_003.conn.ini` and `Net_InstanceNorm_003.conn.json` + +These two files are identical in content but in different formats. + +`.conn` file provides an information how to reconstruct the partitioned models, +`Net_InstanceNorm_003.00001_cpu.circle`, `Net_InstanceNorm_003.00002_acl_cl.circle` +and `Net_InstanceNorm_003.00003_cpu.circle`, so that it will identical to +source `Net_InstanceNorm_003.circle` model in computational results. + +Here, meaning of `reconstruct` is connection of outputs and inputs of partitioned +models. + +```json +$ cat Net_InstanceNorm_003/Net_InstanceNorm_003.conn.json +{ + "source" : { + "file" : "Net_InstanceNorm_003.circle", + "inputs" : [ "Input" ], + "outputs" : [ "Add_as_terminal" ] + }, + "parts" : [ + { + "file" : "Net_InstanceNorm_003.00001_cpu.circle", + "inputs" : [ "Input" ], + "outputs" : [ "Pow", "Sub" ] + }, + { + "file" : "Net_InstanceNorm_003.00002_acl_cl.circle", + "inputs" : [ "Sub", "Pow" ], + "outputs" : [ "Div" ] + }, + { + "file" : "Net_InstanceNorm_003.00003_cpu.circle", + "inputs" : [ "Div" ], + "outputs" : [ "Add_as_terminal" ] + } + ] +} +``` +Above file is in `JSON` format with `source` file and `parts` for partitioned models. +Each `parts` have `file` for the file, `inputs` for input nodes and `outputs` +for output nodes. + +From the `source` we can identify inputs and outputs for the model. + +- Each items in `outputs` should connect to `inputs` of another item of `parts` model, +or should be one of the `outputs` of the `source` model. +- For first `Net_InstanceNorm_003.00001_cpu.circle` model, `inputs` is(are) same +as the `source` model: `[ "Input" ]`. +- `outputs` `[ "Pow", "Sub" ]` have same names in the second model +`Net_InstanceNorm_003.00002_acl_cl.circle` which they should be connected. +- And `outputs` `[ "Div" ]` should be connected to `inputs` of +third model `Net_InstanceNorm_003.00003_cpu.circle`. diff --git a/compiler/circle-partitioner/requires.cmake b/compiler/circle-partitioner/requires.cmake index 507a4d89d..690d9531c 100644 --- a/compiler/circle-partitioner/requires.cmake +++ b/compiler/circle-partitioner/requires.cmake @@ -1,5 +1,6 @@ require("foder") require("crew") +require("pepper-csv2vec") require("safemain") require("luci") require("arser") diff --git a/compiler/circle-partitioner/src/CirclePartitioner.cpp b/compiler/circle-partitioner/src/CirclePartitioner.cpp index 28ff22abc..0151e92d3 100644 --- a/compiler/circle-partitioner/src/CirclePartitioner.cpp +++ b/compiler/circle-partitioner/src/CirclePartitioner.cpp @@ -17,7 +17,6 @@ #include "PartitionRead.h" #include "PartitionExport.h" #include "HelperPath.h" -#include "HelperStrings.h" #include <foder/FileLoader.h> @@ -25,8 +24,12 @@ #include <luci/Service/Validate.h> #include <luci/CircleExporter.h> #include <luci/CircleFileExpContract.h> +#include <luci/CircleOptimizer.h> +#include <luci/PartitionDump.h> +#include <luci/PartitionValidate.h> #include <luci/Log.h> +#include <pepper/csv2vec.h> #include <arser/arser.h> #include <vconone/vconone.h> @@ -109,82 +112,6 @@ std::unique_ptr<luci::Module> load_model(const std::string &input_path) return importer.importModule(circle_model); } -bool validate_module(luci::Module *module) -{ - for (size_t g = 0; g < module->size(); ++g) - { - auto graph = module->graph(g); - if (!luci::validate(graph)) - { - std::cerr << "ERROR: Invalid circle model" << std::endl; - return false; - } - if (!luci::validate_name(graph)) - { - std::cerr << "ERROR: circle model has empty name" << std::endl; - return false; - } - } - - if (!luci::validate_unique_name(module)) - { - std::cerr << "ERROR: circle model has duplicate names" << std::endl; - return false; - } - - return true; -} - -bool validate_partition(luci::PartitionTable &partition) -{ - if (partition.groups.size() == 0) - { - std::cerr << "There is no 'backends' information"; - return false; - } - if (partition.default_group.empty()) - { - std::cerr << "There is no 'default' backend information"; - return false; - } - if (!partee::is_one_of(partition.default_group, partition.groups)) - { - std::cerr << "'default' backend is not one of 'backends' item"; - return false; - } - for (auto &byopcode : partition.byopcodes) - { - if (!partee::is_one_of(byopcode.second, partition.groups)) - { - std::cerr << "OPCODE " << byopcode.first << " is not assigned to one of 'backends' items"; - return false; - } - } - return true; -} - -void dump(std::ostream &os, const luci::PartitionTable &table) -{ - os << "Backends:"; - for (auto &group : table.groups) - { - os << " " << group; - if (table.default_group == group) - os << "(default)"; - } - os << std::endl; - - os << "Assign by OPCODE: " << std::endl; - for (auto &item : table.byopcodes) - os << " " << item.first << "=" << item.second << std::endl; -} - -std::ostream &operator<<(std::ostream &os, const luci::PartitionTable &table) -{ - dump(os, table); - return os; -} - } // namespace int entry(int argc, char **argv) @@ -218,7 +145,17 @@ int entry(int argc, char **argv) { return EXIT_FAILURE; } - if (!validate_module(module.get())) + // Run default shape/dtype inference before validation + // NOTE CircleWhileOut default shape is INVALID as it needs initial shape + // inference. This is cause of WHILE may have dynamic shape. + luci::CircleOptimizer optimizer; + (void)optimizer.options(); // need to call this to make internal member + for (size_t g = 0; g < module->size(); ++g) + { + auto graph = module->graph(g); + optimizer.optimize(graph); + } + if (!luci::validate(module.get())) { return EXIT_FAILURE; } @@ -233,15 +170,16 @@ int entry(int argc, char **argv) if (arser[opt_bks]) { auto backend_backends = arser.get<std::string>(opt_bks); - partition.groups = partee::csv_to_vector<std::string>(backend_backends); + partition.groups = pepper::csv_to_vector<std::string>(backend_backends); } if (arser[opt_def]) { partition.default_group = arser.get<std::string>(opt_def); } } - if (!validate_partition(partition)) + if (!luci::validate(partition)) { + // NOTE error reason/message is put to std::cerr inside validate() return EXIT_FAILURE; } diff --git a/compiler/circle-partitioner/src/PartitionRead.cpp b/compiler/circle-partitioner/src/PartitionRead.cpp index b179ecb59..1bfb2d309 100644 --- a/compiler/circle-partitioner/src/PartitionRead.cpp +++ b/compiler/circle-partitioner/src/PartitionRead.cpp @@ -15,11 +15,11 @@ */ #include "PartitionRead.h" -#include "HelperStrings.h" #include <crew/PConfigIni.h> #include <crew/PConfigIniDump.h> #include <luci/Log.h> +#include <pepper/csv2vec.h> #include <stdexcept> @@ -30,15 +30,24 @@ using namespace partee; const char *_section_partition = "partition"; const char *_section_OPCODE = "OPCODE"; +const char *_section_OPNAME = "OPNAME"; + +const char *_comply_opcode = "opcode"; +const char *_comply_opname = "opname"; const char *_key_backends = "backends"; const char *_key_default = "default"; +const char *_key_comply = "comply"; const char *_key_underscore = "_"; luci::PartitionTable parse_table(const crew::Sections §ions) { luci::PartitionTable table; + // default comply as OPCODE + table.comply = luci::PartitionTable::COMPLY::OPCODE; + + // read main '[partition]' first for (auto §ion : sections) { if (section.name == _section_partition) @@ -53,23 +62,63 @@ luci::PartitionTable parse_table(const crew::Sections §ions) throw std::invalid_argument("'default' is required"); } - table.groups = csv_to_vector<std::string>(items.at(_key_backends)); + table.groups = pepper::csv_to_vector<std::string>(items.at(_key_backends)); table.default_group = items.at(_key_default); + + auto comply = items.at(_key_comply); + + // check valid comply types + if (comply == _comply_opcode) + { + table.comply = luci::PartitionTable::COMPLY::OPCODE; + continue; + } + if (comply == _comply_opname) + { + table.comply = luci::PartitionTable::COMPLY::OPNAME; + continue; + } + throw std::runtime_error("Invalid or comply is not set"); } - else if (section.name == _section_OPCODE) + } + + // read other sections + for (auto §ion : sections) + { + if (section.name == _section_OPCODE) { auto &items = section.items; for (auto &item : items) { if (item.first == _key_underscore) - table.default_group = item.second; + { + if (table.comply == luci::PartitionTable::COMPLY::OPCODE) + table.default_group = item.second; + } else { table.byopcodes.emplace(item.first, item.second); } } } + else if (section.name == _section_OPNAME) + { + auto &items = section.items; + + for (auto &item : items) + { + if (item.first == _key_underscore) + { + if (table.comply == luci::PartitionTable::COMPLY::OPNAME) + table.default_group = item.second; + } + else + { + table.byopnames.emplace(item.first, item.second); + } + } + } } return table; diff --git a/compiler/circle-quantizer/src/CircleQuantizer.cpp b/compiler/circle-quantizer/src/CircleQuantizer.cpp index 720430e5a..5e717d085 100644 --- a/compiler/circle-quantizer/src/CircleQuantizer.cpp +++ b/compiler/circle-quantizer/src/CircleQuantizer.cpp @@ -75,6 +75,12 @@ int entry(int argc, char **argv) .help("Show version information and exit") .exit_with(print_version); + arser.add_argument("-V", "--verbose") + .nargs(0) + .required(false) + .default_value(false) + .help("output additional information to stdout or stderr"); + arser.add_argument(qdqw) .nargs(3) .type(arser::DataType::STR_VEC) @@ -111,7 +117,7 @@ int entry(int argc, char **argv) } catch (const std::runtime_error &err) { - std::cout << err.what() << std::endl; + std::cerr << err.what() << std::endl; std::cout << arser; return 255; } @@ -128,6 +134,13 @@ int entry(int argc, char **argv) } } + if (arser.get<bool>("--verbose")) + { + // The third parameter of setenv means REPLACE. + // If REPLACE is zero, it does not overwrite an existing value. + setenv("LUCI_LOG", "100", 0); + } + if (arser[qdqw]) { auto values = arser.get<std::vector<std::string>>(qdqw); diff --git a/compiler/circle-tensordump/CMakeLists.txt b/compiler/circle-tensordump/CMakeLists.txt index e55901fe2..4524260c4 100644 --- a/compiler/circle-tensordump/CMakeLists.txt +++ b/compiler/circle-tensordump/CMakeLists.txt @@ -2,7 +2,7 @@ if(NOT TARGET mio_circle) return() endif(NOT TARGET mio_circle) -nnas_find_package(HDF5 QUIET) +nnas_find_package(HDF5 COMPONENTS STATIC QUIET) if(NOT HDF5_FOUND) message(STATUS "Build circle-tensordump: FAILED (missing HDF5)") @@ -21,3 +21,5 @@ target_link_libraries(circle-tensordump PRIVATE arser) target_link_libraries(circle-tensordump PRIVATE foder) target_link_libraries(circle-tensordump PRIVATE mio_circle) target_link_libraries(circle-tensordump PRIVATE safemain) + +install(TARGETS circle-tensordump DESTINATION bin) diff --git a/compiler/circle-tensordump/src/Dump.cpp b/compiler/circle-tensordump/src/Dump.cpp index d5c3fe6fa..e477a7417 100644 --- a/compiler/circle-tensordump/src/Dump.cpp +++ b/compiler/circle-tensordump/src/Dump.cpp @@ -113,8 +113,10 @@ void DumpTensors::run(std::ostream &os, const circle::Model *model, const std::s auto tensors = reader.tensors(); for (const auto &tensor : *tensors) { + const auto tensor_name = tensor->name(); + std::string tensor_name_str = tensor_name ? tensor_name->str() : "no_name"; os << std::string(70, '-') << std::endl; - os << "[" << tensor->name()->str() << "]" << std::endl; + os << "[" << tensor_name_str << "]" << std::endl; auto buff_idx = tensor->buffer(); auto buff_data_ptr = reader.buffers()->Get(buff_idx)->data(); auto quant_param = tensor->quantization(); @@ -228,7 +230,9 @@ std::vector<hsize_t> hdf5_dims_cast(const flatbuffers::Vector<T> *data, ret.resize(rank); for (uint32_t d = 0; d < rank; d++) { - ret.at(d) = dims->Get(d); + if (dims->Get(d) < 0) + throw std::runtime_error("Dimensions shouldn't be negative"); + ret.at(d) = static_cast<hsize_t>(dims->Get(d)); } } } @@ -305,8 +309,16 @@ void DumpTensorsToHdf5::run(std::ostream &os, const circle::Model *model, auto tensors = reader.tensors(); for (const auto &tensor : *tensors) { + // If tensor does not have name, do nothing. + const auto tensor_name = tensor->name(); + if (tensor_name == nullptr) + { + assert(false && "There is no tensor name"); + continue; + } + // create a group for each tensor whose name is its tensor name - std::string group_name = ::mangle(tensor->name()->c_str()); + std::string group_name = ::mangle(tensor_name->c_str()); std::unique_ptr<H5::Group> tensor_group = std::make_unique<H5::Group>(file.createGroup(group_name)); diff --git a/compiler/circle2circle-dredd-recipe-test/CMakeLists.txt b/compiler/circle2circle-dredd-recipe-test/CMakeLists.txt index 4bcaae347..ee73d63e3 100644 --- a/compiler/circle2circle-dredd-recipe-test/CMakeLists.txt +++ b/compiler/circle2circle-dredd-recipe-test/CMakeLists.txt @@ -69,7 +69,7 @@ add_custom_command( COMMAND ${CMAKE_COMMAND} -E remove -f ${TEST_CONFIG} COMMAND ${CMAKE_COMMAND} -E echo 'CIRCLE_INSPECT_PATH=\"$<TARGET_FILE:circle-inspect>\"' >> ${TEST_CONFIG} COMMAND ${CMAKE_COMMAND} -E echo 'CIRCLE_VERIFY_PATH=\"$<TARGET_FILE:circle-verify>\"' >> ${TEST_CONFIG} - DEPENDS + DEPENDS circle-inspect circle-verify COMMENT "Generate test configuration" diff --git a/compiler/circle2circle-dredd-recipe-test/test.lst b/compiler/circle2circle-dredd-recipe-test/test.lst index bb944201e..95822c758 100644 --- a/compiler/circle2circle-dredd-recipe-test/test.lst +++ b/compiler/circle2circle-dredd-recipe-test/test.lst @@ -16,7 +16,10 @@ Add(Net_BroadcastTo_AddV2_001 PASS resolve_customop_add) Add(Net_Conv_Add_Mul_000 PASS fuse_batchnorm_with_conv) Add(Net_Conv_Add_Mul_001 PASS fuse_batchnorm_with_conv) Add(Net_Conv_Add_Mul_002 PASS fuse_batchnorm_with_conv) +Add(Net_Conv_FakeQuant_000 PASS remove_fakequant) +Add(Net_Conv_QuantDequant_000 PASS remove_quantdequant) Add(Net_Conv_Min_Max_000 PASS transform_min_max_to_relu6) +Add(Net_Conv_Min_Relu_000 PASS transform_min_relu_to_relu6) Add(Net_Conv_Relu6_000 PASS fuse_activation_function) Add(Net_DwConv_BN_000 PASS fuse_batchnorm_with_dwconv) Add(Net_DwConv_BN_001 PASS fuse_batchnorm_with_dwconv) @@ -29,13 +32,51 @@ Add(Net_TConv_BN_000 PASS fuse_batchnorm_with_tconv) Add(Net_TConv_BN_001 PASS fuse_batchnorm_with_tconv) Add(Net_TConv_BN_002 PASS fuse_batchnorm_with_tconv) Add(Net_InstanceNorm_001 PASS fuse_instnorm) -Add(Net_InstanceNorm_002 PASS fuse_instnorm) Add(Net_InstanceNorm_003 PASS fuse_instnorm) +Add(Net_InstanceNorm_004 PASS fuse_instnorm) +Add(Net_InstanceNorm_005 PASS fuse_instnorm) +Add(Net_InstanceNorm_006 PASS fuse_instnorm) +Add(Net_InstanceNorm_007 PASS fuse_instnorm) Add(Net_Maximum_Minimum_000 PASS transform_min_max_to_relu6) Add(BatchMatMulV2_000 PASS resolve_customop_batchmatmul) Add(MatMul_000 PASS resolve_customop_matmul) Add(DepthwiseConv2D_003 PASS) +Add(StridedSlice_003 PASS substitute_strided_slice_to_reshape) +Add(MaxPoolWithArgmax_000 PASS resolve_customop_max_pool_with_argmax) +Add(MaxPoolWithArgmax_001 PASS resolve_customop_max_pool_with_argmax) +Add(MaxPoolWithArgmax_002 PASS resolve_customop_max_pool_with_argmax) ## CIRCLE RECIPE Add(CircleBatchMatMul_000) + +# REGRESSION test + +Add(REGRESS_ONNX_Conv_BN_001 PASS + convert_nchw_to_nhwc + nchw_to_nhwc_input_shape + nchw_to_nhwc_output_shape + remove_redundant_transpose + substitute_transpose_to_reshape + remove_redundant_reshape + remove_unnecessary_reshape + fuse_batchnorm_with_conv) + +Add(REGRESS_ONNX_Conv_BN_Relu6_001 PASS + convert_nchw_to_nhwc + nchw_to_nhwc_input_shape + nchw_to_nhwc_output_shape + remove_redundant_transpose + transform_min_max_to_relu6 + fuse_batchnorm_with_conv + fuse_activation_function) + +Add(REGRESS_ONNX_Conv_BN_MeanMean_001 PASS + convert_nchw_to_nhwc + nchw_to_nhwc_input_shape + nchw_to_nhwc_output_shape + remove_redundant_transpose + fuse_batchnorm_with_conv + fuse_activation_function + fuse_mean_with_mean + fuse_transpose_with_mean) diff --git a/compiler/circle2circle/CMakeLists.txt b/compiler/circle2circle/CMakeLists.txt index f60c896d8..358fc4e2c 100644 --- a/compiler/circle2circle/CMakeLists.txt +++ b/compiler/circle2circle/CMakeLists.txt @@ -3,7 +3,6 @@ file(GLOB_RECURSE TESTS "src/*.test.cpp") list(REMOVE_ITEM SOURCES ${TESTS}) add_executable(circle2circle "${SOURCES}") -target_include_directories(circle2circle PRIVATE include) target_include_directories(circle2circle PRIVATE src) target_link_libraries(circle2circle foder) target_link_libraries(circle2circle nncc_common) @@ -30,7 +29,6 @@ endif(NOT ENABLE_TEST) nnas_find_package(GTest REQUIRED) GTest_AddTest(circle2circle_test ${TESTS} ${SOURCES}) -target_include_directories(circle2circle_test PRIVATE include) target_include_directories(circle2circle_test PRIVATE src) target_link_libraries(circle2circle_test foder) target_link_libraries(circle2circle_test nncc_common) diff --git a/compiler/circle2circle/src/Circle2Circle.cpp b/compiler/circle2circle/src/Circle2Circle.cpp index da05a0a9a..1998b1646 100644 --- a/compiler/circle2circle/src/Circle2Circle.cpp +++ b/compiler/circle2circle/src/Circle2Circle.cpp @@ -18,6 +18,7 @@ #include <luci/Importer.h> #include <luci/CircleOptimizer.h> +#include <luci/Service/ChangeOutputs.h> #include <luci/Service/Validate.h> #include <luci/CircleExporter.h> #include <luci/CircleFileExpContract.h> @@ -29,7 +30,10 @@ #include <functional> #include <iostream> +#include <sstream> #include <string> +#include <vector> +#include <cstdlib> using Algorithms = luci::CircleOptimizer::Options::Algorithm; using AlgorithmParameters = luci::CircleOptimizer::Options::AlgorithmParameters; @@ -40,6 +44,16 @@ void print_version(void) std::cout << vconone::get_copyright() << std::endl; } +void csv_tokenize(const std::string &data, std::vector<std::string> &result) +{ + const char delim = ','; + std::string token; + std::stringstream ss(data); + + while (std::getline(ss, token, delim)) + result.push_back(token); +} + int entry(int argc, char **argv) { // Simple argument parser (based on map) @@ -57,6 +71,12 @@ int entry(int argc, char **argv) .help("Show version information and exit") .exit_with(print_version); + arser.add_argument("-V", "--verbose") + .nargs(0) + .required(false) + .default_value(false) + .help("output additional information to stdout or stderr"); + arser.add_argument("--O1").nargs(0).required(false).default_value(false).help( "Enable O1 optimize options"); @@ -132,6 +152,19 @@ int entry(int argc, char **argv) .default_value(false) .help("This will fuse operators to InstanceNorm operator"); + arser.add_argument("--fuse_mean_with_mean") + .nargs(0) + .required(false) + .default_value(false) + .help("This will fuse two Mean operations when they follow one by one." + "This will fold them into one operation and merge reduction indices."); + + arser.add_argument("--fuse_transpose_with_mean") + .nargs(0) + .required(false) + .default_value(false) + .help("This will fuse Mean operation with a preceding Transpose under certain conditions."); + arser.add_argument("--make_batchnorm_gamma_positive") .nargs(0) .required(false) @@ -146,6 +179,18 @@ int entry(int argc, char **argv) .default_value(false) .help("This will fuse BatchNorm operators of pre-activations to Convolution operator"); + arser.add_argument("--remove_fakequant") + .nargs(0) + .required(false) + .default_value(false) + .help("This will remove FakeQuant operators"); + + arser.add_argument("--remove_quantdequant") + .nargs(0) + .required(false) + .default_value(false) + .help("This will remove Quantize-Dequantize sequence"); + arser.add_argument("--remove_redundant_reshape") .nargs(0) .required(false) @@ -188,6 +233,12 @@ int entry(int argc, char **argv) .default_value(false) .help("This will replace channel-wise mul/add with DepthwiseConv2D operator"); + arser.add_argument("--replace_sub_with_add") + .nargs(0) + .required(false) + .default_value(false) + .help("This will replace sub with add operator"); + arser.add_argument("--resolve_customop_add") .nargs(0) .required(false) @@ -206,6 +257,12 @@ int entry(int argc, char **argv) .default_value(false) .help("This will convert Custom(Matmul) to Matmul operator"); + arser.add_argument("--resolve_customop_max_pool_with_argmax") + .nargs(0) + .required(false) + .default_value(false) + .help("This will convert Custom(MaxPoolWithArgmax) to equivalent set of operators"); + arser.add_argument("--shuffle_weight_to_16x1float32") .nargs(0) .required(false) @@ -219,12 +276,24 @@ int entry(int argc, char **argv) .default_value(false) .help("This will convert single input Pack to Reshape"); + arser.add_argument("--substitute_padv2_to_pad") + .nargs(0) + .required(false) + .default_value(false) + .help("This will convert certain condition PadV2 to Pad"); + arser.add_argument("--substitute_squeeze_to_reshape") .nargs(0) .required(false) .default_value(false) .help("This will convert certain condition Squeeze to Reshape"); + arser.add_argument("--substitute_strided_slice_to_reshape") + .nargs(0) + .required(false) + .default_value(false) + .help("This will convert certain condition Strided_Slice to Reshape"); + arser.add_argument("--substitute_transpose_to_reshape") .nargs(0) .required(false) @@ -238,23 +307,29 @@ int entry(int argc, char **argv) .help("Experimental: This will convert NCHW operators to NHWC under the assumption that " "input model is NCHW."); - arser.add_argument("--nchw_to_nhwc_preserve_input_shape") + arser.add_argument("--nchw_to_nhwc_input_shape") .nargs(0) .required(false) .default_value(false) - .help("Preserve the input shape of the model (argument for --convert_nchw_to_nhwc)."); + .help("Convert the input shape of the model (argument for --convert_nchw_to_nhwc)."); - arser.add_argument("--nchw_to_nhwc_preserve_output_shape") + arser.add_argument("--nchw_to_nhwc_output_shape") .nargs(0) .required(false) .default_value(false) - .help("Preserve the output shape of the model (argument for --convert_nchw_to_nhwc)."); + .help("Convert the output shape of the model (argument for --convert_nchw_to_nhwc)."); arser.add_argument("--transform_min_max_to_relu6") .nargs(0) .required(false) .default_value(false) - .help("Transform Minimum-Maximum pattern to Relu6 operator"); + .help("Transform Minimum(6)-Maximum(0) pattern to Relu6 operator"); + + arser.add_argument("--transform_min_relu_to_relu6") + .nargs(0) + .required(false) + .default_value(false) + .help("Transform Minimum(6)-Relu pattern to Relu6 operator"); arser.add_argument("--mute_warnings") .nargs(0) @@ -274,6 +349,12 @@ int entry(int argc, char **argv) .default_value(false) .help("This will turn on profiling data generation."); + arser.add_argument("--change_outputs") + .nargs(1) + .type(arser::DataType::STR) + .required(false) + .help("Experimental: Change first subgraph output nodes to CSV names"); + arser.add_argument("input").nargs(1).type(arser::DataType::STR).help("Input circle model"); arser.add_argument("output").nargs(1).type(arser::DataType::STR).help("Output circle model"); @@ -318,11 +399,17 @@ int entry(int argc, char **argv) } catch (const std::runtime_error &err) { - std::cout << err.what() << std::endl; + std::cerr << err.what() << std::endl; std::cout << arser; return 255; } + if (arser.get<bool>("--verbose")) + { + // The third parameter of setenv means REPLACE. + // If REPLACE is zero, it does not overwrite an existing value. + setenv("LUCI_LOG", "100", 0); + } if (arser.get<bool>("--O1")) { options->enable(Algorithms::FuseBCQ); @@ -357,10 +444,18 @@ int entry(int argc, char **argv) options->enable(Algorithms::FuseBCQ); if (arser.get<bool>("--fuse_instnorm")) options->enable(Algorithms::FuseInstanceNorm); + if (arser.get<bool>("--fuse_mean_with_mean")) + options->enable(Algorithms::FuseMeanWithMean); if (arser.get<bool>("--make_batchnorm_gamma_positive")) options->enable(Algorithms::MakeBatchNormGammaPositive); if (arser.get<bool>("--fuse_preactivation_batchnorm")) options->enable(Algorithms::FusePreActivationBatchNorm); + if (arser.get<bool>("--fuse_transpose_with_mean")) + options->enable(Algorithms::FuseTransposeWithMean); + if (arser.get<bool>("--remove_fakequant")) + options->enable(Algorithms::RemoveFakeQuant); + if (arser.get<bool>("--remove_quantdequant")) + options->enable(Algorithms::RemoveQuantDequantSeq); if (arser.get<bool>("--remove_redundant_reshape")) options->enable(Algorithms::RemoveRedundantReshape); if (arser.get<bool>("--remove_redundant_transpose")) @@ -375,22 +470,32 @@ int entry(int argc, char **argv) options->enable(Algorithms::RemoveUnnecessarySplit); if (arser.get<bool>("--replace_cw_mul_add_with_depthwise_conv")) options->enable(Algorithms::ReplaceMulAddWithDepthwiseConv); + if (arser.get<bool>("--replace_sub_with_add")) + options->enable(Algorithms::ReplaceSubWithAdd); if (arser.get<bool>("--resolve_customop_add")) options->enable(Algorithms::ResolveCustomOpAdd); if (arser.get<bool>("--resolve_customop_batchmatmul")) options->enable(Algorithms::ResolveCustomOpBatchMatMul); if (arser.get<bool>("--resolve_customop_matmul")) options->enable(Algorithms::ResolveCustomOpMatMul); + if (arser.get<bool>("--resolve_customop_max_pool_with_argmax")) + options->enable(Algorithms::ResolveCustomOpMaxPoolWithArgmax); if (arser.get<bool>("--shuffle_weight_to_16x1float32")) options->enable(Algorithms::ShuffleWeightTo16x1Float32); if (arser.get<bool>("--substitute_pack_to_reshape")) options->enable(Algorithms::SubstitutePackToReshape); + if (arser.get<bool>("--substitute_padv2_to_pad")) + options->enable(Algorithms::SubstitutePadV2ToPad); if (arser.get<bool>("--substitute_squeeze_to_reshape")) options->enable(Algorithms::SubstituteSqueezeToReshape); + if (arser.get<bool>("--substitute_strided_slice_to_reshape")) + options->enable(Algorithms::SubstituteStridedSliceToReshape); if (arser.get<bool>("--substitute_transpose_to_reshape")) options->enable(Algorithms::SubstituteTransposeToReshape); if (arser.get<bool>("--transform_min_max_to_relu6")) options->enable(Algorithms::TransformMinMaxToRelu6Pass); + if (arser.get<bool>("--transform_min_relu_to_relu6")) + options->enable(Algorithms::TransformMinReluToRelu6Pass); if (arser.get<bool>("--mute_warnings")) settings->set(luci::UserSettings::Key::MuteWarnings, true); @@ -426,10 +531,20 @@ int entry(int argc, char **argv) if (arser.get<bool>("--convert_nchw_to_nhwc")) { options->enable(Algorithms::ConvertNCHWToNHWC); - if (arser.get<bool>("--nchw_to_nhwc_preserve_input_shape")) - options->param(AlgorithmParameters::NCHW_to_NHWC_preserve_input_shape, "true"); - if (arser.get<bool>("--nchw_to_nhwc_preserve_output_shape")) - options->param(AlgorithmParameters::NCHW_to_NHWC_preserve_output_shape, "true"); + if (arser.get<bool>("--nchw_to_nhwc_input_shape")) + options->param(AlgorithmParameters::NCHW_to_NHWC_input_shape, "true"); + if (arser.get<bool>("--nchw_to_nhwc_output_shape")) + options->param(AlgorithmParameters::NCHW_to_NHWC_output_shape, "true"); + } + + // Change output nodes + bool change_outputs = false; + std::vector<std::string> new_outputs; + if (arser["--change_outputs"]) + { + change_outputs = true; + auto csv_nodes = arser.get<std::string>("--change_outputs"); + csv_tokenize(csv_nodes, new_outputs); } // Load model from the file @@ -464,6 +579,12 @@ int entry(int argc, char **argv) luci::Importer importer; auto module = importer.importModule(circle_model); + if (change_outputs) + { + auto graph = module->graph(0); + luci::change_outputs(graph, new_outputs); + } + // call luci optimizations for module optimizer.optimize(module.get()); diff --git a/compiler/circlechef/core/src/Convert.cpp b/compiler/circlechef/core/src/Convert.cpp index d9bbd6e50..6066324b0 100644 --- a/compiler/circlechef/core/src/Convert.cpp +++ b/compiler/circlechef/core/src/Convert.cpp @@ -62,6 +62,8 @@ circle::TensorType as_circle_tensortype(const circlechef::TensorType &value) return circle::TensorType_UINT8; case circlechef::INT64: return circle::TensorType_INT64; + case circlechef::STRING: + return circle::TensorType_STRING; case circlechef::BOOL: return circle::TensorType_BOOL; case circlechef::INT16: diff --git a/compiler/souschef/include/souschef/DataChef.def b/compiler/circlechef/core/src/DataChef.def index d724d0390..c634c047e 100644 --- a/compiler/souschef/include/souschef/DataChef.def +++ b/compiler/circlechef/core/src/DataChef.def @@ -16,6 +16,7 @@ DATA_CHEF(INT16, explicit, ExplicitDataChefFactory<int16_t>) DATA_CHEF(UINT8, explicit, ExplicitDataChefFactory<uint8_t>) DATA_CHEF(BOOL, explicit, ExplicitDataChefFactory<bool>) DATA_CHEF(FLOAT32, explicit, ExplicitDataChefFactory<float>) +DATA_CHEF(STRING, explicit, ExplicitDataChefFactory<std::string>) DATA_CHEF(FLOAT32, gaussian, GaussianFloat32DataChefFactory) DATA_CHEF(INT32, gaussian, GaussianInt32DataChefFactory) DATA_CHEF(INT16, gaussian, GaussianInt16DataChefFactory) diff --git a/compiler/circlechef/core/src/ModelChef.cpp b/compiler/circlechef/core/src/ModelChef.cpp index d7101f618..6975f42a3 100644 --- a/compiler/circlechef/core/src/ModelChef.cpp +++ b/compiler/circlechef/core/src/ModelChef.cpp @@ -89,6 +89,7 @@ DataChefRegistry &data_chef_registry(const circlechef::TensorType &type) static DataChefRegistry s64; static DataChefRegistry fp32; static DataChefRegistry u8; + static DataChefRegistry string; static DataChefRegistry boolean; static DataChefRegistry s16; @@ -102,6 +103,8 @@ DataChefRegistry &data_chef_registry(const circlechef::TensorType &type) return fp32; case circlechef::UINT8: return u8; + case circlechef::STRING: + return string; case circlechef::BOOL: return boolean; case circlechef::INT16: @@ -493,7 +496,7 @@ GeneratedModel cook(const ::circlechef::ModelRecipe &model_recipe) #define DATA_CHEF(TYPE, NAME, FACTORY_CLASS) \ data_chef_registry(::circlechef::TYPE) \ .add(#NAME, std::unique_ptr<FACTORY_CLASS>(new FACTORY_CLASS())); -#include <souschef/DataChef.def> +#include "DataChef.def" #undef DATA_CHEF // diff --git a/compiler/circlechef/proto/circlechef.proto b/compiler/circlechef/proto/circlechef.proto index 1c14b97ff..d5e08576f 100644 --- a/compiler/circlechef/proto/circlechef.proto +++ b/compiler/circlechef/proto/circlechef.proto @@ -18,6 +18,7 @@ enum TensorType { INT32 = 2; UINT8 = 3; INT64 = 4; + STRING = 5; BOOL = 6; INT16 = 7; } diff --git a/compiler/circlechef/tests/string_tensor/test.recipe b/compiler/circlechef/tests/string_tensor/test.recipe new file mode 100644 index 000000000..8ab5bbce0 --- /dev/null +++ b/compiler/circlechef/tests/string_tensor/test.recipe @@ -0,0 +1,31 @@ +operand { + name: "ifm" + shape { } + type: STRING +} +operand { + name: "constant" + type: STRING + shape { } + filler { + tag: "explicit" + arg: "Hello" + } +} +operand { + name: "ofm" + type: STRING + shape { } +} +operation { + type: "BatchMatMul" + input: "ifm" + input: "constant" + output: "ofm" + batch_matmul_options { + adjoint_lhs: false + adjoint_rhs: false + } +} +input: "ifm" +output: "ofm" diff --git a/compiler/circledump/CMakeLists.txt b/compiler/circledump/CMakeLists.txt index fb72b1d66..8ef68370d 100644 --- a/compiler/circledump/CMakeLists.txt +++ b/compiler/circledump/CMakeLists.txt @@ -12,3 +12,5 @@ target_link_libraries(circledump arser) target_link_libraries(circledump mio_circle) target_link_libraries(circledump safemain) target_link_libraries(circledump flatbuffers) + +install(TARGETS circledump DESTINATION bin) diff --git a/compiler/circledump/src/OpPrinter.cpp b/compiler/circledump/src/OpPrinter.cpp index 5319bb88d..7af3ff641 100644 --- a/compiler/circledump/src/OpPrinter.cpp +++ b/compiler/circledump/src/OpPrinter.cpp @@ -85,6 +85,7 @@ public: os << std::boolalpha; os << "adjoint_lhs(" << params->adjoint_lhs() << ") "; os << "adjoint_rhs(" << params->adjoint_rhs() << ") "; + os << std::noboolalpha; os << std::endl; } } @@ -251,6 +252,7 @@ public: os << std::boolalpha; os << "align_corners(" << resize_params->align_corners() << ")"; os << "half_pixel_centers(" << resize_params->half_pixel_centers() << ")"; + os << std::noboolalpha; os << std::endl; } } @@ -266,6 +268,7 @@ public: os << " "; os << std::boolalpha; os << "align_corners(" << resize_params->align_corners() << ")"; + os << std::noboolalpha; os << std::endl; } } diff --git a/compiler/circledump/src/Read.h b/compiler/circledump/src/Read.h index be0e15827..c61a1ab6d 100644 --- a/compiler/circledump/src/Read.h +++ b/compiler/circledump/src/Read.h @@ -28,6 +28,11 @@ namespace circleread template <typename T> std::vector<T> as_index_vector(const flatbuffers::Vector<T> *flat_array) { + if (flat_array == nullptr) + { + throw std::runtime_error("flat array is nullptr"); + } + std::vector<T> ret(flat_array->Length()); for (uint32_t i = 0; i < flat_array->Length(); i++) { @@ -91,12 +96,12 @@ private: const CircleOperators_t *_operators{nullptr}; const CircleMetadata_t *_metadata{nullptr}; - uint32_t _subgraph_index; + uint32_t _subgraph_index = 0; std::string _subgraph_name; std::vector<const circle::OperatorCode *> _op_codes; std::vector<int32_t> _inputs; std::vector<int32_t> _outputs; - circle::DataFormat _data_format; + circle::DataFormat _data_format = circle::DataFormat::DataFormat_CHANNELS_FIRST; }; } // namespace circleread diff --git a/compiler/common-artifacts/CMakeLists.txt b/compiler/common-artifacts/CMakeLists.txt index e93a66ef0..edca29b34 100644 --- a/compiler/common-artifacts/CMakeLists.txt +++ b/compiler/common-artifacts/CMakeLists.txt @@ -258,7 +258,7 @@ foreach(RECIPE IN ITEMS ${RECIPES}) add_custom_command(OUTPUT ${INPUT_HDF5_FILE} ${EXPECTED_HDF5_FILE} COMMAND $<TARGET_FILE:testDataGenerator> --input_data ${INPUT_HDF5_FILE} --expected_data ${EXPECTED_HDF5_FILE} ${MODEL_FILE} DEPENDS $<TARGET_FILE:testDataGenerator> ${MODEL_FILE} ${TC_DIRECTORY} - COMMENT "Generate ${INPUT_HDF5_FILE} and ${EXPECTED_HDF5_FILE}" + COMMENT "Generate input.h5 and expected.h5 in ${NNPKG_FILE}/metadata/tc" ) list(APPEND TEST_DEPS ${INPUT_HDF5_FILE} ${EXPECTED_HDF5_FILE}) endif() diff --git a/compiler/common-artifacts/exclude.lst b/compiler/common-artifacts/exclude.lst index b9b758fe7..f32e00413 100644 --- a/compiler/common-artifacts/exclude.lst +++ b/compiler/common-artifacts/exclude.lst @@ -15,6 +15,8 @@ tcgenerate(Abs_000) tcgenerate(AddN_000) tcgenerate(Add_001) # runtime doesn't support tcgenerate(Add_U8_000) +tcgenerate(Add_STR_000) # STRING is not supported +tcgenerate(Add_STR_001) # STRING is not supported tcgenerate(All_000) tcgenerate(ArgMin_000) tcgenerate(ArgMin_001) @@ -52,17 +54,15 @@ tcgenerate(FullyConnected_U8_000) tcgenerate(Gather_000) tcgenerate(GatherNd_000) tcgenerate(GatherNd_001) -tcgenerate(If_000) -tcgenerate(If_001) tcgenerate(L2Pool2D_U8_000) tcgenerate(Log_000) tcgenerate(MatMul_000) tcgenerate(MatrixBandPart_000) tcgenerate(MatrixDiag_000) tcgenerate(MatrixSetDiag_000) -tcgenerate(MaxPoolWithArgMax_000) -tcgenerate(MaxPoolWithArgMax_001) -tcgenerate(MaxPoolWithArgMax_002) +tcgenerate(MaxPoolWithArgmax_000) +tcgenerate(MaxPoolWithArgmax_001) +tcgenerate(MaxPoolWithArgmax_002) tcgenerate(Mean_dynamic_000) # TestDataGenerator does not support unknown dimension tcgenerate(Mean_dynamic_001) # TestDataGenerator does not support unknown dimension tcgenerate(Mean_U8_dynamic_000) # TestDataGenerator does not support unknown dimension @@ -74,6 +74,8 @@ tcgenerate(MirrorPad_000) tcgenerate(Mul_U8_000) tcgenerate(Neg_000) tcgenerate(Net_BroadcastTo_AddV2_001) # luci-interpreter doesn't support custom operator +tcgenerate(Net_Conv_FakeQuant_000) # luci-interpreter doesn't support FakeQuant yet +tcgenerate(Net_Conv_QuantDequant_000) # luci-interpreter doesn't support Quantize/Dequantize yet tcgenerate(Net_Dangle_001) tcgenerate(Net_ZeroDim_001) # luci-interpreter doesn't support zero dim tcgenerate(OneHot_000) @@ -83,6 +85,7 @@ tcgenerate(OneHot_003) tcgenerate(Pack_000) tcgenerate(Pack_U8_000) tcgenerate(PadV2_000) +tcgenerate(Quantize_000) # runtime and luci-interpreter doesn't support Quantize op yet tcgenerate(Range_000) tcgenerate(Rank_000) tcgenerate(ReduceAny_000) @@ -149,10 +152,10 @@ tcgenerate(Unique_U8_000) tcgenerate(Unique_U8_001) tcgenerate(Where_000) tcgenerate(Where_001) -tcgenerate(While_000) -tcgenerate(While_001) -tcgenerate(While_002) -tcgenerate(While_003) +tcgenerate(While_000) # Needs luci-interpreter int32_t support for ADD, EQUAL +tcgenerate(While_001) # Needs luci-interpreter int32_t support for ADD, EQUAL +tcgenerate(While_002) # Needs luci-interpreter int32_t support for ADD, EQUAL +tcgenerate(While_003) # Needs luci-interpreter int32_t support for ADD, EQUAL, and dynamic shape for WHILE tcgenerate(YUV_TO_RGB_000) tcgenerate(YUV_TO_RGB_U8_000) tcgenerate(ZerosLike_000) diff --git a/compiler/common-artifacts/src/TestDataGenerator.cpp b/compiler/common-artifacts/src/TestDataGenerator.cpp index be6bb5ba9..b00e93e88 100644 --- a/compiler/common-artifacts/src/TestDataGenerator.cpp +++ b/compiler/common-artifacts/src/TestDataGenerator.cpp @@ -65,6 +65,15 @@ template <typename T> void geneate_random_data(std::mt19937 &gen, void *data, ui } } +template <> void geneate_random_data<bool>(std::mt19937 &gen, void *data, uint32_t size) +{ + std::normal_distribution<float> distrib(0, 2); // mean(0), stddev(2) + for (uint32_t i = 0; i < size; i++) + { + static_cast<bool *>(data)[i] = distrib(gen) >= 0 ? true : false; + } +} + void fill_random_data(void *data, uint32_t size, loco::DataType dtype, uint32_t seed) { std::mt19937 gen(seed); // standard mersenne_twister_engine seeded with rd() @@ -83,8 +92,11 @@ void fill_random_data(void *data, uint32_t size, loco::DataType dtype, uint32_t case loco::DataType::FLOAT32: geneate_random_data<float>(gen, data, size); break; - default: + case loco::DataType::BOOL: + geneate_random_data<bool>(gen, data, size); break; + default: + throw std::runtime_error("NYI data type."); } } @@ -166,9 +178,11 @@ int entry(int argc, char **argv) std::random_device rd; // used to obtain a seed for the random number engine uint32_t input_index = 0; - for (uint32_t g = 0; g < circle_model->subgraphs()->size(); g++) + // TODO remove indentation { - const auto input_nodes = loco::input_nodes(module->graph(g)); + // NOTE we only need to prepare data for main graph (subgraph 0) as + // other subgraphs are invoked by the main graph + const auto input_nodes = loco::input_nodes(module->graph(0)); for (const auto &node : input_nodes) { const auto *input_node = dynamic_cast<const luci::CircleInput *>(node); @@ -220,9 +234,9 @@ int entry(int argc, char **argv) // dump output data into hdf5 file uint32_t output_index = 0; - for (uint32_t g = 0; g < circle_model->subgraphs()->size(); g++) + // TODO remove indentation { - const auto output_nodes = loco::output_nodes(module->graph(g)); + const auto output_nodes = loco::output_nodes(module->graph(0)); for (const auto &node : output_nodes) { const auto *output_node = dynamic_cast<const luci::CircleOutput *>(node); diff --git a/compiler/foder/include/foder/FileLoader.h b/compiler/foder/include/foder/FileLoader.h index f0b052a63..cdf5ddef7 100644 --- a/compiler/foder/include/foder/FileLoader.h +++ b/compiler/foder/include/foder/FileLoader.h @@ -33,7 +33,7 @@ public: public: FileLoader(const FileLoader &) = delete; - FileLoader(FileLoader &&) = delete; + FileLoader &operator=(const FileLoader &) = delete; public: DataBuffer load(void) const @@ -41,7 +41,7 @@ public: std::ifstream file(_path, std::ios::binary | std::ios::in); if (!file.good()) { - std::string errmsg = "ERROR: Failed to open file: " + _path; + std::string errmsg = "Failed to open file: " + _path; throw std::runtime_error(errmsg.c_str()); } @@ -58,7 +58,7 @@ public: file.read(data.data(), fileSize); if (file.fail()) { - std::string errmsg = "ERROR: Failed to read file: " + _path; + std::string errmsg = "Failed to read file: " + _path; throw std::runtime_error(errmsg.c_str()); } diff --git a/compiler/loco/CMakeLists.txt b/compiler/loco/CMakeLists.txt index b1f61ade0..52011ec17 100644 --- a/compiler/loco/CMakeLists.txt +++ b/compiler/loco/CMakeLists.txt @@ -14,6 +14,8 @@ target_link_libraries(loco PRIVATE nncc_common) target_link_libraries(loco PUBLIC nncc_coverage) # Q. HOW TO MAKE DEV PACKAGE(?) install(TARGETS loco DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/loco/include/loco/IR/DataType.h b/compiler/loco/include/loco/IR/DataType.h index b07022bf5..b89edf29e 100644 --- a/compiler/loco/include/loco/IR/DataType.h +++ b/compiler/loco/include/loco/IR/DataType.h @@ -44,6 +44,9 @@ enum class DataType // WARNING the size of Bool may vary for NN frameworks // TODO we need to find a way to resolve this issue BOOL, // Boolean + + // WARNING STRING is NOT fully supported yet + STRING, // String }; } // namespace loco diff --git a/compiler/loco/include/loco/IR/DataTypeTraits.h b/compiler/loco/include/loco/IR/DataTypeTraits.h index 3713ac992..1f78c9fec 100644 --- a/compiler/loco/include/loco/IR/DataTypeTraits.h +++ b/compiler/loco/include/loco/IR/DataTypeTraits.h @@ -21,6 +21,7 @@ #include <cassert> #include <cstdint> +#include <stdexcept> namespace loco { @@ -101,6 +102,12 @@ template <> struct DataTypeImpl<DataType::BOOL> using Type = uint8_t; }; +template <> struct DataTypeImpl<DataType::STRING> +{ + // Use C++ std::string type for STRING + using Type = std::string; +}; + /** * @brief Returns the size of the data type. * @note If you need the size at compile time, use `sizeof(typename DataTypeImpl<DT>::Type)`. @@ -131,6 +138,9 @@ inline uint32_t size(DataType data_type) return sizeof(DataTypeImpl<DataType::FLOAT64>::Type); case DataType::BOOL: return sizeof(DataTypeImpl<DataType::BOOL>::Type); + case DataType::STRING: + // STRING is variable length. Cannot decide size by type + throw std::runtime_error("Invalid size call with STRING type"); default: // TODO Support remaining data types. assert(false); diff --git a/compiler/loco/include/loco/IR/NodeMixins.h b/compiler/loco/include/loco/IR/NodeMixins.h index f0e34b0ba..fcadcaba7 100644 --- a/compiler/loco/include/loco/IR/NodeMixins.h +++ b/compiler/loco/include/loco/IR/NodeMixins.h @@ -83,7 +83,7 @@ private: std::vector<Dimension> _dims; }; -template <unsigned N> struct FixedArity +template <uint32_t N> struct FixedArity { template <typename Base> class Mixin : public virtual Base { @@ -99,7 +99,7 @@ template <unsigned N> struct FixedArity virtual ~Mixin() = default; public: - unsigned arity(void) const final { return N; } + uint32_t arity(void) const final { return N; } Node *arg(uint32_t n) const final { return _args.at(n)->node(); } @@ -113,7 +113,7 @@ template <unsigned N> struct FixedArity protected: // This API allows inherited classes to access "_args" field. - Use *at(unsigned n) const { return _args.at(n).get(); } + Use *at(uint32_t n) const { return _args.at(n).get(); } private: std::array<std::unique_ptr<Use>, N> _args{}; diff --git a/compiler/locomotiv/src/Node/Pull.cpp b/compiler/locomotiv/src/Node/Pull.cpp index fe5d7c2e1..e60c646bd 100644 --- a/compiler/locomotiv/src/Node/Pull.cpp +++ b/compiler/locomotiv/src/Node/Pull.cpp @@ -31,15 +31,6 @@ using namespace locomotiv; void execute_node(loco::Pull *pull) { -// TODO Remove deprecated code -#if 0 - validate(annot_data(pull), "Data for Pull is not ready"); - - validate(annot_domain(pull) == loco::Domain::Tensor, "Domain for Pull is not Tensor"); - - // DO NOTHING -#endif - auto input_data = user_data(pull); validate(input_data, "Input not ready"); diff --git a/compiler/locomotiv/src/Node/Pull.test.cpp b/compiler/locomotiv/src/Node/Pull.test.cpp index 53e78776b..ec58c48f1 100644 --- a/compiler/locomotiv/src/Node/Pull.test.cpp +++ b/compiler/locomotiv/src/Node/Pull.test.cpp @@ -45,16 +45,8 @@ TEST(NodeExecution_Pull, check_data_ready) auto pull_data = locomotiv::make_data(pull_buf); locomotiv::user_data(pull, std::move(pull_data)); -// The behavior of Pull is now consistent with that of other nodes. -// - annot_data and annot_domain is available after evaluating that "pull" node. -// TODO Remove this -#if 0 - // Domain not ready yet - ASSERT_ANY_THROW(locomotiv::NodeExecution::get().run(pull)); - - // Set Domain - locomotiv::annot_domain(pull, loco::Domain::Tensor); -#endif + // The behavior of Pull is now consistent with that of other nodes. + // - annot_data and annot_domain is available after evaluating that "pull" node. // Valid run ASSERT_NO_THROW(locomotiv::NodeExecution::get().run(pull)); diff --git a/compiler/locomotiv/src/Node/ReLU6.cpp b/compiler/locomotiv/src/Node/ReLU6.cpp index 586c015fc..d237fd923 100644 --- a/compiler/locomotiv/src/Node/ReLU6.cpp +++ b/compiler/locomotiv/src/Node/ReLU6.cpp @@ -16,25 +16,6 @@ #include "NodeExecution.h" -// TODO Remove deprecated code -#if 0 -#include "NodeDataImpl.h" -#include "NodeDomain.h" -#include "Validation.h" - -#include <nncc/core/ADT/tensor/Shape.h> -#include <nncc/core/ADT/tensor/Buffer.h> -#include <nncc/core/ADT/tensor/IndexEnumerator.h> -#include <nncc/core/ADT/tensor/LexicalLayout.h> - -using nncc::core::ADT::tensor::IndexEnumerator; -using nncc::core::ADT::tensor::LexicalLayout; -using nncc::core::ADT::tensor::make_buffer; - -#include <cassert> -#include <stdexcept> -#endif - namespace { @@ -47,42 +28,6 @@ namespace locomotiv void NodeExecution::execute(loco::ReLU6 *relu6) { -// TODO Remove deprecated code -#if 0 - auto input_data = annot_data(relu6->input()); - - validate(input_data, "Input not ready"); - validate(annot_domain(relu6->input()) != loco::Domain::Unknown, - "Input domain of ReLU is Unknown"); - - std::unique_ptr<NodeData> relu6_data = nullptr; - - switch (input_data->dtype()) - { - case loco::DataType::FLOAT32: - { - auto input_bufptr = input_data->as_f32_bufptr(); - auto *shape = input_data->shape(); - auto relu6_buf = make_buffer<float, LexicalLayout>(*shape); - - for (IndexEnumerator e{*shape}; e.valid(); e.advance()) - { - const auto &index = e.current(); - relu6_buf.at(index) = relu6_ew(input_bufptr->at(index)); - } - - relu6_data = make_data(relu6_buf); - break; - } - default: - throw std::runtime_error("NYI for this DataType"); - } - - assert(relu6_data != nullptr); - annot_data(relu6, std::move(relu6_data)); - annot_domain(relu6, annot_domain(relu6->input())); -#endif - struct Func final : public UnaryFunc { float apply(float v) const final { return relu6_ew(v); } diff --git a/compiler/luci-eval-driver/CMakeLists.txt b/compiler/luci-eval-driver/CMakeLists.txt index 990f9d1a9..c67754a5e 100644 --- a/compiler/luci-eval-driver/CMakeLists.txt +++ b/compiler/luci-eval-driver/CMakeLists.txt @@ -10,3 +10,5 @@ target_link_libraries(luci_eval_driver PRIVATE luci_export) target_link_libraries(luci_eval_driver PRIVATE luci_lang) target_link_libraries(luci_eval_driver PRIVATE luci_interpreter) target_link_libraries(luci_eval_driver PRIVATE safemain) + +install(TARGETS luci_eval_driver DESTINATION bin) diff --git a/compiler/luci-interpreter/CMakeLists.txt b/compiler/luci-interpreter/CMakeLists.txt index 33fdc52aa..ab4ec1f43 100644 --- a/compiler/luci-interpreter/CMakeLists.txt +++ b/compiler/luci-interpreter/CMakeLists.txt @@ -1,4 +1,7 @@ set(LUCI_INTERPRETER_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include") set(LUCI_INTERPRETER_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/src") +if (NOT LUCI_INTERPRETER_PAL_DIR) + set(LUCI_INTERPRETER_PAL_DIR "${CMAKE_CURRENT_SOURCE_DIR}/pal/linux") +endif() add_subdirectory(src) diff --git a/compiler/luci-interpreter/pal/linux/pal.cmake b/compiler/luci-interpreter/pal/linux/pal.cmake new file mode 100644 index 000000000..da880c64c --- /dev/null +++ b/compiler/luci-interpreter/pal/linux/pal.cmake @@ -0,0 +1,49 @@ +macro(initialize_pal) + nnas_find_package(TensorFlowSource EXACT 2.3.0 QUIET) + nnas_find_package(TensorFlowGEMMLowpSource EXACT 2.3.0 QUIET) + nnas_find_package(TensorFlowEigenSource EXACT 2.3.0 QUIET) + nnas_find_package(TensorFlowRuySource EXACT 2.3.0 QUIET) + + if (NOT TensorFlowSource_FOUND) + message(STATUS "Skipping luci-interpreter: TensorFlow not found") + return() + endif () + + if (NOT TensorFlowGEMMLowpSource_FOUND) + message(STATUS "Skipping luci-interpreter: gemmlowp not found") + return() + endif () + + if (NOT TensorFlowEigenSource_FOUND) + message(STATUS "Skipping luci-interpreter: Eigen not found") + return() + endif () + + if (NOT TensorFlowRuySource_FOUND) + message(STATUS "Skipping luci-interpreter: Ruy not found") + return() + endif () + + find_package(Threads REQUIRED) + + set(PAL_INITIALIZED TRUE) +endmacro() + +macro(add_pal_to_target TGT) + target_include_directories(${TGT} PRIVATE "${PAL}") + target_include_directories(${TGT} SYSTEM PRIVATE + "${TensorFlowRuySource_DIR}" + "${TensorFlowGEMMLowpSource_DIR}" + "${TensorFlowEigenSource_DIR}" + "${TensorFlowSource_DIR}") + target_include_directories(${TGT} PRIVATE ${LUCI_INTERPRETER_PAL_DIR}) + + # TODO put it back, I changed my mind. + # instead add sources with visitors in this library + set(PAL_SOURCES ${TensorFlowSource_DIR}/tensorflow/lite/kernels/internal/quantization_util.cc) + add_library(luci_interpreter_linux_pal STATIC ${PAL_SOURCES}) + set_target_properties(luci_interpreter_linux_pal PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_include_directories(luci_interpreter_linux_pal SYSTEM PRIVATE "${TensorFlowSource_DIR}") + + target_link_libraries(${TGT} PRIVATE Threads::Threads luci_interpreter_linux_pal) +endmacro() diff --git a/compiler/luci-interpreter/pal/mcu/pal.cmake b/compiler/luci-interpreter/pal/mcu/pal.cmake new file mode 100644 index 000000000..2307ac727 --- /dev/null +++ b/compiler/luci-interpreter/pal/mcu/pal.cmake @@ -0,0 +1,49 @@ +macro(initialize_pal) + nnas_find_package(TensorFlowSource EXACT 2.3.0 QUIET) + nnas_find_package(TensorFlowGEMMLowpSource EXACT 2.3.0 QUIET) + nnas_find_package(TensorFlowEigenSource EXACT 2.3.0 QUIET) + nnas_find_package(TensorFlowRuySource EXACT 2.3.0 QUIET) + + if (NOT TensorFlowSource_FOUND) + message(STATUS "Skipping luci-interpreter: TensorFlow not found") + return() + endif () + + if (NOT TensorFlowGEMMLowpSource_FOUND) + message(STATUS "Skipping luci-interpreter: gemmlowp not found") + return() + endif () + + if (NOT TensorFlowEigenSource_FOUND) + message(STATUS "Skipping luci-interpreter: Eigen not found") + return() + endif () + + if (NOT TensorFlowRuySource_FOUND) + message(STATUS "Skipping luci-interpreter: Ruy not found") + return() + endif () + #find_package(Threads REQUIRED) + + set(PAL_INITIALIZED TRUE) +endmacro() + +macro(add_pal_to_target TGT) + target_include_directories(${TGT} PRIVATE "${PAL}") + target_include_directories(${TGT} SYSTEM PRIVATE + "${TensorFlowRuySource_DIR}" + "${TensorFlowGEMMLowpSource_DIR}" + "${TensorFlowEigenSource_DIR}" + "${TensorFlowSource_DIR}") + target_include_directories(${TGT} PRIVATE ${LUCI_INTERPRETER_PAL_DIR}) + + # TODO put it back, I changed my mind. + # instead add sources with visitors in this library + set(PAL_SOURCES ${TensorFlowSource_DIR}/tensorflow/lite/kernels/internal/quantization_util.cc) + add_library(luci_interpreter_mcu_pal STATIC ${PAL_SOURCES}) + set_target_properties(luci_interpreter_mcu_pal PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_include_directories(luci_interpreter_mcu_pal SYSTEM PRIVATE "${TensorFlowSource_DIR}") + + target_link_libraries(${TGT} PRIVATE luci_interpreter_mcu_pal) + #target_link_libraries(${TGT} PRIVATE Threads::Threads luci_interpreter_mcu_pal) +endmacro() diff --git a/compiler/luci-interpreter/src/CMakeLists.txt b/compiler/luci-interpreter/src/CMakeLists.txt index 47b68fa40..6f34b6117 100644 --- a/compiler/luci-interpreter/src/CMakeLists.txt +++ b/compiler/luci-interpreter/src/CMakeLists.txt @@ -1,31 +1,21 @@ -nnas_find_package(TensorFlowSource EXACT 2.3.0 QUIET) -nnas_find_package(TensorFlowGEMMLowpSource EXACT 2.3.0 QUIET) -nnas_find_package(TensorFlowEigenSource EXACT 2.3.0 QUIET) -nnas_find_package(TensorFlowRuySource EXACT 2.3.0 QUIET) +include(${LUCI_INTERPRETER_PAL_DIR}/pal.cmake) -if (NOT TensorFlowSource_FOUND) - message(STATUS "Skipping luci-interpreter: TensorFlow not found") - return() -endif () +initialize_pal() -if (NOT TensorFlowGEMMLowpSource_FOUND) - message(STATUS "Skipping luci-interpreter: gemmlowp not found") +if (NOT PAL_INITIALIZED) return() -endif () +endif() -if (NOT TensorFlowEigenSource_FOUND) - message(STATUS "Skipping luci-interpreter: Eigen not found") - return() -endif () - -if (NOT TensorFlowRuySource_FOUND) - message(STATUS "Skipping luci-interpreter: Ruy not found") - return() -endif () +message(STATUS "LUCI INTERPRETER BEGIN") add_subdirectory(core) +message(STATUS "LUCI INTERPRETER CORE") add_subdirectory(kernels) +message(STATUS "LUCI INTERPRETER KERNELS") add_subdirectory(loader) +message(STATUS "LUCI INTERPRETER LOADER") + +message(STATUS "LUCI INTERPTER INITALIZED") set(SOURCES "${LUCI_INTERPRETER_INCLUDE_DIR}/luci_interpreter/Interpreter.h" @@ -39,3 +29,5 @@ target_link_libraries(luci_interpreter PRIVATE nncc_common) install(TARGETS luci_interpreter DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") diff --git a/compiler/luci-interpreter/src/core/KernelParams.h b/compiler/luci-interpreter/src/core/KernelParams.h index dab6ba25f..ee0390fcc 100644 --- a/compiler/luci-interpreter/src/core/KernelParams.h +++ b/compiler/luci-interpreter/src/core/KernelParams.h @@ -19,6 +19,7 @@ #include <luci/IR/AttrPadding.h> #include <luci/IR/AttrFusedActFunc.h> +#include <luci/IR/AttrMirrorPadMode.h> #include <luci_interpreter/core/DataType.h> #include <cstdint> @@ -30,6 +31,7 @@ namespace luci_interpreter // Inject commonly used types into `luci_interpreter` namespace for convenience. using Activation = luci::FusedActFunc; using Padding = luci::Padding; +using MirrorPadMode = luci::MirrorPadMode; struct AddParams { @@ -107,6 +109,11 @@ struct LocalResponseNormalizationParams float beta; }; +struct MirrorPadParams +{ + MirrorPadMode mode; +}; + struct MulParams { Activation activation; diff --git a/compiler/luci-interpreter/src/kernels/CMakeLists.txt b/compiler/luci-interpreter/src/kernels/CMakeLists.txt index d7ab76374..9801e11af 100644 --- a/compiler/luci-interpreter/src/kernels/CMakeLists.txt +++ b/compiler/luci-interpreter/src/kernels/CMakeLists.txt @@ -9,6 +9,8 @@ set(SOURCES AveragePool2D.cpp BatchToSpaceND.h BatchToSpaceND.cpp + Cast.h + Cast.cpp Concatenation.h Concatenation.cpp Conv2D.h @@ -69,6 +71,8 @@ set(SOURCES Mean.cpp Minimum.h Minimum.cpp + MirrorPad.h + MirrorPad.cpp Mul.h Mul.cpp Neg.h @@ -79,10 +83,12 @@ set(SOURCES Pack.cpp Pad.h Pad.cpp + PadV2.h + PadV2.cpp Pow.h Pow.cpp - Prelu.h - Prelu.cpp + PRelu.h + PRelu.cpp Relu.h Relu.cpp Relu6.h @@ -93,8 +99,8 @@ set(SOURCES ResizeBilinear.cpp ResizeNearestNeighbor.h ResizeNearestNeighbor.cpp - Reverse.h - Reverse.cpp + ReverseV2.h + ReverseV2.cpp Rsqrt.h Rsqrt.cpp Slice.h @@ -111,6 +117,8 @@ set(SOURCES StridedSlice.cpp Sqrt.h Sqrt.cpp + Square.h + Square.cpp SquaredDifference.h SquaredDifference.cpp Squeeze.h @@ -124,7 +132,9 @@ set(SOURCES TransposeConv.h TransposeConv.cpp Unpack.h - Unpack.cpp) + Unpack.cpp + While.h + While.cpp) list(APPEND SOURCES BinaryOpCommon.h @@ -155,6 +165,7 @@ set(TEST_SOURCES ArgMax.test.cpp AveragePool2D.test.cpp BatchToSpaceND.test.cpp + Cast.test.cpp Concatenation.test.cpp Conv2D.test.cpp DepthToSpace.test.cpp @@ -190,14 +201,15 @@ set(TEST_SOURCES NotEqual.test.cpp Pack.test.cpp Pad.test.cpp + PadV2.test.cpp Pow.test.cpp - Prelu.test.cpp + PRelu.test.cpp Relu.test.cpp Relu6.test.cpp Reshape.test.cpp ResizeBilinear.test.cpp ResizeNearestNeighbor.test.cpp - Reverse.test.cpp + ReverseV2.test.cpp Rsqrt.test.cpp Slice.test.cpp Softmax.test.cpp @@ -206,13 +218,15 @@ set(TEST_SOURCES Split.test.cpp StridedSlice.test.cpp Sqrt.test.cpp + Square.test.cpp SquaredDifference.test.cpp Squeeze.test.cpp Sub.test.cpp Tanh.test.cpp Transpose.test.cpp TransposeConv.test.cpp - Unpack.test.cpp) + Unpack.test.cpp + While.test.cpp) list(APPEND TEST_SOURCES TestUtils.h TestUtils.cpp) diff --git a/compiler/luci-interpreter/src/kernels/Cast.cpp b/compiler/luci-interpreter/src/kernels/Cast.cpp new file mode 100644 index 000000000..39ee725dc --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Cast.cpp @@ -0,0 +1,143 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Cast.h" +#include "kernels/Utils.h" + +namespace +{ + +using namespace luci_interpreter; +using namespace luci_interpreter::kernels; + +template <typename InT, typename OutT> +void cast_data(const InT *in_data, OutT *out_data, uint32_t elements_count) +{ + std::transform(in_data, in_data + elements_count, out_data, + [](InT a) { return static_cast<OutT>(a); }); +} + +template <typename InT> void cast_from_pointer_to_tensor(const InT *in_data, Tensor *out_tensor) +{ + auto const out_type = out_tensor->element_type(); + auto const elements_count = out_tensor->shape().num_elements(); + + switch (out_type) + { + case loco::DataType::U8: + cast_data(in_data, getTensorData<uint8_t>(out_tensor), elements_count); + break; + case loco::DataType::U16: + cast_data(in_data, getTensorData<uint16_t>(out_tensor), elements_count); + break; + case loco::DataType::U32: + cast_data(in_data, getTensorData<uint32_t>(out_tensor), elements_count); + break; + case loco::DataType::U64: + cast_data(in_data, getTensorData<uint64_t>(out_tensor), elements_count); + break; + case loco::DataType::S8: + cast_data(in_data, getTensorData<int8_t>(out_tensor), elements_count); + break; + case loco::DataType::S16: + cast_data(in_data, getTensorData<int16_t>(out_tensor), elements_count); + break; + case loco::DataType::S32: + cast_data(in_data, getTensorData<int32_t>(out_tensor), elements_count); + break; + case loco::DataType::S64: + cast_data(in_data, getTensorData<int64_t>(out_tensor), elements_count); + break; + case loco::DataType::FLOAT32: + cast_data(in_data, getTensorData<float>(out_tensor), elements_count); + break; + case loco::DataType::BOOL: + cast_data(in_data, getTensorData<bool>(out_tensor), elements_count); + break; + default: + throw std::runtime_error("Unsupported output type."); + } +} + +void cast_from_tensor_to_tensor(const Tensor *in_tensor, Tensor *out_tensor) +{ + auto in_type = in_tensor->element_type(); + + switch (in_type) + { + case loco::DataType::U8: + cast_from_pointer_to_tensor(getTensorData<uint8_t>(in_tensor), out_tensor); + break; + case loco::DataType::U16: + cast_from_pointer_to_tensor(getTensorData<uint16_t>(in_tensor), out_tensor); + break; + case loco::DataType::U32: + cast_from_pointer_to_tensor(getTensorData<uint32_t>(in_tensor), out_tensor); + break; + case loco::DataType::U64: + cast_from_pointer_to_tensor(getTensorData<uint64_t>(in_tensor), out_tensor); + break; + case loco::DataType::S8: + cast_from_pointer_to_tensor(getTensorData<int8_t>(in_tensor), out_tensor); + break; + case loco::DataType::S16: + cast_from_pointer_to_tensor(getTensorData<int16_t>(in_tensor), out_tensor); + break; + case loco::DataType::S32: + cast_from_pointer_to_tensor(getTensorData<int32_t>(in_tensor), out_tensor); + break; + case loco::DataType::S64: + cast_from_pointer_to_tensor(getTensorData<int64_t>(in_tensor), out_tensor); + break; + case loco::DataType::FLOAT32: + cast_from_pointer_to_tensor(getTensorData<float>(in_tensor), out_tensor); + break; + case loco::DataType::BOOL: + cast_from_pointer_to_tensor(getTensorData<bool>(in_tensor), out_tensor); + break; + default: + throw std::runtime_error("Unsupported input type."); + } +} + +} // namespace + +namespace luci_interpreter +{ +namespace kernels +{ + +Cast::Cast(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {} + +void Cast::configure() +{ + LUCI_INTERPRETER_CHECK(input()->element_type() != loco::DataType::Unknown); + LUCI_INTERPRETER_CHECK(output()->element_type() != loco::DataType::Unknown); + + const Shape &shape = input()->shape(); + output()->resize(shape); +} + +void Cast::execute() const +{ + assert(input()->shape().num_elements() == output()->shape().num_elements()); + + cast_from_tensor_to_tensor(input(), output()); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Cast.h b/compiler/luci-interpreter/src/kernels/Cast.h new file mode 100644 index 000000000..f0bd02037 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Cast.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_CAST_H +#define LUCI_INTERPRETER_KERNELS_CAST_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Cast : public Kernel +{ +public: + Cast(const Tensor *input, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_CAST_H diff --git a/compiler/luci-interpreter/src/kernels/Cast.test.cpp b/compiler/luci-interpreter/src/kernels/Cast.test.cpp new file mode 100644 index 000000000..42944628d --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Cast.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Cast.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template <typename T1, typename T2> +void Check(std::initializer_list<int32_t> shape, std::initializer_list<T1> input_data, + std::initializer_list<T2> output_data) +{ + constexpr DataType input_type = getElementType<T1>(); + constexpr DataType output_type = getElementType<T2>(); + + Tensor input_tensor = makeInputTensor<input_type>(shape, input_data); + Tensor output_tensor = makeOutputTensor(output_type); + + Cast kernel(&input_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData<T2>(output_tensor), ::testing::ElementsAreArray(output_data)); + EXPECT_THAT(extractTensorShape(output_tensor), shape); +} + +template <typename T> class CastTest : public ::testing::Test +{ +}; + +using DataTypes = ::testing::Types<uint8_t, int32_t, int64_t>; +TYPED_TEST_CASE(CastTest, DataTypes); + +TYPED_TEST(CastTest, FloatToInt) +{ + Check<float, TypeParam>(/*shape=*/{1, 1, 1, 4}, + /*input_data=*/ + { + 1.43f, 9.99f, 7.0f, 3.12f, // + }, + /*output_data=*/ + { + 1, 9, 7, 3, // + }); + Check<TypeParam, TypeParam>(/*shape=*/{1, 1, 1, 4}, + /*input_data=*/ + { + 1, 9, 7, 3, // + }, + /*output_data=*/ + { + 1, 9, 7, 3, // + }); +} + +TEST(CastTest, UnsupportedType_NEG) +{ + Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1, 1, 2, 4}, { + 1, 2, 7, 8, // + 1, 9, 7, 3, // + }); + Tensor output_tensor = makeOutputTensor(DataType::Unknown); + + Cast kernel(&input_tensor, &output_tensor); + EXPECT_ANY_THROW(kernel.configure()); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/InstanceNorm.cpp b/compiler/luci-interpreter/src/kernels/InstanceNorm.cpp index b8317e2f2..22a329be6 100644 --- a/compiler/luci-interpreter/src/kernels/InstanceNorm.cpp +++ b/compiler/luci-interpreter/src/kernels/InstanceNorm.cpp @@ -37,7 +37,13 @@ void InstanceNorm::configure() LUCI_INTERPRETER_CHECK(input()->shape().num_dims() == 4); LUCI_INTERPRETER_CHECK(input()->element_type() == output()->element_type()); LUCI_INTERPRETER_CHECK(gamma()->element_type() == input()->element_type()); + LUCI_INTERPRETER_CHECK(gamma()->shape().num_dims() == 1); + LUCI_INTERPRETER_CHECK(gamma()->shape().dim(0) == input()->shape().dim(3) || + gamma()->shape().dim(0) == 1); LUCI_INTERPRETER_CHECK(beta()->element_type() == input()->element_type()); + LUCI_INTERPRETER_CHECK(beta()->shape().num_dims() == 1); + LUCI_INTERPRETER_CHECK(beta()->shape().dim(0) == input()->shape().dim(3) || + beta()->shape().dim(0) == 1); output()->resize(input()->shape()); } @@ -65,7 +71,11 @@ void InstanceNorm::evalFloat() const const int32_t channels = tflite::MatchingDim(input_shape, 3, output_shape, 3); const float *input_data = getTensorData<float>(input()); const float *gamma_data = getTensorData<float>(gamma()); + auto gamma_shape = getTensorShape(gamma()); + bool single_gamma = gamma_shape.DimensionsCount() == 1 && gamma_shape.Dims(0) == 1; const float *beta_data = getTensorData<float>(beta()); + auto beta_shape = getTensorShape(beta()); + bool single_beta = beta_shape.DimensionsCount() == 1 && beta_shape.Dims(0) == 1; float *output_data = getTensorData<float>(output()); for (int32_t batch = 0; batch < batches; batch++) { @@ -86,8 +96,8 @@ void InstanceNorm::evalFloat() const double mean = sum / size; double var = square_sum / size - mean * mean; - double gamma = gamma_data[channel]; - double beta = beta_data[channel]; + double gamma = single_gamma ? gamma_data[0] : gamma_data[channel]; + double beta = single_beta ? beta_data[0] : beta_data[channel]; double a = gamma / (std::sqrt(var + params().epsilon)); double b = -mean * a + beta; diff --git a/compiler/luci-interpreter/src/kernels/InstanceNorm.test.cpp b/compiler/luci-interpreter/src/kernels/InstanceNorm.test.cpp index 19f863544..1d4ccb4cd 100644 --- a/compiler/luci-interpreter/src/kernels/InstanceNorm.test.cpp +++ b/compiler/luci-interpreter/src/kernels/InstanceNorm.test.cpp @@ -43,6 +43,40 @@ TEST(InstanceNormTest, Simple) EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 2, 1})); } +TEST(InstanceNormTest, Single_gamma_beta) +{ + Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1, 2, 1, 2}, {1, 1, 1, 1}); + Tensor gamma_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1}); + Tensor beta_tensor = makeInputTensor<DataType::FLOAT32>({1}, {2}); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + InstanceNormParams params{}; + params.epsilon = 0.1f; + params.activation = Activation::NONE; + + InstanceNorm kernel(&input_tensor, &gamma_tensor, &beta_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear({2, 2, 2, 2})); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 1, 2})); +} + +TEST(InstanceNormTest, Wrong_gamma_beta_dim_NEG) +{ + Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1, 2, 1, 2}, {1, 1, 1, 1}); + Tensor gamma_tensor = makeInputTensor<DataType::FLOAT32>({3}, {1, 1, 1}); + Tensor beta_tensor = makeInputTensor<DataType::FLOAT32>({3}, {2, 2, 2}); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + InstanceNormParams params{}; + params.epsilon = 0.1f; + params.activation = Activation::NONE; + + InstanceNorm kernel(&input_tensor, &gamma_tensor, &beta_tensor, &output_tensor, params); + EXPECT_ANY_THROW(kernel.configure()); +} + } // namespace } // namespace kernels } // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/MirrorPad.cpp b/compiler/luci-interpreter/src/kernels/MirrorPad.cpp new file mode 100644 index 000000000..1139167e0 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/MirrorPad.cpp @@ -0,0 +1,108 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/MirrorPad.h" + +#include "kernels/Utils.h" + +#include <tensorflow/lite/kernels/internal/reference/reference_ops.h> + +namespace luci_interpreter +{ +namespace kernels +{ + +MirrorPad::MirrorPad(const Tensor *input, const Tensor *paddings, Tensor *output, + const MirrorPadParams ¶ms) + : KernelWithParams<MirrorPadParams>({input, paddings}, {output}, params) +{ +} + +void MirrorPad::configure() +{ + const Shape &input_shape = input()->shape(); + const int num_dims = input_shape.num_dims(); + + if (num_dims > 4) + throw std::runtime_error("Unsupported number of dimensions."); + + assert(output()->element_type() == input()->element_type()); + assert(paddings()->element_type() == DataType::S32); + // Paddings shape should be [N, 2]. + assert(paddings()->shape().num_dims() == 2); + assert(paddings()->shape().dim(0) == num_dims); + assert(paddings()->shape().dim(1) == 2); + + Shape output_shape(num_dims); + const auto *paddings_data = getTensorData<int32_t>(paddings()); + for (int i = 0; i < num_dims; ++i) + { + const int32_t padding_before = paddings_data[i * 2]; + const int32_t padding_after = paddings_data[i * 2 + 1]; + assert(padding_before >= 0 && padding_after >= 0); + output_shape.dim(i) = input_shape.dim(i) + padding_before + padding_after; + } + + output()->resize(output_shape); +} + +void MirrorPad::execute() const +{ + const int num_dims = input()->shape().num_dims(); + + tflite::PadParams params{}; + params.left_padding_count = num_dims; + params.right_padding_count = num_dims; + + const auto *paddings_data = getTensorData<int32_t>(paddings()); + for (int i = num_dims - 1; i >= 0; --i) + { + params.left_padding[i] = paddings_data[i * 2]; + params.right_padding[i] = paddings_data[i * 2 + 1]; + } + + switch (input()->element_type()) + { + case DataType::FLOAT32: + { + const float pad_value = 0; + + // NOTE: this implementation only obtains min-max values for quantization + // TODO: calculate proper inference values + tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData<float>(input()), + &pad_value, getTensorShape(output()), + getTensorData<float>(output())); + break; + } + case DataType::U8: + { + // NOTE: this implementation only obtains min-max values for quantization + // TODO: calculate proper inference values + assert(output()->zero_point() >= std::numeric_limits<uint8_t>::min()); + assert(output()->zero_point() <= std::numeric_limits<uint8_t>::max()); + const auto pad_value = static_cast<uint8_t>(output()->zero_point()); + tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData<uint8_t>(input()), + &pad_value, getTensorShape(output()), + getTensorData<uint8_t>(output())); + break; + } + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/MirrorPad.h b/compiler/luci-interpreter/src/kernels/MirrorPad.h new file mode 100644 index 000000000..d3e6e858a --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/MirrorPad.h @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_MIRROR_PAD_H +#define LUCI_INTERPRETER_KERNELS_MIRROR_PAD_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class MirrorPad : public KernelWithParams<MirrorPadParams> +{ +public: + MirrorPad(const Tensor *input, const Tensor *paddings, Tensor *output, + const MirrorPadParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *paddings() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_MIRROR_PAD_H diff --git a/compiler/luci-interpreter/src/kernels/Prelu.cpp b/compiler/luci-interpreter/src/kernels/PRelu.cpp index c4b288f1b..a53ac6f80 100644 --- a/compiler/luci-interpreter/src/kernels/Prelu.cpp +++ b/compiler/luci-interpreter/src/kernels/PRelu.cpp @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "kernels/Prelu.h" +#include "kernels/PRelu.h" #include "kernels/BinaryOpCommon.h" #include "kernels/Utils.h" @@ -29,17 +29,17 @@ namespace luci_interpreter namespace kernels { -Prelu::Prelu(const Tensor *input, const Tensor *alpha, Tensor *output) +PRelu::PRelu(const Tensor *input, const Tensor *alpha, Tensor *output) : Kernel({input, alpha}, {output}) { } -Prelu::~Prelu() +PRelu::~PRelu() { // Destructor declared to delete vector of alpha quantized data properly } -void Prelu::configure() +void PRelu::configure() { LUCI_INTERPRETER_CHECK(input()->element_type() == output()->element_type()); LUCI_INTERPRETER_CHECK(alpha()->element_type() == output()->element_type()); @@ -64,7 +64,7 @@ void Prelu::configure() { LUCI_INTERPRETER_CHECK(alpha()->zero_points()[channel] == 0); } - // Prelu specific checks for CWQ + // PRelu specific checks for CWQ LUCI_INTERPRETER_CHECK(alpha()->quantized_dimension() == alpha()->shape().num_dims() - 1); LUCI_INTERPRETER_CHECK(static_cast<int32_t>(alpha()->scales().size()) == alpha()->shape().dim(alpha()->quantized_dimension())); @@ -88,7 +88,7 @@ void Prelu::configure() output()->resize(calculateShapeForBroadcast(input()->shape(), alpha()->shape())); } -void Prelu::execute() const +void PRelu::execute() const { switch (input()->element_type()) { @@ -106,21 +106,21 @@ void Prelu::execute() const } } -void Prelu::evalFloat() const +void PRelu::evalFloat() const { const auto input_data = getTensorData<float>(input()); const auto alpha_data = getTensorData<float>(alpha()); const auto size = getTensorShape(input()).FlatSize(); auto output_data = getTensorData<float>(output()); - auto PreluFunc = [](float input, float alpha) { return input >= 0.0 ? input : input * alpha; }; + auto PReluFunc = [](float input, float alpha) { return input >= 0.0 ? input : input * alpha; }; if (input()->shape() != alpha()->shape()) { tflite::reference_ops::BroadcastBinaryFunction4DSlow<float, float, float>( getTensorShape(input()), getTensorData<float>(input()), getTensorShape(alpha()), getTensorData<float>(alpha()), getTensorShape(output()), getTensorData<float>(output()), - PreluFunc); + PReluFunc); } else { @@ -134,7 +134,7 @@ void Prelu::evalFloat() const } } -void Prelu::evalQuantized() const +void PRelu::evalQuantized() const { tflite::PreluParams op_params{}; @@ -160,7 +160,7 @@ void Prelu::evalQuantized() const } } -static inline int16_t evalElemS16Prelu(int16_t input_val, int16_t alpha_val, +static inline int16_t evalElemS16PRelu(int16_t input_val, int16_t alpha_val, const ChannelQuantMultipliers &identity_mult, const ChannelQuantMultipliers &alpha_mult) { @@ -176,7 +176,7 @@ static inline int16_t evalElemS16Prelu(int16_t input_val, int16_t alpha_val, return clamped_output; } -void Prelu::evalQuantizedS16() const +void PRelu::evalQuantizedS16() const { // Note that this kernel assumes alpha is CWQ tflite::RuntimeShape input_shape = getTensorShape(input()); @@ -201,7 +201,7 @@ void Prelu::evalQuantizedS16() const offset += quant_channel; output_data[offset] = - evalElemS16Prelu(input_data[offset], alpha_data[quant_channel], pos_mult, neg_mult); + evalElemS16PRelu(input_data[offset], alpha_data[quant_channel], pos_mult, neg_mult); } } diff --git a/compiler/luci-interpreter/src/kernels/Prelu.h b/compiler/luci-interpreter/src/kernels/PRelu.h index 08cb0eaa5..e85c3f7e9 100644 --- a/compiler/luci-interpreter/src/kernels/Prelu.h +++ b/compiler/luci-interpreter/src/kernels/PRelu.h @@ -27,12 +27,12 @@ namespace kernels class ChannelQuantMultipliers; -class Prelu : public Kernel +class PRelu : public Kernel { public: - Prelu(const Tensor *input, const Tensor *alpha, Tensor *output); + PRelu(const Tensor *input, const Tensor *alpha, Tensor *output); - ~Prelu(); + ~PRelu(); const Tensor *input() const { return _inputs[0]; } const Tensor *alpha() const { return _inputs[1]; } diff --git a/compiler/luci-interpreter/src/kernels/Prelu.test.cpp b/compiler/luci-interpreter/src/kernels/PRelu.test.cpp index 9d9adf66f..3dbc51cc1 100644 --- a/compiler/luci-interpreter/src/kernels/Prelu.test.cpp +++ b/compiler/luci-interpreter/src/kernels/PRelu.test.cpp @@ -15,7 +15,7 @@ * limitations under the License. */ -#include "kernels/Prelu.h" +#include "kernels/PRelu.h" #include "kernels/TestUtils.h" namespace luci_interpreter @@ -37,7 +37,7 @@ void Check(std::initializer_list<int32_t> input_shape, std::initializer_list<int Tensor alpha_tensor = makeInputTensor<element_type>(alpha_shape, alpha_data); Tensor output_tensor = makeOutputTensor(element_type); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); kernel.configure(); kernel.execute(); @@ -46,7 +46,7 @@ void Check(std::initializer_list<int32_t> input_shape, std::initializer_list<int EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape)); } -TEST(PreluTest, FloatSimple) +TEST(PReluTest, FloatSimple) { Check<float>(/*input_shape=*/{2, 3}, /*alpha_shape=*/{2, 3}, /*output_shape=*/{2, 3}, @@ -69,7 +69,7 @@ TEST(PreluTest, FloatSimple) SUCCEED(); } -TEST(PreluTest, FloatBroadcast) +TEST(PReluTest, FloatBroadcast) { Check<float>(/*input_shape=*/{1, 2, 2, 3}, /*alpha_shape=*/{1, 1, 3}, /*output_shape=*/{1, 2, 2, 3}, @@ -95,7 +95,7 @@ TEST(PreluTest, FloatBroadcast) float GetTolerance(float min, float max) { return (max - min) / 255.0; } -TEST(PreluTest, Uint8Simple) +TEST(PReluTest, Uint8Simple) { std::vector<float> input_data{-0.8f, 0.2f, 0.9f, 0.7f, 0.1f, -0.4f}; std::vector<float> alpha_data{0.5f, 0.5f, 0.5f, 0.25f, 1.0f, 0.25f}; @@ -110,7 +110,7 @@ TEST(PreluTest, Uint8Simple) makeInputTensor<DataType::U8>({1, 2, 3, 1}, quant_param.first, quant_param.second, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); kernel.configure(); kernel.execute(); @@ -121,7 +121,7 @@ TEST(PreluTest, Uint8Simple) SUCCEED(); } -TEST(PreluTest, Uint8Broadcast) +TEST(PReluTest, Uint8Broadcast) { std::vector<float> input_data{ 0.0f, 0.0f, 0.0f, // Row 1, Column 1 @@ -153,7 +153,7 @@ TEST(PreluTest, Uint8Broadcast) makeInputTensor<DataType::U8>({1, 1, 3}, quant_param.first, quant_param.second, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); kernel.configure(); kernel.execute(); @@ -164,7 +164,7 @@ TEST(PreluTest, Uint8Broadcast) ::testing::ElementsAreArray(ref_quant_output_data)); } -TEST(PreluTest, SInt16_LWQ_NEG) +TEST(PReluTest, SInt16_LWQ_NEG) { // Rewrite this test in case layer-wise quantization for sint16 is supported std::vector<float> input_data(6); // data is not important @@ -174,11 +174,11 @@ TEST(PreluTest, SInt16_LWQ_NEG) Tensor alpha_tensor = makeInputTensor<DataType::S16>({1, 2, 3, 1}, 0.1, 0, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::S16, 0.1, 0); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } -TEST(PreluTest, SInt16_CWQ_Simple) +TEST(PReluTest, SInt16_CWQ_Simple) { std::vector<float> input_data{-0.8f, 0.2f, 0.9f, -0.7f, 0.1f, -0.4f}; std::vector<float> alpha_data{0.5f, 0.25f}; @@ -190,7 +190,7 @@ TEST(PreluTest, SInt16_CWQ_Simple) Tensor alpha_tensor = makeInputTensor<DataType::S16>({2}, alpha_scales, zerop, 0, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::S16, 0.025, 0); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); kernel.configure(); kernel.execute(); @@ -198,7 +198,7 @@ TEST(PreluTest, SInt16_CWQ_Simple) EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data)); } -TEST(PreluTest, SInt16_CWQ_spatial_alpha_NEG) +TEST(PReluTest, SInt16_CWQ_spatial_alpha_NEG) { std::vector<float> input_data(6); // data is not important std::vector<float> alpha_data(6); @@ -210,11 +210,11 @@ TEST(PreluTest, SInt16_CWQ_spatial_alpha_NEG) makeInputTensor<DataType::S16>({1, 1, 3, 2}, alpha_scales, zerop, 3, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::S16, 0.1, 0); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } -TEST(PreluTest, SInt16_CWQ_wrong_dim_quant_NEG) +TEST(PReluTest, SInt16_CWQ_wrong_dim_quant_NEG) { std::vector<float> input_data(6); // data is not important std::vector<float> alpha_data(6); @@ -226,11 +226,11 @@ TEST(PreluTest, SInt16_CWQ_wrong_dim_quant_NEG) makeInputTensor<DataType::S16>({1, 1, 1, 2}, alpha_scales, zerop, 1, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::S16, 0.1, 0); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } -TEST(PreluTest, SInt16_CWQ_uneven_shape1) +TEST(PReluTest, SInt16_CWQ_uneven_shape1) { std::vector<float> input_data{-0.8f, 0.2f, 0.9f, -0.7f, 0.1f, -0.4f}; std::vector<float> alpha_data{0.5f, 0.25f}; @@ -243,7 +243,7 @@ TEST(PreluTest, SInt16_CWQ_uneven_shape1) makeInputTensor<DataType::S16>({1, 1, 2}, alpha_scales, zerop, 2, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::S16, 0.025, 0); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); kernel.configure(); kernel.execute(); @@ -251,7 +251,7 @@ TEST(PreluTest, SInt16_CWQ_uneven_shape1) EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data)); } -TEST(PreluTest, SInt16_CWQ_uneven_shape2) +TEST(PReluTest, SInt16_CWQ_uneven_shape2) { std::vector<float> input_data{ 0.0f, 0.0f, 0.0f, // Row 1, Column 1 @@ -274,7 +274,7 @@ TEST(PreluTest, SInt16_CWQ_uneven_shape2) makeInputTensor<DataType::S16>({1, 1, 1, 3}, alpha_scales, zerop, 3, alpha_data); Tensor output_tensor = makeOutputTensor(DataType::S16, 0.001, 0); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); kernel.configure(); kernel.execute(); @@ -282,38 +282,38 @@ TEST(PreluTest, SInt16_CWQ_uneven_shape2) EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data)); } -TEST(PreluTest, Input_Output_Type_NEG) +TEST(PReluTest, Input_Output_Type_NEG) { Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1.f}); Tensor alpha_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1.f}); Tensor output_tensor = makeOutputTensor(DataType::U8); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } -TEST(PreluTest, Input_Alpha_Type_NEG) +TEST(PReluTest, Input_Alpha_Type_NEG) { Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1}, {1.f}); Tensor alpha_tensor = makeInputTensor<DataType::U8>({1}, {1}); Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } -TEST(PreluTest, Invalid_Input_Type_NEG) +TEST(PReluTest, Invalid_Input_Type_NEG) { Tensor input_tensor = makeInputTensor<DataType::S64>({1}, {1}); Tensor alpha_tensor = makeInputTensor<DataType::S64>({1}, {1}); Tensor output_tensor = makeOutputTensor(DataType::S64); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); kernel.configure(); EXPECT_ANY_THROW(kernel.execute()); } -TEST(PreluTest, Input_Output_U8_CWQ_NEG) +TEST(PReluTest, Input_Output_U8_CWQ_NEG) { std::vector<float> scales{1.f, 1.f}; std::vector<int32_t> zerop{0, 0}; @@ -322,11 +322,11 @@ TEST(PreluTest, Input_Output_U8_CWQ_NEG) Tensor alpha_tensor = makeInputTensor<DataType::U8>({2, 2}, scales, zerop, 0, dummy_data); Tensor output_tensor = makeInputTensor<DataType::U8>({2, 2}, scales, zerop, 0, dummy_data); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } -TEST(PreluTest, Input_Output_S16_CWQ_NEG) +TEST(PReluTest, Input_Output_S16_CWQ_NEG) { std::vector<float> scales{1.f, 1.f}; std::vector<int32_t> zerop{0, 0}; @@ -335,18 +335,18 @@ TEST(PreluTest, Input_Output_S16_CWQ_NEG) Tensor alpha_tensor = makeInputTensor<DataType::S16>({2, 2}, scales, zerop, 0, dummy_data); Tensor output_tensor = makeInputTensor<DataType::S16>({2, 2}, scales, zerop, 0, dummy_data); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } -TEST(PreluTest, Mixing_U8_S16_NEG) +TEST(PReluTest, Mixing_U8_S16_NEG) { std::vector<float> dummy_data(4, 0.f); Tensor input_tensor = makeInputTensor<DataType::U8>({2, 2}, 1.f, 0, dummy_data); Tensor alpha_tensor = makeInputTensor<DataType::S16>({2, 2}, 1.f, 0, dummy_data); Tensor output_tensor = makeInputTensor<DataType::U8>({2, 2}, 1.f, 0, dummy_data); - Prelu kernel(&input_tensor, &alpha_tensor, &output_tensor); + PRelu kernel(&input_tensor, &alpha_tensor, &output_tensor); EXPECT_ANY_THROW(kernel.configure()); } diff --git a/compiler/luci-interpreter/src/kernels/PadV2.cpp b/compiler/luci-interpreter/src/kernels/PadV2.cpp new file mode 100644 index 000000000..3c215dbca --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/PadV2.cpp @@ -0,0 +1,106 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/PadV2.h" + +#include "kernels/Utils.h" + +#include <tensorflow/lite/kernels/internal/reference/reference_ops.h> + +namespace luci_interpreter +{ +namespace kernels +{ + +PadV2::PadV2(const Tensor *input, const Tensor *paddings, const Tensor *constant_values, + Tensor *output) + : Kernel({input, paddings, constant_values}, {output}) +{ +} + +void PadV2::configure() +{ + const Shape &input_shape = input()->shape(); + const int num_dims = input_shape.num_dims(); + + if (num_dims > 4) + throw std::runtime_error("Unsupported number of dimensions."); + + assert(output()->element_type() == input()->element_type()); + assert(paddings()->element_type() == DataType::S32); + assert(constant_values()->element_type() == output()->element_type()); + // Paddings shape should be [N, 2]. + assert(paddings()->shape().num_dims() == 2); + assert(paddings()->shape().dim(0) == num_dims); + assert(paddings()->shape().dim(1) == 2); + // Constant values elements number should be 1. + assert(constant_values()->shape().num_elements() == 1); + + Shape output_shape(num_dims); + const auto *paddings_data = getTensorData<int32_t>(paddings()); + for (int i = 0; i < num_dims; ++i) + { + const int32_t padding_before = paddings_data[i * 2]; + const int32_t padding_after = paddings_data[i * 2 + 1]; + assert(padding_before >= 0 && padding_after >= 0); + output_shape.dim(i) = input_shape.dim(i) + padding_before + padding_after; + } + + output()->resize(output_shape); +} + +void PadV2::execute() const +{ + const int num_dims = input()->shape().num_dims(); + + tflite::PadParams params{}; + params.left_padding_count = num_dims; + params.right_padding_count = num_dims; + + const auto *paddings_data = getTensorData<int32_t>(paddings()); + for (int i = num_dims - 1; i >= 0; --i) + { + params.left_padding[i] = paddings_data[i * 2]; + params.right_padding[i] = paddings_data[i * 2 + 1]; + } + + switch (input()->element_type()) + { + case DataType::FLOAT32: + { + const auto pad_value = getTensorData<float>(constant_values())[0]; + tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData<float>(input()), + &pad_value, getTensorShape(output()), + getTensorData<float>(output())); + break; + } + case DataType::U8: + { + assert(output()->zero_point() >= std::numeric_limits<uint8_t>::min()); + assert(output()->zero_point() <= std::numeric_limits<uint8_t>::max()); + const auto pad_value = getTensorData<uint8_t>(constant_values())[0]; + tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData<uint8_t>(input()), + &pad_value, getTensorShape(output()), + getTensorData<uint8_t>(output())); + break; + } + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/PadV2.h b/compiler/luci-interpreter/src/kernels/PadV2.h new file mode 100644 index 000000000..48a31f584 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/PadV2.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_PAD_V2_H +#define LUCI_INTERPRETER_KERNELS_PAD_V2_H + +#include "core/Kernel.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class PadV2 : public Kernel +{ +public: + PadV2(const Tensor *input, const Tensor *paddings, const Tensor *constant_values, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *paddings() const { return _inputs[1]; } + const Tensor *constant_values() const { return _inputs[2]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_PAD_V2_H diff --git a/compiler/luci-interpreter/src/kernels/PadV2.test.cpp b/compiler/luci-interpreter/src/kernels/PadV2.test.cpp new file mode 100644 index 000000000..1ee741401 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/PadV2.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/PadV2.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +float GetTolerance(float min, float max) { return (max - min) / 255.0; } + +TEST(PadV2, Uint8) +{ + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::pair<float, int32_t> quant_param = quantizationParams<uint8_t>(-1.0f, 1.0f); + std::vector<float> input_data{-0.8, 0.2, 0.9, 0.7, 0.1, -0.3}; + std::vector<int32_t> paddings_data{0, 0, 0, 2, 1, 3, 0, 0}; + std::vector<float> constant_values_data{0.5}; + Tensor input_tensor = + makeInputTensor<DataType::U8>({1, 2, 3, 1}, quant_param.first, quant_param.second, input_data); + Tensor paddings_tensor = makeInputTensor<DataType::S32>({4, 2}, paddings_data); + Tensor constant_values = + makeInputTensor<DataType::U8>({1}, quant_param.first, quant_param.second, constant_values_data); + Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); + + PadV2 kernel(&input_tensor, &paddings_tensor, &constant_values, &output_tensor); + kernel.configure(); + kernel.execute(); + + std::vector<float> ref_output_data = { + 0.5, -0.8, 0.2, 0.9, 0.5, 0.5, 0.5, 0.5, 0.7, 0.1, -0.3, 0.5, 0.5, 0.5, // + 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5}; // + EXPECT_THAT(dequantizeTensorData(output_tensor), + FloatArrayNear(ref_output_data, kQuantizedTolerance)); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 4, 7, 1})); +} + +TEST(PadV2, Float) +{ + std::vector<float> input_data{1, 2, 3, 4, 5, 6}; + std::vector<int32_t> paddings_data{1, 0, 0, 2, 0, 3, 0, 0}; + std::vector<float> constant_values_data{7}; + Tensor input_tensor = makeInputTensor<DataType::FLOAT32>({1, 2, 3, 1}, input_data); + Tensor paddings_tensor = makeInputTensor<DataType::S32>({4, 2}, paddings_data); + Tensor constant_values = makeInputTensor<DataType::FLOAT32>({1}, constant_values_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + PadV2 kernel(&input_tensor, &paddings_tensor, &constant_values, &output_tensor); + kernel.configure(); + kernel.execute(); + + std::vector<float> ref_output_data{7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 7, 7, 7, 1, 2, 3, 7, 7, 7, 4, 5, + 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7}; + std::initializer_list<int32_t> ref_output_shape{2, 4, 6, 1}; + EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data)); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Reverse.cpp b/compiler/luci-interpreter/src/kernels/ReverseV2.cpp index e9893fadc..1b6a5cc3b 100644 --- a/compiler/luci-interpreter/src/kernels/Reverse.cpp +++ b/compiler/luci-interpreter/src/kernels/ReverseV2.cpp @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "kernels/Reverse.h" +#include "kernels/ReverseV2.h" #include "kernels/Utils.h" #include <tensorflow/lite/kernels/internal/reference/reference_ops.h> @@ -24,12 +24,12 @@ namespace luci_interpreter namespace kernels { -Reverse::Reverse(const Tensor *input, const Tensor *axes, Tensor *output) +ReverseV2::ReverseV2(const Tensor *input, const Tensor *axes, Tensor *output) : Kernel({input, axes}, {output}) { } -void Reverse::configure() +void ReverseV2::configure() { assert(axes()->shape().num_dims() == 1); assert(input()->shape().num_dims() >= axes()->shape().num_elements()); @@ -57,7 +57,7 @@ void Reverse::configure() output()->resize(input()->shape()); } -void Reverse::execute() const +void ReverseV2::execute() const { int axis_value = getTensorData<int32_t>(axes())[0]; switch (output()->element_type()) diff --git a/compiler/luci-interpreter/src/kernels/Reverse.h b/compiler/luci-interpreter/src/kernels/ReverseV2.h index 3489dae28..51211c703 100644 --- a/compiler/luci-interpreter/src/kernels/Reverse.h +++ b/compiler/luci-interpreter/src/kernels/ReverseV2.h @@ -24,10 +24,10 @@ namespace luci_interpreter namespace kernels { -class Reverse : public Kernel +class ReverseV2 : public Kernel { public: - Reverse(const Tensor *input, const Tensor *axes, Tensor *output); + ReverseV2(const Tensor *input, const Tensor *axes, Tensor *output); const Tensor *input() const { return _inputs[0]; } const Tensor *axes() const { return _inputs[1]; } diff --git a/compiler/luci-interpreter/src/kernels/Reverse.test.cpp b/compiler/luci-interpreter/src/kernels/ReverseV2.test.cpp index 5475a8bd3..6e1e6c03c 100644 --- a/compiler/luci-interpreter/src/kernels/Reverse.test.cpp +++ b/compiler/luci-interpreter/src/kernels/ReverseV2.test.cpp @@ -15,7 +15,7 @@ * limitations under the License. */ -#include "kernels/Reverse.h" +#include "kernels/ReverseV2.h" #include "kernels/TestUtils.h" namespace luci_interpreter @@ -27,14 +27,14 @@ namespace using namespace testing; -template <typename T> class ReverseTest : public ::testing::Test +template <typename T> class ReverseV2Test : public ::testing::Test { }; using DataTypes = ::testing::Types<float, uint8_t>; -TYPED_TEST_CASE(ReverseTest, DataTypes); +TYPED_TEST_CASE(ReverseV2Test, DataTypes); -TYPED_TEST(ReverseTest, MultiDimensions) +TYPED_TEST(ReverseV2Test, MultiDimensions) { // TypeParam std::vector<TypeParam> input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, @@ -52,7 +52,7 @@ TYPED_TEST(ReverseTest, MultiDimensions) Tensor output_tensor = makeOutputTensor(getElementType<TypeParam>()); - Reverse kernel = Reverse(&input_tensor, &axis_tensor, &output_tensor); + ReverseV2 kernel = ReverseV2(&input_tensor, &axis_tensor, &output_tensor); kernel.configure(); kernel.execute(); diff --git a/compiler/luci-interpreter/src/kernels/Square.cpp b/compiler/luci-interpreter/src/kernels/Square.cpp new file mode 100644 index 000000000..bc71905c1 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Square.cpp @@ -0,0 +1,66 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Square.h" +#include "kernels/Utils.h" + +#include <stdexcept> +#include <cmath> + +namespace luci_interpreter +{ + +namespace kernels +{ + +Square::Square(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {} + +void Square::configure() +{ + if (input()->element_type() != output()->element_type()) + { + throw std::runtime_error("Input/output tensor data type mismatch."); + } + output()->resize(input()->shape()); +} + +void Square::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Square::evalFloat() const +{ + auto in = getTensorData<float>(input()); + auto out = getTensorData<float>(output()); + auto size = getTensorShape(input()).FlatSize(); + for (auto i = in; i != in + size; ++i) + { + *out = (*i) * (*i); + ++out; + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Square.h b/compiler/luci-interpreter/src/kernels/Square.h new file mode 100644 index 000000000..73ed5a707 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Square.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_SQUARE_H +#define LUCI_INTERPRETER_KERNELS_SQUARE_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Square : public Kernel +{ +public: + Square(const Tensor *input, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_SQUARE_H diff --git a/compiler/luci-interpreter/src/kernels/Square.test.cpp b/compiler/luci-interpreter/src/kernels/Square.test.cpp new file mode 100644 index 000000000..730d6405c --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Square.test.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Square.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(SquareTest, Float) +{ + Shape input_shape{3, 1, 2}; + std::vector<float> input_data1{1.0, 0.0, -1.0, 11.0, -2.0, -1.44}; + Tensor input_tensor = makeInputTensor<DataType::FLOAT32>(input_shape, input_data1); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Square kernel(&input_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + std::vector<float> ref_output_data{1.0, 0.0, 1.0, 121.0, 4.0, 2.0736}; + EXPECT_THAT(extractTensorData<float>(output_tensor), FloatArrayNear(ref_output_data)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Utils.cpp b/compiler/luci-interpreter/src/kernels/Utils.cpp index 52e76a81c..83faa7d7f 100644 --- a/compiler/luci-interpreter/src/kernels/Utils.cpp +++ b/compiler/luci-interpreter/src/kernels/Utils.cpp @@ -32,6 +32,7 @@ void calculateActivationRange(Activation activation, float *activation_min, floa switch (activation) { case Activation::NONE: + case Activation::TANH: *activation_min = std::numeric_limits<float>::lowest(); *activation_max = std::numeric_limits<float>::max(); break; @@ -66,6 +67,7 @@ static void calculateActivationRangeQuantizedImpl(Activation activation, int32_t switch (activation) { case Activation::NONE: + case Activation::TANH: *activation_min = qmin; *activation_max = qmax; break; diff --git a/compiler/luci-interpreter/src/kernels/While.cpp b/compiler/luci-interpreter/src/kernels/While.cpp new file mode 100644 index 000000000..d4676467d --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/While.cpp @@ -0,0 +1,105 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/While.h" +#include "kernels/Utils.h" + +#include <cstring> + +namespace luci_interpreter +{ +namespace kernels +{ + +namespace +{ + +void copy(const std::vector<const Tensor *> &src, const std::vector<Tensor *> &dst) +{ + for (size_t i = 0; i < src.size(); ++i) + { + LUCI_INTERPRETER_CHECK(dst[i]->element_type() == src[i]->element_type()); + dst[i]->resize(src[i]->shape()); + + const int32_t num_elements = src[i]->shape().num_elements(); + const std::size_t element_size = getDataTypeSize(src[i]->element_type()); + std::memcpy(dst[i]->data<void>(), src[i]->data<void>(), num_elements * element_size); + } +} + +void copy(const std::vector<Tensor *> &src, const std::vector<Tensor *> &dst) +{ + std::vector<const Tensor *> const_src; + for (const auto &t : src) + const_src.push_back(t); + copy(const_src, dst); +} + +} // namespace + +While::While(std::vector<const Tensor *> inputs, std::vector<Tensor *> outputs, + RuntimeGraph *cond_graph, RuntimeGraph *body_graph) + : Kernel(std::move(inputs), std::move(outputs)), _cond_graph(cond_graph), _body_graph(body_graph) +{ +} + +void While::configure() +{ + LUCI_INTERPRETER_CHECK(_body_graph->getInputTensors().size() == getInputTensors().size()); + LUCI_INTERPRETER_CHECK(_body_graph->getOutputTensors().size() == getOutputTensors().size()); + LUCI_INTERPRETER_CHECK(_body_graph->getOutputTensors().size() == getInputTensors().size()); + + LUCI_INTERPRETER_CHECK(_cond_graph->getInputTensors().size() == getInputTensors().size()); + + const auto &cond_outputs = _cond_graph->getOutputTensors(); + LUCI_INTERPRETER_CHECK(cond_outputs.size() == 1) + LUCI_INTERPRETER_CHECK(cond_outputs[0]->element_type() == DataType::BOOL); +} + +/** + * @note Dynamic shape such as {1, 0, 8} may fail in tensor->data() + */ +void While::execute() const +{ + const auto &cond_inputs = _cond_graph->getInputTensors(); + const auto &cond_outputs = _cond_graph->getOutputTensors(); + + copy(getInputTensors(), cond_inputs); + + const auto &body_inputs = _body_graph->getInputTensors(); + const auto &body_outputs = _body_graph->getOutputTensors(); + + while (true) + { + _cond_graph->execute(); + + bool cond_value = cond_outputs[0]->data<bool>()[0]; + if (!cond_value) + break; + + copy(cond_inputs, body_inputs); + + _body_graph->execute(); + + copy(body_outputs, cond_inputs); + } + + copy(cond_inputs, getOutputTensors()); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/While.h b/compiler/luci-interpreter/src/kernels/While.h new file mode 100644 index 000000000..f758df3f3 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/While.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_WHILE_H +#define LUCI_INTERPRETER_KERNELS_WHILE_H + +#include "core/Kernel.h" +#include "core/RuntimeGraph.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class While : public Kernel +{ +public: + While(std::vector<const Tensor *> inputs, std::vector<Tensor *> outputs, RuntimeGraph *cond_graph, + RuntimeGraph *body_graph); + + const Tensor *input(int index) const { return _inputs[index]; } + Tensor *output(int index) const { return _outputs[index]; } + + void configure() override; + void execute() const override; + +private: + RuntimeGraph *const _cond_graph = nullptr; + RuntimeGraph *const _body_graph = nullptr; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_WHILE_H diff --git a/compiler/luci-interpreter/src/kernels/While.test.cpp b/compiler/luci-interpreter/src/kernels/While.test.cpp new file mode 100644 index 000000000..a066d2c12 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/While.test.cpp @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "core/RuntimeModule.h" +#include "kernels/Add.h" +#include "kernels/Less.h" +#include "kernels/While.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +RuntimeGraph *buildCondSubgraph(RuntimeModule *module, DataType dtype, Tensor *input_cond) +{ + RuntimeGraph *graph = module->addGraph(); + Tensor *input = + graph->addTensor(std::make_unique<Tensor>(dtype, Shape{}, AffineQuantization{}, "")); + Tensor *output = + graph->addTensor(std::make_unique<Tensor>(DataType::BOOL, Shape{}, AffineQuantization{}, "")); + + graph->setInputTensors({input}); + graph->setOutputTensors({output}); + + graph->addKernel(std::make_unique<Less>(input, input_cond, output)); + + return graph; +} + +RuntimeGraph *buildBodySubgraph(RuntimeModule *module, DataType dtype, Tensor *input_add) +{ + RuntimeGraph *graph = module->addGraph(); + Tensor *input = + graph->addTensor(std::make_unique<Tensor>(dtype, Shape{}, AffineQuantization{}, "")); + Tensor *output = + graph->addTensor(std::make_unique<Tensor>(dtype, Shape{}, AffineQuantization{}, "")); + + graph->setInputTensors({input}); + graph->setOutputTensors({output}); + + AddParams params{}; + params.activation = Activation::NONE; + graph->addKernel(std::make_unique<Add>(input, input_add, output, params)); + + return graph; +} + +TEST(WhileTest, FloatLoop10) +{ + Tensor input = makeInputTensor<DataType::FLOAT32>({1}, {1}); + Tensor output = makeOutputTensor(DataType::FLOAT32); + + Tensor input_cond = makeInputTensor<DataType::FLOAT32>({1}, {10}); + Tensor input_add = makeInputTensor<DataType::FLOAT32>({1}, {1}); + + RuntimeModule module(nullptr); + RuntimeGraph *cond_graph = buildCondSubgraph(&module, DataType::FLOAT32, &input_cond); + RuntimeGraph *body_graph = buildBodySubgraph(&module, DataType::FLOAT32, &input_add); + + While kernel({&input}, {&output}, cond_graph, body_graph); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData<float>(output), FloatArrayNear({10})); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/loader/CMakeLists.txt b/compiler/luci-interpreter/src/loader/CMakeLists.txt index 20a6f03cd..782f46761 100644 --- a/compiler/luci-interpreter/src/loader/CMakeLists.txt +++ b/compiler/luci-interpreter/src/loader/CMakeLists.txt @@ -1,6 +1,8 @@ set(SOURCES GraphLoader.h GraphLoader.cpp + KernelBuilderHelper.h + KernelBuilderHelper.cpp KernelBuilder.h KernelBuilder.cpp ModuleLoader.h diff --git a/compiler/luci-interpreter/src/loader/GraphLoader.cpp b/compiler/luci-interpreter/src/loader/GraphLoader.cpp index bc44c7efa..ee45ad747 100644 --- a/compiler/luci-interpreter/src/loader/GraphLoader.cpp +++ b/compiler/luci-interpreter/src/loader/GraphLoader.cpp @@ -63,6 +63,8 @@ const void *getNodeData(const luci::CircleConst *node, size_t *data_size) return getNodeDataImpl<DataType::S32>(node, data_size); case DataType::S64: return getNodeDataImpl<DataType::S64>(node, data_size); + case DataType::BOOL: + return getNodeDataImpl<DataType::BOOL>(node, data_size); default: throw std::runtime_error("Unsupported type."); } @@ -81,6 +83,7 @@ bool isExecutableNode(const luci::CircleNode *node) case luci::CircleOpcode::CIRCLEIFOUT: case luci::CircleOpcode::CIRCLESPLITOUT: case luci::CircleOpcode::CIRCLEUNPACKOUT: + case luci::CircleOpcode::CIRCLEWHILEOUT: return false; default: return true; @@ -195,7 +198,7 @@ void GraphLoader::loadOperators() if (isExecutableNode(node)) { - std::unique_ptr<Kernel> kernel = node->accept(&kernel_builder); + std::unique_ptr<Kernel> kernel = kernel_builder.build(node); _runtime_to_ir.kernel_to_node.emplace(kernel.get(), node); _runtime_graph->addKernel(std::move(kernel)); } diff --git a/compiler/luci-interpreter/src/loader/KernelBuilder.cpp b/compiler/luci-interpreter/src/loader/KernelBuilder.cpp index 913a062d7..4cb8bd691 100644 --- a/compiler/luci-interpreter/src/loader/KernelBuilder.cpp +++ b/compiler/luci-interpreter/src/loader/KernelBuilder.cpp @@ -20,6 +20,7 @@ #include "kernels/ArgMax.h" #include "kernels/AveragePool2D.h" #include "kernels/BatchToSpaceND.h" +#include "kernels/Cast.h" #include "kernels/Concatenation.h" #include "kernels/Conv2D.h" #include "kernels/DepthToSpace.h" @@ -50,19 +51,21 @@ #include "kernels/MaxPool2D.h" #include "kernels/Mean.h" #include "kernels/Minimum.h" +#include "kernels/MirrorPad.h" #include "kernels/Mul.h" #include "kernels/Neg.h" #include "kernels/NotEqual.h" #include "kernels/Pack.h" #include "kernels/Pad.h" +#include "kernels/PadV2.h" #include "kernels/Pow.h" -#include "kernels/Prelu.h" +#include "kernels/PRelu.h" #include "kernels/Relu.h" #include "kernels/Relu6.h" #include "kernels/Reshape.h" #include "kernels/ResizeBilinear.h" #include "kernels/ResizeNearestNeighbor.h" -#include "kernels/Reverse.h" +#include "kernels/ReverseV2.h" #include "kernels/Rsqrt.h" #include "kernels/Slice.h" #include "kernels/Softmax.h" @@ -71,6 +74,7 @@ #include "kernels/Split.h" #include "kernels/StridedSlice.h" #include "kernels/Sqrt.h" +#include "kernels/Square.h" #include "kernels/SquaredDifference.h" #include "kernels/Squeeze.h" #include "kernels/Sub.h" @@ -78,14 +82,15 @@ #include "kernels/Unpack.h" #include "kernels/Transpose.h" #include "kernels/TransposeConv.h" +#include "kernels/While.h" #include <stdexcept> -namespace luci_interpreter +namespace { template <typename CircleNodeOut> -static std::vector<const loco::Node *> collectOutputNodes(const luci::CircleNode *node) +std::vector<const loco::Node *> collectOutputNodes(const luci::CircleNode *node) { std::vector<const CircleNodeOut *> output_nodes; for (const loco::Node *loco_node : loco::succs(node)) @@ -99,52 +104,251 @@ static std::vector<const loco::Node *> collectOutputNodes(const luci::CircleNode return {output_nodes.cbegin(), output_nodes.cend()}; } -const Tensor *KernelBuilder::getInputTensor(const loco::Node *node) const +} // namespace + +namespace luci_interpreter { - const Tensor *tensor = _node_to_tensor.at(node); - assert(tensor != nullptr); - return tensor; -} -const Tensor *KernelBuilder::getOptionalInputTensor(const loco::Node *node) const +// TODO move to anonymous namespace +enum class KB { - if (dynamic_cast<const luci::CircleOutputExclude *>(node)) + ABC, + DEF, + GHIJ, + KLMN, + OPQR, + STUV, + WXYZ, +}; + +#define DECLARE_VISIT(CLASS) std::unique_ptr<Kernel> visit(const luci::CLASS *) override + +template <KB kb> class KernelBuilderLet; + +template <> +class KernelBuilderLet<KB::ABC> : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>>, + public KernelBuilderHelper +{ +public: + KernelBuilderLet( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) { - return nullptr; } - return getInputTensor(node); -} -Tensor *KernelBuilder::getOutputTensor(const loco::Node *node) const -{ - Tensor *tensor = _node_to_tensor.at(node); - assert(tensor != nullptr); - return tensor; -} +public: + std::unique_ptr<Kernel> visit(const luci::CircleNode *) { return nullptr; } + +public: + DECLARE_VISIT(CircleAdd); + DECLARE_VISIT(CircleArgMax); + DECLARE_VISIT(CircleAveragePool2D); + DECLARE_VISIT(CircleBatchToSpaceND); + DECLARE_VISIT(CircleCast); + DECLARE_VISIT(CircleConcatenation); + DECLARE_VISIT(CircleConst); + DECLARE_VISIT(CircleConv2D); +}; + +template <> +class KernelBuilderLet<KB::DEF> : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>>, + public KernelBuilderHelper +{ +public: + KernelBuilderLet( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) + { + } -std::vector<Tensor *> -KernelBuilder::getOutputTensors(const std::vector<const loco::Node *> &nodes) const -{ - std::vector<Tensor *> tensors; - tensors.reserve(nodes.size()); - for (const loco::Node *node : nodes) - tensors.push_back(getOutputTensor(node)); - return tensors; -} +public: + std::unique_ptr<Kernel> visit(const luci::CircleNode *) { return nullptr; } + +public: + DECLARE_VISIT(CircleDepthToSpace); + DECLARE_VISIT(CircleDepthwiseConv2D); + DECLARE_VISIT(CircleDiv); + DECLARE_VISIT(CircleElu); + DECLARE_VISIT(CircleEqual); + DECLARE_VISIT(CircleExp); + DECLARE_VISIT(CircleFloor); + DECLARE_VISIT(CircleFloorDiv); + DECLARE_VISIT(CircleFullyConnected); +}; + +template <> +class KernelBuilderLet<KB::GHIJ> : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>>, + public KernelBuilderHelper +{ +public: + KernelBuilderLet( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) + { + } -RuntimeGraph *KernelBuilder::getRuntimeGraph(const loco::Graph *graph) const -{ - RuntimeGraph *runtime_graph = _graph_to_runtime_graph.at(graph); - assert(runtime_graph != nullptr); - return runtime_graph; -} +public: + std::unique_ptr<Kernel> visit(const luci::CircleNode *) { return nullptr; } + +public: + DECLARE_VISIT(CircleGreater); + DECLARE_VISIT(CircleGreaterEqual); + DECLARE_VISIT(CircleIf); + DECLARE_VISIT(CircleInput); + DECLARE_VISIT(CircleInstanceNorm); +}; + +template <> +class KernelBuilderLet<KB::KLMN> : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>>, + public KernelBuilderHelper +{ +public: + KernelBuilderLet( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) + { + } + +public: + std::unique_ptr<Kernel> visit(const luci::CircleNode *) { return nullptr; } + +public: + DECLARE_VISIT(CircleL2Normalize); + DECLARE_VISIT(CircleL2Pool2D); + DECLARE_VISIT(CircleLeakyRelu); + DECLARE_VISIT(CircleLess); + DECLARE_VISIT(CircleLessEqual); + DECLARE_VISIT(CircleLocalResponseNormalization); + DECLARE_VISIT(CircleLogSoftmax); + DECLARE_VISIT(CircleLogicalAnd); + DECLARE_VISIT(CircleLogicalNot); + DECLARE_VISIT(CircleLogicalOr); + DECLARE_VISIT(CircleLogistic); + DECLARE_VISIT(CircleMaxPool2D); + DECLARE_VISIT(CircleMaximum); + DECLARE_VISIT(CircleMean); + DECLARE_VISIT(CircleMinimum); + DECLARE_VISIT(CircleMirrorPad); + DECLARE_VISIT(CircleMul); + DECLARE_VISIT(CircleNeg); + DECLARE_VISIT(CircleNotEqual); +}; + +template <> +class KernelBuilderLet<KB::OPQR> : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>>, + public KernelBuilderHelper +{ +public: + KernelBuilderLet( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) + { + } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleNode *) +public: + std::unique_ptr<Kernel> visit(const luci::CircleNode *) { return nullptr; } + +public: + DECLARE_VISIT(CircleOutput); + DECLARE_VISIT(CirclePRelu); + DECLARE_VISIT(CirclePack); + DECLARE_VISIT(CirclePad); + DECLARE_VISIT(CirclePadV2); + DECLARE_VISIT(CirclePow); + DECLARE_VISIT(CircleRelu); + DECLARE_VISIT(CircleRelu6); + DECLARE_VISIT(CircleReshape); + DECLARE_VISIT(CircleResizeBilinear); + DECLARE_VISIT(CircleResizeNearestNeighbor); + DECLARE_VISIT(CircleReverseV2); + DECLARE_VISIT(CircleRsqrt); +}; + +template <> +class KernelBuilderLet<KB::STUV> : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>>, + public KernelBuilderHelper +{ +public: + KernelBuilderLet( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) + { + } + +public: + std::unique_ptr<Kernel> visit(const luci::CircleNode *) { return nullptr; } + +public: + DECLARE_VISIT(CircleSlice); + DECLARE_VISIT(CircleSoftmax); + DECLARE_VISIT(CircleSpaceToBatchND); + DECLARE_VISIT(CircleSpaceToDepth); + DECLARE_VISIT(CircleSplit); + DECLARE_VISIT(CircleSqrt); + DECLARE_VISIT(CircleSquare); + DECLARE_VISIT(CircleSquaredDifference); + DECLARE_VISIT(CircleSqueeze); + DECLARE_VISIT(CircleStridedSlice); + DECLARE_VISIT(CircleSub); + DECLARE_VISIT(CircleTanh); + DECLARE_VISIT(CircleTranspose); + DECLARE_VISIT(CircleTransposeConv); + DECLARE_VISIT(CircleUnpack); +}; + +template <> +class KernelBuilderLet<KB::WXYZ> : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>>, + public KernelBuilderHelper +{ +public: + KernelBuilderLet( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) + { + } + +public: + std::unique_ptr<Kernel> visit(const luci::CircleNode *) { return nullptr; } + +public: + DECLARE_VISIT(CircleWhile); +}; + +#undef DECLARE_VISIT + +std::unique_ptr<Kernel> KernelBuilder::build(const luci::CircleNode *node) { - throw std::invalid_argument("Unsupported operator."); +#define VISIT_KB(GRP) \ + do \ + { \ + KernelBuilderLet<KB::GRP> kbl(graph_to_runtime_graph(), node_to_tensor()); \ + auto ret = node->accept(&kbl); \ + if (ret != nullptr) \ + return ret; \ + } while (false) + + VISIT_KB(ABC); + VISIT_KB(DEF); + VISIT_KB(GHIJ); + VISIT_KB(KLMN); + VISIT_KB(OPQR); + VISIT_KB(STUV); + VISIT_KB(WXYZ); + +#undef VISIT_KB + std::string msg = "Unsupported operator: "; + msg += std::to_string(static_cast<uint32_t>(node->opcode())) + " " + std::string(node->name()); + throw std::invalid_argument(msg.c_str()); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleAdd *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleAdd *node) { assert(node->arity() == 2); @@ -158,7 +362,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleAdd *node) return std::make_unique<kernels::Add>(input1, input2, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleArgMax *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleArgMax *node) { assert(node->arity() == 2); const Tensor *input = getInputTensor(node->input()); @@ -171,7 +375,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleArgMax *node) return std::make_unique<kernels::ArgMax>(input, axis, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleAveragePool2D *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleAveragePool2D *node) { assert(node->arity() == 1); @@ -189,7 +393,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleAveragePool2D *no return std::make_unique<kernels::AveragePool2D>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleBatchToSpaceND *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleBatchToSpaceND *node) { assert(node->arity() == 3); @@ -201,7 +405,17 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleBatchToSpaceND *n return std::make_unique<kernels::BatchToSpaceND>(input, block_shape, crops, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleConcatenation *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleCast *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->x()); + Tensor *output = getOutputTensor(node); + + return std::make_unique<kernels::Cast>(input, output); +} + +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleConcatenation *node) { std::vector<const Tensor *> inputs(node->numValues()); for (uint32_t i = 0; i < node->numValues(); ++i) @@ -217,12 +431,12 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleConcatenation *no return std::make_unique<kernels::Concatenation>(std::move(inputs), output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleConst *) +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleConst *) { throw std::runtime_error("Const node cannot be executed."); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleConv2D *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::ABC>::visit(const luci::CircleConv2D *node) { assert(node->arity() == 3); @@ -242,7 +456,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleConv2D *node) return std::make_unique<kernels::Conv2D>(input, filter, bias, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleDepthToSpace *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleDepthToSpace *node) { assert(node->arity() == 1); @@ -255,7 +469,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleDepthToSpace *nod return std::make_unique<kernels::DepthToSpace>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleDepthwiseConv2D *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleDepthwiseConv2D *node) { assert(node->arity() == 3); @@ -276,7 +490,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleDepthwiseConv2D * return std::make_unique<kernels::DepthwiseConv2D>(input, filter, bias, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleDiv *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleDiv *node) { assert(node->arity() == 2); const Tensor *input1 = getInputTensor(node->x()); @@ -289,7 +503,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleDiv *node) return std::make_unique<kernels::Div>(input1, input2, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleElu *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleElu *node) { assert(node->arity() == 1); @@ -299,38 +513,38 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleElu *node) return std::make_unique<kernels::Elu>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleExp *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleEqual *node) { - assert(node->arity() == 1); + assert(node->arity() == 2); - const Tensor *input = getInputTensor(node->x()); + const Tensor *x = getInputTensor(node->x()); + const Tensor *y = getInputTensor(node->y()); Tensor *output = getOutputTensor(node); - return std::make_unique<kernels::Exp>(input, output); + return std::make_unique<kernels::Equal>(x, y, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleFloor *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleExp *node) { assert(node->arity() == 1); const Tensor *input = getInputTensor(node->x()); Tensor *output = getOutputTensor(node); - return std::make_unique<kernels::Floor>(input, output); + return std::make_unique<kernels::Exp>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleFloorDiv *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleFloor *node) { - assert(node->arity() == 2); + assert(node->arity() == 1); - const Tensor *x = getInputTensor(node->x()); - const Tensor *y = getInputTensor(node->y()); + const Tensor *input = getInputTensor(node->x()); Tensor *output = getOutputTensor(node); - return std::make_unique<kernels::FloorDiv>(x, y, output); + return std::make_unique<kernels::Floor>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleEqual *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleFloorDiv *node) { assert(node->arity() == 2); @@ -338,10 +552,10 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleEqual *node) const Tensor *y = getInputTensor(node->y()); Tensor *output = getOutputTensor(node); - return std::make_unique<kernels::Equal>(x, y, output); + return std::make_unique<kernels::FloorDiv>(x, y, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleFullyConnected *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::DEF>::visit(const luci::CircleFullyConnected *node) { assert(node->arity() == 3); @@ -356,7 +570,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleFullyConnected *n return std::make_unique<kernels::FullyConnected>(input, weights, bias, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleGreater *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::GHIJ>::visit(const luci::CircleGreater *node) { assert(node->arity() == 2); @@ -367,7 +581,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleGreater *node) return std::make_unique<kernels::Greater>(x, y, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleGreaterEqual *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::GHIJ>::visit(const luci::CircleGreaterEqual *node) { assert(node->arity() == 2); @@ -378,7 +592,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleGreaterEqual *nod return std::make_unique<kernels::GreaterEqual>(x, y, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleIf *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::GHIJ>::visit(const luci::CircleIf *node) { auto output_nodes = collectOutputNodes<luci::CircleIfOut>(node); assert(node->arity() == 1 + node->input_count()); @@ -399,7 +613,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleIf *node) else_graph); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleInstanceNorm *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::GHIJ>::visit(const luci::CircleInstanceNorm *node) { assert(node->arity() == 3); @@ -416,12 +630,12 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleInstanceNorm *nod return std::make_unique<kernels::InstanceNorm>(input, gamma, beta, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleInput *) +std::unique_ptr<Kernel> KernelBuilderLet<KB::GHIJ>::visit(const luci::CircleInput *) { throw std::runtime_error("Input node cannot be executed."); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleL2Normalize *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleL2Normalize *node) { assert(node->arity() == 1); @@ -434,7 +648,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleL2Normalize *node return std::make_unique<kernels::L2Normalize>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleL2Pool2D *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleL2Pool2D *node) { assert(node->arity() == 1); @@ -452,7 +666,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleL2Pool2D *node) return std::make_unique<kernels::L2Pool2D>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLeakyRelu *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLeakyRelu *node) { assert(node->arity() == 1); const Tensor *input = getInputTensor(node->features()); @@ -464,7 +678,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLeakyRelu *node) return std::make_unique<kernels::LeakyRelu>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLess *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLess *node) { assert(node->arity() == 2); @@ -475,7 +689,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLess *node) return std::make_unique<kernels::Less>(x, y, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLessEqual *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLessEqual *node) { assert(node->arity() == 2); @@ -486,7 +700,8 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLessEqual *node) return std::make_unique<kernels::LessEqual>(x, y, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLocalResponseNormalization *node) +std::unique_ptr<Kernel> +KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLocalResponseNormalization *node) { assert(node->arity() == 1); const Tensor *input = getInputTensor(node->input()); @@ -501,7 +716,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLocalResponseNorm return std::make_unique<kernels::LocalResponseNormalization>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogicalAnd *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLogicalAnd *node) { assert(node->arity() == 2); @@ -512,7 +727,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogicalAnd *node) return std::make_unique<kernels::LogicalAnd>(input1, input2, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogicalNot *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLogicalNot *node) { assert(node->arity() == 1); @@ -522,7 +737,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogicalNot *node) return std::make_unique<kernels::LogicalNot>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogicalOr *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLogicalOr *node) { assert(node->arity() == 2); @@ -533,7 +748,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogicalOr *node) return std::make_unique<kernels::LogicalOr>(input1, input2, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogistic *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLogistic *node) { assert(node->arity() == 1); @@ -543,7 +758,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogistic *node) return std::make_unique<kernels::Logistic>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogSoftmax *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleLogSoftmax *node) { assert(node->arity() == 1); @@ -553,7 +768,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleLogSoftmax *node) return std::make_unique<kernels::LogSoftmax>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMaximum *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleMaximum *node) { assert(node->arity() == 2); @@ -564,7 +779,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMaximum *node) return std::make_unique<kernels::Maximum>(input1, input2, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMaxPool2D *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleMaxPool2D *node) { assert(node->arity() == 1); @@ -582,7 +797,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMaxPool2D *node) return std::make_unique<kernels::MaxPool2D>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMean *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleMean *node) { assert(node->arity() == 2); @@ -596,7 +811,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMean *node) return std::make_unique<kernels::Mean>(input, axes, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMinimum *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleMinimum *node) { assert(node->arity() == 2); @@ -607,7 +822,21 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMinimum *node) return std::make_unique<kernels::Minimum>(input1, input2, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMul *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleMirrorPad *node) +{ + assert(node->arity() == 2); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *paddings = getInputTensor(node->paddings()); + Tensor *output = getOutputTensor(node); + + MirrorPadParams params{}; + params.mode = node->mode(); + + return std::make_unique<kernels::MirrorPad>(input, paddings, output, params); +} + +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleMul *node) { assert(node->arity() == 2); @@ -621,7 +850,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleMul *node) return std::make_unique<kernels::Mul>(input1, input2, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleNeg *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleNeg *node) { assert(node->arity() == 1); @@ -631,7 +860,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleNeg *node) return std::make_unique<kernels::Neg>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleNotEqual *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::KLMN>::visit(const luci::CircleNotEqual *node) { assert(node->arity() == 2); @@ -642,12 +871,12 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleNotEqual *node) return std::make_unique<kernels::NotEqual>(x, y, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleOutput *) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CircleOutput *) { throw std::runtime_error("Output node cannot be executed."); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePack *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CirclePack *node) { assert(node->arity() == node->values_count()); @@ -665,7 +894,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePack *node) return std::make_unique<kernels::Pack>(std::move(inputs), output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePad *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CirclePad *node) { assert(node->arity() == 2); @@ -676,7 +905,19 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePad *node) return std::make_unique<kernels::Pad>(input, paddings, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePow *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CirclePadV2 *node) +{ + assert(node->arity() == 3); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *paddings = getInputTensor(node->paddings()); + const Tensor *constant_values = getInputTensor(node->constant_values()); + Tensor *output = getOutputTensor(node); + + return std::make_unique<kernels::PadV2>(input, paddings, constant_values, output); +} + +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CirclePow *node) { assert(node->arity() == 2); @@ -688,7 +929,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePow *node) return std::make_unique<kernels::Pow>(input1, input2, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePRelu *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CirclePRelu *node) { assert(node->arity() == 2); @@ -696,10 +937,10 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CirclePRelu *node) const Tensor *alpha = getInputTensor(node->alpha()); Tensor *output = getOutputTensor(node); - return std::make_unique<kernels::Prelu>(input, alpha, output); + return std::make_unique<kernels::PRelu>(input, alpha, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleRelu *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CircleRelu *node) { assert(node->arity() == 1); @@ -709,7 +950,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleRelu *node) return std::make_unique<kernels::Relu>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleRelu6 *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CircleRelu6 *node) { assert(node->arity() == 1); @@ -719,7 +960,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleRelu6 *node) return std::make_unique<kernels::Relu6>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleReshape *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CircleReshape *node) { assert(node->arity() == 2); @@ -731,7 +972,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleReshape *node) return std::make_unique<kernels::Reshape>(input, shape, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleResizeBilinear *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CircleResizeBilinear *node) { assert(node->arity() == 2); @@ -746,7 +987,8 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleResizeBilinear *n return std::make_unique<kernels::ResizeBilinear>(input, size, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleResizeNearestNeighbor *node) +std::unique_ptr<Kernel> +KernelBuilderLet<KB::OPQR>::visit(const luci::CircleResizeNearestNeighbor *node) { assert(node->arity() == 2); @@ -765,7 +1007,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleResizeNearestNeig return std::make_unique<kernels::ResizeNearestNeighbor>(input, size, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleReverseV2 *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CircleReverseV2 *node) { assert(node->arity() == 2); @@ -773,10 +1015,10 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleReverseV2 *node) const Tensor *axes = getInputTensor(node->axis()); Tensor *output = getOutputTensor(node); - return std::make_unique<kernels::Reverse>(input, axes, output); + return std::make_unique<kernels::ReverseV2>(input, axes, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleRsqrt *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::OPQR>::visit(const luci::CircleRsqrt *node) { assert(node->arity() == 1); @@ -786,7 +1028,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleRsqrt *node) return std::make_unique<kernels::Rsqrt>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSlice *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSlice *node) { assert(node->arity() == 3); @@ -799,7 +1041,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSlice *node) return std::make_unique<kernels::Slice>(input, begin, size, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSoftmax *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSoftmax *node) { assert(node->arity() == 1); @@ -812,7 +1054,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSoftmax *node) return std::make_unique<kernels::Softmax>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSpaceToBatchND *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSpaceToBatchND *node) { assert(node->arity() == 3); @@ -826,7 +1068,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSpaceToBatchND *n ; } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSpaceToDepth *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSpaceToDepth *node) { assert(node->arity() == 1); const Tensor *input = getInputTensor(node->input()); @@ -839,7 +1081,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSpaceToDepth *nod return std::make_unique<kernels::SpaceToDepth>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSplit *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSplit *node) { auto output_nodes = collectOutputNodes<luci::CircleSplitOut>(node); assert(node->arity() == 2); @@ -853,7 +1095,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSplit *node) return std::make_unique<kernels::Split>(axis, input, std::move(outputs)); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSqrt *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSqrt *node) { assert(node->arity() == 1); @@ -863,7 +1105,17 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSqrt *node) return std::make_unique<kernels::Sqrt>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSquaredDifference *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSquare *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->x()); + Tensor *output = getOutputTensor(node); + + return std::make_unique<kernels::Square>(input, output); +} + +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSquaredDifference *node) { assert(node->arity() == 2); @@ -874,7 +1126,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSquaredDifference return std::make_unique<kernels::SquaredDifference>(input1, input2, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSqueeze *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSqueeze *node) { assert(node->arity() == 1); @@ -887,7 +1139,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSqueeze *node) return std::make_unique<kernels::Squeeze>(input, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleStridedSlice *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleStridedSlice *node) { assert(node->arity() == 4); @@ -908,7 +1160,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleStridedSlice *nod return std::make_unique<kernels::StridedSlice>(input, begin, end, strides, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSub *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleSub *node) { assert(node->arity() == 2); @@ -922,7 +1174,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleSub *node) return std::make_unique<kernels::Sub>(input1, input2, output, params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleTanh *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleTanh *node) { assert(node->arity() == 1); @@ -932,7 +1184,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleTanh *node) return std::make_unique<kernels::Tanh>(input, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleTranspose *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleTranspose *node) { assert(node->arity() == 2); @@ -943,7 +1195,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleTranspose *node) return std::make_unique<kernels::Transpose>(input, perm, output); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleTransposeConv *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleTransposeConv *node) { assert(node->arity() == 4); @@ -963,7 +1215,7 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleTransposeConv *no params); } -std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleUnpack *node) +std::unique_ptr<Kernel> KernelBuilderLet<KB::STUV>::visit(const luci::CircleUnpack *node) { auto output_nodes = collectOutputNodes<luci::CircleUnpackOut>(node); assert(node->arity() == 1); @@ -979,4 +1231,24 @@ std::unique_ptr<Kernel> KernelBuilder::visit(const luci::CircleUnpack *node) return std::make_unique<kernels::Unpack>(input, std::move(outputs), params); } +std::unique_ptr<Kernel> KernelBuilderLet<KB::WXYZ>::visit(const luci::CircleWhile *node) +{ + auto output_nodes = collectOutputNodes<luci::CircleWhileOut>(node); + assert(node->arity() == node->input_count()); + assert(output_nodes.size() == static_cast<size_t>(node->output_count())); + + std::vector<const Tensor *> inputs(node->input_count()); + for (uint32_t i = 0; i < node->input_count(); ++i) + { + inputs[i] = getInputTensor(node->input(i)); + } + std::vector<Tensor *> outputs = getOutputTensors(output_nodes); + + RuntimeGraph *cond_graph = getRuntimeGraph(node->cond_graph()); + RuntimeGraph *body_graph = getRuntimeGraph(node->body_graph()); + + return std::make_unique<kernels::While>(std::move(inputs), std::move(outputs), cond_graph, + body_graph); +} + } // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/loader/KernelBuilder.h b/compiler/luci-interpreter/src/loader/KernelBuilder.h index 6f482b29e..406c41ef6 100644 --- a/compiler/luci-interpreter/src/loader/KernelBuilder.h +++ b/compiler/luci-interpreter/src/loader/KernelBuilder.h @@ -17,109 +17,30 @@ #ifndef LUCI_INTERPRETER_LOADER_KERNELBUILDER_H #define LUCI_INTERPRETER_LOADER_KERNELBUILDER_H +#include "loader/KernelBuilderHelper.h" + #include "core/Kernel.h" #include "core/RuntimeGraph.h" #include <luci/IR/CircleNodeVisitor.h> #include <memory> -#include <vector> #include <unordered_map> namespace luci_interpreter { -class KernelBuilder : public luci::CircleNodeVisitor<std::unique_ptr<Kernel>> +class KernelBuilder : public KernelBuilderHelper { public: KernelBuilder( const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) - : _graph_to_runtime_graph(graph_to_runtime_graph), _node_to_tensor(node_to_tensor) + : KernelBuilderHelper(graph_to_runtime_graph, node_to_tensor) { } - std::unique_ptr<Kernel> visit(const luci::CircleNode *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleAdd *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleArgMax *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleAveragePool2D *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleBatchToSpaceND *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleConcatenation *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleConv2D *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleConst *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleDepthToSpace *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleDepthwiseConv2D *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleDiv *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleElu *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleExp *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleFloor *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleFloorDiv *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleEqual *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleFullyConnected *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleGreater *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleGreaterEqual *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleIf *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleInstanceNorm *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleL2Normalize *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleL2Pool2D *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLeakyRelu *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLess *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLessEqual *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLocalResponseNormalization *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLogicalAnd *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLogicalNot *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLogicalOr *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLogistic *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleLogSoftmax *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleInput *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleMaximum *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleMaxPool2D *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleMean *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleMinimum *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleMul *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleNeg *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleNotEqual *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleOutput *node) override; - std::unique_ptr<Kernel> visit(const luci::CirclePack *node) override; - std::unique_ptr<Kernel> visit(const luci::CirclePad *node) override; - std::unique_ptr<Kernel> visit(const luci::CirclePow *node) override; - std::unique_ptr<Kernel> visit(const luci::CirclePRelu *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleRelu *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleRelu6 *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleReshape *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleResizeBilinear *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleResizeNearestNeighbor *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleReverseV2 *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleRsqrt *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSlice *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSoftmax *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSpaceToBatchND *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSpaceToDepth *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSplit *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleStridedSlice *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSqrt *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSquaredDifference *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSqueeze *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleSub *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleTanh *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleTranspose *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleTransposeConv *node) override; - std::unique_ptr<Kernel> visit(const luci::CircleUnpack *node) override; - -private: - const Tensor *getInputTensor(const loco::Node *node) const; - - const Tensor *getOptionalInputTensor(const loco::Node *node) const; - - Tensor *getOutputTensor(const loco::Node *node) const; - - std::vector<Tensor *> getOutputTensors(const std::vector<const loco::Node *> &nodes) const; - - RuntimeGraph *getRuntimeGraph(const loco::Graph *graph) const; - -private: - const std::unordered_map<const loco::Graph *, RuntimeGraph *> &_graph_to_runtime_graph; - const std::unordered_map<const loco::Node *, Tensor *> &_node_to_tensor; + std::unique_ptr<Kernel> build(const luci::CircleNode *node); }; } // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/loader/KernelBuilder.test.cpp b/compiler/luci-interpreter/src/loader/KernelBuilder.test.cpp index b49085325..d8611243e 100644 --- a/compiler/luci-interpreter/src/loader/KernelBuilder.test.cpp +++ b/compiler/luci-interpreter/src/loader/KernelBuilder.test.cpp @@ -20,6 +20,7 @@ #include <kernels/Add.h> #include <kernels/ArgMax.h> #include <kernels/AveragePool2D.h> +#include <kernels/Cast.h> #include <kernels/Concatenation.h> #include <kernels/Conv2D.h> #include <kernels/DepthToSpace.h> @@ -53,14 +54,15 @@ #include <kernels/Neg.h> #include <kernels/NotEqual.h> #include <kernels/Pad.h> +#include <kernels/PadV2.h> #include <kernels/Pow.h> -#include <kernels/Prelu.h> +#include <kernels/PRelu.h> #include <kernels/Relu.h> #include <kernels/Relu6.h> #include <kernels/Reshape.h> #include <kernels/ResizeBilinear.h> #include <kernels/ResizeNearestNeighbor.h> -#include <kernels/Reverse.h> +#include <kernels/ReverseV2.h> #include <kernels/Rsqrt.h> #include <kernels/Slice.h> #include <kernels/Softmax.h> @@ -120,7 +122,7 @@ protected: KernelBuilder kernel_builder(graph_to_runtime_graph, _node_to_tensor); - auto kernel = op->accept(&kernel_builder); + auto kernel = kernel_builder.build(op); return std::unique_ptr<KernelT>(dynamic_cast<KernelT *>(kernel.release())); } @@ -201,6 +203,20 @@ TEST_F(KernelBuilderTest, AveragePool2D) EXPECT_THAT(kernel->params().activation, Eq(op->fusedActivationFunction())); } +TEST_F(KernelBuilderTest, Cast) +{ + auto *input = createInputNode(); + + auto *op = createNode<luci::CircleCast>(); + op->x(input); + + auto kernel = buildKernel<kernels::Cast>(op); + ASSERT_THAT(kernel, NotNull()); + + checkTensor(kernel->input(), input); + checkTensor(kernel->output(), op); +} + TEST_F(KernelBuilderTest, Concatenation) { auto *input1 = createInputNode(); @@ -827,6 +843,26 @@ TEST_F(KernelBuilderTest, Pad) checkTensor(kernel->output(), op); } +TEST_F(KernelBuilderTest, PadV2) +{ + auto *input = createInputNode(); + auto *paddings = createInputNode(); + auto *constant_values = createInputNode(); + + auto *op = createNode<luci::CirclePadV2>(); + op->input(input); + op->paddings(paddings); + op->constant_values(constant_values); + + auto kernel = buildKernel<kernels::PadV2>(op); + ASSERT_THAT(kernel, NotNull()); + + checkTensor(kernel->input(), input); + checkTensor(kernel->paddings(), paddings); + checkTensor(kernel->constant_values(), constant_values); + checkTensor(kernel->output(), op); +} + TEST_F(KernelBuilderTest, Pow) { auto *input1 = createInputNode(); @@ -844,7 +880,7 @@ TEST_F(KernelBuilderTest, Pow) checkTensor(kernel->output(), op); } -TEST_F(KernelBuilderTest, Prelu) +TEST_F(KernelBuilderTest, PRelu) { auto *input = createInputNode(); auto *alpha = createInputNode(); @@ -853,7 +889,7 @@ TEST_F(KernelBuilderTest, Prelu) op->input(input); op->alpha(alpha); - auto kernel = buildKernel<kernels::Prelu>(op); + auto kernel = buildKernel<kernels::PRelu>(op); ASSERT_THAT(kernel, NotNull()); checkTensor(kernel->input(), input); @@ -957,7 +993,7 @@ TEST_F(KernelBuilderTest, ReverseV2) op->tensor(input); op->axis(axes); - auto kernel = buildKernel<kernels::Reverse>(op); + auto kernel = buildKernel<kernels::ReverseV2>(op); ASSERT_THAT(kernel, NotNull()); checkTensor(kernel->input(), input); diff --git a/compiler/luci-interpreter/src/loader/KernelBuilderHelper.cpp b/compiler/luci-interpreter/src/loader/KernelBuilderHelper.cpp new file mode 100644 index 000000000..23c96a6db --- /dev/null +++ b/compiler/luci-interpreter/src/loader/KernelBuilderHelper.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "loader/KernelBuilderHelper.h" + +#include <luci/IR/Nodes/CircleOutput.h> + +namespace luci_interpreter +{ + +const Tensor *KernelBuilderHelper::getInputTensor(const loco::Node *node) const +{ + const Tensor *tensor = _node_to_tensor.at(node); + assert(tensor != nullptr); + return tensor; +} + +const Tensor *KernelBuilderHelper::getOptionalInputTensor(const loco::Node *node) const +{ + if (dynamic_cast<const luci::CircleOutputExclude *>(node)) + { + return nullptr; + } + return getInputTensor(node); +} + +Tensor *KernelBuilderHelper::getOutputTensor(const loco::Node *node) const +{ + Tensor *tensor = _node_to_tensor.at(node); + assert(tensor != nullptr); + return tensor; +} + +std::vector<Tensor *> +KernelBuilderHelper::getOutputTensors(const std::vector<const loco::Node *> &nodes) const +{ + std::vector<Tensor *> tensors; + tensors.reserve(nodes.size()); + for (const loco::Node *node : nodes) + tensors.push_back(getOutputTensor(node)); + return tensors; +} + +RuntimeGraph *KernelBuilderHelper::getRuntimeGraph(const loco::Graph *graph) const +{ + RuntimeGraph *runtime_graph = _graph_to_runtime_graph.at(graph); + assert(runtime_graph != nullptr); + return runtime_graph; +} + +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/loader/KernelBuilderHelper.h b/compiler/luci-interpreter/src/loader/KernelBuilderHelper.h new file mode 100644 index 000000000..4517d1f19 --- /dev/null +++ b/compiler/luci-interpreter/src/loader/KernelBuilderHelper.h @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_LOADER_KERNELBUILDER_HELPER_H +#define LUCI_INTERPRETER_LOADER_KERNELBUILDER_HELPER_H + +#include "core/Kernel.h" +#include "core/RuntimeGraph.h" + +#include <loco/IR/Graph.h> +#include <loco/IR/Node.h> + +#include <vector> +#include <unordered_map> + +namespace luci_interpreter +{ + +class KernelBuilderHelper +{ +public: + KernelBuilderHelper( + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph, + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor) + : _graph_to_runtime_graph(graph_to_runtime_graph), _node_to_tensor(node_to_tensor) + { + } + +protected: + const Tensor *getInputTensor(const loco::Node *node) const; + const Tensor *getOptionalInputTensor(const loco::Node *node) const; + + Tensor *getOutputTensor(const loco::Node *node) const; + std::vector<Tensor *> getOutputTensors(const std::vector<const loco::Node *> &nodes) const; + + RuntimeGraph *getRuntimeGraph(const loco::Graph *graph) const; + +protected: + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &graph_to_runtime_graph() const + { + return _graph_to_runtime_graph; + } + + const std::unordered_map<const loco::Node *, Tensor *> &node_to_tensor() const + { + return _node_to_tensor; + } + +private: + const std::unordered_map<const loco::Graph *, RuntimeGraph *> &_graph_to_runtime_graph; + const std::unordered_map<const loco::Node *, Tensor *> &_node_to_tensor; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_LOADER_KERNELBUILDER_HELPER_H diff --git a/compiler/luci-pass-value-test/test.lst b/compiler/luci-pass-value-test/test.lst index e607904cb..9c408887d 100644 --- a/compiler/luci-pass-value-test/test.lst +++ b/compiler/luci-pass-value-test/test.lst @@ -12,6 +12,7 @@ addeval(Net_Conv_Add_Mul_000 fuse_batchnorm_with_conv) addeval(Net_Conv_Add_Mul_001 fuse_batchnorm_with_conv) addeval(Net_Conv_Add_Mul_002 fuse_batchnorm_with_conv) addeval(Net_Conv_Min_Max_000 transform_min_max_to_relu6) +addeval(Net_Conv_Min_Relu_000 transform_min_relu_to_relu6) addeval(Net_Conv_Relu6_000 fuse_activation_function) addeval(Net_DwConv_BN_000 fuse_batchnorm_with_dwconv) addeval(Net_DwConv_BN_001 fuse_batchnorm_with_dwconv) diff --git a/compiler/luci-value-test/test.lst b/compiler/luci-value-test/test.lst index edf329aff..2b5c93fa3 100644 --- a/compiler/luci-value-test/test.lst +++ b/compiler/luci-value-test/test.lst @@ -182,3 +182,7 @@ addeval(Unpack_003) #addeval(While_003) #addeval(YUV_TO_RGB_U8_000) #addeval(ZerosLike_000) + +# Simple Network test +addeval(Part_While_000) +addeval(Part_While_001) diff --git a/compiler/luci/CMakeLists.txt b/compiler/luci/CMakeLists.txt index 3771176f0..95c349c0d 100644 --- a/compiler/luci/CMakeLists.txt +++ b/compiler/luci/CMakeLists.txt @@ -1,12 +1,12 @@ add_subdirectory(env) add_subdirectory(log) add_subdirectory(lang) +add_subdirectory(logex) add_subdirectory(testhelper) add_subdirectory(service) add_subdirectory(pass) add_subdirectory(profile) add_subdirectory(partition) -add_subdirectory(logex) add_subdirectory(import) add_subdirectory(export) add_subdirectory(tester) diff --git a/compiler/luci/env/CMakeLists.txt b/compiler/luci/env/CMakeLists.txt index 3d8387a47..4d1a89ad1 100644 --- a/compiler/luci/env/CMakeLists.txt +++ b/compiler/luci/env/CMakeLists.txt @@ -6,6 +6,8 @@ add_library(luci_env SHARED ${SOURCES}) target_include_directories(luci_env PUBLIC include) target_link_libraries(luci_env PRIVATE nncc_common) install(TARGETS luci_env DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/luci/export/CMakeLists.txt b/compiler/luci/export/CMakeLists.txt index 01f737110..5c0077625 100644 --- a/compiler/luci/export/CMakeLists.txt +++ b/compiler/luci/export/CMakeLists.txt @@ -18,6 +18,8 @@ target_link_libraries(luci_export PRIVATE nncc_common) target_link_libraries(luci_export PRIVATE locop) target_link_libraries(luci_export PRIVATE oops) install(TARGETS luci_export DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") #if(NOT ENABLE_TEST) # return() diff --git a/compiler/luci/export/src/CircleExporterImpl.cpp b/compiler/luci/export/src/CircleExporterImpl.cpp index 7e218191c..5868c176c 100644 --- a/compiler/luci/export/src/CircleExporterImpl.cpp +++ b/compiler/luci/export/src/CircleExporterImpl.cpp @@ -224,6 +224,7 @@ void CircleExporterImpl::exportModule(Module *module) auto description = _builder.CreateString(description_str); // Metadata + md._metadata.source_table(module->source_table()); auto metadata_vec = createCircleMetadataVector(_builder, md); auto metadata = _builder.CreateVector(std::vector<Offset<Metadata>>(metadata_vec)); diff --git a/compiler/luci/export/src/CircleExporterUtils.cpp b/compiler/luci/export/src/CircleExporterUtils.cpp index 1b21fdd86..3a7ba304f 100644 --- a/compiler/luci/export/src/CircleExporterUtils.cpp +++ b/compiler/luci/export/src/CircleExporterUtils.cpp @@ -69,6 +69,9 @@ circle::TensorType to_circle_tensortype(loco::DataType type) case loco::DataType::BOOL: return circle::TensorType_BOOL; + case loco::DataType::STRING: + return circle::TensorType_STRING; + default: INTERNAL_EXN_V("failed to convert unsupported loco::DataType", oops::to_uint32(type)); } diff --git a/compiler/luci/export/src/CircleOperationExporter.cpp b/compiler/luci/export/src/CircleOperationExporter.cpp index 4bf674b9b..014d9bd61 100644 --- a/compiler/luci/export/src/CircleOperationExporter.cpp +++ b/compiler/luci/export/src/CircleOperationExporter.cpp @@ -606,15 +606,77 @@ void export_node(ExportContext &ctx, luci::CircleWhile *node) ctx.gd._operators.push_back(op_offset); } -class OperationExporter final : public luci::CircleNodeMutableVisitor<void>, - public loco::CanonicalNodeMutableVisitor<void> +class ExportHelper { public: - OperationExporter(ExportContext &ctx) : _ctx{ctx} + ExportHelper(ExportContext &ctx) : _ctx{ctx} { // DO NOTHING } +protected: + /** + * @brief export simple nodes + */ + void export_simple(loco::Node *node, circle::BuiltinOperator bop, circle::BuiltinOptions bot, + flatbuffers::Offset<void> options_offset) + { + export_node(_ctx, node, bop, bot, options_offset); + } + + /** + * @brief export simple nodes having void options + */ + void export_simple(loco::Node *node, circle::BuiltinOperator bop) + { + export_node(_ctx, node, bop); + } + +protected: + ExportContext &_ctx; +}; + +enum class OE +{ + ABC, + DEF, + GHIJ, + KLMN, + OPQR, + STUV, + WXYZ, + CIRC, // circle only + VIRT, // virtual +}; + +class OperationExporter final : public ExportHelper +{ +public: + OperationExporter(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void export_node(luci::CircleNode *); +}; + +template <OE oe> class OpExporterLet; + +template <> +class OpExporterLet<OE::ABC> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + // NOTE visit for luci::CircleNode is added NOT to throw NYI + void visit(luci::CircleNode *) final {} + public: void visit(luci::CircleAbs *) final; void visit(luci::CircleAdd *) final; @@ -632,6 +694,22 @@ public: void visit(luci::CircleConv2D *) final; void visit(luci::CircleCos *) final; void visit(luci::CircleCustom *) final; +}; + +template <> +class OpExporterLet<OE::DEF> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: void visit(luci::CircleDepthToSpace *) final; void visit(luci::CircleDepthwiseConv2D *) final; void visit(luci::CircleDequantize *) final; @@ -646,11 +724,43 @@ public: void visit(luci::CircleFloorDiv *) final; void visit(luci::CircleFloorMod *) final; void visit(luci::CircleFullyConnected *) final; +}; + +template <> +class OpExporterLet<OE::GHIJ> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: void visit(luci::CircleGather *) final; void visit(luci::CircleGatherNd *) final; void visit(luci::CircleGreater *) final; void visit(luci::CircleGreaterEqual *) final; void visit(luci::CircleIf *) final; +}; + +template <> +class OpExporterLet<OE::KLMN> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: void visit(luci::CircleL2Normalize *) final; void visit(luci::CircleL2Pool2D *) final; void visit(luci::CircleLeakyRelu *) final; @@ -675,12 +785,29 @@ public: void visit(luci::CircleNonMaxSuppressionV4 *) final; void visit(luci::CircleNonMaxSuppressionV5 *) final; void visit(luci::CircleNotEqual *) final; +}; + +template <> +class OpExporterLet<OE::OPQR> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: void visit(luci::CircleOneHot *) final; void visit(luci::CirclePack *) final; void visit(luci::CirclePad *) final; void visit(luci::CirclePadV2 *) final; void visit(luci::CirclePow *) final; void visit(luci::CirclePRelu *) final; + void visit(luci::CircleQuantize *) final; void visit(luci::CircleRange *) final; void visit(luci::CircleRank *) final; void visit(luci::CircleReduceAny *) final; @@ -697,6 +824,22 @@ public: void visit(luci::CircleReverseV2 *) final; void visit(luci::CircleRound *) final; void visit(luci::CircleRsqrt *) final; +}; + +template <> +class OpExporterLet<OE::STUV> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: void visit(luci::CircleScatterNd *) final; void visit(luci::CircleSegmentSum *) final; void visit(luci::CircleSelect *) final; @@ -725,13 +868,61 @@ public: void visit(luci::CircleUnidirectionalSequenceLSTM *) final; void visit(luci::CircleUnique *) final; void visit(luci::CircleUnpack *) final; +}; + +template <> +class OpExporterLet<OE::WXYZ> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: void visit(luci::CircleWhere *) final; void visit(luci::CircleWhile *) final; void visit(luci::CircleZerosLike *) final; +}; + +template <> +class OpExporterLet<OE::CIRC> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: // Circle only void visit(luci::CircleBCQFullyConnected *) final; void visit(luci::CircleBCQGather *) final; void visit(luci::CircleInstanceNorm *) final; +}; + +template <> +class OpExporterLet<OE::VIRT> final : public luci::CircleNodeMutableVisitor<void>, + public ExportHelper +{ +public: + OpExporterLet(ExportContext &ctx) : ExportHelper(ctx) + { + // DO NOTHING + } + +public: + void visit(luci::CircleNode *) final {} + +public: // Virtual void visit(luci::CircleInput *) final {} void visit(luci::CircleOutput *) final {} @@ -749,77 +940,73 @@ public: void visit(luci::CircleUniqueOut *) final {} void visit(luci::CircleUnpackOut *) final {} void visit(luci::CircleWhileOut *) final {} - -private: - /** - * @brief export simple nodes - */ - void export_simple(loco::Node *node, circle::BuiltinOperator bop, circle::BuiltinOptions bot, - flatbuffers::Offset<void> options_offset); - - /** - * @brief export simple nodes having void options - */ - void export_simple(loco::Node *node, circle::BuiltinOperator bop); - -private: - ExportContext &_ctx; }; -void OperationExporter::export_simple(loco::Node *node, circle::BuiltinOperator bop, - circle::BuiltinOptions bot, - flatbuffers::Offset<void> options_offset) +void OperationExporter::export_node(luci::CircleNode *node) { - export_node(_ctx, node, bop, bot, options_offset); -} + // TODO revise return type to bool and return if handled +#define VISIT_OE(GRP) \ + do \ + { \ + OpExporterLet<OE::GRP> oe(_ctx); \ + node->accept(&oe); \ + } while (false) -void OperationExporter::export_simple(loco::Node *node, circle::BuiltinOperator bop) -{ - export_node(_ctx, node, bop); + VISIT_OE(ABC); + VISIT_OE(DEF); + VISIT_OE(GHIJ); + VISIT_OE(KLMN); + VISIT_OE(OPQR); + VISIT_OE(STUV); + VISIT_OE(WXYZ); + VISIT_OE(CIRC); + VISIT_OE(VIRT); + +#undef VISIT_OE } -void OperationExporter::visit(luci::CircleAbs *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleAbs *node) { export_simple(node, circle::BuiltinOperator_ABS, circle::BuiltinOptions_AbsOptions, CreateAbsOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleAdd *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleAdd *node) { export_simple( node, circle::BuiltinOperator_ADD, circle::BuiltinOptions_AddOptions, CreateAddOptions(_ctx.builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); } -void OperationExporter::visit(luci::CircleAddN *node) { export_node(_ctx, node); } +void OpExporterLet<OE::ABC>::visit(luci::CircleAddN *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleArgMax *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleArgMax *node) { export_simple( node, circle::BuiltinOperator_ARG_MAX, circle::BuiltinOptions_ArgMaxOptions, CreateArgMaxOptions(_ctx.builder, to_circle_tensortype(node->output_type())).Union()); } -void OperationExporter::visit(luci::CircleArgMin *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleArgMin *node) { export_simple( node, circle::BuiltinOperator_ARG_MIN, circle::BuiltinOptions_ArgMinOptions, CreateArgMinOptions(_ctx.builder, to_circle_tensortype(node->output_type())).Union()); } -void OperationExporter::visit(luci::CircleAveragePool2D *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleAveragePool2D *node) { export_pool_2d<luci::CircleAveragePool2D>(_ctx, node, circle::BuiltinOperator_AVERAGE_POOL_2D); } -void OperationExporter::visit(luci::CircleBatchMatMul *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleBatchMatMul *node) { export_simple(node, circle::BuiltinOperator_BATCH_MATMUL, circle::BuiltinOptions_BatchMatMulOptions, CreateBatchMatMulOptions(_ctx.builder, node->adj_x(), node->adj_y()).Union()); } -void OperationExporter::visit(luci::CircleBidirectionalSequenceLSTM *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleBidirectionalSequenceLSTM *node) { auto bidi_lstm_outs = loco::succs(node); assert((bidi_lstm_outs.size() == 1) || (bidi_lstm_outs.size() == 2)); @@ -861,23 +1048,23 @@ void OperationExporter::visit(luci::CircleBidirectionalSequenceLSTM *node) _ctx.gd._operators.push_back(op_offset); } -void OperationExporter::visit(luci::CircleCast *node) { export_node(_ctx, node); } +void OpExporterLet<OE::ABC>::visit(luci::CircleCast *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleCeil *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleCeil *node) { export_simple(node, circle::BuiltinOperator_CEIL); } -void OperationExporter::visit(luci::CircleConcatenation *node) { export_node(_ctx, node); } +void OpExporterLet<OE::ABC>::visit(luci::CircleConcatenation *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleBatchToSpaceND *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleBatchToSpaceND *node) { export_simple(node, circle::BuiltinOperator_BATCH_TO_SPACE_ND, circle::BuiltinOptions_BatchToSpaceNDOptions, CreateBatchToSpaceNDOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleConv2D *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleConv2D *node) { export_simple(node, circle::BuiltinOperator_CONV_2D, circle::BuiltinOptions_Conv2DOptions, CreateConv2DOptions(_ctx.builder, getOpPadding(node->padding()), @@ -887,22 +1074,22 @@ void OperationExporter::visit(luci::CircleConv2D *node) .Union()); } -void OperationExporter::visit(luci::CircleCos *node) +void OpExporterLet<OE::ABC>::visit(luci::CircleCos *node) { export_simple(node, circle::BuiltinOperator_COS, circle::BuiltinOptions_CosOptions, CreateCosOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleCustom *node) { export_node(_ctx, node); } +void OpExporterLet<OE::ABC>::visit(luci::CircleCustom *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleDepthToSpace *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleDepthToSpace *node) { export_simple(node, circle::BuiltinOperator_DEPTH_TO_SPACE, circle::BuiltinOptions_DepthToSpaceOptions, CreateDepthToSpaceOptions(_ctx.builder, node->block_size()).Union()); } -void OperationExporter::visit(luci::CircleDepthwiseConv2D *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleDepthwiseConv2D *node) { export_simple( node, circle::BuiltinOperator_DEPTHWISE_CONV_2D, circle::BuiltinOptions_DepthwiseConv2DOptions, @@ -913,42 +1100,42 @@ void OperationExporter::visit(luci::CircleDepthwiseConv2D *node) .Union()); } -void OperationExporter::visit(luci::CircleDequantize *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleDequantize *node) { export_simple(node, circle::BuiltinOperator_DEQUANTIZE); } -void OperationExporter::visit(luci::CircleDiv *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleDiv *node) { export_simple( node, circle::BuiltinOperator_DIV, circle::BuiltinOptions_DivOptions, CreateDivOptions(_ctx.builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); } -void OperationExporter::visit(luci::CircleElu *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleElu *node) { export_simple(node, circle::BuiltinOperator_ELU); } -void OperationExporter::visit(luci::CircleEqual *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleEqual *node) { export_simple(node, circle::BuiltinOperator_EQUAL, circle::BuiltinOptions_EqualOptions, CreateEqualOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleExp *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleExp *node) { export_simple(node, circle::BuiltinOperator_EXP, circle::BuiltinOptions_ExpOptions, CreateExpOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleExpandDims *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleExpandDims *node) { export_simple(node, circle::BuiltinOperator_EXPAND_DIMS, circle::BuiltinOptions_ExpandDimsOptions, CreateExpandDimsOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleFakeQuant *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleFakeQuant *node) { export_simple(node, circle::BuiltinOperator_FAKE_QUANT, circle::BuiltinOptions_FakeQuantOptions, CreateFakeQuantOptions(_ctx.builder, node->min(), node->max(), node->num_bits(), @@ -956,30 +1143,30 @@ void OperationExporter::visit(luci::CircleFakeQuant *node) .Union()); } -void OperationExporter::visit(luci::CircleFill *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleFill *node) { export_simple(node, circle::BuiltinOperator_FILL, circle::BuiltinOptions_FillOptions, CreateFillOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleFloor *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleFloor *node) { export_simple(node, circle::BuiltinOperator_FLOOR); } -void OperationExporter::visit(luci::CircleFloorDiv *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleFloorDiv *node) { export_simple(node, circle::BuiltinOperator_FLOOR_DIV, circle::BuiltinOptions_FloorDivOptions, CreateFloorDivOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleFloorMod *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleFloorMod *node) { export_simple(node, circle::BuiltinOperator_FLOOR_MOD, circle::BuiltinOptions_FloorModOptions, CreateFloorModOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleFullyConnected *node) +void OpExporterLet<OE::DEF>::visit(luci::CircleFullyConnected *node) { export_simple( node, circle::BuiltinOperator_FULLY_CONNECTED, circle::BuiltinOptions_FullyConnectedOptions, @@ -988,64 +1175,64 @@ void OperationExporter::visit(luci::CircleFullyConnected *node) .Union()); } -void OperationExporter::visit(luci::CircleGather *node) +void OpExporterLet<OE::GHIJ>::visit(luci::CircleGather *node) { export_simple(node, circle::BuiltinOperator_GATHER, circle::BuiltinOptions_GatherOptions, CreateGatherOptions(_ctx.builder, node->axis()).Union()); } -void OperationExporter::visit(luci::CircleGatherNd *node) +void OpExporterLet<OE::GHIJ>::visit(luci::CircleGatherNd *node) { export_simple(node, circle::BuiltinOperator_GATHER_ND, circle::BuiltinOptions_GatherNdOptions, CreateGatherNdOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleGreater *node) +void OpExporterLet<OE::GHIJ>::visit(luci::CircleGreater *node) { export_simple(node, circle::BuiltinOperator_GREATER, circle::BuiltinOptions_GreaterOptions, CreateGreaterOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleGreaterEqual *node) +void OpExporterLet<OE::GHIJ>::visit(luci::CircleGreaterEqual *node) { export_simple(node, circle::BuiltinOperator_GREATER_EQUAL, circle::BuiltinOptions_GreaterEqualOptions, CreateGreaterEqualOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleIf *node) { export_node(_ctx, node); } +void OpExporterLet<OE::GHIJ>::visit(luci::CircleIf *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleL2Normalize *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleL2Normalize *node) { export_simple( node, circle::BuiltinOperator_L2_NORMALIZATION, circle::BuiltinOptions_L2NormOptions, CreateL2NormOptions(_ctx.builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); } -void OperationExporter::visit(luci::CircleL2Pool2D *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleL2Pool2D *node) { export_pool_2d<luci::CircleL2Pool2D>(_ctx, node, circle::BuiltinOperator_L2_POOL_2D); } -void OperationExporter::visit(luci::CircleLeakyRelu *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLeakyRelu *node) { export_simple(node, circle::BuiltinOperator_LEAKY_RELU, circle::BuiltinOptions_LeakyReluOptions, CreateLeakyReluOptions(_ctx.builder, node->alpha()).Union()); } -void OperationExporter::visit(luci::CircleLess *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLess *node) { export_simple(node, circle::BuiltinOperator_LESS, circle::BuiltinOptions_LessOptions, CreateLessOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleLessEqual *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLessEqual *node) { export_simple(node, circle::BuiltinOperator_LESS_EQUAL, circle::BuiltinOptions_LessEqualOptions, CreateLessEqualOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleLocalResponseNormalization *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLocalResponseNormalization *node) { export_simple(node, circle::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, circle::BuiltinOptions_LocalResponseNormalizationOptions, @@ -1054,193 +1241,204 @@ void OperationExporter::visit(luci::CircleLocalResponseNormalization *node) .Union()); } -void OperationExporter::visit(luci::CircleLog *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLog *node) { export_simple(node, circle::BuiltinOperator_LOG); } -void OperationExporter::visit(luci::CircleLogicalAnd *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLogicalAnd *node) { export_simple(node, circle::BuiltinOperator_LOGICAL_AND, circle::BuiltinOptions_LogicalAndOptions, CreateLogicalAndOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleLogicalNot *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLogicalNot *node) { export_simple(node, circle::BuiltinOperator_LOGICAL_NOT, circle::BuiltinOptions_LogicalNotOptions, CreateLogicalNotOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleLogicalOr *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLogicalOr *node) { export_simple(node, circle::BuiltinOperator_LOGICAL_OR, circle::BuiltinOptions_LogicalOrOptions, CreateLogicalOrOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleLogistic *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLogistic *node) { export_simple(node, circle::BuiltinOperator_LOGISTIC); } -void OperationExporter::visit(luci::CircleLogSoftmax *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleLogSoftmax *node) { export_simple(node, circle::BuiltinOperator_LOG_SOFTMAX, circle::BuiltinOptions_LogSoftmaxOptions, CreateLogSoftmaxOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleMatrixDiag *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMatrixDiag *node) { export_simple(node, circle::BuiltinOperator_MATRIX_DIAG, circle::BuiltinOptions_MatrixDiagOptions, CreateMatrixDiagOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleMatrixSetDiag *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMatrixSetDiag *node) { export_simple(node, circle::BuiltinOperator_MATRIX_SET_DIAG, circle::BuiltinOptions_MatrixSetDiagOptions, CreateMatrixSetDiagOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleMaximum *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMaximum *node) { export_simple(node, circle::BuiltinOperator_MAXIMUM, circle::BuiltinOptions_MaximumMinimumOptions, CreateMaximumMinimumOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleMaxPool2D *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMaxPool2D *node) { export_pool_2d<luci::CircleMaxPool2D>(_ctx, node, circle::BuiltinOperator_MAX_POOL_2D); } -void OperationExporter::visit(luci::CircleMean *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMean *node) { export_simple(node, circle::BuiltinOperator_MEAN, circle::BuiltinOptions_ReducerOptions, CreateReducerOptions(_ctx.builder, node->keep_dims()).Union()); } -void OperationExporter::visit(luci::CircleMinimum *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMinimum *node) { export_simple(node, circle::BuiltinOperator_MINIMUM, circle::BuiltinOptions_MaximumMinimumOptions, CreateMaximumMinimumOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleMirrorPad *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMirrorPad *node) { export_simple( node, circle::BuiltinOperator_MIRROR_PAD, circle::BuiltinOptions_MirrorPadOptions, CreateMirrorPadOptions(_ctx.builder, to_circle_mirrorpadmode(node->mode())).Union()); } -void OperationExporter::visit(luci::CircleMul *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleMul *node) { export_simple( node, circle::BuiltinOperator_MUL, circle::BuiltinOptions_MulOptions, CreateMulOptions(_ctx.builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); } -void OperationExporter::visit(luci::CircleNeg *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleNeg *node) { export_simple(node, circle::BuiltinOperator_NEG, circle::BuiltinOptions_NegOptions, CreateNegOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleNonMaxSuppressionV4 *node) { export_node(_ctx, node); } +void OpExporterLet<OE::KLMN>::visit(luci::CircleNonMaxSuppressionV4 *node) +{ + export_node(_ctx, node); +} -void OperationExporter::visit(luci::CircleNonMaxSuppressionV5 *node) { export_node(_ctx, node); } +void OpExporterLet<OE::KLMN>::visit(luci::CircleNonMaxSuppressionV5 *node) +{ + export_node(_ctx, node); +} -void OperationExporter::visit(luci::CircleNotEqual *node) +void OpExporterLet<OE::KLMN>::visit(luci::CircleNotEqual *node) { export_simple(node, circle::BuiltinOperator_NOT_EQUAL, circle::BuiltinOptions_NotEqualOptions, CreateNotEqualOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleOneHot *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleOneHot *node) { export_simple(node, circle::BuiltinOperator_ONE_HOT, circle::BuiltinOptions_OneHotOptions, CreateOneHotOptions(_ctx.builder, node->axis()).Union()); } -void OperationExporter::visit(luci::CirclePack *node) +void OpExporterLet<OE::OPQR>::visit(luci::CirclePack *node) { export_simple(node, circle::BuiltinOperator_PACK, circle::BuiltinOptions_PackOptions, CreatePackOptions(_ctx.builder, node->values_count(), node->axis()).Union()); } -void OperationExporter::visit(luci::CirclePad *node) +void OpExporterLet<OE::OPQR>::visit(luci::CirclePad *node) { export_simple(node, circle::BuiltinOperator_PAD, circle::BuiltinOptions_PadOptions, CreatePadOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CirclePadV2 *node) +void OpExporterLet<OE::OPQR>::visit(luci::CirclePadV2 *node) { export_simple(node, circle::BuiltinOperator_PADV2, circle::BuiltinOptions_PadV2Options, CreatePadV2Options(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CirclePow *node) +void OpExporterLet<OE::OPQR>::visit(luci::CirclePow *node) { export_simple(node, circle::BuiltinOperator_POW, circle::BuiltinOptions_PowOptions, CreatePowOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CirclePRelu *node) +void OpExporterLet<OE::OPQR>::visit(luci::CirclePRelu *node) { export_simple(node, circle::BuiltinOperator_PRELU); } -void OperationExporter::visit(luci::CircleRange *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleQuantize *node) +{ + export_simple(node, circle::BuiltinOperator_QUANTIZE); +} + +void OpExporterLet<OE::OPQR>::visit(luci::CircleRange *node) { export_simple(node, circle::BuiltinOperator_RANGE, circle::BuiltinOptions_RangeOptions, CreateRangeOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleRank *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleRank *node) { export_simple(node, circle::BuiltinOperator_RANK, circle::BuiltinOptions_RankOptions, CreateRankOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleReduceAny *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleReduceAny *node) { export_simple(node, circle::BuiltinOperator_REDUCE_ANY, circle::BuiltinOptions_ReducerOptions, CreateReducerOptions(_ctx.builder, node->keep_dims()).Union()); } -void OperationExporter::visit(luci::CircleReduceMax *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleReduceMax *node) { export_simple(node, circle::BuiltinOperator_REDUCE_MAX, circle::BuiltinOptions_ReducerOptions, CreateReducerOptions(_ctx.builder, node->keep_dims()).Union()); } -void OperationExporter::visit(luci::CircleReduceMin *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleReduceMin *node) { export_simple(node, circle::BuiltinOperator_REDUCE_MIN, circle::BuiltinOptions_ReducerOptions, CreateReducerOptions(_ctx.builder, node->keep_dims()).Union()); } -void OperationExporter::visit(luci::CircleReduceProd *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleReduceProd *node) { export_simple(node, circle::BuiltinOperator_REDUCE_PROD, circle::BuiltinOptions_ReducerOptions, CreateReducerOptions(_ctx.builder, node->keep_dims()).Union()); } -void OperationExporter::visit(luci::CircleRelu *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleRelu *node) { export_simple(node, circle::BuiltinOperator_RELU); } -void OperationExporter::visit(luci::CircleRelu6 *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleRelu6 *node) { export_simple(node, circle::BuiltinOperator_RELU6); } -void OperationExporter::visit(luci::CircleReluN1To1 *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleReluN1To1 *node) { export_simple(node, circle::BuiltinOperator_RELU_N1_TO_1); } -void OperationExporter::visit(luci::CircleReshape *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleReshape *node) { auto new_shape = _ctx.builder.CreateVector<int32_t>( node->newShape()->rank(), [node](size_t i) { return node->newShape()->dim(i); }); @@ -1249,7 +1447,7 @@ void OperationExporter::visit(luci::CircleReshape *node) CreateReshapeOptions(_ctx.builder, new_shape).Union()); } -void OperationExporter::visit(luci::CircleResizeBilinear *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleResizeBilinear *node) { export_simple( node, circle::BuiltinOperator_RESIZE_BILINEAR, circle::BuiltinOptions_ResizeBilinearOptions, @@ -1257,130 +1455,130 @@ void OperationExporter::visit(luci::CircleResizeBilinear *node) .Union()); } -void OperationExporter::visit(luci::CircleResizeNearestNeighbor *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleResizeNearestNeighbor *node) { export_simple(node, circle::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, circle::BuiltinOptions_ResizeNearestNeighborOptions, CreateResizeNearestNeighborOptions(_ctx.builder, node->align_corners()).Union()); } -void OperationExporter::visit(luci::CircleReverseSequence *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleReverseSequence *node) { export_simple( node, circle::BuiltinOperator_REVERSE_SEQUENCE, circle::BuiltinOptions_ReverseSequenceOptions, CreateReverseSequenceOptions(_ctx.builder, node->seq_axis(), node->batch_axis()).Union()); } -void OperationExporter::visit(luci::CircleReverseV2 *node) { export_node(_ctx, node); } +void OpExporterLet<OE::OPQR>::visit(luci::CircleReverseV2 *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleRound *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleRound *node) { export_simple(node, circle::BuiltinOperator_ROUND); } -void OperationExporter::visit(luci::CircleRsqrt *node) +void OpExporterLet<OE::OPQR>::visit(luci::CircleRsqrt *node) { export_simple(node, circle::BuiltinOperator_RSQRT); } -void OperationExporter::visit(luci::CircleScatterNd *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleScatterNd *node) { export_simple(node, circle::BuiltinOperator_SCATTER_ND, circle::BuiltinOptions_ScatterNdOptions, CreateScatterNdOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleSegmentSum *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSegmentSum *node) { export_simple(node, circle::BuiltinOperator_SEGMENT_SUM, circle::BuiltinOptions_SegmentSumOptions, CreateSegmentSumOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleSelect *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSelect *node) { export_simple(node, circle::BuiltinOperator_SELECT, circle::BuiltinOptions_SelectOptions, CreateSelectOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleSelectV2 *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSelectV2 *node) { export_simple(node, circle::BuiltinOperator_SELECT_V2, circle::BuiltinOptions_SelectV2Options, CreateSelectV2Options(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleShape *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleShape *node) { export_simple(node, circle::BuiltinOperator_SHAPE, circle::BuiltinOptions_ShapeOptions, CreateShapeOptions(_ctx.builder, to_circle_tensortype(node->out_type())).Union()); } -void OperationExporter::visit(luci::CircleSin *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSin *node) { export_simple(node, circle::BuiltinOperator_SIN); } -void OperationExporter::visit(luci::CircleSlice *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSlice *node) { export_simple(node, circle::BuiltinOperator_SLICE, circle::BuiltinOptions_SliceOptions, CreateSliceOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleSoftmax *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSoftmax *node) { export_simple(node, circle::BuiltinOperator_SOFTMAX, circle::BuiltinOptions_SoftmaxOptions, CreateSoftmaxOptions(_ctx.builder, node->beta()).Union()); } -void OperationExporter::visit(luci::CircleSpaceToBatchND *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSpaceToBatchND *node) { export_simple(node, circle::BuiltinOperator_SPACE_TO_BATCH_ND, circle::BuiltinOptions_SpaceToBatchNDOptions, CreateSpaceToBatchNDOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleSpaceToDepth *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSpaceToDepth *node) { export_simple(node, circle::BuiltinOperator_SPACE_TO_DEPTH, circle::BuiltinOptions_SpaceToDepthOptions, CreateSpaceToDepthOptions(_ctx.builder, node->block_size()).Union()); } -void OperationExporter::visit(luci::CircleSparseToDense *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSparseToDense *node) { export_simple(node, circle::BuiltinOperator_SPARSE_TO_DENSE, circle::BuiltinOptions_SparseToDenseOptions, CreateSparseToDenseOptions(_ctx.builder, node->validate_indices()).Union()); } -void OperationExporter::visit(luci::CircleSplit *node) { export_node(_ctx, node); } +void OpExporterLet<OE::STUV>::visit(luci::CircleSplit *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleSplitV *node) { export_node(_ctx, node); } +void OpExporterLet<OE::STUV>::visit(luci::CircleSplitV *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleSqrt *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSqrt *node) { export_simple(node, circle::BuiltinOperator_SQRT); } -void OperationExporter::visit(luci::CircleSquare *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSquare *node) { export_simple(node, circle::BuiltinOperator_SQUARE, circle::BuiltinOptions_SquareOptions, CreateSquareOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleSquaredDifference *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSquaredDifference *node) { export_simple(node, circle::BuiltinOperator_SQUARED_DIFFERENCE, circle::BuiltinOptions_SquaredDifferenceOptions, CreateSquaredDifferenceOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleSqueeze *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSqueeze *node) { auto squeeze_dims = _ctx.builder.CreateVector<int32_t>(node->squeeze_dims()); export_simple(node, circle::BuiltinOperator_SQUEEZE, circle::BuiltinOptions_SqueezeOptions, CreateSqueezeOptions(_ctx.builder, squeeze_dims).Union()); } -void OperationExporter::visit(luci::CircleStridedSlice *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleStridedSlice *node) { export_simple(node, circle::BuiltinOperator_STRIDED_SLICE, circle::BuiltinOptions_StridedSliceOptions, @@ -1390,39 +1588,39 @@ void OperationExporter::visit(luci::CircleStridedSlice *node) .Union()); } -void OperationExporter::visit(luci::CircleSub *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSub *node) { export_simple( node, circle::BuiltinOperator_SUB, circle::BuiltinOptions_SubOptions, CreateSubOptions(_ctx.builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); } -void OperationExporter::visit(luci::CircleSum *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleSum *node) { export_simple(node, circle::BuiltinOperator_SUM, circle::BuiltinOptions_ReducerOptions, CreateReducerOptions(_ctx.builder, node->keep_dims()).Union()); } -void OperationExporter::visit(luci::CircleTanh *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleTanh *node) { export_simple(node, circle::BuiltinOperator_TANH); } -void OperationExporter::visit(luci::CircleTile *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleTile *node) { export_simple(node, circle::BuiltinOperator_TILE, circle::BuiltinOptions_TileOptions, CreateTileOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleTopKV2 *node) { export_node(_ctx, node); } +void OpExporterLet<OE::STUV>::visit(luci::CircleTopKV2 *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleTranspose *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleTranspose *node) { export_simple(node, circle::BuiltinOperator_TRANSPOSE, circle::BuiltinOptions_TransposeOptions, CreateTransposeOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleTransposeConv *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleTransposeConv *node) { export_simple(node, circle::BuiltinOperator_TRANSPOSE_CONV, circle::BuiltinOptions_TransposeConvOptions, @@ -1431,7 +1629,7 @@ void OperationExporter::visit(luci::CircleTransposeConv *node) .Union()); } -void OperationExporter::visit(luci::CircleUnidirectionalSequenceLSTM *node) +void OpExporterLet<OE::STUV>::visit(luci::CircleUnidirectionalSequenceLSTM *node) { export_simple(node, circle::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, circle::BuiltinOptions_UnidirectionalSequenceLSTMOptions, @@ -1442,25 +1640,25 @@ void OperationExporter::visit(luci::CircleUnidirectionalSequenceLSTM *node) .Union()); } -void OperationExporter::visit(luci::CircleUnique *node) { export_node(_ctx, node); } +void OpExporterLet<OE::STUV>::visit(luci::CircleUnique *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleUnpack *node) { export_node(_ctx, node); } +void OpExporterLet<OE::STUV>::visit(luci::CircleUnpack *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleWhere *node) +void OpExporterLet<OE::WXYZ>::visit(luci::CircleWhere *node) { export_simple(node, circle::BuiltinOperator_WHERE, circle::BuiltinOptions_WhereOptions, CreateWhereOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleWhile *node) { export_node(_ctx, node); } +void OpExporterLet<OE::WXYZ>::visit(luci::CircleWhile *node) { export_node(_ctx, node); } -void OperationExporter::visit(luci::CircleZerosLike *node) +void OpExporterLet<OE::WXYZ>::visit(luci::CircleZerosLike *node) { export_simple(node, circle::BuiltinOperator_ZEROS_LIKE, circle::BuiltinOptions_ZerosLikeOptions, CreateZerosLikeOptions(_ctx.builder).Union()); } -void OperationExporter::visit(luci::CircleBCQFullyConnected *node) +void OpExporterLet<OE::CIRC>::visit(luci::CircleBCQFullyConnected *node) { export_simple(node, circle::BuiltinOperator_BCQ_FULLY_CONNECTED, circle::BuiltinOptions_BCQFullyConnectedOptions, @@ -1469,14 +1667,14 @@ void OperationExporter::visit(luci::CircleBCQFullyConnected *node) .Union()); } -void OperationExporter::visit(luci::CircleBCQGather *node) +void OpExporterLet<OE::CIRC>::visit(luci::CircleBCQGather *node) { export_simple( node, circle::BuiltinOperator_BCQ_GATHER, circle::BuiltinOptions_BCQGatherOptions, CreateBCQGatherOptions(_ctx.builder, node->input_hidden_size(), node->axis()).Union()); } -void OperationExporter::visit(luci::CircleInstanceNorm *node) +void OpExporterLet<OE::CIRC>::visit(luci::CircleInstanceNorm *node) { export_simple(node, circle::BuiltinOperator_INSTANCE_NORM, circle::BuiltinOptions_InstanceNormOptions, @@ -1495,13 +1693,12 @@ void exportNode(loco::Node *node, flatbuffers::FlatBufferBuilder &builder, Seria const auto ops_size = gd._operators.size(); - circle_node->accept(&exporter); + exporter.export_node(circle_node); if (has_origin(circle_node) && ops_size != gd._operators.size()) { const auto node_id = gd._operators.size() - 1; for (auto source : get_origin(circle_node)->sources()) { - md._metadata.add_source_table(source->id(), source->name()); md._metadata.add_op_table(node_id, source->id()); } } diff --git a/compiler/luci/export/src/CircleTensorExporter.cpp b/compiler/luci/export/src/CircleTensorExporter.cpp index fefdf4e73..615402aa8 100644 --- a/compiler/luci/export/src/CircleTensorExporter.cpp +++ b/compiler/luci/export/src/CircleTensorExporter.cpp @@ -28,6 +28,8 @@ #include <loco/IR/DataTypeTraits.h> #include <oops/InternalExn.h> +#include <string.h> + using namespace circle; using namespace flatbuffers; @@ -353,6 +355,54 @@ flatbuffers::Offset<circle::Buffer> encodeOpBufferByDType(FlatBufferBuilder &bui } template <> +flatbuffers::Offset<circle::Buffer> +encodeOpBufferByDType<loco::DataType::STRING>(FlatBufferBuilder &builder, luci::CircleConst *c) +{ + const uint32_t count = c->size<loco::DataType::STRING>(); + uint32_t raw_size = sizeof(int32_t) * (count + 2); + for (uint32_t i = 0; i < count; ++i) + { + auto &value = c->at<loco::DataType::STRING>(i); + raw_size += value.length(); + } + + // serialize string data + // int32_t count + // int32_t offsets[count + 1] + // string values[count] + std::vector<uint8_t> raw_data; + raw_data.reserve(raw_size); + + auto *i32d = reinterpret_cast<int32_t *>(raw_data.data()); + int32_t start = sizeof(int32_t) * (count + 2); + int32_t offset = start; + std::vector<int32_t> offsets; + + *i32d++ = count; + *i32d++ = start; + offsets.push_back(start); + for (uint32_t i = 0; i < count; ++i) + { + auto &value = c->at<loco::DataType::STRING>(i); + offset += value.length(); + *i32d++ = offset; + offsets.push_back(offset); + } + + auto *data = reinterpret_cast<uint8_t *>(i32d); + for (uint32_t i = 0; i < count; ++i) + { + int32_t length = offsets[i + 1] - offsets[i]; + auto &value = c->at<loco::DataType::STRING>(i); + memcpy(data, value.c_str(), length); + data += length; + } + + auto array_offset = builder.CreateVector(reinterpret_cast<uint8_t *>(raw_data.data()), raw_size); + return CreateBuffer(builder, array_offset); +} + +template <> flatbuffers::Offset<circle::Buffer> encodeOpBuffer(FlatBufferBuilder &builder, luci::CircleConst *c) { switch (c->dtype()) @@ -371,6 +421,8 @@ flatbuffers::Offset<circle::Buffer> encodeOpBuffer(FlatBufferBuilder &builder, l return encodeOpBufferByDType<loco::DataType::U8>(builder, c); case loco::DataType::BOOL: return encodeOpBufferByDType<loco::DataType::BOOL>(builder, c); + case loco::DataType::STRING: + return encodeOpBufferByDType<loco::DataType::STRING>(builder, c); default: break; } @@ -510,9 +562,14 @@ uint32_t get_buffer_id(FlatBufferBuilder &builder, SerializedModelData &md, luci } else { - // When there is no CircleConst, the operation do not use buffer. - // So return buffer id as 0 which means empty buffer in circle schema. - return 0; + // When there is no CircleConst, there is nothing to cache. + // So return new buffer id. + auto buffer = encodeOpBuffer(builder); + + auto buffer_id = static_cast<uint32_t>(md._buffers.size()); + md._buffers.push_back(buffer); + + return buffer_id; } } diff --git a/compiler/luci/export/src/SerializedData.h b/compiler/luci/export/src/SerializedData.h index df71e5c21..95f7b5755 100644 --- a/compiler/luci/export/src/SerializedData.h +++ b/compiler/luci/export/src/SerializedData.h @@ -51,14 +51,7 @@ struct OpCode class CircleExportMetadata { public: - void add_source_table(uint32_t source_id, std::string origin_name) - { - // Model with multiple subgraph may have different origin_name - // even if source_id is same. However, as we do not consider about - // multiple subgraph in profiling for now, just do not care those cases - // and support them correctly in the future. - _source_table.emplace(source_id, origin_name); - } + void source_table(const std::map<uint32_t, std::string> &table) { _source_table = table; } void add_op_table(uint32_t node_id, uint32_t source_id) { diff --git a/compiler/luci/import/CMakeLists.txt b/compiler/luci/import/CMakeLists.txt index 642751ca6..4e200f6ae 100644 --- a/compiler/luci/import/CMakeLists.txt +++ b/compiler/luci/import/CMakeLists.txt @@ -15,6 +15,8 @@ target_link_libraries(luci_import PRIVATE nncc_common) target_link_libraries(luci_import PRIVATE locop) target_link_libraries(luci_import PRIVATE oops) install(TARGETS luci_import DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/luci/import/include/luci/Import/Nodes.h b/compiler/luci/import/include/luci/Import/Nodes.h index b084c7dbc..f7d22e7aa 100644 --- a/compiler/luci/import/include/luci/Import/Nodes.h +++ b/compiler/luci/import/include/luci/Import/Nodes.h @@ -85,6 +85,7 @@ #include "Nodes/CirclePadV2.h" #include "Nodes/CirclePow.h" #include "Nodes/CirclePRelu.h" +#include "Nodes/CircleQuantize.h" #include "Nodes/CircleRange.h" #include "Nodes/CircleRank.h" #include "Nodes/CircleReduceAny.h" diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleQuantize.h b/compiler/luci/import/include/luci/Import/Nodes/CircleQuantize.h new file mode 100644 index 000000000..b6d52f503 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleQuantize.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_QUANTIZE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_QUANTIZE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleQuantizeGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector<CircleNode *> &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_QUANTIZE_H__ diff --git a/compiler/luci/import/src/CircleImportMetadata.h b/compiler/luci/import/src/CircleImportMetadata.h index 80176db94..007985dcc 100644 --- a/compiler/luci/import/src/CircleImportMetadata.h +++ b/compiler/luci/import/src/CircleImportMetadata.h @@ -45,6 +45,8 @@ public: */ const OriginTable origin_table(void); + const std::map<uint32_t, std::string> &source_table(void) const { return _source_table; } + private: // Decoded metadata is stored std::map<uint32_t, std::string> _source_table; diff --git a/compiler/luci/import/src/CircleReader.cpp b/compiler/luci/import/src/CircleReader.cpp index 861c1bbe3..6c9bf3af9 100644 --- a/compiler/luci/import/src/CircleReader.cpp +++ b/compiler/luci/import/src/CircleReader.cpp @@ -86,7 +86,7 @@ loco::DataType luci_datatype(const circle::TensorType type) case circle::TensorType_INT64: return loco::DataType::S64; case circle::TensorType_STRING: - break; + return loco::DataType::STRING; case circle::TensorType_BOOL: return loco::DataType::BOOL; case circle::TensorType_INT16: diff --git a/compiler/luci/import/src/GraphBuilderMultiOutput.cpp b/compiler/luci/import/src/GraphBuilderMultiOutput.cpp index 9b42e997e..be553f4c0 100644 --- a/compiler/luci/import/src/GraphBuilderMultiOutput.cpp +++ b/compiler/luci/import/src/GraphBuilderMultiOutput.cpp @@ -60,7 +60,8 @@ CircleNode *GraphBuilderMultiOutput::build(const circle::OperatorT &op, auto *node = build_node(bna); uint32_t output_count = outputs.size(); - assert(output_count > 0); + // NOTE CustomOp inherits GraphBuilderMultiOutput and can have 0 output + if (output_count > 0) { // Let's use attributes from output 0 for this node const circle::TensorT &output_tensor = *tensors[outputs[0]]; @@ -82,6 +83,7 @@ CircleNode *GraphBuilderMultiOutput::build(const circle::OperatorT &op, auto *nodeout = build_out(boa); copy_tensor_attributes(output_tensor, nodeout); + // NOTE name of CxxxOut nodes may have same name // mark shape_status if (tensors_ptr->Get(outputs[n])->shape() == nullptr) nodeout->shape_status(ShapeStatus::NOSHAPE); diff --git a/compiler/luci/import/src/GraphBuilderRegistry.cpp b/compiler/luci/import/src/GraphBuilderRegistry.cpp index 7f98aab78..df07d9e48 100644 --- a/compiler/luci/import/src/GraphBuilderRegistry.cpp +++ b/compiler/luci/import/src/GraphBuilderRegistry.cpp @@ -94,6 +94,7 @@ GraphBuilderRegistry::GraphBuilderRegistry() CIRCLE_NODE(PADV2, CirclePadV2GraphBuilder); // 60 CIRCLE_NODE(POW, CirclePowGraphBuilder); // 78 CIRCLE_NODE(PRELU, CirclePReluGraphBuilder); // 54, + CIRCLE_NODE(QUANTIZE, CircleQuantizeGraphBuilder); // 114, CIRCLE_NODE(RANGE, CircleRangeGraphBuilder); // 96 CIRCLE_NODE(RANK, CircleRankGraphBuilder); // 110 CIRCLE_NODE(REDUCE_ANY, CircleReduceAnyGraphBuilder); // 91 @@ -158,7 +159,6 @@ GraphBuilderRegistry::GraphBuilderRegistry() // BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, // BuiltinOperator_DELEGATE = 51, // BuiltinOperator_ARG_MAX = 56, - // BuiltinOperator_QUANTIZE = 114, // BuiltinOperator_HARD_SWISH = 117, // BuiltinOperator_DENSIFY = 124, } diff --git a/compiler/luci/import/src/Importer.cpp b/compiler/luci/import/src/Importer.cpp index 193afffcb..68baefab0 100644 --- a/compiler/luci/import/src/Importer.cpp +++ b/compiler/luci/import/src/Importer.cpp @@ -315,6 +315,35 @@ std::unique_ptr<Module> Importer::importModule(const circle::Model *model) const post_import_graph(module.get(), reader); + // Initialize 'source_table' + auto circle_metadata = std::make_unique<luci::CircleImportMetadata>(reader); + if (circle_metadata->source_table().size() > 0) + { + // If there is 'source_table' metadata in circle model, copy the table. + module->source_table(circle_metadata->source_table()); + } + else + { + // If there is no 'source_table' metadata in circle model, + // create new table with circle nodes. + std::map<uint32_t, std::string> table; + + // NOTE Only first subgraph is considered + for (auto node : loco::all_nodes(module->graph(0))) + { + auto circle_node = loco::must_cast<luci::CircleNode *>(node); + + // Virtual nodes may not have id + if (!has_node_id(circle_node)) + continue; + + assert(table.find(get_node_id(circle_node)) == table.end()); + table.insert({get_node_id(circle_node), circle_node->name()}); + } + + module->source_table(table); + } + return module; } diff --git a/compiler/luci/import/src/Nodes/CircleConst.cpp b/compiler/luci/import/src/Nodes/CircleConst.cpp index f69448dfe..11fbb4e54 100644 --- a/compiler/luci/import/src/Nodes/CircleConst.cpp +++ b/compiler/luci/import/src/Nodes/CircleConst.cpp @@ -23,6 +23,9 @@ #include <oops/UserExn.h> #include <cassert> +#include <ostream> +#include <string> +#include <vector> namespace { @@ -40,14 +43,10 @@ std::ostream &operator<<(std::ostream &os, const std::vector<int32_t> &vect) return os; } -} // namespace - -namespace luci -{ +using namespace luci; template <loco::DataType DT> -static void copy_data(const std::vector<uint8_t> &raw_data, uint32_t num_elements, - CircleConst *const_node) +void copy_data(const std::vector<uint8_t> &raw_data, uint32_t num_elements, CircleConst *const_node) { using T = typename loco::DataTypeImpl<DT>::Type; @@ -67,6 +66,46 @@ static void copy_data(const std::vector<uint8_t> &raw_data, uint32_t num_element } } +template <> +void copy_data<loco::DataType::STRING>(const std::vector<uint8_t> &raw_data, uint32_t num_elements, + CircleConst *const_node) +{ + assert(const_node->sparsityparam() == nullptr); + + const auto *data = reinterpret_cast<const char *>(raw_data.data()); + const auto *i32d = reinterpret_cast<const int32_t *>(raw_data.data()); + + // de-serialize string data + // int32_t count + // int32_t offsets[count + 1] + // string values[count] + assert(static_cast<uint32_t>(*i32d) == num_elements); + i32d++; // skip count + + std::vector<int32_t> offsets; + offsets.push_back(*i32d++); + for (uint32_t i = 0; i < num_elements; ++i) + { + offsets.push_back(*i32d++); + } + assert(offsets.size() == num_elements + 1); + + const_node->size<loco::DataType::STRING>(num_elements); + for (uint32_t i = 0; i < num_elements; ++i) + { + int32_t start = offsets[i]; + int32_t next = offsets[i + 1]; + + std::string value(data + start, next - start); + const_node->at<loco::DataType::STRING>(i) = value; + } +} + +} // namespace + +namespace luci +{ + CircleConst *create_circleconst(GraphBuilderContext *context, int32_t tensor_index) { LOGGER(l); @@ -141,6 +180,10 @@ CircleConst *create_circleconst(GraphBuilderContext *context, int32_t tensor_ind copy_data<loco::DataType::BOOL>(buffer, num_elements, const_node); break; + case loco::DataType::STRING: + copy_data<loco::DataType::STRING>(buffer, num_elements, const_node); + break; + default: throw oops::UserExn("Unsupported tensor type", circle::EnumNameTensorType(const_tensor.type)); diff --git a/compiler/luci/import/src/Nodes/CircleElu.cpp b/compiler/luci/import/src/Nodes/CircleElu.cpp index 461da9517..41696a65a 100644 --- a/compiler/luci/import/src/Nodes/CircleElu.cpp +++ b/compiler/luci/import/src/Nodes/CircleElu.cpp @@ -36,8 +36,14 @@ bool CircleEluGraphBuilder::validate(const ValidateArgs &args) const switch (tensor->type) { + case circle::TensorType_FLOAT64: + break; case circle::TensorType_FLOAT32: break; + case circle::TensorType_INT16: + break; + case circle::TensorType_UINT8: + break; default: return false; } diff --git a/compiler/luci/import/src/Nodes/CircleExp.cpp b/compiler/luci/import/src/Nodes/CircleExp.cpp index 64f18fbd4..5bb7bb664 100644 --- a/compiler/luci/import/src/Nodes/CircleExp.cpp +++ b/compiler/luci/import/src/Nodes/CircleExp.cpp @@ -38,6 +38,10 @@ bool CircleExpGraphBuilder::validate(const ValidateArgs &args) const case circle::TensorType_FLOAT32: case circle::TensorType_FLOAT64: break; + // Additional support for quantized tensors + case circle::TensorType_UINT8: + case circle::TensorType_INT16: + break; // TODO support TensorType_COMPLEX64, complex128, bfloat16 default: return false; diff --git a/compiler/luci/import/src/Nodes/CircleQuantize.cpp b/compiler/luci/import/src/Nodes/CircleQuantize.cpp new file mode 100644 index 000000000..9247a76d9 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleQuantize.cpp @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleQuantize.h" + +#include <luci/IR/Nodes/CircleQuantize.h> + +#include <loco.h> + +namespace luci +{ + +bool CircleQuantizeGraphBuilder::validate(const ValidateArgs &args) const +{ + return GraphBuilder::validate(args, 1); +} + +CircleNode *CircleQuantizeGraphBuilder::build_node(const circle::OperatorT &, + const std::vector<CircleNode *> &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create<CircleQuantize>(); + node->input(inputs.at(0)); + + // No options for Quantize + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSquaredDifference.cpp b/compiler/luci/import/src/Nodes/CircleSquaredDifference.cpp index f4e193713..33440d5ab 100644 --- a/compiler/luci/import/src/Nodes/CircleSquaredDifference.cpp +++ b/compiler/luci/import/src/Nodes/CircleSquaredDifference.cpp @@ -44,6 +44,10 @@ bool CircleSquaredDifferenceGraphBuilder::validate(const ValidateArgs &args) con case circle::TensorType_COMPLEX64: break; // TODO support bfloat16, complex128 + // Additional support for quantized tensors + case circle::TensorType_UINT8: + case circle::TensorType_INT16: + break; default: return false; } diff --git a/compiler/luci/lang/CMakeLists.txt b/compiler/luci/lang/CMakeLists.txt index c618fdd6f..669a866b1 100644 --- a/compiler/luci/lang/CMakeLists.txt +++ b/compiler/luci/lang/CMakeLists.txt @@ -12,6 +12,8 @@ target_link_libraries(luci_lang PRIVATE logo) target_link_libraries(luci_lang PRIVATE nncc_common) install(TARGETS luci_lang DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/luci/lang/include/luci/IR/CircleNodeMixins.h b/compiler/luci/lang/include/luci/IR/CircleNodeMixins.h index 3f8ab7d61..158d65d90 100644 --- a/compiler/luci/lang/include/luci/IR/CircleNodeMixins.h +++ b/compiler/luci/lang/include/luci/IR/CircleNodeMixins.h @@ -67,7 +67,7 @@ public: * * TODO Deprecated this class, and use loco::FixedArity instead */ -template <unsigned N, typename Base> class FixedArityNode : public Base +template <uint32_t N, typename Base> class FixedArityNode : public Base { public: FixedArityNode() @@ -82,7 +82,7 @@ public: virtual ~FixedArityNode() = default; public: - unsigned arity(void) const final { return N; } + uint32_t arity(void) const final { return N; } loco::Node *arg(uint32_t n) const final { return _args.at(n)->node(); } @@ -96,7 +96,7 @@ public: protected: // This API allows inherited classes to access "_args" field. - loco::Use *at(unsigned n) const { return _args.at(n).get(); } + loco::Use *at(uint32_t n) const { return _args.at(n).get(); } private: std::vector<std::unique_ptr<loco::Use>> _args{}; diff --git a/compiler/luci/lang/include/luci/IR/CircleNodes.h b/compiler/luci/lang/include/luci/IR/CircleNodes.h index 69a82a7b9..a313f9d5b 100644 --- a/compiler/luci/lang/include/luci/IR/CircleNodes.h +++ b/compiler/luci/lang/include/luci/IR/CircleNodes.h @@ -81,6 +81,7 @@ #include "Nodes/CirclePad.h" #include "Nodes/CirclePadV2.h" #include "Nodes/CirclePow.h" +#include "Nodes/CircleQuantize.h" #include "Nodes/CirclePRelu.h" #include "Nodes/CircleRange.h" #include "Nodes/CircleRank.h" diff --git a/compiler/luci/lang/include/luci/IR/CircleNodes.lst b/compiler/luci/lang/include/luci/IR/CircleNodes.lst index b93fdc89d..914aa16e4 100644 --- a/compiler/luci/lang/include/luci/IR/CircleNodes.lst +++ b/compiler/luci/lang/include/luci/IR/CircleNodes.lst @@ -15,135 +15,136 @@ // .Input("value: T") <-- Input name is 'value' // -CIRCLE_NODE(ABS, luci::CircleAbs) -CIRCLE_NODE(ADD, luci::CircleAdd) -CIRCLE_NODE(ADD_N, luci::CircleAddN) -CIRCLE_NODE(ARG_MAX, luci::CircleArgMax) -CIRCLE_NODE(ARG_MIN, luci::CircleArgMin) -CIRCLE_NODE(AVERAGE_POOL_2D, luci::CircleAveragePool2D) -CIRCLE_NODE(BATCH_TO_SPACE_ND, luci::CircleBatchToSpaceND) -CIRCLE_NODE(BATCH_MATMUL, luci::CircleBatchMatMul) -CIRCLE_NODE(BIDIRECTIONAL_SEQUENCE_LSTM, luci::CircleBidirectionalSequenceLSTM) -CIRCLE_NODE(CAST, luci::CircleCast) -CIRCLE_NODE(CEIL, luci::CircleCeil) -CIRCLE_NODE(CONCATENATION, luci::CircleConcatenation) -CIRCLE_NODE(CONV_2D, luci::CircleConv2D) -CIRCLE_NODE(COS, luci::CircleCos) -CIRCLE_NODE(CUSTOM, luci::CircleCustom) -CIRCLE_NODE(DEPTH_TO_SPACE, luci::CircleDepthToSpace) -CIRCLE_NODE(DEPTHWISE_CONV_2D, luci::CircleDepthwiseConv2D) -CIRCLE_NODE(DEQUANTIZE, luci::CircleDequantize) -CIRCLE_NODE(DIV, luci::CircleDiv) -CIRCLE_NODE(ELU, luci::CircleElu) -CIRCLE_NODE(EQUAL, luci::CircleEqual) -CIRCLE_NODE(EXP, luci::CircleExp) -CIRCLE_NODE(EXPAND_DIMS, luci::CircleExpandDims) -CIRCLE_NODE(FAKE_QUANT, luci::CircleFakeQuant) -CIRCLE_NODE(FILL, luci::CircleFill) -CIRCLE_NODE(FLOOR, luci::CircleFloor) -CIRCLE_NODE(FLOOR_DIV, luci::CircleFloorDiv) -CIRCLE_NODE(FLOOR_MOD, luci::CircleFloorMod) -CIRCLE_NODE(FULLY_CONNECTED, luci::CircleFullyConnected) -CIRCLE_NODE(GATHER, luci::CircleGather) -CIRCLE_NODE(GATHER_ND, luci::CircleGatherNd) -CIRCLE_NODE(GREATER, luci::CircleGreater) -CIRCLE_NODE(GREATER_EQUAL, luci::CircleGreaterEqual) -CIRCLE_NODE(IF, luci::CircleIf) -CIRCLE_NODE(L2_NORMALIZATION, luci::CircleL2Normalize) -CIRCLE_NODE(L2_POOL_2D, luci::CircleL2Pool2D) -CIRCLE_NODE(LEAKY_RELU, luci::CircleLeakyRelu) -CIRCLE_NODE(LESS, luci::CircleLess) -CIRCLE_NODE(LESS_EQUAL, luci::CircleLessEqual) -CIRCLE_NODE(LOCAL_RESPONSE_NORMALIZATION, luci::CircleLocalResponseNormalization) -CIRCLE_NODE(LOG, luci::CircleLog) -CIRCLE_NODE(LOGICAL_AND, luci::CircleLogicalAnd) -CIRCLE_NODE(LOGICAL_NOT, luci::CircleLogicalNot) -CIRCLE_NODE(LOGICAL_OR, luci::CircleLogicalOr) -CIRCLE_NODE(LOGISTIC, luci::CircleLogistic) -CIRCLE_NODE(LOG_SOFTMAX, luci::CircleLogSoftmax) -CIRCLE_NODE(MATRIX_DIAG, luci::CircleMatrixDiag) -CIRCLE_NODE(MAX_POOL_2D, luci::CircleMaxPool2D) -CIRCLE_NODE(MATRIX_SET_DIAG, luci::CircleMatrixSetDiag) -CIRCLE_NODE(MAXIMUM, luci::CircleMaximum) -CIRCLE_NODE(MEAN, luci::CircleMean) -CIRCLE_NODE(MINIMUM, luci::CircleMinimum) -CIRCLE_NODE(MIRROR_PAD, luci::CircleMirrorPad) -CIRCLE_NODE(MUL, luci::CircleMul) -CIRCLE_NODE(NEG, luci::CircleNeg) -CIRCLE_NODE(NON_MAX_SUPPRESSION_V4, luci::CircleNonMaxSuppressionV4) -CIRCLE_NODE(NON_MAX_SUPPRESSION_V5, luci::CircleNonMaxSuppressionV5) -CIRCLE_NODE(NOT_EQUAL, luci::CircleNotEqual) -CIRCLE_NODE(ONE_HOT, luci::CircleOneHot) -CIRCLE_NODE(PACK, luci::CirclePack) -CIRCLE_NODE(PAD, luci::CirclePad) -CIRCLE_NODE(PADV2, luci::CirclePadV2) -CIRCLE_NODE(POW, luci::CirclePow) -CIRCLE_NODE(PRELU, luci::CirclePRelu) -CIRCLE_NODE(RANGE, luci::CircleRange) -CIRCLE_NODE(RANK, luci::CircleRank) -CIRCLE_NODE(REDUCE_ANY, luci::CircleReduceAny) -CIRCLE_NODE(REDUCE_MAX, luci::CircleReduceMax) -CIRCLE_NODE(REDUCE_MIN, luci::CircleReduceMin) -CIRCLE_NODE(REDUCE_PROD, luci::CircleReduceProd) -CIRCLE_NODE(RELU, luci::CircleRelu) -CIRCLE_NODE(RELU6, luci::CircleRelu6) -CIRCLE_NODE(RELU_N1_TO_1, luci::CircleReluN1To1) -CIRCLE_NODE(RESHAPE, luci::CircleReshape) -CIRCLE_NODE(RESIZE_BILINEAR, luci::CircleResizeBilinear) -CIRCLE_NODE(RESIZE_NEAREST_NEIGHBOR, luci::CircleResizeNearestNeighbor) -CIRCLE_NODE(REVERSE_SEQUENCE, luci::CircleReverseSequence) -CIRCLE_NODE(REVERSE_V2, luci::CircleReverseV2) -CIRCLE_NODE(ROUND, luci::CircleRound) -CIRCLE_NODE(RSQRT, luci::CircleRsqrt) -CIRCLE_NODE(SCATTER_ND, luci::CircleScatterNd) -CIRCLE_NODE(SEGMENT_SUM, luci::CircleSegmentSum) -CIRCLE_NODE(SELECT, luci::CircleSelect) -CIRCLE_NODE(SELECT_V2, luci::CircleSelectV2) -CIRCLE_NODE(SHAPE, luci::CircleShape) -CIRCLE_NODE(SIN, luci::CircleSin) -CIRCLE_NODE(SLICE, luci::CircleSlice) -CIRCLE_NODE(SOFTMAX, luci::CircleSoftmax) -CIRCLE_NODE(SPACE_TO_BATCH_ND, luci::CircleSpaceToBatchND) -CIRCLE_NODE(SPACE_TO_DEPTH, luci::CircleSpaceToDepth) -CIRCLE_NODE(SPARSE_TO_DENSE, luci::CircleSparseToDense) -CIRCLE_NODE(SPLIT, luci::CircleSplit) -CIRCLE_NODE(SPLIT_V, luci::CircleSplitV) -CIRCLE_NODE(SQRT, luci::CircleSqrt) -CIRCLE_NODE(SQUARE, luci::CircleSquare) -CIRCLE_NODE(SQUARED_DIFFERENCE, luci::CircleSquaredDifference) -CIRCLE_NODE(SQUEEZE, luci::CircleSqueeze) -CIRCLE_NODE(STRIDED_SLICE, luci::CircleStridedSlice) -CIRCLE_NODE(SUB, luci::CircleSub) -CIRCLE_NODE(SUM, luci::CircleSum) -CIRCLE_NODE(TANH, luci::CircleTanh) -CIRCLE_NODE(TILE, luci::CircleTile) -CIRCLE_NODE(TOPK_V2, luci::CircleTopKV2) -CIRCLE_NODE(TRANSPOSE, luci::CircleTranspose) -CIRCLE_NODE(TRANSPOSE_CONV, luci::CircleTransposeConv) -CIRCLE_NODE(UNIDIRECTIONAL_SEQUENCE_LSTM, luci::CircleUnidirectionalSequenceLSTM) -CIRCLE_NODE(UNIQUE, luci::CircleUnique) -CIRCLE_NODE(UNPACK, luci::CircleUnpack) -CIRCLE_NODE(WHERE, luci::CircleWhere) -CIRCLE_NODE(WHILE, luci::CircleWhile) -CIRCLE_NODE(ZEROS_LIKE, luci::CircleZerosLike) +CIRCLE_NODE(ABS, CircleAbs) +CIRCLE_NODE(ADD, CircleAdd) +CIRCLE_NODE(ADD_N, CircleAddN) +CIRCLE_NODE(ARG_MAX, CircleArgMax) +CIRCLE_NODE(ARG_MIN, CircleArgMin) +CIRCLE_NODE(AVERAGE_POOL_2D, CircleAveragePool2D) +CIRCLE_NODE(BATCH_TO_SPACE_ND, CircleBatchToSpaceND) +CIRCLE_NODE(BATCH_MATMUL, CircleBatchMatMul) +CIRCLE_NODE(BIDIRECTIONAL_SEQUENCE_LSTM, CircleBidirectionalSequenceLSTM) +CIRCLE_NODE(CAST, CircleCast) +CIRCLE_NODE(CEIL, CircleCeil) +CIRCLE_NODE(CONCATENATION, CircleConcatenation) +CIRCLE_NODE(CONV_2D, CircleConv2D) +CIRCLE_NODE(COS, CircleCos) +CIRCLE_NODE(CUSTOM, CircleCustom) +CIRCLE_NODE(DEPTH_TO_SPACE, CircleDepthToSpace) +CIRCLE_NODE(DEPTHWISE_CONV_2D, CircleDepthwiseConv2D) +CIRCLE_NODE(DEQUANTIZE, CircleDequantize) +CIRCLE_NODE(DIV, CircleDiv) +CIRCLE_NODE(ELU, CircleElu) +CIRCLE_NODE(EQUAL, CircleEqual) +CIRCLE_NODE(EXP, CircleExp) +CIRCLE_NODE(EXPAND_DIMS, CircleExpandDims) +CIRCLE_NODE(FAKE_QUANT, CircleFakeQuant) +CIRCLE_NODE(FILL, CircleFill) +CIRCLE_NODE(FLOOR, CircleFloor) +CIRCLE_NODE(FLOOR_DIV, CircleFloorDiv) +CIRCLE_NODE(FLOOR_MOD, CircleFloorMod) +CIRCLE_NODE(FULLY_CONNECTED, CircleFullyConnected) +CIRCLE_NODE(GATHER, CircleGather) +CIRCLE_NODE(GATHER_ND, CircleGatherNd) +CIRCLE_NODE(GREATER, CircleGreater) +CIRCLE_NODE(GREATER_EQUAL, CircleGreaterEqual) +CIRCLE_NODE(IF, CircleIf) +CIRCLE_NODE(L2_NORMALIZATION, CircleL2Normalize) +CIRCLE_NODE(L2_POOL_2D, CircleL2Pool2D) +CIRCLE_NODE(LEAKY_RELU, CircleLeakyRelu) +CIRCLE_NODE(LESS, CircleLess) +CIRCLE_NODE(LESS_EQUAL, CircleLessEqual) +CIRCLE_NODE(LOCAL_RESPONSE_NORMALIZATION, CircleLocalResponseNormalization) +CIRCLE_NODE(LOG, CircleLog) +CIRCLE_NODE(LOGICAL_AND, CircleLogicalAnd) +CIRCLE_NODE(LOGICAL_NOT, CircleLogicalNot) +CIRCLE_NODE(LOGICAL_OR, CircleLogicalOr) +CIRCLE_NODE(LOGISTIC, CircleLogistic) +CIRCLE_NODE(LOG_SOFTMAX, CircleLogSoftmax) +CIRCLE_NODE(MATRIX_DIAG, CircleMatrixDiag) +CIRCLE_NODE(MAX_POOL_2D, CircleMaxPool2D) +CIRCLE_NODE(MATRIX_SET_DIAG, CircleMatrixSetDiag) +CIRCLE_NODE(MAXIMUM, CircleMaximum) +CIRCLE_NODE(MEAN, CircleMean) +CIRCLE_NODE(MINIMUM, CircleMinimum) +CIRCLE_NODE(MIRROR_PAD, CircleMirrorPad) +CIRCLE_NODE(MUL, CircleMul) +CIRCLE_NODE(NEG, CircleNeg) +CIRCLE_NODE(NON_MAX_SUPPRESSION_V4, CircleNonMaxSuppressionV4) +CIRCLE_NODE(NON_MAX_SUPPRESSION_V5, CircleNonMaxSuppressionV5) +CIRCLE_NODE(NOT_EQUAL, CircleNotEqual) +CIRCLE_NODE(ONE_HOT, CircleOneHot) +CIRCLE_NODE(PACK, CirclePack) +CIRCLE_NODE(PAD, CirclePad) +CIRCLE_NODE(PADV2, CirclePadV2) +CIRCLE_NODE(POW, CirclePow) +CIRCLE_NODE(PRELU, CirclePRelu) +CIRCLE_NODE(QUANTIZE, CircleQuantize) +CIRCLE_NODE(RANGE, CircleRange) +CIRCLE_NODE(RANK, CircleRank) +CIRCLE_NODE(REDUCE_ANY, CircleReduceAny) +CIRCLE_NODE(REDUCE_MAX, CircleReduceMax) +CIRCLE_NODE(REDUCE_MIN, CircleReduceMin) +CIRCLE_NODE(REDUCE_PROD, CircleReduceProd) +CIRCLE_NODE(RELU, CircleRelu) +CIRCLE_NODE(RELU6, CircleRelu6) +CIRCLE_NODE(RELU_N1_TO_1, CircleReluN1To1) +CIRCLE_NODE(RESHAPE, CircleReshape) +CIRCLE_NODE(RESIZE_BILINEAR, CircleResizeBilinear) +CIRCLE_NODE(RESIZE_NEAREST_NEIGHBOR, CircleResizeNearestNeighbor) +CIRCLE_NODE(REVERSE_SEQUENCE, CircleReverseSequence) +CIRCLE_NODE(REVERSE_V2, CircleReverseV2) +CIRCLE_NODE(ROUND, CircleRound) +CIRCLE_NODE(RSQRT, CircleRsqrt) +CIRCLE_NODE(SCATTER_ND, CircleScatterNd) +CIRCLE_NODE(SEGMENT_SUM, CircleSegmentSum) +CIRCLE_NODE(SELECT, CircleSelect) +CIRCLE_NODE(SELECT_V2, CircleSelectV2) +CIRCLE_NODE(SHAPE, CircleShape) +CIRCLE_NODE(SIN, CircleSin) +CIRCLE_NODE(SLICE, CircleSlice) +CIRCLE_NODE(SOFTMAX, CircleSoftmax) +CIRCLE_NODE(SPACE_TO_BATCH_ND, CircleSpaceToBatchND) +CIRCLE_NODE(SPACE_TO_DEPTH, CircleSpaceToDepth) +CIRCLE_NODE(SPARSE_TO_DENSE, CircleSparseToDense) +CIRCLE_NODE(SPLIT, CircleSplit) +CIRCLE_NODE(SPLIT_V, CircleSplitV) +CIRCLE_NODE(SQRT, CircleSqrt) +CIRCLE_NODE(SQUARE, CircleSquare) +CIRCLE_NODE(SQUARED_DIFFERENCE, CircleSquaredDifference) +CIRCLE_NODE(SQUEEZE, CircleSqueeze) +CIRCLE_NODE(STRIDED_SLICE, CircleStridedSlice) +CIRCLE_NODE(SUB, CircleSub) +CIRCLE_NODE(SUM, CircleSum) +CIRCLE_NODE(TANH, CircleTanh) +CIRCLE_NODE(TILE, CircleTile) +CIRCLE_NODE(TOPK_V2, CircleTopKV2) +CIRCLE_NODE(TRANSPOSE, CircleTranspose) +CIRCLE_NODE(TRANSPOSE_CONV, CircleTransposeConv) +CIRCLE_NODE(UNIDIRECTIONAL_SEQUENCE_LSTM, CircleUnidirectionalSequenceLSTM) +CIRCLE_NODE(UNIQUE, CircleUnique) +CIRCLE_NODE(UNPACK, CircleUnpack) +CIRCLE_NODE(WHERE, CircleWhere) +CIRCLE_NODE(WHILE, CircleWhile) +CIRCLE_NODE(ZEROS_LIKE, CircleZerosLike) // Circle Only -CIRCLE_NODE(BCQ_FULLY_CONNECTED, luci::CircleBCQFullyConnected) -CIRCLE_NODE(BCQ_GATHER, luci::CircleBCQGather) -CIRCLE_NODE(INSTANCE_NORM, luci::CircleInstanceNorm) +CIRCLE_NODE(BCQ_FULLY_CONNECTED, CircleBCQFullyConnected) +CIRCLE_NODE(BCQ_GATHER, CircleBCQGather) +CIRCLE_NODE(INSTANCE_NORM, CircleInstanceNorm) // Virtual node(s) -CIRCLE_VNODE(CIRCLEBIDIRECTIONAL_SEQUENCE_LSTM_OUT, luci::CircleBidirectionalSequenceLSTMOut) -CIRCLE_VNODE(CIRCLECONST, luci::CircleConst) -CIRCLE_VNODE(CIRCLEINPUT, luci::CircleInput) -CIRCLE_VNODE(CIRCLEOUTPUT, luci::CircleOutput) -CIRCLE_VNODE(CIRCLEOUTPUTDUMMY, luci::CircleOutputDummy) -CIRCLE_VNODE(CIRCLEOUTPUTEXCLUDE, luci::CircleOutputExclude) -CIRCLE_VNODE(CIRCLECUSTOMOUT, luci::CircleCustomOut) -CIRCLE_VNODE(CIRCLEIFOUT, luci::CircleIfOut) -CIRCLE_VNODE(CIRCLENONMAXSUPPRESSIONV4OUT, luci::CircleNonMaxSuppressionV4Out) -CIRCLE_VNODE(CIRCLENONMAXSUPPRESSIONV5OUT, luci::CircleNonMaxSuppressionV5Out) -CIRCLE_VNODE(CIRCLESPLITOUT, luci::CircleSplitOut) -CIRCLE_VNODE(CIRCLESPLITVOUT, luci::CircleSplitVOut) -CIRCLE_VNODE(CIRCLETOPKV2OUT, luci::CircleTopKV2Out) -CIRCLE_VNODE(CIRCLEUNIQUEOUT, luci::CircleUniqueOut) -CIRCLE_VNODE(CIRCLEUNPACKOUT, luci::CircleUnpackOut) -CIRCLE_VNODE(CIRCLEWHILEOUT, luci::CircleWhileOut) +CIRCLE_VNODE(CIRCLEBIDIRECTIONAL_SEQUENCE_LSTM_OUT, CircleBidirectionalSequenceLSTMOut) +CIRCLE_VNODE(CIRCLECONST, CircleConst) +CIRCLE_VNODE(CIRCLEINPUT, CircleInput) +CIRCLE_VNODE(CIRCLEOUTPUT, CircleOutput) +CIRCLE_VNODE(CIRCLEOUTPUTDUMMY, CircleOutputDummy) +CIRCLE_VNODE(CIRCLEOUTPUTEXCLUDE, CircleOutputExclude) +CIRCLE_VNODE(CIRCLECUSTOMOUT, CircleCustomOut) +CIRCLE_VNODE(CIRCLEIFOUT, CircleIfOut) +CIRCLE_VNODE(CIRCLENONMAXSUPPRESSIONV4OUT, CircleNonMaxSuppressionV4Out) +CIRCLE_VNODE(CIRCLENONMAXSUPPRESSIONV5OUT, CircleNonMaxSuppressionV5Out) +CIRCLE_VNODE(CIRCLESPLITOUT, CircleSplitOut) +CIRCLE_VNODE(CIRCLESPLITVOUT, CircleSplitVOut) +CIRCLE_VNODE(CIRCLETOPKV2OUT, CircleTopKV2Out) +CIRCLE_VNODE(CIRCLEUNIQUEOUT, CircleUniqueOut) +CIRCLE_VNODE(CIRCLEUNPACKOUT, CircleUnpackOut) +CIRCLE_VNODE(CIRCLEWHILEOUT, CircleWhileOut) diff --git a/compiler/luci/lang/include/luci/IR/Module.h b/compiler/luci/lang/include/luci/IR/Module.h index 30eac59ce..75cf67905 100644 --- a/compiler/luci/lang/include/luci/IR/Module.h +++ b/compiler/luci/lang/include/luci/IR/Module.h @@ -19,6 +19,7 @@ #include <loco/IR/Graph.h> +#include <map> #include <memory> #include <vector> @@ -59,8 +60,27 @@ public: // TODO provide graph accessor with a name +public: + void source_table(const std::map<uint32_t, std::string> &table) { _source_table = table; } + + const std::map<uint32_t, std::string> &source_table(void) const { return _source_table; } + private: std::vector<std::unique_ptr<loco::Graph>> _graphs; + +private: + /** + * @brief Metadata about source table for profiling + * + * @note Key is ID of node and value is name of node. + * + * If there was originally imported 'source_table' in circle model, + * the table will be stored as it is. + * Otherwise, new 'source_table' is created with imported nodes. + * + * Even if Module has multiple subgraphs, only first subgraph is considered. + */ + std::map<uint32_t, std::string> _source_table; }; std::unique_ptr<Module> make_module(void); diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h index e44363d14..3e9a274e0 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h @@ -44,6 +44,8 @@ public: private: std::vector<uint8_t> _data; + // TODO use _data for STRING and remove _strings + std::vector<std::string> _strings; // for STRING type }; } // namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleCustom.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleCustom.h index b21cc679f..5709e2cd5 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleCustom.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleCustom.h @@ -32,9 +32,8 @@ public: CircleCustom(uint32_t arity, uint32_t out) : VariadicArityNode<CircleNodeImpl<CircleOpcode::CUSTOM>>(arity), _output_count(out) { - // TODO Support when arity is 0 - assert(arity >= 1); - assert(out > 0); + // NOTE Custom can have 0 input or 0 output but not both + assert(arity != 0 || out != 0); } public: diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleQuantize.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleQuantize.h new file mode 100644 index 000000000..8018a76c2 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleQuantize.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCELEQUANTIZE_H__ +#define __LUCI_IR_CIRCELEQUANTIZE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/CircleNodeMixins.h" + +namespace luci +{ + +/** + * @brief QUANTIZE in Circle + */ +class CircleQuantize final : public FixedArityNode<1, CircleNodeImpl<CircleOpcode::QUANTIZE>> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCELEDUANTIZE_H__ diff --git a/compiler/luci/lang/src/Nodes/CircleConst.cpp b/compiler/luci/lang/src/Nodes/CircleConst.cpp index 0d02d32dc..c2d82c8a2 100644 --- a/compiler/luci/lang/src/Nodes/CircleConst.cpp +++ b/compiler/luci/lang/src/Nodes/CircleConst.cpp @@ -80,4 +80,48 @@ INSTANTIATE(loco::DataType::BOOL); #undef INSTANTIATE +// CircleConst implementations for loco::DataType::STRING + +template <> uint32_t CircleConst::size<loco::DataType::STRING>(void) const +{ + assert(dtype() == loco::DataType::STRING); + assert(_data.size() == 0); + return _strings.size(); +} + +template <> void CircleConst::size<loco::DataType::STRING>(uint32_t l) +{ + assert(dtype() == loco::DataType::STRING); + assert(_data.size() == 0); + _strings.resize(l); +} + +template <> const std::string &CircleConst::at<loco::DataType::STRING>(uint32_t n) const +{ + assert(dtype() == loco::DataType::STRING); + assert(n < _strings.size()); + return _strings.at(n); +} + +template <> std::string &CircleConst::at<loco::DataType::STRING>(uint32_t n) +{ + assert(dtype() == loco::DataType::STRING); + assert(n < _strings.size()); + return _strings.at(n); +} + +template <> const std::string &CircleConst::scalar<loco::DataType::STRING>(void) const +{ + assert(dtype() == loco::DataType::STRING); + assert(1 == _strings.size()); + return _strings.at(0); +} + +template <> std::string &CircleConst::scalar<loco::DataType::STRING>(void) +{ + assert(dtype() == loco::DataType::STRING); + assert(1 == _strings.size()); + return _strings.at(0); +} + } // namespace luci diff --git a/compiler/luci/lang/src/Nodes/CircleConst.test.cpp b/compiler/luci/lang/src/Nodes/CircleConst.test.cpp index a81f4b00d..e8c892b83 100644 --- a/compiler/luci/lang/src/Nodes/CircleConst.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleConst.test.cpp @@ -51,3 +51,16 @@ TEST(CircleConstTest, scalar) auto const &cs = const_node.scalar<loco::DataType::S32>(); ASSERT_EQ(1, cs); } + +TEST(CircleConstTest, string) +{ + luci::CircleConst const_node; + + const_node.dtype(loco::DataType::STRING); + const_node.size<loco::DataType::STRING>(1); + const_node.at<loco::DataType::STRING>(0) = std::string("Hello"); + + ASSERT_EQ(loco::DataType::STRING, const_node.dtype()); + ASSERT_EQ(1, const_node.size<loco::DataType::STRING>()); + EXPECT_TRUE(std::string("Hello") == const_node.at<loco::DataType::STRING>(0)); +} diff --git a/compiler/luci/lang/src/Nodes/CircleQuantize.test.cpp b/compiler/luci/lang/src/Nodes/CircleQuantize.test.cpp new file mode 100644 index 000000000..098716fdc --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleQuantize.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleQuantize.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include <gtest/gtest.h> + +#include <memory> + +TEST(CircleQuantizeTest, constructor) +{ + luci::CircleQuantize quant_node; + + ASSERT_EQ(luci::CircleDialect::get(), quant_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::QUANTIZE, quant_node.opcode()); + + ASSERT_EQ(nullptr, quant_node.input()); +} + +TEST(CircleQuantizeTest, common_NEG) +{ + luci::CircleQuantize quant_node; + + quant_node.name("name"); + ASSERT_EQ("name", quant_node.name()); + + auto q = std::make_unique<luci::CircleQuantParam>(); + quant_node.quantparam(std::move(q)); + ASSERT_NE(nullptr, quant_node.quantparam()); + + ASSERT_EQ(luci::ShapeStatus::UNDEFINED, quant_node.shape_status()); + quant_node.shape_status(luci::ShapeStatus::NOSHAPE); + ASSERT_NE(luci::ShapeStatus::UNDEFINED, quant_node.shape_status()); +} + +TEST(CircleQuantizeTest, input_NEG) +{ + luci::CircleQuantize quant_node; + luci::CircleQuantize node; + + quant_node.input(&node); + ASSERT_NE(nullptr, quant_node.input()); + + quant_node.input(nullptr); + ASSERT_EQ(nullptr, quant_node.input()); +} + +TEST(CircleQuantizeTest, arity_NEG) +{ + luci::CircleQuantize quant_node; + + ASSERT_NO_THROW(quant_node.arg(0)); + ASSERT_THROW(quant_node.arg(1), std::out_of_range); +} + +TEST(CircleQuantizeTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor<void> + { + }; + + luci::CircleQuantize quant_node; + + TestVisitor tv; + ASSERT_THROW(quant_node.accept(&tv), std::exception); +} + +TEST(CircleQuantizeTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor<void> + { + }; + + luci::CircleQuantize quant_node; + + TestVisitor tv; + ASSERT_THROW(quant_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/log/CMakeLists.txt b/compiler/luci/log/CMakeLists.txt index 5e822871b..23bd00828 100644 --- a/compiler/luci/log/CMakeLists.txt +++ b/compiler/luci/log/CMakeLists.txt @@ -8,3 +8,5 @@ target_link_libraries(luci_log PRIVATE hermes_std) target_link_libraries(luci_log PRIVATE nncc_common) target_link_libraries(luci_log PRIVATE luci_env) install(TARGETS luci_log DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") diff --git a/compiler/luci/logex/CMakeLists.txt b/compiler/luci/logex/CMakeLists.txt index fa2ea123c..cd2571ba1 100644 --- a/compiler/luci/logex/CMakeLists.txt +++ b/compiler/luci/logex/CMakeLists.txt @@ -11,3 +11,5 @@ target_link_libraries(luci_logex PRIVATE hermes_std) target_link_libraries(luci_logex PRIVATE nncc_common) target_link_libraries(luci_logex PRIVATE pepper_str) install(TARGETS luci_logex DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") diff --git a/compiler/luci/logex/src/FormattedGraph.cpp b/compiler/luci/logex/src/FormattedGraph.cpp index f1337e3e6..0588ed79e 100644 --- a/compiler/luci/logex/src/FormattedGraph.cpp +++ b/compiler/luci/logex/src/FormattedGraph.cpp @@ -25,6 +25,7 @@ #include <sstream> #include <vector> +using namespace luci; /** * @brief dump std::vector<int64_t> values to stream */ @@ -170,13 +171,8 @@ public: bool build(const loco::Node *, locop::NodeSummary &s) const final; protected: -#define CIRCLE_NODE(OPCODE, CLASS) \ - virtual bool summary(const CLASS *, locop::NodeSummary &s) const \ - { \ - s.comments().append("Emitted by Default CircleNodeSummaryBuilder"); \ - s.state(locop::NodeSummary::State::PartiallyKnown); \ - return true; \ - } +#define CIRCLE_NODE(OPCODE, CLASS) \ + virtual bool summary(const CLASS *, locop::NodeSummary &) const { return false; } #define CIRCLE_VNODE CIRCLE_NODE #include <luci/IR/CircleNodes.lst> #undef CIRCLE_VNODE @@ -185,149 +181,8 @@ protected: protected: const locop::SymbolTable *tbl(void) const { return _tbl; } - // Please do not use _tbl directly and use tbl(). - // This will be changed to private in near future. -protected: - const locop::SymbolTable *_tbl; -}; - -class CircleNodeSummaryBuilder final : public CircleNodeSummaryBuilderBase -{ -public: - CircleNodeSummaryBuilder(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) - { - // DO NOTHING - } - private: -#define IMPLEMENT(CLASS) bool summary(const CLASS *, locop::NodeSummary &) const final; - IMPLEMENT(luci::CircleAbs) - IMPLEMENT(luci::CircleAdd) - IMPLEMENT(luci::CircleAddN) - IMPLEMENT(luci::CircleArgMax) - IMPLEMENT(luci::CircleArgMin) - IMPLEMENT(luci::CircleAveragePool2D) - IMPLEMENT(luci::CircleBatchMatMul) - IMPLEMENT(luci::CircleBatchToSpaceND) - IMPLEMENT(luci::CircleBidirectionalSequenceLSTM) - IMPLEMENT(luci::CircleCast) - IMPLEMENT(luci::CircleCeil) - IMPLEMENT(luci::CircleConcatenation) - IMPLEMENT(luci::CircleConst) - IMPLEMENT(luci::CircleConv2D) - IMPLEMENT(luci::CircleCos) - IMPLEMENT(luci::CircleCustom) - IMPLEMENT(luci::CircleDepthToSpace) - IMPLEMENT(luci::CircleDepthwiseConv2D) - IMPLEMENT(luci::CircleDequantize) - IMPLEMENT(luci::CircleDiv) - IMPLEMENT(luci::CircleElu) - IMPLEMENT(luci::CircleExp) - IMPLEMENT(luci::CircleExpandDims) - IMPLEMENT(luci::CircleFakeQuant) - IMPLEMENT(luci::CircleFill) - IMPLEMENT(luci::CircleFloor) - IMPLEMENT(luci::CircleFloorDiv) - IMPLEMENT(luci::CircleFloorMod) - IMPLEMENT(luci::CircleFullyConnected) - IMPLEMENT(luci::CircleGather) - IMPLEMENT(luci::CircleGatherNd) - IMPLEMENT(luci::CircleGreater) - IMPLEMENT(luci::CircleGreaterEqual) - IMPLEMENT(luci::CircleIf) - IMPLEMENT(luci::CircleL2Normalize) - IMPLEMENT(luci::CircleLeakyRelu) - IMPLEMENT(luci::CircleLess) - IMPLEMENT(luci::CircleLessEqual) - IMPLEMENT(luci::CircleLocalResponseNormalization) - IMPLEMENT(luci::CircleLog) - IMPLEMENT(luci::CircleLogicalAnd) - IMPLEMENT(luci::CircleLogicalNot) - IMPLEMENT(luci::CircleLogicalOr) - IMPLEMENT(luci::CircleLogistic) - IMPLEMENT(luci::CircleLogSoftmax) - IMPLEMENT(luci::CircleMatrixDiag) - IMPLEMENT(luci::CircleMatrixSetDiag) - IMPLEMENT(luci::CircleMaximum) - IMPLEMENT(luci::CircleMaxPool2D) - IMPLEMENT(luci::CircleMean) - IMPLEMENT(luci::CircleMinimum) - IMPLEMENT(luci::CircleMirrorPad) - IMPLEMENT(luci::CircleMul) - IMPLEMENT(luci::CircleNeg) - IMPLEMENT(luci::CircleNonMaxSuppressionV4) - IMPLEMENT(luci::CircleNonMaxSuppressionV5) - IMPLEMENT(luci::CircleNotEqual) - IMPLEMENT(luci::CircleOneHot) - IMPLEMENT(luci::CirclePack) - IMPLEMENT(luci::CirclePad) - IMPLEMENT(luci::CirclePadV2) - IMPLEMENT(luci::CirclePow) - IMPLEMENT(luci::CirclePRelu) - IMPLEMENT(luci::CircleRange) - IMPLEMENT(luci::CircleRank) - IMPLEMENT(luci::CircleReduceAny) - IMPLEMENT(luci::CircleReduceMax) - IMPLEMENT(luci::CircleReduceMin) - IMPLEMENT(luci::CircleReduceProd) - IMPLEMENT(luci::CircleRelu) - IMPLEMENT(luci::CircleRelu6) - IMPLEMENT(luci::CircleReluN1To1) - IMPLEMENT(luci::CircleReshape) - IMPLEMENT(luci::CircleResizeBilinear) - IMPLEMENT(luci::CircleResizeNearestNeighbor) - IMPLEMENT(luci::CircleReverseSequence) - IMPLEMENT(luci::CircleReverseV2) - IMPLEMENT(luci::CircleRound) - IMPLEMENT(luci::CircleRsqrt) - IMPLEMENT(luci::CircleScatterNd) - IMPLEMENT(luci::CircleSegmentSum) - IMPLEMENT(luci::CircleSelect) - IMPLEMENT(luci::CircleSelectV2) - IMPLEMENT(luci::CircleShape) - IMPLEMENT(luci::CircleSin) - IMPLEMENT(luci::CircleSlice) - IMPLEMENT(luci::CircleSoftmax) - IMPLEMENT(luci::CircleSpaceToBatchND) - IMPLEMENT(luci::CircleSpaceToDepth) - IMPLEMENT(luci::CircleSparseToDense) - IMPLEMENT(luci::CircleSplit) - IMPLEMENT(luci::CircleSplitV) - IMPLEMENT(luci::CircleSqrt) - IMPLEMENT(luci::CircleSquare) - IMPLEMENT(luci::CircleSquaredDifference) - IMPLEMENT(luci::CircleSqueeze) - IMPLEMENT(luci::CircleStridedSlice) - IMPLEMENT(luci::CircleSub) - IMPLEMENT(luci::CircleSum) - IMPLEMENT(luci::CircleTanh) - IMPLEMENT(luci::CircleTile) - IMPLEMENT(luci::CircleTopKV2) - IMPLEMENT(luci::CircleTranspose) - IMPLEMENT(luci::CircleTransposeConv) - IMPLEMENT(luci::CircleUnidirectionalSequenceLSTM) - IMPLEMENT(luci::CircleUnique) - IMPLEMENT(luci::CircleUnpack) - IMPLEMENT(luci::CircleWhere) - IMPLEMENT(luci::CircleWhile) - IMPLEMENT(luci::CircleZerosLike) - // Circle Only - IMPLEMENT(luci::CircleBCQFullyConnected) - IMPLEMENT(luci::CircleBCQGather) - IMPLEMENT(luci::CircleInstanceNorm) - // Virtual nodes - IMPLEMENT(luci::CircleInput) - IMPLEMENT(luci::CircleOutput) - IMPLEMENT(luci::CircleIfOut) - IMPLEMENT(luci::CircleNonMaxSuppressionV4Out) - IMPLEMENT(luci::CircleNonMaxSuppressionV5Out) - IMPLEMENT(luci::CircleSplitOut) - IMPLEMENT(luci::CircleSplitVOut) - IMPLEMENT(luci::CircleTopKV2Out) - IMPLEMENT(luci::CircleUniqueOut) - IMPLEMENT(luci::CircleUnpackOut) - IMPLEMENT(luci::CircleWhileOut) -#undef IMPLEMENT + const locop::SymbolTable *_tbl; }; template <class CIRCLENODE> @@ -699,6 +554,20 @@ bool summary_node(const locop::SymbolTable *tbl, const luci::CircleL2Normalize * return true; } +bool summary_node(const locop::SymbolTable *tbl, const luci::CircleL2Pool2D *node, + locop::NodeSummary &s) +{ + assert(node->fusedActivationFunction() != luci::FusedActFunc::UNDEFINED); + + s.args().append("value", tbl->lookup(node->value())); + s.args().append("filter(h,w)", to_str(node->filter())); + s.args().append("stride(h,w)", to_str(node->stride())); + s.args().append("padding", to_str(node->padding())); + s.args().append("fused", to_str(node->fusedActivationFunction())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + bool summary_node(const locop::SymbolTable *tbl, const luci::CircleLeakyRelu *node, locop::NodeSummary &s) { @@ -1236,6 +1105,20 @@ bool summary_node(const locop::SymbolTable *tbl, const luci::CircleOutput *node, return true; } +bool summary_node(const locop::SymbolTable *, const luci::CircleOutputDummy *, + locop::NodeSummary &s) +{ + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool summary_node(const locop::SymbolTable *, const luci::CircleOutputExclude *, + locop::NodeSummary &s) +{ + s.state(locop::NodeSummary::State::Complete); + return true; +} + bool summary_node(const locop::SymbolTable *tbl, const luci::CircleBCQFullyConnected *node, locop::NodeSummary &s) { @@ -1279,6 +1162,255 @@ bool summary_node(const locop::SymbolTable *tbl, const luci::CircleInstanceNorm return true; } +// SummaryBuilderLet type +enum class SB +{ + ABC, + DEF, + GHIJ, + KLMN, + OPQR, + STUV, + WXYZ, + CIRC, // circle only + VIRT, // virtual +}; + +template <SB sb> class SummaryBuilderLet; + +#define IMPLEMENT(CLASS) bool summary(const CLASS *, locop::NodeSummary &) const final; + +template <> class SummaryBuilderLet<SB::ABC> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleAbs) + IMPLEMENT(luci::CircleAdd) + IMPLEMENT(luci::CircleAddN) + IMPLEMENT(luci::CircleArgMax) + IMPLEMENT(luci::CircleArgMin) + IMPLEMENT(luci::CircleAveragePool2D) + IMPLEMENT(luci::CircleBatchMatMul) + IMPLEMENT(luci::CircleBatchToSpaceND) + IMPLEMENT(luci::CircleBidirectionalSequenceLSTM) + IMPLEMENT(luci::CircleCast) + IMPLEMENT(luci::CircleCeil) + IMPLEMENT(luci::CircleConcatenation) + IMPLEMENT(luci::CircleConst) + IMPLEMENT(luci::CircleConv2D) + IMPLEMENT(luci::CircleCos) + IMPLEMENT(luci::CircleCustom) +}; + +template <> class SummaryBuilderLet<SB::DEF> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleDepthToSpace) + IMPLEMENT(luci::CircleDepthwiseConv2D) + IMPLEMENT(luci::CircleDequantize) + IMPLEMENT(luci::CircleDiv) + IMPLEMENT(luci::CircleElu) + IMPLEMENT(luci::CircleEqual) + IMPLEMENT(luci::CircleExp) + IMPLEMENT(luci::CircleExpandDims) + IMPLEMENT(luci::CircleFakeQuant) + IMPLEMENT(luci::CircleFill) + IMPLEMENT(luci::CircleFloor) + IMPLEMENT(luci::CircleFloorDiv) + IMPLEMENT(luci::CircleFloorMod) + IMPLEMENT(luci::CircleFullyConnected) +}; + +template <> class SummaryBuilderLet<SB::GHIJ> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleGather) + IMPLEMENT(luci::CircleGatherNd) + IMPLEMENT(luci::CircleGreater) + IMPLEMENT(luci::CircleGreaterEqual) + IMPLEMENT(luci::CircleIf) +}; + +template <> class SummaryBuilderLet<SB::KLMN> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleL2Normalize) + IMPLEMENT(luci::CircleL2Pool2D) + IMPLEMENT(luci::CircleLeakyRelu) + IMPLEMENT(luci::CircleLess) + IMPLEMENT(luci::CircleLessEqual) + IMPLEMENT(luci::CircleLocalResponseNormalization) + IMPLEMENT(luci::CircleLog) + IMPLEMENT(luci::CircleLogicalAnd) + IMPLEMENT(luci::CircleLogicalNot) + IMPLEMENT(luci::CircleLogicalOr) + IMPLEMENT(luci::CircleLogistic) + IMPLEMENT(luci::CircleLogSoftmax) + IMPLEMENT(luci::CircleMatrixDiag) + IMPLEMENT(luci::CircleMatrixSetDiag) + IMPLEMENT(luci::CircleMaximum) + IMPLEMENT(luci::CircleMaxPool2D) + IMPLEMENT(luci::CircleMean) + IMPLEMENT(luci::CircleMinimum) + IMPLEMENT(luci::CircleMirrorPad) + IMPLEMENT(luci::CircleMul) + IMPLEMENT(luci::CircleNeg) + IMPLEMENT(luci::CircleNonMaxSuppressionV4) + IMPLEMENT(luci::CircleNonMaxSuppressionV5) + IMPLEMENT(luci::CircleNotEqual) +}; + +template <> class SummaryBuilderLet<SB::OPQR> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleOneHot) + IMPLEMENT(luci::CirclePack) + IMPLEMENT(luci::CirclePad) + IMPLEMENT(luci::CirclePadV2) + IMPLEMENT(luci::CirclePow) + IMPLEMENT(luci::CirclePRelu) + IMPLEMENT(luci::CircleQuantize) + IMPLEMENT(luci::CircleRange) + IMPLEMENT(luci::CircleRank) + IMPLEMENT(luci::CircleReduceAny) + IMPLEMENT(luci::CircleReduceMax) + IMPLEMENT(luci::CircleReduceMin) + IMPLEMENT(luci::CircleReduceProd) + IMPLEMENT(luci::CircleRelu) + IMPLEMENT(luci::CircleRelu6) + IMPLEMENT(luci::CircleReluN1To1) + IMPLEMENT(luci::CircleReshape) + IMPLEMENT(luci::CircleResizeBilinear) + IMPLEMENT(luci::CircleResizeNearestNeighbor) + IMPLEMENT(luci::CircleReverseSequence) + IMPLEMENT(luci::CircleReverseV2) + IMPLEMENT(luci::CircleRound) + IMPLEMENT(luci::CircleRsqrt) +}; + +template <> class SummaryBuilderLet<SB::STUV> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleScatterNd) + IMPLEMENT(luci::CircleSegmentSum) + IMPLEMENT(luci::CircleSelect) + IMPLEMENT(luci::CircleSelectV2) + IMPLEMENT(luci::CircleShape) + IMPLEMENT(luci::CircleSin) + IMPLEMENT(luci::CircleSlice) + IMPLEMENT(luci::CircleSoftmax) + IMPLEMENT(luci::CircleSpaceToBatchND) + IMPLEMENT(luci::CircleSpaceToDepth) + IMPLEMENT(luci::CircleSparseToDense) + IMPLEMENT(luci::CircleSplit) + IMPLEMENT(luci::CircleSplitV) + IMPLEMENT(luci::CircleSqrt) + IMPLEMENT(luci::CircleSquare) + IMPLEMENT(luci::CircleSquaredDifference) + IMPLEMENT(luci::CircleSqueeze) + IMPLEMENT(luci::CircleStridedSlice) + IMPLEMENT(luci::CircleSub) + IMPLEMENT(luci::CircleSum) + IMPLEMENT(luci::CircleTanh) + IMPLEMENT(luci::CircleTile) + IMPLEMENT(luci::CircleTopKV2) + IMPLEMENT(luci::CircleTranspose) + IMPLEMENT(luci::CircleTransposeConv) + IMPLEMENT(luci::CircleUnidirectionalSequenceLSTM) + IMPLEMENT(luci::CircleUnique) + IMPLEMENT(luci::CircleUnpack) +}; + +template <> class SummaryBuilderLet<SB::WXYZ> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleWhere) + IMPLEMENT(luci::CircleWhile) + IMPLEMENT(luci::CircleZerosLike) +}; + +template <> class SummaryBuilderLet<SB::CIRC> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleBCQFullyConnected) + IMPLEMENT(luci::CircleBCQGather) + IMPLEMENT(luci::CircleInstanceNorm) +}; + +template <> class SummaryBuilderLet<SB::VIRT> final : public CircleNodeSummaryBuilderBase +{ +public: + SummaryBuilderLet(const locop::SymbolTable *tbl) : CircleNodeSummaryBuilderBase(tbl) + { + // DO NOTHING + } + +private: + IMPLEMENT(luci::CircleInput) + IMPLEMENT(luci::CircleOutput) + IMPLEMENT(luci::CircleCustomOut) + IMPLEMENT(luci::CircleIfOut) + IMPLEMENT(luci::CircleNonMaxSuppressionV4Out) + IMPLEMENT(luci::CircleNonMaxSuppressionV5Out) + IMPLEMENT(luci::CircleOutputDummy) + IMPLEMENT(luci::CircleOutputExclude) + IMPLEMENT(luci::CircleSplitOut) + IMPLEMENT(luci::CircleSplitVOut) + IMPLEMENT(luci::CircleTopKV2Out) + IMPLEMENT(luci::CircleUniqueOut) + IMPLEMENT(luci::CircleUnpackOut) + IMPLEMENT(luci::CircleWhileOut) +}; + +#undef IMPLEMENT + bool CircleNodeSummaryBuilderBase::build(const loco::Node *node, locop::NodeSummary &s) const { if (node->dialect() != luci::CircleDialect::get()) @@ -1290,12 +1422,20 @@ bool CircleNodeSummaryBuilderBase::build(const loco::Node *node, locop::NodeSumm return ss.str(); }; -#define CIRCLE_NODE(OPCODE, CLASS) \ - if (dynamic_cast<const CLASS *>(node)) \ - { \ - s.opname(circle_opname(node->opnum())); \ - s.comments().append("Mem = " + ptr_to_str(node)); \ - return summary(dynamic_cast<const CLASS *>(node), s); \ + auto add_comment = [&]() { + auto cnode = loco::must_cast<const luci::CircleNode *>(node); + s.opname(circle_opname(node->opnum())); + s.comments().append("[" + cnode->name() + "] = " + ptr_to_str(node)); + }; + +#define CIRCLE_NODE(OPCODE, CLASS) \ + if (dynamic_cast<const CLASS *>(node)) \ + { \ + if (summary(dynamic_cast<const CLASS *>(node), s)) \ + { \ + add_comment(); \ + return true; \ + } \ } #define CIRCLE_VNODE CIRCLE_NODE #include <luci/IR/CircleNodes.lst> @@ -1305,687 +1445,753 @@ bool CircleNodeSummaryBuilderBase::build(const loco::Node *node, locop::NodeSumm return false; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleAbs *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleAbs *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleAdd *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleAdd *node, locop::NodeSummary &s) const { return use_xy_act(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleAddN *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleAddN *node, locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleArgMax *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleArgMax *node, + locop::NodeSummary &s) const { return use_ido(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleArgMin *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleArgMin *node, + locop::NodeSummary &s) const { return use_ido(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleAveragePool2D *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleAveragePool2D *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleBatchMatMul *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleBatchMatMul *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleBatchToSpaceND *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleBatchToSpaceND *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleBidirectionalSequenceLSTM *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleBidirectionalSequenceLSTM *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleCast *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleCast *node, locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleCeil *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleCeil *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleConcatenation *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleConcatenation *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleConst *, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleConst *, locop::NodeSummary &s) const { s.state(locop::NodeSummary::State::PartiallyKnown); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleConv2D *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleConv2D *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleCos *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleCos *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleCustom *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::ABC>::summary(const luci::CircleCustom *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleDepthToSpace *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleDepthToSpace *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleDepthwiseConv2D *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleDepthwiseConv2D *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleDequantize *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleDequantize *node, + locop::NodeSummary &s) const { return use_input(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleDiv *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleDiv *node, locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleElu *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleElu *node, locop::NodeSummary &s) const { return use_features(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleExp *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleEqual *node, locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleExp *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleExpandDims *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleExpandDims *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleFakeQuant *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleFakeQuant *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleFill *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleFill *node, locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleFloor *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleFloor *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleFloorDiv *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleFloorDiv *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleFloorMod *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleFloorMod *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleFullyConnected *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::DEF>::summary(const luci::CircleFullyConnected *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleGather *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::GHIJ>::summary(const luci::CircleGather *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleGatherNd *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::GHIJ>::summary(const luci::CircleGatherNd *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleGreater *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::GHIJ>::summary(const luci::CircleGreater *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleGreaterEqual *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::GHIJ>::summary(const luci::CircleGreaterEqual *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleIf *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::GHIJ>::summary(const luci::CircleIf *node, locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleL2Normalize *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleL2Normalize *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLess *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleL2Pool2D *node, + locop::NodeSummary &s) const +{ + return summary_node(tbl(), node, s); +} + +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLess *node, locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLessEqual *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLessEqual *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLeakyRelu *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLeakyRelu *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLocalResponseNormalization *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLocalResponseNormalization *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLog *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLog *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalAnd *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLogicalAnd *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalNot *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLogicalNot *node, + locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalOr *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLogicalOr *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLogistic *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLogistic *node, + locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLogSoftmax *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleLogSoftmax *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMatrixDiag *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMatrixDiag *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMatrixSetDiag *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMatrixSetDiag *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMaximum *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMaximum *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMaxPool2D *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMaxPool2D *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMean *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMean *node, locop::NodeSummary &s) const { return use_reducer(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMinimum *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMinimum *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMirrorPad *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMirrorPad *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMul *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleMul *node, locop::NodeSummary &s) const { return use_xy_act(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleNeg *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleNeg *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleNonMaxSuppressionV4 *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleNonMaxSuppressionV4 *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleNonMaxSuppressionV5 *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleNonMaxSuppressionV5 *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleNotEqual *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::KLMN>::summary(const luci::CircleNotEqual *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleOneHot *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleOneHot *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CirclePack *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CirclePack *node, locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CirclePad *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CirclePad *node, locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CirclePadV2 *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CirclePadV2 *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CirclePow *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CirclePow *node, locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CirclePRelu *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CirclePRelu *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRange *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleQuantize *node, + locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleRange *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRank *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleRank *node, locop::NodeSummary &s) const { return use_input(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceAny *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReduceAny *node, + locop::NodeSummary &s) const { return use_reducer(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceMax *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReduceMax *node, + locop::NodeSummary &s) const { return use_reducer(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceMin *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReduceMin *node, + locop::NodeSummary &s) const { return use_reducer(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceProd *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReduceProd *node, + locop::NodeSummary &s) const { return use_reducer(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRelu *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleRelu *node, locop::NodeSummary &s) const { return use_features(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRelu6 *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleRelu6 *node, + locop::NodeSummary &s) const { return use_features(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReluN1To1 *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReluN1To1 *node, + locop::NodeSummary &s) const { return use_features(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReshape *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReshape *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleResizeBilinear *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleResizeBilinear *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleResizeNearestNeighbor *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleResizeNearestNeighbor *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReverseSequence *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReverseSequence *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReverseV2 *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleReverseV2 *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRound *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleRound *node, + locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRsqrt *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::OPQR>::summary(const luci::CircleRsqrt *node, + locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleScatterNd *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleScatterNd *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSegmentSum *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSegmentSum *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSelect *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSelect *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSelectV2 *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSelectV2 *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleShape *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleShape *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSin *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSin *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSlice *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSlice *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSoftmax *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSoftmax *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSpaceToBatchND *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSpaceToBatchND *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSpaceToDepth *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSpaceToDepth *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSparseToDense *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSparseToDense *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSplit *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSplit *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSplitV *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSplitV *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSqrt *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSqrt *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSquare *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSquare *node, + locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSquaredDifference *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSquaredDifference *node, + locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSqueeze *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSqueeze *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleStridedSlice *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleStridedSlice *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSub *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSub *node, locop::NodeSummary &s) const { return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSum *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleSum *node, locop::NodeSummary &s) const { return use_reducer(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleTanh *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleTanh *node, locop::NodeSummary &s) const { return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleTile *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleTile *node, locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleTopKV2 *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleTopKV2 *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleTranspose *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleTranspose *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleTransposeConv *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleTransposeConv *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleUnidirectionalSequenceLSTM *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleUnidirectionalSequenceLSTM *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleUnique *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleUnique *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleUnpack *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::STUV>::summary(const luci::CircleUnpack *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleWhere *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::WXYZ>::summary(const luci::CircleWhere *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleWhile *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::WXYZ>::summary(const luci::CircleWhile *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleZerosLike *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::WXYZ>::summary(const luci::CircleZerosLike *node, + locop::NodeSummary &s) const { return use_input(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSplitOut *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::CIRC>::summary(const luci::CircleBCQFullyConnected *node, + locop::NodeSummary &s) const { - return use_input(tbl(), node, s); + return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSplitVOut *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::CIRC>::summary(const luci::CircleBCQGather *node, + locop::NodeSummary &s) const { - return use_input(tbl(), node, s); + return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleTopKV2Out *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::CIRC>::summary(const luci::CircleInstanceNorm *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleUniqueOut *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleInput *, locop::NodeSummary &s) const { - return summary_node(tbl(), node, s); + s.state(locop::NodeSummary::State::Complete); + return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleUnpackOut *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleOutput *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleIfOut *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleCustomOut *node, + locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleIfOut *node, + locop::NodeSummary &s) const { return use_input(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleNonMaxSuppressionV4Out *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleNonMaxSuppressionV4Out *node, + locop::NodeSummary &s) const { return use_input(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleNonMaxSuppressionV5Out *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleNonMaxSuppressionV5Out *node, + locop::NodeSummary &s) const { return use_input(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleWhileOut *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleOutputDummy *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleInput *, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleOutputExclude *node, + locop::NodeSummary &s) const { - s.state(locop::NodeSummary::State::Complete); - return true; + return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleOutput *node, locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleSplitOut *node, + locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleSplitVOut *node, + locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleTopKV2Out *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleBCQFullyConnected *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleUniqueOut *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleBCQGather *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleUnpackOut *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleInstanceNorm *node, - locop::NodeSummary &s) const +bool SummaryBuilderLet<SB::VIRT>::summary(const luci::CircleWhileOut *node, + locop::NodeSummary &s) const { return summary_node(tbl(), node, s); } @@ -2002,10 +2208,22 @@ bool NodeSummaryBuilder::build(const loco::Node *node, locop::NodeSummary &s) co return true; } - if (CircleNodeSummaryBuilder(_tbl).build(node, s)) - { - return true; - } +#define BUILD_GRP(GRP) \ + do \ + { \ + if (SummaryBuilderLet<SB::GRP>(_tbl).build(node, s)) \ + return true; \ + } while (false) + + BUILD_GRP(ABC); + BUILD_GRP(DEF); + BUILD_GRP(GHIJ); + BUILD_GRP(KLMN); + BUILD_GRP(OPQR); + BUILD_GRP(STUV); + BUILD_GRP(WXYZ); + BUILD_GRP(CIRC); + BUILD_GRP(VIRT); return false; } diff --git a/compiler/luci/partition/CMakeLists.txt b/compiler/luci/partition/CMakeLists.txt index 838642b6e..236b689c4 100644 --- a/compiler/luci/partition/CMakeLists.txt +++ b/compiler/luci/partition/CMakeLists.txt @@ -11,9 +11,12 @@ target_link_libraries(luci_partition PRIVATE luci_log) target_link_libraries(luci_partition PRIVATE luci_logex) target_link_libraries(luci_partition PRIVATE mio_circle) target_link_libraries(luci_partition PRIVATE nncc_common) +target_link_libraries(luci_partition PRIVATE pepper_csv2vec) target_link_libraries(luci_partition PRIVATE oops) install(TARGETS luci_partition DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/luci/partition/include/luci/Partition.h b/compiler/luci/partition/include/luci/Partition.h index cf90e448b..6189ed9f2 100644 --- a/compiler/luci/partition/include/luci/Partition.h +++ b/compiler/luci/partition/include/luci/Partition.h @@ -32,13 +32,22 @@ namespace luci */ struct PartitionTable { + enum class COMPLY + { + UNDEFINED, + OPCODE, + OPNAME, + }; + std::vector<std::string> groups; std::string default_group; + COMPLY comply = COMPLY::UNDEFINED; // assign by opcode name: OPCODENAME=group std::unordered_map<std::string /* OPCODENAME */, std::string /* group */> byopcodes; - // TODO add assign by OP name + // assign by op name: OPNAME=group + std::unordered_map<std::string /* OPNAME */, std::string /* group */> byopnames; }; /** diff --git a/compiler/luci/partition/include/luci/PartitionDump.h b/compiler/luci/partition/include/luci/PartitionDump.h new file mode 100644 index 000000000..f395e57bf --- /dev/null +++ b/compiler/luci/partition/include/luci/PartitionDump.h @@ -0,0 +1,26 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_PARTITION_DUMP_H__ +#define __LUCI_PARTITION_DUMP_H__ + +#include "luci/Partition.h" + +#include <iostream> + +std::ostream &operator<<(std::ostream &os, const luci::PartitionTable &table); + +#endif // __LUCI_PARTITION_DUMP_H__ diff --git a/compiler/luci/partition/include/luci/PartitionValidate.h b/compiler/luci/partition/include/luci/PartitionValidate.h new file mode 100644 index 000000000..9f910c8cc --- /dev/null +++ b/compiler/luci/partition/include/luci/PartitionValidate.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_PARTITION_VALIDATE_H__ +#define __LUCI_PARTITION_VALIDATE_H__ + +#include "luci/Partition.h" + +#include <luci/IR/Module.h> + +namespace luci +{ + +bool validate(luci::PartitionTable &partition); + +} // namespace luci + +#endif // __LUCI_PARTITION_VALIDATE_H__ diff --git a/compiler/luci/partition/src/ConnectNode.h b/compiler/luci/partition/src/ConnectNode.h index 017c587e5..ebbff7a6a 100644 --- a/compiler/luci/partition/src/ConnectNode.h +++ b/compiler/luci/partition/src/ConnectNode.h @@ -50,6 +50,9 @@ struct CloneContext MapNode2Clone::iterator find(const CircleNode *org) { return node2clone.find(org); } MapNode2Clone::iterator end(void) { return node2clone.end(); } + MapNode2Clone::const_iterator find(const CircleNode *org) const { return node2clone.find(org); } + MapNode2Clone::const_iterator end(void) const { return node2clone.end(); } + MapNode2Clone node2clone; }; @@ -59,138 +62,142 @@ public: ConnectNode(luci::CloneContext &clonecontext) : _clonecontext(clonecontext){}; public: - // void visit(const luci::CircleAbs *) final; + void visit(const luci::CircleAbs *) final; void visit(const luci::CircleAdd *) final; - // void visit(const luci::CircleAddN *) final; - // void visit(const luci::CircleArgMax *) final; - // void visit(const luci::CircleArgMin *) final; - // void visit(const luci::CircleAveragePool2D *) final; - // void visit(const luci::CircleBatchMatMul *) final; - // void visit(const luci::CircleBatchToSpaceND *) final; - // void visit(const luci::CircleCast *) final; - // void visit(const luci::CircleCeil *) final; - // void visit(const luci::CircleConcatenation *) final; + void visit(const luci::CircleAddN *) final; + void visit(const luci::CircleArgMax *) final; + void visit(const luci::CircleArgMin *) final; + void visit(const luci::CircleAveragePool2D *) final; + void visit(const luci::CircleBatchMatMul *) final; + void visit(const luci::CircleBatchToSpaceND *) final; + void visit(const luci::CircleCast *) final; + void visit(const luci::CircleCeil *) final; + void visit(const luci::CircleConcatenation *) final; void visit(const luci::CircleConst *) final; - // void visit(const luci::CircleConv2D *) final; - // void visit(const luci::CircleCos *) final; - // void visit(const luci::CircleCustom *) final; - // void visit(const luci::CircleDepthToSpace *) final; - // void visit(const luci::CircleDepthwiseConv2D *) final; - // void visit(const luci::CircleDequantize *) final; + void visit(const luci::CircleConv2D *) final; + void visit(const luci::CircleCos *) final; + void visit(const luci::CircleCustom *) final; + void visit(const luci::CircleDepthToSpace *) final; + void visit(const luci::CircleDepthwiseConv2D *) final; + void visit(const luci::CircleDequantize *) final; void visit(const luci::CircleDiv *) final; - // void visit(const luci::CircleElu *) final; - // void visit(const luci::CircleEqual *) final; - // void visit(const luci::CircleExp *) final; - // void visit(const luci::CircleExpandDims *) final; - // void visit(const luci::CircleFakeQuant *) final; - // void visit(const luci::CircleFill *) final; - // void visit(const luci::CircleFloor *) final; - // void visit(const luci::CircleFloorDiv *) final; - // void visit(const luci::CircleFloorMod *) final; - // void visit(const luci::CircleFullyConnected *) final; - // void visit(const luci::CircleGather *) final; - // void visit(const luci::CircleGatherNd *) final; - // void visit(const luci::CircleGreater *) final; - // void visit(const luci::CircleGreaterEqual *) final; - // void visit(const luci::CircleIf *) final; - // void visit(const luci::CircleL2Normalize *) final; - // void visit(const luci::CircleL2Pool2D *) final; - // void visit(const luci::CircleLeakyRelu *) final; - // void visit(const luci::CircleLess *) final; - // void visit(const luci::CircleLessEqual *) final; - // void visit(const luci::CircleLocalResponseNormalization *) final; - // void visit(const luci::CircleLog *) final; - // void visit(const luci::CircleLogicalAnd *) final; - // void visit(const luci::CircleLogicalNot *) final; - // void visit(const luci::CircleLogicalOr *) final; - // void visit(const luci::CircleLogistic *) final; - // void visit(const luci::CircleLogSoftmax *) final; - // void visit(const luci::CircleMatrixDiag *) final; - // void visit(const luci::CircleMatrixSetDiag *) final; - // void visit(const luci::CircleMaximum *) final; - // void visit(const luci::CircleMaxPool2D *) final; + void visit(const luci::CircleElu *) final; + void visit(const luci::CircleEqual *) final; + void visit(const luci::CircleExp *) final; + void visit(const luci::CircleExpandDims *) final; + void visit(const luci::CircleFakeQuant *) final; + void visit(const luci::CircleFill *) final; + void visit(const luci::CircleFloor *) final; + void visit(const luci::CircleFloorDiv *) final; + void visit(const luci::CircleFloorMod *) final; + void visit(const luci::CircleFullyConnected *) final; + void visit(const luci::CircleGather *) final; + void visit(const luci::CircleGatherNd *) final; + void visit(const luci::CircleGreater *) final; + void visit(const luci::CircleGreaterEqual *) final; + void visit(const luci::CircleIf *) final; + void visit(const luci::CircleL2Normalize *) final; + void visit(const luci::CircleL2Pool2D *) final; + void visit(const luci::CircleLeakyRelu *) final; + void visit(const luci::CircleLess *) final; + void visit(const luci::CircleLessEqual *) final; + void visit(const luci::CircleLocalResponseNormalization *) final; + void visit(const luci::CircleLog *) final; + void visit(const luci::CircleLogicalAnd *) final; + void visit(const luci::CircleLogicalNot *) final; + void visit(const luci::CircleLogicalOr *) final; + void visit(const luci::CircleLogistic *) final; + void visit(const luci::CircleLogSoftmax *) final; + void visit(const luci::CircleMatrixDiag *) final; + void visit(const luci::CircleMatrixSetDiag *) final; + void visit(const luci::CircleMaximum *) final; + void visit(const luci::CircleMaxPool2D *) final; void visit(const luci::CircleMean *) final; - // void visit(const luci::CircleMinimum *) final; - // void visit(const luci::CircleMirrorPad *) final; + void visit(const luci::CircleMinimum *) final; + void visit(const luci::CircleMirrorPad *) final; void visit(const luci::CircleMul *) final; - // void visit(const luci::CircleNeg *) final; - // void visit(const luci::CircleNonMaxSuppressionV4 *) final; - // void visit(const luci::CircleNonMaxSuppressionV5 *) final; - // void visit(const luci::CircleNotEqual *) final; - // void visit(const luci::CircleOneHot *) final; - // void visit(const luci::CirclePack *) final; - // void visit(const luci::CirclePad *) final; - // void visit(const luci::CirclePadV2 *) final; + void visit(const luci::CircleNeg *) final; + void visit(const luci::CircleNonMaxSuppressionV4 *) final; + void visit(const luci::CircleNonMaxSuppressionV5 *) final; + void visit(const luci::CircleNotEqual *) final; + void visit(const luci::CircleOneHot *) final; + void visit(const luci::CirclePack *) final; + void visit(const luci::CirclePad *) final; + void visit(const luci::CirclePadV2 *) final; void visit(const luci::CirclePow *) final; - // void visit(const luci::CirclePRelu *) final; - // void visit(const luci::CircleRange *) final; - // void visit(const luci::CircleRank *) final; - // void visit(const luci::CircleReduceAny *) final; - // void visit(const luci::CircleReduceMax *) final; - // void visit(const luci::CircleReduceMin *) final; - // void visit(const luci::CircleReduceProd *) final; - // void visit(const luci::CircleRelu *) final; - // void visit(const luci::CircleRelu6 *) final; - // void visit(const luci::CircleReluN1To1 *) final; - // void visit(const luci::CircleReshape *) final; - // void visit(const luci::CircleResizeBilinear *) final; - // void visit(const luci::CircleResizeNearestNeighbor *) final; - // void visit(const luci::CircleReverseSequence *) final; - // void visit(const luci::CircleReverseV2 *) final; - // void visit(const luci::CircleRound *) final; + void visit(const luci::CirclePRelu *) final; + void visit(const luci::CircleQuantize *) final; + void visit(const luci::CircleRange *) final; + void visit(const luci::CircleRank *) final; + void visit(const luci::CircleReduceAny *) final; + void visit(const luci::CircleReduceMax *) final; + void visit(const luci::CircleReduceMin *) final; + void visit(const luci::CircleReduceProd *) final; + void visit(const luci::CircleRelu *) final; + void visit(const luci::CircleRelu6 *) final; + void visit(const luci::CircleReluN1To1 *) final; + void visit(const luci::CircleReshape *) final; + void visit(const luci::CircleResizeBilinear *) final; + void visit(const luci::CircleResizeNearestNeighbor *) final; + void visit(const luci::CircleReverseSequence *) final; + void visit(const luci::CircleReverseV2 *) final; + void visit(const luci::CircleRound *) final; void visit(const luci::CircleRsqrt *) final; - // void visit(const luci::CircleScatterNd *) final; - // void visit(const luci::CircleSegmentSum *) final; - // void visit(const luci::CircleSelect *) final; - // void visit(const luci::CircleSelectV2 *) final; - // void visit(const luci::CircleShape *) final; - // void visit(const luci::CircleSin *) final; - // void visit(const luci::CircleSlice *) final; - // void visit(const luci::CircleSoftmax *) final; - // void visit(const luci::CircleSpaceToBatchND *) final; - // void visit(const luci::CircleSpaceToDepth *) final; - // void visit(const luci::CircleSparseToDense *) final; - // void visit(const luci::CircleSplit *) final; - // void visit(const luci::CircleSplitV *) final; + void visit(const luci::CircleScatterNd *) final; + void visit(const luci::CircleSegmentSum *) final; + void visit(const luci::CircleSelect *) final; + void visit(const luci::CircleSelectV2 *) final; + void visit(const luci::CircleShape *) final; + void visit(const luci::CircleSin *) final; + void visit(const luci::CircleSlice *) final; + void visit(const luci::CircleSoftmax *) final; + void visit(const luci::CircleSpaceToBatchND *) final; + void visit(const luci::CircleSpaceToDepth *) final; + void visit(const luci::CircleSparseToDense *) final; + void visit(const luci::CircleSplit *) final; + void visit(const luci::CircleSplitV *) final; void visit(const luci::CircleSqrt *) final; - // void visit(const luci::CircleSquare *) final; + void visit(const luci::CircleSquare *) final; void visit(const luci::CircleSquaredDifference *) final; - // void visit(const luci::CircleSqueeze *) final; - // void visit(const luci::CircleStridedSlice *) final; + void visit(const luci::CircleSqueeze *) final; + void visit(const luci::CircleStridedSlice *) final; void visit(const luci::CircleSub *) final; - // void visit(const luci::CircleSum *) final; - // void visit(const luci::CircleTanh *) final; - // void visit(const luci::CircleTile *) final; - // void visit(const luci::CircleTopKV2 *) final; - // void visit(const luci::CircleTranspose *) final; - // void visit(const luci::CircleTransposeConv *) final; - // void visit(const luci::CircleUnidirectionalSequenceLSTM *) final; - // void visit(const luci::CircleUnique *) final; - // void visit(const luci::CircleUnpack *) final; - // void visit(const luci::CircleWhere *) final; - // void visit(const luci::CircleWhile *) final; - // void visit(const luci::CircleZerosLike *) final; + void visit(const luci::CircleSum *) final; + void visit(const luci::CircleTanh *) final; + void visit(const luci::CircleTile *) final; + void visit(const luci::CircleTopKV2 *) final; + void visit(const luci::CircleTranspose *) final; + void visit(const luci::CircleTransposeConv *) final; + void visit(const luci::CircleUnidirectionalSequenceLSTM *) final; + void visit(const luci::CircleUnique *) final; + void visit(const luci::CircleUnpack *) final; + void visit(const luci::CircleWhere *) final; + void visit(const luci::CircleWhile *) final; + void visit(const luci::CircleZerosLike *) final; // Circle Only - // void visit(const luci::CircleBCQFullyConnected *) final; - // void visit(const luci::CircleBCQGather *) final; - // void visit(const luci::CircleInstanceNorm *) final; + void visit(const luci::CircleBCQFullyConnected *) final; + void visit(const luci::CircleBCQGather *) final; + void visit(const luci::CircleInstanceNorm *) final; + + // NOTE CircleInput and CircleOutput are not handled here as these need + // link with graph I/O // Virtual - // void visit(const luci::CircleCustomOut *) final; - // void visit(const luci::CircleIfOut *) final; + void visit(const luci::CircleCustomOut *) final; + void visit(const luci::CircleIfOut *) final; // void visit(const luci::CircleInput *) final; - // void visit(const luci::CircleNonMaxSuppressionV4Out *) final; - // void visit(const luci::CircleNonMaxSuppressionV5Out *) final; + void visit(const luci::CircleNonMaxSuppressionV4Out *) final; + void visit(const luci::CircleNonMaxSuppressionV5Out *) final; // void visit(const luci::CircleOutput *) final; - // void visit(const luci::CircleOutputDummy *) final; - // void visit(const luci::CircleOutputExclude *) final; - // void visit(const luci::CircleSplitOut *) final; - // void visit(const luci::CircleSplitVOut *) final; - // void visit(const luci::CircleTopKV2Out *) final; - // void visit(const luci::CircleUniqueOut *) final; - // void visit(const luci::CircleUnpackOut *) final; - // void visit(const luci::CircleWhileOut *) final; + void visit(const luci::CircleOutputDummy *) final; + void visit(const luci::CircleOutputExclude *) final; + void visit(const luci::CircleSplitOut *) final; + void visit(const luci::CircleSplitVOut *) final; + void visit(const luci::CircleTopKV2Out *) final; + void visit(const luci::CircleUniqueOut *) final; + void visit(const luci::CircleUnpackOut *) final; + void visit(const luci::CircleWhileOut *) final; public: luci::CircleNode *find_clone(const luci::CircleNode *node); diff --git a/compiler/luci/partition/src/ConnectNode.test.h b/compiler/luci/partition/src/ConnectNode.test.h index f7333ff99..ac4878a15 100644 --- a/compiler/luci/partition/src/ConnectNode.test.h +++ b/compiler/luci/partition/src/ConnectNode.test.h @@ -45,8 +45,9 @@ public: if (shape_in.size() != N) throw std::runtime_error("Failed to init TestIsOGraph"); - TestIsGraphlet<N>::init(TestIsGraphlet<N>::g(), shape_in); - TestOGraphlet::init(TestIsGraphlet<N>::g(), shape_out); + auto g = TestIsGraphlet<N>::g(); + TestIsGraphlet<N>::init(g, shape_in); + TestOGraphlet::init(g, shape_out); } }; @@ -82,6 +83,43 @@ protected: T *_node{nullptr}; }; +template <class T> class NodeIsOsGraphletT +{ +public: + virtual void init(loco::Graph *g, uint32_t n, uint32_t m) + { + _node = g->nodes()->create<T>(n, m); + _node->dtype(loco::DataType::S32); + _node->name("node"); + } + + T *node(void) const { return _node; } + +protected: + T *_node{nullptr}; +}; + +template <unsigned N, unsigned M> +class TestIsOsGraph : public TestIsGraphlet<N>, public TestOsGraphlet<M> +{ +public: + TestIsOsGraph() = default; + +public: + virtual void init(const std::initializer_list<ShapeU32> shape_in, + const std::initializer_list<ShapeU32> shape_out) + { + if (shape_in.size() != N) + throw std::runtime_error("Failed to init TestIsOsGraph"); + if (shape_out.size() != M) + throw std::runtime_error("Failed to init TestIsOsGraph"); + + auto g = TestIsGraphlet<N>::g(); + TestIsGraphlet<N>::init(g, shape_in); + TestOsGraphlet<M>::init(g, shape_out); + } +}; + /** * @brief ConnectionTestHelper provides common framework for testing * cloned CircleNode connection @@ -105,6 +143,33 @@ public: } } + template <unsigned N, unsigned M> void prepare_inputs(TestIsOsGraph<N, M> *isosgraph) + { + assert(N == isosgraph->num_inputs()); + assert(M == isosgraph->num_outputs()); + + for (uint32_t i = 0; i < N; ++i) + { + auto *input = _graph_clone->nodes()->create<luci::CircleInput>(); + luci::copy_common_attributes(isosgraph->input(i), input); + _clonectx.emplace(isosgraph->input(i), input); + _inputs.push_back(input); + } + } + + /** + * @note although there is only one input, method name has 's' to make test simple + */ + void prepare_inputs(TestIOGraph *isograph) + { + assert(1 == isograph->num_inputs()); + + auto *input = _graph_clone->nodes()->create<luci::CircleInput>(); + luci::copy_common_attributes(isograph->input(), input); + _clonectx.emplace(isograph->input(), input); + _inputs.push_back(input); + } + /** * @note prepare_inputs_miss is for negative testing */ @@ -122,6 +187,31 @@ public: } } + template <unsigned N, unsigned M> void prepare_inputs_miss(TestIsOsGraph<N, M> *isograph) + { + assert(N == isograph->num_inputs()); + assert(M == isograph->num_outputs()); + + for (uint32_t i = 0; i < N; ++i) + { + auto *input = _graph_clone->nodes()->create<luci::CircleInput>(); + luci::copy_common_attributes(isograph->input(i), input); + if (i != 0) + _clonectx.emplace(isograph->input(i), input); + _inputs.push_back(input); + } + } + + void prepare_inputs_miss(TestIOGraph *isograph) + { + assert(1 == isograph->num_inputs()); + + auto *input = _graph_clone->nodes()->create<luci::CircleInput>(); + luci::copy_common_attributes(isograph->input(), input); + // _clonectx.emplace() is NOT called on purpose + _inputs.push_back(input); + } + void clone_connect(luci::CircleNode *node, luci::CircleNode *clone) { _clonectx.emplace(node, clone); diff --git a/compiler/luci/partition/src/Nodes/CircleAbs.cpp b/compiler/luci/partition/src/Nodes/CircleAbs.cpp new file mode 100644 index 000000000..a3fde4c45 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleAbs.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +/** + * @note This method and all other connect() are just to reduce LOC of ConnectNode class + */ +void connect(luci::ConnectNode *cn, const luci::CircleAbs *node) +{ + auto *cloned = loco::must_cast<luci::CircleAbs *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleAbs *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleAbs.test.cpp b/compiler/luci/partition/src/Nodes/CircleAbs.test.cpp new file mode 100644 index 000000000..f3e721525 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleAbs.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleAbs> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Abs) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAbs *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAbs *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Abs_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAbs *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAbs *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleAddN.cpp b/compiler/luci/partition/src/Nodes/CircleAddN.cpp new file mode 100644 index 000000000..81e5e0949 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleAddN.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleAddN *node) +{ + auto *cloned = loco::must_cast<luci::CircleAddN *>(cn->find_clone(node)); + + uint32_t num_inputs = cloned->arity(); + for (uint32_t i = 0; i < num_inputs; ++i) + { + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->inputs(i)); + + cloned->inputs(i, cn->find_clone(input)); + } +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleAddN *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleAddN.test.cpp b/compiler/luci/partition/src/Nodes/CircleAddN.test.cpp new file mode 100644 index 000000000..5d0a7489f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleAddN.test.cpp @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeIsGraphletT<luci::CircleAddN> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g(), 3); + + for (uint32_t i = 0; i < 3; ++i) + { + node()->inputs(i, input(i)); + } + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_AddN) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAddN *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAddN *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_AddN_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAddN *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAddN *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleArgMax.cpp b/compiler/luci/partition/src/Nodes/CircleArgMax.cpp new file mode 100644 index 000000000..1409586d7 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleArgMax.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleArgMax *node) +{ + auto *cloned = loco::must_cast<luci::CircleArgMax *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *dimension = loco::must_cast<luci::CircleNode *>(node->dimension()); + + cloned->input(cn->find_clone(input)); + cloned->dimension(cn->find_clone(dimension)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleArgMax *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleArgMax.test.cpp b/compiler/luci/partition/src/Nodes/CircleArgMax.test.cpp new file mode 100644 index 000000000..c816fbeb8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleArgMax.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleArgMax> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->dimension(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ArgMax) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMax *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ArgMax_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMax *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleArgMin.cpp b/compiler/luci/partition/src/Nodes/CircleArgMin.cpp new file mode 100644 index 000000000..6151aa98a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleArgMin.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleArgMin *node) +{ + auto *cloned = loco::must_cast<luci::CircleArgMin *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *dimension = loco::must_cast<luci::CircleNode *>(node->dimension()); + + cloned->input(cn->find_clone(input)); + cloned->dimension(cn->find_clone(dimension)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleArgMin *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleArgMin.test.cpp b/compiler/luci/partition/src/Nodes/CircleArgMin.test.cpp new file mode 100644 index 000000000..d150be4d6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleArgMin.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleArgMin> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->dimension(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ArgMin) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMin *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMin *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ArgMin_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMin *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleArgMin *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleAveragePool2D.cpp b/compiler/luci/partition/src/Nodes/CircleAveragePool2D.cpp new file mode 100644 index 000000000..547665771 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleAveragePool2D.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleAveragePool2D *node) +{ + auto *cloned = loco::must_cast<luci::CircleAveragePool2D *>(cn->find_clone(node)); + + luci::CircleNode *value = loco::must_cast<luci::CircleNode *>(node->value()); + + cloned->value(cn->find_clone(value)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleAveragePool2D *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleAveragePool2D.test.cpp b/compiler/luci/partition/src/Nodes/CircleAveragePool2D.test.cpp new file mode 100644 index 000000000..fba2be835 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleAveragePool2D.test.cpp @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleAveragePool2D> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleAveragePool2D>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + _node->padding(luci::Padding::VALID); + } +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->value(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_AveragePool2D) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAveragePool2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAveragePool2D *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_AveragePool2D_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAveragePool2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleAveragePool2D *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleBCQFullyConnected.cpp b/compiler/luci/partition/src/Nodes/CircleBCQFullyConnected.cpp new file mode 100644 index 000000000..5b1dd8543 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBCQFullyConnected.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleBCQFullyConnected *node) +{ + auto *cloned = loco::must_cast<luci::CircleBCQFullyConnected *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *weights_scales = loco::must_cast<luci::CircleNode *>(node->weights_scales()); + luci::CircleNode *weights_binary = loco::must_cast<luci::CircleNode *>(node->weights_binary()); + luci::CircleNode *bias = loco::must_cast<luci::CircleNode *>(node->bias()); + luci::CircleNode *weights_clusters = + loco::must_cast<luci::CircleNode *>(node->weights_clusters()); + + cloned->input(cn->find_clone(input)); + cloned->weights_scales(cn->find_clone(weights_scales)); + cloned->weights_binary(cn->find_clone(weights_binary)); + cloned->bias(cn->find_clone(bias)); + cloned->weights_clusters(cn->find_clone(weights_clusters)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleBCQFullyConnected *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleBCQFullyConnected.test.cpp b/compiler/luci/partition/src/Nodes/CircleBCQFullyConnected.test.cpp new file mode 100644 index 000000000..3d64f4b29 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBCQFullyConnected.test.cpp @@ -0,0 +1,106 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleBCQFullyConnected> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleBCQFullyConnected>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + } +}; + +class TestNodeGraph : public TestIsOGraph<5>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<5>::init({shape, shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->weights_scales(input(1)); + node()->weights_binary(input(2)); + node()->bias(input(3)); + node()->weights_clusters(input(4)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_BCQFullyConnected) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQFullyConnected *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQFullyConnected *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(5, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); + ASSERT_EQ(cth.inputs(4), clone->arg(4)); +} + +TEST(ConnectNodeTest, connect_BCQFullyConnected_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQFullyConnected *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQFullyConnected *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleBCQGather.cpp b/compiler/luci/partition/src/Nodes/CircleBCQGather.cpp new file mode 100644 index 000000000..90c4d9ef3 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBCQGather.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleBCQGather *node) +{ + auto *cloned = loco::must_cast<luci::CircleBCQGather *>(cn->find_clone(node)); + + luci::CircleNode *input_scales = loco::must_cast<luci::CircleNode *>(node->input_scales()); + luci::CircleNode *input_binary = loco::must_cast<luci::CircleNode *>(node->input_binary()); + luci::CircleNode *indices = loco::must_cast<luci::CircleNode *>(node->indices()); + luci::CircleNode *input_clusters = loco::must_cast<luci::CircleNode *>(node->input_clusters()); + + cloned->input_scales(cn->find_clone(input_scales)); + cloned->input_binary(cn->find_clone(input_binary)); + cloned->indices(cn->find_clone(indices)); + cloned->input_clusters(cn->find_clone(input_clusters)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleBCQGather *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleBCQGather.test.cpp b/compiler/luci/partition/src/Nodes/CircleBCQGather.test.cpp new file mode 100644 index 000000000..bbbd3f157 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBCQGather.test.cpp @@ -0,0 +1,96 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleBCQGather> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<4>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<4>::init({shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input_scales(input(0)); + node()->input_binary(input(1)); + node()->indices(input(2)); + node()->input_clusters(input(3)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_BCQGather) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQGather *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQGather *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(4, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); +} + +TEST(ConnectNodeTest, connect_BCQGather_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQGather *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBCQGather *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleBatchMatMul.cpp b/compiler/luci/partition/src/Nodes/CircleBatchMatMul.cpp new file mode 100644 index 000000000..c3992a64e --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBatchMatMul.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleBatchMatMul *node) +{ + auto *cloned = loco::must_cast<luci::CircleBatchMatMul *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleBatchMatMul *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleBatchMatMul.test.cpp b/compiler/luci/partition/src/Nodes/CircleBatchMatMul.test.cpp new file mode 100644 index 000000000..94336d36a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBatchMatMul.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleBatchMatMul> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_BatchMatMul) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchMatMul *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchMatMul *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_BatchMatMul_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchMatMul *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchMatMul *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleBatchToSpaceND.cpp b/compiler/luci/partition/src/Nodes/CircleBatchToSpaceND.cpp new file mode 100644 index 000000000..2a463afb1 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBatchToSpaceND.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleBatchToSpaceND *node) +{ + auto *cloned = loco::must_cast<luci::CircleBatchToSpaceND *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *block_shape = loco::must_cast<luci::CircleNode *>(node->block_shape()); + luci::CircleNode *crops = loco::must_cast<luci::CircleNode *>(node->crops()); + + cloned->input(cn->find_clone(input)); + cloned->block_shape(cn->find_clone(block_shape)); + cloned->crops(cn->find_clone(crops)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleBatchToSpaceND *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleBatchToSpaceND.test.cpp b/compiler/luci/partition/src/Nodes/CircleBatchToSpaceND.test.cpp new file mode 100644 index 000000000..544f5e127 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleBatchToSpaceND.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleBatchToSpaceND> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->block_shape(input(1)); + node()->crops(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_BatchToSpaceND) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchToSpaceND *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchToSpaceND *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_BatchToSpaceND_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchToSpaceND *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleBatchToSpaceND *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleCast.cpp b/compiler/luci/partition/src/Nodes/CircleCast.cpp new file mode 100644 index 000000000..f7630cd85 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCast.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleCast *node) +{ + auto *cloned = loco::must_cast<luci::CircleCast *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleCast *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleCast.test.cpp b/compiler/luci/partition/src/Nodes/CircleCast.test.cpp new file mode 100644 index 000000000..005119060 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCast.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleCast> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Cast) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCast *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCast *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Cast_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCast *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCast *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleCeil.cpp b/compiler/luci/partition/src/Nodes/CircleCeil.cpp new file mode 100644 index 000000000..a0c94033e --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCeil.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleCeil *node) +{ + auto *cloned = loco::must_cast<luci::CircleCeil *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleCeil *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleCeil.test.cpp b/compiler/luci/partition/src/Nodes/CircleCeil.test.cpp new file mode 100644 index 000000000..dbd7e5390 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCeil.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleCeil> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Ceil) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCeil *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCeil *>(node)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Ceil_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCeil *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCeil *>(node)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleConcatenation.cpp b/compiler/luci/partition/src/Nodes/CircleConcatenation.cpp new file mode 100644 index 000000000..fb24d21ca --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleConcatenation.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleConcatenation *node) +{ + auto *cloned = loco::must_cast<luci::CircleConcatenation *>(cn->find_clone(node)); + + uint32_t num_inputs = cloned->numValues(); + for (uint32_t i = 0; i < num_inputs; ++i) + { + luci::CircleNode *value = loco::must_cast<luci::CircleNode *>(node->values(i)); + + cloned->values(i, cn->find_clone(value)); + } +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleConcatenation *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleConcatenation.test.cpp b/compiler/luci/partition/src/Nodes/CircleConcatenation.test.cpp new file mode 100644 index 000000000..4d64b85a2 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleConcatenation.test.cpp @@ -0,0 +1,103 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeIsGraphletT<luci::CircleConcatenation> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g, uint32_t n) override + { + NodeIsGraphletT<luci::CircleConcatenation>::init(g, n); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + } +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g(), 3); + + for (uint32_t i = 0; i < 3; ++i) + { + node()->values(i, input(i)); + } + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Concatenation) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConcatenation *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConcatenation *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_Concatenation_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConcatenation *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConcatenation *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleConv2D.cpp b/compiler/luci/partition/src/Nodes/CircleConv2D.cpp new file mode 100644 index 000000000..46716f0ec --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleConv2D.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleConv2D *node) +{ + auto *cloned = loco::must_cast<luci::CircleConv2D *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *filter = loco::must_cast<luci::CircleNode *>(node->filter()); + luci::CircleNode *bias = loco::must_cast<luci::CircleNode *>(node->bias()); + + cloned->input(cn->find_clone(input)); + cloned->filter(cn->find_clone(filter)); + cloned->bias(cn->find_clone(bias)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleConv2D *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleConv2D.test.cpp b/compiler/luci/partition/src/Nodes/CircleConv2D.test.cpp new file mode 100644 index 000000000..829adec9b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleConv2D.test.cpp @@ -0,0 +1,103 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleConv2D> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleConv2D>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + _node->padding(luci::Padding::VALID); + } +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->filter(input(1)); + node()->bias(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Conv2D) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConv2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConv2D *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_Conv2D_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConv2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleConv2D *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleCos.cpp b/compiler/luci/partition/src/Nodes/CircleCos.cpp new file mode 100644 index 000000000..9dcf81e83 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCos.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleCos *node) +{ + auto *cloned = loco::must_cast<luci::CircleCos *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleCos *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleCos.test.cpp b/compiler/luci/partition/src/Nodes/CircleCos.test.cpp new file mode 100644 index 000000000..6c92b93fb --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCos.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleCos> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Cos) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCos *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCos *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Cos_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCos *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCos *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleCustom.cpp b/compiler/luci/partition/src/Nodes/CircleCustom.cpp new file mode 100644 index 000000000..ac16ebe40 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCustom.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleCustom *node) +{ + auto *cloned = loco::must_cast<luci::CircleCustom *>(cn->find_clone(node)); + + uint32_t numInputs = cloned->numInputs(); + for (uint32_t i = 0; i < numInputs; ++i) + { + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->inputs(i)); + + cloned->inputs(i, cn->find_clone(input)); + } +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleCustom *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleCustom.test.cpp b/compiler/luci/partition/src/Nodes/CircleCustom.test.cpp new file mode 100644 index 000000000..9f40b5220 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCustom.test.cpp @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +/** + * @note Does not use template like others as only Custom have both multiple in/out + */ +class NodeGraphlet +{ +public: + NodeGraphlet() = default; + +public: + virtual void init(loco::Graph *g, uint32_t in, uint32_t out) + { + _node = g->nodes()->create<luci::CircleCustom>(in, out); + _node->dtype(loco::DataType::S32); + _node->name("node"); + } + + luci::CircleCustom *node(void) const { return _node; } + +protected: + luci::CircleCustom *_node = nullptr; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g(), 3, 3); + + for (uint32_t i = 0; i < 3; ++i) + { + node()->inputs(i, input(i)); + } + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Custom) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustom *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustom *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_Custom_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustom *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustom *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleCustomOut.cpp b/compiler/luci/partition/src/Nodes/CircleCustomOut.cpp new file mode 100644 index 000000000..fee1a1a8c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCustomOut.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleCustomOut *node) +{ + auto *cloned = loco::must_cast<luci::CircleCustomOut *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleCustomOut *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleCustomOut.test.cpp b/compiler/luci/partition/src/Nodes/CircleCustomOut.test.cpp new file mode 100644 index 000000000..0a293970e --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleCustomOut.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleCustomOut> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_CustomOut) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustomOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustomOut *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_CustomOut_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustomOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleCustomOut *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleDepthToSpace.cpp b/compiler/luci/partition/src/Nodes/CircleDepthToSpace.cpp new file mode 100644 index 000000000..ade266e41 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleDepthToSpace.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleDepthToSpace *node) +{ + auto *cloned = loco::must_cast<luci::CircleDepthToSpace *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleDepthToSpace *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleDepthToSpace.test.cpp b/compiler/luci/partition/src/Nodes/CircleDepthToSpace.test.cpp new file mode 100644 index 000000000..997360a9b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleDepthToSpace.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleDepthToSpace> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_DepthToSpace) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthToSpace *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthToSpace *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_DepthToSpace_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthToSpace *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthToSpace *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleDepthwiseConv2D.cpp b/compiler/luci/partition/src/Nodes/CircleDepthwiseConv2D.cpp new file mode 100644 index 000000000..19d1d5f42 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleDepthwiseConv2D.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleDepthwiseConv2D *node) +{ + auto *cloned = loco::must_cast<luci::CircleDepthwiseConv2D *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *filter = loco::must_cast<luci::CircleNode *>(node->filter()); + luci::CircleNode *bias = loco::must_cast<luci::CircleNode *>(node->bias()); + + cloned->input(cn->find_clone(input)); + cloned->filter(cn->find_clone(filter)); + cloned->bias(cn->find_clone(bias)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleDepthwiseConv2D *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleDepthwiseConv2D.test.cpp b/compiler/luci/partition/src/Nodes/CircleDepthwiseConv2D.test.cpp new file mode 100644 index 000000000..681f98bdb --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleDepthwiseConv2D.test.cpp @@ -0,0 +1,103 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleDepthwiseConv2D> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleDepthwiseConv2D>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + _node->padding(luci::Padding::VALID); + } +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->filter(input(1)); + node()->bias(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_DepthwiseConv2D) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthwiseConv2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthwiseConv2D *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_DepthwiseConv2D_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthwiseConv2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDepthwiseConv2D *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleDequantize.cpp b/compiler/luci/partition/src/Nodes/CircleDequantize.cpp new file mode 100644 index 000000000..3a520d4e9 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleDequantize.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleDequantize *node) +{ + auto *cloned = loco::must_cast<luci::CircleDequantize *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleDequantize *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleDequantize.test.cpp b/compiler/luci/partition/src/Nodes/CircleDequantize.test.cpp new file mode 100644 index 000000000..7f6006c1d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleDequantize.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleDequantize> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Dequantize) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDequantize *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDequantize *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Dequantize_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDequantize *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleDequantize *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleElu.cpp b/compiler/luci/partition/src/Nodes/CircleElu.cpp new file mode 100644 index 000000000..d21cd4c01 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleElu.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleElu *node) +{ + auto *cloned = loco::must_cast<luci::CircleElu *>(cn->find_clone(node)); + + luci::CircleNode *features = loco::must_cast<luci::CircleNode *>(node->features()); + + cloned->features(cn->find_clone(features)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleElu *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleElu.test.cpp b/compiler/luci/partition/src/Nodes/CircleElu.test.cpp new file mode 100644 index 000000000..94774cca8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleElu.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleElu> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->features(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Elu) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleElu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleElu *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Elu_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleElu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleElu *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleEqual.cpp b/compiler/luci/partition/src/Nodes/CircleEqual.cpp new file mode 100644 index 000000000..6a126c0e2 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleEqual.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleEqual *node) +{ + auto *cloned = loco::must_cast<luci::CircleEqual *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleEqual *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleEqual.test.cpp b/compiler/luci/partition/src/Nodes/CircleEqual.test.cpp new file mode 100644 index 000000000..20b539199 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleEqual.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleEqual> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Equal) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleEqual *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Equal_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleEqual *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleExp.cpp b/compiler/luci/partition/src/Nodes/CircleExp.cpp new file mode 100644 index 000000000..95fb1cd67 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleExp.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleExp *node) +{ + auto *cloned = loco::must_cast<luci::CircleExp *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleExp *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleExp.test.cpp b/compiler/luci/partition/src/Nodes/CircleExp.test.cpp new file mode 100644 index 000000000..16d7244ab --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleExp.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleExp> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Exp) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExp *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExp *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Exp_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExp *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExp *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleExpandDims.cpp b/compiler/luci/partition/src/Nodes/CircleExpandDims.cpp new file mode 100644 index 000000000..6fccd6310 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleExpandDims.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleExpandDims *node) +{ + auto *cloned = loco::must_cast<luci::CircleExpandDims *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *axis = loco::must_cast<luci::CircleNode *>(node->axis()); + + cloned->input(cn->find_clone(input)); + cloned->axis(cn->find_clone(axis)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleExpandDims *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleExpandDims.test.cpp b/compiler/luci/partition/src/Nodes/CircleExpandDims.test.cpp new file mode 100644 index 000000000..8a5156509 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleExpandDims.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleExpandDims> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->axis(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ExpandDims) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExpandDims *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExpandDims *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ExpandDims_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExpandDims *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleExpandDims *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleFakeQuant.cpp b/compiler/luci/partition/src/Nodes/CircleFakeQuant.cpp new file mode 100644 index 000000000..4855d80ae --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFakeQuant.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleFakeQuant *node) +{ + auto *cloned = loco::must_cast<luci::CircleFakeQuant *>(cn->find_clone(node)); + + luci::CircleNode *inputs = loco::must_cast<luci::CircleNode *>(node->inputs()); + + cloned->inputs(cn->find_clone(inputs)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleFakeQuant *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleFakeQuant.test.cpp b/compiler/luci/partition/src/Nodes/CircleFakeQuant.test.cpp new file mode 100644 index 000000000..3821d755a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFakeQuant.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleFakeQuant> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->inputs(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_FakeQuant) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFakeQuant *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFakeQuant *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_FakeQuant_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFakeQuant *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFakeQuant *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleFill.cpp b/compiler/luci/partition/src/Nodes/CircleFill.cpp new file mode 100644 index 000000000..06fca7b41 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFill.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleFill *node) +{ + auto *cloned = loco::must_cast<luci::CircleFill *>(cn->find_clone(node)); + + luci::CircleNode *dims = loco::must_cast<luci::CircleNode *>(node->dims()); + luci::CircleNode *value = loco::must_cast<luci::CircleNode *>(node->value()); + + cloned->dims(cn->find_clone(dims)); + cloned->value(cn->find_clone(value)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleFill *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleFill.test.cpp b/compiler/luci/partition/src/Nodes/CircleFill.test.cpp new file mode 100644 index 000000000..97a5a348d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFill.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleFill> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->dims(input(0)); + node()->value(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Fill) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFill *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFill *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Fill_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFill *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFill *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleFloor.cpp b/compiler/luci/partition/src/Nodes/CircleFloor.cpp new file mode 100644 index 000000000..7ad392461 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFloor.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleFloor *node) +{ + auto *cloned = loco::must_cast<luci::CircleFloor *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleFloor *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleFloor.test.cpp b/compiler/luci/partition/src/Nodes/CircleFloor.test.cpp new file mode 100644 index 000000000..1a964ea21 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFloor.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleFloor> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Floor) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloor *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloor *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Floor_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloor *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloor *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleFloorDiv.cpp b/compiler/luci/partition/src/Nodes/CircleFloorDiv.cpp new file mode 100644 index 000000000..3b92b00c6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFloorDiv.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleFloorDiv *node) +{ + auto *cloned = loco::must_cast<luci::CircleFloorDiv *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleFloorDiv *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleFloorDiv.test.cpp b/compiler/luci/partition/src/Nodes/CircleFloorDiv.test.cpp new file mode 100644 index 000000000..3d2801566 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFloorDiv.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleFloorDiv> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_FloorDiv) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorDiv *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorDiv *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_FloorDiv_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorDiv *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorDiv *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleFloorMod.cpp b/compiler/luci/partition/src/Nodes/CircleFloorMod.cpp new file mode 100644 index 000000000..9f868d0e5 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFloorMod.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleFloorMod *node) +{ + auto *cloned = loco::must_cast<luci::CircleFloorMod *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleFloorMod *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleFloorMod.test.cpp b/compiler/luci/partition/src/Nodes/CircleFloorMod.test.cpp new file mode 100644 index 000000000..89a09411b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFloorMod.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleFloorMod> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_FloorMod) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorMod *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorMod *>(node)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_FloorMod_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorMod *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFloorMod *>(node)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleFullyConnected.cpp b/compiler/luci/partition/src/Nodes/CircleFullyConnected.cpp new file mode 100644 index 000000000..da273037a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFullyConnected.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleFullyConnected *node) +{ + auto *cloned = loco::must_cast<luci::CircleFullyConnected *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *weights = loco::must_cast<luci::CircleNode *>(node->weights()); + luci::CircleNode *bias = loco::must_cast<luci::CircleNode *>(node->bias()); + + cloned->input(cn->find_clone(input)); + cloned->weights(cn->find_clone(weights)); + cloned->bias(cn->find_clone(bias)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleFullyConnected *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleFullyConnected.test.cpp b/compiler/luci/partition/src/Nodes/CircleFullyConnected.test.cpp new file mode 100644 index 000000000..fc88204bd --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleFullyConnected.test.cpp @@ -0,0 +1,103 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleFullyConnected> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleFullyConnected>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + _node->weights_format(luci::CircleFullyConnected::WeightsFormat::DEFAULT); + } +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->weights(input(1)); + node()->bias(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_FullyConnected) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFullyConnected *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFullyConnected *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_FullyConnected_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFullyConnected *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleFullyConnected *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleGather.cpp b/compiler/luci/partition/src/Nodes/CircleGather.cpp new file mode 100644 index 000000000..0ee458394 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGather.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleGather *node) +{ + auto *cloned = loco::must_cast<luci::CircleGather *>(cn->find_clone(node)); + + luci::CircleNode *params = loco::must_cast<luci::CircleNode *>(node->params()); + luci::CircleNode *indices = loco::must_cast<luci::CircleNode *>(node->indices()); + + cloned->params(cn->find_clone(params)); + cloned->indices(cn->find_clone(indices)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleGather *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleGather.test.cpp b/compiler/luci/partition/src/Nodes/CircleGather.test.cpp new file mode 100644 index 000000000..7f4e08435 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGather.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleGather> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->params(input(0)); + node()->indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Gather) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGather *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGather *>(node)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Gather_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGather *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGather *>(node)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleGatherNd.cpp b/compiler/luci/partition/src/Nodes/CircleGatherNd.cpp new file mode 100644 index 000000000..4be05ca94 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGatherNd.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleGatherNd *node) +{ + auto *cloned = loco::must_cast<luci::CircleGatherNd *>(cn->find_clone(node)); + + luci::CircleNode *params = loco::must_cast<luci::CircleNode *>(node->params()); + luci::CircleNode *indices = loco::must_cast<luci::CircleNode *>(node->indices()); + + cloned->params(cn->find_clone(params)); + cloned->indices(cn->find_clone(indices)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleGatherNd *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleGatherNd.test.cpp b/compiler/luci/partition/src/Nodes/CircleGatherNd.test.cpp new file mode 100644 index 000000000..d673698e1 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGatherNd.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleGatherNd> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->params(input(0)); + node()->indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_GatherNd) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGatherNd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGatherNd *>(node)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_GatherNd_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGatherNd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGatherNd *>(node)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleGreater.cpp b/compiler/luci/partition/src/Nodes/CircleGreater.cpp new file mode 100644 index 000000000..7bc2a14c9 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGreater.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleGreater *node) +{ + auto *cloned = loco::must_cast<luci::CircleGreater *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleGreater *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleGreater.test.cpp b/compiler/luci/partition/src/Nodes/CircleGreater.test.cpp new file mode 100644 index 000000000..842370d42 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGreater.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleGreater> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Greater) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreater *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreater *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Greater_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreater *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreater *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleGreaterEqual.cpp b/compiler/luci/partition/src/Nodes/CircleGreaterEqual.cpp new file mode 100644 index 000000000..536a0aed6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGreaterEqual.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleGreaterEqual *node) +{ + auto *cloned = loco::must_cast<luci::CircleGreaterEqual *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleGreaterEqual *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleGreaterEqual.test.cpp b/compiler/luci/partition/src/Nodes/CircleGreaterEqual.test.cpp new file mode 100644 index 000000000..76dc770f8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleGreaterEqual.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleGreaterEqual> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_GreaterEqual) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreaterEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreaterEqual *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_GreaterEqual_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreaterEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleGreaterEqual *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleIf.cpp b/compiler/luci/partition/src/Nodes/CircleIf.cpp new file mode 100644 index 000000000..1672a136d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleIf.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleIf *node) +{ + auto *cloned = loco::must_cast<luci::CircleIf *>(cn->find_clone(node)); + + luci::CircleNode *cond = loco::must_cast<luci::CircleNode *>(node->cond()); + + cloned->cond(cn->find_clone(cond)); + + auto input_count = node->input_count(); + for (uint32_t in = 0; in < input_count; ++in) + { + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input(in)); + + cloned->input(in, cn->find_clone(input)); + } +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleIf *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleIf.test.cpp b/compiler/luci/partition/src/Nodes/CircleIf.test.cpp new file mode 100644 index 000000000..dbd25c822 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleIf.test.cpp @@ -0,0 +1,102 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeIsOsGraphletT<luci::CircleIf> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g, uint32_t n, uint32_t m) override + { + // cond() will take one input + NodeIsOsGraphletT::init(g, n - 1, m); + } +}; + +class TestNodeGraph : public TestIsOsGraph<3, 1>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOsGraph<3, 1>::init({shape, shape, shape}, {shape}); + NodeGraphlet::init(g(), 3, 1); + + node()->cond(input(0)); + node()->input(0, input(1)); + node()->input(1, input(2)); + + output(0)->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_If) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs<3, 1>(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIf *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIf *>(clone)); + + cth.clone_connect(node, clone); + + // aritiy(3) = cond + input(2) + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_If_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss<3, 1>(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIf *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIf *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleIfOut.cpp b/compiler/luci/partition/src/Nodes/CircleIfOut.cpp new file mode 100644 index 000000000..969bdd93c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleIfOut.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleIfOut *node) +{ + auto *cloned = loco::must_cast<luci::CircleIfOut *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleIfOut *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleIfOut.test.cpp b/compiler/luci/partition/src/Nodes/CircleIfOut.test.cpp new file mode 100644 index 000000000..9207654bc --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleIfOut.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleIfOut> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_IfOut) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIfOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIfOut *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_IfOut_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIfOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleIfOut *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleInstanceNorm.cpp b/compiler/luci/partition/src/Nodes/CircleInstanceNorm.cpp new file mode 100644 index 000000000..386652fb1 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleInstanceNorm.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleInstanceNorm *node) +{ + auto *cloned = loco::must_cast<luci::CircleInstanceNorm *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *gamma = loco::must_cast<luci::CircleNode *>(node->gamma()); + luci::CircleNode *beta = loco::must_cast<luci::CircleNode *>(node->beta()); + + cloned->input(cn->find_clone(input)); + cloned->gamma(cn->find_clone(gamma)); + cloned->beta(cn->find_clone(beta)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleInstanceNorm *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleInstanceNorm.test.cpp b/compiler/luci/partition/src/Nodes/CircleInstanceNorm.test.cpp new file mode 100644 index 000000000..b932223d0 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleInstanceNorm.test.cpp @@ -0,0 +1,102 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleInstanceNorm> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleInstanceNorm>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + } +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->gamma(input(1)); + node()->beta(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_InstanceNorm) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleInstanceNorm *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleInstanceNorm *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_InstanceNorm_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleInstanceNorm *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleInstanceNorm *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleL2Normalize.cpp b/compiler/luci/partition/src/Nodes/CircleL2Normalize.cpp new file mode 100644 index 000000000..61ddba264 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleL2Normalize.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleL2Normalize *node) +{ + auto *cloned = loco::must_cast<luci::CircleL2Normalize *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleL2Normalize *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleL2Normalize.test.cpp b/compiler/luci/partition/src/Nodes/CircleL2Normalize.test.cpp new file mode 100644 index 000000000..4fc23727a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleL2Normalize.test.cpp @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleL2Normalize> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleL2Normalize>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + } +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_L2Normalize) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Normalize *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Normalize *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_L2Normalize_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Normalize *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Normalize *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleL2Pool2D.cpp b/compiler/luci/partition/src/Nodes/CircleL2Pool2D.cpp new file mode 100644 index 000000000..24333d507 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleL2Pool2D.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleL2Pool2D *node) +{ + auto *cloned = loco::must_cast<luci::CircleL2Pool2D *>(cn->find_clone(node)); + + luci::CircleNode *value = loco::must_cast<luci::CircleNode *>(node->value()); + + cloned->value(cn->find_clone(value)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleL2Pool2D *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleL2Pool2D.test.cpp b/compiler/luci/partition/src/Nodes/CircleL2Pool2D.test.cpp new file mode 100644 index 000000000..40328488c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleL2Pool2D.test.cpp @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleL2Pool2D> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleL2Pool2D>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + _node->padding(luci::Padding::VALID); + } +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->value(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_L2Pool2D) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Pool2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Pool2D *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_L2Pool2D_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Pool2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleL2Pool2D *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLeakyRelu.cpp b/compiler/luci/partition/src/Nodes/CircleLeakyRelu.cpp new file mode 100644 index 000000000..3da1ba287 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLeakyRelu.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLeakyRelu *node) +{ + auto *cloned = loco::must_cast<luci::CircleLeakyRelu *>(cn->find_clone(node)); + + luci::CircleNode *features = loco::must_cast<luci::CircleNode *>(node->features()); + + cloned->features(cn->find_clone(features)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLeakyRelu *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLeakyRelu.test.cpp b/compiler/luci/partition/src/Nodes/CircleLeakyRelu.test.cpp new file mode 100644 index 000000000..5a0d1dd87 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLeakyRelu.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLeakyRelu> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->features(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_LeakyRelu) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLeakyRelu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLeakyRelu *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_LeakyRelu_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLeakyRelu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLeakyRelu *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLess.cpp b/compiler/luci/partition/src/Nodes/CircleLess.cpp new file mode 100644 index 000000000..aab495fcc --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLess.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLess *node) +{ + auto *cloned = loco::must_cast<luci::CircleLess *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLess *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLess.test.cpp b/compiler/luci/partition/src/Nodes/CircleLess.test.cpp new file mode 100644 index 000000000..ab65e5d18 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLess.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLess> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Less) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLess *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLess *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Less_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLess *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLess *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLessEqual.cpp b/compiler/luci/partition/src/Nodes/CircleLessEqual.cpp new file mode 100644 index 000000000..ec129dbe8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLessEqual.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLessEqual *node) +{ + auto *cloned = loco::must_cast<luci::CircleLessEqual *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLessEqual *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLessEqual.test.cpp b/compiler/luci/partition/src/Nodes/CircleLessEqual.test.cpp new file mode 100644 index 000000000..0dd8986b6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLessEqual.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLessEqual> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_LessEqual) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLessEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLessEqual *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_LessEqual_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLessEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLessEqual *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLocalResponseNormalization.cpp b/compiler/luci/partition/src/Nodes/CircleLocalResponseNormalization.cpp new file mode 100644 index 000000000..6b0d1cd12 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLocalResponseNormalization.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLocalResponseNormalization *node) +{ + auto *cloned = loco::must_cast<luci::CircleLocalResponseNormalization *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLocalResponseNormalization *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLocalResponseNormalization.test.cpp b/compiler/luci/partition/src/Nodes/CircleLocalResponseNormalization.test.cpp new file mode 100644 index 000000000..e1973387d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLocalResponseNormalization.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLocalResponseNormalization> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_LocalResponseNormalization) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLocalResponseNormalization *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLocalResponseNormalization *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_LocalResponseNormalization_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLocalResponseNormalization *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLocalResponseNormalization *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLog.cpp b/compiler/luci/partition/src/Nodes/CircleLog.cpp new file mode 100644 index 000000000..c43570fa2 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLog.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLog *node) +{ + auto *cloned = loco::must_cast<luci::CircleLog *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLog *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLog.test.cpp b/compiler/luci/partition/src/Nodes/CircleLog.test.cpp new file mode 100644 index 000000000..8a43f6f01 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLog.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLog> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Log) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLog *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLog *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Log_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLog *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLog *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLogSoftmax.cpp b/compiler/luci/partition/src/Nodes/CircleLogSoftmax.cpp new file mode 100644 index 000000000..de582c80d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogSoftmax.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLogSoftmax *node) +{ + auto *cloned = loco::must_cast<luci::CircleLogSoftmax *>(cn->find_clone(node)); + + luci::CircleNode *logits = loco::must_cast<luci::CircleNode *>(node->logits()); + + cloned->logits(cn->find_clone(logits)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLogSoftmax *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLogSoftmax.test.cpp b/compiler/luci/partition/src/Nodes/CircleLogSoftmax.test.cpp new file mode 100644 index 000000000..1e60bf54c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogSoftmax.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLogSoftmax> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->logits(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_LogSoftmax) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogSoftmax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogSoftmax *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_LogSoftmax_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogSoftmax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogSoftmax *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLogicalAnd.cpp b/compiler/luci/partition/src/Nodes/CircleLogicalAnd.cpp new file mode 100644 index 000000000..28e8f42e5 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogicalAnd.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLogicalAnd *node) +{ + auto *cloned = loco::must_cast<luci::CircleLogicalAnd *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLogicalAnd *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLogicalAnd.test.cpp b/compiler/luci/partition/src/Nodes/CircleLogicalAnd.test.cpp new file mode 100644 index 000000000..a1189f06f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogicalAnd.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLogicalAnd> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_LogicalAnd) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalAnd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalAnd *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_LogicalAnd_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalAnd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalAnd *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLogicalNot.cpp b/compiler/luci/partition/src/Nodes/CircleLogicalNot.cpp new file mode 100644 index 000000000..e2657824c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogicalNot.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLogicalNot *node) +{ + auto *cloned = loco::must_cast<luci::CircleLogicalNot *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLogicalNot *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLogicalNot.test.cpp b/compiler/luci/partition/src/Nodes/CircleLogicalNot.test.cpp new file mode 100644 index 000000000..f6b34596e --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogicalNot.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLogicalNot> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_LogicalNot) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalNot *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalNot *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_LogicalNot_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalNot *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalNot *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLogicalOr.cpp b/compiler/luci/partition/src/Nodes/CircleLogicalOr.cpp new file mode 100644 index 000000000..418dc023b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogicalOr.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLogicalOr *node) +{ + auto *cloned = loco::must_cast<luci::CircleLogicalOr *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLogicalOr *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLogicalOr.test.cpp b/compiler/luci/partition/src/Nodes/CircleLogicalOr.test.cpp new file mode 100644 index 000000000..fee3f4779 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogicalOr.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLogicalOr> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_LogicalOr) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalOr *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalOr *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_LogicalOr_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalOr *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogicalOr *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleLogistic.cpp b/compiler/luci/partition/src/Nodes/CircleLogistic.cpp new file mode 100644 index 000000000..7d788512d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogistic.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleLogistic *node) +{ + auto *cloned = loco::must_cast<luci::CircleLogistic *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleLogistic *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleLogistic.test.cpp b/compiler/luci/partition/src/Nodes/CircleLogistic.test.cpp new file mode 100644 index 000000000..c4b3f7fe3 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleLogistic.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleLogistic> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Logistic) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogistic *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogistic *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Logistic_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogistic *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleLogistic *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleMatrixDiag.cpp b/compiler/luci/partition/src/Nodes/CircleMatrixDiag.cpp new file mode 100644 index 000000000..e92806aff --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMatrixDiag.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleMatrixDiag *node) +{ + auto *cloned = loco::must_cast<luci::CircleMatrixDiag *>(cn->find_clone(node)); + + luci::CircleNode *diagonal = loco::must_cast<luci::CircleNode *>(node->diagonal()); + + cloned->diagonal(cn->find_clone(diagonal)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleMatrixDiag *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleMatrixDiag.test.cpp b/compiler/luci/partition/src/Nodes/CircleMatrixDiag.test.cpp new file mode 100644 index 000000000..03e3c3c3e --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMatrixDiag.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleMatrixDiag> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->diagonal(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_MatrixDiag) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixDiag *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixDiag *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_MatrixDiag_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixDiag *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixDiag *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleMatrixSetDiag.cpp b/compiler/luci/partition/src/Nodes/CircleMatrixSetDiag.cpp new file mode 100644 index 000000000..29bb7fe5f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMatrixSetDiag.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleMatrixSetDiag *node) +{ + auto *cloned = loco::must_cast<luci::CircleMatrixSetDiag *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *diagonal = loco::must_cast<luci::CircleNode *>(node->diagonal()); + + cloned->input(cn->find_clone(input)); + cloned->diagonal(cn->find_clone(diagonal)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleMatrixSetDiag *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleMatrixSetDiag.test.cpp b/compiler/luci/partition/src/Nodes/CircleMatrixSetDiag.test.cpp new file mode 100644 index 000000000..5503ea18f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMatrixSetDiag.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleMatrixSetDiag> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->diagonal(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_MatrixSetDiag) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixSetDiag *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixSetDiag *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_MatrixSetDiag_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixSetDiag *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMatrixSetDiag *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleMaxPool2D.cpp b/compiler/luci/partition/src/Nodes/CircleMaxPool2D.cpp new file mode 100644 index 000000000..75a665aee --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMaxPool2D.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleMaxPool2D *node) +{ + auto *cloned = loco::must_cast<luci::CircleMaxPool2D *>(cn->find_clone(node)); + + luci::CircleNode *value = loco::must_cast<luci::CircleNode *>(node->value()); + + cloned->value(cn->find_clone(value)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleMaxPool2D *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleMaxPool2D.test.cpp b/compiler/luci/partition/src/Nodes/CircleMaxPool2D.test.cpp new file mode 100644 index 000000000..16996497a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMaxPool2D.test.cpp @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleMaxPool2D> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleMaxPool2D>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + _node->padding(luci::Padding::VALID); + } +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->value(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_MaxPool2D) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaxPool2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaxPool2D *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_MaxPool2D_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaxPool2D *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaxPool2D *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleMaximum.cpp b/compiler/luci/partition/src/Nodes/CircleMaximum.cpp new file mode 100644 index 000000000..2ba6055b4 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMaximum.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleMaximum *node) +{ + auto *cloned = loco::must_cast<luci::CircleMaximum *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleMaximum *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleMaximum.test.cpp b/compiler/luci/partition/src/Nodes/CircleMaximum.test.cpp new file mode 100644 index 000000000..370174c37 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMaximum.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleMaximum> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Maximum) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaximum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaximum *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Maximum_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaximum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMaximum *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleMean.test.cpp b/compiler/luci/partition/src/Nodes/CircleMean.test.cpp new file mode 100644 index 000000000..53435d9dc --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMean.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleMean> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->reduction_indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Mean) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMean *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMean *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Mean_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMean *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMean *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleMinimum.cpp b/compiler/luci/partition/src/Nodes/CircleMinimum.cpp new file mode 100644 index 000000000..cdf757583 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMinimum.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleMinimum *node) +{ + auto *cloned = loco::must_cast<luci::CircleMinimum *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleMinimum *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleMinimum.test.cpp b/compiler/luci/partition/src/Nodes/CircleMinimum.test.cpp new file mode 100644 index 000000000..2fe6b0da6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMinimum.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleMinimum> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Minimum) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMinimum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMinimum *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Minimum_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMinimum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMinimum *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleMirrorPad.cpp b/compiler/luci/partition/src/Nodes/CircleMirrorPad.cpp new file mode 100644 index 000000000..16a24abf7 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMirrorPad.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleMirrorPad *node) +{ + auto *cloned = loco::must_cast<luci::CircleMirrorPad *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *paddings = loco::must_cast<luci::CircleNode *>(node->paddings()); + + cloned->input(cn->find_clone(input)); + cloned->paddings(cn->find_clone(paddings)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleMirrorPad *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleMirrorPad.test.cpp b/compiler/luci/partition/src/Nodes/CircleMirrorPad.test.cpp new file mode 100644 index 000000000..605a126c9 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleMirrorPad.test.cpp @@ -0,0 +1,100 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleMirrorPad> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleMirrorPad>::init(g); + + _node->mode(luci::MirrorPadMode::REFLECT); + } +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->paddings(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_MirrorPad) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMirrorPad *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMirrorPad *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_MirrorPad_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMirrorPad *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleMirrorPad *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleNeg.cpp b/compiler/luci/partition/src/Nodes/CircleNeg.cpp new file mode 100644 index 000000000..413ad4930 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNeg.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleNeg *node) +{ + auto *cloned = loco::must_cast<luci::CircleNeg *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleNeg *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleNeg.test.cpp b/compiler/luci/partition/src/Nodes/CircleNeg.test.cpp new file mode 100644 index 000000000..bd74a3665 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNeg.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleNeg> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Neg) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNeg *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNeg *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Neg_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNeg *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNeg *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4.cpp new file mode 100644 index 000000000..63ff3f021 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleNonMaxSuppressionV4 *node) +{ + auto *cloned = loco::must_cast<luci::CircleNonMaxSuppressionV4 *>(cn->find_clone(node)); + + luci::CircleNode *boxes = loco::must_cast<luci::CircleNode *>(node->boxes()); + luci::CircleNode *scores = loco::must_cast<luci::CircleNode *>(node->scores()); + luci::CircleNode *max_output_size = loco::must_cast<luci::CircleNode *>(node->max_output_size()); + luci::CircleNode *iou_threshold = loco::must_cast<luci::CircleNode *>(node->iou_threshold()); + luci::CircleNode *score_threshold = loco::must_cast<luci::CircleNode *>(node->score_threshold()); + + cloned->boxes(cn->find_clone(boxes)); + cloned->scores(cn->find_clone(scores)); + cloned->max_output_size(cn->find_clone(max_output_size)); + cloned->iou_threshold(cn->find_clone(iou_threshold)); + cloned->score_threshold(cn->find_clone(score_threshold)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleNonMaxSuppressionV4 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4.test.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4.test.cpp new file mode 100644 index 000000000..2771aef49 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4.test.cpp @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleNonMaxSuppressionV4> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<5>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<5>::init({shape, shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->boxes(input(0)); + node()->scores(input(1)); + node()->max_output_size(input(2)); + node()->iou_threshold(input(3)); + node()->score_threshold(input(4)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV4) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(5, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); + ASSERT_EQ(cth.inputs(4), clone->arg(4)); +} + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV4_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4Out.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4Out.cpp new file mode 100644 index 000000000..80e4704b9 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4Out.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleNonMaxSuppressionV4Out *node) +{ + auto *cloned = loco::must_cast<luci::CircleNonMaxSuppressionV4Out *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleNonMaxSuppressionV4Out *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4Out.test.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4Out.test.cpp new file mode 100644 index 000000000..5a0a8da8c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV4Out.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleNonMaxSuppressionV4Out> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV4Out) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4Out *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4Out *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV4Out_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4Out *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV4Out *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5.cpp new file mode 100644 index 000000000..c1f117724 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5.cpp @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleNonMaxSuppressionV5 *node) +{ + auto *cloned = loco::must_cast<luci::CircleNonMaxSuppressionV5 *>(cn->find_clone(node)); + + luci::CircleNode *boxes = loco::must_cast<luci::CircleNode *>(node->boxes()); + luci::CircleNode *scores = loco::must_cast<luci::CircleNode *>(node->scores()); + luci::CircleNode *max_output_size = loco::must_cast<luci::CircleNode *>(node->max_output_size()); + luci::CircleNode *iou_threshold = loco::must_cast<luci::CircleNode *>(node->iou_threshold()); + luci::CircleNode *score_threshold = loco::must_cast<luci::CircleNode *>(node->score_threshold()); + luci::CircleNode *soft_nms_sigma = loco::must_cast<luci::CircleNode *>(node->soft_nms_sigma()); + + cloned->boxes(cn->find_clone(boxes)); + cloned->scores(cn->find_clone(scores)); + cloned->max_output_size(cn->find_clone(max_output_size)); + cloned->iou_threshold(cn->find_clone(iou_threshold)); + cloned->score_threshold(cn->find_clone(score_threshold)); + cloned->soft_nms_sigma(cn->find_clone(soft_nms_sigma)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleNonMaxSuppressionV5 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5.test.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5.test.cpp new file mode 100644 index 000000000..1f20fbb0f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5.test.cpp @@ -0,0 +1,100 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleNonMaxSuppressionV5> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<6>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<6>::init({shape, shape, shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->boxes(input(0)); + node()->scores(input(1)); + node()->max_output_size(input(2)); + node()->iou_threshold(input(3)); + node()->score_threshold(input(4)); + node()->soft_nms_sigma(input(5)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV5) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(6, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); + ASSERT_EQ(cth.inputs(4), clone->arg(4)); + ASSERT_EQ(cth.inputs(5), clone->arg(5)); +} + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV5_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5Out.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5Out.cpp new file mode 100644 index 000000000..69e3cc8e8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5Out.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleNonMaxSuppressionV5Out *node) +{ + auto *cloned = loco::must_cast<luci::CircleNonMaxSuppressionV5Out *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleNonMaxSuppressionV5Out *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5Out.test.cpp b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5Out.test.cpp new file mode 100644 index 000000000..e001b0b0b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNonMaxSuppressionV5Out.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleNonMaxSuppressionV5Out> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV5Out) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5Out *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5Out *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_NonMaxSuppressionV5Out_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5Out *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNonMaxSuppressionV5Out *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleNotEqual.cpp b/compiler/luci/partition/src/Nodes/CircleNotEqual.cpp new file mode 100644 index 000000000..c40c2a21a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNotEqual.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleNotEqual *node) +{ + auto *cloned = loco::must_cast<luci::CircleNotEqual *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + luci::CircleNode *y = loco::must_cast<luci::CircleNode *>(node->y()); + + cloned->x(cn->find_clone(x)); + cloned->y(cn->find_clone(y)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleNotEqual *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleNotEqual.test.cpp b/compiler/luci/partition/src/Nodes/CircleNotEqual.test.cpp new file mode 100644 index 000000000..360940ca7 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleNotEqual.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleNotEqual> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_NotEqual) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNotEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNotEqual *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_NotEqual_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNotEqual *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleNotEqual *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleOneHot.cpp b/compiler/luci/partition/src/Nodes/CircleOneHot.cpp new file mode 100644 index 000000000..d76f49255 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleOneHot.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleOneHot *node) +{ + auto *cloned = loco::must_cast<luci::CircleOneHot *>(cn->find_clone(node)); + + luci::CircleNode *indices = loco::must_cast<luci::CircleNode *>(node->indices()); + luci::CircleNode *depth = loco::must_cast<luci::CircleNode *>(node->depth()); + luci::CircleNode *on_value = loco::must_cast<luci::CircleNode *>(node->on_value()); + luci::CircleNode *off_value = loco::must_cast<luci::CircleNode *>(node->off_value()); + + cloned->indices(cn->find_clone(indices)); + cloned->depth(cn->find_clone(depth)); + cloned->on_value(cn->find_clone(on_value)); + cloned->off_value(cn->find_clone(off_value)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleOneHot *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleOneHot.test.cpp b/compiler/luci/partition/src/Nodes/CircleOneHot.test.cpp new file mode 100644 index 000000000..3c555c290 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleOneHot.test.cpp @@ -0,0 +1,96 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleOneHot> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<4>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<4>::init({shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->indices(input(0)); + node()->depth(input(1)); + node()->on_value(input(2)); + node()->off_value(input(3)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_OneHot) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleOneHot *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleOneHot *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(4, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); +} + +TEST(ConnectNodeTest, connect_OneHot_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleOneHot *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleOneHot *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleOutputDummy.cpp b/compiler/luci/partition/src/Nodes/CircleOutputDummy.cpp new file mode 100644 index 000000000..a033e80a8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleOutputDummy.cpp @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleOutputDummy *) +{ + // Nothing to do +} + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleOutputExclude.cpp b/compiler/luci/partition/src/Nodes/CircleOutputExclude.cpp new file mode 100644 index 000000000..106eb405d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleOutputExclude.cpp @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleOutputExclude *) +{ + // Nothing to do +} + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CirclePRelu.cpp b/compiler/luci/partition/src/Nodes/CirclePRelu.cpp new file mode 100644 index 000000000..b8a2341c8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePRelu.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CirclePRelu *node) +{ + auto *cloned = loco::must_cast<luci::CirclePRelu *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *alpha = loco::must_cast<luci::CircleNode *>(node->alpha()); + + cloned->input(cn->find_clone(input)); + cloned->alpha(cn->find_clone(alpha)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CirclePRelu *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CirclePRelu.test.cpp b/compiler/luci/partition/src/Nodes/CirclePRelu.test.cpp new file mode 100644 index 000000000..e5bcedcf6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePRelu.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CirclePRelu> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->alpha(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_PRelu) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePRelu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePRelu *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_PRelu_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePRelu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePRelu *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CirclePack.cpp b/compiler/luci/partition/src/Nodes/CirclePack.cpp new file mode 100644 index 000000000..326881067 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePack.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CirclePack *node) +{ + auto *cloned = loco::must_cast<luci::CirclePack *>(cn->find_clone(node)); + + uint32_t values_count = cloned->values_count(); + for (uint32_t i = 0; i < values_count; ++i) + { + luci::CircleNode *value = loco::must_cast<luci::CircleNode *>(node->values(i)); + + cloned->values(i, cn->find_clone(value)); + } +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CirclePack *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CirclePack.test.cpp b/compiler/luci/partition/src/Nodes/CirclePack.test.cpp new file mode 100644 index 000000000..68c513848 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePack.test.cpp @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeIsGraphletT<luci::CirclePack> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g(), 3); + + for (uint32_t i = 0; i < 3; ++i) + { + node()->values(i, input(i)); + } + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Pack) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePack *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePack *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_Pack_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePack *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePack *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CirclePad.cpp b/compiler/luci/partition/src/Nodes/CirclePad.cpp new file mode 100644 index 000000000..eb2a89c85 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePad.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CirclePad *node) +{ + auto *cloned = loco::must_cast<luci::CirclePad *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *paddings = loco::must_cast<luci::CircleNode *>(node->paddings()); + + cloned->input(cn->find_clone(input)); + cloned->paddings(cn->find_clone(paddings)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CirclePad *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CirclePad.test.cpp b/compiler/luci/partition/src/Nodes/CirclePad.test.cpp new file mode 100644 index 000000000..24ea83fa3 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePad.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CirclePad> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->paddings(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Pad) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePad *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePad *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Pad_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePad *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePad *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CirclePadV2.cpp b/compiler/luci/partition/src/Nodes/CirclePadV2.cpp new file mode 100644 index 000000000..001fecbcb --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePadV2.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CirclePadV2 *node) +{ + auto *cloned = loco::must_cast<luci::CirclePadV2 *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *paddings = loco::must_cast<luci::CircleNode *>(node->paddings()); + luci::CircleNode *constant_values = loco::must_cast<luci::CircleNode *>(node->constant_values()); + + cloned->input(cn->find_clone(input)); + cloned->paddings(cn->find_clone(paddings)); + cloned->constant_values(cn->find_clone(constant_values)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CirclePadV2 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CirclePadV2.test.cpp b/compiler/luci/partition/src/Nodes/CirclePadV2.test.cpp new file mode 100644 index 000000000..aea8e0cce --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePadV2.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CirclePadV2> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->paddings(input(1)); + node()->constant_values(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_PadV2) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePadV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePadV2 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_PadV2_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePadV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePadV2 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CirclePow.test.cpp b/compiler/luci/partition/src/Nodes/CirclePow.test.cpp new file mode 100644 index 000000000..7a5be4d13 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CirclePow.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CirclePow> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Pow) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePow *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePow *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Pow_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePow *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CirclePow *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleQuantize.cpp b/compiler/luci/partition/src/Nodes/CircleQuantize.cpp new file mode 100644 index 000000000..340c1da42 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleQuantize.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleQuantize *node) +{ + auto *cloned = loco::must_cast<luci::CircleQuantize *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleQuantize *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleQuantize.test.cpp b/compiler/luci/partition/src/Nodes/CircleQuantize.test.cpp new file mode 100644 index 000000000..1f348b45c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleQuantize.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleQuantize> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Quantize) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleQuantize *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleQuantize *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Quantize_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleQuantize *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleQuantize *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleRange.cpp b/compiler/luci/partition/src/Nodes/CircleRange.cpp new file mode 100644 index 000000000..f295338d8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRange.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleRange *node) +{ + auto *cloned = loco::must_cast<luci::CircleRange *>(cn->find_clone(node)); + + luci::CircleNode *start = loco::must_cast<luci::CircleNode *>(node->start()); + luci::CircleNode *limit = loco::must_cast<luci::CircleNode *>(node->limit()); + luci::CircleNode *delta = loco::must_cast<luci::CircleNode *>(node->delta()); + + cloned->start(cn->find_clone(start)); + cloned->limit(cn->find_clone(limit)); + cloned->delta(cn->find_clone(delta)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleRange *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleRange.test.cpp b/compiler/luci/partition/src/Nodes/CircleRange.test.cpp new file mode 100644 index 000000000..59a95f119 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRange.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleRange> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->start(input(0)); + node()->limit(input(1)); + node()->delta(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Range) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRange *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRange *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_Range_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRange *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRange *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleRank.cpp b/compiler/luci/partition/src/Nodes/CircleRank.cpp new file mode 100644 index 000000000..f7cce762b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRank.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleRank *node) +{ + auto *cloned = loco::must_cast<luci::CircleRank *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleRank *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleRank.test.cpp b/compiler/luci/partition/src/Nodes/CircleRank.test.cpp new file mode 100644 index 000000000..74c520bee --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRank.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleRank> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Rank) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRank *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRank *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Rank_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRank *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRank *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReduceAny.cpp b/compiler/luci/partition/src/Nodes/CircleReduceAny.cpp new file mode 100644 index 000000000..ed762dbc6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceAny.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReduceAny *node) +{ + auto *cloned = loco::must_cast<luci::CircleReduceAny *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *reduction_indices = + loco::must_cast<luci::CircleNode *>(node->reduction_indices()); + + cloned->input(cn->find_clone(input)); + cloned->reduction_indices(cn->find_clone(reduction_indices)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReduceAny *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReduceAny.test.cpp b/compiler/luci/partition/src/Nodes/CircleReduceAny.test.cpp new file mode 100644 index 000000000..792f51187 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceAny.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReduceAny> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->reduction_indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ReduceAny) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceAny *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceAny *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ReduceAny_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceAny *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceAny *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReduceMax.cpp b/compiler/luci/partition/src/Nodes/CircleReduceMax.cpp new file mode 100644 index 000000000..09586ecee --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceMax.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReduceMax *node) +{ + auto *cloned = loco::must_cast<luci::CircleReduceMax *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *reduction_indices = + loco::must_cast<luci::CircleNode *>(node->reduction_indices()); + + cloned->input(cn->find_clone(input)); + cloned->reduction_indices(cn->find_clone(reduction_indices)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReduceMax *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReduceMax.test.cpp b/compiler/luci/partition/src/Nodes/CircleReduceMax.test.cpp new file mode 100644 index 000000000..8fbaf653e --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceMax.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReduceMax> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->reduction_indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ReduceMax) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMax *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ReduceMax_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMax *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReduceMin.cpp b/compiler/luci/partition/src/Nodes/CircleReduceMin.cpp new file mode 100644 index 000000000..105214d0b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceMin.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReduceMin *node) +{ + auto *cloned = loco::must_cast<luci::CircleReduceMin *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *reduction_indices = + loco::must_cast<luci::CircleNode *>(node->reduction_indices()); + + cloned->input(cn->find_clone(input)); + cloned->reduction_indices(cn->find_clone(reduction_indices)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReduceMin *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReduceMin.test.cpp b/compiler/luci/partition/src/Nodes/CircleReduceMin.test.cpp new file mode 100644 index 000000000..c37d6248f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceMin.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReduceMin> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->reduction_indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ReduceMin) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMin *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMin *>(node)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ReduceMin_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMin *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceMin *>(node)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReduceProd.cpp b/compiler/luci/partition/src/Nodes/CircleReduceProd.cpp new file mode 100644 index 000000000..2fb4e3e01 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceProd.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReduceProd *node) +{ + auto *cloned = loco::must_cast<luci::CircleReduceProd *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *reduction_indices = + loco::must_cast<luci::CircleNode *>(node->reduction_indices()); + + cloned->input(cn->find_clone(input)); + cloned->reduction_indices(cn->find_clone(reduction_indices)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReduceProd *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReduceProd.test.cpp b/compiler/luci/partition/src/Nodes/CircleReduceProd.test.cpp new file mode 100644 index 000000000..cc1ac83ad --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReduceProd.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReduceProd> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->reduction_indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ReduceProd) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceProd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceProd *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ReduceProd_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceProd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReduceProd *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleRelu.cpp b/compiler/luci/partition/src/Nodes/CircleRelu.cpp new file mode 100644 index 000000000..d3617bdbd --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRelu.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleRelu *node) +{ + auto *cloned = loco::must_cast<luci::CircleRelu *>(cn->find_clone(node)); + + luci::CircleNode *features = loco::must_cast<luci::CircleNode *>(node->features()); + + cloned->features(cn->find_clone(features)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleRelu *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleRelu.test.cpp b/compiler/luci/partition/src/Nodes/CircleRelu.test.cpp new file mode 100644 index 000000000..ccaf5760b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRelu.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleRelu> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->features(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Relu) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Relu_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleRelu6.cpp b/compiler/luci/partition/src/Nodes/CircleRelu6.cpp new file mode 100644 index 000000000..fb9ba6f36 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRelu6.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleRelu6 *node) +{ + auto *cloned = loco::must_cast<luci::CircleRelu6 *>(cn->find_clone(node)); + + luci::CircleNode *features = loco::must_cast<luci::CircleNode *>(node->features()); + + cloned->features(cn->find_clone(features)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleRelu6 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleRelu6.test.cpp b/compiler/luci/partition/src/Nodes/CircleRelu6.test.cpp new file mode 100644 index 000000000..1341b0e06 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRelu6.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleRelu6> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->features(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Relu6) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu6 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu6 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Relu6_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu6 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRelu6 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReluN1To1.cpp b/compiler/luci/partition/src/Nodes/CircleReluN1To1.cpp new file mode 100644 index 000000000..476195b71 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReluN1To1.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReluN1To1 *node) +{ + auto *cloned = loco::must_cast<luci::CircleReluN1To1 *>(cn->find_clone(node)); + + luci::CircleNode *features = loco::must_cast<luci::CircleNode *>(node->features()); + + cloned->features(cn->find_clone(features)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReluN1To1 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReluN1To1.test.cpp b/compiler/luci/partition/src/Nodes/CircleReluN1To1.test.cpp new file mode 100644 index 000000000..7dc63c6ef --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReluN1To1.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReluN1To1> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->features(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ReluN1To1) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReluN1To1 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReluN1To1 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_ReluN1To1_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReluN1To1 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReluN1To1 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReshape.cpp b/compiler/luci/partition/src/Nodes/CircleReshape.cpp new file mode 100644 index 000000000..e59670453 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReshape.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReshape *node) +{ + auto *cloned = loco::must_cast<luci::CircleReshape *>(cn->find_clone(node)); + + luci::CircleNode *tensor = loco::must_cast<luci::CircleNode *>(node->tensor()); + luci::CircleNode *shape = loco::must_cast<luci::CircleNode *>(node->shape()); + + cloned->tensor(cn->find_clone(tensor)); + cloned->shape(cn->find_clone(shape)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReshape *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReshape.test.cpp b/compiler/luci/partition/src/Nodes/CircleReshape.test.cpp new file mode 100644 index 000000000..73cbbdfcc --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReshape.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReshape> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->tensor(input(0)); + node()->shape(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Reshape) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReshape *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReshape *>(node)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Reshape_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReshape *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReshape *>(node)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleResizeBilinear.cpp b/compiler/luci/partition/src/Nodes/CircleResizeBilinear.cpp new file mode 100644 index 000000000..0f504015b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleResizeBilinear.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleResizeBilinear *node) +{ + auto *cloned = loco::must_cast<luci::CircleResizeBilinear *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *size = loco::must_cast<luci::CircleNode *>(node->size()); + + cloned->input(cn->find_clone(input)); + cloned->size(cn->find_clone(size)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleResizeBilinear *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleResizeBilinear.test.cpp b/compiler/luci/partition/src/Nodes/CircleResizeBilinear.test.cpp new file mode 100644 index 000000000..c2d8b714b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleResizeBilinear.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleResizeBilinear> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->size(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ResizeBilinear) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeBilinear *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeBilinear *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ResizeBilinear_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeBilinear *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeBilinear *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleResizeNearestNeighbor.cpp b/compiler/luci/partition/src/Nodes/CircleResizeNearestNeighbor.cpp new file mode 100644 index 000000000..c985b7f51 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleResizeNearestNeighbor.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleResizeNearestNeighbor *node) +{ + auto *cloned = loco::must_cast<luci::CircleResizeNearestNeighbor *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *size = loco::must_cast<luci::CircleNode *>(node->size()); + + cloned->input(cn->find_clone(input)); + cloned->size(cn->find_clone(size)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleResizeNearestNeighbor *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleResizeNearestNeighbor.test.cpp b/compiler/luci/partition/src/Nodes/CircleResizeNearestNeighbor.test.cpp new file mode 100644 index 000000000..9cc2e558e --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleResizeNearestNeighbor.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleResizeNearestNeighbor> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->size(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ResizeNearestNeighbor) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeNearestNeighbor *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeNearestNeighbor *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ResizeNearestNeighbor_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeNearestNeighbor *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleResizeNearestNeighbor *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReverseSequence.cpp b/compiler/luci/partition/src/Nodes/CircleReverseSequence.cpp new file mode 100644 index 000000000..225d29ea5 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReverseSequence.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReverseSequence *node) +{ + auto *cloned = loco::must_cast<luci::CircleReverseSequence *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *seq_lengths = loco::must_cast<luci::CircleNode *>(node->seq_lengths()); + + cloned->input(cn->find_clone(input)); + cloned->seq_lengths(cn->find_clone(seq_lengths)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReverseSequence *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReverseSequence.test.cpp b/compiler/luci/partition/src/Nodes/CircleReverseSequence.test.cpp new file mode 100644 index 000000000..408fc0c9c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReverseSequence.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReverseSequence> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->seq_lengths(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ReverseSequence) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseSequence *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseSequence *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ReverseSequence_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseSequence *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseSequence *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleReverseV2.cpp b/compiler/luci/partition/src/Nodes/CircleReverseV2.cpp new file mode 100644 index 000000000..d59a7de93 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReverseV2.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleReverseV2 *node) +{ + auto *cloned = loco::must_cast<luci::CircleReverseV2 *>(cn->find_clone(node)); + + luci::CircleNode *tensor = loco::must_cast<luci::CircleNode *>(node->tensor()); + luci::CircleNode *axis = loco::must_cast<luci::CircleNode *>(node->axis()); + + cloned->tensor(cn->find_clone(tensor)); + cloned->axis(cn->find_clone(axis)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleReverseV2 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleReverseV2.test.cpp b/compiler/luci/partition/src/Nodes/CircleReverseV2.test.cpp new file mode 100644 index 000000000..d41ad8e66 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleReverseV2.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleReverseV2> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->tensor(input(0)); + node()->axis(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ReverseV2) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseV2 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_ReverseV2_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleReverseV2 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleRound.cpp b/compiler/luci/partition/src/Nodes/CircleRound.cpp new file mode 100644 index 000000000..9170bcdd9 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRound.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleRound *node) +{ + auto *cloned = loco::must_cast<luci::CircleRound *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleRound *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleRound.test.cpp b/compiler/luci/partition/src/Nodes/CircleRound.test.cpp new file mode 100644 index 000000000..fad090476 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRound.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleRound> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Round) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRound *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRound *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Round_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRound *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRound *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleRsqrt.test.cpp b/compiler/luci/partition/src/Nodes/CircleRsqrt.test.cpp new file mode 100644 index 000000000..d76b96e14 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleRsqrt.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleRsqrt> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Rsqrt) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRsqrt *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRsqrt *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Rsqrt_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRsqrt *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleRsqrt *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleScatterNd.cpp b/compiler/luci/partition/src/Nodes/CircleScatterNd.cpp new file mode 100644 index 000000000..62912b791 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleScatterNd.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleScatterNd *node) +{ + auto *cloned = loco::must_cast<luci::CircleScatterNd *>(cn->find_clone(node)); + + luci::CircleNode *indices = loco::must_cast<luci::CircleNode *>(node->indices()); + luci::CircleNode *updates = loco::must_cast<luci::CircleNode *>(node->updates()); + luci::CircleNode *shape = loco::must_cast<luci::CircleNode *>(node->shape()); + + cloned->indices(cn->find_clone(indices)); + cloned->updates(cn->find_clone(updates)); + cloned->shape(cn->find_clone(shape)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleScatterNd *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleScatterNd.test.cpp b/compiler/luci/partition/src/Nodes/CircleScatterNd.test.cpp new file mode 100644 index 000000000..f271f8843 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleScatterNd.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleScatterNd> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->indices(input(0)); + node()->updates(input(1)); + node()->shape(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ScatterNd) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleScatterNd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleScatterNd *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_ScatterNd_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleScatterNd *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleScatterNd *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSegmentSum.cpp b/compiler/luci/partition/src/Nodes/CircleSegmentSum.cpp new file mode 100644 index 000000000..5fc320a16 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSegmentSum.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSegmentSum *node) +{ + auto *cloned = loco::must_cast<luci::CircleSegmentSum *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *segment_ids = loco::must_cast<luci::CircleNode *>(node->segment_ids()); + + cloned->input(cn->find_clone(input)); + cloned->segment_ids(cn->find_clone(segment_ids)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSegmentSum *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSegmentSum.test.cpp b/compiler/luci/partition/src/Nodes/CircleSegmentSum.test.cpp new file mode 100644 index 000000000..a6bcff20a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSegmentSum.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSegmentSum> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->segment_ids(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SegmentSum) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSegmentSum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSegmentSum *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_SegmentSum_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSegmentSum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSegmentSum *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSelect.cpp b/compiler/luci/partition/src/Nodes/CircleSelect.cpp new file mode 100644 index 000000000..dbe1dd48f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSelect.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSelect *node) +{ + auto *cloned = loco::must_cast<luci::CircleSelect *>(cn->find_clone(node)); + + luci::CircleNode *condition = loco::must_cast<luci::CircleNode *>(node->condition()); + luci::CircleNode *t = loco::must_cast<luci::CircleNode *>(node->t()); + luci::CircleNode *e = loco::must_cast<luci::CircleNode *>(node->e()); + + cloned->condition(cn->find_clone(condition)); + cloned->t(cn->find_clone(t)); + cloned->e(cn->find_clone(e)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSelect *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSelect.test.cpp b/compiler/luci/partition/src/Nodes/CircleSelect.test.cpp new file mode 100644 index 000000000..912934b8b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSelect.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSelect> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->condition(input(0)); + node()->t(input(1)); + node()->e(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Select) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelect *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelect *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_Select_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelect *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelect *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSelectV2.cpp b/compiler/luci/partition/src/Nodes/CircleSelectV2.cpp new file mode 100644 index 000000000..28072c860 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSelectV2.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSelectV2 *node) +{ + auto *cloned = loco::must_cast<luci::CircleSelectV2 *>(cn->find_clone(node)); + + luci::CircleNode *condition = loco::must_cast<luci::CircleNode *>(node->condition()); + luci::CircleNode *t = loco::must_cast<luci::CircleNode *>(node->t()); + luci::CircleNode *e = loco::must_cast<luci::CircleNode *>(node->e()); + + cloned->condition(cn->find_clone(condition)); + cloned->t(cn->find_clone(t)); + cloned->e(cn->find_clone(e)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSelectV2 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSelectV2.test.cpp b/compiler/luci/partition/src/Nodes/CircleSelectV2.test.cpp new file mode 100644 index 000000000..e8d128e93 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSelectV2.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSelectV2> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->condition(input(0)); + node()->t(input(1)); + node()->e(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SelectV2) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelectV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelectV2 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_SelectV2_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelectV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSelectV2 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleShape.cpp b/compiler/luci/partition/src/Nodes/CircleShape.cpp new file mode 100644 index 000000000..f93cf1458 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleShape.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleShape *node) +{ + auto *cloned = loco::must_cast<luci::CircleShape *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleShape *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleShape.test.cpp b/compiler/luci/partition/src/Nodes/CircleShape.test.cpp new file mode 100644 index 000000000..9b4afdcc2 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleShape.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleShape> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Shape) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleShape *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleShape *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Shape_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleShape *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleShape *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSin.cpp b/compiler/luci/partition/src/Nodes/CircleSin.cpp new file mode 100644 index 000000000..62c776ef6 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSin.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSin *node) +{ + auto *cloned = loco::must_cast<luci::CircleSin *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSin *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSin.test.cpp b/compiler/luci/partition/src/Nodes/CircleSin.test.cpp new file mode 100644 index 000000000..fbee6f662 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSin.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSin> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Sin) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSin *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSin *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Sin_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSin *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSin *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSlice.cpp b/compiler/luci/partition/src/Nodes/CircleSlice.cpp new file mode 100644 index 000000000..7895d9ece --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSlice.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSlice *node) +{ + auto *cloned = loco::must_cast<luci::CircleSlice *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *begin = loco::must_cast<luci::CircleNode *>(node->begin()); + luci::CircleNode *size = loco::must_cast<luci::CircleNode *>(node->size()); + + cloned->input(cn->find_clone(input)); + cloned->begin(cn->find_clone(begin)); + cloned->size(cn->find_clone(size)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSlice *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSlice.test.cpp b/compiler/luci/partition/src/Nodes/CircleSlice.test.cpp new file mode 100644 index 000000000..3c666ad6c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSlice.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSlice> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->begin(input(1)); + node()->size(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Slice) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSlice *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSlice *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_Slice_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSlice *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSlice *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSoftmax.cpp b/compiler/luci/partition/src/Nodes/CircleSoftmax.cpp new file mode 100644 index 000000000..0a93787e7 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSoftmax.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSoftmax *node) +{ + auto *cloned = loco::must_cast<luci::CircleSoftmax *>(cn->find_clone(node)); + + luci::CircleNode *logits = loco::must_cast<luci::CircleNode *>(node->logits()); + + cloned->logits(cn->find_clone(logits)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSoftmax *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSoftmax.test.cpp b/compiler/luci/partition/src/Nodes/CircleSoftmax.test.cpp new file mode 100644 index 000000000..b25629863 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSoftmax.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSoftmax> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->logits(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Softmax) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSoftmax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSoftmax *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Softmax_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSoftmax *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSoftmax *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSpaceToBatchND.cpp b/compiler/luci/partition/src/Nodes/CircleSpaceToBatchND.cpp new file mode 100644 index 000000000..b94948bee --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSpaceToBatchND.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSpaceToBatchND *node) +{ + auto *cloned = loco::must_cast<luci::CircleSpaceToBatchND *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *block_shape = loco::must_cast<luci::CircleNode *>(node->block_shape()); + luci::CircleNode *paddings = loco::must_cast<luci::CircleNode *>(node->paddings()); + + cloned->input(cn->find_clone(input)); + cloned->block_shape(cn->find_clone(block_shape)); + cloned->paddings(cn->find_clone(paddings)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSpaceToBatchND *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSpaceToBatchND.test.cpp b/compiler/luci/partition/src/Nodes/CircleSpaceToBatchND.test.cpp new file mode 100644 index 000000000..279e9b232 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSpaceToBatchND.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSpaceToBatchND> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->block_shape(input(1)); + node()->paddings(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SpaceToBatchND) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToBatchND *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToBatchND *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_SpaceToBatchND_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToBatchND *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToBatchND *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSpaceToDepth.cpp b/compiler/luci/partition/src/Nodes/CircleSpaceToDepth.cpp new file mode 100644 index 000000000..bd4523ca8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSpaceToDepth.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSpaceToDepth *node) +{ + auto *cloned = loco::must_cast<luci::CircleSpaceToDepth *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSpaceToDepth *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSpaceToDepth.test.cpp b/compiler/luci/partition/src/Nodes/CircleSpaceToDepth.test.cpp new file mode 100644 index 000000000..207163d08 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSpaceToDepth.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSpaceToDepth> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SpaceToDepth) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToDepth *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToDepth *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_SpaceToDepth_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToDepth *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSpaceToDepth *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSparseToDense.cpp b/compiler/luci/partition/src/Nodes/CircleSparseToDense.cpp new file mode 100644 index 000000000..d1ed18818 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSparseToDense.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSparseToDense *node) +{ + auto *cloned = loco::must_cast<luci::CircleSparseToDense *>(cn->find_clone(node)); + + luci::CircleNode *indices = loco::must_cast<luci::CircleNode *>(node->indices()); + luci::CircleNode *output_shape = loco::must_cast<luci::CircleNode *>(node->output_shape()); + luci::CircleNode *values = loco::must_cast<luci::CircleNode *>(node->values()); + luci::CircleNode *default_value = loco::must_cast<luci::CircleNode *>(node->default_value()); + + cloned->indices(cn->find_clone(indices)); + cloned->output_shape(cn->find_clone(output_shape)); + cloned->values(cn->find_clone(values)); + cloned->default_value(cn->find_clone(default_value)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSparseToDense *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSparseToDense.test.cpp b/compiler/luci/partition/src/Nodes/CircleSparseToDense.test.cpp new file mode 100644 index 000000000..2257186e8 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSparseToDense.test.cpp @@ -0,0 +1,96 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSparseToDense> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<4>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<4>::init({shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->indices(input(0)); + node()->output_shape(input(1)); + node()->values(input(2)); + node()->default_value(input(3)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SparseToDense) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSparseToDense *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSparseToDense *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(4, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); +} + +TEST(ConnectNodeTest, connect_SparseToDense_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSparseToDense *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSparseToDense *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSplit.cpp b/compiler/luci/partition/src/Nodes/CircleSplit.cpp new file mode 100644 index 000000000..d6d62a8ed --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplit.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSplit *node) +{ + auto *cloned = loco::must_cast<luci::CircleSplit *>(cn->find_clone(node)); + + luci::CircleNode *split_dim = loco::must_cast<luci::CircleNode *>(node->split_dim()); + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->split_dim(cn->find_clone(split_dim)); + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSplit *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSplit.test.cpp b/compiler/luci/partition/src/Nodes/CircleSplit.test.cpp new file mode 100644 index 000000000..d8d0953e0 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplit.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSplit> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->split_dim(input(0)); + node()->input(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Split) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplit *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplit *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Split_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplit *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplit *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSplitOut.cpp b/compiler/luci/partition/src/Nodes/CircleSplitOut.cpp new file mode 100644 index 000000000..4021f2042 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplitOut.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSplitOut *node) +{ + auto *cloned = loco::must_cast<luci::CircleSplitOut *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSplitOut *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSplitOut.test.cpp b/compiler/luci/partition/src/Nodes/CircleSplitOut.test.cpp new file mode 100644 index 000000000..85fe2685b --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplitOut.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSplitOut> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SplitOut) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitOut *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_SplitOut_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitOut *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSplitV.cpp b/compiler/luci/partition/src/Nodes/CircleSplitV.cpp new file mode 100644 index 000000000..f13205725 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplitV.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSplitV *node) +{ + auto *cloned = loco::must_cast<luci::CircleSplitV *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *size_splits = loco::must_cast<luci::CircleNode *>(node->size_splits()); + luci::CircleNode *split_dim = loco::must_cast<luci::CircleNode *>(node->split_dim()); + + cloned->input(cn->find_clone(input)); + cloned->size_splits(cn->find_clone(size_splits)); + cloned->split_dim(cn->find_clone(split_dim)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSplitV *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSplitV.test.cpp b/compiler/luci/partition/src/Nodes/CircleSplitV.test.cpp new file mode 100644 index 000000000..3ac1d6c27 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplitV.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSplitV> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<3>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<3>::init({shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->size_splits(input(1)); + node()->split_dim(input(2)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SplitV) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitV *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitV *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(3, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); +} + +TEST(ConnectNodeTest, connect_SplitV_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitV *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitV *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSplitVOut.cpp b/compiler/luci/partition/src/Nodes/CircleSplitVOut.cpp new file mode 100644 index 000000000..2034805cd --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplitVOut.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSplitVOut *node) +{ + auto *cloned = loco::must_cast<luci::CircleSplitVOut *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSplitVOut *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSplitVOut.test.cpp b/compiler/luci/partition/src/Nodes/CircleSplitVOut.test.cpp new file mode 100644 index 000000000..434dfb0ad --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSplitVOut.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSplitVOut> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SplitVOut) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitVOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitVOut *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_SplitVOut_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitVOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSplitVOut *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSqrt.test.cpp b/compiler/luci/partition/src/Nodes/CircleSqrt.test.cpp new file mode 100644 index 000000000..fa7f7fe2a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSqrt.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSqrt> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Sqrt) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqrt *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqrt *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Sqrt_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqrt *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqrt *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSquare.cpp b/compiler/luci/partition/src/Nodes/CircleSquare.cpp new file mode 100644 index 000000000..1476a8694 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSquare.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSquare *node) +{ + auto *cloned = loco::must_cast<luci::CircleSquare *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSquare *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSquare.test.cpp b/compiler/luci/partition/src/Nodes/CircleSquare.test.cpp new file mode 100644 index 000000000..bb6a7c33f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSquare.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSquare> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Square) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquare *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquare *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Square_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquare *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquare *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSquaredDifference.test.cpp b/compiler/luci/partition/src/Nodes/CircleSquaredDifference.test.cpp new file mode 100644 index 000000000..9cfe9eefb --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSquaredDifference.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSquaredDifference> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->x(input(0)); + node()->y(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_SquaredDifference) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquaredDifference *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquaredDifference *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_SquaredDifference_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquaredDifference *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSquaredDifference *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSqueeze.cpp b/compiler/luci/partition/src/Nodes/CircleSqueeze.cpp new file mode 100644 index 000000000..bc9fda296 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSqueeze.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSqueeze *node) +{ + auto *cloned = loco::must_cast<luci::CircleSqueeze *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSqueeze *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSqueeze.test.cpp b/compiler/luci/partition/src/Nodes/CircleSqueeze.test.cpp new file mode 100644 index 000000000..1f0971043 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSqueeze.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSqueeze> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Squeeze) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqueeze *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqueeze *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Squeeze_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqueeze *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSqueeze *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleStridedSlice.cpp b/compiler/luci/partition/src/Nodes/CircleStridedSlice.cpp new file mode 100644 index 000000000..3bdca8a8a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleStridedSlice.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleStridedSlice *node) +{ + auto *cloned = loco::must_cast<luci::CircleStridedSlice *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *begin = loco::must_cast<luci::CircleNode *>(node->begin()); + luci::CircleNode *end = loco::must_cast<luci::CircleNode *>(node->end()); + luci::CircleNode *strides = loco::must_cast<luci::CircleNode *>(node->strides()); + + cloned->input(cn->find_clone(input)); + cloned->begin(cn->find_clone(begin)); + cloned->end(cn->find_clone(end)); + cloned->strides(cn->find_clone(strides)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleStridedSlice *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleStridedSlice.test.cpp b/compiler/luci/partition/src/Nodes/CircleStridedSlice.test.cpp new file mode 100644 index 000000000..130ff9159 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleStridedSlice.test.cpp @@ -0,0 +1,96 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleStridedSlice> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<4>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<4>::init({shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->begin(input(1)); + node()->end(input(2)); + node()->strides(input(3)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_StridedSlice) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleStridedSlice *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleStridedSlice *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(4, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); +} + +TEST(ConnectNodeTest, connect_StridedSlice_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleStridedSlice *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleStridedSlice *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleSum.cpp b/compiler/luci/partition/src/Nodes/CircleSum.cpp new file mode 100644 index 000000000..bef1d4676 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSum.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleSum *node) +{ + auto *cloned = loco::must_cast<luci::CircleSum *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *reduction_indices = + loco::must_cast<luci::CircleNode *>(node->reduction_indices()); + + cloned->input(cn->find_clone(input)); + cloned->reduction_indices(cn->find_clone(reduction_indices)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleSum *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleSum.test.cpp b/compiler/luci/partition/src/Nodes/CircleSum.test.cpp new file mode 100644 index 000000000..1ed65c04f --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleSum.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleSum> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->reduction_indices(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Sum) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSum *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Sum_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSum *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleSum *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleTanh.cpp b/compiler/luci/partition/src/Nodes/CircleTanh.cpp new file mode 100644 index 000000000..e6c56ebf7 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTanh.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleTanh *node) +{ + auto *cloned = loco::must_cast<luci::CircleTanh *>(cn->find_clone(node)); + + luci::CircleNode *x = loco::must_cast<luci::CircleNode *>(node->x()); + + cloned->x(cn->find_clone(x)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleTanh *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleTanh.test.cpp b/compiler/luci/partition/src/Nodes/CircleTanh.test.cpp new file mode 100644 index 000000000..17cd48731 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTanh.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleTanh> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->x(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Tanh) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTanh *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTanh *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Tanh_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTanh *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTanh *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleTile.cpp b/compiler/luci/partition/src/Nodes/CircleTile.cpp new file mode 100644 index 000000000..0381b4dac --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTile.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleTile *node) +{ + auto *cloned = loco::must_cast<luci::CircleTile *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *multiples = loco::must_cast<luci::CircleNode *>(node->multiples()); + + cloned->input(cn->find_clone(input)); + cloned->multiples(cn->find_clone(multiples)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleTile *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleTile.test.cpp b/compiler/luci/partition/src/Nodes/CircleTile.test.cpp new file mode 100644 index 000000000..79d1ba16c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTile.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleTile> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->multiples(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Tile) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTile *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTile *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Tile_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTile *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTile *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleTopKV2.cpp b/compiler/luci/partition/src/Nodes/CircleTopKV2.cpp new file mode 100644 index 000000000..ce8a6f5df --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTopKV2.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleTopKV2 *node) +{ + auto *cloned = loco::must_cast<luci::CircleTopKV2 *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + luci::CircleNode *k = loco::must_cast<luci::CircleNode *>(node->k()); + + cloned->input(cn->find_clone(input)); + cloned->k(cn->find_clone(k)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleTopKV2 *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleTopKV2.test.cpp b/compiler/luci/partition/src/Nodes/CircleTopKV2.test.cpp new file mode 100644 index 000000000..f08f3f315 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTopKV2.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleTopKV2> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + node()->k(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_TopKV2) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2 *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_TopKV2_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2 *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2 *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleTopKV2Out.cpp b/compiler/luci/partition/src/Nodes/CircleTopKV2Out.cpp new file mode 100644 index 000000000..6ca6e3d29 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTopKV2Out.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleTopKV2Out *node) +{ + auto *cloned = loco::must_cast<luci::CircleTopKV2Out *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleTopKV2Out *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleTopKV2Out.test.cpp b/compiler/luci/partition/src/Nodes/CircleTopKV2Out.test.cpp new file mode 100644 index 000000000..a5c1c43f7 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTopKV2Out.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleTopKV2Out> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_TopKV2Out) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2Out *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2Out *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_TopKV2Out_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2Out *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTopKV2Out *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleTranspose.cpp b/compiler/luci/partition/src/Nodes/CircleTranspose.cpp new file mode 100644 index 000000000..1cbb54666 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTranspose.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleTranspose *node) +{ + auto *cloned = loco::must_cast<luci::CircleTranspose *>(cn->find_clone(node)); + + luci::CircleNode *a = loco::must_cast<luci::CircleNode *>(node->a()); + luci::CircleNode *perm = loco::must_cast<luci::CircleNode *>(node->perm()); + + cloned->a(cn->find_clone(a)); + cloned->perm(cn->find_clone(perm)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleTranspose *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleTranspose.test.cpp b/compiler/luci/partition/src/Nodes/CircleTranspose.test.cpp new file mode 100644 index 000000000..b3b16307c --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTranspose.test.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleTranspose> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIsOGraph<2>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<2>::init({shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->a(input(0)); + node()->perm(input(1)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Transpose) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTranspose *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTranspose *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(2, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); +} + +TEST(ConnectNodeTest, connect_Transpose_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTranspose *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTranspose *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleTransposeConv.cpp b/compiler/luci/partition/src/Nodes/CircleTransposeConv.cpp new file mode 100644 index 000000000..469cc9a1a --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTransposeConv.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleTransposeConv *node) +{ + auto *cloned = loco::must_cast<luci::CircleTransposeConv *>(cn->find_clone(node)); + + luci::CircleNode *inputSizes = loco::must_cast<luci::CircleNode *>(node->inputSizes()); + luci::CircleNode *filter = loco::must_cast<luci::CircleNode *>(node->filter()); + luci::CircleNode *outBackprop = loco::must_cast<luci::CircleNode *>(node->outBackprop()); + luci::CircleNode *bias = loco::must_cast<luci::CircleNode *>(node->bias()); + + cloned->inputSizes(cn->find_clone(inputSizes)); + cloned->filter(cn->find_clone(filter)); + cloned->outBackprop(cn->find_clone(outBackprop)); + cloned->bias(cn->find_clone(bias)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleTransposeConv *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleTransposeConv.test.cpp b/compiler/luci/partition/src/Nodes/CircleTransposeConv.test.cpp new file mode 100644 index 000000000..ee9fb0e78 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleTransposeConv.test.cpp @@ -0,0 +1,104 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleTransposeConv> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleTransposeConv>::init(g); + + _node->padding(luci::Padding::VALID); + } +}; + +class TestNodeGraph : public TestIsOGraph<4>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<4>::init({shape, shape, shape, shape}, shape); + NodeGraphlet::init(g()); + + node()->inputSizes(input(0)); + node()->filter(input(1)); + node()->outBackprop(input(2)); + node()->bias(input(3)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_TransposeConv) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTransposeConv *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTransposeConv *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(4, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); + ASSERT_EQ(cth.inputs(1), clone->arg(1)); + ASSERT_EQ(cth.inputs(2), clone->arg(2)); + ASSERT_EQ(cth.inputs(3), clone->arg(3)); +} + +TEST(ConnectNodeTest, connect_TransposeConv_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTransposeConv *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleTransposeConv *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleUnidirectionalSequenceLSTM.cpp b/compiler/luci/partition/src/Nodes/CircleUnidirectionalSequenceLSTM.cpp new file mode 100644 index 000000000..3f0374aac --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnidirectionalSequenceLSTM.cpp @@ -0,0 +1,117 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleUnidirectionalSequenceLSTM *node) +{ + auto *cloned = loco::must_cast<luci::CircleUnidirectionalSequenceLSTM *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + luci::CircleNode *input_to_input_weights = + loco::must_cast<luci::CircleNode *>(node->input_to_input_weights()); + luci::CircleNode *input_to_forget_weights = + loco::must_cast<luci::CircleNode *>(node->input_to_forget_weights()); + luci::CircleNode *input_to_cell_weights = + loco::must_cast<luci::CircleNode *>(node->input_to_cell_weights()); + luci::CircleNode *input_to_output_weights = + loco::must_cast<luci::CircleNode *>(node->input_to_output_weights()); + + luci::CircleNode *recurrent_to_input_weights = + loco::must_cast<luci::CircleNode *>(node->recurrent_to_input_weights()); + luci::CircleNode *recurrent_to_forget_weights = + loco::must_cast<luci::CircleNode *>(node->recurrent_to_forget_weights()); + luci::CircleNode *recurrent_to_cell_weights = + loco::must_cast<luci::CircleNode *>(node->recurrent_to_cell_weights()); + luci::CircleNode *recurrent_to_output_weights = + loco::must_cast<luci::CircleNode *>(node->recurrent_to_output_weights()); + + luci::CircleNode *cell_to_input_weights = + loco::must_cast<luci::CircleNode *>(node->cell_to_input_weights()); + luci::CircleNode *cell_to_forget_weights = + loco::must_cast<luci::CircleNode *>(node->cell_to_forget_weights()); + luci::CircleNode *cell_to_output_weights = + loco::must_cast<luci::CircleNode *>(node->cell_to_output_weights()); + + luci::CircleNode *input_gate_bias = loco::must_cast<luci::CircleNode *>(node->input_gate_bias()); + luci::CircleNode *forget_gate_bias = + loco::must_cast<luci::CircleNode *>(node->forget_gate_bias()); + luci::CircleNode *cell_gate_bias = loco::must_cast<luci::CircleNode *>(node->cell_gate_bias()); + luci::CircleNode *output_gate_bias = + loco::must_cast<luci::CircleNode *>(node->output_gate_bias()); + + luci::CircleNode *projection_weights = + loco::must_cast<luci::CircleNode *>(node->projection_weights()); + luci::CircleNode *projection_bias = loco::must_cast<luci::CircleNode *>(node->projection_bias()); + + luci::CircleNode *activation_state = + loco::must_cast<luci::CircleNode *>(node->activation_state()); + luci::CircleNode *cell_state = loco::must_cast<luci::CircleNode *>(node->cell_state()); + + luci::CircleNode *input_layer_norm_coefficients = + loco::must_cast<luci::CircleNode *>(node->input_layer_norm_coefficients()); + luci::CircleNode *forget_layer_norm_coefficients = + loco::must_cast<luci::CircleNode *>(node->forget_layer_norm_coefficients()); + luci::CircleNode *cell_layer_norm_coefficients = + loco::must_cast<luci::CircleNode *>(node->cell_layer_norm_coefficients()); + luci::CircleNode *output_layer_norm_coefficients = + loco::must_cast<luci::CircleNode *>(node->output_layer_norm_coefficients()); + + cloned->input(cn->find_clone(input)); + + cloned->input_to_input_weights(cn->find_clone(input_to_input_weights)); + cloned->input_to_forget_weights(cn->find_clone(input_to_forget_weights)); + cloned->input_to_cell_weights(cn->find_clone(input_to_cell_weights)); + cloned->input_to_output_weights(cn->find_clone(input_to_output_weights)); + + cloned->recurrent_to_input_weights(cn->find_clone(recurrent_to_input_weights)); + cloned->recurrent_to_forget_weights(cn->find_clone(recurrent_to_forget_weights)); + cloned->recurrent_to_cell_weights(cn->find_clone(recurrent_to_cell_weights)); + cloned->recurrent_to_output_weights(cn->find_clone(recurrent_to_output_weights)); + + cloned->cell_to_input_weights(cn->find_clone(cell_to_input_weights)); + cloned->cell_to_forget_weights(cn->find_clone(cell_to_forget_weights)); + cloned->cell_to_output_weights(cn->find_clone(cell_to_output_weights)); + + cloned->input_gate_bias(cn->find_clone(input_gate_bias)); + cloned->forget_gate_bias(cn->find_clone(forget_gate_bias)); + cloned->cell_gate_bias(cn->find_clone(cell_gate_bias)); + cloned->output_gate_bias(cn->find_clone(output_gate_bias)); + + cloned->projection_weights(cn->find_clone(projection_weights)); + cloned->projection_bias(cn->find_clone(projection_bias)); + + cloned->activation_state(cn->find_clone(activation_state)); + cloned->cell_state(cn->find_clone(cell_state)); + + cloned->input_layer_norm_coefficients(cn->find_clone(input_layer_norm_coefficients)); + cloned->forget_layer_norm_coefficients(cn->find_clone(forget_layer_norm_coefficients)); + cloned->cell_layer_norm_coefficients(cn->find_clone(cell_layer_norm_coefficients)); + cloned->output_layer_norm_coefficients(cn->find_clone(output_layer_norm_coefficients)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleUnidirectionalSequenceLSTM *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleUnidirectionalSequenceLSTM.test.cpp b/compiler/luci/partition/src/Nodes/CircleUnidirectionalSequenceLSTM.test.cpp new file mode 100644 index 000000000..aeefef093 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnidirectionalSequenceLSTM.test.cpp @@ -0,0 +1,133 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleUnidirectionalSequenceLSTM> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g) override + { + NodeGraphletT<luci::CircleUnidirectionalSequenceLSTM>::init(g); + + _node->fusedActivationFunction(luci::FusedActFunc::RELU); + } +}; + +class TestNodeGraph : public TestIsOGraph<24>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOGraph<24>::init({shape, shape, shape, shape, shape, shape, shape, shape, + shape, shape, shape, shape, shape, shape, shape, shape, + shape, shape, shape, shape, shape, shape, shape, shape}, + shape); + NodeGraphlet::init(g()); + + node()->input(input(0)); + + node()->input_to_input_weights(input(1)); + node()->input_to_forget_weights(input(2)); + node()->input_to_cell_weights(input(3)); + node()->input_to_output_weights(input(4)); + + node()->recurrent_to_input_weights(input(5)); + node()->recurrent_to_forget_weights(input(6)); + node()->recurrent_to_cell_weights(input(7)); + node()->recurrent_to_output_weights(input(8)); + + node()->cell_to_input_weights(input(9)); + node()->cell_to_forget_weights(input(10)); + node()->cell_to_output_weights(input(11)); + + node()->input_gate_bias(input(12)); + node()->forget_gate_bias(input(13)); + node()->cell_gate_bias(input(14)); + node()->output_gate_bias(input(15)); + + node()->projection_weights(input(16)); + node()->projection_bias(input(17)); + + node()->activation_state(input(18)); + node()->cell_state(input(19)); + + node()->input_layer_norm_coefficients(input(20)); + node()->forget_layer_norm_coefficients(input(21)); + node()->cell_layer_norm_coefficients(input(22)); + node()->output_layer_norm_coefficients(input(23)); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_UnidirectionalSequenceLSTM) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnidirectionalSequenceLSTM *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnidirectionalSequenceLSTM *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(24, clone->arity()); + // 24 separate checks is too much + for (uint32_t i = 0; i < 24; ++i) + ASSERT_EQ(cth.inputs(i), clone->arg(i)); +} + +TEST(ConnectNodeTest, connect_UnidirectionalSequenceLSTM_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnidirectionalSequenceLSTM *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnidirectionalSequenceLSTM *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleUnique.cpp b/compiler/luci/partition/src/Nodes/CircleUnique.cpp new file mode 100644 index 000000000..79ca59466 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnique.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleUnique *node) +{ + auto *cloned = loco::must_cast<luci::CircleUnique *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleUnique *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleUnique.test.cpp b/compiler/luci/partition/src/Nodes/CircleUnique.test.cpp new file mode 100644 index 000000000..23f299840 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnique.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleUnique> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Unique) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnique *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnique *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Unique_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnique *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnique *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleUniqueOut.cpp b/compiler/luci/partition/src/Nodes/CircleUniqueOut.cpp new file mode 100644 index 000000000..f244dd6eb --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUniqueOut.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleUniqueOut *node) +{ + auto *cloned = loco::must_cast<luci::CircleUniqueOut *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleUniqueOut *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleUniqueOut.test.cpp b/compiler/luci/partition/src/Nodes/CircleUniqueOut.test.cpp new file mode 100644 index 000000000..887640790 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUniqueOut.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleUniqueOut> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_UniqueOut) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUniqueOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUniqueOut *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_UniqueOut_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUniqueOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUniqueOut *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleUnpack.cpp b/compiler/luci/partition/src/Nodes/CircleUnpack.cpp new file mode 100644 index 000000000..f83c5d810 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnpack.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleUnpack *node) +{ + auto *cloned = loco::must_cast<luci::CircleUnpack *>(cn->find_clone(node)); + + luci::CircleNode *value = loco::must_cast<luci::CircleNode *>(node->value()); + + cloned->value(cn->find_clone(value)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleUnpack *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleUnpack.test.cpp b/compiler/luci/partition/src/Nodes/CircleUnpack.test.cpp new file mode 100644 index 000000000..b164cc3bc --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnpack.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleUnpack> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->value(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Unpack) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpack *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpack *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Unpack_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpack *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpack *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleUnpackOut.cpp b/compiler/luci/partition/src/Nodes/CircleUnpackOut.cpp new file mode 100644 index 000000000..b8982fff5 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnpackOut.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleUnpackOut *node) +{ + auto *cloned = loco::must_cast<luci::CircleUnpackOut *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleUnpackOut *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleUnpackOut.test.cpp b/compiler/luci/partition/src/Nodes/CircleUnpackOut.test.cpp new file mode 100644 index 000000000..9ed440966 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleUnpackOut.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleUnpackOut> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_UnpackOut) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpackOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpackOut *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_UnpackOut_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpackOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleUnpackOut *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleWhere.cpp b/compiler/luci/partition/src/Nodes/CircleWhere.cpp new file mode 100644 index 000000000..8ef274268 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleWhere.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleWhere *node) +{ + auto *cloned = loco::must_cast<luci::CircleWhere *>(cn->find_clone(node)); + + luci::CircleNode *condition = loco::must_cast<luci::CircleNode *>(node->condition()); + + cloned->condition(cn->find_clone(condition)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleWhere *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleWhere.test.cpp b/compiler/luci/partition/src/Nodes/CircleWhere.test.cpp new file mode 100644 index 000000000..942f804c2 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleWhere.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleWhere> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->condition(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_Where) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhere *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhere *>(node)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_Where_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhere *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhere *>(node)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleWhile.cpp b/compiler/luci/partition/src/Nodes/CircleWhile.cpp new file mode 100644 index 000000000..7820aca01 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleWhile.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleWhile *node) +{ + auto *cloned = loco::must_cast<luci::CircleWhile *>(cn->find_clone(node)); + + auto input_count = node->input_count(); + for (uint32_t in = 0; in < input_count; ++in) + { + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input(in)); + + cloned->input(in, cn->find_clone(input)); + } +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleWhile *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleWhile.test.cpp b/compiler/luci/partition/src/Nodes/CircleWhile.test.cpp new file mode 100644 index 000000000..bffb7869d --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleWhile.test.cpp @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeIsOsGraphletT<luci::CircleWhile> +{ +public: + NodeGraphlet() = default; + +public: + void init(loco::Graph *g, uint32_t n, uint32_t m) override { NodeIsOsGraphletT::init(g, n, m); } +}; + +class TestNodeGraph : public TestIsOsGraph<1, 1>, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIsOsGraph<1, 1>::init({shape}, {shape}); + NodeGraphlet::init(g(), 1, 1); + + node()->input(0, input(0)); + + output(0)->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_While) +{ + TestNodeGraph tng; + tng.init({1}); + + ConnectionTestHelper cth; + cth.prepare_inputs<1, 1>(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhile *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhile *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_While_NEG) +{ + TestNodeGraph tng; + tng.init({1}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss<1, 1>(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhile *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhile *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleWhileOut.cpp b/compiler/luci/partition/src/Nodes/CircleWhileOut.cpp new file mode 100644 index 000000000..1cb4419db --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleWhileOut.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleWhileOut *node) +{ + auto *cloned = loco::must_cast<luci::CircleWhileOut *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleWhileOut *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleWhileOut.test.cpp b/compiler/luci/partition/src/Nodes/CircleWhileOut.test.cpp new file mode 100644 index 000000000..901f31b01 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleWhileOut.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleWhileOut> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_WhileOut) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhileOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhileOut *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_WhileOut_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhileOut *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleWhileOut *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Nodes/CircleZerosLike.cpp b/compiler/luci/partition/src/Nodes/CircleZerosLike.cpp new file mode 100644 index 000000000..715042d86 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleZerosLike.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +namespace +{ + +void connect(luci::ConnectNode *cn, const luci::CircleZerosLike *node) +{ + auto *cloned = loco::must_cast<luci::CircleZerosLike *>(cn->find_clone(node)); + + luci::CircleNode *input = loco::must_cast<luci::CircleNode *>(node->input()); + + cloned->input(cn->find_clone(input)); +} + +} // namespace + +namespace luci +{ + +void ConnectNode::visit(const luci::CircleZerosLike *node) { connect(this, node); } + +} // namespace luci diff --git a/compiler/luci/partition/src/Nodes/CircleZerosLike.test.cpp b/compiler/luci/partition/src/Nodes/CircleZerosLike.test.cpp new file mode 100644 index 000000000..74c873cb2 --- /dev/null +++ b/compiler/luci/partition/src/Nodes/CircleZerosLike.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConnectNode.h" + +#include "ConnectNode.test.h" + +#include <luci/Service/CircleNodeClone.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class NodeGraphlet : public NodeGraphletT<luci::CircleZerosLike> +{ +public: + NodeGraphlet() = default; +}; + +class TestNodeGraph : public TestIOGraph, public NodeGraphlet +{ +public: + TestNodeGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + NodeGraphlet::init(g()); + + node()->input(input()); + + output()->from(node()); + } +}; + +} // namespace + +TEST(ConnectNodeTest, connect_ZerosLike) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleZerosLike *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleZerosLike *>(clone)); + + cth.clone_connect(node, clone); + + ASSERT_EQ(1, clone->arity()); + ASSERT_EQ(cth.inputs(0), clone->arg(0)); +} + +TEST(ConnectNodeTest, connect_ZerosLike_NEG) +{ + TestNodeGraph tng; + tng.init({2, 3}); + + ConnectionTestHelper cth; + cth.prepare_inputs_miss(&tng); + + auto *node = tng.node(); + ASSERT_NO_THROW(loco::must_cast<luci::CircleZerosLike *>(node)); + + auto *clone = luci::clone_node(node, cth.graph_clone()); + ASSERT_NO_THROW(loco::must_cast<luci::CircleZerosLike *>(clone)); + + EXPECT_ANY_THROW(cth.clone_connect(node, clone)); +} diff --git a/compiler/luci/partition/src/Partition.test.cpp b/compiler/luci/partition/src/Partition.test.cpp index 9e24c441c..42fcc5189 100644 --- a/compiler/luci/partition/src/Partition.test.cpp +++ b/compiler/luci/partition/src/Partition.test.cpp @@ -73,6 +73,7 @@ TEST(PartitionTest, simple_apply) luci::PartitionTable pt; pt.default_group = "A"; + pt.comply = luci::PartitionTable::COMPLY::OPCODE; auto pms = apply(&module, pt); diff --git a/compiler/luci/partition/src/PartitionCleanup.cpp b/compiler/luci/partition/src/PartitionCleanup.cpp index 6545295df..7bf51518a 100644 --- a/compiler/luci/partition/src/PartitionCleanup.cpp +++ b/compiler/luci/partition/src/PartitionCleanup.cpp @@ -71,9 +71,6 @@ void remove_unused_inputoutputs(luci::PGroups *pgroups, const luci::Module *sour LOGGER(l); - // TODO support multiple subgraph - assert(source->size() == 1); - INFO(l) << "--- Cleanup unused inputs/outputs"; // remove input within same pgroup diff --git a/compiler/luci/partition/src/PartitionDump.cpp b/compiler/luci/partition/src/PartitionDump.cpp new file mode 100644 index 000000000..69aec610d --- /dev/null +++ b/compiler/luci/partition/src/PartitionDump.cpp @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/PartitionDump.h" + +namespace +{ + +void dump(std::ostream &os, const luci::PartitionTable &table) +{ + os << "Backends:"; + for (auto &group : table.groups) + { + os << " " << group; + if (table.default_group == group) + os << "(default)"; + } + os << std::endl; + + os << "Assign by OPCODE: " << std::endl; + for (auto &item : table.byopcodes) + os << " " << item.first << "=" << item.second << std::endl; + + os << "Assign by OPNAME: " << std::endl; + for (auto &item : table.byopnames) + os << " " << item.first << "=" << item.second << std::endl; +} + +} // namespace + +std::ostream &operator<<(std::ostream &os, const luci::PartitionTable &table) +{ + dump(os, table); + return os; +} diff --git a/compiler/luci/partition/src/PartitionIR.cpp b/compiler/luci/partition/src/PartitionIR.cpp index ebd6b25fa..60dc74f89 100644 --- a/compiler/luci/partition/src/PartitionIR.cpp +++ b/compiler/luci/partition/src/PartitionIR.cpp @@ -67,7 +67,7 @@ std::unique_ptr<PGroups> PGroups::make_copy(void) const return std::move(d_pgroups); } -std::string PGroups::group_of(luci::CircleNode *node) const +GroupKey PGroups::group_of(luci::CircleNode *node) const { assert(node != nullptr); diff --git a/compiler/luci/partition/src/PartitionIR.h b/compiler/luci/partition/src/PartitionIR.h index 852e38cc0..c91b2f2ab 100644 --- a/compiler/luci/partition/src/PartitionIR.h +++ b/compiler/luci/partition/src/PartitionIR.h @@ -29,6 +29,8 @@ namespace luci struct PGroup; +using GroupKey = std::string; + /** * @brief Partition Node with CircleNode with group name * @note node just points to source luci::CircleNode, NOT the cloned node @@ -37,7 +39,7 @@ struct PGroup; struct PNode { const luci::CircleNode *node = nullptr; - std::string group; + GroupKey group; const PGroup *pgroup = nullptr; }; @@ -48,7 +50,7 @@ struct PNode struct PGroup { std::vector<std::unique_ptr<PNode>> pnodes; - std::string group; + GroupKey group; uint32_t id = 0; // I/O while partitioning @@ -61,13 +63,13 @@ struct PGroups std::vector<std::unique_ptr<PGroup>> pgroups; // node2group is to find group key from source node - std::map<const luci::CircleNode *, std::string> node2group; + std::map<const luci::CircleNode *, GroupKey> node2group; // id2pngroup is to find *pngroup from pngroup id std::map<uint32_t, PGroup *> id2pgroup; // default group key for reference - std::string default_group; + GroupKey default_group; public: /** @@ -78,7 +80,7 @@ public: /** * @brief return group key of node, empty string if not found */ - std::string group_of(luci::CircleNode *node) const; + GroupKey group_of(luci::CircleNode *node) const; /** * @brief return holding pgroup of node, nullptr if not found diff --git a/compiler/luci/partition/src/PartitionMerge.cpp b/compiler/luci/partition/src/PartitionMerge.cpp index 038fc2a0c..b767c77ae 100644 --- a/compiler/luci/partition/src/PartitionMerge.cpp +++ b/compiler/luci/partition/src/PartitionMerge.cpp @@ -50,9 +50,18 @@ bool is_input_same(const luci::PGroup *pgroup, const luci::PGroups *pgroups) std::string group; for (auto &input : pgroup->inputs) { + // We ignore below logic for CircleConst. + // CircleConst will be cloned if they are not found in pgroup as an input. + // Refer build_graph(), "add CircleConst for inputs" + // Reason: CircleConst can be shared as input to multiple nodes + // where each node can be placed in different groups. For this case + // we need to clone this CircleConst for each graph of the group. + if (dynamic_cast<const luci::CircleConst *>(input) != nullptr) + continue; + auto input_group = pgroups->group_of(input); // NOTE: all the nodes should be registered and return should be valid group. - // convert_to_proups() should ensure this. + // produce_pgroups() should ensure this, except CircleConst, Input, Outputs. // assert here to find if there is any problem with this. assert(not input_group.empty()); if (input_group.empty()) diff --git a/compiler/luci/partition/src/PartitionPGroups.cpp b/compiler/luci/partition/src/PartitionPGroups.cpp index 594ed6c40..e0b4e8e0d 100644 --- a/compiler/luci/partition/src/PartitionPGroups.cpp +++ b/compiler/luci/partition/src/PartitionPGroups.cpp @@ -67,8 +67,8 @@ std::unique_ptr<luci::PGroups> produce_pgroups(const luci::Module *source, const luci::PartitionTable &partition) { assert(source != nullptr); - // TODO support multiple subgraphs - assert(source->size() == 1); + // NOTE Only main graph (subgraph index 0) will be partitioned. + // Other subgraphs will follow the owner (IF/WHILE/...) group LOGGER(l); @@ -86,13 +86,36 @@ std::unique_ptr<luci::PGroups> produce_pgroups(const luci::Module *source, // check if node is normal node that we are interested if (check_allocate_partition(node)) { - auto opcodename = luci::opcode_name(node); - assert(!opcodename.empty()); - auto group = partition.default_group; - auto it = partition.byopcodes.find(opcodename); - if (it != partition.byopcodes.end()) - group = it->second; + + std::string opcodename; // opcodename or opname + + switch (partition.comply) + { + case luci::PartitionTable::COMPLY::OPCODE: + { + opcodename = luci::opcode_name(node); + assert(!opcodename.empty()); + + auto it = partition.byopcodes.find(opcodename); + if (it != partition.byopcodes.end()) + group = it->second; + break; + } + case luci::PartitionTable::COMPLY::OPNAME: + { + opcodename = node->name(); + assert(!opcodename.empty()); + + auto it = partition.byopnames.find(opcodename); + if (it != partition.byopnames.end()) + group = it->second; + break; + } + + default: + throw std::runtime_error("Unsupported partition.comply"); + } INFO(l) << "Op: " << node->name() << ": " << opcodename << ", " << node << ", " << group << std::endl; diff --git a/compiler/luci/partition/src/PartitionPGroups.test.cpp b/compiler/luci/partition/src/PartitionPGroups.test.cpp index 960f3cde9..f31641be4 100644 --- a/compiler/luci/partition/src/PartitionPGroups.test.cpp +++ b/compiler/luci/partition/src/PartitionPGroups.test.cpp @@ -73,6 +73,7 @@ TEST(PartitionPGroupsTest, simple_produce) luci::PartitionTable pt; pt.default_group = "A"; + pt.comply = luci::PartitionTable::COMPLY::OPCODE; auto pgs = produce_pgroups(&module, pt); diff --git a/compiler/luci/partition/src/PartitionPModules.cpp b/compiler/luci/partition/src/PartitionPModules.cpp index 36f4d47a4..beaaf6093 100644 --- a/compiler/luci/partition/src/PartitionPModules.cpp +++ b/compiler/luci/partition/src/PartitionPModules.cpp @@ -25,6 +25,12 @@ namespace { +// forward declare +void clone_ifnode_subgraphs(luci::PartedModule &pm, const luci::CircleIf *if_node, + const luci::CloneContext &clonectx); +void clone_whilenode_subgraphs(luci::PartedModule &pm, const luci::CircleWhile *while_node, + const luci::CloneContext &clonectx); + void add_graph_input(loco::Graph *graph, luci::CircleInput *input_node) { assert(graph != nullptr); @@ -76,9 +82,177 @@ void add_graph_output(loco::Graph *graph, luci::CircleOutput *output_node) } /** + * @brief make a clone of graph + */ +std::unique_ptr<loco::Graph> clone_graph(loco::Graph *graph_org, luci::CloneContext &clonectx) +{ + auto graph = loco::make_graph(); + auto graph_clone = graph.get(); + + graph_clone->name(graph_org->name()); + + // clone inputs + for (uint32_t n = 0; n < graph_org->inputs()->size(); ++n) + { + auto input_org = luci::input_node(graph_org, n); + assert(input_org != nullptr); + + auto *input_clone = graph_clone->nodes()->create<luci::CircleInput>(); + luci::copy_common_attributes(input_org, input_clone); + + add_graph_input(graph_clone, input_clone); + clonectx.emplace(input_org, input_clone); + } + + // clone nodes + auto nodes = graph_org->nodes(); + for (uint32_t n = 0; n < nodes->size(); ++n) + { + auto node = nodes->at(n); + + // skip for CircleInput, CircleOutput + if (dynamic_cast<luci::CircleInput *>(node) != nullptr) + continue; + if (dynamic_cast<luci::CircleOutput *>(node) != nullptr) + continue; + + auto node_org = loco::must_cast<luci::CircleNode *>(node); + assert(clonectx.find(node_org) == clonectx.end()); + + auto *node_clone = clone_node(node_org, graph_clone); + clonectx.emplace(node_org, node_clone); + } + + // connect nodes + for (uint32_t n = 0; n < nodes->size(); ++n) + { + auto node = nodes->at(n); + + // skip for CircleInput, CircleOutput + if (dynamic_cast<luci::CircleInput *>(node) != nullptr) + continue; + if (dynamic_cast<luci::CircleOutput *>(node) != nullptr) + continue; + + auto node_org = loco::must_cast<luci::CircleNode *>(node); + clone_connect(node_org, clonectx); + } + + // clone outputs + for (uint32_t n = 0; n < graph_org->outputs()->size(); ++n) + { + auto output_org = luci::output_node(graph_org, n); + assert(output_org != nullptr); + + auto *output_clone = graph_clone->nodes()->create<luci::CircleOutput>(); + luci::copy_common_attributes(output_org, output_clone); + // note: we don't add output_clone to clonectx. + // logically, output is not used as an input to any other nodes. + auto output_from = loco::must_cast<luci::CircleNode *>(output_org->from()); + auto it = clonectx.find(output_from); + assert(it != clonectx.end()); + output_clone->from(it->second); + + add_graph_output(graph_clone, output_clone); + } + + return std::move(graph); +} + +void clone_recursive_subgraphs(luci::PartedModule &pm, loco::Graph *graph, + const luci::CloneContext &clonectx) +{ + auto nodes = graph->nodes(); + for (uint32_t n = 0; n < nodes->size(); ++n) + { + { + auto if_node = dynamic_cast<luci::CircleIf *>(nodes->at(n)); + if (if_node != nullptr) + { + clone_ifnode_subgraphs(pm, if_node, clonectx); + } + } + { + auto while_node = dynamic_cast<luci::CircleWhile *>(nodes->at(n)); + if (while_node != nullptr) + { + clone_whilenode_subgraphs(pm, while_node, clonectx); + } + } + // TODO handle others + } +} + +void clone_ifnode_subgraphs(luci::PartedModule &pm, const luci::CircleIf *if_node, + const luci::CloneContext &clonectx) +{ + assert(if_node != nullptr); + + auto it = clonectx.find(if_node); + assert(it != clonectx.end()); + auto if_clone = loco::must_cast<luci::CircleIf *>(it->second); + + luci::CloneContext then_clonectx; + luci::CloneContext else_clonectx; + + auto then_graph = if_node->then_graph(); + auto else_graph = if_node->else_graph(); + + auto then_clone = clone_graph(then_graph, then_clonectx); + auto else_clone = clone_graph(else_graph, else_clonectx); + if_clone->then_graph(then_clone.get()); + if_clone->else_graph(else_clone.get()); + + pm.module->add(std::move(then_clone)); + int32_t then_index = pm.module->size() - 1; + pm.module->add(std::move(else_clone)); + int32_t else_index = pm.module->size() - 1; + if_clone->then_branch(then_index); + if_clone->else_branch(else_index); + + // do recursive copy subgraphs of CircleIf if there are any, + // inside then_graph or else_graph. + clone_recursive_subgraphs(pm, then_graph, then_clonectx); + clone_recursive_subgraphs(pm, else_graph, else_clonectx); +} + +void clone_whilenode_subgraphs(luci::PartedModule &pm, const luci::CircleWhile *while_node, + const luci::CloneContext &clonectx) +{ + assert(while_node != nullptr); + + auto it = clonectx.find(while_node); + assert(it != clonectx.end()); + auto while_clone = loco::must_cast<luci::CircleWhile *>(it->second); + + luci::CloneContext cond_clonectx; + luci::CloneContext body_clonectx; + + auto cond_graph = while_node->cond_graph(); + auto body_graph = while_node->body_graph(); + + auto cond_clone = clone_graph(cond_graph, cond_clonectx); + auto body_clone = clone_graph(body_graph, body_clonectx); + while_clone->cond_graph(cond_clone.get()); + while_clone->body_graph(body_clone.get()); + + pm.module->add(std::move(cond_clone)); + int32_t cond_index = pm.module->size() - 1; + pm.module->add(std::move(body_clone)); + int32_t body_index = pm.module->size() - 1; + while_clone->cond_branch(cond_index); + while_clone->body_branch(body_index); + + // do recursive copy subgraphs of CircleWhile if there are any, + // inside cond_graph or body_graph. + clone_recursive_subgraphs(pm, cond_graph, cond_clonectx); + clone_recursive_subgraphs(pm, body_graph, body_clonectx); +} + +/** * @brief Build loco::graph from pgroup into graph */ -void build_graph(loco::Graph *graph, const luci::PGroup *pgroup) +void build_graph(luci::PartedModule &pm, loco::Graph *graph, const luci::PGroup *pgroup) { LOGGER(l); @@ -153,6 +327,27 @@ void build_graph(loco::Graph *graph, const luci::PGroup *pgroup) << "output(" << output << ") -> " << output_clone << "(" << output_clone->name() << ")" << ": from " << it->second << "(" << it->second->name() << ")"; } + + // TODO relocate this if needed + // subgraphs for IF/WHILE/... nodes + for (auto &pnode : pgroup->pnodes) + { + { + auto if_node = dynamic_cast<const luci::CircleIf *>(pnode->node); + if (if_node != nullptr) + { + clone_ifnode_subgraphs(pm, if_node, clonectx); + } + } + { + auto while_node = dynamic_cast<const luci::CircleWhile *>(pnode->node); + if (while_node != nullptr) + { + clone_whilenode_subgraphs(pm, while_node, clonectx); + } + } + // TODO handle others + } } std::string make_name(const luci::PGroup *pgroup) @@ -184,16 +379,20 @@ luci::PartedModules produce_pmodules(const luci::PGroups *pgroups) pm.module = std::make_unique<luci::Module>(); pm.group = pgroup->group; + // the main graph for this module auto graph = loco::make_graph(); + auto graph_ptr = graph.get(); auto graph_name = make_name(pgroup.get()); graph->name(graph_name); + // Add main graph so that other subgraphs can be added inside build_graph + pm.module->add(std::move(graph)); + INFO(l) << "--- Partition Graph build----------------------"; INFO(l) << "--- name: " << graph_name; - build_graph(graph.get(), pgroup.get()); + build_graph(pm, graph_ptr, pgroup.get()); - pm.module->add(std::move(graph)); pms.pmodules.emplace_back(std::move(pm)); } diff --git a/compiler/luci/partition/src/PartitionPModules.test.cpp b/compiler/luci/partition/src/PartitionPModules.test.cpp index 99c39e839..9b949c2de 100644 --- a/compiler/luci/partition/src/PartitionPModules.test.cpp +++ b/compiler/luci/partition/src/PartitionPModules.test.cpp @@ -74,6 +74,7 @@ TEST(PartitionPModulesTest, simple_convert) luci::PartitionTable pt; pt.default_group = "A"; + pt.comply = luci::PartitionTable::COMPLY::OPCODE; auto pgs = produce_pgroups(&module, pt); auto pms = produce_pmodules(pgs.get()); diff --git a/compiler/luci/partition/src/PartitionValidate.cpp b/compiler/luci/partition/src/PartitionValidate.cpp new file mode 100644 index 000000000..5aceb98ca --- /dev/null +++ b/compiler/luci/partition/src/PartitionValidate.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/PartitionValidate.h" + +#include <luci/Service/Validate.h> + +#include <pepper/csv2vec.h> + +#include <iostream> + +namespace luci +{ + +bool validate(luci::PartitionTable &partition) +{ + if (partition.groups.size() == 0) + { + std::cerr << "There is no 'backends' information"; + return false; + } + if (partition.default_group.empty()) + { + std::cerr << "There is no 'default' backend information"; + return false; + } + if (!pepper::is_one_of<std::string>(partition.default_group, partition.groups)) + { + std::cerr << "'default' backend is not one of 'backends' item"; + return false; + } + for (auto &byopcode : partition.byopcodes) + { + if (!pepper::is_one_of<std::string>(byopcode.second, partition.groups)) + { + std::cerr << "OPCODE " << byopcode.first << " is not assigned to one of 'backends' items"; + return false; + } + } + for (auto &byopname : partition.byopnames) + { + if (!pepper::is_one_of<std::string>(byopname.second, partition.groups)) + { + std::cerr << "OPNAME " << byopname.first << " is not assigned to one of 'backends' items"; + return false; + } + } + return true; +} + +} // namespace luci diff --git a/compiler/luci/pass/CMakeLists.txt b/compiler/luci/pass/CMakeLists.txt index 2977fbed7..fd06c6d52 100644 --- a/compiler/luci/pass/CMakeLists.txt +++ b/compiler/luci/pass/CMakeLists.txt @@ -14,8 +14,11 @@ target_link_libraries(luci_pass PRIVATE luci_service) target_link_libraries(luci_pass PRIVATE luci_logex) target_link_libraries(luci_pass PRIVATE luci_profile) target_link_libraries(luci_pass PRIVATE nncc_common) +target_link_libraries(luci_pass PRIVATE pepper_csv2vec) target_link_libraries(luci_pass PRIVATE oops) install(TARGETS luci_pass DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/luci/pass/include/luci/CircleOptimizer.h b/compiler/luci/pass/include/luci/CircleOptimizer.h index 1f5e1c8b9..3bcc7c5bb 100644 --- a/compiler/luci/pass/include/luci/CircleOptimizer.h +++ b/compiler/luci/pass/include/luci/CircleOptimizer.h @@ -40,9 +40,12 @@ public: FuseBatchNormWithTConv, FuseBCQ, FuseInstanceNorm, + FuseMeanWithMean, + FuseTransposeWithMean, ResolveCustomOpAdd, ResolveCustomOpBatchMatMul, ResolveCustomOpMatMul, + ResolveCustomOpMaxPoolWithArgmax, QuantizeDequantizeWeights, QuantizeWithMinMax, Requantize, @@ -58,7 +61,9 @@ public: ShuffleWeightTo16x1Float32, RemoveRedundantTranspose, ReplaceMulAddWithDepthwiseConv, + ReplaceSubWithAdd, SubstitutePackToReshape, + SubstitutePadV2ToPad, SubstituteSqueezeToReshape, ConvertNCHWToNHWC, RemoveUnnecessarySlice, @@ -66,8 +71,12 @@ public: RemoveUnnecessarySplit, RemoveUnnecessaryReshape, TransformMinMaxToRelu6Pass, + TransformMinReluToRelu6Pass, + SubstituteStridedSliceToReshape, SubstituteTransposeToReshape, RemoveRedundantReshape, + RemoveFakeQuant, + RemoveQuantDequantSeq, }; enum AlgorithmParameters @@ -85,8 +94,8 @@ public: Sparsify_block_map, // convert NCHW to NHWC - NCHW_to_NHWC_preserve_input_shape, - NCHW_to_NHWC_preserve_output_shape, + NCHW_to_NHWC_input_shape, + NCHW_to_NHWC_output_shape, }; virtual ~Options() = default; diff --git a/compiler/luci/pass/include/luci/Pass/FuseMeanWithMeanPass.h b/compiler/luci/pass/include/luci/Pass/FuseMeanWithMeanPass.h new file mode 100644 index 000000000..8bbce6947 --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/FuseMeanWithMeanPass.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_FUSE_MEAN_WITH_MEAN_PASS_H__ +#define __LUCI_FUSE_MEAN_WITH_MEAN_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to fuse two Mean operations follow one by one into one Mean + * with merge reduction indices + */ +struct FuseMeanWithMeanPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::FuseMeanWithMeanPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_FUSE_MEAN_WITH_MEAN_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/FuseTransposeWithMeanPass.h b/compiler/luci/pass/include/luci/Pass/FuseTransposeWithMeanPass.h new file mode 100644 index 000000000..74dd04e2b --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/FuseTransposeWithMeanPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_FUSE_TRANSPOSE_WITH_MEAN_PASS_H__ +#define __LUCI_FUSE_TRANSPOSE_WITH_MEAN_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to fuse Mean operation with a preceding Transpose + */ +struct FuseTransposeWithMeanPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::FuseTransposeWithMeanPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_FUSE_TRANSPOSE_WITH_MEAN_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/RemoveFakeQuantPass.h b/compiler/luci/pass/include/luci/Pass/RemoveFakeQuantPass.h new file mode 100644 index 000000000..b477c8733 --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/RemoveFakeQuantPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_REMOVE_FAKEQUANT_PASS_H__ +#define __LUCI_REMOVE_FAKEQUANT_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to Remove FakeQuant node. + */ +struct RemoveFakeQuantPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::RemoveFakeQuantPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_REMOVE_FAKEQUANT_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/RemoveQuantDequantSeqPass.h b/compiler/luci/pass/include/luci/Pass/RemoveQuantDequantSeqPass.h new file mode 100644 index 000000000..3eac626c2 --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/RemoveQuantDequantSeqPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_REMOVE_QUANTDEQUANTSEQ_PASS_H__ +#define __LUCI_REMOVE_QUANTDEQUANTSEQ_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to Remove Quantize-Dequantize sequence. + */ +struct RemoveQuantDequantSeqPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::RemoveQuantDequantSeqPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_REMOVE_QUANTDEQUANTSEQ_PASS_H__ diff --git a/compiler/circle2circle/include/Model.h b/compiler/luci/pass/include/luci/Pass/ReplaceSubWithAddPass.h index 35b7e3239..4878f728f 100644 --- a/compiler/circle2circle/include/Model.h +++ b/compiler/luci/pass/include/luci/Pass/ReplaceSubWithAddPass.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,30 +14,25 @@ * limitations under the License. */ -#ifndef __CIRCLE2CIRCLE_MODEL_H__ -#define __CIRCLE2CIRCLE_MODEL_H__ +#ifndef __LUCI_REPLACE_SUB_WITH_ADD_PASS_H__ +#define __LUCI_REPLACE_SUB_WITH_ADD_PASS_H__ -#include <mio/circle/schema_generated.h> - -#include <memory> +#include <logo/Pass.h> namespace luci { -struct Model -{ - virtual ~Model() = default; - - virtual const ::circle::Model *model(void) = 0; -}; - /** - * @brief Load Circle model (as a raw Model) from a given path + * @brief Class to Replace Sub With Add * - * @note May return a nullptr */ -std::unique_ptr<Model> load_model(const std::string &path); +struct ReplaceSubWithAddPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::ReplaceSubWithAddPass"; } + + bool run(loco::Graph *g) final; +}; } // namespace luci -#endif // __CIRCLE2CIRCLE_MODEL_H__ +#endif // __LUCI_REPLACE_SUB_WITH_ADD_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/ResolveCustomOpMaxPoolWithArgmaxPass.h b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpMaxPoolWithArgmaxPass.h new file mode 100644 index 000000000..ab8fce8fe --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpMaxPoolWithArgmaxPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_RESOLVE_CUSTOM_OP_MAXPOOL_WITH_ARGMAX_PASS_H__ +#define __LUCI_RESOLVE_CUSTOM_OP_MAXPOOL_WITH_ARGMAX_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to resolve custom op MaxPoolWithArgmax to subgraph with circle's MaxPool and ArgMax. + */ +struct ResolveCustomOpMaxPoolWithArgmaxPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::ResolveCustomOpMaxPoolWithArgmaxPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_RESOLVE_CUSTOM_OP_MAXPOOL_WITH_ARGMAX_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/SubstitutePadV2ToPadPass.h b/compiler/luci/pass/include/luci/Pass/SubstitutePadV2ToPadPass.h new file mode 100644 index 000000000..c4ecc1086 --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/SubstitutePadV2ToPadPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_SUBSTITUTE_PADV2_TO_PAD_PASS_H__ +#define __LUCI_SUBSTITUTE_PADV2_TO_PAD_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to substitute PadV2 in certain condition to Pad. + */ +struct SubstitutePadV2ToPadPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::SubstitutePadV2ToPadPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_SUBSTITUTE_PADV2_TO_PAD_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/SubstituteStridedSliceToReshapePass.h b/compiler/luci/pass/include/luci/Pass/SubstituteStridedSliceToReshapePass.h new file mode 100644 index 000000000..a8229a6cd --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/SubstituteStridedSliceToReshapePass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_SUBSTITUTE_STRIDED_SLICE_TO_RESHAPE_PASS_H__ +#define __LUCI_SUBSTITUTE_STRIDED_SLICE_TO_RESHAPE_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to substitute Strided_Slice with certain condition to single reshape node. + */ +struct SubstituteStridedSliceToReshapePass final : public logo::Pass +{ + const char *name(void) const final { return "luci::SubstituteStridedSliceToReshapePass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_SUBSTITUTE_STRIDED_SLICE_TO_RESHAPE_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/TransformMinReluToRelu6Pass.h b/compiler/luci/pass/include/luci/Pass/TransformMinReluToRelu6Pass.h new file mode 100644 index 000000000..2c83e6e85 --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/TransformMinReluToRelu6Pass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_TRANSFORM_MIN_RELU_TO_RELU6_PASS_H__ +#define __LUCI_TRANSFORM_MIN_RELU_TO_RELU6_PASS_H__ + +#include <logo/Pass.h> + +namespace luci +{ + +/** + * @brief Class to transform Relu(Minimum(input, 6)) to Relu6 + */ +struct TransformMinReluToRelu6Pass final : public logo::Pass +{ + const char *name(void) const final { return "luci::TransformMinReluToRelu6Pass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_TRANSFORM_MIN_RELU_TO_RELU6_PASS_H__ diff --git a/compiler/luci/pass/src/CircleOptimizer.cpp b/compiler/luci/pass/src/CircleOptimizer.cpp index bddad34fa..98c22a07a 100644 --- a/compiler/luci/pass/src/CircleOptimizer.cpp +++ b/compiler/luci/pass/src/CircleOptimizer.cpp @@ -29,9 +29,13 @@ #include "luci/Pass/FuseBatchNormWithTConvPass.h" #include "luci/Pass/FuseBCQPass.h" #include "luci/Pass/FuseInstanceNormPass.h" +#include "luci/Pass/FuseMeanWithMeanPass.h" #include "luci/Pass/FusePreActivationBatchNormPass.h" +#include "luci/Pass/FuseTransposeWithMeanPass.h" #include "luci/Pass/MakeBatchNormGammaPositivePass.h" #include "luci/Pass/PropagateQuantParamPass.h" +#include "luci/Pass/RemoveFakeQuantPass.h" +#include "luci/Pass/RemoveQuantDequantSeqPass.h" #include "luci/Pass/RemoveRedundantReshapePass.h" #include "luci/Pass/RemoveRedundantTransposePass.h" #include "luci/Pass/RemoveUnnecessaryReshapePass.h" @@ -39,18 +43,23 @@ #include "luci/Pass/RemoveUnnecessaryStridedSlicePass.h" #include "luci/Pass/RemoveUnnecessarySplitPass.h" #include "luci/Pass/ReplaceMulAddWithDepthwiseConvPass.h" +#include "luci/Pass/ReplaceSubWithAddPass.h" #include "luci/Pass/ResolveCustomOpAddPass.h" #include "luci/Pass/ResolveCustomOpBatchMatMulPass.h" #include "luci/Pass/ResolveCustomOpMatMulPass.h" +#include "luci/Pass/ResolveCustomOpMaxPoolWithArgmaxPass.h" #include "luci/Pass/RequantizePass.h" #include "luci/Pass/QuantizeWithMinMaxPass.h" #include "luci/Pass/QuantizeDequantizeWeightsPass.h" #include "luci/Pass/SparsifyTensorPass.h" #include "luci/Pass/ShuffleWeightTo16x1Float32Pass.h" #include "luci/Pass/SubstitutePackToReshapePass.h" +#include "luci/Pass/SubstitutePadV2ToPadPass.h" #include "luci/Pass/SubstituteSqueezeToReshapePass.h" +#include "luci/Pass/SubstituteStridedSliceToReshapePass.h" #include "luci/Pass/SubstituteTransposeToReshapePass.h" #include "luci/Pass/TransformMinMaxToRelu6Pass.h" +#include "luci/Pass/TransformMinReluToRelu6Pass.h" // TODO add more passes #include "luci/Pass/CircleShapeInferencePass.h" @@ -67,6 +76,7 @@ #include <luci/IR/CircleNodes.h> #include <logo/Phase.h> +#include <pepper/csv2vec.h> #include <memory> #include <sstream> @@ -118,6 +128,23 @@ bool OptimizeOptionsImpl::query(Algorithm algo) return true; } +void convert_nchw_to_nhwc(loco::Graph *g, bool preserve_input, bool preserve_output) +{ + logo::Phase phase; + + phase.emplace_back(std::make_unique<logo::RemoveDeadNodeWithQueryPass>()); + phase.emplace_back(std::make_unique<luci::CircleShapeInferencePass>()); + phase.emplace_back(std::make_unique<luci::CircleTypeInferencePass>()); + + phase.emplace_back( + std::make_unique<luci::ConvertNCHWToNHWCPass>(preserve_input, preserve_output)); + + ProgressReporter prog(g, logo::PhaseStrategy::Restart); + logo::PhaseRunner<logo::PhaseStrategy::Restart> phase_runner{g}; + phase_runner.attach(&prog); + phase_runner.run(phase); +} + } // namespace namespace luci @@ -156,6 +183,17 @@ void CircleOptimizer::optimize(loco::Graph *g) const { logo::Phase phase; + // Conversion from NCHW to NHWC is done first to avoid interference with other optimizations. + if (_options->query(Options::Algorithm::ConvertNCHWToNHWC)) + { + bool preserve_input = + _options->param(Options::AlgorithmParameters::NCHW_to_NHWC_input_shape) != "true"; + bool preserve_output = + _options->param(Options::AlgorithmParameters::NCHW_to_NHWC_output_shape) != "true"; + + convert_nchw_to_nhwc(g, preserve_input, preserve_output); + } + /* TRANSFORM DECLARATION BEGIN */ phase.emplace_back(std::make_unique<logo::RemoveDeadNodeWithQueryPass>()); @@ -175,6 +213,14 @@ void CircleOptimizer::optimize(loco::Graph *g) const { phase.emplace_back(std::make_unique<luci::ResolveCustomOpMatMulPass>()); } + if (_options->query(Options::Algorithm::FuseMeanWithMean)) + { + phase.emplace_back(std::make_unique<FuseMeanWithMeanPass>()); + } + if (_options->query(Options::Algorithm::ResolveCustomOpMaxPoolWithArgmax)) + { + phase.emplace_back(std::make_unique<luci::ResolveCustomOpMaxPoolWithArgmaxPass>()); + } if (_options->query(Options::Algorithm::FuseInstanceNorm)) { phase.emplace_back(std::make_unique<FuseInstanceNormPass>()); @@ -199,6 +245,10 @@ void CircleOptimizer::optimize(loco::Graph *g) const { phase.emplace_back(std::make_unique<FuseActivationFunctionPass>()); } + if (_options->query(Options::Algorithm::FuseTransposeWithMean)) + { + phase.emplace_back(std::make_unique<FuseTransposeWithMeanPass>()); + } if (_options->query(Options::Algorithm::FoldAddV2)) { phase.emplace_back(std::make_unique<luci::FoldAddV2Pass>()); @@ -231,6 +281,14 @@ void CircleOptimizer::optimize(loco::Graph *g) const { phase.emplace_back(std::make_unique<luci::ShuffleWeightTo16x1Float32Pass>()); } + if (_options->query(Options::Algorithm::RemoveFakeQuant)) + { + phase.emplace_back(std::make_unique<luci::RemoveFakeQuantPass>()); + } + if (_options->query(Options::Algorithm::RemoveQuantDequantSeq)) + { + phase.emplace_back(std::make_unique<luci::RemoveQuantDequantSeqPass>()); + } if (_options->query(Options::Algorithm::RemoveUnnecessaryReshape)) { phase.emplace_back(std::make_unique<luci::RemoveUnnecessaryReshapePass>()); @@ -259,14 +317,26 @@ void CircleOptimizer::optimize(loco::Graph *g) const { phase.emplace_back(std::make_unique<luci::ReplaceMulAddWithDepthwiseConvPass>()); } + if (_options->query(Options::Algorithm::ReplaceSubWithAdd)) + { + phase.emplace_back(std::make_unique<luci::ReplaceSubWithAddPass>()); + } if (_options->query(Options::Algorithm::SubstitutePackToReshape)) { phase.emplace_back(std::make_unique<luci::SubstitutePackToReshapePass>()); } + if (_options->query(Options::Algorithm::SubstitutePadV2ToPad)) + { + phase.emplace_back(std::make_unique<luci::SubstitutePadV2ToPadPass>()); + } if (_options->query(Options::Algorithm::SubstituteSqueezeToReshape)) { phase.emplace_back(std::make_unique<luci::SubstituteSqueezeToReshapePass>()); } + if (_options->query(Options::Algorithm::SubstituteStridedSliceToReshape)) + { + phase.emplace_back(std::make_unique<luci::SubstituteStridedSliceToReshapePass>()); + } if (_options->query(Options::Algorithm::SubstituteTransposeToReshape)) { phase.emplace_back(std::make_unique<luci::SubstituteTransposeToReshapePass>()); @@ -275,15 +345,9 @@ void CircleOptimizer::optimize(loco::Graph *g) const { phase.emplace_back(std::make_unique<luci::TransformMinMaxToRelu6Pass>()); } - if (_options->query(Options::Algorithm::ConvertNCHWToNHWC)) + if (_options->query(Options::Algorithm::TransformMinReluToRelu6Pass)) { - bool preserve_input = - _options->param(Options::AlgorithmParameters::NCHW_to_NHWC_preserve_input_shape) == "true"; - bool preserve_output = - _options->param(Options::AlgorithmParameters::NCHW_to_NHWC_preserve_output_shape) == "true"; - - phase.emplace_back( - std::make_unique<luci::ConvertNCHWToNHWCPass>(preserve_input, preserve_output)); + phase.emplace_back(std::make_unique<luci::TransformMinReluToRelu6Pass>()); } /* TRANSFORM DECLARATION END */ @@ -432,7 +496,7 @@ void CircleOptimizer::sparsify(loco::Graph *g) const std::string str_block_map = _options->param(Options::AlgorithmParameters::Sparsify_block_map); // traversal order - std::vector<int32_t> traversal_order = csv_to_vector<int32_t>(str_tarversal_order); + std::vector<int32_t> traversal_order = pepper::csv_to_vector<int32_t>(str_tarversal_order); // format std::vector<DimensionType> format; std::istringstream is(str_format); @@ -447,9 +511,9 @@ void CircleOptimizer::sparsify(loco::Graph *g) const is.ignore(); } // block size - std::vector<int32_t> block_size = csv_to_vector<int32_t>(str_block_size); + std::vector<int32_t> block_size = pepper::csv_to_vector<int32_t>(str_block_size); // block map - std::vector<int32_t> block_map = csv_to_vector<int32_t>(str_block_map); + std::vector<int32_t> block_map = pepper::csv_to_vector<int32_t>(str_block_map); luci::SparsifyTensorPass sparsifier{tensor_name, traversal_order, format, block_size, block_map}; diff --git a/compiler/luci/pass/src/CircleOptimizer.test.cpp b/compiler/luci/pass/src/CircleOptimizer.test.cpp index ca6dc77f3..43d96feaf 100644 --- a/compiler/luci/pass/src/CircleOptimizer.test.cpp +++ b/compiler/luci/pass/src/CircleOptimizer.test.cpp @@ -42,6 +42,7 @@ TEST(CircleOptimizerTest, optimize_algorithms) options->enable(Algorithms::RemoveUnnecessarySlice); options->enable(Algorithms::RemoveUnnecessarySplit); options->enable(Algorithms::ReplaceMulAddWithDepthwiseConv); + options->enable(Algorithms::SubstituteStridedSliceToReshape); options->enable(Algorithms::SubstituteTransposeToReshape); options->enable(Algorithms::ConvertNCHWToNHWC); diff --git a/compiler/luci/pass/src/ConvertNCHWToNHWCPass.cpp b/compiler/luci/pass/src/ConvertNCHWToNHWCPass.cpp index c9022f122..95e23e1b8 100644 --- a/compiler/luci/pass/src/ConvertNCHWToNHWCPass.cpp +++ b/compiler/luci/pass/src/ConvertNCHWToNHWCPass.cpp @@ -20,8 +20,11 @@ #include <luci/IR/CircleNodes.h> #include <luci/IR/CircleNodeVisitor.h> #include <luci/Profile/CircleNodeOrigin.h> +#include <luci/Service/Nodes/CircleConst.h> #include <luci/Log.h> +#include <functional> + namespace { @@ -66,6 +69,37 @@ DataFormat get_data_format(loco::Node *node) bool has_data_format(loco::Node *node) { return node->annot<DataFormatAnnotation>() != nullptr; } +bool check_4d_transpose(loco::Node *node, const std::vector<int32_t> indices) +{ + assert(indices.size() == 4); + + auto trans = dynamic_cast<luci::CircleTranspose *>(node); + if (not trans) + return false; + + if (not trans->perm()) + return false; + + auto perm = dynamic_cast<luci::CircleConst *>(trans->perm()); + // Only const perm is supported + if (not perm) + return false; + + if (perm->dtype() != loco::DataType::S32) + return false; + + if (perm->size<loco::DataType::S32>() != 4) + return false; + + for (uint32_t i = 0; i < 4; i++) + { + if (perm->at<loco::DataType::S32>(i) != indices[i]) + return false; + } + + return true; +} + luci::CircleTranspose *create_4d_transpose(luci::CircleNode *node, const std::vector<int32_t> indices) { @@ -106,6 +140,44 @@ luci::CircleTranspose *create_4d_transpose(luci::CircleNode *node, return trans; } +luci::CircleTranspose *create_Nd_transpose(luci::CircleNode *node, + const std::vector<int32_t> indices) +{ + auto name = node->name(); + assert(name.length() > 0); + + auto perm = node->graph()->nodes()->create<luci::CircleConst>(); + perm->dtype(loco::DataType::S32); + perm->size<loco::DataType::S32>(indices.size()); + perm->rank(1); + perm->dim(0) = indices.size(); + for (uint32_t i = 0; i < indices.size(); i++) + perm->at<loco::DataType::S32>(i) = indices[i]; + perm->shape_status(luci::ShapeStatus::VALID); + + auto make_string = [](const std::vector<int32_t> &nums) { + std::string str; + for (auto num : nums) + { + if (str.length() > 0) + str += "."; + str += std::to_string(num); + } + return str; + }; + + auto str_indices = make_string(indices); + + perm->name(name + "/Transpose_" + str_indices + "/perm"); + + auto trans = node->graph()->nodes()->create<luci::CircleTranspose>(); + trans->perm(perm); + trans->name(name + "/Transpose_" + str_indices); + luci::add_origin(trans, luci::get_origin(node)); + + return trans; +} + int32_t nchw_axis_to_nhwc(int32_t axis) { uint32_t pos_axis = axis >= 0 ? static_cast<uint32_t>(axis) : static_cast<uint32_t>(axis + 4); @@ -125,6 +197,42 @@ luci::CircleTranspose *create_pre_transpose(luci::CircleNode *node) return create_4d_transpose(node, {0, 2, 3, 1}); } +bool check_4d_reshape(loco::Node *node, const std::vector<int32_t> indices) +{ + assert(indices.size() == 4); // FIX_CALLER_UNLESS + + auto reshape = dynamic_cast<luci::CircleReshape *>(node); + if (not reshape) + return false; + + if (reshape->rank() != 4) + return false; + + auto input = loco::must_cast<luci::CircleNode *>(reshape->tensor()); + if (input->shape_status() != luci::ShapeStatus::VALID) + return false; + + if (reshape->shape_status() != luci::ShapeStatus::VALID) + return false; + + if (!(input->dim(0) == reshape->dim(indices[0])) || + !(input->dim(1) == reshape->dim(indices[1])) || + !(input->dim(2) == reshape->dim(indices[2])) || !(input->dim(3) == reshape->dim(indices[3]))) + return false; + + return true; +} + +// Check if Reshape that converts NCHW -> NHWC +bool is_pre_reshape(loco::Node *node) { return check_4d_reshape(node, {0, 3, 1, 2}); } + +// Check if Reshape that converts NHWC -> NCHW +bool is_post_reshape(loco::Node *node) { return check_4d_reshape(node, {0, 2, 3, 1}); } + +bool is_post_transpose(loco::Node *node) { return check_4d_transpose(node, {0, 3, 1, 2}); } + +bool is_pre_transpose(loco::Node *node) { return check_4d_transpose(node, {0, 2, 3, 1}); } + uint32_t cal_offset(const loco::TensorShape &dimension, const uint32_t *indices) { return indices[0] * dimension.dim(1).value() * dimension.dim(2).value() * @@ -180,6 +288,28 @@ luci::CircleConst *create_NHWC_paddings(luci::CircleConst *paddings) return nhwc_paddings; } +luci::CircleConst *create_NHWC_rindices(luci::CircleConst *rindices) +{ + assert(rindices != nullptr); // FIX_CALLER_UNLESS + + if (rindices->dtype() != loco::DataType::S32) + return nullptr; + + auto nhwc_rindices = luci::clone(rindices); + auto name = rindices->name(); + assert(name.length() > 0); // FIX_CALLER_UNLESS + nhwc_rindices->name(name + "_NHWC"); + + auto size = nhwc_rindices->size<loco::DataType::S32>(); + for (uint32_t i = 0; i < size; i++) + { + nhwc_rindices->at<loco::DataType::S32>(i) = + nchw_axis_to_nhwc(rindices->at<loco::DataType::S32>(i)); + } + + return nhwc_rindices; +} + luci::CircleConst *create_NHWC_from_NCHW(luci::CircleConst *constant) { LOGGER(l); @@ -263,11 +393,79 @@ bool is_NCHW(const luci::CirclePad *node) return true; } +// NOTE Copied from is_NCHW(CirclePad) +bool is_NCHW(const luci::CirclePadV2 *node) +{ + const auto paddings = dynamic_cast<luci::CircleConst *>(node->paddings()); + // Non-const paddings is not supported + if (paddings == nullptr) + return false; + + if (paddings->rank() != 2) + return false; + + if (paddings->dim(0).value() != 4 || paddings->dim(1).value() != 2) + return false; + + // Only check the first two dimensions + for (uint32_t dim = 0; dim < 2; dim++) + { + for (uint32_t i = 0; i < 2; i++) + { + auto data = paddings->at<loco::DataType::S32>(dim * 2 + i); + if (data != 0) + return false; + } + } + + return true; +} + +// NOTE Following conditions can be extended later +// NOTE Used for Maximum, Miminum as ReLU/ReLU6 +// +// Find T with an NCHW pattern described below +// - Input (non-constant) shape : [N, C, H, W] +// - Input (constant) shape : [1] or [] +// - Output shape : [N, C, H, W] +template <class T> +bool is_NCHW_with_s_const(const T *node, luci::CircleNode *&pred_node, + luci::CircleConst *&comp_const) +{ + auto x = dynamic_cast<luci::CircleConst *>(node->x()); + auto y = dynamic_cast<luci::CircleConst *>(node->y()); + + if (x != nullptr && y == nullptr) + { + pred_node = loco::must_cast<luci::CircleNode *>(node->y()); + comp_const = x; + } + else if (x == nullptr && y != nullptr) + { + pred_node = loco::must_cast<luci::CircleNode *>(node->x()); + comp_const = y; + } + else + { + // Ignore if T does not have a comp_const input. + return false; + } + + if (pred_node->rank() != 4) + return false; + + // Check if scalar + const auto const_rank = comp_const->rank(); + if (const_rank == 0 || (const_rank == 1 && comp_const->dim(0).value() == 1)) + return true; + return false; +} + // NOTE Following conditions can be extended later // // Find MUL with an NCHW pattern described below // - Input (non-constant) shape : [N, C, H, W] -// - Input (constant) shape : [1, C, 1, 1] +// - Input (constant) shape : [1, C, 1, 1] or a scalar (1) // - Output shape : [N, C, H, W] bool is_NCHW_with_const(const luci::CircleMul *node, luci::CircleNode *&pred_node, luci::CircleConst *&multiplier) @@ -295,20 +493,32 @@ bool is_NCHW_with_const(const luci::CircleMul *node, luci::CircleNode *&pred_nod return false; const auto const_rank = multiplier->rank(); - if (const_rank != 4) + // Support Rank 4 or scalar (rank 0 or 1) + if (const_rank != 4 && const_rank != 0 && const_rank != 1) return false; - for (uint32_t i = 0; i < const_rank; i++) + if (const_rank == 4) { - if (i != 1 && multiplier->dim(i).value() != 1) - return false; + for (uint32_t i = 0; i < const_rank; i++) + { + if (i != 1 && multiplier->dim(i).value() != 1) + return false; + } } - const auto const_cdim = multiplier->dim(1); const auto input_cdim = pred_node->dim(1); const auto output_cdim = node->dim(1); - if (const_cdim == input_cdim && input_cdim == output_cdim) + if (const_rank == 4) + { + const auto const_cdim = multiplier->dim(1); + // Check Input, Output, Const have the same channel size + if (const_cdim == input_cdim && input_cdim == output_cdim) + return true; + else + return false; + } + if (input_cdim == output_cdim) return true; else return false; @@ -317,7 +527,7 @@ bool is_NCHW_with_const(const luci::CircleMul *node, luci::CircleNode *&pred_nod // We assume ADD with const input is NCHW if, // Input shape: (N, C, H, W) // Output shape: (N, C, H, W) -// 1. Const shape is (1, C, 1, 1) +// 1. Const shape is (1, C, 1, 1) or a scalar (1) // 2. Input, Output, Const have the same C. bool is_NCHW_with_const(const luci::CircleAdd *node, luci::CircleNode *&pred_node, luci::CircleConst *&beta) @@ -345,25 +555,86 @@ bool is_NCHW_with_const(const luci::CircleAdd *node, luci::CircleNode *&pred_nod return false; const auto const_rank = beta->rank(); - if (const_rank != 4) + // Support Rank 4 or scalar (rank 0 or 1) + if (const_rank != 4 && const_rank != 0 && const_rank != 1) return false; - // Check the shape is (1, C, 1, 1) - for (uint32_t i = 0; i < const_rank; i++) + if (const_rank == 4) { - if (i == 1) - continue; + // Check the shape is (1, C, 1, 1) + for (uint32_t i = 0; i < const_rank; i++) + { + if (i == 1) + continue; - if (beta->dim(i).value() != 1) + if (beta->dim(i).value() != 1) + return false; + } + } + + const auto input_cdim = pred_node->dim(1); + const auto output_cdim = node->dim(1); + + if (const_rank == 4) + { + const auto const_cdim = beta->dim(1); + // Check Input, Output, Const have the same channel size + if (const_cdim == input_cdim && input_cdim == output_cdim) + return true; + else return false; } + if (input_cdim == output_cdim) + return true; + else + return false; +} + +// We assume SUB with const input is NCHW if, +// Input shape: (N, C, H, W) +// Output shape: (N, C, H, W) +// 1. Const shape is (1, C, 1, 1) or a scalar (1) +// 2. Input, Output, Const have the same C. +bool is_NCHW_with_const(const luci::CircleSub *node, const luci::CircleNode *pred_node, + const luci::CircleConst *subtract) +{ + assert(pred_node != nullptr); + assert(subtract != nullptr); + + if (pred_node->rank() != 4) + return false; + + const auto const_rank = subtract->rank(); + // Support Rank 4 or scalar (rank 0 or 1) + if (const_rank != 4 && const_rank != 0 && const_rank != 1) + return false; + + if (const_rank == 4) + { + // Check the shape is (1, C, 1, 1) + for (uint32_t i = 0; i < const_rank; i++) + { + if (i == 1) + continue; + + if (subtract->dim(i).value() != 1) + return false; + } + } - const auto const_cdim = beta->dim(1); const auto input_cdim = pred_node->dim(1); const auto output_cdim = node->dim(1); - // Check Input, Output, Const have the same channel size - if (const_cdim == input_cdim && input_cdim == output_cdim) + if (const_rank == 4) + { + const auto const_cdim = subtract->dim(1); + // Check Input, Output, Const have the same channel size + if (const_cdim == input_cdim && input_cdim == output_cdim) + return true; + else + return false; + } + if (input_cdim == output_cdim) return true; else return false; @@ -387,6 +658,24 @@ template <class T> bool convert_unary_features(T *node) return true; } +template <class T> bool convert_unary_x(T *node) +{ + const auto pred_node = loco::must_cast<luci::CircleNode *>(node->x()); + auto pre_trans = create_pre_transpose(node); + pre_trans->a(pred_node); + node->x(pre_trans); + + // Do shape inference for this node again. + node->shape_status(luci::ShapeStatus::UNDEFINED); + + auto post_trans = create_post_transpose(node); + loco::replace(node).with(post_trans); + + post_trans->a(node); + + return true; +} + class ConvertNCHWToNHWC final : public luci::CircleNodeMutableVisitor<bool> { // Default @@ -457,12 +746,15 @@ class ConvertNCHWToNHWC final : public luci::CircleNodeMutableVisitor<bool> auto pre_trans = create_pre_transpose(node); pre_trans->a(pred_node); - auto nhwc_const = create_NHWC_from_NCHW(beta); - if (nhwc_const == nullptr) - return false; + if (beta->rank() == 4) + { + auto nhwc_const = create_NHWC_from_NCHW(beta); + if (nhwc_const == nullptr) + return false; + node->y(nhwc_const); + } node->x(pre_trans); - node->y(nhwc_const); } else if (beta == nullptr) { @@ -521,6 +813,137 @@ class ConvertNCHWToNHWC final : public luci::CircleNodeMutableVisitor<bool> return convert_unary_features<luci::CircleLeakyRelu>(node); } + bool visit(luci::CircleLogistic *node) { return convert_unary_x<luci::CircleLogistic>(node); } + + bool visit(luci::CircleMaximum *node) + { + luci::CircleNode *pred_node = nullptr; + luci::CircleConst *comp_constant = nullptr; + + if (is_NCHW_with_s_const<luci::CircleMaximum>(node, pred_node, comp_constant)) + { + auto pre_trans = create_pre_transpose(node); + pre_trans->a(pred_node); + node->x(pre_trans); + } + else + { + // TODO support other cases + return false; + } + + // Do shape inference for this node again. + node->shape_status(luci::ShapeStatus::UNDEFINED); + + auto post_trans = create_post_transpose(node); + loco::replace(node).with(post_trans); + + post_trans->a(node); + return true; + } + + bool visit(luci::CircleMean *node) + { + auto input = loco::must_cast<luci::CircleNode *>(node->input()); + if (input->rank() != 4) + return false; + + auto rindices = dynamic_cast<luci::CircleConst *>(node->reduction_indices()); + if (not rindices) + return false; + + auto nhwc_rindices = create_NHWC_rindices(rindices); + if (not nhwc_rindices) + return false; + + auto pre_trans = create_pre_transpose(node); + pre_trans->a(input); + node->input(pre_trans); + + // Do shape inference for this node again. + node->shape_status(luci::ShapeStatus::UNDEFINED); + + node->reduction_indices(nhwc_rindices); + + if (node->keep_dims()) + { + auto post_trans = create_post_transpose(node); + loco::replace(node).with(post_trans); + + post_trans->a(node); + + return true; + } + + // node->keep_dims() == false + // 1D output never needs a transpose + if (node->rank() <= 1) + return true; + + std::vector<bool> reduced_dims_nhwc(4, false); + uint32_t num_reduced_indices = nhwc_rindices->size<loco::DataType::S32>(); + + for (uint32_t ri = 0; ri < num_reduced_indices; ++ri) + { + reduced_dims_nhwc[nhwc_rindices->at<loco::DataType::S32>(ri)] = true; + } + + // if channel dimension has been reduced, we don't need a transpose + if (reduced_dims_nhwc[3]) + return true; + + // likewise, if both space dimensions are reduced, no transpose is needed + if (reduced_dims_nhwc[1] && reduced_dims_nhwc[2]) + return true; + + std::vector<int32_t> post_trans_ind; + // case 1: only N is reduced + if (num_reduced_indices == 1 && reduced_dims_nhwc[0]) + post_trans_ind = {2, 0, 1}; + + // case 2: only H or W is reduced + if (num_reduced_indices == 1 && (reduced_dims_nhwc[1] || reduced_dims_nhwc[2])) + post_trans_ind = {0, 2, 1}; + + // case 3: N and either H or W are reduced + if (num_reduced_indices == 2) + post_trans_ind = {1, 0}; + + auto post_trans = create_Nd_transpose(node, post_trans_ind); + loco::replace(node).with(post_trans); + + post_trans->a(node); + + return true; + } + + bool visit(luci::CircleMinimum *node) + { + luci::CircleNode *pred_node = nullptr; + luci::CircleConst *comp_constant = nullptr; + + if (is_NCHW_with_s_const<luci::CircleMinimum>(node, pred_node, comp_constant)) + { + auto pre_trans = create_pre_transpose(node); + pre_trans->a(pred_node); + node->x(pre_trans); + } + else + { + // TODO support other cases + return false; + } + + // Do shape inference for this node again. + node->shape_status(luci::ShapeStatus::UNDEFINED); + + auto post_trans = create_post_transpose(node); + loco::replace(node).with(post_trans); + + post_trans->a(node); + return true; + } + bool visit(luci::CircleMul *node) { LOGGER(l); @@ -534,14 +957,29 @@ class ConvertNCHWToNHWC final : public luci::CircleNodeMutableVisitor<bool> pre_trans->a(pred_node); node->x(pre_trans); - auto nhwc_const = create_NHWC_from_NCHW(multiplier); - node->y(nhwc_const); + if (multiplier->rank() == 4) + { + auto nhwc_const = create_NHWC_from_NCHW(multiplier); + node->y(nhwc_const); + } } else if (multiplier == nullptr) { - // TODO : Implement this case. - INFO(l) << "Not yet implemented. Both inputs of MUL are non-const." << std::endl; - return false; + // Only support for input rank 4 + auto input_x = loco::must_cast<luci::CircleNode *>(node->x()); + if (input_x->rank() != 4) + return false; + auto input_y = loco::must_cast<luci::CircleNode *>(node->y()); + if (input_y->rank() != 4) + return false; + + auto pre_trans_x = create_pre_transpose(node); + pre_trans_x->a(input_x); + node->x(pre_trans_x); + + auto pre_trans_y = create_pre_transpose(node); + pre_trans_y->a(input_y); + node->y(pre_trans_y); } else { @@ -558,12 +996,21 @@ class ConvertNCHWToNHWC final : public luci::CircleNodeMutableVisitor<bool> return true; } - bool visit(luci::CircleNeg *node) + bool visit(luci::CircleNeg *node) { return convert_unary_x<luci::CircleNeg>(node); } + + bool visit(luci::CirclePad *node) { - const auto pred_node = loco::must_cast<luci::CircleNode *>(node->x()); + if (!is_NCHW(node)) + return false; + + const auto pred_node = loco::must_cast<luci::CircleNode *>(node->input()); auto pre_trans = create_pre_transpose(node); pre_trans->a(pred_node); - node->x(pre_trans); + node->input(pre_trans); + + auto nchw_paddings = loco::must_cast<luci::CircleConst *>(node->paddings()); + const auto nhwc_paddings = create_NHWC_paddings(nchw_paddings); + node->paddings(nhwc_paddings); // Do shape inference for this node again. node->shape_status(luci::ShapeStatus::UNDEFINED); @@ -576,7 +1023,7 @@ class ConvertNCHWToNHWC final : public luci::CircleNodeMutableVisitor<bool> return true; } - bool visit(luci::CirclePad *node) + bool visit(luci::CirclePadV2 *node) { if (!is_NCHW(node)) return false; @@ -604,6 +1051,124 @@ class ConvertNCHWToNHWC final : public luci::CircleNodeMutableVisitor<bool> bool visit(luci::CircleRelu *node) { return convert_unary_features<luci::CircleRelu>(node); } bool visit(luci::CircleRelu6 *node) { return convert_unary_features<luci::CircleRelu6>(node); } + + bool visit(luci::CircleRsqrt *node) { return convert_unary_x<luci::CircleRsqrt>(node); } + + bool visit(luci::CircleSquaredDifference *node) + { + // TODO support CircleConst input + if (dynamic_cast<luci::CircleConst *>(node->x()) != nullptr) + return false; + if (dynamic_cast<luci::CircleConst *>(node->y()) != nullptr) + return false; + + auto input_x = loco::must_cast<luci::CircleNode *>(node->x()); + if (input_x->rank() != 4) + return false; + auto input_y = loco::must_cast<luci::CircleNode *>(node->y()); + if (input_y->rank() != 4) + return false; + + auto pre_trans_x = create_pre_transpose(node); + pre_trans_x->a(input_x); + node->x(pre_trans_x); + + auto pre_trans_y = create_pre_transpose(node); + pre_trans_y->a(input_y); + node->y(pre_trans_y); + + // Do shape inference for this node again. + node->shape_status(luci::ShapeStatus::UNDEFINED); + + auto post_trans = create_post_transpose(node); + loco::replace(node).with(post_trans); + + post_trans->a(node); + return true; + } + + bool visit(luci::CircleSub *node) + { + luci::CircleNode *pred_node = nullptr; + luci::CircleConst *subtract = nullptr; + + auto const_x = dynamic_cast<luci::CircleConst *>(node->x()); + auto const_y = dynamic_cast<luci::CircleConst *>(node->y()); + + if (const_x != nullptr && const_y == nullptr) + { + // case of subtract - pred_node + pred_node = loco::must_cast<luci::CircleNode *>(node->y()); + subtract = const_x; + + if (!is_NCHW_with_const(node, pred_node, subtract)) + return false; + + auto pre_trans = create_pre_transpose(node); + pre_trans->a(pred_node); + + if (subtract->rank() == 4) + { + auto nhwc_const = create_NHWC_from_NCHW(subtract); + if (nhwc_const == nullptr) + return false; + node->x(nhwc_const); + } + node->y(pre_trans); + } + else if (const_x == nullptr && const_y != nullptr) + { + // case of pred_node - subtract + pred_node = loco::must_cast<luci::CircleNode *>(node->x()); + subtract = const_y; + + if (!is_NCHW_with_const(node, pred_node, subtract)) + return false; + + auto pre_trans = create_pre_transpose(node); + pre_trans->a(pred_node); + + if (subtract->rank() == 4) + { + auto nhwc_const = create_NHWC_from_NCHW(subtract); + if (nhwc_const == nullptr) + return false; + node->y(nhwc_const); + } + + node->x(pre_trans); + } + else if (const_x == nullptr && const_y == nullptr) + { + // Both inputs are not constant. + // In this case, we cannot distinguish NCHW from NHWC, + // so just insert Transpose Ops. + // Only support for input rank 4. + auto input_x = loco::must_cast<luci::CircleNode *>(node->x()); + if (input_x->rank() != 4) + return false; + auto input_y = loco::must_cast<luci::CircleNode *>(node->y()); + if (input_y->rank() != 4) + return false; + + auto pre_trans_x = create_pre_transpose(node); + pre_trans_x->a(input_x); + node->x(pre_trans_x); + + auto pre_trans_y = create_pre_transpose(node); + pre_trans_y->a(input_y); + node->y(pre_trans_y); + } + + // Do shape inference for this node again. + node->shape_status(luci::ShapeStatus::UNDEFINED); + + auto post_trans = create_post_transpose(node); + loco::replace(node).with(post_trans); + + post_trans->a(node); + return true; + } }; } // namespace @@ -616,6 +1181,48 @@ bool ConvertNCHWToNHWCPass::run(loco::Graph *g) LOGGER(l); INFO(l) << "ConvertNCHWToNHWCPass Start" << std::endl; + // Annotate NHWC operators + // NHWC operators are detected by pattern matching + // + // Pattern + // pre-Transose (or pre-Reshape) + [intermediate Ops] + post-Transpose (or post-Reshape) + // + // [intermediate Ops] are annotated as NHWC + // + // NOTE A single pre-Transpose/Reshape can have multiple post-Transpose/Reshape. + // For example, + // pre-Transpose --- [intermediate Ops] --- post-Transpose + // | + // +--[intermediate Ops] --- post-Transpose + for (auto node : loco::postorder_traversal(loco::output_nodes(g))) + { + if (has_data_format(node)) + continue; + + if (is_pre_transpose(node) || is_pre_reshape(node)) + { + // For recursive call of lambda + std::function<void(loco::Node *)> set_data_format_to_succs; + set_data_format_to_succs = [&](loco::Node *n) { + for (auto succ : loco::succs(n)) + { + // Exit condition + if (is_post_transpose(succ) || is_post_reshape(succ)) + continue; + + if (not has_data_format(succ)) + { + set_data_format(succ, DataFormat::NHWC); + } + + set_data_format_to_succs(succ); + } + }; + + set_data_format_to_succs(node); + } + } + // Annotate NCHW operators for (auto node : loco::active_nodes(loco::output_nodes(g))) { @@ -638,11 +1245,19 @@ bool ConvertNCHWToNHWCPass::run(loco::Graph *g) case luci::CircleOpcode::ADD: case luci::CircleOpcode::CONCATENATION: case luci::CircleOpcode::LEAKY_RELU: + case luci::CircleOpcode::LOGISTIC: + case luci::CircleOpcode::MAXIMUM: + case luci::CircleOpcode::MEAN: + case luci::CircleOpcode::MINIMUM: case luci::CircleOpcode::MUL: case luci::CircleOpcode::NEG: case luci::CircleOpcode::PAD: + case luci::CircleOpcode::PADV2: case luci::CircleOpcode::RELU: case luci::CircleOpcode::RELU6: + case luci::CircleOpcode::RSQRT: + case luci::CircleOpcode::SQUARED_DIFFERENCE: + case luci::CircleOpcode::SUB: if (!has_data_format(node)) { set_data_format(node, DataFormat::NCHW); @@ -677,7 +1292,11 @@ bool ConvertNCHWToNHWCPass::run(loco::Graph *g) ConvertNCHWToNHWC converter; auto circle_node = loco::must_cast<luci::CircleNode *>(node); if (circle_node->rank() != 4) - continue; + { + // TODO replace the check above with the input rank check, and remove the condition below + if (not dynamic_cast<luci::CircleMean *>(node)) + continue; + } if (circle_node->accept(&converter)) { diff --git a/compiler/luci/pass/src/ConvertNCHWToNHWCPass.test.cpp b/compiler/luci/pass/src/ConvertNCHWToNHWCPass.test.cpp index 831d5f89a..d844246f8 100644 --- a/compiler/luci/pass/src/ConvertNCHWToNHWCPass.test.cpp +++ b/compiler/luci/pass/src/ConvertNCHWToNHWCPass.test.cpp @@ -134,6 +134,93 @@ public: luci::CircleConst *beta = nullptr; }; +class NHWCReluGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + relu = g.nodes()->create<luci::CircleRelu>(); + pre_reshape = g.nodes()->create<luci::CircleReshape>(); + post_reshape = g.nodes()->create<luci::CircleReshape>(); + pre_shape = g.nodes()->create<luci::CircleConst>(); + post_shape = g.nodes()->create<luci::CircleConst>(); + + pre_shape->dtype(loco::DataType::S32); + post_shape->dtype(loco::DataType::S32); + + uint32_t channel_size = 16; + auto in = loco::must_cast<luci::CircleNode *>(input); + in->shape({1, channel_size, 4, 4}); + pre_shape->shape({4}); + post_shape->shape({4}); + + pre_shape->size<loco::DataType::S32>(4); + pre_shape->at<loco::DataType::S32>(0) = 1; + pre_shape->at<loco::DataType::S32>(1) = 4; + pre_shape->at<loco::DataType::S32>(2) = 4; + pre_shape->at<loco::DataType::S32>(3) = channel_size; + + post_shape->size<loco::DataType::S32>(4); + post_shape->at<loco::DataType::S32>(0) = 1; + post_shape->at<loco::DataType::S32>(1) = channel_size; + post_shape->at<loco::DataType::S32>(2) = 4; + post_shape->at<loco::DataType::S32>(3) = 4; + + pre_reshape->tensor(input); + pre_reshape->shape(pre_shape); + + relu->features(pre_reshape); + + post_reshape->tensor(relu); + post_reshape->shape(post_shape); + + relu->name("Relu"); + pre_reshape->name("pre-reshape"); + post_reshape->name("post-reshape"); + + return post_reshape; + } + +public: + luci::CircleRelu *relu = nullptr; + luci::CircleReshape *pre_reshape = nullptr; + luci::CircleReshape *post_reshape = nullptr; + luci::CircleConst *pre_shape = nullptr; + luci::CircleConst *post_shape = nullptr; +}; + +class AddScalarGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + add = g.nodes()->create<luci::CircleAdd>(); + beta = g.nodes()->create<luci::CircleConst>(); + + add->dtype(loco::DataType::FLOAT32); + beta->dtype(loco::DataType::FLOAT32); + + uint32_t channel_size = 16; + add->shape({1, channel_size, 4, 4}); + beta->shape({1}); + + beta->size<loco::DataType::FLOAT32>(1); + beta->at<loco::DataType::FLOAT32>(0) = 3.14; + + add->x(input); + add->y(beta); + + add->name("add"); + beta->name("beta"); + + return add; + } + +public: + luci::CircleAdd *add = nullptr; + luci::CircleConst *beta = nullptr; +}; + class ConcatenationGraph final : public SimpleGraph { protected: @@ -180,6 +267,129 @@ public: luci::CircleLeakyRelu *leakyrelu = nullptr; }; +class LogisticGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + logistic = g.nodes()->create<luci::CircleLogistic>(); + logistic->x(input); + logistic->name("logistic"); + + return logistic; + } + +public: + luci::CircleLogistic *logistic = nullptr; +}; + +class MaximumGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + max = g.nodes()->create<luci::CircleMaximum>(); + limit = g.nodes()->create<luci::CircleConst>(); + + max->dtype(loco::DataType::FLOAT32); + limit->dtype(loco::DataType::FLOAT32); + + max->shape({1, 16, 4, 4}); + limit->shape({}); + + limit->size<loco::DataType::FLOAT32>(1); + limit->at<loco::DataType::FLOAT32>(0) = 100; + + max->x(input); + max->y(limit); + + max->name("max"); + limit->name("limit"); + + return max; + } + +public: + luci::CircleMaximum *max = nullptr; + luci::CircleConst *limit = nullptr; +}; + +class MeanGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + mean = g.nodes()->create<luci::CircleMean>(); + rindices = g.nodes()->create<luci::CircleConst>(); + + mean->dtype(loco::DataType::FLOAT32); + rindices->dtype(loco::DataType::S32); + + mean->shape(_shape); + rindices->shape({static_cast<uint32_t>(_axes.size())}); + + rindices->size<loco::DataType::S32>(_axes.size()); + for (uint32_t i = 0; i < _axes.size(); ++i) + { + rindices->at<loco::DataType::S32>(i) = _axes[i]; + } + + mean->input(input); + mean->reduction_indices(rindices); + mean->keep_dims(_keep_dims); + + mean->name("mean"); + rindices->name("rindices"); + + return mean; + } + +public: + void keep_dims(bool val) { _keep_dims = val; } + void axes(std::vector<int32_t> val) { _axes = val; } + void shape(std::initializer_list<uint32_t> val) { _shape = val; } + +public: + luci::CircleMean *mean = nullptr; + luci::CircleConst *rindices = nullptr; + +private: + bool _keep_dims = true; + std::vector<int32_t> _axes = {2, 3}; + std::initializer_list<uint32_t> _shape = {1, 16, 1, 1}; +}; + +class MinimumGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + min = g.nodes()->create<luci::CircleMinimum>(); + limit = g.nodes()->create<luci::CircleConst>(); + + min->dtype(loco::DataType::FLOAT32); + limit->dtype(loco::DataType::FLOAT32); + + min->shape({1, 16, 4, 4}); + limit->shape({}); + + limit->size<loco::DataType::FLOAT32>(1); + limit->at<loco::DataType::FLOAT32>(0) = 100; + + min->x(input); + min->y(limit); + + min->name("min"); + limit->name("limit"); + + return min; + } + +public: + luci::CircleMinimum *min = nullptr; + luci::CircleConst *limit = nullptr; +}; + class MulGraph final : public SimpleGraph { protected: @@ -215,6 +425,62 @@ public: luci::CircleConst *multiplier = nullptr; }; +class MulScalarGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + mul = g.nodes()->create<luci::CircleMul>(); + multiplier = g.nodes()->create<luci::CircleConst>(); + + mul->dtype(loco::DataType::FLOAT32); + multiplier->dtype(loco::DataType::FLOAT32); + + uint32_t channel_size = 16; + mul->shape({1, channel_size, 4, 4}); + multiplier->shape({1}); + + multiplier->size<loco::DataType::FLOAT32>(1); + multiplier->at<loco::DataType::FLOAT32>(0) = 2; + + mul->x(input); + mul->y(multiplier); + + mul->name("mul"); + multiplier->name("multiplier"); + + return mul; + } + +public: + luci::CircleMul *mul = nullptr; + luci::CircleConst *multiplier = nullptr; +}; + +class MulBothNormGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + mul = g.nodes()->create<luci::CircleMul>(); + + mul->dtype(loco::DataType::FLOAT32); + + uint32_t channel_size = 16; + mul->shape({1, channel_size, 4, 4}); + + mul->x(input); + mul->y(input); + + mul->name("mul"); + + return mul; + } + +public: + luci::CircleMul *mul = nullptr; +}; + class NegGraph final : public SimpleGraph { protected: @@ -278,6 +544,62 @@ public: luci::CircleConst *paddings = nullptr; }; +class PadV2Graph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + pad = g.nodes()->create<luci::CirclePadV2>(); + paddings = g.nodes()->create<luci::CircleConst>(); + const_value = g.nodes()->create<luci::CircleConst>(); + + pad->dtype(loco::DataType::FLOAT32); + paddings->dtype(loco::DataType::S32); + const_value->dtype(loco::DataType::FLOAT32); + + uint32_t channel_size = 16; + pad->shape({1, channel_size, 4, 4}); + paddings->shape({4, 2}); + const_value->shape({1}); + + // paddings data (NCHW) + // [[0,0], [0,0], [1,1], [2,2]] + paddings->size<loco::DataType::S32>(8); + for (uint32_t dim = 0; dim < 4; dim++) + { + for (uint32_t i = 0; i < 2; i++) + { + int32_t data = 0; + + if (dim == 2) + data = 1; + else if (dim == 3) + data = 2; + + paddings->at<loco::DataType::S32>(dim * 2 + i) = data; + } + } + + const_value->size<loco::DataType::FLOAT32>(1); + const_value->at<loco::DataType::FLOAT32>(0) = -3.4; + + pad->input(input); + pad->paddings(paddings); + pad->constant_values(paddings); + + pad->name("padV2"); + paddings->name("paddings"); + const_value->name("constant_values"); + + return pad; + } + +public: + luci::CirclePadV2 *pad = nullptr; + luci::CircleConst *paddings = nullptr; + luci::CircleConst *const_value = nullptr; +}; + class ReluGraph final : public SimpleGraph { protected: @@ -310,6 +632,106 @@ public: luci::CircleRelu6 *relu6 = nullptr; }; +class RsqrtGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + rsqrt = g.nodes()->create<luci::CircleRsqrt>(); + rsqrt->x(input); + rsqrt->name("rsqrt"); + + return rsqrt; + } + +public: + luci::CircleRsqrt *rsqrt = nullptr; +}; + +class SquaredDifferenceGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + sqdiff = g.nodes()->create<luci::CircleSquaredDifference>(); + sqdiff->x(input); + sqdiff->y(input); + sqdiff->name("sqdiff"); + + return sqdiff; + } + +public: + luci::CircleSquaredDifference *sqdiff = nullptr; +}; + +class SubGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + sub = g.nodes()->create<luci::CircleSub>(); + beta = g.nodes()->create<luci::CircleConst>(); + + sub->dtype(loco::DataType::FLOAT32); + beta->dtype(loco::DataType::FLOAT32); + + uint32_t channel_size = 16; + sub->shape({1, channel_size, 4, 4}); + beta->shape({1, channel_size, 1, 1}); + + beta->size<loco::DataType::FLOAT32>(channel_size); + for (uint32_t i = 0; i < channel_size; i++) + { + beta->at<loco::DataType::FLOAT32>(i) = i; + } + + sub->x(input); + sub->y(beta); + + sub->name("sub"); + beta->name("beta"); + + return sub; + } + +public: + luci::CircleSub *sub = nullptr; + luci::CircleConst *beta = nullptr; +}; + +class SubScalarGraph final : public SimpleGraph +{ +protected: + loco::Node *insertGraphBody(loco::Node *input) override + { + sub = g.nodes()->create<luci::CircleSub>(); + beta = g.nodes()->create<luci::CircleConst>(); + + sub->dtype(loco::DataType::FLOAT32); + beta->dtype(loco::DataType::FLOAT32); + + uint32_t channel_size = 16; + sub->shape({1, channel_size, 4, 4}); + beta->shape({1}); + + beta->size<loco::DataType::FLOAT32>(1); + beta->at<loco::DataType::FLOAT32>(0) = 5; + + sub->x(beta); + sub->y(input); + + sub->name("sub"); + beta->name("beta"); + + return sub; + } + +public: + luci::CircleSub *sub = nullptr; + luci::CircleConst *beta = nullptr; +}; + void check_pre_trans(loco::Node *node) { auto pre_trans = dynamic_cast<luci::CircleTranspose *>(node); @@ -393,6 +815,47 @@ TEST(ConvertNCHWToNHWC, Add) check_pre_trans(g.output->from()); } +TEST(ConvertNCHWToNHWC, NHWC_Relu) +{ + // Relu is already NHWC, so it should not be converted + // i.e., the graph is not changed + NHWCReluGraph g; + g.init(); + + run_phase(&g.g, false, false); + + EXPECT_EQ(g.pre_reshape, g.relu->features()); + + auto relu_succs = loco::succs(g.relu); + EXPECT_EQ(1, relu_succs.size()); + EXPECT_EQ(g.post_reshape, *relu_succs.begin()); +} + +TEST(ConvertNCHWToNHWC, AddScalar) +{ + AddScalarGraph g; + g.init(); + + run_phase(&g.g, false, false); + + auto input_succs = loco::succs(g.input); + EXPECT_EQ(1, input_succs.size()); + check_post_trans(*input_succs.begin()); + + check_pre_trans(g.add->x()); + + auto add_succs = loco::succs(g.add); + EXPECT_EQ(1, add_succs.size()); + check_post_trans(*add_succs.begin()); + + auto new_beta = dynamic_cast<luci::CircleConst *>(g.add->y()); + EXPECT_NE(nullptr, new_beta); + EXPECT_EQ(1, new_beta->rank()); + EXPECT_EQ(1, new_beta->dim(0).value()); + + check_pre_trans(g.output->from()); +} + TEST(ConvertNCHWToNHWC, Concatenation) { ConcatenationGraph g; @@ -435,6 +898,202 @@ TEST(ConvertNCHWToNHWC, LeakyRelu) EXPECT_EQ(16, g.leakyrelu->dim(3).value()); } +TEST(ConvertNCHWToNHWC, Logistic) +{ + LogisticGraph g; + g.init(); + + run_phase(&g.g, true, true); + + check_pre_trans(g.logistic->x()); + + auto logistic_succs = loco::succs(g.logistic); + EXPECT_EQ(1, logistic_succs.size()); + check_post_trans(*logistic_succs.begin()); + + // Check logistic shape + EXPECT_EQ(1, g.logistic->dim(0).value()); + EXPECT_EQ(4, g.logistic->dim(1).value()); + EXPECT_EQ(4, g.logistic->dim(2).value()); + EXPECT_EQ(16, g.logistic->dim(3).value()); +} + +TEST(ConvertNCHWToNHWC, Maximum) +{ + MaximumGraph g; + g.init(); + + run_phase(&g.g, false, false); + + auto input_succs = loco::succs(g.input); + EXPECT_EQ(1, input_succs.size()); + check_post_trans(*input_succs.begin()); + + check_pre_trans(g.max->x()); + + auto max_succs = loco::succs(g.max); + EXPECT_EQ(1, max_succs.size()); + check_post_trans(*max_succs.begin()); + + check_pre_trans(g.output->from()); +} + +TEST(ConvertNCHWToNHWC, Mean) +{ + MeanGraph g; + g.init(); + + run_phase(&g.g, false, false); + + check_pre_trans(g.mean->input()); + + auto mean_succs = loco::succs(g.mean); + EXPECT_EQ(1, mean_succs.size()); + check_post_trans(*mean_succs.begin()); + + auto new_rindices = dynamic_cast<luci::CircleConst *>(g.mean->reduction_indices()); + EXPECT_NE(nullptr, new_rindices); + EXPECT_EQ(1, new_rindices->rank()); + EXPECT_EQ(2, new_rindices->dim(0).value()); + EXPECT_EQ(2, new_rindices->size<loco::DataType::S32>()); + EXPECT_EQ(1, new_rindices->at<loco::DataType::S32>(0)); + EXPECT_EQ(2, new_rindices->at<loco::DataType::S32>(1)); +} + +TEST(ConvertNCHWToNHWC, Mean_keep_dims_false) +{ + struct TC + { + std::vector<int32_t> nchw_ind; + std::vector<int32_t> nhwc_ind; + std::initializer_list<uint32_t> shape; + bool needs_transpose = false; + }; + + uint32_t n = 1; + uint32_t c = 16; + uint32_t h = 4; + uint32_t w = 4; + + std::vector<TC> test_cases{{{0}, {0}, {c, h, w}, true}, {{1}, {3}, {n, h, w}, false}, + {{2}, {1}, {n, c, w}, true}, {{3}, {2}, {n, c, h}, true}, + {{0, 1}, {0, 3}, {h, w}, false}, {{0, 2}, {0, 1}, {c, w}, true}, + {{0, 3}, {0, 2}, {c, h}, true}, {{1, 2}, {3, 1}, {n, w}, false}, + {{1, 3}, {3, 2}, {n, h}, false}, {{2, 3}, {1, 2}, {n, c}, false}, + {{0, 1, 2}, {0, 3, 1}, {w}, false}}; + + for (auto &tc : test_cases) + { + MeanGraph g; + g.keep_dims(false); + g.axes(tc.nchw_ind); + g.shape(tc.shape); + g.init(); + + run_phase(&g.g, false, true); + + check_pre_trans(g.mean->input()); + + auto mean_succs = loco::succs(g.mean); + EXPECT_EQ(1, mean_succs.size()); + if (tc.needs_transpose) + { + EXPECT_NE(nullptr, dynamic_cast<luci::CircleTranspose *>(*mean_succs.begin())); + } + else + { + EXPECT_NE(nullptr, dynamic_cast<luci::CircleOutput *>(*mean_succs.begin())); + } + + auto new_rindices = dynamic_cast<luci::CircleConst *>(g.mean->reduction_indices()); + EXPECT_NE(nullptr, new_rindices); + EXPECT_EQ(1, new_rindices->rank()); + EXPECT_EQ(tc.nhwc_ind.size(), new_rindices->dim(0).value()); + EXPECT_EQ(tc.nhwc_ind.size(), new_rindices->size<loco::DataType::S32>()); + for (uint32_t i = 0; i < tc.nhwc_ind.size(); ++i) + { + EXPECT_EQ(tc.nhwc_ind[i], new_rindices->at<loco::DataType::S32>(i)); + } + } +} + +TEST(ConvertNCHWToNHWC, ConvertNCHWToNHWC_Mean_keep_dims_false_NEG) +{ + loco::Graph g; + auto input = g.nodes()->create<luci::CircleInput>(); + auto output = g.nodes()->create<luci::CircleOutput>(); + input->name("input"); + output->name("output"); + + auto graph_input = g.inputs()->create(); + input->index(graph_input->index()); + auto graph_output = g.outputs()->create(); + output->index(graph_output->index()); + + graph_input->dtype(loco::DataType::FLOAT32); + input->dtype(loco::DataType::FLOAT32); + output->dtype(loco::DataType::FLOAT32); + graph_output->dtype(loco::DataType::FLOAT32); + + uint32_t channel_size = 16; + graph_input->shape({channel_size, 4, 4}); + input->shape({channel_size, 4, 4}); + output->shape({channel_size}); + graph_output->shape({channel_size}); + + auto mean = g.nodes()->create<luci::CircleMean>(); + auto rindices = g.nodes()->create<luci::CircleConst>(); + + mean->dtype(loco::DataType::FLOAT32); + rindices->dtype(loco::DataType::S32); + + mean->shape({channel_size}); + rindices->shape({2}); + + rindices->size<loco::DataType::S32>(2); + rindices->at<loco::DataType::S32>(0) = 1; + rindices->at<loco::DataType::S32>(1) = 2; + + mean->input(input); + mean->reduction_indices(rindices); + mean->keep_dims(false); + + mean->name("mean"); + rindices->name("rindices"); + + output->from(mean); + + run_phase(&g, true, true); + + auto new_rindices = dynamic_cast<luci::CircleConst *>(mean->reduction_indices()); + EXPECT_NE(nullptr, new_rindices); + EXPECT_EQ(1, new_rindices->rank()); + EXPECT_EQ(2, new_rindices->dim(0).value()); + EXPECT_EQ(2, new_rindices->size<loco::DataType::S32>()); + EXPECT_EQ(1, new_rindices->at<loco::DataType::S32>(0)); + EXPECT_EQ(2, new_rindices->at<loco::DataType::S32>(1)); +} + +TEST(ConvertNCHWToNHWC, Minimum) +{ + MinimumGraph g; + g.init(); + + run_phase(&g.g, false, false); + + auto input_succs = loco::succs(g.input); + EXPECT_EQ(1, input_succs.size()); + check_post_trans(*input_succs.begin()); + + check_pre_trans(g.min->x()); + + auto min_succs = loco::succs(g.min); + EXPECT_EQ(1, min_succs.size()); + check_post_trans(*min_succs.begin()); + + check_pre_trans(g.output->from()); +} + TEST(ConvertNCHWToNHWC, Mul) { MulGraph g; @@ -464,6 +1123,52 @@ TEST(ConvertNCHWToNHWC, Mul) check_pre_trans(g.output->from()); } +TEST(ConvertNCHWToNHWC, MulScalar) +{ + MulScalarGraph g; + g.init(); + + run_phase(&g.g, false, false); + + auto input_succs = loco::succs(g.input); + EXPECT_EQ(1, input_succs.size()); + check_post_trans(*input_succs.begin()); + + check_pre_trans(g.mul->x()); + + auto mul_succs = loco::succs(g.mul); + EXPECT_EQ(1, mul_succs.size()); + check_post_trans(*mul_succs.begin()); + + auto new_multiplier = dynamic_cast<luci::CircleConst *>(g.mul->y()); + EXPECT_NE(nullptr, new_multiplier); + EXPECT_EQ(1, new_multiplier->rank()); + EXPECT_EQ(1, new_multiplier->dim(0).value()); + + check_pre_trans(g.output->from()); +} + +TEST(ConvertNCHWToNHWC, MulBothNorm) +{ + MulBothNormGraph g; + g.init(); + + run_phase(&g.g, false, false); + + auto input_succs = loco::succs(g.input); + EXPECT_EQ(1, input_succs.size()); + check_post_trans(*input_succs.begin()); + + check_pre_trans(g.mul->x()); + check_pre_trans(g.mul->y()); + + auto mul_succs = loco::succs(g.mul); + EXPECT_EQ(1, mul_succs.size()); + check_post_trans(*mul_succs.begin()); + + check_pre_trans(g.output->from()); +} + TEST(ConvertNCHWToNHWC, Neg) { NegGraph g; @@ -518,6 +1223,34 @@ TEST(ConvertNCHWToNHWC, Pad) check_pre_trans(g.output->from()); } +TEST(ConvertNCHWToNHWC, PadV2) +{ + PadV2Graph g; + g.init(); + + run_phase(&g.g, false, false); + + check_pre_trans(g.pad->input()); + + auto pad_succs = loco::succs(g.pad); + EXPECT_EQ(1, pad_succs.size()); + check_post_trans(*pad_succs.begin()); + + auto new_paddings = dynamic_cast<luci::CircleConst *>(g.pad->paddings()); + EXPECT_NE(nullptr, new_paddings); + EXPECT_EQ(2, new_paddings->rank()); + EXPECT_EQ(4, new_paddings->dim(0).value()); + EXPECT_EQ(2, new_paddings->dim(1).value()); + EXPECT_EQ(0, new_paddings->at<loco::DataType::S32>(0)); + EXPECT_EQ(0, new_paddings->at<loco::DataType::S32>(1)); + EXPECT_EQ(1, new_paddings->at<loco::DataType::S32>(2)); + EXPECT_EQ(1, new_paddings->at<loco::DataType::S32>(3)); + EXPECT_EQ(2, new_paddings->at<loco::DataType::S32>(4)); + EXPECT_EQ(2, new_paddings->at<loco::DataType::S32>(5)); + EXPECT_EQ(0, new_paddings->at<loco::DataType::S32>(6)); + EXPECT_EQ(0, new_paddings->at<loco::DataType::S32>(7)); +} + TEST(ConvertNCHWToNHWC, Unknown_Shape_NEG) { AddGraph g; @@ -634,3 +1367,91 @@ TEST(ConvertNCHWToNHWC, Relu6) EXPECT_EQ(4, g.relu6->dim(2).value()); EXPECT_EQ(16, g.relu6->dim(3).value()); } + +TEST(ConvertNCHWToNHWC, Rsqrt) +{ + RsqrtGraph g; + g.init(); + + run_phase(&g.g, true, true); + + check_pre_trans(g.rsqrt->x()); + + auto rsqrt_succs = loco::succs(g.rsqrt); + EXPECT_EQ(1, rsqrt_succs.size()); + check_post_trans(*rsqrt_succs.begin()); + + // Check rsqrt shape + EXPECT_EQ(1, g.rsqrt->dim(0).value()); + EXPECT_EQ(4, g.rsqrt->dim(1).value()); + EXPECT_EQ(4, g.rsqrt->dim(2).value()); + EXPECT_EQ(16, g.rsqrt->dim(3).value()); +} + +TEST(ConvertNCHWToNHWC, SquaredDifference) +{ + SquaredDifferenceGraph g; + g.init(); + + run_phase(&g.g, true, true); + + check_pre_trans(g.sqdiff->x()); + check_pre_trans(g.sqdiff->y()); + + auto sqdiff_succs = loco::succs(g.sqdiff); + EXPECT_EQ(1, sqdiff_succs.size()); + check_post_trans(*sqdiff_succs.begin()); +} + +TEST(ConvertNCHWToNHWC, Sub) +{ + SubGraph g; + g.init(); + + run_phase(&g.g, false, false); + + auto input_succs = loco::succs(g.input); + EXPECT_EQ(1, input_succs.size()); + check_post_trans(*input_succs.begin()); + + check_pre_trans(g.sub->x()); + + auto add_succs = loco::succs(g.sub); + EXPECT_EQ(1, add_succs.size()); + check_post_trans(*add_succs.begin()); + + uint32_t channel_size = 16; + auto new_beta = dynamic_cast<luci::CircleConst *>(g.sub->y()); + EXPECT_NE(nullptr, new_beta); + EXPECT_EQ(4, new_beta->rank()); + EXPECT_EQ(1, new_beta->dim(0).value()); + EXPECT_EQ(1, new_beta->dim(1).value()); + EXPECT_EQ(1, new_beta->dim(2).value()); + EXPECT_EQ(channel_size, new_beta->dim(3).value()); + + check_pre_trans(g.output->from()); +} + +TEST(ConvertNCHWToNHWC, SubScalar) +{ + SubScalarGraph g; + g.init(); + + run_phase(&g.g, false, false); + + auto input_succs = loco::succs(g.input); + EXPECT_EQ(1, input_succs.size()); + check_post_trans(*input_succs.begin()); + + check_pre_trans(g.sub->y()); + + auto add_succs = loco::succs(g.sub); + EXPECT_EQ(1, add_succs.size()); + check_post_trans(*add_succs.begin()); + + auto new_beta = dynamic_cast<luci::CircleConst *>(g.sub->x()); + EXPECT_NE(nullptr, new_beta); + EXPECT_EQ(1, new_beta->rank()); + + check_pre_trans(g.output->from()); +} diff --git a/compiler/luci/pass/src/FuseBatchNormWithDwConvPass.cpp b/compiler/luci/pass/src/FuseBatchNormWithDwConvPass.cpp index 8b2286f43..64e8daa69 100644 --- a/compiler/luci/pass/src/FuseBatchNormWithDwConvPass.cpp +++ b/compiler/luci/pass/src/FuseBatchNormWithDwConvPass.cpp @@ -96,10 +96,6 @@ bool fused_batch_norm_with_dwconv(luci::CircleAdd *add) return false; if (add->dtype() != loco::DataType::FLOAT32) return false; - // TODO support more Activations - if (add->fusedActivationFunction() != luci::FusedActFunc::NONE && - add->fusedActivationFunction() != luci::FusedActFunc::RELU6) - return false; // get weight of dwconv auto filter = dynamic_cast<luci::CircleConst *>(dwconv->filter()); diff --git a/compiler/luci/pass/src/FuseInstanceNormPass.cpp b/compiler/luci/pass/src/FuseInstanceNormPass.cpp index ab7baa1fa..f3ec6cd9e 100644 --- a/compiler/luci/pass/src/FuseInstanceNormPass.cpp +++ b/compiler/luci/pass/src/FuseInstanceNormPass.cpp @@ -21,6 +21,7 @@ #include <luci/IR/CircleNodes.h> #include <luci/Profile/CircleNodeOrigin.h> +#include <luci/Service/CircleNodeClone.h> #include <cassert> #include <set> @@ -40,23 +41,7 @@ bool is_1D_with_dummy_dim(luci::CircleConst *node, uint32_t depth) return node->dim(axis).value() == depth; } -/// @return true if node shape consists of ones, except the one before the last dim: 1,...1,depth,1 -bool is_quasi_1D_with_dummy_dim(luci::CircleConst *node, uint32_t depth) -{ - auto rank = node->rank(); - // minimal accepted shape is [1 x depth x 1] - if (rank < 3) - return false; - const auto depth_axis = rank - 2; - for (uint32_t axis = 0; axis < rank; ++axis) - { - if (axis != depth_axis && node->dim(axis).value() != 1) - return false; - } - return node->dim(depth_axis).value() == depth; -} - -bool is_instance_mean_v0(luci::CircleMean *mean) +bool is_instance_mean_v1(luci::CircleMean *mean) { // // CHECK 1) input is rank 4 @@ -100,53 +85,6 @@ bool is_instance_mean_v0(luci::CircleMean *mean) return mean->keep_dims(); } -bool is_instance_mean_v1(luci::CircleMean *mean) -{ - // - // CHECK 1) input is rank 5 (NHWCX) - // - auto input = loco::must_cast<luci::CircleNode *>(mean->input()); - if (input->shape_status() != luci::ShapeStatus::VALID) - return false; - if (input->rank() != 5) - return false; - - // - // CHECK 2) 'reduction indices' is CircleConst of value [1,2,4], that is HWX of NHWCX input shape - // - // TODO Support equivalent case, like [-3,-2] - // TODO Support non-Const case? - // TODO What if input is NCHW format in Circle? - auto red_indices = dynamic_cast<luci::CircleConst *>(mean->reduction_indices()); - if (not red_indices) - return false; - if (red_indices->rank() != 1) - return false; - std::set<int32_t> red_indices_set; - - // TODO Currently only support S32, support other types - if (red_indices->dtype() != loco::DataType::S32) - return false; - for (uint32_t i = 0; i < red_indices->dim(0).value(); ++i) - red_indices_set.insert(red_indices->at<loco::DataType::S32>(i)); - - if (red_indices_set.size() != 3) - return false; - if (red_indices_set.find(1) == red_indices_set.end()) - return false; - if (red_indices_set.find(2) == red_indices_set.end()) - return false; - if (red_indices_set.find(4) == red_indices_set.end()) - return false; - - // - // CHECK 3) keep_dims == true (?) - // - // We only have case of 'keep_dims == true' so far, but it might be okay with 'keep_dims == false' - // TODO Check this fact, and if true, return true regardless of keep_dims - return mean->keep_dims(); -} - /// @return true When node has the shape of 1D channel_size bool is_1D_float32_const(const luci::CircleConst *node, uint32_t channel_size) { @@ -182,6 +120,7 @@ namespace * * TODO support other semantically same patterns for instance norm * + * Version_1 * [In] * | * V @@ -216,46 +155,7 @@ namespace * V * [Out] *------------------------------------------------------------------- - * [In] - * | - * V - * ifm - * | - * V - * +---------reshape_of_ifm ----+ (reduction indicies) - * | | | | - * | | V V - * | | mean_of_reshape -------------+ - * | V | | - * | sqdiff <--+ (reduction indicies) | - * | | | | - * | V | | - * | mean_as_variance <---+ const_as_epsilon | - * | | | | - * | V | | - * | add_as_variance <--------+ | - * | | | - * | V | - * | rsqrt const_as_gamma | - * | | | | - * | V | | - * | mul_gamma <--+ | - * | | | | - * V V V | - * mul_as_scaled_reshape mul_as_scaled_mean <-----------+ - * | | - * | const_as_beta | - * | | V - * | +------> sub - * V | - * add_as_terminal <----------+ - * | - * V - * reshape_as_terminal - * | - * V - * [Out] - *------------------------------------------------------------------- + * Version_2 * [In] * | * V @@ -288,15 +188,126 @@ namespace * | * V * [Out] + *------------------------------------------------------------------- + * Version_3 + * [In] + * | + * V + * +----+-------------- ifm ---+ + * | | (reduction | | (reduction + * | | indicies) | | indicies) + * | | | | | | + * | V V | V V + * | mean_of_ifm | mean_of_ifm_2 + * | | | | + * V | V | + * sub <----+ sub_2 <---+ + * | | + * | V + * | square + * | | (reduction indicies) + * | | | + * | V | + * | mean_as_variance <---+ + * | | + * | V + * | sqrt const_as_epsilon + * | | | + * | V | + * | add_as_variance <---+ + * | | + * V | + * div <------------------+ const_as_gamma + * | | + * V | + * mul_gamma <----------------------+ + * | const_as_beta + * V | + * add_as_terminal <--------+ + * | + * V + * [Out] + *------------------------------------------------------------------- + * Version_4 + * - mul_gamma and add_as_terminal are removed for const_as_gamma = 1.0 + * and const_as_beta = 0.0 + * [In] + * | + * V + * +----+-------------- ifm ---+ + * | | (reduction | | (reduction + * | | indicies) | | indicies) + * | | | | | | + * | V V | V V + * | mean_of_ifm | mean_of_ifm_2 + * | | | | + * V | V | + * sub <----+ sub_2 <---+ + * | | + * | V + * | square + * | | (reduction indicies) + * | | | + * | V | + * | mean_as_variance <---+ + * | | + * | V + * | sqrt const_as_epsilon + * | | | + * | V | + * | add_as_variance <---+ + * | | + * V | + * div <------------------+ + * | + * V + * [Out] + *------------------------------------------------------------------- + * Version_5 + * [In] + * | + * V + * +----------- ifm -----+ (reduction indicies) + * | | | | + * | | V V + * | | mean_of_ifm ----------------+ + * | V | | + * | sqdiff <--+ (reduction indicies) | + * | | | | + * | V | | + * | mean_as_variance <---+ const_as_epsilon | + * | | | | + * | V | | + * | add_as_variance <--------+ | + * | | | + * | V | + * | rsqrt | + * | | | + * | +--+--+ | + * | | | | + * V V V | + * mul_as_scaled_ifm mul_as_scaled_mean <-------------+ + * | | + * | const_as_beta | + * | | V + * | +------> sub + * V | + * add_as_terminal <----------+ + * | + * V + * [Out] */ class InstanceNormPattern final { public: enum PatternVersion { - Version_0, + Version_Unknown, Version_1, Version_2, + Version_3, + Version_4, + Version_5, }; InstanceNormPattern(luci::CircleAdd *candidate, PatternVersion pv) @@ -306,6 +317,16 @@ public: _pv = pv; } + InstanceNormPattern(luci::CircleDiv *candidate, PatternVersion pv) + { + assert(candidate); + div = candidate; + _pv = pv; + } + +private: + template <enum PatternVersion> bool match(); + public: bool matched(); bool matched() const { return _matched; } @@ -317,8 +338,10 @@ public: loco::Node *ifm = nullptr; luci::CircleReshape *reshape_of_ifm = nullptr; luci::CircleMean *mean_of_ifm = nullptr; + luci::CircleMean *mean_of_ifm_2 = nullptr; luci::CircleMean *mean_of_reshape = nullptr; luci::CircleSquaredDifference *sqdiff = nullptr; + luci::CircleSquare *square = nullptr; luci::CircleMean *mean_as_variance = nullptr; luci::CircleConst *const_as_epsilon = nullptr; luci::CircleAdd *add_as_variance = nullptr; @@ -330,8 +353,10 @@ public: luci::CircleMul *mul_as_scaled_reshape = nullptr; luci::CircleConst *const_as_beta = nullptr; luci::CircleSub *sub = nullptr; + luci::CircleSub *sub_2 = nullptr; luci::CircleAdd *add_as_terminal = nullptr; luci::CirclePow *pow = nullptr; + luci::CircleSqrt *sqrt = nullptr; luci::CircleDiv *div = nullptr; private: @@ -339,93 +364,247 @@ private: PatternVersion _pv; }; -bool InstanceNormPattern::matched() -{ - if (_matched) - return true; - #define CHECK_OR_FALSE(condition) \ if (not(condition)) \ return false; - // Check order is DFS +template <> bool InstanceNormPattern::match<InstanceNormPattern::PatternVersion::Version_1>() +{ + CHECK_OR_FALSE(luci::fill(&mul_as_scaled_ifm, &sub).with_commutative_args_of(add_as_terminal)); + CHECK_OR_FALSE(luci::fill(&ifm, &mul_gamma).with_commutative_args_of(mul_as_scaled_ifm)); - // Version 2 is quite different from Version 0 and 1. - // So it is handled in the separate if statement - if (_pv == PatternVersion::Version_2) - { - CHECK_OR_FALSE( - luci::fill(&mul_gamma, &const_as_beta).with_commutative_args_of(add_as_terminal)); - CHECK_OR_FALSE(luci::fill(&div, &const_as_gamma).with_commutative_args_of(mul_gamma)); + auto ifm_circle = loco::must_cast<luci::CircleNode *>(ifm); + CHECK_OR_FALSE(ifm_circle->shape_status() == luci::ShapeStatus::VALID); + CHECK_OR_FALSE(ifm_circle->rank() == 4); + CHECK_OR_FALSE(ifm_circle->dim(3).known()); + uint32_t ifm_channel_depth = ifm_circle->dim(3).value(); - sub = dynamic_cast<luci::CircleSub *>(div->x()); - CHECK_OR_FALSE(sub); + CHECK_OR_FALSE(luci::fill(&rsqrt, &const_as_gamma).with_commutative_args_of(mul_gamma)); - ifm = sub->x(); - CHECK_OR_FALSE(ifm); + CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_gamma, ifm_channel_depth)); - luci::CircleNode *ifm_node = loco::must_cast<luci::CircleNode *>(ifm); - CHECK_OR_FALSE(ifm_node->rank() == 4); - CHECK_OR_FALSE(ifm_node->dim(3).known()); - uint32_t ifm_channel_depth = ifm_node->dim(3).value(); + add_as_variance = dynamic_cast<luci::CircleAdd *>(rsqrt->x()); + CHECK_OR_FALSE(add_as_variance); - mean_of_ifm = dynamic_cast<luci::CircleMean *>(sub->y()); - CHECK_OR_FALSE(mean_of_ifm); + CHECK_OR_FALSE( + luci::fill(&mean_as_variance, &const_as_epsilon).with_commutative_args_of(add_as_variance)); - CHECK_OR_FALSE(ifm == mean_of_ifm->input()); + CHECK_OR_FALSE(const_as_epsilon->dtype() == loco::DataType::FLOAT32); + // TODO Support regarding broadcast + CHECK_OR_FALSE(const_as_epsilon->size<loco::DataType::FLOAT32>() == 1); - pow = dynamic_cast<luci::CirclePow *>(div->y()); - CHECK_OR_FALSE(pow); + CHECK_OR_FALSE(is_instance_mean_v1(mean_as_variance)); - add_as_variance = dynamic_cast<luci::CircleAdd *>(pow->x()); - CHECK_OR_FALSE(add_as_variance); + sqdiff = dynamic_cast<luci::CircleSquaredDifference *>(mean_as_variance->input()); + CHECK_OR_FALSE(sqdiff); - luci::CircleConst *zero_point_five = dynamic_cast<luci::CircleConst *>(pow->y()); - CHECK_OR_FALSE(zero_point_five); - CHECK_OR_FALSE(zero_point_five->dtype() == loco::DataType::FLOAT32); - // TODO Support regarding broadcast - CHECK_OR_FALSE(zero_point_five->size<loco::DataType::FLOAT32>() == 1); - CHECK_OR_FALSE(zero_point_five->at<loco::DataType::FLOAT32>(0) == 0.5); + loco::Node *ifm_should_be = nullptr; + CHECK_OR_FALSE(luci::fill(&ifm_should_be, &mean_of_ifm).with_commutative_args_of(sqdiff)); + CHECK_OR_FALSE(ifm == ifm_should_be); + CHECK_OR_FALSE(is_instance_mean_v1(mean_of_ifm)); + CHECK_OR_FALSE(ifm == mean_of_ifm->input()); - CHECK_OR_FALSE( - luci::fill(&mean_as_variance, &const_as_epsilon).with_commutative_args_of(add_as_variance)); - CHECK_OR_FALSE(const_as_epsilon->dtype() == loco::DataType::FLOAT32); - // TODO Support regarding broadcast - CHECK_OR_FALSE(const_as_epsilon->size<loco::DataType::FLOAT32>() == 1); + const_as_beta = dynamic_cast<luci::CircleConst *>(sub->x()); + CHECK_OR_FALSE(const_as_beta); + CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_beta, ifm_channel_depth)); - CHECK_OR_FALSE(is_instance_mean_v0(mean_as_variance)); + luci::CircleMul *mul_gamma_should_be = nullptr; + luci::CircleMean *mean_of_ifm_should_be = nullptr; - sqdiff = dynamic_cast<luci::CircleSquaredDifference *>(mean_as_variance->input()); - CHECK_OR_FALSE(sqdiff); + mul_as_scaled_mean = dynamic_cast<luci::CircleMul *>(sub->y()); + CHECK_OR_FALSE(mul_as_scaled_mean); + CHECK_OR_FALSE(luci::fill(&mul_gamma_should_be, &mean_of_ifm_should_be) + .with_commutative_args_of(mul_as_scaled_mean)); + CHECK_OR_FALSE(mul_gamma == mul_gamma_should_be); + CHECK_OR_FALSE(mean_of_ifm == mean_of_ifm_should_be); - loco::Node *ifm_should_be = nullptr; - luci::CircleMean *mean_of_ifm_should_be = nullptr; - CHECK_OR_FALSE( - luci::fill(&ifm_should_be, &mean_of_ifm_should_be).with_commutative_args_of(sqdiff)); - CHECK_OR_FALSE(ifm == ifm_should_be); - CHECK_OR_FALSE(mean_of_ifm == mean_of_ifm_should_be); + _matched = true; + return true; +} - // Check for channel size - CHECK_OR_FALSE(is_1D_float32_const(const_as_gamma, ifm_channel_depth)); - CHECK_OR_FALSE(is_1D_float32_const(const_as_beta, ifm_channel_depth)); +template <> bool InstanceNormPattern::match<InstanceNormPattern::PatternVersion::Version_2>() +{ + CHECK_OR_FALSE(luci::fill(&mul_gamma, &const_as_beta).with_commutative_args_of(add_as_terminal)); + CHECK_OR_FALSE(luci::fill(&div, &const_as_gamma).with_commutative_args_of(mul_gamma)); - _matched = true; - return true; - } + sub = dynamic_cast<luci::CircleSub *>(div->x()); + CHECK_OR_FALSE(sub); - if (_pv == PatternVersion::Version_0) - { - CHECK_OR_FALSE(luci::fill(&mul_as_scaled_ifm, &sub).with_commutative_args_of(add_as_terminal)); - CHECK_OR_FALSE(luci::fill(&ifm, &mul_gamma).with_commutative_args_of(mul_as_scaled_ifm)); - } - if (_pv == PatternVersion::Version_1) - { - CHECK_OR_FALSE( - luci::fill(&mul_as_scaled_reshape, &sub).with_commutative_args_of(add_as_terminal)); - CHECK_OR_FALSE( - luci::fill(&reshape_of_ifm, &mul_gamma).with_commutative_args_of(mul_as_scaled_reshape)); - ifm = reshape_of_ifm->tensor(); - } + ifm = sub->x(); + CHECK_OR_FALSE(ifm); + + luci::CircleNode *ifm_node = loco::must_cast<luci::CircleNode *>(ifm); + CHECK_OR_FALSE(ifm_node->rank() == 4); + CHECK_OR_FALSE(ifm_node->dim(3).known()); + uint32_t ifm_channel_depth = ifm_node->dim(3).value(); + + mean_of_ifm = dynamic_cast<luci::CircleMean *>(sub->y()); + CHECK_OR_FALSE(mean_of_ifm); + + CHECK_OR_FALSE(ifm == mean_of_ifm->input()); + + pow = dynamic_cast<luci::CirclePow *>(div->y()); + CHECK_OR_FALSE(pow); + + add_as_variance = dynamic_cast<luci::CircleAdd *>(pow->x()); + CHECK_OR_FALSE(add_as_variance); + + luci::CircleConst *zero_point_five = dynamic_cast<luci::CircleConst *>(pow->y()); + CHECK_OR_FALSE(zero_point_five); + CHECK_OR_FALSE(zero_point_five->dtype() == loco::DataType::FLOAT32); + // TODO Support regarding broadcast + CHECK_OR_FALSE(zero_point_five->size<loco::DataType::FLOAT32>() == 1); + CHECK_OR_FALSE(zero_point_five->at<loco::DataType::FLOAT32>(0) == 0.5); + + CHECK_OR_FALSE( + luci::fill(&mean_as_variance, &const_as_epsilon).with_commutative_args_of(add_as_variance)); + CHECK_OR_FALSE(const_as_epsilon->dtype() == loco::DataType::FLOAT32); + // TODO Support regarding broadcast + CHECK_OR_FALSE(const_as_epsilon->size<loco::DataType::FLOAT32>() == 1); + + CHECK_OR_FALSE(is_instance_mean_v1(mean_as_variance)); + + sqdiff = dynamic_cast<luci::CircleSquaredDifference *>(mean_as_variance->input()); + CHECK_OR_FALSE(sqdiff); + + loco::Node *ifm_should_be = nullptr; + luci::CircleMean *mean_of_ifm_should_be = nullptr; + CHECK_OR_FALSE( + luci::fill(&ifm_should_be, &mean_of_ifm_should_be).with_commutative_args_of(sqdiff)); + CHECK_OR_FALSE(ifm == ifm_should_be); + CHECK_OR_FALSE(mean_of_ifm == mean_of_ifm_should_be); + + // Check for channel size + CHECK_OR_FALSE(is_1D_float32_const(const_as_gamma, ifm_channel_depth)); + CHECK_OR_FALSE(is_1D_float32_const(const_as_beta, ifm_channel_depth)); + + _matched = true; + return true; +} + +template <> bool InstanceNormPattern::match<InstanceNormPattern::PatternVersion::Version_3>() +{ + CHECK_OR_FALSE(luci::fill(&mul_gamma, &const_as_beta).with_commutative_args_of(add_as_terminal)); + CHECK_OR_FALSE(luci::fill(&div, &const_as_gamma).with_commutative_args_of(mul_gamma)); + CHECK_OR_FALSE(luci::fill(&sub, &add_as_variance).with_commutative_args_of(div)); + + // check left sub + ifm = sub->x(); + CHECK_OR_FALSE(ifm); + + luci::CircleNode *ifm_node = loco::must_cast<luci::CircleNode *>(ifm); + CHECK_OR_FALSE(ifm_node->rank() == 4); + CHECK_OR_FALSE(ifm_node->dim(3).known()); + + mean_of_ifm = dynamic_cast<luci::CircleMean *>(sub->y()); + CHECK_OR_FALSE(mean_of_ifm); + CHECK_OR_FALSE(ifm == mean_of_ifm->input()); + + // continue search from add_as_variance + CHECK_OR_FALSE(luci::fill(&sqrt, &const_as_epsilon).with_commutative_args_of(add_as_variance)); + CHECK_OR_FALSE(const_as_epsilon->dtype() == loco::DataType::FLOAT32); + // TODO Support regarding broadcast + CHECK_OR_FALSE(const_as_epsilon->size<loco::DataType::FLOAT32>() == 1); + + mean_as_variance = dynamic_cast<luci::CircleMean *>(sqrt->x()); + CHECK_OR_FALSE(mean_as_variance); + + square = dynamic_cast<luci::CircleSquare *>(mean_as_variance->input()); + CHECK_OR_FALSE(square); + + sub_2 = dynamic_cast<luci::CircleSub *>(square->x()); + CHECK_OR_FALSE(sub_2); + CHECK_OR_FALSE(ifm == sub_2->x()); + + mean_of_ifm_2 = dynamic_cast<luci::CircleMean *>(sub_2->y()); + CHECK_OR_FALSE(mean_of_ifm_2); + CHECK_OR_FALSE(ifm == mean_of_ifm_2->input()); + + loco::Node *ifm_should_be = nullptr; + luci::CircleMean *mean_of_ifm_2_should_be = nullptr; + CHECK_OR_FALSE( + luci::fill(&ifm_should_be, &mean_of_ifm_2_should_be).with_commutative_args_of(sub_2)); + CHECK_OR_FALSE(ifm == ifm_should_be); + CHECK_OR_FALSE(mean_of_ifm_2 == mean_of_ifm_2_should_be); + + _matched = true; + return true; +} + +luci::CircleConst *make_const_one(loco::Graph *graph, float value) +{ + auto const_one = graph->nodes()->create<luci::CircleConst>(); + const_one->dtype(loco::DataType::FLOAT32); + const_one->rank(1); + const_one->size<loco::DataType::FLOAT32>(1); + const_one->at<loco::DataType::FLOAT32>(0) = value; + return const_one; +} + +template <> bool InstanceNormPattern::match<InstanceNormPattern::PatternVersion::Version_4>() +{ + CHECK_OR_FALSE(div); + CHECK_OR_FALSE(luci::fill(&sub, &add_as_variance).with_commutative_args_of(div)); + + // check left sub + ifm = sub->x(); + CHECK_OR_FALSE(ifm); + + luci::CircleNode *ifm_node = loco::must_cast<luci::CircleNode *>(ifm); + CHECK_OR_FALSE(ifm_node->rank() == 4); + CHECK_OR_FALSE(ifm_node->dim(3).known()); + + mean_of_ifm = dynamic_cast<luci::CircleMean *>(sub->y()); + CHECK_OR_FALSE(mean_of_ifm); + CHECK_OR_FALSE(ifm == mean_of_ifm->input()); + + // continue search from add_as_variance + CHECK_OR_FALSE(luci::fill(&sqrt, &const_as_epsilon).with_commutative_args_of(add_as_variance)); + CHECK_OR_FALSE(const_as_epsilon->dtype() == loco::DataType::FLOAT32); + // TODO Support regarding broadcast + CHECK_OR_FALSE(const_as_epsilon->size<loco::DataType::FLOAT32>() == 1); + + mean_as_variance = dynamic_cast<luci::CircleMean *>(sqrt->x()); + CHECK_OR_FALSE(mean_as_variance); + + square = dynamic_cast<luci::CircleSquare *>(mean_as_variance->input()); + CHECK_OR_FALSE(square); + + sub_2 = dynamic_cast<luci::CircleSub *>(square->x()); + CHECK_OR_FALSE(sub_2); + CHECK_OR_FALSE(ifm == sub_2->x()); + + mean_of_ifm_2 = dynamic_cast<luci::CircleMean *>(sub_2->y()); + CHECK_OR_FALSE(mean_of_ifm_2); + CHECK_OR_FALSE(ifm == mean_of_ifm_2->input()); + + loco::Node *ifm_should_be = nullptr; + luci::CircleMean *mean_of_ifm_2_should_be = nullptr; + CHECK_OR_FALSE( + luci::fill(&ifm_should_be, &mean_of_ifm_2_should_be).with_commutative_args_of(sub_2)); + CHECK_OR_FALSE(ifm == ifm_should_be); + CHECK_OR_FALSE(mean_of_ifm_2 == mean_of_ifm_2_should_be); + + assert(const_as_gamma == nullptr); + assert(const_as_beta == nullptr); + assert(mul_gamma == nullptr); + assert(add_as_terminal == nullptr); + + // create 1.0 gamma and 0.0 beta + auto graph = div->graph(); + const_as_gamma = make_const_one(graph, 1.0f); + const_as_beta = make_const_one(graph, 0.0f); + const_as_gamma->name(div->name() + "/gamma"); + const_as_beta->name(div->name() + "/beta"); + + _matched = true; + return true; +} + +template <> bool InstanceNormPattern::match<InstanceNormPattern::PatternVersion::Version_5>() +{ + CHECK_OR_FALSE(luci::fill(&mul_as_scaled_ifm, &sub).with_commutative_args_of(add_as_terminal)); + CHECK_OR_FALSE(luci::fill(&ifm, &rsqrt).with_commutative_args_of(mul_as_scaled_ifm)); auto ifm_circle = loco::must_cast<luci::CircleNode *>(ifm); CHECK_OR_FALSE(ifm_circle->shape_status() == luci::ShapeStatus::VALID); @@ -433,17 +612,6 @@ bool InstanceNormPattern::matched() CHECK_OR_FALSE(ifm_circle->dim(3).known()); uint32_t ifm_channel_depth = ifm_circle->dim(3).value(); - CHECK_OR_FALSE(luci::fill(&rsqrt, &const_as_gamma).with_commutative_args_of(mul_gamma)); - - if (_pv == PatternVersion::Version_0) - { - CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_gamma, ifm_channel_depth)); - } - if (_pv == PatternVersion::Version_1) - { - CHECK_OR_FALSE(is_quasi_1D_with_dummy_dim(const_as_gamma, ifm_channel_depth)); - } - add_as_variance = dynamic_cast<luci::CircleAdd *>(rsqrt->x()); CHECK_OR_FALSE(add_as_variance); @@ -454,151 +622,495 @@ bool InstanceNormPattern::matched() // TODO Support regarding broadcast CHECK_OR_FALSE(const_as_epsilon->size<loco::DataType::FLOAT32>() == 1); - if (_pv == PatternVersion::Version_0) - { - CHECK_OR_FALSE(is_instance_mean_v0(mean_as_variance)); - } - if (_pv == PatternVersion::Version_1) - { - CHECK_OR_FALSE(is_instance_mean_v1(mean_as_variance)); - } + CHECK_OR_FALSE(is_instance_mean_v1(mean_as_variance)); sqdiff = dynamic_cast<luci::CircleSquaredDifference *>(mean_as_variance->input()); CHECK_OR_FALSE(sqdiff); - if (_pv == PatternVersion::Version_0) - { - loco::Node *ifm_should_be = nullptr; - CHECK_OR_FALSE(luci::fill(&ifm_should_be, &mean_of_ifm).with_commutative_args_of(sqdiff)); - CHECK_OR_FALSE(ifm == ifm_should_be); - CHECK_OR_FALSE(is_instance_mean_v0(mean_of_ifm)); - CHECK_OR_FALSE(ifm == mean_of_ifm->input()); - } - if (_pv == PatternVersion::Version_1) - { - loco::Node *reshape_should_be = nullptr; - CHECK_OR_FALSE( - luci::fill(&reshape_should_be, &mean_of_reshape).with_commutative_args_of(sqdiff)); - CHECK_OR_FALSE(reshape_of_ifm == reshape_should_be); - CHECK_OR_FALSE(is_instance_mean_v1(mean_of_reshape)); - CHECK_OR_FALSE(reshape_of_ifm == mean_of_reshape->input()); - } + loco::Node *ifm_should_be = nullptr; + CHECK_OR_FALSE(luci::fill(&ifm_should_be, &mean_of_ifm).with_commutative_args_of(sqdiff)); + CHECK_OR_FALSE(ifm == ifm_should_be); + CHECK_OR_FALSE(is_instance_mean_v1(mean_of_ifm)); + CHECK_OR_FALSE(ifm == mean_of_ifm->input()); const_as_beta = dynamic_cast<luci::CircleConst *>(sub->x()); CHECK_OR_FALSE(const_as_beta); + CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_beta, ifm_channel_depth)); - if (_pv == PatternVersion::Version_0) - { - CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_beta, ifm_channel_depth)); - } - if (_pv == PatternVersion::Version_1) - { - CHECK_OR_FALSE(is_quasi_1D_with_dummy_dim(const_as_beta, ifm_channel_depth)); - } + luci::CircleRsqrt *rsqrt_should_be = nullptr; + luci::CircleMean *mean_of_ifm_should_be = nullptr; mul_as_scaled_mean = dynamic_cast<luci::CircleMul *>(sub->y()); CHECK_OR_FALSE(mul_as_scaled_mean); + CHECK_OR_FALSE(luci::fill(&rsqrt_should_be, &mean_of_ifm_should_be) + .with_commutative_args_of(mul_as_scaled_mean)); + CHECK_OR_FALSE(rsqrt == rsqrt_should_be); + CHECK_OR_FALSE(mean_of_ifm == mean_of_ifm_should_be); - luci::CircleMul *mul_gamma_should_be = nullptr; - luci::CircleMean *mean_of_ifm_should_be = nullptr; - luci::CircleMean *mean_of_reshape_should_be = nullptr; + // mul_gamma is absent + // const_as_gamma assume to be 1.0 + auto graph = add_as_terminal->graph(); + const_as_gamma = make_const_one(graph, 1.0f); + const_as_gamma->name(add_as_terminal->name() + "/gamma"); - if (_pv == PatternVersion::Version_0) - { - CHECK_OR_FALSE(luci::fill(&mul_gamma_should_be, &mean_of_ifm_should_be) - .with_commutative_args_of(mul_as_scaled_mean)); - CHECK_OR_FALSE(mul_gamma == mul_gamma_should_be); - CHECK_OR_FALSE(mean_of_ifm == mean_of_ifm_should_be); - } - if (_pv == PatternVersion::Version_1) + _matched = true; + return true; +} + +bool InstanceNormPattern::matched() +{ + if (_matched) + return true; + + // Check order is DFS + + switch (_pv) { - CHECK_OR_FALSE(luci::fill(&mul_gamma_should_be, &mean_of_reshape_should_be) - .with_commutative_args_of(mul_as_scaled_mean)); - CHECK_OR_FALSE(mul_gamma == mul_gamma_should_be); - CHECK_OR_FALSE(mean_of_reshape == mean_of_reshape_should_be); + case PatternVersion::Version_1: + return match<PatternVersion::Version_1>(); + case PatternVersion::Version_2: + return match<PatternVersion::Version_2>(); + case PatternVersion::Version_3: + return match<PatternVersion::Version_3>(); + case PatternVersion::Version_4: + return match<PatternVersion::Version_4>(); + case PatternVersion::Version_5: + return match<PatternVersion::Version_5>(); + + default: + break; } -#undef CHECK_OR_FALSE - _matched = true; - return true; + throw std::runtime_error("Invalid InstanceNorm PatternVersion."); } +#undef CHECK_OR_FALSE + /** * Instance norm pattern would be fused like following diagram: * - * [In] --------------------------- CircleInstanceNorm --- [Out] - * / / - * const_as_gamma --- TFLReshape --- / - * / - * const_as_beta ---- TFLReshape --- + * [In] -------------- CircleInstanceNorm --- [Out] + * / / + * const_as_gamma ---- / + * / + * const_as_beta ----- * * Note * - 'const_as_gamma' and 'const_as_beta' are from original graph * - Value of 'const_as_epsilon' would be copied to CircleInstanceNorm's attribute - * - TFLReshape is added as CircleInstanceNorm only accept 1D tensor + * - Two CircleConst shape is updated as CircleInstanceNorm only accept 1D tensor * - 'CircleConst --- TFLReshape' is expected to be fused in constant folding for Reshape */ -void fuse_instance_norm(const InstanceNormPattern &p) + +class FuseInstanceNorm final { - assert(p.matched()); +public: + FuseInstanceNorm(const InstanceNormPattern &p) : _p(p) {} - auto graph = p.add_as_terminal->graph(); +public: + void apply(void); - // Version 0 and 1 need to reshape - if (p.version() != InstanceNormPattern::Version_2) +private: + template <InstanceNormPattern::PatternVersion> void apply(void); + +private: + void reshape_gamma_beta(void); + luci::CircleInstanceNorm *create_inst_norm(loco::Graph *graph); + +private: + const InstanceNormPattern &_p; +}; + +void FuseInstanceNorm::reshape_gamma_beta() +{ + // Version 1 and 3 need to reshape { - p.const_as_gamma->rank(1); - p.const_as_gamma->dim(0).set(p.const_as_gamma->size<loco::DataType::FLOAT32>()); - p.const_as_beta->rank(1); - p.const_as_beta->dim(0).set(p.const_as_beta->size<loco::DataType::FLOAT32>()); + _p.const_as_gamma->rank(1); + _p.const_as_gamma->dim(0).set(_p.const_as_gamma->size<loco::DataType::FLOAT32>()); + _p.const_as_beta->rank(1); + _p.const_as_beta->dim(0).set(_p.const_as_beta->size<loco::DataType::FLOAT32>()); - p.const_as_gamma->shape_status(luci::ShapeStatus::UNDEFINED); - p.const_as_beta->shape_status(luci::ShapeStatus::UNDEFINED); + _p.const_as_gamma->shape_status(luci::ShapeStatus::UNDEFINED); + _p.const_as_beta->shape_status(luci::ShapeStatus::UNDEFINED); } +} +luci::CircleInstanceNorm *FuseInstanceNorm::create_inst_norm(loco::Graph *graph) +{ // Make Instance Norm to replace auto instance_norm = graph->nodes()->create<luci::CircleInstanceNorm>(); - instance_norm->input(p.ifm); - instance_norm->gamma(p.const_as_gamma); - instance_norm->beta(p.const_as_beta); - float epsilon = p.const_as_epsilon->at<loco::DataType::FLOAT32>(0); + instance_norm->input(_p.ifm); + instance_norm->gamma(_p.const_as_gamma); + instance_norm->beta(_p.const_as_beta); + float epsilon = _p.const_as_epsilon->at<loco::DataType::FLOAT32>(0); instance_norm->epsilon(epsilon); - instance_norm->fusedActivationFunction(p.add_as_terminal->fusedActivationFunction()); - // NOTE unique name should be assigned in export - instance_norm->name("InstanceNorm"); + if (_p.add_as_terminal != nullptr) + { + instance_norm->fusedActivationFunction(_p.add_as_terminal->fusedActivationFunction()); + // NOTE unique name should be assigned in export + instance_norm->name("FusedInstanceNorm/" + _p.add_as_terminal->name()); + } + else + { + // VERSION_4 + assert(_p.div != nullptr); + instance_norm->fusedActivationFunction(_p.div->fusedActivationFunction()); + instance_norm->name("FusedInstanceNorm/" + _p.div->name()); + } + + return instance_norm; +} + +template <> void FuseInstanceNorm::apply<InstanceNormPattern::PatternVersion::Version_1>() +{ + auto graph = _p.add_as_terminal->graph(); + + reshape_gamma_beta(); + + auto instance_norm = create_inst_norm(graph); + + // set origin + std::vector<std::shared_ptr<luci::CircleNodeOrigin>> origin_vec{ + luci::get_origin(_p.mean_of_ifm), + luci::get_origin(_p.sqdiff), + luci::get_origin(_p.mean_as_variance), + luci::get_origin(_p.add_as_variance), + luci::get_origin(_p.rsqrt), + luci::get_origin(_p.mul_gamma), + luci::get_origin(_p.mul_as_scaled_ifm), + luci::get_origin(_p.mul_as_scaled_mean), + luci::get_origin(_p.sub), + luci::get_origin(_p.add_as_terminal)}; + + luci::add_origin(instance_norm, luci::composite_origin(origin_vec)); + + replace(_p.add_as_terminal).with(instance_norm); +} + +template <> void FuseInstanceNorm::apply<InstanceNormPattern::PatternVersion::Version_2>() +{ + auto graph = _p.add_as_terminal->graph(); + + auto instance_norm = create_inst_norm(graph); + + // set origin + std::vector<std::shared_ptr<luci::CircleNodeOrigin>> origin_vec{ + luci::get_origin(_p.mean_of_ifm), + luci::get_origin(_p.sqdiff), + luci::get_origin(_p.mean_as_variance), + luci::get_origin(_p.add_as_variance), + luci::get_origin(_p.pow), + luci::get_origin(_p.sub), + luci::get_origin(_p.div), + luci::get_origin(_p.mul_gamma), + luci::get_origin(_p.add_as_terminal)}; + + luci::add_origin(instance_norm, luci::composite_origin(origin_vec)); + + replace(_p.add_as_terminal).with(instance_norm); +} + +template <> void FuseInstanceNorm::apply<InstanceNormPattern::PatternVersion::Version_3>() +{ + auto graph = _p.add_as_terminal->graph(); + + reshape_gamma_beta(); + + auto instance_norm = create_inst_norm(graph); + + // set origin + std::vector<std::shared_ptr<luci::CircleNodeOrigin>> origin_vec{ + luci::get_origin(_p.mean_of_ifm), + luci::get_origin(_p.sub), + luci::get_origin(_p.mean_of_ifm_2), + luci::get_origin(_p.sub_2), + luci::get_origin(_p.square), + luci::get_origin(_p.mean_as_variance), + luci::get_origin(_p.sqrt), + luci::get_origin(_p.add_as_variance), + luci::get_origin(_p.div), + luci::get_origin(_p.mul_gamma), + luci::get_origin(_p.add_as_terminal)}; + + luci::add_origin(instance_norm, luci::composite_origin(origin_vec)); + + replace(_p.add_as_terminal).with(instance_norm); +} + +template <> void FuseInstanceNorm::apply<InstanceNormPattern::PatternVersion::Version_4>() +{ + auto graph = _p.div->graph(); + + auto instance_norm = create_inst_norm(graph); // set origin std::vector<std::shared_ptr<luci::CircleNodeOrigin>> origin_vec{ - luci::get_origin(p.sqdiff), - luci::get_origin(p.mean_as_variance), - luci::get_origin(p.add_as_variance), - luci::get_origin(p.mul_gamma), - luci::get_origin(p.sub), - luci::get_origin(p.add_as_terminal)}; - if (p.version() == InstanceNormPattern::PatternVersion::Version_0) + luci::get_origin(_p.mean_of_ifm), + luci::get_origin(_p.sub), + luci::get_origin(_p.mean_of_ifm_2), + luci::get_origin(_p.sub_2), + luci::get_origin(_p.square), + luci::get_origin(_p.mean_as_variance), + luci::get_origin(_p.sqrt), + luci::get_origin(_p.add_as_variance), + luci::get_origin(_p.div)}; + + luci::add_origin(instance_norm, luci::composite_origin(origin_vec)); + + replace(_p.div).with(instance_norm); +} + +template <> void FuseInstanceNorm::apply<InstanceNormPattern::PatternVersion::Version_5>() +{ + auto graph = _p.add_as_terminal->graph(); + + reshape_gamma_beta(); + + auto instance_norm = create_inst_norm(graph); + + // set origin + std::vector<std::shared_ptr<luci::CircleNodeOrigin>> origin_vec{ + luci::get_origin(_p.mean_of_ifm), + luci::get_origin(_p.sqdiff), + luci::get_origin(_p.mean_as_variance), + luci::get_origin(_p.add_as_variance), + luci::get_origin(_p.rsqrt), + luci::get_origin(_p.mul_as_scaled_ifm), + luci::get_origin(_p.mul_as_scaled_mean), + luci::get_origin(_p.sub), + luci::get_origin(_p.add_as_terminal)}; + + luci::add_origin(instance_norm, luci::composite_origin(origin_vec)); + + replace(_p.add_as_terminal).with(instance_norm); +} + +void FuseInstanceNorm::apply() +{ + assert(_p.matched()); + + switch (_p.version()) { - origin_vec.push_back(luci::get_origin(p.mean_of_ifm)); - origin_vec.push_back(luci::get_origin(p.rsqrt)); - origin_vec.push_back(luci::get_origin(p.mul_as_scaled_ifm)); - origin_vec.push_back(luci::get_origin(p.mul_as_scaled_mean)); + case InstanceNormPattern::PatternVersion::Version_1: + apply<InstanceNormPattern::PatternVersion::Version_1>(); + break; + case InstanceNormPattern::PatternVersion::Version_2: + apply<InstanceNormPattern::PatternVersion::Version_2>(); + break; + case InstanceNormPattern::PatternVersion::Version_3: + apply<InstanceNormPattern::PatternVersion::Version_3>(); + break; + case InstanceNormPattern::PatternVersion::Version_4: + apply<InstanceNormPattern::PatternVersion::Version_4>(); + break; + case InstanceNormPattern::PatternVersion::Version_5: + apply<InstanceNormPattern::PatternVersion::Version_5>(); + break; + + default: + break; } - if (p.version() == InstanceNormPattern::PatternVersion::Version_1) +} + +} // namespace + +namespace +{ + +class PostFusion final +{ +public: + PostFusion(luci::CircleInstanceNorm *inst_norm) : _inst_norm(inst_norm) {} + +private: + uint32_t input_channel(void); + + luci::CircleConst *match_const_channel(luci::CircleConst *, uint32_t); + bool match_const_gamma_channel(void); + bool match_const_beta_channel(void); + +public: + bool process(void); + +private: + luci::CircleInstanceNorm *_inst_norm = nullptr; +}; + +/** + * @brief return C value or 0 if shape status is not valid + */ +uint32_t PostFusion::input_channel(void) +{ + auto input = dynamic_cast<luci::CircleNode *>(_inst_norm->input()); + if (input == nullptr) + return 0; + if (input->shape_status() != luci::ShapeStatus::VALID) + return 0; + + auto input_rank = input->rank(); + if (input_rank < 1) + return 0; + + // assume channel-last + return input->dim(input_rank - 1).value(); +} + +/** + * @brief return new CircleConst with C channel if input_const channel != C + */ +luci::CircleConst *PostFusion::match_const_channel(luci::CircleConst *input_const, uint32_t C) +{ + luci::CircleConst *new_input_const = nullptr; + + auto input_chn = input_const->dim(0).value(); + if (input_chn == 1 && input_chn != C) + { + float value = input_const->at<loco::DataType::FLOAT32>(0); + auto clone = luci::clone_node(input_const, input_const->graph()); + + new_input_const = loco::must_cast<luci::CircleConst *>(clone); + new_input_const->rank(1); + new_input_const->dim(0).set(C); + new_input_const->size<loco::DataType::FLOAT32>(C); + for (uint32_t c = 0; c < C; ++c) + new_input_const->at<loco::DataType::FLOAT32>(c) = value; + } + + return new_input_const; +} + +/** + * @brief Broadcast gamma to match input channel if CircleConst + */ +bool PostFusion::match_const_gamma_channel(void) +{ + auto const_as_gamma = dynamic_cast<luci::CircleConst *>(_inst_norm->gamma()); + if (const_as_gamma == nullptr) + return false; + + auto C = input_channel(); + if (C == 0) + return false; + + auto new_const_as_gamma = match_const_channel(const_as_gamma, C); + if (new_const_as_gamma == nullptr) + return false; + + _inst_norm->gamma(new_const_as_gamma); + + return true; +} + +/** + * @brief Broadcast beta to match input channel if CircleConst + */ +bool PostFusion::match_const_beta_channel(void) +{ + auto const_as_beta = dynamic_cast<luci::CircleConst *>(_inst_norm->beta()); + if (const_as_beta == nullptr) + return false; + + auto C = input_channel(); + if (C == 0) + return false; + + auto new_const_as_beta = match_const_channel(const_as_beta, C); + if (new_const_as_beta == nullptr) + return false; + + _inst_norm->beta(new_const_as_beta); + + return true; +} + +bool PostFusion::process(void) +{ + bool changed = false; + + if (match_const_gamma_channel()) + changed = true; + if (match_const_beta_channel()) + changed = true; + + return changed; +} + +} // namespace + +namespace +{ + +bool is_add_input_mul_const(luci::CircleAdd *add) +{ + luci::CircleMul *p_mul = nullptr; + luci::CircleConst *p_const = nullptr; + + return luci::fill(&p_mul, &p_const).with_commutative_args_of(add); +} + +bool fuse_instance_norm(luci::CircleAdd *add) +{ + InstanceNormPattern::PatternVersion pv = InstanceNormPattern::PatternVersion::Version_1; + + if (is_add_input_mul_const(add)) + pv = InstanceNormPattern::PatternVersion::Version_2; + + InstanceNormPattern pattern(add, pv); + if (pattern.matched()) + { + FuseInstanceNorm fuse(pattern); + fuse.apply(); + return true; + } + + if (pv == InstanceNormPattern::PatternVersion::Version_1) { - origin_vec.push_back(luci::get_origin(p.reshape_of_ifm)); - origin_vec.push_back(luci::get_origin(p.mean_of_reshape)); - origin_vec.push_back(luci::get_origin(p.rsqrt)); - origin_vec.push_back(luci::get_origin(p.mul_as_scaled_mean)); - origin_vec.push_back(luci::get_origin(p.mul_as_scaled_reshape)); + // if Version_1 failed, try with Version_5 + pv = InstanceNormPattern::PatternVersion::Version_5; + InstanceNormPattern pattern(add, pv); + if (pattern.matched()) + { + FuseInstanceNorm fuse(pattern); + fuse.apply(); + return true; + } } - if (p.version() == InstanceNormPattern::PatternVersion::Version_2) + else if (pv == InstanceNormPattern::PatternVersion::Version_2) { - origin_vec.push_back(luci::get_origin(p.mean_of_ifm)); - origin_vec.push_back(luci::get_origin(p.pow)); - origin_vec.push_back(luci::get_origin(p.div)); + // if Version_2 failed, try with Version_3 + pv = InstanceNormPattern::PatternVersion::Version_3; + InstanceNormPattern pattern(add, pv); + if (pattern.matched()) + { + FuseInstanceNorm fuse(pattern); + fuse.apply(); + return true; + } } - luci::add_origin(instance_norm, luci::composite_origin(origin_vec)); - replace(p.add_as_terminal).with(instance_norm); + return false; +} + +bool fuse_instance_norm(luci::CircleDiv *div) +{ + InstanceNormPattern::PatternVersion pv = InstanceNormPattern::PatternVersion::Version_4; + + InstanceNormPattern pattern(div, pv); + if (pattern.matched()) + { + FuseInstanceNorm fuse(pattern); + fuse.apply(); + return true; + } + + return false; +} + +bool post_fusion(luci::CircleInstanceNorm *inst_norm) +{ + PostFusion postfusion(inst_norm); + + return postfusion.process(); } } // namespace @@ -609,41 +1121,38 @@ namespace luci bool FuseInstanceNormPass::run(loco::Graph *g) { bool changed = false; - luci::CircleAdd *add; - InstanceNormPattern::PatternVersion pv; + // Check Version_1, Version_2, Version_3, Version_5 for (auto node : loco::active_nodes(loco::output_nodes(g))) { - auto reshape = dynamic_cast<luci::CircleReshape *>(node); - if (not reshape) - { - add = dynamic_cast<luci::CircleAdd *>(node); - if (not add) - continue; - pv = InstanceNormPattern::PatternVersion::Version_0; - - auto x = loco::must_cast<luci::CircleNode *>(add->x()); - auto y = loco::must_cast<luci::CircleNode *>(add->y()); - if ((x->opcode() == luci::CircleOpcode::MUL && - y->opcode() == luci::CircleOpcode::CIRCLECONST) || - (x->opcode() == luci::CircleOpcode::CIRCLECONST && - y->opcode() == luci::CircleOpcode::MUL)) - pv = InstanceNormPattern::PatternVersion::Version_2; - } - else - { - add = dynamic_cast<luci::CircleAdd *>(reshape->tensor()); - if (not add) - continue; - pv = InstanceNormPattern::PatternVersion::Version_1; - } + auto add = dynamic_cast<luci::CircleAdd *>(node); + if (not add) + continue; - InstanceNormPattern pattern(add, pv); - if (not pattern.matched()) + if (fuse_instance_norm(add)) + changed = true; + } + + // Check Version_4(from DIV) if MUL-ADD pattern is not found + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto div = dynamic_cast<luci::CircleDiv *>(node); + if (not div) continue; - fuse_instance_norm(pattern); - changed = true; + if (fuse_instance_norm(div)) + changed = true; + } + + // Post processing of FuseInstanceNorm + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto inst_norm = dynamic_cast<luci::CircleInstanceNorm *>(node); + if (not inst_norm) + continue; + + if (post_fusion(inst_norm)) + changed = true; } return changed; diff --git a/compiler/luci/pass/src/FuseInstanceNormPass.test.cpp b/compiler/luci/pass/src/FuseInstanceNormPass.test.cpp index b83ccca50..83489fdea 100644 --- a/compiler/luci/pass/src/FuseInstanceNormPass.test.cpp +++ b/compiler/luci/pass/src/FuseInstanceNormPass.test.cpp @@ -42,32 +42,3 @@ TEST(FuseInstanceNormPassTest, name) auto const name = pass.name(); ASSERT_NE(nullptr, name); } - -TEST(FuseInstanceNormPass, is_quasi_1D_with_dummy_dim) -{ - luci::CircleConst const_node; - - setShape(const_node, {}); - EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); - - setShape(const_node, {1}); - EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); - - setShape(const_node, {8}); - EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); - - setShape(const_node, {1, 2, 1, 8, 1}); - EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); - - setShape(const_node, {8, 3}); - EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); - - setShape(const_node, {8, 1}); - EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); - - setShape(const_node, {1, 8, 1}); - EXPECT_TRUE(is_quasi_1D_with_dummy_dim(&const_node, 8)); - - setShape(const_node, {1, 1, 1, 8, 1}); - EXPECT_TRUE(is_quasi_1D_with_dummy_dim(&const_node, 8)); -} diff --git a/compiler/luci/pass/src/FuseMeanWithMeanPass.cpp b/compiler/luci/pass/src/FuseMeanWithMeanPass.cpp new file mode 100644 index 000000000..75113f94b --- /dev/null +++ b/compiler/luci/pass/src/FuseMeanWithMeanPass.cpp @@ -0,0 +1,172 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/FuseMeanWithMeanPass.h" + +#include <luci/IR/CircleNode.h> +#include <luci/Profile/CircleNodeOrigin.h> + +namespace +{ +/** + * Fuse two Mean operations to one Mean operation with merged reduction indices + * + * BEFORE + * | + * [CircleMean, axis<1>] + * | + * [CircleMean, axis<1>] + * | + * + * AFTER + * | + * [CircleMean, axis<1,2>] [CircleMean, axis<1>] + * | | + * [CircleMean, axis<1>] + * + */ +luci::CircleConst *create_fused_indices(luci::CircleConst *indices, + const std::set<uint32_t> &indices_set) +{ + auto name = indices->name(); + + auto fused_indices_const = indices->graph()->nodes()->create<luci::CircleConst>(); + fused_indices_const->dtype(indices->dtype()); + fused_indices_const->rank(1); + fused_indices_const->dim(0) = indices_set.size(); + fused_indices_const->size<loco::DataType::S32>(indices_set.size()); + fused_indices_const->shape_status(luci::ShapeStatus::VALID); + fused_indices_const->name(name); + + auto curr_index = 0; + for (auto it = indices_set.begin(); it != indices_set.end(); it++) + { + fused_indices_const->at<loco::DataType::S32>(curr_index) = *it; + curr_index++; + } + + return fused_indices_const; +} + +bool fuse_mean_with_mean(luci::CircleMean *mean) +{ + // Get reduction indices of current CircleMean operation. + auto indices = dynamic_cast<luci::CircleConst *>(mean->reduction_indices()); + if (not indices) + return false; + assert(indices->dtype() == loco::DataType::S32); + + // Check whether previous node is CircleMean operation or not. + auto prev_mean = dynamic_cast<luci::CircleMean *>(mean->input()); + if (not prev_mean) + return false; + + // Check whether input rank of previous CircleMean operation is less 2 or not. + // This optimization works only if doesn't. + auto input = loco::must_cast<luci::CircleNode *>(prev_mean->input()); + if (input->shape_status() != luci::ShapeStatus::VALID) + return false; + auto input_rank = input->rank(); + if (input_rank < 2) + return false; + + // Check whether current CircleMean and previous CircleMean + // has the same keep_dims parameter or not. + // If it doesn't, keep the graph unchanged. + if (mean->keep_dims() != prev_mean->keep_dims()) + return false; + + // Get reduction indices of previous CircleMean operation. + auto prev_indices = dynamic_cast<luci::CircleConst *>(prev_mean->reduction_indices()); + if (not prev_indices) + return false; + assert(prev_indices->dtype() == loco::DataType::S32); + + // Get sizes of indices of current CircleMean operation and previous CircleMean operation. + auto indices_size = indices->size<loco::DataType::S32>(); + auto prev_indices_size = prev_indices->size<loco::DataType::S32>(); + + // Get set of indices of previous CircleMean operation. + std::set<uint32_t> indices_set; + for (uint32_t i = 0; i < prev_indices_size; i++) + { + auto index = prev_indices->at<loco::DataType::S32>(i); + if (index < 0) + index += input_rank; + indices_set.insert(index); + } + + // Get the vector of input indexes, that remained untouched + // after the current CircleMean operation. + std::vector<uint32_t> input_indices_vector; + for (uint32_t i = 0; i < input_rank; i++) + { + if (indices_set.find(i) == indices_set.end()) + input_indices_vector.push_back(i); + } + + // Get final set of merged indices. + for (uint32_t i = 0; i < indices_size; i++) + { + auto index = indices->at<loco::DataType::S32>(i); + if (index < 0) + index += input_rank; + indices_set.insert(input_indices_vector.at(index)); + } + + // Create merged indices. + auto fused_indices_const = create_fused_indices(indices, indices_set); + + auto name = mean->name(); + assert(name.length() > 0); + + // Create and configure new CircleMean operation. + auto fused_mean = mean->graph()->nodes()->create<luci::CircleMean>(); + fused_mean->reduction_indices(fused_indices_const); + fused_mean->input(prev_mean->input()); + fused_mean->keep_dims(mean->keep_dims()); + fused_mean->name(name + "/Mean"); + + // Replace old CircleMean operations with new CircleMean operation with merged indices. + replace(mean).with(fused_mean); + luci::add_origin(fused_mean, + luci::composite_origin({luci::get_origin(mean), luci::get_origin(prev_mean)})); + + return true; +} + +} // namespace + +namespace luci +{ + +bool FuseMeanWithMeanPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto mean = dynamic_cast<luci::CircleMean *>(node); + if (not mean) + continue; + + if (fuse_mean_with_mean(mean)) + changed = true; + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/FuseMeanWithMeanPass.test.cpp b/compiler/luci/pass/src/FuseMeanWithMeanPass.test.cpp new file mode 100644 index 000000000..30511e2d6 --- /dev/null +++ b/compiler/luci/pass/src/FuseMeanWithMeanPass.test.cpp @@ -0,0 +1,142 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/FuseMeanWithMeanPass.h" + +#include <luci/IR/CircleNodes.h> + +#include <luci/test/TestIOGraph.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +/** + * Simple graph for test + * + * BEFORE + * | + * [CircleMean, axis<1>] + * | + * [CircleMean, axis<1>] + * | + * + * AFTER + * | + * [CircleMean, axis<1,2>] [CircleMean, axis<1>] + * | | + * [CircleMean, axis<1>] + * + */ +class MeansGraphlet +{ +public: + MeansGraphlet() = default; + + void init(loco::Graph *g) + { + _mean1 = g->nodes()->create<luci::CircleMean>(); + _mean2 = g->nodes()->create<luci::CircleMean>(); + _indices1 = g->nodes()->create<luci::CircleConst>(); + _indices2 = g->nodes()->create<luci::CircleConst>(); + + _mean1->name("mean1"); + _mean2->name("mean2"); + _indices1->name("indices1"); + _indices2->name("indices2"); + } + +public: + luci::CircleMean *mean1() { return _mean1; } + luci::CircleMean *mean2() { return _mean2; } + +protected: + luci::CircleMean *_mean1 = nullptr; + luci::CircleMean *_mean2 = nullptr; + luci::CircleConst *_indices1 = nullptr; + luci::CircleConst *_indices2 = nullptr; +}; + +class FuseMeanWithMeanTestGraph : public TestIOGraph, public MeansGraphlet +{ +public: + FuseMeanWithMeanTestGraph() = default; + + void init(void) + { + TestIOGraph::init({1, 64, 20, 32}, {1, 20}); + MeansGraphlet::init(g()); + + _indices1->rank(1); + _indices1->dtype(loco::DataType::S32); + _indices1->size<loco::DataType::S32>(1); + _indices1->at<loco::DataType::S32>(0) = static_cast<int32_t>(1); + _indices1->shape_status(luci::ShapeStatus::VALID); + + _indices2->rank(1); + _indices2->dtype(loco::DataType::S32); + _indices2->size<loco::DataType::S32>(1); + _indices2->at<loco::DataType::S32>(0) = static_cast<int32_t>(2); + _indices2->shape_status(luci::ShapeStatus::VALID); + + _mean1->input(input()); + _mean1->reduction_indices(_indices1); + + _mean2->input(_mean1); + _mean2->reduction_indices(_indices2); + + output()->from(_mean2); + } +}; + +} // namespace + +TEST(FuseMeanWithMeanPassTest, name) +{ + luci::FuseMeanWithMeanPass pass; + auto const name = pass.name(); + ASSERT_NE(nullptr, name); +} + +TEST(FuseMeanWithMeanPassTest, fuse_mean_with_mean) +{ + FuseMeanWithMeanTestGraph g; + luci::FuseMeanWithMeanPass pass; + + g.init(); + + EXPECT_TRUE(pass.run(g.g())); +} + +TEST(FuseMeanWithMeanPassTest, fus_mean_with_mean_NEG) +{ + FuseMeanWithMeanTestGraph g; + luci::FuseMeanWithMeanPass pass; + + g.init(); + + // Add CircleRelu operation between CircleMeans operations + auto relu = g.g()->nodes()->create<luci::CircleRelu>(); + relu->name("relu"); + relu->features(g.mean1()); + g.mean2()->input(relu); + + // Due to the CircleRelu operation, pass will not be applied + EXPECT_FALSE(pass.run(g.g())); +} diff --git a/compiler/luci/pass/src/FuseTransposeWithMeanPass.cpp b/compiler/luci/pass/src/FuseTransposeWithMeanPass.cpp new file mode 100644 index 000000000..e5fac5af9 --- /dev/null +++ b/compiler/luci/pass/src/FuseTransposeWithMeanPass.cpp @@ -0,0 +1,164 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/FuseTransposeWithMeanPass.h" + +#include <luci/IR/CircleNode.h> +#include <luci/Profile/CircleNodeOrigin.h> +#include <luci/Service/Nodes/CircleConst.h> + +namespace +{ + +/** + * Fuse Transpose with Mean if possible + * + * BEFORE + * | + * [CircleTranspose, perm<0, 2, 3, 1>] + * | + * [CircleMean, axis<3>] + * | + * + * AFTER + * | | + * [CircleMean, axis<1>] [CircleTranspose, perm<0, 2, 3, 1>] + * | | + * [CircleMean, axis<3>] + * + */ + +/** + * @brief Create a const for fused reduction indices + */ +luci::CircleConst *create_fused_indices(luci::CircleConst *rindices, + const std::vector<uint32_t> &fused_rindices) +{ + assert(rindices != nullptr); // FIX_CALLER_UNLESS + + if (rindices->dtype() != loco::DataType::S32) + return nullptr; + + assert(fused_rindices.size() == rindices->size<loco::DataType::S32>()); + + auto fused_rindices_const = luci::clone(rindices); + auto name = rindices->name(); + assert(name.length() > 0); // FIX_CALLER_UNLESS + fused_rindices_const->name(name + "_fused"); + + for (uint32_t i = 0; i < fused_rindices.size(); ++i) + { + fused_rindices_const->at<loco::DataType::S32>(i) = fused_rindices.at(i); + } + + return fused_rindices_const; +} + +bool const_has_value_s32(const luci::CircleConst *circle_const, int32_t value) +{ + if (circle_const->dtype() != loco::DataType::S32) + return false; + + uint32_t size = circle_const->size<loco::DataType::S32>(); + for (uint32_t i = 0; i < size; ++i) + { + if (circle_const->at<loco::DataType::S32>(i) == value) + return true; + } + + return false; +} + +bool fuse_transpose_with_mean(luci::CircleMean *mean) +{ + auto transpose = dynamic_cast<luci::CircleTranspose *>(mean->input()); + if (not transpose) + return false; + + // Get reduction indices of CircleMean operation. + auto rindices = dynamic_cast<luci::CircleConst *>(mean->reduction_indices()); + if (not rindices) + return false; + + if (rindices->dtype() != loco::DataType::S32) + return false; + + if (mean->keep_dims() != false) + return false; + + auto perm = dynamic_cast<luci::CircleConst *>(transpose->perm()); + if (not perm) + return false; + + std::vector<uint32_t> axes_after_reduction; + std::vector<uint32_t> orig_reduced_axes; + for (uint32_t axis = 0; axis < perm->size<loco::DataType::S32>(); ++axis) + { + uint32_t original_axis = static_cast<uint32_t>(perm->at<loco::DataType::S32>(axis)); + + if (const_has_value_s32(rindices, axis)) + { + orig_reduced_axes.push_back(original_axis); + continue; + } + + axes_after_reduction.push_back(original_axis); + } + + if (not std::is_sorted(axes_after_reduction.begin(), axes_after_reduction.end())) + return false; + + auto fused_rindices = create_fused_indices(rindices, orig_reduced_axes); + if (not fused_rindices) + return false; + + // Create and configure new CircleMean operation. + auto fused_mean = mean->graph()->nodes()->create<luci::CircleMean>(); + fused_mean->reduction_indices(fused_rindices); + fused_mean->input(transpose->a()); + fused_mean->keep_dims(false); + fused_mean->name(mean->name() + "/Transpose"); + + // Replace old CircleMean operation with new CircleMean operation with merged indices. + replace(mean).with(fused_mean); + luci::add_origin(fused_mean, + luci::composite_origin({luci::get_origin(mean), luci::get_origin(transpose)})); + + return true; +} + +} // namespace + +namespace luci +{ + +bool FuseTransposeWithMeanPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto mean = dynamic_cast<luci::CircleMean *>(node); + if (not mean) + continue; + + if (fuse_transpose_with_mean(mean)) + changed = true; + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/FuseTransposeWithMeanPass.test.cpp b/compiler/luci/pass/src/FuseTransposeWithMeanPass.test.cpp new file mode 100644 index 000000000..f015d1cf7 --- /dev/null +++ b/compiler/luci/pass/src/FuseTransposeWithMeanPass.test.cpp @@ -0,0 +1,147 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/FuseTransposeWithMeanPass.h" + +#include <luci/IR/CircleNodes.h> + +#include <luci/test/TestIOGraph.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +/** + * Simple graph for test + * + * BEFORE + * | + * [CircleTranspose, perm<0, 2, 3, 1>] + * | + * [CircleMean, axis<3>] + * | + * + * AFTER + * | + * [CircleMean, axis<1>] [CircleTranspose, perm<0, 2, 3, 1>] + * | | + * [CircleMean, axis<3>] + * + */ +class FuseTransposeWithMeanTestGraph : public TestIOGraph +{ +public: + FuseTransposeWithMeanTestGraph() = default; + + void init(void) + { + TestIOGraph::init({1, 64, 20, 32}, {1, 20, 32}); + + _mean = g()->nodes()->create<luci::CircleMean>(); + _transpose = g()->nodes()->create<luci::CircleTranspose>(); + _indices = g()->nodes()->create<luci::CircleConst>(); + _perm = g()->nodes()->create<luci::CircleConst>(); + + _mean->name("mean"); + _transpose->name("transpose"); + _indices->name("indices"); + _perm->name("perm"); + + _indices->rank(1); + _indices->dtype(loco::DataType::S32); + _indices->size<loco::DataType::S32>(1); + _indices->at<loco::DataType::S32>(0) = static_cast<int32_t>(3); + _indices->dim(0) = 1; + _indices->shape_status(luci::ShapeStatus::VALID); + + _perm->rank(1); + _perm->dtype(loco::DataType::S32); + _perm->size<loco::DataType::S32>(4); + _perm->dim(0) = 4; + _perm->at<loco::DataType::S32>(0) = static_cast<int32_t>(0); + _perm->at<loco::DataType::S32>(1) = static_cast<int32_t>(2); + _perm->at<loco::DataType::S32>(2) = static_cast<int32_t>(3); + _perm->at<loco::DataType::S32>(3) = static_cast<int32_t>(1); + _perm->shape_status(luci::ShapeStatus::VALID); + + _transpose->a(input()); + _transpose->perm(_perm); + + _mean->input(_transpose); + _mean->reduction_indices(_indices); + + output()->from(_mean); + } + + luci::CircleTranspose *transpose(void) const { return _transpose; } + luci::CircleMean *mean(void) const { return _mean; } + +private: + luci::CircleTranspose *_transpose = nullptr; + luci::CircleMean *_mean = nullptr; + luci::CircleConst *_indices = nullptr; + luci::CircleConst *_perm = nullptr; +}; + +} // namespace + +TEST(FuseTransposeWithMeanPassTest, name) +{ + luci::FuseTransposeWithMeanPass pass; + auto const name = pass.name(); + ASSERT_NE(nullptr, name); +} + +TEST(FuseTransposeWithMeanPassTest, fuse_transpose_with_mean) +{ + FuseTransposeWithMeanTestGraph g; + luci::FuseTransposeWithMeanPass pass; + + g.init(); + + EXPECT_TRUE(pass.run(g.g())); + + auto fused_mean = dynamic_cast<luci::CircleMean *>(g.output()->from()); + EXPECT_NE(nullptr, fused_mean); + + auto rindices = dynamic_cast<luci::CircleConst *>(fused_mean->reduction_indices()); + EXPECT_NE(nullptr, rindices); + + EXPECT_EQ(1, rindices->rank()); + EXPECT_EQ(1, rindices->dim(0)); + EXPECT_EQ(1, rindices->size<loco::DataType::S32>()); + EXPECT_EQ(1, rindices->at<loco::DataType::S32>(0)); +} + +TEST(FuseTransposeWithMeanPassTest, fuse_transpose_with_mean_NEG) +{ + FuseTransposeWithMeanTestGraph g; + luci::FuseTransposeWithMeanPass pass; + + g.init(); + + // Add CircleRelu operation between CircleMean and Transpose + auto relu = g.g()->nodes()->create<luci::CircleRelu>(); + relu->name("relu"); + relu->features(g.transpose()); + g.mean()->input(relu); + + // Due to the CircleRelu operation, pass will not be applied + EXPECT_FALSE(pass.run(g.g())); +} diff --git a/compiler/luci/pass/src/PropagateQuantParamPass.cpp b/compiler/luci/pass/src/PropagateQuantParamPass.cpp index 26282086b..10c113574 100644 --- a/compiler/luci/pass/src/PropagateQuantParamPass.cpp +++ b/compiler/luci/pass/src/PropagateQuantParamPass.cpp @@ -70,7 +70,7 @@ struct PropagateQuantParam final : public luci::CircleNodeMutableVisitor<bool> return false; auto input_node = loco::must_cast<luci::CircleNode *>(input); - return copy_qparam(node, input_node); + return copy_qparam(input_node, node); } // TODO : Add more Ops (e.g., Transpose) diff --git a/compiler/luci/pass/src/PropagateQuantParamPass.test.cpp b/compiler/luci/pass/src/PropagateQuantParamPass.test.cpp index ed1f96828..0f1564223 100644 --- a/compiler/luci/pass/src/PropagateQuantParamPass.test.cpp +++ b/compiler/luci/pass/src/PropagateQuantParamPass.test.cpp @@ -98,12 +98,12 @@ TEST(PropagateQuantParam, simple) while (pass.run(&g.g)) ; - EXPECT_FLOAT_EQ(0.2, g.conv->quantparam()->scale[0]); - EXPECT_FLOAT_EQ(0.4, g.conv->quantparam()->scale[1]); - EXPECT_FLOAT_EQ(0.6, g.conv->quantparam()->scale[2]); - EXPECT_EQ(-10, g.conv->quantparam()->zerop[0]); - EXPECT_EQ(0, g.conv->quantparam()->zerop[1]); - EXPECT_EQ(10, g.conv->quantparam()->zerop[2]); + EXPECT_FLOAT_EQ(0.1, g.reshape->quantparam()->scale[0]); + EXPECT_FLOAT_EQ(0.2, g.reshape->quantparam()->scale[1]); + EXPECT_FLOAT_EQ(0.3, g.reshape->quantparam()->scale[2]); + EXPECT_EQ(0, g.reshape->quantparam()->zerop[0]); + EXPECT_EQ(10, g.reshape->quantparam()->zerop[1]); + EXPECT_EQ(20, g.reshape->quantparam()->zerop[2]); } TEST(PropagateQuantParam, wrong_op_NEG) diff --git a/compiler/luci/pass/src/QuantizationUtils.cpp b/compiler/luci/pass/src/QuantizationUtils.cpp index 85d600e47..2f6fed46e 100644 --- a/compiler/luci/pass/src/QuantizationUtils.cpp +++ b/compiler/luci/pass/src/QuantizationUtils.cpp @@ -160,6 +160,11 @@ void compute_sym_scale_zp(float min, float max, float &scaling_factor, int64_t & scaling_factor = scale_factor_from_min_side > scale_factor_from_max_side ? scale_factor_from_min_side : scale_factor_from_max_side; + + // protect scale from being very low to avoid overflow/underflow + if (scaling_factor < 1e-9) + scaling_factor = 1e-9; + zp = 0; nudged_min = static_cast<float>(qmin_double * scaling_factor); nudged_max = static_cast<float>(qmax_double * scaling_factor); diff --git a/compiler/luci/pass/src/QuantizationUtils.h b/compiler/luci/pass/src/QuantizationUtils.h index c8c558d3c..605f6a77e 100644 --- a/compiler/luci/pass/src/QuantizationUtils.h +++ b/compiler/luci/pass/src/QuantizationUtils.h @@ -44,6 +44,8 @@ uint32_t cal_offset(loco::TensorShape &dimension, uint32_t *indices); void propagate_concat_quantparam(luci::CircleConcatenation *concat, loco::DataType quant_type); +void propagate_pad_v2_quantparam(luci::CirclePadV2 *pad_v2, loco::DataType quant_type); + bool is_weights(CircleNode *node); bool is_quantized(const CircleNode *node); diff --git a/compiler/luci/pass/src/QuantizeWithMinMaxPass.cpp b/compiler/luci/pass/src/QuantizeWithMinMaxPass.cpp index 4707ad0e9..6afc2084f 100644 --- a/compiler/luci/pass/src/QuantizeWithMinMaxPass.cpp +++ b/compiler/luci/pass/src/QuantizeWithMinMaxPass.cpp @@ -95,11 +95,11 @@ luci::CircleConst *create_empty_const_from(luci::CircleConst *node, uint32_t siz return new_node; } -void overwrite_quantparam(luci::CircleConcatenation *concat, luci::CircleNode *target) +void overwrite_quantparam(luci::CircleNode *source, luci::CircleNode *target) { - auto concat_qparam = concat->quantparam(); - if (concat_qparam == nullptr) - throw std::runtime_error("quantparam of concat is not found during overwrite"); + auto source_qparam = source->quantparam(); + if (source_qparam == nullptr) + throw std::runtime_error("source quantparam is not found during overwrite"); auto target_qparam = target->quantparam(); if (target_qparam == nullptr) @@ -111,11 +111,11 @@ void overwrite_quantparam(luci::CircleConcatenation *concat, luci::CircleNode *t if (target_qparam == nullptr) throw std::runtime_error("Creating new quant param failed"); } - target_qparam->min = concat_qparam->min; - target_qparam->max = concat_qparam->max; - target_qparam->scale = concat_qparam->scale; - target_qparam->zerop = concat_qparam->zerop; - target_qparam->quantized_dimension = concat_qparam->quantized_dimension; + target_qparam->min = source_qparam->min; + target_qparam->max = source_qparam->max; + target_qparam->scale = source_qparam->scale; + target_qparam->zerop = source_qparam->zerop; + target_qparam->quantized_dimension = source_qparam->quantized_dimension; } void quant_const_values(luci::CircleConst *const_node, float scaling_factor, float zerop, @@ -127,8 +127,13 @@ void quant_const_values(luci::CircleConst *const_node, float scaling_factor, flo std::vector<int32_t> quantized_values(size); for (uint32_t i = 0; i < size; ++i) { - auto data = const_node->at<loco::DataType::FLOAT32>(i); - quantized_values[i] = static_cast<int32_t>(std::round(data * scaling_factor_inv) + zerop); + auto data = static_cast<double>(const_node->at<loco::DataType::FLOAT32>(i)); + double quantized_float = std::round(data * scaling_factor_inv) + zerop; + constexpr auto int_max = static_cast<double>(std::numeric_limits<int32_t>::max()); + constexpr auto int_min = static_cast<double>(std::numeric_limits<int32_t>::min()); + quantized_float = std::min(int_max, std::max(int_min, quantized_float)); + + quantized_values[i] = static_cast<int32_t>(quantized_float); } switch (quant_type) @@ -525,6 +530,102 @@ void set_bias(luci::CircleNode *node, luci::CircleConst *bias) "fully-connected layer have bias"); } +void set_act_qparam(luci::CircleNode *node, float scale, int64_t zp) +{ + assert(node); // FIX_CALLER_UNLESS + assert(node->quantparam()); // FIX_CALLER_UNLESS + + auto qparam = node->quantparam(); + assert(qparam->scale.size() == 1); // FIX_CALLER_UNLESS + assert(qparam->zerop.size() == 1); // FIX_CALLER_UNLESS + qparam->scale[0] = scale; + qparam->zerop[0] = zp; +} + +/** + * @brief Manually set scale/zp of output tensor of special Ops + */ +struct QuantizeSpecialActivation final : public luci::CircleNodeMutableVisitor<void> +{ + QuantizeSpecialActivation(loco::DataType input, loco::DataType output) + : input_type(input), output_type(output) + { + } + + loco::DataType input_type; + loco::DataType output_type; + + void visit(luci::CircleNode *) + { + // Do nothing by default + } + + void visit(luci::CircleLogistic *node) + { + if (output_type == loco::DataType::U8) + set_act_qparam(node, 1.0f / 256.0f, 0); + else + { + assert(output_type == loco::DataType::S16); + set_act_qparam(node, 1.0f / 32768.0f, 0); + } + } + + void visit(luci::CircleTanh *node) + { + if (output_type == loco::DataType::U8) + set_act_qparam(node, 2.0f / 256.0f, 128); + else + { + assert(output_type == loco::DataType::S16); + set_act_qparam(node, 1.0f / 32768.0f, 0); + } + } + + void visit(luci::CircleStridedSlice *node) + { + auto input = loco::must_cast<luci::CircleNode *>(node->input()); + auto i_qparam = input->quantparam(); + assert(i_qparam); + assert(i_qparam->scale.size() == 1); // FIX_CALLER_UNLESS + assert(i_qparam->zerop.size() == 1); // FIX_CALLER_UNLESS + auto i_scale = i_qparam->scale[0]; + auto i_zp = i_qparam->zerop[0]; + + set_act_qparam(node, i_scale, i_zp); + } + + void visit(luci::CircleSplitOut *node) + { + auto split = loco::must_cast<luci::CircleSplit *>(node->input()); + auto input = loco::must_cast<luci::CircleNode *>(split->input()); + auto i_qparam = input->quantparam(); + assert(i_qparam); + assert(i_qparam->scale.size() == 1); // FIX_CALLER_UNLESS + assert(i_qparam->zerop.size() == 1); // FIX_CALLER_UNLESS + auto i_scale = i_qparam->scale[0]; + auto i_zp = i_qparam->zerop[0]; + + set_act_qparam(node, i_scale, i_zp); + } + + void visit(luci::CircleUnpackOut *node) + { + auto unpack = loco::must_cast<luci::CircleUnpack *>(node->input()); + auto input = loco::must_cast<luci::CircleNode *>(unpack->value()); + auto i_qparam = input->quantparam(); + assert(i_qparam); + assert(i_qparam->scale.size() == 1); // FIX_CALLER_UNLESS + assert(i_qparam->zerop.size() == 1); // FIX_CALLER_UNLESS + auto i_scale = i_qparam->scale[0]; + auto i_zp = i_qparam->zerop[0]; + + set_act_qparam(node, i_scale, i_zp); + } + + // TODO Move Softmax, Floor, Ceil from QuantizeActivation to here +}; + /** * @brief QuantizeActivation quantizes tensors for activations * @details Quantize using recorded min/max values @@ -569,11 +670,19 @@ struct QuantizeActivation final : public luci::CircleNodeMutableVisitor<bool> { // Quantize using recorded min/max auto quantparam = circle_node->quantparam(); + assert(quantparam); assert(quantparam->min.size() == 1); // only support layer-wise quant assert(quantparam->max.size() == 1); // only support layer-wise quant auto min = quantparam->min[0]; auto max = quantparam->max[0]; + // Special values + if (circle_node->opcode() == luci::CircleOpcode::SOFTMAX) + { + min = 0.0f; + max = 1.0f; + } + float scaling_factor{0}; int64_t zp{0}; float nudged_min{0}; @@ -590,11 +699,52 @@ struct QuantizeActivation final : public luci::CircleNodeMutableVisitor<bool> circle_node->dtype(loco::DataType::S16); } + // Nodes fused with activation functions which need special quantization + auto fused_act_node = + dynamic_cast<CircleNodeMixin<CircleNodeTrait::FusedActFunc> *>(circle_node); + if (fused_act_node != nullptr && + fused_act_node->fusedActivationFunction() == FusedActFunc::TANH) + { + if (output_type == loco::DataType::U8) + { + scaling_factor = 2.0f / 256.0f; + zp = 128; + } + else + { + assert(output_type == loco::DataType::S16); + scaling_factor = 1.0f / 32768.0f; + zp = 0; + } + } + + // The output of these Ops should be integer, so scale should be integer + // TODO Handle cases where the integer scale needs to be propagated + if (circle_node->opcode() == CircleOpcode::FLOOR || + circle_node->opcode() == CircleOpcode::FLOOR_DIV || + circle_node->opcode() == CircleOpcode::FLOOR_MOD || + circle_node->opcode() == CircleOpcode::CEIL) + { + assert(scaling_factor >= 0); // FIX_ME_UNLESS + scaling_factor = scaling_factor < 1 ? 1.0f : std::round(scaling_factor); + } + circle_node->quantparam()->min.clear(); circle_node->quantparam()->max.clear(); circle_node->quantparam()->scale.push_back(scaling_factor); circle_node->quantparam()->zerop.push_back(zp); } + // Fix special attributes + if (circle_node->opcode() == luci::CircleOpcode::CAST) + { + auto *cast = loco::must_cast<luci::CircleCast *>(circle_node); + auto *cast_input = loco::must_cast<luci::CircleNode *>(cast->x()); + + // make sure that cast_input is already quantized + assert(cast_input->dtype() != loco::DataType::FLOAT32); + cast->in_data_type(cast_input->dtype()); + cast->out_data_type(cast->dtype()); + } } return false; } @@ -631,12 +781,20 @@ struct QuantizeBias final : public luci::CircleNodeMutableVisitor<bool> auto const_bias = loco::must_cast<luci::CircleConst *>(node); assert(const_bias->dtype() == loco::DataType::FLOAT32); + // If input is const, it is quantized here, not in QuantizeActivation + if (auto const_input = dynamic_cast<luci::CircleConst *>(input)) + { + quant_const(const_input, output_type); + } + CircleConst *new_bias = nullptr; if (granularity == QuantizationGranularity::ChannelWise) { - assert(input->quantparam()->scale.size() == 1); // input scale's layer-wise - auto input_scale = input->quantparam()->scale[0]; + auto input_q = input->quantparam(); + assert(input_q); + assert(input_q->scale.size() == 1); // input scale's layer-wise + auto input_scale = input_q->scale[0]; assert(weight->quantparam() != nullptr); // weight scale's channel-wise auto weight_scale = weight->quantparam()->scale; @@ -671,11 +829,15 @@ struct QuantizeBias final : public luci::CircleNodeMutableVisitor<bool> } else { - assert(input->quantparam()->scale.size() == 1); // Only support per-layer quant - auto input_scale = input->quantparam()->scale[0]; + auto input_q = input->quantparam(); + assert(input_q); + assert(input_q->scale.size() == 1); // Only support per-layer quant + auto input_scale = input_q->scale[0]; - assert(weight->quantparam()->scale.size() == 1); // Only support per-layer quant - auto weight_scale = weight->quantparam()->scale[0]; + auto weight_q = weight->quantparam(); + assert(weight_q); + assert(weight_q->scale.size() == 1); // Only support per-layer quant + auto weight_scale = weight_q->scale[0]; float scaling_factor{0}; int64_t zp{0}; @@ -878,6 +1040,76 @@ private: bool visit(luci::CircleNode *) { return false; } }; +/** EXAMPLE + * + * BEFORE + * + * [CircleNode] [CircleConst] + * (qparam1) (FP32) + * \ / + * \ / + * [CirclePack] + * (qparam2) + * + * AFTER + * + * [CircleNode] [CircleConst] [CircleConst] <- Dead node + * (qparam2) (qparam2) (FP32) + * \ / + * \ / + * [CirclePack] + * (qparam2) + * + * NOTE Quantization parameter of CirclePack (qparam2) is propagated to the inputs. + */ +void propagate_pack_quantparam(luci::CirclePack *pack, loco::DataType quant_type) +{ + assert(pack->quantparam() != nullptr); + + const auto num_inputs = pack->values_count(); + + for (uint32_t i = 0; i < num_inputs; i++) + { + auto node = loco::must_cast<luci::CircleNode *>(pack->arg(i)); + + // Skip if this input is PACK Op + if (node->opcode() == luci::CircleOpcode::PACK) + continue; + + // Quantize constant values + if (node->opcode() == luci::CircleOpcode::CIRCLECONST) + { + luci::CircleConst *const_node = loco::must_cast<luci::CircleConst *>(node); + if (const_node->dtype() != loco::DataType::FLOAT32) + throw std::runtime_error("Unsupported data type for constant input of pack Op"); + + const auto pack_qparam = pack->quantparam(); + if (pack_qparam == nullptr) + throw std::runtime_error("quantparam of pack is not found during propagation"); + + assert(pack_qparam->scale.size() == 1); + assert(pack_qparam->zerop.size() == 1); + const auto scaling_factor = pack_qparam->scale[0]; + const auto zerop = pack_qparam->zerop[0]; + + auto new_const = luci::clone(const_node); + quant_const_values(new_const, scaling_factor, zerop, quant_type); + pack->values(i, new_const); + overwrite_quantparam(pack, new_const); + } + else + { + const auto succs = loco::succs(node); + if (succs.size() > 1) + continue; + + // Non-const input must have been quantized + assert(node->quantparam() != nullptr); + overwrite_quantparam(pack, node); + } + } +} + /** * @brief Quantize const input tensors using min/max of const values */ @@ -911,7 +1143,9 @@ void quantize_const_inputs(luci::CircleNode *node, loco::DataType output_type) case luci::CircleOpcode::ARG_MAX: case luci::CircleOpcode::ARG_MIN: case luci::CircleOpcode::BATCH_TO_SPACE_ND: + case luci::CircleOpcode::LOCAL_RESPONSE_NORMALIZATION: case luci::CircleOpcode::MEAN: + case luci::CircleOpcode::MIRROR_PAD: case luci::CircleOpcode::PAD: case luci::CircleOpcode::REDUCE_ANY: case luci::CircleOpcode::REDUCE_PROD: @@ -960,6 +1194,7 @@ void quantize_const_inputs(luci::CircleNode *node, loco::DataType output_type) case luci::CircleOpcode::SQRT: case luci::CircleOpcode::SUB: case luci::CircleOpcode::TANH: + case luci::CircleOpcode::UNPACK: // Quantize all const inputs using their values for (uint32_t i = 0; i < arity; i++) { @@ -979,6 +1214,19 @@ void quantize_const_inputs(luci::CircleNode *node, loco::DataType output_type) quant_const(const_node, output_type); break; + case luci::CircleOpcode::PADV2: + // First and third constant inputs are quantized + // Second input should not be quantized (e.g., paddings) + // Quant params are propagated either from output range to the non-constant input + // or from input to output and constant values + propagate_pad_v2_quantparam(loco::must_cast<CirclePadV2 *>(node), output_type); + break; + + case luci::CircleOpcode::PACK: + // Quant param is propagated from output to inputs + propagate_pack_quantparam(loco::must_cast<CirclePack *>(node), output_type); + break; + default: for (uint32_t i = 0; i < arity; i++) { @@ -1074,6 +1322,134 @@ void propagate_concat_quantparam(luci::CircleConcatenation *concat, loco::DataTy } } +/** + * tells if pad_v2 quantization should ignore padding value + * In that case padding const will be quantized with input parameters, and probably clipped + */ +bool ignore_pad_v2_const_quantization(luci::CirclePadV2 *pad) +{ + // This is a workaround to quantize pad generated from MaxPoolWithArgmax operation properly + // TODO use metadata hints to detect this case + auto const_value_node = dynamic_cast<luci::CircleConst *>(pad->arg(2)); + if (!const_value_node) + return false; + if (const_value_node->dtype() == loco::DataType::FLOAT32) + { + float const_value = const_value_node->at<loco::DataType::FLOAT32>(0); + if (const_value == std::numeric_limits<float>::lowest()) + return true; + } + return false; +} + +/** BEFORE + * + * [CircleNode] [CircleConst] [CircleConst] + * (U8 qparam1) (S32) (FP32) + * \ | / + * \ | / + * [CirclePadV2] + * (U8 qparam2) + * + * AFTER (case 1) + * + * By default qparam is propagated from output to inputs to meet backend requirements. + * + * [CircleNode] [CircleConst] [CircleConst] [CircleConst] <- Dead node + * (U8 qparam2) (S32) (U8 qparam2) (FP32) + * \ | / + * \ | / + * [CirclePadV2] + * (U8 qparam2) + * + * AFTER (case 2) + * + * In case padded value is the lowest float value + * Qparam is propagated from input to output and constant. + * + * This is a special case for optimization constructed pad, needed to guarantee that + * extremely large negative constant do not stretch output quantization range. + * + * [CircleNode] [CircleConst] [CircleConst] [CircleConst] <- Dead node + * (U8 qparam1) (S32) (U8 qparam1) (FP32) + * \ | / + * \ | / + * [CirclePadV2] + * (U8 qparam1) + */ +void propagate_pad_v2_quantparam(luci::CirclePadV2 *pad_v2, loco::DataType quant_type) +{ + if (ignore_pad_v2_const_quantization(pad_v2)) + { + // propagate input quantization paramters from input to output and padding const value + auto pad_v2_input = loco::must_cast<luci::CircleNode *>(pad_v2->arg(0)); + overwrite_quantparam(pad_v2_input, pad_v2); + + auto const_value_node = dynamic_cast<luci::CircleConst *>(pad_v2->arg(2)); + auto new_const = luci::clone(const_value_node); + + const auto pad_v2_input_qparam = pad_v2_input->quantparam(); + assert(pad_v2_input_qparam != nullptr); + assert(pad_v2_input_qparam->scale.size() == 1); + const auto scaling_factor = pad_v2_input_qparam->scale.at(0); + const auto zerop = pad_v2_input_qparam->zerop.at(0); + + quant_const_values(new_const, scaling_factor, zerop, quant_type); + overwrite_quantparam(pad_v2_input, new_const); + pad_v2->constant_values(new_const); + return; + } + + // Propagate quantization paramters from output to inputs, + // to fit both input and counstant_value in one quant range. + auto quant_input = [pad_v2, quant_type](void (CirclePadV2::*arg_setter)(loco::Node *), + uint32_t arg) { + auto node = loco::must_cast<luci::CircleNode *>(pad_v2->arg(arg)); + + // Quantize constant values + if (node->opcode() == luci::CircleOpcode::CIRCLECONST) + { + luci::CircleConst *const_node = loco::must_cast<luci::CircleConst *>(node); + if (is_quantized(const_node)) + return; + + if (const_node->dtype() != loco::DataType::FLOAT32) + throw std::runtime_error("Unsupported data type for constant input of PadV2 Op"); + + const auto pad_v2_qparam = pad_v2->quantparam(); + if (pad_v2_qparam == nullptr) + throw std::runtime_error("quantparam of PadV2 is not found during propagation"); + + assert(pad_v2_qparam->scale.size() == 1); + const auto scaling_factor = pad_v2_qparam->scale.at(0); + const auto zerop = pad_v2_qparam->zerop.at(0); + + auto new_const = luci::clone(const_node); + quant_const_values(new_const, scaling_factor, zerop, quant_type); + overwrite_quantparam(pad_v2, new_const); + (pad_v2->*arg_setter)(new_const); + } + // Subsequent PadV2 Ops quant params are not propagated + else if (node->opcode() == luci::CircleOpcode::PADV2) + { + return; + } + else + { + const auto succs = loco::succs(node); + if (succs.size() > 1) + return; + + // Non-const input must have been quantized + assert(node->quantparam() != nullptr); + overwrite_quantparam(pad_v2, node); + } + }; + + quant_input(&CirclePadV2::input, 0); + quant_input(&CirclePadV2::constant_values, 2); +} + bool QuantizeWithMinMaxPass::run(loco::Graph *g) { LOGGER(l); @@ -1125,6 +1501,14 @@ bool QuantizeWithMinMaxPass::run(loco::Graph *g) quantize_const_inputs(circle_node, _output_dtype); } + // Update qparam of output of special Ops + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + QuantizeSpecialActivation qsa(_input_dtype, _output_dtype); + auto circle_node = loco::must_cast<luci::CircleNode *>(node); + circle_node->accept(&qsa); + } + // Update output dtype auto graph_outputs = g->outputs(); for (auto node : loco::output_nodes(g)) diff --git a/compiler/luci/pass/src/QuantizedModelVerifier.cpp b/compiler/luci/pass/src/QuantizedModelVerifier.cpp index 5ea803cc9..f02301ed1 100644 --- a/compiler/luci/pass/src/QuantizedModelVerifier.cpp +++ b/compiler/luci/pass/src/QuantizedModelVerifier.cpp @@ -38,18 +38,25 @@ void QuantizedModelVerifier::verify(loco::Graph *g) { auto circle_node = loco::must_cast<luci::CircleNode *>(node); + auto node_name = [&circle_node]() { + if (circle_node->name().length() == 0) + return std::string("(noname)"); + + return circle_node->name(); + }; + // Verify Type if (_quantized_dtype == Type::U8) { VerifyQuantizedNodeU8Type vt; if (!circle_node->accept(&vt)) - throw std::runtime_error("Wrong data type"); + throw std::runtime_error("Wrong data type detected in " + node_name()); } else if (_quantized_dtype == Type::S16) { VerifyQuantizedNodeS16Type vt; if (!circle_node->accept(&vt)) - throw std::runtime_error("Wrong data type"); + throw std::runtime_error("Wrong data type detected in " + node_name()); } // Verify Granularity @@ -57,13 +64,13 @@ void QuantizedModelVerifier::verify(loco::Graph *g) { VerifyQuantizedNodeLayerWiseGranularity vg; if (!circle_node->accept(&vg)) - throw std::runtime_error("Wrong granularity"); + throw std::runtime_error("Wrong granularity detected in " + node_name()); } else if (_granularity == Granularity::ChannelWise) { VerifyQuantizedNodeChannelWiseGranularity vg; if (!circle_node->accept(&vg)) - throw std::runtime_error("Wrong granularity"); + throw std::runtime_error("Wrong granularity detected in " + node_name()); } } } diff --git a/compiler/luci/pass/src/QuantizedModelVerifier.test.cpp b/compiler/luci/pass/src/QuantizedModelVerifier.test.cpp index eae1b0c1f..b8cc09955 100644 --- a/compiler/luci/pass/src/QuantizedModelVerifier.test.cpp +++ b/compiler/luci/pass/src/QuantizedModelVerifier.test.cpp @@ -127,6 +127,19 @@ void quantize_and_verify_with_wrong_type(luci::test::TestIOGraph *g, Type quanti verifier.verify(g->g()); } +void quantize_and_verify_with_wrong_type(luci::test::TestIOGraph *g, Type quantized_dtype, + Granularity granularity, Type wrong_dtype, + luci::CircleNode *target) +{ + luci::QuantizeWithMinMaxPass pass(Type::FLOAT32, quantized_dtype, granularity); + pass.run(g->g()); + + target->dtype(wrong_dtype); + + luci::QuantizedModelVerifier verifier(quantized_dtype, granularity); + verifier.verify(g->g()); +} + // Helper function to reduce duplicate test codes // Assumption: g->output()->from() is the target node void quantize_and_verify_with_wrong_granularity(luci::test::TestIOGraph *g, Type quantized_dtype, @@ -221,7 +234,7 @@ public: loco::Node *gamma(void) const { return _instnorm->gamma(); } loco::Node *beta(void) const { return _instnorm->beta(); } -public: +private: luci::CircleInstanceNorm *_instnorm = nullptr; luci::CircleConst *_input = nullptr; luci::CircleConst *_gamma = nullptr; @@ -243,10 +256,29 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleLogistic *_logistic = nullptr; }; +class LocalResponseNormalizationTestGraph final : public SimpleTestGraph +{ +public: + void init(void) override + { + TestIOGraph::init({1, 2, 2, 32}, {1, 2, 2, 32}); + _lrn = g()->nodes()->create<luci::CircleLocalResponseNormalization>(); + { + _lrn->input(input()); + } + output()->from(_lrn); + + set_minmax_to_non_const(g(), -1, 1); + } + +private: + luci::CircleLocalResponseNormalization *_lrn = nullptr; +}; + class SoftmaxTestGraph final : public SimpleTestGraph { public: @@ -263,7 +295,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleSoftmax *_softmax = nullptr; }; @@ -292,7 +324,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleSpaceToBatchND *_stob = nullptr; luci::CircleConst *_block_shape = nullptr; luci::CircleConst *_paddings = nullptr; @@ -314,7 +346,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleSpaceToDepth *_stod = nullptr; }; @@ -343,7 +375,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleSlice *_slice = nullptr; luci::CircleConst *_begin = nullptr; luci::CircleConst *_size = nullptr; @@ -372,7 +404,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleSplit *_split = nullptr; luci::CircleSplitOut *_split_o1 = nullptr; luci::CircleConst *_split_dim = nullptr; @@ -408,7 +440,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleStridedSlice *_slice = nullptr; luci::CircleConst *_begin = nullptr; luci::CircleConst *_end = nullptr; @@ -435,7 +467,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleReshape *_reshape = nullptr; luci::CircleConst *_shape = nullptr; }; @@ -455,7 +487,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleTanh *_tanh = nullptr; }; @@ -474,7 +506,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleFloor *_floor = nullptr; }; @@ -503,6 +535,11 @@ public: } public: + // NOTE: Do not override `luci::CircleNode* input(void)` incidentally + loco::Node *input_argmax(void) { return _argmax->input(); } + loco::Node *dimension(void) { return _argmax->dimension(); } + +private: luci::CircleArgMax *_argmax = nullptr; luci::CircleConst *_dimension = nullptr; }; @@ -532,7 +569,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleBatchToSpaceND *_btos = nullptr; luci::CircleConst *_block_shape = nullptr; luci::CircleConst *_crops = nullptr; @@ -554,10 +591,43 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleDepthToSpace *_dtos = nullptr; }; +class PackTestGraph final : public SimpleTestGraph +{ +public: + void init(void) override + { + TestIOGraph::init({16}, {32}); + _param = create_dummy_const<Type::FLOAT32>(g(), {16}); + _pack = g()->nodes()->create<luci::CirclePack>(2); + { + _pack->values(0, input()); + _pack->values(1, _param); + _pack->axis(0); + } + output()->from(_pack); + + set_minmax_to_non_const(g(), -1, 1); + + // Set min/max of the input + // pack's qparam will be propagted, overwritten to the input + auto input = loco::must_cast<luci::CircleNode *>(pack()->values(0)); + auto qp = input->quantparam(); + qp->min[0] = -0.5; + qp->max[0] = 0.5; + } + +public: + luci::CirclePack *pack(void) { return _pack; } + +private: + luci::CirclePack *_pack = nullptr; + luci::CircleConst *_param = nullptr; +}; + class PadTestGraph final : public SimpleTestGraph { public: @@ -578,11 +648,67 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CirclePad *_pad = nullptr; luci::CircleConst *_paddings = nullptr; }; +class PadV2TestGraph final : public SimpleTestGraph +{ +public: + void init(void) override + { + TestIOGraph::init({32}, {32}); + _paddings = g()->nodes()->create<luci::CircleConst>(); + { + _paddings->dtype(Type::S32); + } + _constant_values = create_dummy_const<Type::FLOAT32>(g(), {1}); + _pad = g()->nodes()->create<luci::CirclePadV2>(); + { + _pad->input(input()); + _pad->paddings(_paddings); + _pad->constant_values(_constant_values); + } + output()->from(_pad); + + set_minmax_to_non_const(g(), -1, 1); + } + +private: + luci::CirclePadV2 *_pad = nullptr; + luci::CircleConst *_paddings = nullptr; + luci::CircleConst *_constant_values = nullptr; +}; + +class MirrorPadTestGraph final : public SimpleTestGraph +{ +public: + void init(void) override + { + TestIOGraph::init({32}, {32}); + _paddings = g()->nodes()->create<luci::CircleConst>(); + { + _paddings->dtype(Type::S32); + } + _constant_values = create_dummy_const<Type::FLOAT32>(g(), {1}); + _mirror_pad = g()->nodes()->create<luci::CircleMirrorPad>(); + { + _mirror_pad->input(input()); + _mirror_pad->paddings(_paddings); + _mirror_pad->mode(luci::MirrorPadMode::REFLECT); + } + output()->from(_mirror_pad); + + set_minmax_to_non_const(g(), -1, 1); + } + +private: + luci::CircleMirrorPad *_mirror_pad = nullptr; + luci::CircleConst *_paddings = nullptr; + luci::CircleConst *_constant_values = nullptr; +}; + class TransposeTestGraph final : public SimpleTestGraph { public: @@ -603,7 +729,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleTranspose *_transpose = nullptr; luci::CircleConst *_perm = nullptr; }; @@ -626,7 +752,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleConcatenation *_concat = nullptr; luci::CircleConst *_param = nullptr; }; @@ -655,7 +781,7 @@ public: loco::Node *x(void) const { return _op->x(); } loco::Node *y(void) const { return _op->y(); } -public: +private: Op *_op = nullptr; luci::CircleConst *_y = nullptr; }; @@ -685,7 +811,7 @@ public: loco::Node *x(void) const { return _op->x(); } loco::Node *y(void) const { return _op->y(); } -public: +private: Op *_op = nullptr; luci::CircleConst *_y = nullptr; }; @@ -759,7 +885,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleRsqrt *_rsqrt = nullptr; }; @@ -778,7 +904,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleSqrt *_sqrt = nullptr; }; @@ -797,7 +923,7 @@ public: set_minmax_to_non_const(g(), -1, 1); } -public: +private: luci::CircleElu *_elu = nullptr; }; @@ -851,6 +977,58 @@ private: luci::CircleConst *_size = nullptr; }; +class ResizeNearestNeighborTestGraph final : public luci::test::TestIOGraph +{ +public: + void init(void) + { + TestIOGraph::init({1, 4, 4, 1}, {1, 8, 8, 1}); + + _size = create_const<Type::S32, int32_t>(g(), {2}, {8, 8}); + _resize_nearest_neighbor = g()->nodes()->create<luci::CircleResizeNearestNeighbor>(); + { + _resize_nearest_neighbor->input(input()); + _resize_nearest_neighbor->size(_size); + } + output()->from(_resize_nearest_neighbor); + + set_minmax_to_non_const(g(), -1, 1); + } + +private: + luci::CircleResizeNearestNeighbor *_resize_nearest_neighbor = nullptr; + luci::CircleConst *_size = nullptr; +}; + +class UnpackTestGraph final : public luci::test::TestIOGraph +{ +public: + void init(void) + { + TestIOGraph::init({1, 32}, {32}); + _unpack = g()->nodes()->create<luci::CircleUnpack>(); + { + _unpack->value(input()); + _unpack->axis(0); + _unpack->num(1); + } + _unpack_o1 = g()->nodes()->create<luci::CircleUnpackOut>(); + { + _unpack_o1->input(_unpack); + _unpack_o1->index(0); + } + + output()->from(_unpack_o1); + + set_minmax_to_non_const(g(), -1, 1); + } + +private: + luci::CircleUnpack *_unpack = nullptr; + luci::CircleUnpackOut *_unpack_o1 = nullptr; + luci::CircleConst *_unpack_dim = nullptr; +}; + } // namespace // Quantize and verify with given configurations @@ -880,6 +1058,18 @@ private: EXPECT_ANY_THROW(quantize_and_verify_with_wrong_granularity(&g, type, granularity)); \ } while (0) +// Quantize and verify with wrong type +// Users can specify the test target +#define TEST_WITH_WRONG_TYPE_TARGET(graph, type, granularity, wrong_dtype, target) \ + do \ + { \ + graph g; \ + g.init(); \ + auto node = loco::must_cast<luci::CircleNode *>(target); \ + EXPECT_ANY_THROW( \ + quantize_and_verify_with_wrong_type(&g, type, granularity, wrong_dtype, node)); \ + } while (0) + // Quantize and verify with wrong granularity // Users can specify the test target #define TEST_WITH_WRONG_GRANULARITY_TARGET(graph, type, granularity, target) \ @@ -936,6 +1126,36 @@ TEST(QuantizedModelVerifierTest, InstanceNorm_wrong_granularity_NEG) SUCCEED(); } +TEST(QuantizedModelVerifierTest, LocalResponseNormalization) +{ + TEST_WITH_GRAPH(LocalResponseNormalizationTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_GRAPH(LocalResponseNormalizationTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_GRAPH(LocalResponseNormalizationTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, LocalResponseNormalization_wrong_type_NEG) +{ + TEST_WITH_WRONG_TYPE(LocalResponseNormalizationTestGraph, Type::U8, Granularity::LayerWise, + Type::S16); + TEST_WITH_WRONG_TYPE(LocalResponseNormalizationTestGraph, Type::U8, Granularity::ChannelWise, + Type::S16); + TEST_WITH_WRONG_TYPE(LocalResponseNormalizationTestGraph, Type::S16, Granularity::ChannelWise, + Type::U8); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, LocalResponseNormalization_wrong_granularity_NEG) +{ + TEST_WITH_WRONG_GRANULARITY(LocalResponseNormalizationTestGraph, Type::U8, + Granularity::LayerWise); + TEST_WITH_WRONG_GRANULARITY(LocalResponseNormalizationTestGraph, Type::U8, + Granularity::ChannelWise); + TEST_WITH_WRONG_GRANULARITY(LocalResponseNormalizationTestGraph, Type::S16, + Granularity::ChannelWise); + SUCCEED(); +} + TEST(QuantizedModelVerifierTest, Logistic) { TEST_WITH_GRAPH(LogisticTestGraph, Type::U8, Granularity::LayerWise); @@ -1128,31 +1348,52 @@ TEST(QuantizedModelVerifierTest, ArgMax) SUCCEED(); } -TEST(QuantizedModelVerifierTest, ArgMax_wrong_dimension_type_NEG) +TEST(QuantizedModelVerifierTest, ArgMax_wrong_input_type_NEG) { - ArgMaxTestGraph<Type::S32> g; - g.init(); - luci::QuantizeWithMinMaxPass pass(Type::FLOAT32, Type::U8, Granularity::LayerWise); - pass.run(g.g()); + TEST_WITH_WRONG_TYPE(ArgMaxTestGraph<Type::S32>, Type::U8, Granularity::LayerWise, Type::S16); + TEST_WITH_WRONG_TYPE(ArgMaxTestGraph<Type::S32>, Type::U8, Granularity::ChannelWise, Type::S16); + TEST_WITH_WRONG_TYPE(ArgMaxTestGraph<Type::S32>, Type::S16, Granularity::ChannelWise, Type::U8); - g._dimension->dtype(Type::U8); - - luci::QuantizedModelVerifier verifier(Type::U8, Granularity::LayerWise); - EXPECT_ANY_THROW(verifier.verify(g.g())); + TEST_WITH_WRONG_TYPE(ArgMaxTestGraph<Type::S64>, Type::U8, Granularity::LayerWise, Type::S16); + TEST_WITH_WRONG_TYPE(ArgMaxTestGraph<Type::S64>, Type::U8, Granularity::ChannelWise, Type::S16); + TEST_WITH_WRONG_TYPE(ArgMaxTestGraph<Type::S64>, Type::S16, Granularity::ChannelWise, Type::U8); + SUCCEED(); } -TEST(QuantizedModelVerifierTest, ArgMax_wrong_input_granularity_NEG) +TEST(QuantizedModelVerifierTest, ArgMax_wrong_dimension_type_NEG) { - ArgMaxTestGraph<Type::S32> g; - g.init(); + TEST_WITH_WRONG_TYPE_TARGET(ArgMaxTestGraph<Type::S32>, Type::U8, Granularity::LayerWise, + Type::S16, g.dimension()); + TEST_WITH_WRONG_TYPE_TARGET(ArgMaxTestGraph<Type::S32>, Type::U8, Granularity::ChannelWise, + Type::S16, g.dimension()); + TEST_WITH_WRONG_TYPE_TARGET(ArgMaxTestGraph<Type::S32>, Type::S16, Granularity::ChannelWise, + Type::U8, g.dimension()); - luci::QuantizeWithMinMaxPass pass(Type::FLOAT32, Type::U8, Granularity::LayerWise); - pass.run(g.g()); + TEST_WITH_WRONG_TYPE_TARGET(ArgMaxTestGraph<Type::S64>, Type::U8, Granularity::LayerWise, + Type::S16, g.dimension()); + TEST_WITH_WRONG_TYPE_TARGET(ArgMaxTestGraph<Type::S64>, Type::U8, Granularity::ChannelWise, + Type::S16, g.dimension()); + TEST_WITH_WRONG_TYPE_TARGET(ArgMaxTestGraph<Type::S64>, Type::S16, Granularity::ChannelWise, + Type::U8, g.dimension()); + SUCCEED(); +} - insert_scale_zp(loco::must_cast<luci::CircleNode *>(g._argmax->input()), 1.0, 1); +TEST(QuantizedModelVerifierTest, ArgMax_wrong_granularity_NEG) +{ + TEST_WITH_WRONG_GRANULARITY_TARGET(ArgMaxTestGraph<Type::S32>, Type::U8, Granularity::LayerWise, + g.input_argmax()); + TEST_WITH_WRONG_GRANULARITY_TARGET(ArgMaxTestGraph<Type::S32>, Type::U8, Granularity::ChannelWise, + g.input_argmax()); + TEST_WITH_WRONG_GRANULARITY_TARGET(ArgMaxTestGraph<Type::S32>, Type::S16, + Granularity::ChannelWise, g.input_argmax()); - luci::QuantizedModelVerifier verifier(Type::U8, Granularity::LayerWise); - EXPECT_ANY_THROW(verifier.verify(g.g())); + TEST_WITH_WRONG_GRANULARITY_TARGET(ArgMaxTestGraph<Type::S64>, Type::U8, Granularity::LayerWise, + g.input_argmax()); + TEST_WITH_WRONG_GRANULARITY_TARGET(ArgMaxTestGraph<Type::S64>, Type::U8, Granularity::ChannelWise, + g.input_argmax()); + TEST_WITH_WRONG_GRANULARITY_TARGET(ArgMaxTestGraph<Type::S64>, Type::S16, + Granularity::ChannelWise, g.input_argmax()); + SUCCEED(); } TEST(QuantizedModelVerifierTest, BatchToSpaceND) @@ -1297,6 +1538,41 @@ TEST(QuantizedModelVerifierTest, Tanh_wrong_granularity_NEG) SUCCEED(); } +TEST(QuantizedModelVerifierTest, Pack) +{ + TEST_WITH_GRAPH(PackTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_GRAPH(PackTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_GRAPH(PackTestGraph, Type::S16, Granularity::ChannelWise); + + // Test if Pack's qparam is propagated to the input + { + PackTestGraph g; + g.init(); + quantize_and_verify(g.g(), Type::U8, Granularity::ChannelWise); + auto input = loco::must_cast<luci::CircleNode *>(g.pack()->values(0)); + auto qp = input->quantparam(); + EXPECT_FLOAT_EQ(2.0 / 255.0, qp->scale[0]); + EXPECT_FLOAT_EQ(128, qp->zerop[0]); + } + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, Pack_wrong_type_NEG) +{ + TEST_WITH_WRONG_TYPE(PackTestGraph, Type::U8, Granularity::LayerWise, Type::S16); + TEST_WITH_WRONG_TYPE(PackTestGraph, Type::U8, Granularity::ChannelWise, Type::S16); + TEST_WITH_WRONG_TYPE(PackTestGraph, Type::S16, Granularity::ChannelWise, Type::U8); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, Pack_wrong_granularity_NEG) +{ + TEST_WITH_WRONG_GRANULARITY(PackTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_WRONG_GRANULARITY(PackTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_WRONG_GRANULARITY(PackTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + TEST(QuantizedModelVerifierTest, Pad) { TEST_WITH_GRAPH(PadTestGraph, Type::U8, Granularity::LayerWise); @@ -1321,6 +1597,54 @@ TEST(QuantizedModelVerifierTest, Pad_wrong_granularity_NEG) SUCCEED(); } +TEST(QuantizedModelVerifierTest, PadV2) +{ + TEST_WITH_GRAPH(PadV2TestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_GRAPH(PadV2TestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_GRAPH(PadV2TestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, PadV2_wrong_type_NEG) +{ + TEST_WITH_WRONG_TYPE(PadV2TestGraph, Type::U8, Granularity::LayerWise, Type::S16); + TEST_WITH_WRONG_TYPE(PadV2TestGraph, Type::U8, Granularity::ChannelWise, Type::S16); + TEST_WITH_WRONG_TYPE(PadV2TestGraph, Type::S16, Granularity::ChannelWise, Type::U8); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, PadV2_wrong_granularity_NEG) +{ + TEST_WITH_WRONG_GRANULARITY(PadV2TestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_WRONG_GRANULARITY(PadV2TestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_WRONG_GRANULARITY(PadV2TestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, MirrorPad) +{ + TEST_WITH_GRAPH(MirrorPadTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_GRAPH(MirrorPadTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_GRAPH(MirrorPadTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, MirrorPad_wrong_type_NEG) +{ + TEST_WITH_WRONG_TYPE(MirrorPadTestGraph, Type::U8, Granularity::LayerWise, Type::S16); + TEST_WITH_WRONG_TYPE(MirrorPadTestGraph, Type::U8, Granularity::ChannelWise, Type::S16); + TEST_WITH_WRONG_TYPE(MirrorPadTestGraph, Type::S16, Granularity::ChannelWise, Type::U8); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, MirrorPad_wrong_granularity_NEG) +{ + TEST_WITH_WRONG_GRANULARITY(MirrorPadTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_WRONG_GRANULARITY(MirrorPadTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_WRONG_GRANULARITY(MirrorPadTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + TEST(QuantizedModelVerifierTest, Transpose) { TEST_WITH_GRAPH(TransposeTestGraph, Type::U8, Granularity::LayerWise); @@ -1663,6 +1987,56 @@ TEST(QuantizedModelVerifierTest, ResizeBilinear_wrong_granularity_NEG) SUCCEED(); } +TEST(QuantizedModelVerifierTest, ResizeNearestNeighbor) +{ + TEST_WITH_GRAPH(ResizeNearestNeighborTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_GRAPH(ResizeNearestNeighborTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_GRAPH(ResizeNearestNeighborTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, ResizeNearestNeighbor_wrong_type_NEG) +{ + TEST_WITH_WRONG_TYPE(ResizeNearestNeighborTestGraph, Type::U8, Granularity::LayerWise, Type::S16); + TEST_WITH_WRONG_TYPE(ResizeNearestNeighborTestGraph, Type::U8, Granularity::ChannelWise, + Type::S16); + TEST_WITH_WRONG_TYPE(ResizeNearestNeighborTestGraph, Type::S16, Granularity::ChannelWise, + Type::U8); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, ResizeNearestNeighbor_wrong_granularity_NEG) +{ + TEST_WITH_WRONG_GRANULARITY(ResizeNearestNeighborTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_WRONG_GRANULARITY(ResizeNearestNeighborTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_WRONG_GRANULARITY(ResizeNearestNeighborTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, Unpack) +{ + TEST_WITH_GRAPH(UnpackTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_GRAPH(UnpackTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_GRAPH(UnpackTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, Unpack_wrong_type_NEG) +{ + TEST_WITH_WRONG_TYPE(UnpackTestGraph, Type::U8, Granularity::LayerWise, Type::S16); + TEST_WITH_WRONG_TYPE(UnpackTestGraph, Type::U8, Granularity::ChannelWise, Type::S16); + TEST_WITH_WRONG_TYPE(UnpackTestGraph, Type::S16, Granularity::ChannelWise, Type::U8); + SUCCEED(); +} + +TEST(QuantizedModelVerifierTest, Unpack_wrong_granularity_NEG) +{ + TEST_WITH_WRONG_GRANULARITY(UnpackTestGraph, Type::U8, Granularity::LayerWise); + TEST_WITH_WRONG_GRANULARITY(UnpackTestGraph, Type::U8, Granularity::ChannelWise); + TEST_WITH_WRONG_GRANULARITY(UnpackTestGraph, Type::S16, Granularity::ChannelWise); + SUCCEED(); +} + #undef TEST_WITH_GRAPH #undef TEST_WITH_WRONG_TYPE #undef TEST_WITH_WRONG_GRANULARITY diff --git a/compiler/luci/pass/src/RemoveFakeQuantPass.cpp b/compiler/luci/pass/src/RemoveFakeQuantPass.cpp new file mode 100644 index 000000000..adc0aa480 --- /dev/null +++ b/compiler/luci/pass/src/RemoveFakeQuantPass.cpp @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/RemoveFakeQuantPass.h" + +#include <luci/IR/CircleNodes.h> + +namespace +{ + +void remove_fake_quant(luci::CircleFakeQuant *fakequant) +{ + assert(fakequant != nullptr); + + auto input_node = loco::must_cast<luci::CircleNode *>(fakequant->inputs()); + + replace(fakequant).with(input_node); +} + +} // namespace + +namespace luci +{ +/** + * BEFORE + * + * [CircleNode] + * | + * [CircleFakeQuant] + * | + * [CircleNode] + * + * AFTER + * + * [CircleNode] + * | + * [CircleNode] [CircleFakeQuant] + * + * CircleFakeQuant OP will be removed from the output graph + */ +bool RemoveFakeQuantPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto target_node = dynamic_cast<luci::CircleFakeQuant *>(node); + if (target_node != nullptr) + { + remove_fake_quant(target_node); + changed = true; + } + } + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/RemoveFakeQuantPass.test.cpp b/compiler/luci/pass/src/RemoveFakeQuantPass.test.cpp new file mode 100644 index 000000000..5e1d28d2b --- /dev/null +++ b/compiler/luci/pass/src/RemoveFakeQuantPass.test.cpp @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/RemoveFakeQuantPass.h" + +#include <luci/IR/CircleNodes.h> + +#include <luci/test/TestIOGraph.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class FakeQuantGraphlet +{ +public: + FakeQuantGraphlet() = default; + +public: + void init(loco::Graph *g) + { + _fq = g->nodes()->create<luci::CircleFakeQuant>(); + _fq->name("fq"); + } + +protected: + luci::CircleFakeQuant *_fq = nullptr; +}; + +class FakeQuantGraph : public TestIOGraph, public FakeQuantGraphlet +{ +public: + FakeQuantGraph() = default; + +public: + void init(void) + { + TestIOGraph::init({1}, {1}); + FakeQuantGraphlet::init(g()); + + _fq->inputs(input()); + + output()->from(_fq); + } +}; + +} // namespace + +TEST(RemoveFakeQuantPass, name) +{ + luci::RemoveFakeQuantPass pass; + auto const name = pass.name(); + ASSERT_NE(nullptr, name); +} + +TEST(RemoveFakeQuantPass, remove_fakequant) +{ + FakeQuantGraph g; + luci::RemoveFakeQuantPass pass; + + g.init(); + + EXPECT_TRUE(pass.run(g.g())); + + auto *node1 = loco::must_cast<luci::CircleNode *>(g.output()->from()); + auto *node2 = loco::must_cast<luci::CircleNode *>(g.input()); + EXPECT_EQ(node1, node2); +} diff --git a/compiler/luci/pass/src/RemoveQuantDequantSeqPass.cpp b/compiler/luci/pass/src/RemoveQuantDequantSeqPass.cpp new file mode 100644 index 000000000..2a3157c45 --- /dev/null +++ b/compiler/luci/pass/src/RemoveQuantDequantSeqPass.cpp @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/RemoveQuantDequantSeqPass.h" + +#include <luci/IR/CircleNodes.h> + +namespace +{ + +bool remove_quant_dequant(luci::CircleDequantize *dequant) +{ + assert(dequant != nullptr); + + auto quantize = dynamic_cast<luci::CircleQuantize *>(dequant->input()); + if (quantize == nullptr) + return false; + + auto input_node = loco::must_cast<luci::CircleNode *>(quantize->input()); + + replace(dequant).with(input_node); + + return true; +} + +} // namespace + +namespace luci +{ +/** + * BEFORE + * + * [CircleNode] + * | + * [CircleQuantize] + * | + * [CircleDequantize] + * | + * [CircleNode] + * + * AFTER + * + * [CircleNode] [CircleQuantize] + * | | + * [CircleNode] [CircleDequantize] + * + * CircleQuant-CircleDequant sequance will be removed from the output graph + */ +bool RemoveQuantDequantSeqPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto target_node = dynamic_cast<luci::CircleDequantize *>(node); + if (target_node != nullptr) + { + if (remove_quant_dequant(target_node)) + changed = true; + } + } + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/RemoveQuantDequantSeqPass.test.cpp b/compiler/luci/pass/src/RemoveQuantDequantSeqPass.test.cpp new file mode 100644 index 000000000..9186e56d7 --- /dev/null +++ b/compiler/luci/pass/src/RemoveQuantDequantSeqPass.test.cpp @@ -0,0 +1,89 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/RemoveQuantDequantSeqPass.h" + +#include <luci/IR/CircleNodes.h> + +#include <luci/test/TestIOGraph.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class QuantDequantGraphlet +{ +public: + QuantDequantGraphlet() = default; + +public: + void init(loco::Graph *g) + { + _qu = g->nodes()->create<luci::CircleQuantize>(); + _qu->name("qu"); + + _de = g->nodes()->create<luci::CircleDequantize>(); + _de->name("de"); + } + +protected: + luci::CircleQuantize *_qu = nullptr; + luci::CircleDequantize *_de = nullptr; +}; + +class QuantDequantGraph : public TestIOGraph, public QuantDequantGraphlet +{ +public: + QuantDequantGraph() = default; + +public: + void init(void) + { + TestIOGraph::init({1}, {1}); + QuantDequantGraphlet::init(g()); + + _qu->input(input()); + _de->input(_qu); + + output()->from(_de); + } +}; + +} // namespace + +TEST(RemoveQuantDequantSeqPass, name) +{ + luci::RemoveQuantDequantSeqPass pass; + auto const name = pass.name(); + ASSERT_NE(nullptr, name); +} + +TEST(RemoveQuantDequantSeqPass, remove_quantdequant) +{ + QuantDequantGraph g; + luci::RemoveQuantDequantSeqPass pass; + + g.init(); + + EXPECT_TRUE(pass.run(g.g())); + + auto *node1 = loco::must_cast<luci::CircleNode *>(g.output()->from()); + auto *node2 = loco::must_cast<luci::CircleNode *>(g.input()); + EXPECT_EQ(node1, node2); +} diff --git a/compiler/luci/pass/src/ReplaceSubWithAddPass.cpp b/compiler/luci/pass/src/ReplaceSubWithAddPass.cpp new file mode 100644 index 000000000..6bd83f5c5 --- /dev/null +++ b/compiler/luci/pass/src/ReplaceSubWithAddPass.cpp @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/ReplaceSubWithAddPass.h" + +#include <luci/IR/CircleNodes.h> +#include <luci/Service/Nodes/CircleConst.h> + +namespace +{ + +bool replace_sub_with_const_rhs(luci::CircleSub *sub) +{ + auto const_rhs = dynamic_cast<luci::CircleConst *>(sub->y()); + if (const_rhs == nullptr) + return false; + + auto graph = sub->graph(); + + auto neg_const_rhs = luci::clone(const_rhs); + if (neg_const_rhs->dtype() == loco::DataType::FLOAT32) + { + for (uint32_t i = 0; i < neg_const_rhs->size<loco::DataType::FLOAT32>(); ++i) + neg_const_rhs->at<loco::DataType::FLOAT32>(i) *= -1.0; + } + else + { + // TODO Support more data type + return false; + } + + auto add = graph->nodes()->create<luci::CircleAdd>(); + add->x(sub->x()); + add->y(neg_const_rhs); + add->name(sub->name()); + add->fusedActivationFunction(sub->fusedActivationFunction()); + loco::replace(sub).with(add); + return true; +} + +} // namespace + +namespace luci +{ + +bool ReplaceSubWithAddPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + if (auto sub = dynamic_cast<luci::CircleSub *>(node)) + { + if (replace_sub_with_const_rhs(sub)) + changed = true; + } + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/ReplaceSubWithAddPass.test.cpp b/compiler/luci/pass/src/ReplaceSubWithAddPass.test.cpp new file mode 100644 index 000000000..c1d5752b2 --- /dev/null +++ b/compiler/luci/pass/src/ReplaceSubWithAddPass.test.cpp @@ -0,0 +1,126 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/ReplaceSubWithAddPass.h" + +#include <luci/IR/CircleNodes.h> + +#include <gtest/gtest.h> + +namespace +{ + +/** + * Simple graph for test + * + * BEFORE + * + * [lhs] ------------+ + * +-- [Sub] -- + * [rhs_const] ------+ + * + * AFTER + * + * [lhs] ------------+ + * +-- [Add] -- + * [neg_rhs_const] --+ + */ +class SimpleGraph +{ +public: + SimpleGraph() + { + lhs = g.nodes()->create<luci::CircleInput>(); + rhs_const = g.nodes()->create<luci::CircleConst>(); + sub = g.nodes()->create<luci::CircleSub>(); + output = g.nodes()->create<luci::CircleOutput>(); + + auto graph_input = g.inputs()->create(); + lhs->index(graph_input->index()); + auto graph_output = g.outputs()->create(); + output->index(graph_output->index()); + + lhs->dtype(loco::DataType::FLOAT32); + rhs_const->dtype(loco::DataType::FLOAT32); + sub->dtype(loco::DataType::FLOAT32); + output->dtype(loco::DataType::FLOAT32); + + lhs->shape({1, 3, 4, 5}); + rhs_const->shape({}); // scalar + sub->shape({1, 3, 4, 5}); + output->shape({1, 3, 4, 5}); + + rhs_const->size<loco::DataType::FLOAT32>(1); + rhs_const->at<loco::DataType::FLOAT32>(0) = 1.1; + + sub->x(lhs); + sub->y(rhs_const); + output->from(sub); + + lhs->name("lhs"); + rhs_const->name("rhs_const"); + sub->name("sub"); + output->name("output"); + } + +public: + loco::Graph g; + luci::CircleInput *lhs = nullptr; + luci::CircleConst *rhs_const = nullptr; + luci::CircleSub *sub = nullptr; + luci::CircleOutput *output = nullptr; +}; + +} // namespace + +TEST(ReplaceSubWithAdd, name) +{ + luci::ReplaceSubWithAddPass pass; + auto const name = pass.name(); + ASSERT_NE(nullptr, name); +} + +TEST(ReplaceSubWithAdd, simple) +{ + SimpleGraph g; + + luci::ReplaceSubWithAddPass pass; + while (pass.run(&g.g)) + ; + + auto add = dynamic_cast<luci::CircleAdd *>(g.output->from()); + EXPECT_NE(nullptr, add); + + auto neg_rhs_const = dynamic_cast<luci::CircleConst *>(add->y()); + EXPECT_NE(nullptr, neg_rhs_const); + EXPECT_EQ(0, neg_rhs_const->rank()); + EXPECT_FLOAT_EQ(-1.1, neg_rhs_const->at<loco::DataType::FLOAT32>(0)); +} + +TEST(ReplaceSubWithAdd, wrong_op_NEG) +{ + SimpleGraph g; + + auto mul = g.g.nodes()->create<luci::CircleMul>(); + mul->x(g.sub->x()); + mul->y(g.sub->y()); + loco::replace(g.sub).with(mul); + + luci::ReplaceSubWithAddPass pass; + auto changed = pass.run(&g.g); + + EXPECT_EQ(false, changed); +} diff --git a/compiler/luci/pass/src/ResolveCustomOpMaxPoolWithArgmaxPass.cpp b/compiler/luci/pass/src/ResolveCustomOpMaxPoolWithArgmaxPass.cpp new file mode 100644 index 000000000..d78a587ac --- /dev/null +++ b/compiler/luci/pass/src/ResolveCustomOpMaxPoolWithArgmaxPass.cpp @@ -0,0 +1,903 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/ResolveCustomOpMaxPoolWithArgmaxPass.h" + +#include "flatbuffers/flexbuffers.h" +#include <loco/IR/DataTypeTraits.h> + +#include <luci/IR/CircleNodes.h> +#include <luci/Profile/CircleNodeOrigin.h> + +#include <loco.h> +#include <oops/InternalExn.h> + +namespace +{ + +template <typename T> std::vector<T> to_vector(const flexbuffers::TypedVector &typed_vec) +{ + std::vector<T> answer(typed_vec.size()); + + for (uint32_t i = 0; i < answer.size(); ++i) + { + answer[i] = typed_vec[i].As<T>(); + } + + return answer; +} + +luci::Padding string_to_padding(const std::string &pad_str) +{ + if (pad_str == "VALID") + return luci::Padding::VALID; + if (pad_str == "SAME") + return luci::Padding::SAME; + + return luci::Padding::UNDEFINED; +} + +template <typename NodeT> void set_stride(NodeT *node, const luci::Stride &stride) +{ + node->stride()->h(stride.h()); + node->stride()->w(stride.w()); +} + +template <typename NodeT> void set_filter(NodeT *node, const luci::Filter &filter) +{ + node->filter()->h(filter.h()); + node->filter()->w(filter.w()); +} + +void init_name_and_origin(luci::CircleNode *node, const std::string &name, + const std::shared_ptr<luci::CircleNodeOrigin> &origin) +{ + node->name(name); + luci::add_origin(node, origin); +} + +template <typename NodeT> NodeT *none_act_func(NodeT *node) +{ + node->fusedActivationFunction(luci::FusedActFunc::NONE); + return node; +} + +luci::CircleCast *create_cast(luci::CircleNode *input, loco::DataType in_type, + loco::DataType out_type) +{ + auto cast = input->graph()->nodes()->create<luci::CircleCast>(); + + cast->in_data_type(in_type); + cast->out_data_type(out_type); + cast->dtype(out_type); + + cast->x(input); + + return cast; +} + +template <loco::DataType DT> void fill_conv_weights(luci::CircleConst *weights) +{ + assert(weights->rank() == 4); + + auto const kn = weights->dim(0).value(); + auto const kh = weights->dim(1).value(); + auto const kw = weights->dim(2).value(); + + auto elements_size = kn * kh * kw * 1; + weights->size<DT>(elements_size); + + for (uint32_t b = 0; b < kn; ++b) + { + for (uint32_t y = 0; y < kh; ++y) + { + for (uint32_t x = 0; x < kw; ++x) + { + auto const idx = (b * kh + y) * kw + x; + weights->at<DT>(idx) = (y * kw + x == b) ? 1 : 0; + } + } + } +} + +luci::CircleConst *create_conv_filter(loco::Graph *graph, const uint32_t kh, const uint32_t kw, + const uint32_t kn) +{ + auto weights = graph->nodes()->create<luci::CircleConst>(); + + weights->dtype(loco::DataType::FLOAT32); + + weights->rank(4); + weights->dim(0).set(kn); + weights->dim(1).set(kh); + weights->dim(2).set(kw); + weights->dim(3).set(1); + weights->shape_status(luci::ShapeStatus::VALID); + + fill_conv_weights<loco::DataType::FLOAT32>(weights); + + return weights; +} + +template <loco::DataType DT> void fill_zero_bias(luci::CircleConst *bias) +{ + assert(bias->rank() == 1); + + auto const depth = bias->dim(0).value(); + + bias->size<DT>(depth); + + for (uint32_t i = 0; i < depth; ++i) + { + bias->at<DT>(i) = 0; + } +} + +luci::CircleConst *create_zero_bias(loco::Graph *graph, uint32_t depth) +{ + auto bias = graph->nodes()->create<luci::CircleConst>(); + + bias->dtype(loco::DataType::FLOAT32); + + bias->rank(1); + bias->dim(0).set(depth); + + fill_zero_bias<loco::DataType::FLOAT32>(bias); + + return bias; +} + +luci::CircleConst *create_padding_const(loco::Graph *graph, int32_t left_pad, int32_t right_pad, + int32_t top_pad, int32_t bottom_pad) +{ + auto paddings = graph->nodes()->create<luci::CircleConst>(); + + paddings->dtype(loco::DataType::S32); + + paddings->rank(2); + paddings->dim(0).set(4); + paddings->dim(1).set(2); + paddings->size<loco::DataType::S32>(8); + paddings->shape_status(luci::ShapeStatus::VALID); + + paddings->at<loco::DataType::S32>(0) = 0; + paddings->at<loco::DataType::S32>(1) = 0; + + paddings->at<loco::DataType::S32>(2) = left_pad; + paddings->at<loco::DataType::S32>(3) = right_pad; + + paddings->at<loco::DataType::S32>(4) = top_pad; + paddings->at<loco::DataType::S32>(5) = bottom_pad; + + paddings->at<loco::DataType::S32>(6) = 0; + paddings->at<loco::DataType::S32>(7) = 0; + + return paddings; +} + +template <loco::DataType DT, typename Numeric> +luci::CircleConst *create_scalar(loco::Graph *graph, Numeric value) +{ + auto scalar = graph->nodes()->create<luci::CircleConst>(); + + scalar->dtype(DT); + + scalar->rank(0); + scalar->size<DT>(1); + scalar->shape_status(luci::ShapeStatus::VALID); + + scalar->scalar<DT>() = value; + + return scalar; +} + +luci::CircleConst *create_shape_tensor(loco::Graph *graph, const std::vector<uint32_t> &dims_vec) +{ + auto shape = graph->nodes()->create<luci::CircleConst>(); + + shape->dtype(loco::DataType::S32); + + shape->rank(1); + shape->dim(0).set(dims_vec.size()); + shape->shape_status(luci::ShapeStatus::VALID); + + shape->size<loco::DataType::S32>(dims_vec.size()); + + for (uint32_t i = 0; i < dims_vec.size(); ++i) + { + shape->at<loco::DataType::S32>(i) = dims_vec[i]; + } + + return shape; +} + +int32_t compute_full_padding(int32_t input_size, int32_t output_size, int32_t stride, + int32_t filter_size) +{ + int32_t effective_input = (output_size - 1) * stride + filter_size; + int32_t full = effective_input - input_size; + // some extreme cases when part of input was not used in computations + if (full < 0) + full = 0; + return full; +} + +template <loco::DataType DT> +void fill_coords_addition(luci::Padding padding, const luci::Stride &stride, + const luci::Filter &filter, uint32_t input_height, uint32_t input_width, + uint32_t depth, luci::CircleConst *cords) +{ + assert(cords->rank() == 4); + + auto const output_height = static_cast<int32_t>(cords->dim(1).value()); + auto const output_width = static_cast<int32_t>(cords->dim(2).value()); + { + auto const element_counts = 1 * output_height * output_width * 1; + cords->size<DT>(element_counts); + } + + assert(padding != luci::Padding::UNDEFINED); + + // For VALID padding: + int32_t start_y = 0; + int32_t start_x = 0; + + // For SAME padding: + if (padding == luci::Padding::SAME) + { + start_y = -compute_full_padding(input_height, output_height, stride.h(), filter.h()) / 2; + start_x = -compute_full_padding(input_width, output_width, stride.w(), filter.w()) / 2; + } + + auto const step_y = static_cast<int32_t>(stride.h()); + auto const step_x = static_cast<int32_t>(stride.w()); + + for (int32_t y_o = 0, y_i = start_y; y_o < output_height; ++y_o, y_i += step_y) + { + for (int32_t x_o = 0, x_i = start_x; x_o < output_width; ++x_o, x_i += step_x) + { + auto const output_idx = y_o * output_width + x_o; + auto const input_idx = y_i * static_cast<int32_t>(input_width) + x_i; + + // Add small adjustment value to fix cast operation result that follows "coord addition" + // in generated subgraph. + // + // Cast operation discards fractional part of value, so 1.9996 will be transformed to 1 + // This is not a problem when working with float32, because it represents integers precisely, + // but leads to wrong results, when working with quantized numbers. + // + // This value is larger than quantization error, + // and small enough to not affect following computations + // (in particular multiplication with depth) + const float round_adjustment = 1.0f / (depth + 1); + + cords->at<DT>(output_idx) = input_idx + round_adjustment; + } + } +} + +luci::CircleConst *create_coords_addition(loco::Graph *graph, luci::Padding padding, + const luci::Stride &stride, const luci::Filter &filter, + uint32_t input_height, uint32_t input_width, + uint32_t depth, uint32_t output_height, + uint32_t output_width) +{ + auto cords = graph->nodes()->create<luci::CircleConst>(); + + cords->dtype(loco::DataType::FLOAT32); + + cords->rank(4); + cords->dim(0).set(1); + cords->dim(1).set(output_height); + cords->dim(2).set(output_width); + cords->dim(3).set(1); + + fill_coords_addition<loco::DataType::FLOAT32>(padding, stride, filter, input_height, input_width, + depth, cords); + + return cords; +} + +luci::CircleNode *get_custom_output(const luci::CircleCustom *cop, int32_t idx) +{ + auto const outputs = loco::succs(cop); + assert(outputs.size() == 2); + + auto output = loco::must_cast<luci::CircleCustomOut *>(*outputs.begin()); + if (output->index() != idx) + { + output = loco::must_cast<luci::CircleCustomOut *>(*outputs.rbegin()); + } + + return output; +} + +luci::CircleNode *max_pool_branch(luci::Padding padding, const luci::Stride &stride, + const luci::Filter filter, luci::CircleCustom *cop) +{ + auto graph = cop->graph(); + auto input = cop->inputs(0); + + auto origin = luci::get_origin(cop); + auto name = cop->name() + "/Argmax"; + + // Create MaxPool + auto maxpool = none_act_func(graph->nodes()->create<luci::CircleMaxPool2D>()); + { + init_name_and_origin(maxpool, name + "/MaxPool2D", origin); + + set_stride(maxpool, stride); + set_filter(maxpool, filter); + maxpool->padding(padding); + + maxpool->value(input); + } + + return maxpool; +} + +luci::CircleNode *window_flattened_coord(const std::string &name, luci::Padding padding, + const luci::Stride &stride, const luci::Filter filter, + int32_t input_height, int32_t input_width, + uint32_t output_height, uint32_t output_width, + luci::CircleNode *input) +{ + auto const graph = input->graph(); + auto const origin = luci::get_origin(input); + + auto const depth_dimension = 3; + + // Create pad in case of SAME padding + luci::CircleNode *conv_input = input; + if (padding == luci::Padding::SAME) + { + // Create redundant add to combine two nodes with special quantization restrictions: + // PadV2 and Split in this case + // TODO Introduce special requantize node and fix quantizer? + auto requantize = none_act_func(graph->nodes()->create<luci::CircleMul>()); + init_name_and_origin(requantize, name + "/Requantize", origin); + auto zero_const = create_scalar<loco::DataType::FLOAT32>(graph, 1.0f); + init_name_and_origin(zero_const, name + "Requantize_const", origin); + + requantize->x(input); + requantize->y(zero_const); + + auto pad = graph->nodes()->create<luci::CirclePadV2>(); + init_name_and_origin(pad, name + "/Pad", origin); + + pad->input(requantize); + + int32_t full_w_pad = compute_full_padding(input_width, output_width, stride.w(), filter.w()); + int32_t full_h_pad = compute_full_padding(input_height, output_height, stride.h(), filter.h()); + int32_t left_pad = full_w_pad / 2; + int32_t right_pad = full_w_pad - left_pad; + int32_t top_pad = full_h_pad / 2; + int32_t bottom_pad = full_h_pad - top_pad; + auto padding_const = create_padding_const(graph, left_pad, right_pad, top_pad, bottom_pad); + init_name_and_origin(padding_const, name + "/Pad_shape", origin); + pad->paddings(padding_const); + + auto padding_value = + create_scalar<loco::DataType::FLOAT32, float>(graph, std::numeric_limits<float>::lowest()); + init_name_and_origin(padding_value, name + "/Pad_value", origin); + pad->constant_values(padding_value); + + conv_input = pad; + } + // Create Conv2D to move spatial dimensions to depth + auto conv = none_act_func(graph->nodes()->create<luci::CircleConv2D>()); + { + init_name_and_origin(conv, name + "/Conv2D", origin); + + // Padding, Stride and kernel size equal to MaxPool's + set_stride(conv, stride); + conv->padding(luci::Padding::VALID); + + // depth of kernel is equal to square size + auto const kh = filter.h(); + auto const kw = filter.w(); + auto const kd = kh * kw; + + // use zero bias + auto bias = create_zero_bias(graph, kd); + init_name_and_origin(bias, conv->name() + "/Bias", origin); + + // create filter + // TODO make shared + auto weights = create_conv_filter(graph, kh, kw, kd); + init_name_and_origin(weights, conv->name() + "/Weights", origin); + + conv->bias(bias); + conv->filter(weights); + conv->input(conv_input); + } + + // Create ArgMax + auto argmax = graph->nodes()->create<luci::CircleArgMax>(); + { + init_name_and_origin(argmax, name + "/ArgMax", origin); + + argmax->output_type(loco::DataType::S32); + + // Create argmax_dim + auto argmax_dim = create_scalar<loco::DataType::S32>(graph, depth_dimension); + init_name_and_origin(argmax_dim, argmax->name() + "/Dimension", origin); + + argmax->dimension(argmax_dim); + argmax->input(conv); + } + + // Create Reshape to 4-rank back, because argmax decrease rank of tensor by 1 + auto reshape = graph->nodes()->create<luci::CircleReshape>(); + { + init_name_and_origin(reshape, name + "/Reshape", origin); + + auto shape = create_shape_tensor(graph, {1, output_height, output_width, 1}); + init_name_and_origin(shape, reshape->name() + "/Shape", origin); + + reshape->tensor(argmax); + reshape->shape(shape); + } + + // Create Cast to use float32 instead int32 + auto argmax_cast = create_cast(reshape, loco::DataType::S32, loco::DataType::FLOAT32); + init_name_and_origin(argmax_cast, argmax->name() + "/Cast", origin); + + return argmax_cast; +} + +// Creates "identity operation" after Floor +// to force circle-quantizer requantize output tensor with scale << 1. +// +// Dealing with values of extremely different scales +// in following binary operations hurts backend precision. +luci::CircleNode *create_post_floor_requantize_node(luci::CircleFloor *floor) +{ + auto graph = floor->graph(); + auto const origin = luci::get_origin(floor); + auto name = floor->name(); + + // Use DepthwiseConv2D with identity filter as an "identity operation". + // + // This operation do not change values, but forces circle-quantizer to use + // statistics to compute qparam scale instead of fixed scale == 1.0 after floor. + // DepthwiseConv2d is not eliminated by optimizations, + // so desired scale will reach backend. + auto requantizer = none_act_func(graph->nodes()->create<luci::CircleDepthwiseConv2D>()); + init_name_and_origin(requantizer, name + "/Requantizer", origin); + + requantizer->input(floor); + + auto requantizer_filter = create_scalar<loco::DataType::FLOAT32>(graph, 1.0f); + init_name_and_origin(requantizer_filter, name + "/Requantizer/filter", origin); + requantizer_filter->rank(4); + for (uint32_t i = 0; i < 4; ++i) + { + requantizer_filter->dim(i) = 1; + } + requantizer->filter(requantizer_filter); + + auto requantizer_bias = create_zero_bias(graph, 1); + init_name_and_origin(requantizer_bias, name + "/Requantizer/bias", origin); + requantizer->bias(requantizer_bias); + + requantizer->padding(luci::Padding::VALID); + requantizer->stride()->w(1); + requantizer->stride()->h(1); + requantizer->depthMultiplier(1); + requantizer->dilation()->w(1); + requantizer->dilation()->h(1); + + return requantizer; +} + +luci::CircleNode *window_y_coord(const std::string &name, const luci::Filter &filter, + luci::CircleNode *flattened) +{ + auto const graph = flattened->graph(); + auto const origin = luci::get_origin(flattened); + + auto div = none_act_func(graph->nodes()->create<luci::CircleMul>()); + { + init_name_and_origin(div, name + "/Div", origin); + + // Adjustment_coeff is needed to fix computation of quantized tensors + // + // For example float32 value 2.0 could be quantized to 1.996 + // after floor it will be transformed to 1.0, but desired answer is still something close to 2.0 + // + // rounding_adjustment is chosen so it is small enough to not affect float32 computations, + // but "Div" change is larger then potential quantization error. + // + // This computation exploits the fact that div is an x coord in maxpool window, + // and lies in defined range [0, filter.h()) + const float rounding_adjustment = 1.0f / (filter.w() * filter.h()); + const float divider_value = filter.w() - rounding_adjustment; + auto divider = create_scalar<loco::DataType::FLOAT32>(graph, 1.0f / divider_value); + init_name_and_origin(divider, div->name() + "/Divider", origin); + + div->x(flattened); + div->y(divider); + } + + auto floor = graph->nodes()->create<luci::CircleFloor>(); + { + init_name_and_origin(floor, name + "/Floor", origin); + floor->x(div); + } + + auto requantizer = create_post_floor_requantize_node(floor); + + return requantizer; +} + +luci::CircleNode *window_x_coord(const std::string &name, float filter_width, + luci::CircleNode *flattened, luci::CircleNode *y_coord) +{ + auto const graph = flattened->graph(); + auto const origin = luci::get_origin(flattened); + + auto mod = none_act_func(graph->nodes()->create<luci::CircleAdd>()); + { + init_name_and_origin(mod, name + "/Mod", origin); + + auto neg = graph->nodes()->create<luci::CircleNeg>(); + { + init_name_and_origin(neg, mod->name() + "/Neg", origin); + + auto mul = none_act_func(graph->nodes()->create<luci::CircleMul>()); + { + init_name_and_origin(mul, neg->name() + "/Neg", origin); + + auto multipler = create_scalar<loco::DataType::FLOAT32>(graph, filter_width); + init_name_and_origin(multipler, mul->name() + "/Multipler", origin); + + mul->x(y_coord); + mul->y(multipler); + } + + neg->x(mul); + } + + mod->x(flattened); + mod->y(neg); + } + + return mod; +} + +luci::CircleNode *plane_flattened_coord(const std::string &name, uint32_t input_width, + luci::CircleNode *y_coord, luci::CircleNode *x_coord, + luci::CircleNode *corners) +{ + auto const graph = corners->graph(); + auto const origin = luci::get_origin(corners); + + auto add = none_act_func(graph->nodes()->create<luci::CircleAdd>()); + { + init_name_and_origin(add, name + "/Add", origin); + + auto addition = none_act_func(graph->nodes()->create<luci::CircleAdd>()); + { + init_name_and_origin(addition, add->name() + "/Add", origin); + + auto y_addition = none_act_func(graph->nodes()->create<luci::CircleMul>()); + { + init_name_and_origin(y_addition, addition->name() + "/Mul", origin); + + auto width_scalar = create_scalar<loco::DataType::FLOAT32>(graph, input_width); + init_name_and_origin(width_scalar, y_addition->name() + "/Const", origin); + + y_addition->x(y_coord); + y_addition->y(width_scalar); + } + + addition->x(x_coord); + addition->y(y_addition); + } + + add->x(addition); + add->y(corners); + } + + return add; +} + +luci::CircleNode *volume_flattened_coords(const std::string &name, uint32_t channel, + uint32_t input_depth, luci::CircleNode *plane) +{ + auto const graph = plane->graph(); + auto const origin = luci::get_origin(plane); + + // Create Mul + auto mul = none_act_func(graph->nodes()->create<luci::CircleMul>()); + { + init_name_and_origin(mul, name + "/Mul", origin); + + auto depth_scalar = create_scalar<loco::DataType::FLOAT32>(graph, input_depth); + init_name_and_origin(depth_scalar, mul->name() + "/Const", origin); + + mul->x(plane); + mul->y(depth_scalar); + } + + luci::CircleNode *volume = mul; + + // Add channel number to output + if (channel > 0) + { + // Create Add + auto add_ch = none_act_func(graph->nodes()->create<luci::CircleAdd>()); + init_name_and_origin(add_ch, name + "/Add_Channel", origin); + + auto channel_scalar = create_scalar<loco::DataType::FLOAT32>(graph, channel); + init_name_and_origin(channel_scalar, add_ch->name() + "/Const", origin); + + add_ch->x(mul); + add_ch->y(channel_scalar); + + volume = add_ch; + } + + return volume; +} + +luci::CircleNode *argmax_branch(luci::Padding padding, const luci::Stride &stride, + const luci::Filter filter, luci::CircleCustom *cop) +{ + auto graph = cop->graph(); + auto input = loco::must_cast<luci::CircleNode *>(cop->inputs(0)); + auto output = get_custom_output(cop, 1); + + auto const depth_dimension = 3; + auto const input_depth = input->dim(depth_dimension).value(); + auto const input_height = input->dim(1).value(); + auto const input_width = input->dim(2).value(); + + assert(output->rank() == 4); + auto const output_height = output->dim(1).value(); + auto const output_width = output->dim(2).value(); + + auto origin = luci::get_origin(cop); + auto name = cop->name() + "/Argmax"; + + // Create Split + auto split = graph->nodes()->create<luci::CircleSplit>(); + { + init_name_and_origin(split, name + "/Split", origin); + + // Create split_dim + auto split_dim = create_scalar<loco::DataType::S32>(graph, depth_dimension); + init_name_and_origin(split_dim, split->name() + "/Dim", origin); + + split->num_split(int32_t(input_depth)); + + split->split_dim(split_dim); + split->input(input); + } + + /** + * Note: we need define idx from input_tensor of maximum element in MaxPool's sliding window. + * For this we split input tensor by channels, define idx in sliding window and convert this idx + * to idx from source input_tensor using FloorDiv, Mul and Add operations with constant tensors. + */ + std::vector<luci::CircleNode *> branch_outputs(input_depth); + + for (uint32_t br_n = 0; br_n < input_depth; ++br_n) + { + auto const branch_name = name + "/depth_" + std::to_string(br_n); + + // Create CircleSplitOut + auto split_out = graph->nodes()->create<luci::CircleSplitOut>(); + init_name_and_origin(split_out, branch_name + "/SplitOut", origin); + split_out->index(int32_t(br_n)); + split_out->input(split); + + // Define idx of max element in Window: + auto window_coords = + window_flattened_coord(branch_name + "/WindowFlat", padding, stride, filter, input_height, + input_width, output_height, output_width, split_out); + + auto const window_y = window_y_coord(branch_name + "/WindowY", filter, window_coords); + auto const window_x = + window_x_coord(branch_name + "/WindowX", filter.w(), window_coords, window_y); + + // Define idx of max element in Plane + // This tensor contains coords of left top corners for each window from input tensor + auto corners = create_coords_addition(graph, padding, stride, filter, input_height, input_width, + input_depth, output_height, output_width); + init_name_and_origin(corners, branch_name + "/Const", origin); + + auto plane_coord = + plane_flattened_coord(branch_name + "/PlaneFlat", input_width, window_y, window_x, corners); + + // Define volume coords as final value + branch_outputs[br_n] = + volume_flattened_coords(branch_name + "/VolumeFlat", br_n, input_depth, plane_coord); + } + + // Create Concatenation + auto concat = none_act_func(graph->nodes()->create<luci::CircleConcatenation>(input_depth)); + { + init_name_and_origin(concat, name + "/Concatenation", origin); + concat->axis(depth_dimension); + + for (uint32_t i = 0; i < input_depth; ++i) + { + concat->values(i, branch_outputs[i]); + } + } + + // Output of argmax_with_maxpool should be S64 or S32 + loco::DataType output_dtype = get_custom_output(cop, 1)->dtype(); + auto output_cast = create_cast(concat, loco::DataType::FLOAT32, output_dtype); + init_name_and_origin(output_cast, name + "/Cast", origin); + + return output_cast; +} + +bool resolve_max_pool_with_argmax(luci::CircleCustom *cop) +{ +#define CHECK_OR_FALSE(condition) \ + if (not(condition)) \ + return false; + + const std::vector<uint8_t> custom_options = cop->custom_options(); + auto map = flexbuffers::GetRoot(custom_options).AsMap(); + + // Define params + // Note: Only `Targmax` equal to DT_INT64 is supported by tflite converter + // Note: Only `data_format` equal to "NHWC" is supported by tflite converter + // TODO add support of `include_batch_in_index` param + auto ksize_param = to_vector<uint32_t>(map["ksize"].AsTypedVector()); + auto strides_param = to_vector<uint32_t>(map["strides"].AsTypedVector()); + auto padding_param = map["padding"].As<std::string>(); + + // Batch size and depth of ksize more than 1 is not supported. + CHECK_OR_FALSE(ksize_param.size() == 4); + CHECK_OR_FALSE(ksize_param[0] == 1 && ksize_param[3] == 1); + + CHECK_OR_FALSE(strides_param.size() == 4); + CHECK_OR_FALSE(strides_param[0] == 1 && strides_param[3] == 1); + + // define Padding + auto padding = string_to_padding(padding_param); + + // define Filter + luci::Filter filter; + filter.h(ksize_param[1]); + filter.w(ksize_param[2]); + + // define Stride + luci::Stride stride; + stride.h(strides_param[1]); + stride.w(strides_param[2]); + + // input node + auto const input = loco::must_cast<luci::CircleNode *>(cop->inputs(0)); + CHECK_OR_FALSE(input->dtype() == loco::DataType::FLOAT32); + CHECK_OR_FALSE(input->rank() == 4); + + // TODO support batch size > 1 and `include_batch_in_index` option + CHECK_OR_FALSE(input->dim(0).value() == 1); + + // output nodes + auto const outputs = loco::succs(cop); + CHECK_OR_FALSE(outputs.size() == 2); + assert(outputs.size() == cop->numOutputs()); + + auto output0 = get_custom_output(cop, 0); + auto output1 = get_custom_output(cop, 1); + + // From TF documentation: output of maxpool must has same type as input + assert(output0->dtype() == input->dtype()); + assert(output1->dtype() == loco::DataType::S64 || output1->dtype() == loco::DataType::S32); + + // Create MaxPool + auto maxpool = max_pool_branch(padding, stride, filter, cop); + auto argmax = argmax_branch(padding, stride, filter, cop); + + // last argmax branch op is cast, it should have dtype initialized + assert(argmax->dtype() == output1->dtype()); + + // replace old node with new subgraph + cop->inputs(0, nullptr); + loco::replace(output0).with(maxpool); + loco::replace(output1).with(argmax); + + return true; +} + +} // namespace + +namespace luci +{ + +/** + * BEFORE + * | + * [CircleNode] + * | + * [CUSTOM(MaxPoolWithArgmax)] + * | | + * [MaxPool output] [Argmax output] + * + * AFTER + * | + * [CircleNode] + * / \ + * [Split over channels] [MaxPool2D] + * / | \ \ + * [Requantize] ... ... [MaxPool output] + * | + * [PadV2] + * | + * [Conv2D] + * | + * [ArgMax] + * | + * [Reshape to 4d] + * | + * [Cast to float32] + * / | + * | [Mul 1/<window width>] + * | \ + * | [Floor] + * | | + * | [DepthwiseConv2D for requantize] + * | / \ + * | [Mul window width] | + * \ / / + * \ [Neg] [Mul input width] + * \ / / + * [Add] / + * \ / + * [Add] + * | + * [Add const] + * | + * [Mul number of channels] + * \ + * [Optional Add with channels id] ... ... + * \ | / + * [Concatenation] + * | + * [Cast to int] + * | + * [Argmax output] + */ +bool ResolveCustomOpMaxPoolWithArgmaxPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto cop = dynamic_cast<luci::CircleCustom *>(node); + if (not cop) + continue; + + if (cop->custom_code() != "MaxPoolWithArgmax") + continue; + + if (!resolve_max_pool_with_argmax(cop)) + continue; + + changed = true; + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/ResolveCustomOpMaxPoolWithArgmaxPass.test.cpp b/compiler/luci/pass/src/ResolveCustomOpMaxPoolWithArgmaxPass.test.cpp new file mode 100644 index 000000000..cad6ca214 --- /dev/null +++ b/compiler/luci/pass/src/ResolveCustomOpMaxPoolWithArgmaxPass.test.cpp @@ -0,0 +1,26 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/ResolveCustomOpMaxPoolWithArgmaxPass.h" + +#include <gtest/gtest.h> + +TEST(ResolveCustomOpMaxPoolWithArgmaxPassTest, name) +{ + luci::ResolveCustomOpMaxPoolWithArgmaxPass pass; + auto const name = pass.name(); + ASSERT_NE(nullptr, name); +} diff --git a/compiler/luci/pass/src/SubstitutePadV2ToPadPass.cpp b/compiler/luci/pass/src/SubstitutePadV2ToPadPass.cpp new file mode 100644 index 000000000..549ed22ec --- /dev/null +++ b/compiler/luci/pass/src/SubstitutePadV2ToPadPass.cpp @@ -0,0 +1,469 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/SubstitutePadV2ToPadPass.h" + +#include <luci/IR/CircleNodes.h> +#include <luci/Profile/CircleNodeOrigin.h> + +#include <vector> + +/** + * @brief Convert PadV2 op in a certain condition to Pad + * @details Condition to convert PadV2 to Pad is like below: + * + * Basic Condition) + * + * C1) For all i, PadV2.input[i] >= 0 + * C2) For all c, PadV2.constant_values[c] <= 0 + * C3) PadV2 == MaxPool2D.value() + * C4) number of padded values at left < MaxPool2D.Filter.W + * number of padded values at right < MaxPool2D.Filter.W + * number of padded values at top < MaxPool2D.Filter.H + * number of padded values at bottom < MaxPool2D.Filter.H + * + * Example graph is as follows: + * + * %1 = CircleRelu # relu_output[i] >= 0 + * %2 = CirclePadV2(%1, constant_values <= 0) + * %3 = CircleMaxPool2D(%2, ...) # output will be chosen from relu_output + * + * In this case, it's OK to replace PadV2 with Pad, which uses 0 as padding constant. + * + * Optional Condition) + * + * Terminology) + * - 'reshaping op' : op that does not change the value of tensor + * but changes position of tensor value, e.g., Transpose, Reshape, Slice, etc. + * + * C5) Input of PadV2 could be 'reshaping op'. Example is as follow: + * + * %1 = CircleRelu # output[i] >= 0 + * %2 = CircleTranspose(%1) # reshaping op + * ... # more reshaping ops + * %n = CirclePadV2(%n-1, constant_values <= 0) + * %n+1 = CircleMaxPool2D(%n, ...) + * + * C6) PadV2 could be an input of 'reshaping op'. Example is as follow: + * + * %1 = CircleRelu + * %2 = CirclePadV2(%1, constant_values <= 0) + * %3 = CircleTranspose(%2) # reshaping op + * ... # more reshaping ops + * %n = CircleMaxPool2D(%n-1, ...) + * + * Why is this pass required? + * + * When PyTorch model is converted into Circle model, sometimes PadV2 is inserted with + * the following pattern: + * + * %1 = Circle.Conv2D(..., activation = Relu) + * %2 = Circle.Transpose(%1, perm=[0,3,1,2]) + * %3 = Circle.PadV2(%2, constant_values = -3.4028234663852886e+38) + * %4 = Circle.Transpose(%3, perm=[0,2,3,1]) + * %5 = Circle.MaxPool2D(%4, filter=[3,3], padding="VALID") + * + * Large negative padding constant of %3 caused problem when we quantized this model. + * So we need to convert the negative number to some number in reasonable range for + * quantization, e.g., zero. + */ +namespace +{ + +struct Paddings +{ + struct Pad + { + int32_t front; + int32_t end; + }; + /** + * @brief Store paddings position information. + * @details _padding_pos[k] stores Pad object at axis k + * + * @note Paddings must be for rank 4 tensor + */ + std::vector<Pad> _padding_pos; + + Paddings(luci::CircleConst *paddings) + { + assert(paddings->dtype() == loco::DataType::S32); + assert(paddings->rank() == 2); + assert(paddings->dim(1).value() == 2); + assert(paddings->size<loco::DataType::S32>() == paddings->rank() * 4); + + for (uint32_t i = 0; i < paddings->dim(0).value(); i++) + { + Pad pad{.front = paddings->at<loco::DataType::S32>(i * 2), + .end = paddings->at<loco::DataType::S32>(i * 2 + 1)}; + _padding_pos.emplace_back(pad); + } + + assert(_padding_pos.size() == 4); + } + + /** + * @brief Check if this padding area is covered by filter + * + * @note This is to check condition C4). + * _padding_pos should store values according to NHWC. + */ + bool smaller_than(int32_t filter_h, int32_t filter_w) + { + auto &pad_H = _padding_pos.at(1); + auto &pad_W = _padding_pos.at(2); + + return (pad_H.front < filter_h) && (pad_H.end < filter_h) && (pad_W.front < filter_w) && + (pad_W.end < filter_w); + } + + /** + * @brief Track how paddings change after CircleTranspose + * @details Consider the following graph, + * + * %1 = Circle.Input + * %2 = Circle.PadV2(%1, + * paddings=[[0, 0], [0, 0], [2, 3], [4, 5]], + * padding_value = -100) + * %3 = Circle.Transpose(%2, perm[0, 2, 3, 1]) + * + * Output of %3 has padding constant value(-100) from %2 at position below: + * + * - axis | front | end + * ------|-------|----- + * 0 | 0 | 0 + * 1 | 2 | 3 + * 2 | 4 | 5 + * 3 | 0 | 0 + * + * This method keeps track of such change of padding position. + */ + void apply(luci::CircleTranspose *transpose) + { + assert(transpose); + luci::CircleConst *perm = loco::must_cast<luci::CircleConst *>(transpose->perm()); + + std::vector<Pad> transposed_pos; + transposed_pos.resize(4); + + for (uint32_t to = 0; to < 4; to++) + { + int32_t from = perm->at<loco::DataType::S32>(to); + transposed_pos.at(to) = _padding_pos.at(from); + } + + _padding_pos = transposed_pos; + } +}; + +struct ReshapingNode +{ + /// @brief Check if node is 'reshaping op' + static bool check(loco::Node *node) + { + if (dynamic_cast<luci::CircleTranspose *>(node)) + return true; + // add more 'reshaping op' + + return false; + } + + /// @brief Retuen reshaping op's input + static loco::Node *input(loco::Node *node) + { + if (auto transpose = dynamic_cast<luci::CircleTranspose *>(node)) + return transpose->a(); + // add more 'reshaping op' + + throw std::runtime_error("Not yet supported reshaping op"); + } +}; + +/// @brief Get only successor node +loco::Node *get_only_succ(loco::Node *parent) +{ + assert(parent); + + auto successors = loco::succs(parent); + if (successors.size() != 1) + return nullptr; + + return *successors.begin(); +} + +// Check condition C1) and C5) +bool positive_or_zero(loco::Node *ifm) +{ + assert(ifm); + + if (ReshapingNode::check(ifm)) + return positive_or_zero(ReshapingNode::input(ifm)); + + // Since Relu.output[i] >= 0 + if (dynamic_cast<luci::CircleRelu *>(ifm)) + return true; + if (auto conv = dynamic_cast<luci::CircleConv2D *>(ifm)) + { + if (conv->fusedActivationFunction() == luci::FusedActFunc::RELU) + return true; + // Add more FusedActFunc + } + // Add more ops of which output[i] >= 0 + + return false; +} + +template <loco::DataType DT> bool has_all_positive_values(luci::CircleConst *node) +{ + // Only numeric datatype is allowed + static_assert(DT != loco::DataType::Unknown); + static_assert(DT != loco::DataType::STRING); + + assert(node); + + auto size = node->size<DT>(); + for (decltype(size) t = 0; t < size; t++) + { + typename loco::DataTypeImpl<DT>::Type val = node->at<DT>(t); + if (val <= 0) + return false; + } + + return true; +} + +// To check condition C2) +bool has_all_positive_values(luci::CircleConst *node) +{ + assert(node); + + if (node->dtype() == loco::DataType::FLOAT32) + return has_all_positive_values<loco::DataType::FLOAT32>(node); + // Add more datatype + + throw std::runtime_error("Not yet supported datatype"); +} + +bool used_by_maxpool_only(luci::CircleNode *node, Paddings &paddings) +{ + auto successor = get_only_succ(node); + + // when successor is not only-succ + if (successor == nullptr) + return false; + + if (auto maxpool = dynamic_cast<luci::CircleMaxPool2D *>(successor)) + { + // Let's check condition C4) + return paddings.smaller_than(maxpool->filter()->h(), maxpool->filter()->w()); + } + + // Let's check condition C6) + if (auto transpose = dynamic_cast<luci::CircleTranspose *>(successor)) + { + auto appropriate = [](luci::CircleTranspose *transpose) { + luci::CircleConst *perm = loco::must_cast<luci::CircleConst *>(transpose->perm()); + + // For Transpose to be an input for MaxPool2D + return (transpose->rank() == 4) && (perm && perm->dtype() == loco::DataType::S32) && + (perm->size<loco::DataType::S32>() == 4); + }; + + if (not appropriate(transpose)) + return false; + + paddings.apply(transpose); + return used_by_maxpool_only(transpose, paddings); + } + // Support more 'reshaping op' later + + return false; +} + +// Check condition C3), C4) and C6) +bool used_by_maxpool_only(luci::CirclePadV2 *pad_v2) +{ + // For PadV2 to be an input for MaxPool2D + if (pad_v2->rank() != 4) + return false; + + Paddings paddings(loco::must_cast<luci::CircleConst *>(pad_v2->paddings())); + + return used_by_maxpool_only(pad_v2, paddings); +} + +loco::Node *build_pad_from(luci::CirclePadV2 *pad_v2) +{ + auto copy_shape = [](const luci::CircleNode *src, luci::CircleNode *dest) { + auto rank = src->rank(); + dest->rank(rank); + + for (decltype(rank) axis = 0; axis < rank; axis++) + dest->dim(axis) = src->dim(axis); + }; + + auto g = pad_v2->graph(); + + auto pad = g->nodes()->create<luci::CirclePad>(); + { + pad->name(pad_v2->name() + "/pad"); + luci::add_origin(pad, luci::get_origin(pad_v2)); + + pad->dtype(pad_v2->dtype()); + copy_shape(pad_v2, pad); + + pad->input(pad_v2->input()); + pad->paddings(pad_v2->paddings()); + } + + return pad; +} + +luci::CirclePadV2 *get_padv2(loco::Node *node) +{ + if (auto padv2 = dynamic_cast<luci::CirclePadV2 *>(node)) + return padv2; + + if (ReshapingNode::check(node)) + return get_padv2(ReshapingNode::input(node)); + + return nullptr; +} + +bool substitute_padv2_to_pad(luci::CircleMaxPool2D *maxp) +{ + // precondition + assert(maxp); + assert(maxp->value()); + + auto pad_v2 = get_padv2(maxp->value()); + + if (pad_v2 == nullptr) + return false; + + assert(pad_v2->input()); + + auto paddings = loco::must_cast<luci::CircleConst *>(pad_v2->paddings()); + auto constant_values = loco::must_cast<luci::CircleConst *>(pad_v2->constant_values()); + + (void)paddings; + assert(paddings); + assert(paddings->dtype() == loco::DataType::S32); + assert(constant_values); + assert(constant_values->dtype() == pad_v2->dtype()); + + if (not positive_or_zero(pad_v2->input())) + return false; + + if (has_all_positive_values(constant_values)) + return false; + + if (not used_by_maxpool_only(pad_v2)) + return false; + + auto pad = build_pad_from(pad_v2); + + replace(pad_v2).with(pad); + + return true; +} + +} // namespace + +namespace luci +{ + +/** + * Case 1) Basic case + * + * BEFORE + * [CircleRelu] + * | + * | [CircleConst] [CircleConst] + * | | | + * -------+---------------------- + * | + * [CirclePadV2] + * | + * [CircleMaxPool2D] + * | + * + * AFTER + * [CircleRelu] + * | + * | [CircleConst] [CircleNode] [CircleConst] + * | | | | | + * -------+------- -------------+--------------+ + * | | + * [CirclePad] [CirclePadV2] + * | + * [CircleMaxPool2D] + * | + * + * Case 2) During conversion from a PyTorch model into a Circle model, + * it is common that some 'Reshaping op', e.g., CircleTranspose, + * are inserted in-between operations to swith NCHW into NHWC and vice versa. + * This pass also needs to handle such situation. + * + * BEFORE + * [CircleRelu] + * | + * | [CircleConst] [CircleConst] + * | | | + * -------+---------------------- + * | + * [CircleTranspose] + * | + * [CirclePadV2] + * | + * [CircleTranspose] + * | + * [CircleMaxPool2D] + * | + * + * AFTER + * [CircleRelu] + * | + * | [CircleConst] [CircleNode] [CircleConst] + * | | | | | + * -------+------- -------------+--------------+ + * | | + * [CircleTranspose] [CirclePadV2] + * | + * [CirclePad] + * | + * [CircleTranspose] + * | + * [CircleMaxPool2D] + * | + */ +bool SubstitutePadV2ToPadPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + if (auto circle_node = dynamic_cast<luci::CircleMaxPool2D *>(node)) + { + if (substitute_padv2_to_pad(circle_node)) + { + changed = true; + } + } + } + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/SubstitutePadV2ToPadPass.test.cpp b/compiler/luci/pass/src/SubstitutePadV2ToPadPass.test.cpp new file mode 100644 index 000000000..872968250 --- /dev/null +++ b/compiler/luci/pass/src/SubstitutePadV2ToPadPass.test.cpp @@ -0,0 +1,555 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "luci/Pass/SubstitutePadV2ToPadPass.h" +#include "luci/Pass/CircleShapeInferencePass.h" + +#include <luci/IR/CircleNodes.h> + +#include <gtest/gtest.h> + +namespace +{ + +using UIntList = std::initializer_list<uint32_t>; +using IntList = std::initializer_list<int32_t>; + +// convert shape in UIntList to loco::TensorShape +std::unique_ptr<loco::TensorShape> tensor_shape(const UIntList &values) +{ + auto shape = std::make_unique<loco::TensorShape>(); + { + shape->rank(values.size()); + + uint32_t r = 0; + for (auto v : values) + shape->dim(r++).set(v); + } + return shape; +} + +class TestGraph +{ +public: + void init(const UIntList &input_shape, const UIntList &output_shape) + { + _input = _g.nodes()->create<luci::CircleInput>(); + { + _input->name("input"); + _input->dtype(loco::DataType::FLOAT32); + _input->shape(input_shape); + + auto graph_input = _g.inputs()->create(); + { + _input->index(graph_input->index()); + graph_input->shape(std::move(tensor_shape(input_shape))); + } + } + + _output = _g.nodes()->create<luci::CircleOutput>(); + { + _output->name("output"); + _output->dtype(loco::DataType::FLOAT32); + _output->shape(output_shape); + + auto graph_output = _g.outputs()->create(); + { + _output->index(graph_output->index()); + graph_output->shape(std::move(tensor_shape(output_shape))); + } + } + + // subclass should implement build_body() + auto graphlet_before_output = build_body(_input); + + _output->from(graphlet_before_output); + } + + // build luci::CircleConst for paddings + luci::CircleConst *paddings_const(const std::vector<int32_t> &plist) + { + assert(plist.size() == 8); + + auto node = _g.nodes()->create<luci::CircleConst>(); + { + node->dtype(loco::DataType::S32); + node->shape({4, 2}); + node->size<loco::DataType::S32>(8); + + for (int32_t t = 0; t < 8; t++) + node->at<loco::DataType::S32>(t) = plist.at(t); + } + + return node; + } + + // build luci::CircleConst for paddings value + luci::CircleConst *padding_val_const(float val) + { + auto node = _g.nodes()->create<luci::CircleConst>(); + { + node->dtype(loco::DataType::FLOAT32); + node->shape({1}); + node->size<loco::DataType::FLOAT32>(1); + + node->at<loco::DataType::FLOAT32>(0) = val; + } + + return node; + } + + // build luci::CirclePadV2 + luci::CirclePadV2 *padV2(loco::Node *input, const std::vector<int32_t> &paddings, + float padding_constant) + { + auto padv2 = _g.nodes()->create<luci::CirclePadV2>(); + { + padv2->name("PadV2"); + padv2->dtype(loco::DataType::FLOAT32); + + padv2->input(input); + padv2->paddings(paddings_const(paddings)); + padv2->constant_values(padding_val_const(padding_constant)); + // No shape setting. ShapeInference should be run later + } + return padv2; + } + + // build luci::CircleMaxPool2D + luci::CircleMaxPool2D *maxpool2d(loco::Node *input, + const std::pair<uint32_t, uint32_t> &kernel_HW) + { + auto mp = _g.nodes()->create<luci::CircleMaxPool2D>(); + { + mp->value(input); + mp->fusedActivationFunction(luci::FusedActFunc::NONE); + mp->padding(luci::Padding::VALID); + mp->filter()->h(kernel_HW.first); + mp->filter()->w(kernel_HW.second); + mp->stride()->h(1); + mp->stride()->w(1); + + mp->dtype(loco::DataType::FLOAT32); + // No shape setting. ShapeInference should be run later + } + return mp; + } + + // build luci::CircleRelu + luci::CircleRelu *relu(loco::Node *input) + { + auto relu = _g.nodes()->create<luci::CircleRelu>(); + { + relu->features(input); + relu->dtype(loco::DataType::FLOAT32); + // No shape setting. ShapeInference should be run later + } + return relu; + } + + // build luci::CircleTranspose + luci::CircleTranspose *transpose(loco::Node *input, const std::vector<int32_t> &perm_v) + { + auto perm = _g.nodes()->create<luci::CircleConst>(); + { + auto rank = static_cast<uint32_t>(perm_v.size()); + perm->dtype(loco::DataType::S32); + perm->size<loco::DataType::S32>(rank); + perm->shape({rank}); + for (decltype(rank) d = 0; d < rank; d++) + perm->at<loco::DataType::S32>(d) = perm_v.at(d); + } + auto transpose_node = _g.nodes()->create<luci::CircleTranspose>(); + { + transpose_node->a(input); + transpose_node->perm(perm); + transpose_node->dtype(loco::DataType::S32); + // No shape setting. ShapeInference should be run later + } + return transpose_node; + } + + loco::Graph *g() { return &_g; } + luci::CircleOutput *output() { return _output; } + + virtual loco::Node *build_body(loco::Node *input) = 0; + +private: + loco::Graph _g; + luci::CircleInput *_input = nullptr; + luci::CircleOutput *_output = nullptr; +}; + +class SubstitutePadV2ToPadPassTest : public ::testing::Test +{ +public: + SubstitutePadV2ToPadPassTest() = default; + + bool run_pass(loco::Graph *g) + { + _shapeinf_pass.run(g); + + return _pad_pass.run(g); + } + +protected: + luci::SubstitutePadV2ToPadPass _pad_pass; + luci::CircleShapeInferencePass _shapeinf_pass; +}; + +} // namespace + +/** + * Graph that is changed by SubstitutePadV2ToPadPass + * + * [CircleInput] + * | + * [Relu] + * | + * [CirclePadV2] pad.H.front = 1, pad.H.end = 1, pad.W.front = 1, pad.W.end = 1 + * | + * [MaxPool2D] filter.H = 2, filter.W = 2 + * | + * [CircleOutput] + */ +TEST_F(SubstitutePadV2ToPadPassTest, basic_case) +{ + struct Graph_basic : public TestGraph + { + Graph_basic() + { + UIntList input_shape = {1, 4, 4, 3}; + UIntList output_shape = {1, 6, 6, 3}; + init(input_shape, output_shape); + } + + loco::Node *build_body(loco::Node *input) final + { + auto relu_node = relu(input); + + IntList paddings = {0, 0, 1, 1, 1, 1, 0, 0}; + auto padding_const = -10.0; + auto padV2_node = padV2(relu_node, paddings, padding_const); + + return maxpool2d(padV2_node, {2, 2}); + } + } graph; + + auto result = run_pass(graph.g()); + ASSERT_TRUE(result); + + // Checking CircleMaxPool2D + auto maxpool = dynamic_cast<luci::CircleMaxPool2D *>(graph.output()->from()); + ASSERT_TRUE(maxpool != nullptr); + + // Checking CirclePad + auto pad = dynamic_cast<luci::CirclePad *>(maxpool->value()); + ASSERT_TRUE(pad != nullptr); + + // Checking CircleRelu + auto relu = dynamic_cast<luci::CircleRelu *>(pad->input()); + ASSERT_TRUE(relu != nullptr); + + auto input = dynamic_cast<luci::CircleInput *>(relu->features()); + ASSERT_TRUE(input != nullptr); +} + +/** + * Graph that is changed by SubstitutePadV2ToPadPass + * + * Transpose ops are inserted, e.g., to switch layout between NHWC and NCHW + * + * [CircleInput] + * | + * [Relu] + * | 1x4x4x3 (NHWC) + * [Transpose] perm=[0,3,1,2] + * | 1x3x4x4 (NCHW) + * [CirclePadV2] paddings=[0,0,0,0,1,1,1,1] + * | 1x3x6x6 (NCHW) + * [Transpose] perm=[0,2,3,1] + * | 1x6x6x3 (NHWC) + * [MaxPool2D] filter.H = 3, filter.W = 3 + * | 1x4x4x3 (NHWC) + * [CircleOutput] + */ +TEST_F(SubstitutePadV2ToPadPassTest, reshaping_op_case) +{ + struct Graph_Reshaping_Op : public TestGraph + { + Graph_Reshaping_Op() + { + UIntList input_shape = {1, 4, 4, 3}; + UIntList output_shape = {1, 4, 4, 3}; + init(input_shape, output_shape); + } + + loco::Node *build_body(loco::Node *input) final + { + auto relu_node = relu(input); + + auto transpose1_node = transpose(relu_node, {0, 3, 1, 2}); + + IntList paddings = {0, 0, 0, 0, 1, 1, 1, 1}; + auto padding_const = -10.0; + auto padV2_node = padV2(transpose1_node, paddings, padding_const); + + auto transpose2_node = transpose(padV2_node, {0, 2, 3, 1}); + + return maxpool2d(transpose2_node, {3, 3}); + } + } graph; + + auto result = run_pass(graph.g()); + ASSERT_TRUE(result); + + // Checking CircleMaxPool2D + auto maxpool = dynamic_cast<luci::CircleMaxPool2D *>(graph.output()->from()); + ASSERT_TRUE(maxpool != nullptr); + + // Checking Transpose + auto transpose1 = dynamic_cast<luci::CircleTranspose *>(maxpool->value()); + ASSERT_TRUE(transpose1 != nullptr); + + // Checking CirclePad + auto pad = dynamic_cast<luci::CirclePad *>(transpose1->a()); + ASSERT_TRUE(pad != nullptr); + + // Checking Transpose + auto transpose2 = dynamic_cast<luci::CircleTranspose *>(pad->input()); + ASSERT_TRUE(transpose2 != nullptr); + + // Checking CircleRelu + auto relu = dynamic_cast<luci::CircleRelu *>(transpose2->a()); + ASSERT_TRUE(relu != nullptr); + + auto input = dynamic_cast<luci::CircleInput *>(relu->features()); + ASSERT_TRUE(input != nullptr); +} + +// +// Negative Tests +// + +/** + * Graph that is not changed by SubstitutePadV2ToPadPass + * + * [CircleInput] + * | + * [CirclePadV2] + * | + * [CircleOutput] + */ +TEST_F(SubstitutePadV2ToPadPassTest, no_relu_maxpool_NEG) +{ + struct Graph_No_MaxPool : public TestGraph + { + Graph_No_MaxPool() + { + UIntList input_shape = {1, 4, 4, 3}; + UIntList output_shape = {1, 6, 8, 3}; + init(input_shape, output_shape); + } + + loco::Node *build_body(loco::Node *input) final + { + IntList paddings = {0, 0, 1, 1, 2, 2, 0, 0}; + auto padding_const = -10.0; + return padV2(input, paddings, padding_const); + } + } graph; + + auto result = run_pass(graph.g()); + + ASSERT_FALSE(result); +} + +/** + * Graph that is not changed by SubstitutePadV2ToPadPass + * + * There is no CircleMaxPool2D. + * + * [CircleInput] + * | + * [CircleRelu] + * | + * [CirclePadV2] + * | + * [CircleOutput] + */ +TEST_F(SubstitutePadV2ToPadPassTest, no_maxpool_NEG) +{ + struct Graph_No_MaxPool : public TestGraph + { + Graph_No_MaxPool() + { + UIntList input_shape = {1, 4, 4, 3}; + UIntList output_shape = {1, 6, 8, 3}; + init(input_shape, output_shape); + } + + loco::Node *build_body(loco::Node *input) final + { + auto relu_node = relu(input); + + IntList paddings = {0, 0, 1, 1, 2, 2, 0, 0}; + auto padding_const = -10.0; + return padV2(relu_node, paddings, padding_const); + } + } graph; + + auto result = run_pass(graph.g()); + + ASSERT_FALSE(result); +} + +/** + * Graph where PadV2 has non-negative constant value + * + * [CircleInput] + * | + * [Relu] + * | + * [CirclePadV2] + * | + * [MaxPool2D] + * | + * [CircleOutput] + */ +TEST_F(SubstitutePadV2ToPadPassTest, non_negative_NEG) +{ + struct NegGraph : public TestGraph + { + NegGraph() + { + UIntList input_shape = {1, 4, 4, 3}; + UIntList output_shape = {1, 6, 6, 3}; + init(input_shape, output_shape); + } + + loco::Node *build_body(loco::Node *input) final + { + constexpr auto POSITIVE_CONST_VALUE = 0.1f; + + auto relu_node = relu(input); + + IntList paddings = {0, 0, 1, 1, 1, 1, 0, 0}; + auto padV2_node = padV2(relu_node, paddings, POSITIVE_CONST_VALUE); + + return maxpool2d(padV2_node, {2, 2}); + } + } graph; + + auto result = run_pass(graph.g()); + + ASSERT_FALSE(result); +} + +/** + * Graph that has PadV2.padding wider than MaxPool2D.Filter + * + * [CircleInput] + * | + * [CircleRelu] + * | + * [CirclePadV2] paddings=[0, 0, 3, 3, 1, 1, 0, 0] + * | + * [CircleMaxPool2D] Filter_H = 2, Filter_W = 2 (Filter_H < paddings for H) + * | + * [CircleOutput] + */ +TEST_F(SubstitutePadV2ToPadPassTest, wider_paddings_01_NEG) +{ + struct NegGraph : public TestGraph + { + NegGraph() + { + UIntList input_shape = {1, 4, 4, 3}; + UIntList output_shape = {1, 9, 5, 3}; + init(input_shape, output_shape); + } + + loco::Node *build_body(loco::Node *input) final + { + auto relu_node = relu(input); + + constexpr auto TOO_WIDE_H_FRONT = 3; + constexpr auto TOO_WIDE_H_END = 3; + + IntList paddings = {0, 0, TOO_WIDE_H_FRONT, TOO_WIDE_H_END, 1, 1, 0, 0}; + auto padding_const = -10.0; + auto padv2 = padV2(relu_node, paddings, padding_const); + + return maxpool2d(padv2, {2, 2}); + } + } graph; + + auto result = run_pass(graph.g()); + + ASSERT_FALSE(result); +} + +/** + * Graph that has PadV2.paddings wider than MaxPool2D.Filter + * + * Transpose ops are inserted, e.g., to switch layout between NHWC and NCHW + * + * [CircleInput] + * | + * [Relu] + * | 1x4x4x3 (NHWC) + * [Transpose] perm=[0,3,1,2] + * | 1x3x4x4 (NCHW) + * [CirclePadV2] paddings=[0,0,0,0,3,3,1,1] + * | 1x3x6x6 (NCHW) + * [Transpose] perm=[0,2,3,1] + * | 1x6x6x3 (NHWC) + * [MaxPool2D] filter.H = 2, filter.W = 2 + * | 1x4x4x3 + * [CircleOutput] + */ +TEST_F(SubstitutePadV2ToPadPassTest, wider_paddings_02_NEG) +{ + struct Graph_Reshaping_Op : public TestGraph + { + Graph_Reshaping_Op() + { + UIntList input_shape = {1, 4, 4, 3}; + UIntList output_shape = {1, 9, 5, 3}; + init(input_shape, output_shape); + } + + loco::Node *build_body(loco::Node *input) final + { + auto relu_node = relu(input); + + auto transpose1_node = transpose(relu_node, {0, 3, 1, 2}); + + constexpr auto TOO_WIDE_H_FRONT = 3; + constexpr auto TOO_WIDE_H_END = 3; + + IntList paddings = {0, 0, 0, 0, TOO_WIDE_H_FRONT, TOO_WIDE_H_END, 1, 1}; + auto padding_const = -10.0; + auto padV2_node = padV2(transpose1_node, paddings, padding_const); + + auto transpose2_node = transpose(padV2_node, {0, 2, 3, 1}); + + return maxpool2d(transpose2_node, {3, 3}); + } + } graph; + + auto result = run_pass(graph.g()); + ASSERT_FALSE(result); +} diff --git a/compiler/luci/pass/src/SubstituteStridedSliceToReshapePass.cpp b/compiler/luci/pass/src/SubstituteStridedSliceToReshapePass.cpp new file mode 100644 index 000000000..f50f2f54f --- /dev/null +++ b/compiler/luci/pass/src/SubstituteStridedSliceToReshapePass.cpp @@ -0,0 +1,215 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/SubstituteStridedSliceToReshapePass.h" + +#include <luci/IR/CircleNodes.h> +#include <luci/Profile/CircleNodeOrigin.h> + +#include <bitset> +#include <vector> + +/** + * @brief Convert strided_slice op in a certain condition to reshape op + * @details Convert strided_slice op if the op meets all of the following condition: + * For all i, 0 <= i < input.rank + * - begin[i] == 0 + * - end[i] >= input.shape.dim[i] + * - strides[i] == 1 + * For all k (0 <= k < input.rank) where kth bit of shrink_axis_mask == 1 + * - end[k] == 1 + * + * Example: + * input.shape = [1,1,2,3] + * strided_slice(input, begin=[0,0,0,0], end=[1,1,2,3], strides=[1,1,1,1], + * shrink_axis_mask=0011b) // k = 0, 1 + * + * can be converted to + * + * reshape(input, [2,3]) + */ +namespace +{ + +/** + * @brief Return newly-created CircleConst whose rank is 1 + */ +luci::CircleConst *build_rank1_const(loco::Graph *graph, const std::vector<uint32_t> &values) +{ + auto const_node = graph->nodes()->create<luci::CircleConst>(); + const_node->dtype(loco::DataType::S32); + const_node->size<loco::DataType::S32>(values.size()); + const_node->shape_status(luci::ShapeStatus::VALID); + const_node->rank(1); + const_node->dim(0) = values.size(); + + for (size_t i = 0; i < values.size(); i++) + { + const_node->at<loco::DataType::S32>(i) = values.at(i); + } + + return const_node; +} + +/** + * @brief Return newly-created CircleReshape node + */ +luci::CircleNode *build_reshape(loco::Graph *graph, const std::string &name, + const std::shared_ptr<luci::CircleNodeOrigin> &origin, + luci::CircleNode *input, const std::vector<uint32_t> &new_shape) +{ + auto reshape_node = graph->nodes()->create<luci::CircleReshape>(); + reshape_node->tensor(input); + reshape_node->name(name); + luci::add_origin(reshape_node, origin); + + auto new_shape_const = build_rank1_const(graph, new_shape); + { + new_shape_const->name(name + "/new_shape"); + luci::add_origin(new_shape_const, origin); + } + + reshape_node->shape(new_shape_const); + + return reshape_node; +} + +/** + * @brief Return value in position on CircleConst with int64 format. + */ +int64_t value_from_circle_const(const luci::CircleConst *node, uint32_t idx) +{ + assert(node->rank() == 1 && node->dim(0).value() > idx); + assert(node->dtype() == loco::DataType::S64 || node->dtype() == loco::DataType::S32); + + if (node->dtype() == loco::DataType::S64) + return node->at<loco::DataType::S64>(idx); + return static_cast<int64_t>(node->at<loco::DataType::S32>(idx)); +} + +bool substitute_strided_slice_to_reshape(luci::CircleStridedSlice *ss_node) +{ + if (ss_node->shrink_axis_mask() == 0) + return false; + + // TODO Consider cases with ellipsis_mask and new_axis_mask + // NOT YET SUPPORTED + if (ss_node->ellipsis_mask() != 0 or ss_node->new_axis_mask() != 0) + return false; + + auto begin_const = dynamic_cast<luci::CircleConst *>(ss_node->begin()); + auto strides_const = dynamic_cast<luci::CircleConst *>(ss_node->strides()); + auto end_const = dynamic_cast<luci::CircleConst *>(ss_node->end()); + + if (not(begin_const && strides_const && end_const)) + return false; + + auto input_node = loco::must_cast<luci::CircleNode *>(ss_node->input()); + + // condition check + std::bitset<32> begin_mask(ss_node->begin_mask()); + std::bitset<32> end_mask(ss_node->end_mask()); + std::bitset<32> shrink_axis_mask(ss_node->shrink_axis_mask()); + + uint input_rank = input_node->rank(); + for (uint32_t i = 0; i < input_rank; i++) + { + if (!input_node->dim(i).known()) + return false; + + auto begin_dim = value_from_circle_const(begin_const, i); + if (begin_dim != 0 and begin_mask.test(i) == false) + return false; + + // NOTE: + // In Tensorflow and TFLite, e.g., if input_shape = [2,3], + // strided_slice.end = [10,20] (larger value than actual dim) + // is treated as strided_slice.end = [2,3] + int64_t end_dim = value_from_circle_const(end_const, i); + if (end_dim < input_node->dim(i).value() and end_mask.test(i) == false) + return false; + + int64_t strides_value = value_from_circle_const(strides_const, i); + if (strides_value != 1) + return false; + + if (shrink_axis_mask.test(i) && input_node->dim(i).value() != 1) + return false; + } + + // build shape for Reshape op + bool found = false; + std::vector<uint32_t> shrunk_shape; + for (uint32_t i = 0; i < input_rank; i++) + { + if (input_node->dim(i) == 1 and shrink_axis_mask.test(i)) + found = true; + else + shrunk_shape.emplace_back(input_node->dim(i).value()); + } + + if (not found) + return false; + + auto reshape_node = build_reshape(input_node->graph(), ss_node->name(), luci::get_origin(ss_node), + input_node, shrunk_shape); + + replace(ss_node).with(reshape_node); + return true; +} + +} // namespace + +namespace luci +{ + +/** + * BEFORE + * | + * [CircleNode] [CircleConst] [CircleConst] [CircleConst] + * | | | | + * -------+------------------------------------ + * | + * [CircleStridedSlice] + * | + * [CircleNode] + * | + * AFTER + * | + * [CircleConst] [CircleNode] [CircleConst] [CircleConst] [CircleConst] + * \ / \ | | | + * [CircleReshape] -------------------+---------------------- + * | | + * [CircleNode] [CircleStridedSlice] + * | + */ +bool SubstituteStridedSliceToReshapePass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + if (auto circle_node = dynamic_cast<luci::CircleStridedSlice *>(node)) + { + if (substitute_strided_slice_to_reshape(circle_node)) + { + changed = true; + } + } + } + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/SubstituteStridedSliceToReshapePass.test.cpp b/compiler/luci/pass/src/SubstituteStridedSliceToReshapePass.test.cpp new file mode 100644 index 000000000..57e692efa --- /dev/null +++ b/compiler/luci/pass/src/SubstituteStridedSliceToReshapePass.test.cpp @@ -0,0 +1,277 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "luci/Pass/SubstituteStridedSliceToReshapePass.h" + +#include <luci/IR/CircleNodes.h> + +#include <gtest/gtest.h> + +namespace +{ + +luci::CircleConst *build_rank1_const(loco::Graph *graph, const std::vector<uint32_t> values) +{ + auto const_node = graph->nodes()->create<luci::CircleConst>(); + const_node->dtype(loco::DataType::S32); + const_node->size<loco::DataType::S32>(values.size()); + const_node->shape_status(luci::ShapeStatus::VALID); + const_node->rank(1); + const_node->dim(0) = values.size(); + + for (int32_t i = 0; i < values.size(); i++) + { + const_node->at<loco::DataType::S32>(i) = values.at(i); + } + + return const_node; +} + +class SubstituteStridedSliceToReshapeTest : public ::testing::Test +{ +public: + SubstituteStridedSliceToReshapeTest() {} + + void buildGraph(const std::initializer_list<uint32_t> input_shape, + const std::initializer_list<uint32_t> begin_vals, + const std::initializer_list<uint32_t> end_vals, + const std::initializer_list<uint32_t> strides_vals, int32_t begin_mask, + int32_t end_mask, int32_t ellipsis_mask, int32_t new_axis_mask, + int32_t shrink_axis_mask) + { + // Input node + input = g.nodes()->create<luci::CircleInput>(); + { + auto graph_input = g.inputs()->create(); + input->index(graph_input->index()); + input->shape_status(luci::ShapeStatus::VALID); + input->rank(input_shape.size()); + input->shape(input_shape); + input->name("input"); + } + + // StridedSlice node + auto ss_node = g.nodes()->create<luci::CircleStridedSlice>(); + { + auto *graph = &g; + auto build_attr = [&graph](const std::string &name, + const std::initializer_list<uint32_t> vals) { + auto node = build_rank1_const(graph, vals); + node->name(name); + + return node; + }; + + ss_node->input(input); + auto begin = build_attr("begin", begin_vals); + auto end = build_attr("end", end_vals); + auto strides = build_attr("strides", strides_vals); + + ss_node->begin(begin); + ss_node->end(end); + ss_node->strides(strides); + + ss_node->begin_mask(begin_mask); + ss_node->end_mask(end_mask); + ss_node->ellipsis_mask(ellipsis_mask); + ss_node->new_axis_mask(new_axis_mask); + ss_node->shrink_axis_mask(shrink_axis_mask); + } + + // Output node + output = g.nodes()->create<luci::CircleOutput>(); + output->from(ss_node); + auto graph_output = g.outputs()->create(); + output->index(graph_output->index()); + output->name("output"); + } + + void assert_not_converted() + { + luci::SubstituteStridedSliceToReshapePass pass; + while (pass.run(&g)) + ; + + auto reshape_node = dynamic_cast<luci::CircleReshape *>(output->from()); + ASSERT_TRUE(reshape_node == nullptr); + + auto strided_slice_node = dynamic_cast<luci::CircleStridedSlice *>(output->from()); + ASSERT_TRUE(strided_slice_node != nullptr); + } + +public: + loco::Graph g; + luci::CircleInput *input = nullptr; + luci::CircleOutput *output = nullptr; +}; + +} // namespace + +TEST(SubstituteStridedSliceToReshapePassTest, name) +{ + luci::SubstituteStridedSliceToReshapePass pass; + auto const name = pass.name(); + ASSERT_NE(nullptr, name); +} + +TEST_F(SubstituteStridedSliceToReshapeTest, simple_case) +{ + buildGraph({1, 1, 5, 1, 9}, // input shape + {0, 0, 0, 0, 0}, // begin + {1, 1, 5, 1, 9}, // end + {1, 1, 1, 1, 1}, // strides + 0, // begin mask + 0, // end mask + 0, // ellipsis axis mask + 0, // new axis mask + 0b01001 // shrink axis mask, 0th and 3rd dim will be shrunk + ); + + luci::SubstituteStridedSliceToReshapePass pass; + while (pass.run(&g)) + ; + + auto reshape_node = dynamic_cast<luci::CircleReshape *>(output->from()); + ASSERT_TRUE(reshape_node != nullptr); + + auto new_shape = loco::must_cast<luci::CircleConst *>(reshape_node->shape()); + ASSERT_EQ(new_shape->rank(), 1); + ASSERT_EQ(new_shape->dim(0).value(), 3); + ASSERT_EQ(new_shape->at<loco::DataType::S32>(0), 1); + ASSERT_EQ(new_shape->at<loco::DataType::S32>(1), 5); + ASSERT_EQ(new_shape->at<loco::DataType::S32>(2), 9); +} + +TEST_F(SubstituteStridedSliceToReshapeTest, with_begin_end_mask) +{ + buildGraph({5, 1, 9}, // input shape + {0, 0, 5}, // begin + {3, 1, 9}, // end + {1, 1, 1}, // strides + 0b100, // begin mask + 0b001, // end mask + 0, // ellipsis axis mask + 0, // new axis mask + 0b010 // shrink axis mask, 0th and 3rd dim will be shrunk + ); + + luci::SubstituteStridedSliceToReshapePass pass; + while (pass.run(&g)) + ; + + auto reshape_node = dynamic_cast<luci::CircleReshape *>(output->from()); + ASSERT_TRUE(reshape_node != nullptr); + + auto new_shape = loco::must_cast<luci::CircleConst *>(reshape_node->shape()); + ASSERT_EQ(new_shape->rank(), 1); + ASSERT_EQ(new_shape->dim(0).value(), 2); + ASSERT_EQ(new_shape->at<loco::DataType::S32>(0), 5); + ASSERT_EQ(new_shape->at<loco::DataType::S32>(1), 9); +} + +TEST_F(SubstituteStridedSliceToReshapeTest, with_large_end_mask) +{ + buildGraph({5, 1, 9}, // input shape + {0, 0, 0}, // begin + {100, 100, 100}, // large end + {1, 1, 1}, // strides + 0, // begin mask + 0, // end mask + 0, // ellipsis axis mask + 0, // new axis mask + 0b010 // shrink axis mask, 0th and 3rd dim will be shrunk + ); + + luci::SubstituteStridedSliceToReshapePass pass; + while (pass.run(&g)) + ; + + auto reshape_node = dynamic_cast<luci::CircleReshape *>(output->from()); + ASSERT_TRUE(reshape_node != nullptr); + + auto new_shape = loco::must_cast<luci::CircleConst *>(reshape_node->shape()); + ASSERT_EQ(new_shape->rank(), 1); + ASSERT_EQ(new_shape->dim(0).value(), 2); + ASSERT_EQ(new_shape->at<loco::DataType::S32>(0), 5); + ASSERT_EQ(new_shape->at<loco::DataType::S32>(1), 9); +} + +TEST_F(SubstituteStridedSliceToReshapeTest, not_matching_begin_index_NEG) +{ + buildGraph({1, 3, 5, 7}, // input shape + {0, 0, 2, 0}, // begin[2] does not start from 0 + {1, 3, 5, 7}, // end + {1, 1, 1, 1}, // strides + 0, // begin mask + 0, // end mask + 0, // ellipsis axis mask + 0, // new axis mask + 0b0001 // shrink axis mask + ); + + assert_not_converted(); + SUCCEED(); +} + +TEST_F(SubstituteStridedSliceToReshapeTest, not_matching_end_index_NEG) +{ + buildGraph({1, 3, 5, 7}, // input shape + {0, 0, 0, 0}, // begin + {1, 3, 3, 7}, // end[2] does not meet condition + {1, 1, 1, 1}, // strides + 0, // begin mask + 0, // end mask + 0, // ellipsis axis mask + 0, // new axis mask + 0b0001 // shrink axis mask + ); + + assert_not_converted(); + SUCCEED(); +} + +TEST_F(SubstituteStridedSliceToReshapeTest, not_matching_strides_NEG) +{ + buildGraph({1, 3, 5, 7}, // input shape + {0, 0, 0, 0}, // begin + {1, 3, 5, 7}, // end + {1, 1, 2, 1}, // strides[2] does not meet condition + 0, // begin mask + 0, // end mask + 0, // ellipsis axis mask + 0, // new axis mask + 0b0001 // shrink axis mask + ); + + assert_not_converted(); + SUCCEED(); +} + +TEST_F(SubstituteStridedSliceToReshapeTest, not_matching_shrink_axis_mask_NEG) +{ + buildGraph({1, 3, 5, 7}, // input shape + {0, 0, 0, 0}, // begin + {1, 3, 5, 7}, // end + {1, 1, 1, 1}, // strides + 0, // begin mask + 0, // end mask + 0, // ellipsis axis mask + 0, // new axis mask + 0b0101 // shrink axis mask[1] does not meet condition + ); + + assert_not_converted(); + SUCCEED(); +} diff --git a/compiler/luci/pass/src/TransformMinMaxToRelu6Pass.cpp b/compiler/luci/pass/src/TransformMinMaxToRelu6Pass.cpp index c15a3b676..9d1dfc1e3 100644 --- a/compiler/luci/pass/src/TransformMinMaxToRelu6Pass.cpp +++ b/compiler/luci/pass/src/TransformMinMaxToRelu6Pass.cpp @@ -86,8 +86,8 @@ template <loco::DataType DT> bool transform_min_max_pattern(luci::CircleMaximum loco::Node *mini_input = nullptr; // There are two ways Miminum takes inputs. - // 1. Miminum(x = CircleNode, y = CircleMinimum) - // 2. Miminum(x = CircleMinimum, y = CircleNode) + // 1. Miminum(x = CircleNode, y = CircleConst) + // 2. Miminum(x = CircleConst, y = CircleNode) if (not luci::fill(&mini_const, &mini_input).with_commutative_args_of(mini)) return false; diff --git a/compiler/luci/pass/src/TransformMinReluToRelu6Pass.cpp b/compiler/luci/pass/src/TransformMinReluToRelu6Pass.cpp new file mode 100644 index 000000000..cccc0134c --- /dev/null +++ b/compiler/luci/pass/src/TransformMinReluToRelu6Pass.cpp @@ -0,0 +1,124 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/TransformMinReluToRelu6Pass.h" + +#include "helpers/NodeFiller.h" +#include "helpers/TypeMapper.h" + +#include <luci/IR/CircleNodes.h> +#include <luci/Profile/CircleNodeOrigin.h> + +namespace +{ + +template <loco::DataType DT> +bool is_scalar_with_value(luci::CircleConst *node, typename loco::DataTypeImpl<DT>::Type val) +{ + if (node->dtype() != DT) + return false; + if (node->rank() != 0) + return false; + if (node->size<DT>() != 1) + return false; + if (node->at<DT>(0) != static_cast<typename loco::DataTypeImpl<DT>::Type>(val)) + return false; + + return true; +} + +/** + * BEFORE + * [CircleNode] + * | + * [CircleMinimum] + * | + * [CircleRelu] + * | + * [CircleNode] + * + * AFTER + * + * [CircleNode] + * | + * [CircleRelu6] + * | + * [CircleNode] + * + * NOTE Only relu(min(input, 6)) pattern will be transformed. + */ +template <loco::DataType DT> bool transform_min_relu_pattern(luci::CircleRelu *relu) +{ + if (not relu) + return false; + + if (relu->dtype() != DT) + return false; + + auto *mini = dynamic_cast<luci::CircleMinimum *>(relu->features()); + if (not mini) + return false; + + luci::CircleConst *mini_const = nullptr; + loco::Node *mini_input = nullptr; + + // There are two ways Miminum takes inputs. + // 1. Miminum(x = CircleNode, y = CircleConst) + // 2. Miminum(x = CircleConst, y = CircleNode) + if (not luci::fill(&mini_const, &mini_input).with_commutative_args_of(mini)) + return false; + + // Miminum constant should be scalar whose value is 6. + if (not is_scalar_with_value<DT>(mini_const, + static_cast<typename loco::DataTypeImpl<DT>::Type>(6))) + return false; + + auto name = relu->name(); + assert(name.length() > 0); + + // Create Relu6 op + auto relu6 = mini->graph()->nodes()->create<luci::CircleRelu6>(); + relu6->features(mini_input); + relu6->name(name + "/Relu6"); + luci::add_origin(relu6, luci::composite_origin({luci::get_origin(relu), luci::get_origin(mini)})); + + replace(relu).with(relu6); + + return true; +} + +} // namespace + +namespace luci +{ + +bool TransformMinReluToRelu6Pass::run(loco::Graph *g) +{ + bool changed = false; + + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + if (auto relu = dynamic_cast<luci::CircleRelu *>(node)) + { + if (transform_min_relu_pattern<loco::DataType::FLOAT32>(relu)) + changed = true; + } + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/TransformMinReluToRelu6Pass.test.cpp b/compiler/luci/pass/src/TransformMinReluToRelu6Pass.test.cpp new file mode 100644 index 000000000..01ba722a8 --- /dev/null +++ b/compiler/luci/pass/src/TransformMinReluToRelu6Pass.test.cpp @@ -0,0 +1,141 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/TransformMinReluToRelu6Pass.h" + +#include <luci/IR/CircleNodes.h> + +#include <gtest/gtest.h> + +namespace +{ + +/** + * Minimum-Relu pattern graph + * + * [CircleInput] [CircleConst] + * \ / + * [CircleMinimum] + * | + * [CircleRelu] + * | + * [CircleOutput] + */ +struct MinReluGraph +{ + loco::Graph _g; + luci::CircleInput *_input = nullptr; + luci::CircleMinimum *_mini = nullptr; + luci::CircleConst *_mini_const = nullptr; + luci::CircleRelu *_relu = nullptr; + luci::CircleOutput *_output = nullptr; +}; + +class TransformMinReluToRelu6PassTest : public ::testing::Test +{ +protected: + virtual void SetUp() + { + const int N = 1; + const int H = 4; + const int W = 4; + const int C = 3; + + // graph input and output + auto graph_input = _min_relu_g._g.inputs()->create(); + auto graph_output = _min_relu_g._g.outputs()->create(); + + // CircleInput + _min_relu_g._input = _min_relu_g._g.nodes()->create<luci::CircleInput>(); + _min_relu_g._input->index(graph_input->index()); + _min_relu_g._input->shape({N, H, W, C}); + _min_relu_g._input->dtype(loco::DataType::FLOAT32); + _min_relu_g._input->name("input"); + + // CircleConst + _min_relu_g._mini_const = _min_relu_g._g.nodes()->create<luci::CircleConst>(); + _min_relu_g._mini_const->shape({}); // scalar + _min_relu_g._mini_const->dtype(loco::DataType::FLOAT32); + _min_relu_g._mini_const->size<loco::DataType::FLOAT32>(1); + _min_relu_g._mini_const->at<loco::DataType::FLOAT32>(0) = 6.; + _min_relu_g._mini_const->name("mini_const"); + + // CircleMinimum + _min_relu_g._mini = _min_relu_g._g.nodes()->create<luci::CircleMinimum>(); + _min_relu_g._mini->x(_min_relu_g._input); + _min_relu_g._mini->y(_min_relu_g._mini_const); + _min_relu_g._mini->shape({N, H, W, C}); + _min_relu_g._mini->dtype(loco::DataType::FLOAT32); + _min_relu_g._mini->name("mini"); + + // CircleRelu + _min_relu_g._relu = _min_relu_g._g.nodes()->create<luci::CircleRelu>(); + _min_relu_g._relu->features(_min_relu_g._mini); + _min_relu_g._relu->shape({N, H, W, C}); + _min_relu_g._relu->dtype(loco::DataType::FLOAT32); + _min_relu_g._relu->name("relu"); + + // CircleOutput + _min_relu_g._output = _min_relu_g._g.nodes()->create<luci::CircleOutput>(); + _min_relu_g._output->index(graph_output->index()); + _min_relu_g._output->from(_min_relu_g._relu); + _min_relu_g._output->shape({N, H, W, C}); + _min_relu_g._output->dtype(loco::DataType::FLOAT32); + _min_relu_g._output->name("output"); + } + +protected: + luci::TransformMinReluToRelu6Pass _pass; + MinReluGraph _min_relu_g; +}; + +} // namespace + +TEST_F(TransformMinReluToRelu6PassTest, name) +{ + auto const name = _pass.name(); + ASSERT_NE(nullptr, name); +} + +/** + * Optimized graph looks like below. + * + * [CircleInput] + * | + * [CircleRelu6] + * | + * [CircleOutput] + */ +TEST_F(TransformMinReluToRelu6PassTest, simple_test) +{ + auto ret = _pass.run(&_min_relu_g._g); + EXPECT_TRUE(ret); + + auto relu6 = dynamic_cast<luci::CircleRelu6 *>(_min_relu_g._output->from()); + EXPECT_NE(nullptr, relu6); + + auto input = dynamic_cast<luci::CircleInput *>(relu6->features()); + EXPECT_NE(nullptr, input); +} + +TEST_F(TransformMinReluToRelu6PassTest, wrong_condition_NEG) +{ + _min_relu_g._mini_const->at<loco::DataType::FLOAT32>(0) = 2.; + + auto ret = _pass.run(&_min_relu_g._g); + + EXPECT_FALSE(ret); +} diff --git a/compiler/luci/pass/src/VerifyQuantizedNodeChannelWiseGranularity.h b/compiler/luci/pass/src/VerifyQuantizedNodeChannelWiseGranularity.h index 32f0d1a34..1706b9e43 100644 --- a/compiler/luci/pass/src/VerifyQuantizedNodeChannelWiseGranularity.h +++ b/compiler/luci/pass/src/VerifyQuantizedNodeChannelWiseGranularity.h @@ -93,7 +93,9 @@ private: RETURN_FALSE_UNLESS(is_lwq(node)) RETURN_FALSE_UNLESS(is_lwq(node->input())) RETURN_FALSE_UNLESS(is_cwq_const(node->filter(), 0)) - RETURN_FALSE_UNLESS(is_cwq_const(node->bias(), rank(node->bias()) - 1)) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + if (bias != nullptr) + RETURN_FALSE_UNLESS(is_cwq_const(node->bias(), rank(node->bias()) - 1)) return true; } @@ -119,7 +121,9 @@ private: RETURN_FALSE_UNLESS(is_lwq(node)) RETURN_FALSE_UNLESS(is_lwq(node->input())) RETURN_FALSE_UNLESS(is_cwq_const(node->filter(), 3)) - RETURN_FALSE_UNLESS(is_cwq_const(node->bias(), rank(node->bias()) - 1)) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + if (bias != nullptr) + RETURN_FALSE_UNLESS(is_cwq_const(node->bias(), rank(node->bias()) - 1)) return true; } @@ -132,6 +136,16 @@ private: return true; } + bool visit(const luci::CirclePack *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + for (uint32_t i = 0; i < node->values_count(); i++) + { + RETURN_FALSE_UNLESS(is_lwq(node->values(i))); + } + return true; + } + bool visit(const luci::CirclePad *node) { RETURN_FALSE_UNLESS(is_lwq(node)) @@ -139,6 +153,21 @@ private: return true; } + bool visit(const luci::CirclePadV2 *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + RETURN_FALSE_UNLESS(is_lwq(node->input())) + RETURN_FALSE_UNLESS(is_lwq(node->constant_values())) + return true; + } + + bool visit(const luci::CircleMirrorPad *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + RETURN_FALSE_UNLESS(is_lwq(node->input())) + return true; + } + bool visit(const luci::CirclePRelu *node) { RETURN_FALSE_UNLESS(is_lwq(node)) @@ -164,7 +193,10 @@ private: RETURN_FALSE_UNLESS(is_lwq(node)) RETURN_FALSE_UNLESS(is_lwq(node->input())) RETURN_FALSE_UNLESS(is_cwq_const(node->weights(), 0)) - RETURN_FALSE_UNLESS(is_cwq_const(node->bias(), rank(node->bias()) - 1)) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + // Bias is optional (it can be CircleOutputExclude) + if (bias != nullptr) + RETURN_FALSE_UNLESS(is_cwq_const(node->bias(), rank(node->bias()) - 1)) return true; } @@ -197,6 +229,13 @@ private: return true; } + bool visit(const luci::CircleLocalResponseNormalization *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + RETURN_FALSE_UNLESS(is_lwq(node->input())); + return true; + } + bool visit(const luci::CircleMean *node) { RETURN_FALSE_UNLESS(is_lwq(node)); @@ -228,8 +267,12 @@ private: bool visit(const luci::CircleReshape *node) { - RETURN_FALSE_UNLESS(is_lwq(node)) - RETURN_FALSE_UNLESS(is_lwq(node->tensor())); + auto input = loco::must_cast<const luci::CircleNode *>(node->tensor()); + bool input_quantized = input->quantparam() != nullptr; + bool node_quantized = node->quantparam() != nullptr; + RETURN_FALSE_UNLESS(input_quantized == node_quantized); + RETURN_FALSE_UNLESS(not node_quantized or is_lwq(node)) + RETURN_FALSE_UNLESS(not input_quantized or is_lwq(input)); return true; } @@ -389,6 +432,36 @@ private: return true; } + bool visit(const luci::CircleResizeNearestNeighbor *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)); + RETURN_FALSE_UNLESS(is_lwq(node->input())); + return true; + } + + bool visit(const luci::CircleUnpack *node) + { + // node's output is the input of CircleUnpackOut, thus not quantized + RETURN_FALSE_UNLESS(is_lwq(node->value())); + return true; + } + + bool visit(const luci::CircleUnpackOut *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)); + return true; + } + + bool visit(const luci::CircleCast *node) + { + auto input = loco::must_cast<const luci::CircleNode *>(node->x()); + bool input_quantized = input->quantparam() != nullptr; + bool node_quantized = node->quantparam() != nullptr; + RETURN_FALSE_UNLESS(not input_quantized or is_lwq(input)); + RETURN_FALSE_UNLESS(not node_quantized or is_lwq(node)); + return true; + } + // TODO: Implement more Ops bool visit(const luci::CircleNode *) { return true; } diff --git a/compiler/luci/pass/src/VerifyQuantizedNodeLayerWiseGranularity.h b/compiler/luci/pass/src/VerifyQuantizedNodeLayerWiseGranularity.h index 1e6fd53c0..3954bf216 100644 --- a/compiler/luci/pass/src/VerifyQuantizedNodeLayerWiseGranularity.h +++ b/compiler/luci/pass/src/VerifyQuantizedNodeLayerWiseGranularity.h @@ -81,7 +81,9 @@ private: RETURN_FALSE_UNLESS(is_lwq(node)) RETURN_FALSE_UNLESS(is_lwq(node->input())) RETURN_FALSE_UNLESS(is_lwq_const(node->filter())) - RETURN_FALSE_UNLESS(is_lwq_const(node->bias())) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + if (bias != nullptr) + RETURN_FALSE_UNLESS(is_lwq_const(node->bias())) return true; } @@ -107,7 +109,9 @@ private: RETURN_FALSE_UNLESS(is_lwq(node)) RETURN_FALSE_UNLESS(is_lwq(node->input())) RETURN_FALSE_UNLESS(is_lwq_const(node->filter())) - RETURN_FALSE_UNLESS(is_lwq_const(node->bias())) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + if (bias != nullptr) + RETURN_FALSE_UNLESS(is_lwq_const(node->bias())) return true; } @@ -120,6 +124,16 @@ private: return true; } + bool visit(const luci::CirclePack *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + for (uint32_t i = 0; i < node->values_count(); i++) + { + RETURN_FALSE_UNLESS(is_lwq(node->values(i))); + } + return true; + } + bool visit(const luci::CirclePad *node) { RETURN_FALSE_UNLESS(is_lwq(node)) @@ -127,6 +141,21 @@ private: return true; } + bool visit(const luci::CirclePadV2 *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + RETURN_FALSE_UNLESS(is_lwq(node->input())) + RETURN_FALSE_UNLESS(is_lwq(node->constant_values())) + return true; + } + + bool visit(const luci::CircleMirrorPad *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + RETURN_FALSE_UNLESS(is_lwq(node->input())) + return true; + } + bool visit(const luci::CirclePRelu *node) { RETURN_FALSE_UNLESS(is_lwq(node)) @@ -151,7 +180,9 @@ private: RETURN_FALSE_UNLESS(is_lwq(node)) RETURN_FALSE_UNLESS(is_lwq(node->input())) RETURN_FALSE_UNLESS(is_lwq_const(node->weights())) - RETURN_FALSE_UNLESS(is_lwq_const(node->bias())) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + if (bias != nullptr) + RETURN_FALSE_UNLESS(is_lwq_const(node->bias())) return true; } @@ -184,6 +215,13 @@ private: return true; } + bool visit(const luci::CircleLocalResponseNormalization *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)) + RETURN_FALSE_UNLESS(is_lwq(node->input())); + return true; + } + bool visit(const luci::CircleMean *node) { RETURN_FALSE_UNLESS(is_lwq(node)) @@ -215,8 +253,12 @@ private: bool visit(const luci::CircleReshape *node) { - RETURN_FALSE_UNLESS(is_lwq(node)) - RETURN_FALSE_UNLESS(is_lwq(node->tensor())); + auto input = loco::must_cast<const luci::CircleNode *>(node->tensor()); + bool input_quantized = input->quantparam() != nullptr; + bool node_quantized = node->quantparam() != nullptr; + RETURN_FALSE_UNLESS(input_quantized == node_quantized); + RETURN_FALSE_UNLESS(not node_quantized or is_lwq(node)) + RETURN_FALSE_UNLESS(not input_quantized or is_lwq(input)); return true; } @@ -376,6 +418,36 @@ private: return true; } + bool visit(const luci::CircleResizeNearestNeighbor *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)); + RETURN_FALSE_UNLESS(is_lwq(node->input())); + return true; + } + + bool visit(const luci::CircleUnpack *node) + { + // node's output is the input of CircleUnpackOut, thus not quantized + RETURN_FALSE_UNLESS(is_lwq(node->value())); + return true; + } + + bool visit(const luci::CircleUnpackOut *node) + { + RETURN_FALSE_UNLESS(is_lwq(node)); + return true; + } + + bool visit(const luci::CircleCast *node) + { + auto input = loco::must_cast<const luci::CircleNode *>(node->x()); + bool input_quantized = input->quantparam() != nullptr; + bool node_quantized = node->quantparam() != nullptr; + RETURN_FALSE_UNLESS(not input_quantized or is_lwq(input)); + RETURN_FALSE_UNLESS(not node_quantized or is_lwq(node)); + return true; + } + // TODO: Implement more Ops bool visit(const luci::CircleNode *) { return true; } diff --git a/compiler/luci/pass/src/VerifyQuantizedNodeS16Type.h b/compiler/luci/pass/src/VerifyQuantizedNodeS16Type.h index e05d8325f..560abd2ff 100644 --- a/compiler/luci/pass/src/VerifyQuantizedNodeS16Type.h +++ b/compiler/luci/pass/src/VerifyQuantizedNodeS16Type.h @@ -19,6 +19,8 @@ #include <luci/IR/CircleNodes.h> #include <luci/IR/CircleNodeVisitor.h> +#include <cmath> + using Type = loco::DataType; // This macro is undef at the end of the file @@ -93,6 +95,16 @@ private: return true; } + bool visit(const luci::CirclePack *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + for (uint32_t i = 0; i < node->values_count(); i++) + { + RETURN_FALSE_UNLESS(has_type(node->values(i), Type::S16)) + } + return true; + } + bool visit(const luci::CirclePad *node) { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) @@ -101,6 +113,23 @@ private: return true; } + bool visit(const luci::CirclePadV2 *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::S16)) + RETURN_FALSE_UNLESS(has_type(node->paddings(), Type::S32)) + RETURN_FALSE_UNLESS(has_type(node->constant_values(), Type::S16)) + return true; + } + + bool visit(const luci::CircleMirrorPad *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::S16)) + RETURN_FALSE_UNLESS(has_type(node->paddings(), Type::S32)) + return true; + } + bool visit(const luci::CirclePRelu *node) { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) @@ -125,7 +154,9 @@ private: RETURN_FALSE_UNLESS(has_type(node, Type::S16)) RETURN_FALSE_UNLESS(has_type(node->input(), Type::S16)) RETURN_FALSE_UNLESS(has_type(node->weights(), Type::S16)) - RETURN_FALSE_UNLESS(has_type(node->bias(), Type::S64)) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + if (bias != nullptr) + RETURN_FALSE_UNLESS(has_type(bias, Type::S64)) return true; } @@ -159,6 +190,13 @@ private: return true; } + bool visit(const luci::CircleLocalResponseNormalization *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::S16)) + return true; + } + bool visit(const luci::CircleMean *node) { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) @@ -192,8 +230,15 @@ private: bool visit(const luci::CircleReshape *node) { - RETURN_FALSE_UNLESS(has_type(node, Type::S16)) - RETURN_FALSE_UNLESS(has_type(node->tensor(), Type::S16)) + if (node->quantparam()) + { + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + RETURN_FALSE_UNLESS(has_type(node->tensor(), Type::S16)) + } + else + { + RETURN_FALSE_UNLESS(has_type(node->tensor(), node->dtype())) + } luci::CircleConst *shape = dynamic_cast<luci::CircleConst *>(node->shape()); if (shape != nullptr) RETURN_FALSE_UNLESS(has_type(shape, Type::S32)) @@ -204,6 +249,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::S16)) + + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == 1.0f / 32768.0f); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == 0); return true; } @@ -211,6 +260,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) RETURN_FALSE_UNLESS(has_type(node->logits(), Type::S16)) + + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == 1.0f / 32767.0f); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == 0); return true; } @@ -247,6 +300,13 @@ private: bool visit(const luci::CircleSplitOut *node) { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + + // SplitOut has the same qparam with the input of Split + auto split = loco::must_cast<luci::CircleSplit *>(node->input()); + auto input = loco::must_cast<luci::CircleNode *>(split->input()); + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == input->quantparam()->scale[0]); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == input->quantparam()->zerop[0]); return true; } @@ -254,6 +314,11 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) RETURN_FALSE_UNLESS(has_type(node->input(), Type::S16)) + + auto input = loco::must_cast<luci::CircleNode *>(node->input()); + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == input->quantparam()->scale[0]); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == input->quantparam()->zerop[0]); return true; } @@ -277,6 +342,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::S16)) + + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == 1.0f / 32768.0f); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == 0); return true; } @@ -292,6 +361,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::S16)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::S16)) + + // This checks the value of scale is an integer + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(std::roundf(node->quantparam()->scale[0]) == node->quantparam()->scale[0]); return true; } @@ -324,6 +397,10 @@ private: RETURN_FALSE_UNLESS(has_type(node, Type::S16)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::S16)) RETURN_FALSE_UNLESS(has_type(node->y(), Type::S16)) + + // This checks the value of scale is an integer + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(std::roundf(node->quantparam()->scale[0]) == node->quantparam()->scale[0]); return true; } @@ -363,6 +440,50 @@ private: return true; } + bool visit(const luci::CircleResizeNearestNeighbor *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::S16)) + return true; + } + + bool visit(const luci::CircleUnpack *node) + { + // node's output is the input of CircleUnpackOut, thus not quantized + RETURN_FALSE_UNLESS(has_type(node->value(), Type::S16)) + return true; + } + + bool visit(const luci::CircleUnpackOut *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + + // UnpackOut has the same qparam with the input of Unpack + auto Unpack = loco::must_cast<luci::CircleUnpack *>(node->input()); + auto input = loco::must_cast<luci::CircleNode *>(Unpack->value()); + RETURN_FALSE_UNLESS(node->quantparam() && input->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == input->quantparam()->scale[0]); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == input->quantparam()->zerop[0]); + return true; + } + + bool visit(const luci::CircleCast *node) + { + auto *input = loco::must_cast<luci::CircleNode *>(node->x()); + RETURN_FALSE_UNLESS(has_type(input, node->in_data_type())) + + bool input_quantized = input->quantparam() != nullptr; + if (input_quantized) + RETURN_FALSE_UNLESS(has_type(input, Type::S16)) + + RETURN_FALSE_UNLESS(has_type(node, node->out_data_type())) + + bool node_quantized = node->quantparam() != nullptr; + if (node_quantized) + RETURN_FALSE_UNLESS(has_type(node, Type::S16)) + return true; + } + // TODO: Implement more Ops bool visit(const luci::CircleNode *) { return true; } diff --git a/compiler/luci/pass/src/VerifyQuantizedNodeU8Type.h b/compiler/luci/pass/src/VerifyQuantizedNodeU8Type.h index 72ce5b8f8..42cd1ce55 100644 --- a/compiler/luci/pass/src/VerifyQuantizedNodeU8Type.h +++ b/compiler/luci/pass/src/VerifyQuantizedNodeU8Type.h @@ -19,6 +19,8 @@ #include <luci/IR/CircleNodes.h> #include <luci/IR/CircleNodeVisitor.h> +#include <cmath> + using Type = loco::DataType; // This macro is undef at the end of the file @@ -93,6 +95,16 @@ private: return true; } + bool visit(const luci::CirclePack *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + for (uint32_t i = 0; i < node->values_count(); i++) + { + RETURN_FALSE_UNLESS(has_type(node->values(i), Type::U8)) + } + return true; + } + bool visit(const luci::CirclePad *node) { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) @@ -101,6 +113,23 @@ private: return true; } + bool visit(const luci::CirclePadV2 *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::U8)) + RETURN_FALSE_UNLESS(has_type(node->paddings(), Type::S32)) + RETURN_FALSE_UNLESS(has_type(node->constant_values(), Type::U8)) + return true; + } + + bool visit(const luci::CircleMirrorPad *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::U8)) + RETURN_FALSE_UNLESS(has_type(node->paddings(), Type::S32)) + return true; + } + bool visit(const luci::CirclePRelu *node) { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) @@ -125,7 +154,9 @@ private: RETURN_FALSE_UNLESS(has_type(node, Type::U8)) RETURN_FALSE_UNLESS(has_type(node->input(), Type::U8)) RETURN_FALSE_UNLESS(has_type(node->weights(), Type::U8)) - RETURN_FALSE_UNLESS(has_type(node->bias(), Type::S32)) + luci::CircleConst *bias = dynamic_cast<luci::CircleConst *>(node->bias()); + if (bias != nullptr) + RETURN_FALSE_UNLESS(has_type(bias, Type::S32)) return true; } @@ -166,6 +197,13 @@ private: return true; } + bool visit(const luci::CircleLocalResponseNormalization *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::U8)) + return true; + } + bool visit(const luci::CircleMean *node) { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) @@ -199,8 +237,15 @@ private: bool visit(const luci::CircleReshape *node) { - RETURN_FALSE_UNLESS(has_type(node, Type::U8)) - RETURN_FALSE_UNLESS(has_type(node->tensor(), Type::U8)) + if (node->quantparam()) + { + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + RETURN_FALSE_UNLESS(has_type(node->tensor(), Type::U8)) + } + else + { + RETURN_FALSE_UNLESS(has_type(node->tensor(), node->dtype())) + } luci::CircleConst *shape = dynamic_cast<luci::CircleConst *>(node->shape()); if (shape != nullptr) RETURN_FALSE_UNLESS(has_type(shape, Type::S32)) @@ -211,6 +256,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::U8)) + + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == 1.0f / 256.0f); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == 0); return true; } @@ -218,6 +267,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) RETURN_FALSE_UNLESS(has_type(node->logits(), Type::U8)) + + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == 1.0f / 255.0f); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == 0); return true; } @@ -254,6 +307,13 @@ private: bool visit(const luci::CircleSplitOut *node) { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + + // SplitOut has the same qparam with the input of Split + auto split = loco::must_cast<luci::CircleSplit *>(node->input()); + auto input = loco::must_cast<luci::CircleNode *>(split->input()); + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == input->quantparam()->scale[0]); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == input->quantparam()->zerop[0]); return true; } @@ -261,6 +321,11 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) RETURN_FALSE_UNLESS(has_type(node->input(), Type::U8)) + + auto input = loco::must_cast<luci::CircleNode *>(node->input()); + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == input->quantparam()->scale[0]); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == input->quantparam()->zerop[0]); return true; } @@ -277,6 +342,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::U8)) + + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == 2.0f / 256.0f); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == 128); return true; } @@ -292,6 +361,10 @@ private: { RETURN_FALSE_UNLESS(has_type(node, Type::U8)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::U8)) + + // This checks the value of scale is an integer + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(std::roundf(node->quantparam()->scale[0]) == node->quantparam()->scale[0]); return true; } @@ -324,6 +397,10 @@ private: RETURN_FALSE_UNLESS(has_type(node, Type::U8)) RETURN_FALSE_UNLESS(has_type(node->x(), Type::U8)) RETURN_FALSE_UNLESS(has_type(node->y(), Type::U8)) + + // This checks the value of scale is an integer + RETURN_FALSE_UNLESS(node->quantparam()); + RETURN_FALSE_UNLESS(std::roundf(node->quantparam()->scale[0]) == node->quantparam()->scale[0]); return true; } @@ -363,6 +440,52 @@ private: return true; } + bool visit(const luci::CircleResizeNearestNeighbor *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + RETURN_FALSE_UNLESS(has_type(node->input(), Type::U8)) + return true; + } + + bool visit(const luci::CircleUnpack *node) + { + // node's output is the input of CircleUnpackOut, thus not quantized + RETURN_FALSE_UNLESS(has_type(node->value(), Type::U8)) + return true; + } + + bool visit(const luci::CircleUnpackOut *node) + { + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + + // UnpackOut has the same qparam with the input of Unpack + auto Unpack = loco::must_cast<luci::CircleUnpack *>(node->input()); + auto input = loco::must_cast<luci::CircleNode *>(Unpack->value()); + RETURN_FALSE_UNLESS(node->quantparam() && input->quantparam()); + RETURN_FALSE_UNLESS(node->quantparam()->scale[0] == input->quantparam()->scale[0]); + RETURN_FALSE_UNLESS(node->quantparam()->zerop[0] == input->quantparam()->zerop[0]); + return true; + } + + bool visit(const luci::CircleCast *node) + { + auto *input = loco::must_cast<luci::CircleNode *>(node->x()); + bool input_quantized = input->quantparam() != nullptr; + if (input_quantized) + { + RETURN_FALSE_UNLESS(has_type(input, node->in_data_type())) + RETURN_FALSE_UNLESS(has_type(input, Type::U8)) + } + + bool node_quantized = node->quantparam() != nullptr; + if (node_quantized) + { + RETURN_FALSE_UNLESS(has_type(node, node->out_data_type())) + RETURN_FALSE_UNLESS(has_type(node, Type::U8)) + } + return true; + } + // TODO: Implement more Ops bool visit(const luci::CircleNode *) { return true; } diff --git a/compiler/luci/pass/src/helpers/Strings.h b/compiler/luci/pass/src/helpers/Strings.h index 793d137fb..0e7818517 100644 --- a/compiler/luci/pass/src/helpers/Strings.h +++ b/compiler/luci/pass/src/helpers/Strings.h @@ -38,20 +38,6 @@ loco::DataType str_to_dtype(const std::string &); QuantizationGranularity str_to_granularity(const std::string &); -template <typename T> std::vector<T> csv_to_vector(const std::string &str) -{ - std::vector<T> ret; - std::istringstream is(str); - for (T i; is >> i;) - { - assert(i != ','); - ret.push_back(i); - if (is.peek() == ',') - is.ignore(); - } - return ret; -} - } // namespace luci #endif // __LUCI_PASS_HELPERS_STRINGS_H__ diff --git a/compiler/luci/pass/src/helpers/Strings.test.cpp b/compiler/luci/pass/src/helpers/Strings.test.cpp index f6bb48951..d77b65038 100644 --- a/compiler/luci/pass/src/helpers/Strings.test.cpp +++ b/compiler/luci/pass/src/helpers/Strings.test.cpp @@ -48,11 +48,3 @@ TEST(StringsTest, str_to_granularity) EXPECT_THROW(luci::str_to_granularity("foo"), std::runtime_error); } - -TEST(StringsTest, csv_to_vector_int32) -{ - auto ret = luci::csv_to_vector<int32_t>("1,2,3"); - ASSERT_EQ(3, ret.size()); - ASSERT_EQ(1, ret.at(0)); - ASSERT_EQ(3, ret.at(2)); -} diff --git a/compiler/luci/profile/CMakeLists.txt b/compiler/luci/profile/CMakeLists.txt index f2c6665da..fdfcaf1de 100644 --- a/compiler/luci/profile/CMakeLists.txt +++ b/compiler/luci/profile/CMakeLists.txt @@ -9,6 +9,8 @@ target_link_libraries(luci_profile PUBLIC loco) target_link_libraries(luci_profile PUBLIC luci_lang) install(TARGETS luci_profile DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/luci/profile/src/CircleNodeOrigin.cpp b/compiler/luci/profile/src/CircleNodeOrigin.cpp index 0a731a9ad..c1aeb66ae 100644 --- a/compiler/luci/profile/src/CircleNodeOrigin.cpp +++ b/compiler/luci/profile/src/CircleNodeOrigin.cpp @@ -125,13 +125,25 @@ std::shared_ptr<CircleNodeOrigin> single_origin(uint32_t id, const std::string & std::shared_ptr<CircleNodeOrigin> composite_origin(const std::initializer_list<std::shared_ptr<CircleNodeOrigin>> origins) { - return std::make_shared<CompositeOrigin>(origins); + auto origin = std::make_shared<CompositeOrigin>(origins); + + // For empty source, no need to create origin + if (origin->sources().empty()) + return nullptr; + + return origin; } std::shared_ptr<CircleNodeOrigin> composite_origin(const std::vector<std::shared_ptr<CircleNodeOrigin>> &origins) { - return std::make_shared<CompositeOrigin>(origins); + auto origin = std::make_shared<CompositeOrigin>(origins); + + // For empty source, no need to create origin + if (origin->sources().empty()) + return nullptr; + + return origin; } } // namespace luci @@ -141,7 +153,12 @@ namespace luci bool has_origin(const luci::CircleNode *circle_node) { - return circle_node->annot<CircleNodeOriginAnnotation>() != nullptr; + if (circle_node->annot<CircleNodeOriginAnnotation>() == nullptr) + return false; + + assert(!circle_node->annot<CircleNodeOriginAnnotation>()->origin()->sources().empty()); + + return true; } /** @@ -151,6 +168,10 @@ bool has_origin(const luci::CircleNode *circle_node) */ void add_origin(luci::CircleNode *circle_node, const std::shared_ptr<CircleNodeOrigin> origin) { + // Nothing to add + if (origin == nullptr) + return; + auto new_origin = composite_origin({get_origin(circle_node), origin}); circle_node->annot<CircleNodeOriginAnnotation>(nullptr); circle_node->annot(std::make_unique<CircleNodeOriginAnnotation>(new_origin)); diff --git a/compiler/luci/profile/src/CircleNodeOrigin.test.cpp b/compiler/luci/profile/src/CircleNodeOrigin.test.cpp index 34618e1ab..8748a5ce0 100644 --- a/compiler/luci/profile/src/CircleNodeOrigin.test.cpp +++ b/compiler/luci/profile/src/CircleNodeOrigin.test.cpp @@ -106,3 +106,27 @@ TEST(LuciCircleNodeOrigin, composite_origin_empty_ctor_NEG) { ASSERT_ANY_THROW(luci::composite_origin({})); } + +TEST(LuciCircleNodeOrigin, add_null_origin_NEG) +{ + auto g = loco::make_graph(); + auto add = g->nodes()->create<luci::CircleAdd>(); + + ASSERT_FALSE(has_origin(add)); + + add_origin(add, nullptr); + + ASSERT_FALSE(has_origin(add)); +} + +TEST(LuciCircleNodeOrigin, add_empty_origin_NEG) +{ + auto g = loco::make_graph(); + auto add = g->nodes()->create<luci::CircleAdd>(); + + ASSERT_FALSE(has_origin(add)); + + add_origin(add, luci::composite_origin({nullptr, nullptr})); + + ASSERT_FALSE(has_origin(add)); +} diff --git a/compiler/luci/requires.cmake b/compiler/luci/requires.cmake index e52523d45..687bf573a 100644 --- a/compiler/luci/requires.cmake +++ b/compiler/luci/requires.cmake @@ -1,4 +1,5 @@ require("foder") +require("pepper-csv2vec") require("loco") require("locop") require("logo") diff --git a/compiler/luci/service/CMakeLists.txt b/compiler/luci/service/CMakeLists.txt index 1c78031ab..781e6d6de 100644 --- a/compiler/luci/service/CMakeLists.txt +++ b/compiler/luci/service/CMakeLists.txt @@ -9,9 +9,12 @@ target_link_libraries(luci_service PUBLIC luci_lang) target_link_libraries(luci_service PUBLIC mio_circle) target_link_libraries(luci_service PUBLIC logo_core) target_link_libraries(luci_service PRIVATE luci_log) +target_link_libraries(luci_service PRIVATE luci_logex) target_link_libraries(luci_service PRIVATE nncc_common) target_link_libraries(luci_service PRIVATE oops) install(TARGETS luci_service DESTINATION lib) +install(DIRECTORY include/ DESTINATION include + FILES_MATCHING PATTERN "*.h") if(NOT ENABLE_TEST) return() diff --git a/compiler/luci/service/include/luci/Service/ChangeOutputs.h b/compiler/luci/service/include/luci/Service/ChangeOutputs.h new file mode 100644 index 000000000..553a3a3ab --- /dev/null +++ b/compiler/luci/service/include/luci/Service/ChangeOutputs.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_SVC_CHANGE_OUTPUTS_H__ +#define __LUCI_SVC_CHANGE_OUTPUTS_H__ + +#include <loco/IR/Graph.h> + +#include <string> +#include <vector> + +namespace luci +{ + +/** + * @brief Change output to nodes with string name. + * + * @note Should match existing number of nodes and all names should exist. + * Will throw exception if failed. + */ +void change_outputs(loco::Graph *, const std::vector<std::string> &); + +} // namespace luci + +#endif // __LUCI_SVC_CHANGE_OUTPUTS_H__ diff --git a/compiler/luci/service/include/luci/Service/CircleShapeInference.h b/compiler/luci/service/include/luci/Service/CircleShapeInference.h index 60bc16e48..ead12d074 100644 --- a/compiler/luci/service/include/luci/Service/CircleShapeInference.h +++ b/compiler/luci/service/include/luci/Service/CircleShapeInference.h @@ -109,6 +109,7 @@ public: // loco::TensorShape visit(const luci::CirclePadV2 *node) final; // loco::TensorShape visit(const luci::CirclePow *node) final; // loco::TensorShape visit(const luci::CirclePRelu *node) final; + // loco::TensorShape visit(const luci::CircleQuantize *node) final; // loco::TensorShape visit(const luci::CircleRange *node) final; // loco::TensorShape visit(const luci::CircleRank *node) final; // loco::TensorShape visit(const luci::CircleReduceAny *node) final; diff --git a/compiler/luci/service/include/luci/Service/CircleTypeInference.h b/compiler/luci/service/include/luci/Service/CircleTypeInference.h index 8eef469ac..d62731380 100644 --- a/compiler/luci/service/include/luci/Service/CircleTypeInference.h +++ b/compiler/luci/service/include/luci/Service/CircleTypeInference.h @@ -113,6 +113,7 @@ public: // loco::DataType visit(const luci::CircleRank *node) final; // loco::DataType visit(const luci::CircleMul *node) final; // loco::DataType visit(const luci::CircleOneHot *node) final; + // loco::DataType visit(const luci::CircleQuantize *node) final; // loco::DataType visit(const luci::CircleReduceAny *node) final; // loco::DataType visit(const luci::CircleReduceMax *node) final; // loco::DataType visit(const luci::CircleReduceMin *node) final; diff --git a/compiler/luci/service/include/luci/Service/Validate.h b/compiler/luci/service/include/luci/Service/Validate.h index 456d6e504..c6a2b0e0d 100644 --- a/compiler/luci/service/include/luci/Service/Validate.h +++ b/compiler/luci/service/include/luci/Service/Validate.h @@ -37,6 +37,8 @@ bool validate_name(loco::Graph *); */ bool validate_unique_name(luci::Module *); +bool validate(luci::Module *); + } // namespace luci #endif // __LUCI_SERVICE_VALIDATE_H__ diff --git a/compiler/luci/service/src/ChangeOutputs.cpp b/compiler/luci/service/src/ChangeOutputs.cpp new file mode 100644 index 000000000..1f8000061 --- /dev/null +++ b/compiler/luci/service/src/ChangeOutputs.cpp @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Service/ChangeOutputs.h" + +#include <luci/IR/CircleNode.h> + +#include <loco/IR/Graph.h> + +#include <oops/UserExn.h> + +#include <cassert> +#include <iostream> +#include <map> + +namespace +{ + +luci::CircleNode *find_by_name(loco::Graph *g, const std::string &name) +{ + for (auto node : loco::all_nodes(g)) + { + auto cnode = loco::must_cast<luci::CircleNode *>(node); + if (cnode->name() == name) + return cnode; + } + return nullptr; +} + +} // namespace + +namespace luci +{ + +void change_outputs(loco::Graph *graph, const std::vector<std::string> &new_outputs) +{ + if (new_outputs.size() != graph->outputs()->size()) + { + throw oops::UserExn("Change outputs failed: number of outputs should be ", + graph->outputs()->size()); + } + + std::map<std::string, luci::CircleNode *> named_nodes; + + for (auto &node_name : new_outputs) + { + auto node = find_by_name(graph, node_name); + if (node == nullptr) + { + throw oops::UserExn("Change outputs failed: node not found: ", node_name); + } + named_nodes[node_name] = node; + } + // just to be sure + assert(graph->outputs()->size() == named_nodes.size()); + + for (uint32_t out = 0; out < graph->outputs()->size(); ++out) + { + auto output = luci::output_node(graph, out); // output is CircleOutput + assert(output != nullptr); + + auto node_name = new_outputs.at(out); + auto node = named_nodes[node_name]; + assert(node != nullptr); + + output->from(node); + + // update GraphOutput shape, dtype to node + auto graph_out = graph->outputs()->at(out); + auto output_shape = std::make_unique<loco::TensorShape>(); + + output_shape->rank(node->rank()); + for (uint32_t r = 0; r < node->rank(); ++r) + { + if (node->dim(r).known()) + output_shape->dim(r).set(node->dim(r).value()); + else + output_shape->dim(r).unset(); + } + graph_out->shape(std::move(output_shape)); + graph_out->dtype(node->dtype()); + } +} + +} // namespace luci diff --git a/compiler/luci/service/src/ChangeOutputs.test.cpp b/compiler/luci/service/src/ChangeOutputs.test.cpp new file mode 100644 index 000000000..e37860e8a --- /dev/null +++ b/compiler/luci/service/src/ChangeOutputs.test.cpp @@ -0,0 +1,118 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Service/ChangeOutputs.h" + +#include <luci/test/TestIOGraph.h> + +#include <luci/IR/Nodes/CircleSqrt.h> + +#include <gtest/gtest.h> + +namespace +{ + +using namespace luci::test; + +class Sqrt2xGraphlet +{ +public: + Sqrt2xGraphlet() = default; + +public: + void init(loco::Graph *g, const ShapeU32 input_shape) + { + _sqrt1 = g->nodes()->create<luci::CircleSqrt>(); + _sqrt1->dtype(loco::DataType::S32); + _sqrt1->name("sqrt1"); + + _sqrt2 = g->nodes()->create<luci::CircleSqrt>(); + _sqrt2->dtype(loco::DataType::S32); + _sqrt2->name("sqrt2"); + } + +public: + luci::CircleSqrt *sqrt1(void) const { return _sqrt1; } + luci::CircleSqrt *sqrt2(void) const { return _sqrt2; } + +protected: + luci::CircleSqrt *_sqrt1 = nullptr; + luci::CircleSqrt *_sqrt2 = nullptr; +}; + +class Sqrt2xGraph : public TestIOGraph, public Sqrt2xGraphlet +{ +public: + Sqrt2xGraph() = default; + +public: + void init(const ShapeU32 shape) + { + TestIOGraph::init(shape, shape); + Sqrt2xGraphlet::init(g(), shape); + + _sqrt1->x(input()); + + _sqrt2->x(_sqrt1); + + output()->from(_sqrt2); + } +}; + +} // namespace + +TEST(ChangeOutputsTest, change) +{ + Sqrt2xGraph g; + + g.init({3, 3}); + + { + auto output = luci::output_node(g.g(), 0); + ASSERT_EQ(g.sqrt2(), output->from()); + } + + std::vector<std::string> names{"sqrt1"}; + + EXPECT_NO_THROW(luci::change_outputs(g.g(), names)); + + { + auto output = luci::output_node(g.g(), 0); + ASSERT_EQ(g.sqrt1(), output->from()); + } +} + +TEST(ChangeOutputsTest, name_not_found_NEG) +{ + Sqrt2xGraph g; + + g.init({3, 3}); + + std::vector<std::string> names{"sqrt33"}; + + EXPECT_ANY_THROW(luci::change_outputs(g.g(), names)); +} + +TEST(ChangeOutputsTest, number_names_NEG) +{ + Sqrt2xGraph g; + + g.init({3, 3}); + + std::vector<std::string> names{"sqrt1", "sqrt2"}; + + EXPECT_ANY_THROW(luci::change_outputs(g.g(), names)); +} diff --git a/compiler/luci/service/src/CircleCloneNode.cpp b/compiler/luci/service/src/CircleCloneNode.cpp new file mode 100644 index 000000000..a8895ea08 --- /dev/null +++ b/compiler/luci/service/src/CircleCloneNode.cpp @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleCloneNode.h" + +namespace luci +{ + +luci::CircleNode *CloneNode::visit(const luci::CircleNode *node) +{ +#define CNVISIT_GRP(GRP) \ + { \ + CloneNodeLet<CN::GRP> cn(_graph); \ + auto cloned = node->accept(&cn); \ + if (cloned != nullptr) \ + return cloned; \ + } + + CNVISIT_GRP(ABC); + CNVISIT_GRP(DEF); + CNVISIT_GRP(GHIJ); + CNVISIT_GRP(KLMN); + CNVISIT_GRP(OPQR); + CNVISIT_GRP(STUV); + CNVISIT_GRP(WXYZ); + +#undef CNVISIT_GRP + + return nullptr; +} + +} // namespace luci diff --git a/compiler/luci/service/src/CircleCloneNode.h b/compiler/luci/service/src/CircleCloneNode.h index 02c7cd256..3926147f5 100644 --- a/compiler/luci/service/src/CircleCloneNode.h +++ b/compiler/luci/service/src/CircleCloneNode.h @@ -24,10 +24,24 @@ namespace luci { -class CloneNode final : public luci::CircleNodeVisitor<luci::CircleNode *> +// CloneNode-let type +enum class CN +{ + ABC, + DEF, + GHIJ, + KLMN, + OPQR, + STUV, + WXYZ, +}; + +template <CN ct> class CloneNodeLet; + +template <> class CloneNodeLet<CN::ABC> final : public luci::CircleNodeVisitor<luci::CircleNode *> { public: - CloneNode(loco::Graph *graph) : _graph(graph){}; + CloneNodeLet(loco::Graph *graph) : _graph(graph){}; public: luci::CircleNode *visit(const luci::CircleAbs *) final; @@ -45,6 +59,19 @@ public: luci::CircleNode *visit(const luci::CircleConv2D *) final; luci::CircleNode *visit(const luci::CircleCos *) final; luci::CircleNode *visit(const luci::CircleCustom *) final; + + luci::CircleNode *visit(const luci::CircleNode *) final { return nullptr; } + +protected: + loco::Graph *_graph = nullptr; +}; + +template <> class CloneNodeLet<CN::DEF> final : public luci::CircleNodeVisitor<luci::CircleNode *> +{ +public: + CloneNodeLet(loco::Graph *graph) : _graph(graph){}; + +public: luci::CircleNode *visit(const luci::CircleDepthToSpace *) final; luci::CircleNode *visit(const luci::CircleDepthwiseConv2D *) final; luci::CircleNode *visit(const luci::CircleDequantize *) final; @@ -59,11 +86,37 @@ public: luci::CircleNode *visit(const luci::CircleFloorDiv *) final; luci::CircleNode *visit(const luci::CircleFloorMod *) final; luci::CircleNode *visit(const luci::CircleFullyConnected *) final; + + luci::CircleNode *visit(const luci::CircleNode *) final { return nullptr; } + +protected: + loco::Graph *_graph = nullptr; +}; + +template <> class CloneNodeLet<CN::GHIJ> final : public luci::CircleNodeVisitor<luci::CircleNode *> +{ +public: + CloneNodeLet(loco::Graph *graph) : _graph(graph){}; + +public: luci::CircleNode *visit(const luci::CircleGather *) final; luci::CircleNode *visit(const luci::CircleGatherNd *) final; luci::CircleNode *visit(const luci::CircleGreater *) final; luci::CircleNode *visit(const luci::CircleGreaterEqual *) final; - // luci::CircleNode *visit(const luci::CircleIf *) final; + luci::CircleNode *visit(const luci::CircleIf *) final; + + luci::CircleNode *visit(const luci::CircleNode *) final { return nullptr; } + +protected: + loco::Graph *_graph = nullptr; +}; + +template <> class CloneNodeLet<CN::KLMN> final : public luci::CircleNodeVisitor<luci::CircleNode *> +{ +public: + CloneNodeLet(loco::Graph *graph) : _graph(graph){}; + +public: luci::CircleNode *visit(const luci::CircleL2Normalize *) final; luci::CircleNode *visit(const luci::CircleL2Pool2D *) final; luci::CircleNode *visit(const luci::CircleLeakyRelu *) final; @@ -88,12 +141,26 @@ public: luci::CircleNode *visit(const luci::CircleNonMaxSuppressionV4 *) final; luci::CircleNode *visit(const luci::CircleNonMaxSuppressionV5 *) final; luci::CircleNode *visit(const luci::CircleNotEqual *) final; + + luci::CircleNode *visit(const luci::CircleNode *) final { return nullptr; } + +protected: + loco::Graph *_graph = nullptr; +}; + +template <> class CloneNodeLet<CN::OPQR> final : public luci::CircleNodeVisitor<luci::CircleNode *> +{ +public: + CloneNodeLet(loco::Graph *graph) : _graph(graph){}; + +public: luci::CircleNode *visit(const luci::CircleOneHot *) final; luci::CircleNode *visit(const luci::CirclePack *) final; luci::CircleNode *visit(const luci::CirclePad *) final; luci::CircleNode *visit(const luci::CirclePadV2 *) final; luci::CircleNode *visit(const luci::CirclePow *) final; luci::CircleNode *visit(const luci::CirclePRelu *) final; + luci::CircleNode *visit(const luci::CircleQuantize *) final; luci::CircleNode *visit(const luci::CircleRange *) final; luci::CircleNode *visit(const luci::CircleRank *) final; luci::CircleNode *visit(const luci::CircleReduceAny *) final; @@ -110,6 +177,19 @@ public: luci::CircleNode *visit(const luci::CircleReverseV2 *) final; luci::CircleNode *visit(const luci::CircleRound *) final; luci::CircleNode *visit(const luci::CircleRsqrt *) final; + + luci::CircleNode *visit(const luci::CircleNode *) final { return nullptr; } + +protected: + loco::Graph *_graph = nullptr; +}; + +template <> class CloneNodeLet<CN::STUV> final : public luci::CircleNodeVisitor<luci::CircleNode *> +{ +public: + CloneNodeLet(loco::Graph *graph) : _graph(graph){}; + +public: luci::CircleNode *visit(const luci::CircleScatterNd *) final; luci::CircleNode *visit(const luci::CircleSegmentSum *) final; luci::CircleNode *visit(const luci::CircleSelect *) final; @@ -138,18 +218,46 @@ public: luci::CircleNode *visit(const luci::CircleUnidirectionalSequenceLSTM *) final; luci::CircleNode *visit(const luci::CircleUnique *) final; luci::CircleNode *visit(const luci::CircleUnpack *) final; + + luci::CircleNode *visit(const luci::CircleNode *) final { return nullptr; } + +protected: + loco::Graph *_graph = nullptr; +}; + +template <> class CloneNodeLet<CN::WXYZ> final : public luci::CircleNodeVisitor<luci::CircleNode *> +{ +public: + CloneNodeLet(loco::Graph *graph) : _graph(graph){}; + +public: luci::CircleNode *visit(const luci::CircleWhere *) final; - // luci::CircleNode *visit(const luci::CircleWhile *) final; + luci::CircleNode *visit(const luci::CircleWhile *) final; luci::CircleNode *visit(const luci::CircleZerosLike *) final; + luci::CircleNode *visit(const luci::CircleNode *) final { return nullptr; } + +protected: + loco::Graph *_graph = nullptr; +}; + +class CloneNode final : public luci::CircleNodeVisitor<luci::CircleNode *> +{ +public: + CloneNode(loco::Graph *graph) : _graph(graph){}; + +public: // Circle Only luci::CircleNode *visit(const luci::CircleBCQFullyConnected *) final; luci::CircleNode *visit(const luci::CircleBCQGather *) final; luci::CircleNode *visit(const luci::CircleInstanceNorm *) final; + // NOTE CircleInput and CircleOutput are not handled here as these need + // link with graph I/O + // Virtual luci::CircleNode *visit(const luci::CircleCustomOut *) final; - // luci::CircleNode *visit(const luci::CircleIfOut *) final; + luci::CircleNode *visit(const luci::CircleIfOut *) final; // luci::CircleNode *visit(const luci::CircleInput *) final; luci::CircleNode *visit(const luci::CircleNonMaxSuppressionV4Out *) final; luci::CircleNode *visit(const luci::CircleNonMaxSuppressionV5Out *) final; @@ -161,7 +269,10 @@ public: luci::CircleNode *visit(const luci::CircleTopKV2Out *) final; luci::CircleNode *visit(const luci::CircleUniqueOut *) final; luci::CircleNode *visit(const luci::CircleUnpackOut *) final; - // luci::CircleNode *visit(const luci::CircleWhileOut *) final; + luci::CircleNode *visit(const luci::CircleWhileOut *) final; + + // Handle in CircleNode + luci::CircleNode *visit(const luci::CircleNode *) final; // NOTE CircleNodeVisitor will throw if not supported here diff --git a/compiler/luci/service/src/CircleShapeInferenceRule.cpp b/compiler/luci/service/src/CircleShapeInferenceRule.cpp index c6d8232c3..5d6a31050 100644 --- a/compiler/luci/service/src/CircleShapeInferenceRule.cpp +++ b/compiler/luci/service/src/CircleShapeInferenceRule.cpp @@ -345,6 +345,8 @@ template <class Pool2DType> loco::NodeShape infer_pool_2d_shape(const Pool2DType { auto ifm_shape = luci::shape_get(node->value()).template as<loco::TensorShape>(); assert(ifm_shape.rank() == 4); + assert(ifm_shape.dim(1).known()); + assert(ifm_shape.dim(2).known()); uint32_t input_height = ifm_shape.dim(1).value(); uint32_t input_width = ifm_shape.dim(2).value(); @@ -362,6 +364,8 @@ template <class Pool2DType> loco::NodeShape infer_pool_2d_shape(const Pool2DType if (node->padding() == luci::Padding::VALID) { + LUCI_ASSERT(input_height + stride_height > effective_window_height, "Invalid shape"); + LUCI_ASSERT(input_width + stride_width > effective_window_width, "Invalid shape"); output_height = (input_height + stride_height - effective_window_height) / stride_height; output_width = (input_width + stride_width - effective_window_width) / stride_width; } @@ -442,6 +446,10 @@ template <class Conv2DType> OutputSize infer_conv2d_type(const Conv2DType *node) auto ker_shape = luci::shape_get(node->filter()).template as<loco::TensorShape>(); assert(ifm_shape.rank() == 4); assert(ker_shape.rank() == 4); + assert(ifm_shape.dim(1).known()); + assert(ifm_shape.dim(2).known()); + assert(ker_shape.dim(1).known()); + assert(ker_shape.dim(2).known()); uint32_t input_height = ifm_shape.dim(1).value(); uint32_t input_width = ifm_shape.dim(2).value(); @@ -459,6 +467,8 @@ template <class Conv2DType> OutputSize infer_conv2d_type(const Conv2DType *node) if (node->padding() == luci::Padding::VALID) { + LUCI_ASSERT(input_height + stride_height > effective_ker_height, "Invalid shape"); + LUCI_ASSERT(input_width + stride_width > effective_ker_width, "Invalid shape"); output_height = (input_height + stride_height - effective_ker_height) / stride_height; output_width = (input_width + stride_width - effective_ker_width) / stride_width; } @@ -569,9 +579,6 @@ loco::NodeShape infer_conv2d(const luci::CircleConv2D *node) auto ifm_shape = luci::shape_get(node->input()).as<loco::TensorShape>(); // in NHWC auto ker_shape = luci::shape_get(node->filter()).as<loco::TensorShape>(); // in OHWI - INFO(l) << "[luci] CircleConv2D ShapeInf ifm(" << ifm_shape.rank() << ") ker(" << ker_shape.rank() - << ")" << std::endl; - assert(ifm_shape.rank() == 4); assert(ker_shape.rank() == 4); assert(ifm_shape.dim(3) == ker_shape.dim(3)); @@ -585,6 +592,11 @@ loco::NodeShape infer_conv2d(const luci::CircleConv2D *node) ofm_shape.dim(2) = os.width; ofm_shape.dim(3) = ker_shape.dim(0); + INFO(l) << "[luci] CircleConv2D ShapeInf ifm(" << ifm_shape.rank() << ") ker(" << ker_shape.rank() + << ") output(" << ofm_shape.dim(0).value() << "," << ofm_shape.dim(1).value() << "," + << ofm_shape.dim(2).value() << "," << ofm_shape.dim(3).value() << ") " << node->name() + << std::endl; + return loco::NodeShape{ofm_shape}; } @@ -2208,6 +2220,12 @@ public: loco::NodeShape visit(const luci::CirclePRelu *node) final { return infer_p_relu(node); } + loco::NodeShape visit(const luci::CircleQuantize *node) final + { + const auto input_shape = luci::shape_get(node->input()).as<loco::TensorShape>(); + return loco::NodeShape{input_shape}; + } + loco::NodeShape visit(const luci::CircleRange *node) final { return infer_range(node); } loco::NodeShape visit(const luci::CircleRank *) final diff --git a/compiler/luci/service/src/CircleTypeInferenceRule.cpp b/compiler/luci/service/src/CircleTypeInferenceRule.cpp index 0b8d2af9e..fade2cbd0 100644 --- a/compiler/luci/service/src/CircleTypeInferenceRule.cpp +++ b/compiler/luci/service/src/CircleTypeInferenceRule.cpp @@ -314,6 +314,9 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitor<loco::DataT return input_type; } + // TODO support S16 + loco::DataType visit(const luci::CircleQuantize *) final { return loco::DataType::U8; } + loco::DataType visit(const luci::CircleRange *node) final { return luci::dtype_get(node->start()); diff --git a/compiler/luci/service/src/Nodes/CircleAbs.cpp b/compiler/luci/service/src/Nodes/CircleAbs.cpp index 132760957..f7eb71a4b 100644 --- a/compiler/luci/service/src/Nodes/CircleAbs.cpp +++ b/compiler/luci/service/src/Nodes/CircleAbs.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleAbs *) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleAbs *) { return _graph->nodes()->create<luci::CircleAbs>(); } diff --git a/compiler/luci/service/src/Nodes/CircleAdd.cpp b/compiler/luci/service/src/Nodes/CircleAdd.cpp index 08634320e..2bdc6deed 100644 --- a/compiler/luci/service/src/Nodes/CircleAdd.cpp +++ b/compiler/luci/service/src/Nodes/CircleAdd.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleAdd *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleAdd *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleAddN.cpp b/compiler/luci/service/src/Nodes/CircleAddN.cpp index e536e54bb..a43c455b6 100644 --- a/compiler/luci/service/src/Nodes/CircleAddN.cpp +++ b/compiler/luci/service/src/Nodes/CircleAddN.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleAddN *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleAddN *node) { auto arity = node->arity(); return _graph->nodes()->create<luci::CircleAddN>(arity); diff --git a/compiler/luci/service/src/Nodes/CircleArgMax.cpp b/compiler/luci/service/src/Nodes/CircleArgMax.cpp index 1b3bafa86..b6efc2feb 100644 --- a/compiler/luci/service/src/Nodes/CircleArgMax.cpp +++ b/compiler/luci/service/src/Nodes/CircleArgMax.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleArgMax *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleArgMax *node) { auto *cloned = _graph->nodes()->create<luci::CircleArgMax>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleArgMin.cpp b/compiler/luci/service/src/Nodes/CircleArgMin.cpp index fa54f7b76..ab079267e 100644 --- a/compiler/luci/service/src/Nodes/CircleArgMin.cpp +++ b/compiler/luci/service/src/Nodes/CircleArgMin.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleArgMin *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleArgMin *node) { auto *cloned = _graph->nodes()->create<luci::CircleArgMin>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleAveragePool2D.cpp b/compiler/luci/service/src/Nodes/CircleAveragePool2D.cpp index 4d2791833..c64b1b864 100644 --- a/compiler/luci/service/src/Nodes/CircleAveragePool2D.cpp +++ b/compiler/luci/service/src/Nodes/CircleAveragePool2D.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleAveragePool2D *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleAveragePool2D *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleBatchMatMul.cpp b/compiler/luci/service/src/Nodes/CircleBatchMatMul.cpp index c7a8bbd52..b21b78dab 100644 --- a/compiler/luci/service/src/Nodes/CircleBatchMatMul.cpp +++ b/compiler/luci/service/src/Nodes/CircleBatchMatMul.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleBatchMatMul *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleBatchMatMul *node) { auto *cloned = _graph->nodes()->create<luci::CircleBatchMatMul>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleBatchToSpaceND.cpp b/compiler/luci/service/src/Nodes/CircleBatchToSpaceND.cpp index 70aa05f72..276f71bf1 100644 --- a/compiler/luci/service/src/Nodes/CircleBatchToSpaceND.cpp +++ b/compiler/luci/service/src/Nodes/CircleBatchToSpaceND.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleBatchToSpaceND *) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleBatchToSpaceND *) { return _graph->nodes()->create<luci::CircleBatchToSpaceND>(); } diff --git a/compiler/luci/service/src/Nodes/CircleCast.cpp b/compiler/luci/service/src/Nodes/CircleCast.cpp index 75f15f9de..1c20152cc 100644 --- a/compiler/luci/service/src/Nodes/CircleCast.cpp +++ b/compiler/luci/service/src/Nodes/CircleCast.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleCast *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleCast *node) { auto *cloned = _graph->nodes()->create<luci::CircleCast>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleCeil.cpp b/compiler/luci/service/src/Nodes/CircleCeil.cpp index 92d039a7d..3359afa57 100644 --- a/compiler/luci/service/src/Nodes/CircleCeil.cpp +++ b/compiler/luci/service/src/Nodes/CircleCeil.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleCeil *) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleCeil *) { return _graph->nodes()->create<luci::CircleCeil>(); } diff --git a/compiler/luci/service/src/Nodes/CircleConcatenation.cpp b/compiler/luci/service/src/Nodes/CircleConcatenation.cpp index 75d6a53e6..051c35c5b 100644 --- a/compiler/luci/service/src/Nodes/CircleConcatenation.cpp +++ b/compiler/luci/service/src/Nodes/CircleConcatenation.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleConcatenation *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleConcatenation *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleConst.cpp b/compiler/luci/service/src/Nodes/CircleConst.cpp index 0306ef4eb..017dcc8ad 100644 --- a/compiler/luci/service/src/Nodes/CircleConst.cpp +++ b/compiler/luci/service/src/Nodes/CircleConst.cpp @@ -110,7 +110,7 @@ luci::CircleConst *clone(luci::CircleConst *node) namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleConst *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleConst *node) { return clone_circleconst(node, _graph); } diff --git a/compiler/luci/service/src/Nodes/CircleConv2D.cpp b/compiler/luci/service/src/Nodes/CircleConv2D.cpp index 08cd87ef7..bd2a28988 100644 --- a/compiler/luci/service/src/Nodes/CircleConv2D.cpp +++ b/compiler/luci/service/src/Nodes/CircleConv2D.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleConv2D *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleConv2D *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleCos.cpp b/compiler/luci/service/src/Nodes/CircleCos.cpp index c46e3741b..d3377a23e 100644 --- a/compiler/luci/service/src/Nodes/CircleCos.cpp +++ b/compiler/luci/service/src/Nodes/CircleCos.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleCos *) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleCos *) { return _graph->nodes()->create<luci::CircleCos>(); } diff --git a/compiler/luci/service/src/Nodes/CircleCustom.cpp b/compiler/luci/service/src/Nodes/CircleCustom.cpp index a9764c373..9a99b192a 100644 --- a/compiler/luci/service/src/Nodes/CircleCustom.cpp +++ b/compiler/luci/service/src/Nodes/CircleCustom.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleCustom *node) +luci::CircleNode *CloneNodeLet<CN::ABC>::visit(const luci::CircleCustom *node) { uint32_t num_in = node->numInputs(); uint32_t num_out = node->numOutputs(); diff --git a/compiler/luci/service/src/Nodes/CircleDepthToSpace.cpp b/compiler/luci/service/src/Nodes/CircleDepthToSpace.cpp index 7e0bc7d74..6b5705a0e 100644 --- a/compiler/luci/service/src/Nodes/CircleDepthToSpace.cpp +++ b/compiler/luci/service/src/Nodes/CircleDepthToSpace.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleDepthToSpace *node) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleDepthToSpace *node) { auto *cloned = _graph->nodes()->create<luci::CircleDepthToSpace>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleDepthwiseConv2D.cpp b/compiler/luci/service/src/Nodes/CircleDepthwiseConv2D.cpp index 8e0b23d94..ba34a221c 100644 --- a/compiler/luci/service/src/Nodes/CircleDepthwiseConv2D.cpp +++ b/compiler/luci/service/src/Nodes/CircleDepthwiseConv2D.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleDepthwiseConv2D *node) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleDepthwiseConv2D *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleDequantize.cpp b/compiler/luci/service/src/Nodes/CircleDequantize.cpp index 79983e4d3..427617cb0 100644 --- a/compiler/luci/service/src/Nodes/CircleDequantize.cpp +++ b/compiler/luci/service/src/Nodes/CircleDequantize.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleDequantize *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleDequantize *) { return _graph->nodes()->create<luci::CircleDequantize>(); } diff --git a/compiler/luci/service/src/Nodes/CircleDiv.cpp b/compiler/luci/service/src/Nodes/CircleDiv.cpp index 7c48d8b76..6d40c5ee5 100644 --- a/compiler/luci/service/src/Nodes/CircleDiv.cpp +++ b/compiler/luci/service/src/Nodes/CircleDiv.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleDiv *node) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleDiv *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleElu.cpp b/compiler/luci/service/src/Nodes/CircleElu.cpp index e2df30285..885ecd6ad 100644 --- a/compiler/luci/service/src/Nodes/CircleElu.cpp +++ b/compiler/luci/service/src/Nodes/CircleElu.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleElu *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleElu *) { return _graph->nodes()->create<luci::CircleElu>(); } diff --git a/compiler/luci/service/src/Nodes/CircleEqual.cpp b/compiler/luci/service/src/Nodes/CircleEqual.cpp index 5dd382d0b..ee49e92cf 100644 --- a/compiler/luci/service/src/Nodes/CircleEqual.cpp +++ b/compiler/luci/service/src/Nodes/CircleEqual.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleEqual *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleEqual *) { return _graph->nodes()->create<luci::CircleEqual>(); } diff --git a/compiler/luci/service/src/Nodes/CircleExp.cpp b/compiler/luci/service/src/Nodes/CircleExp.cpp index 3d4918320..896dc0279 100644 --- a/compiler/luci/service/src/Nodes/CircleExp.cpp +++ b/compiler/luci/service/src/Nodes/CircleExp.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleExp *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleExp *) { return _graph->nodes()->create<luci::CircleExp>(); } diff --git a/compiler/luci/service/src/Nodes/CircleExpandDims.cpp b/compiler/luci/service/src/Nodes/CircleExpandDims.cpp index 4dd1cec86..a7fe5372b 100644 --- a/compiler/luci/service/src/Nodes/CircleExpandDims.cpp +++ b/compiler/luci/service/src/Nodes/CircleExpandDims.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleExpandDims *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleExpandDims *) { return _graph->nodes()->create<luci::CircleExpandDims>(); } diff --git a/compiler/luci/service/src/Nodes/CircleFakeQuant.cpp b/compiler/luci/service/src/Nodes/CircleFakeQuant.cpp index 7abaca685..8e0150b07 100644 --- a/compiler/luci/service/src/Nodes/CircleFakeQuant.cpp +++ b/compiler/luci/service/src/Nodes/CircleFakeQuant.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleFakeQuant *node) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleFakeQuant *node) { auto *cloned = _graph->nodes()->create<luci::CircleFakeQuant>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleFill.cpp b/compiler/luci/service/src/Nodes/CircleFill.cpp index d9b74c63a..9377c0c9e 100644 --- a/compiler/luci/service/src/Nodes/CircleFill.cpp +++ b/compiler/luci/service/src/Nodes/CircleFill.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleFill *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleFill *) { return _graph->nodes()->create<luci::CircleFill>(); } diff --git a/compiler/luci/service/src/Nodes/CircleFloor.cpp b/compiler/luci/service/src/Nodes/CircleFloor.cpp index 532808bc8..e7d42d57e 100644 --- a/compiler/luci/service/src/Nodes/CircleFloor.cpp +++ b/compiler/luci/service/src/Nodes/CircleFloor.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleFloor *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleFloor *) { return _graph->nodes()->create<luci::CircleFloor>(); } diff --git a/compiler/luci/service/src/Nodes/CircleFloorDiv.cpp b/compiler/luci/service/src/Nodes/CircleFloorDiv.cpp index 65be3e868..456eddc2e 100644 --- a/compiler/luci/service/src/Nodes/CircleFloorDiv.cpp +++ b/compiler/luci/service/src/Nodes/CircleFloorDiv.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleFloorDiv *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleFloorDiv *) { return _graph->nodes()->create<luci::CircleFloorDiv>(); } diff --git a/compiler/luci/service/src/Nodes/CircleFloorMod.cpp b/compiler/luci/service/src/Nodes/CircleFloorMod.cpp index 00e6a0499..759abcda9 100644 --- a/compiler/luci/service/src/Nodes/CircleFloorMod.cpp +++ b/compiler/luci/service/src/Nodes/CircleFloorMod.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleFloorMod *) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleFloorMod *) { return _graph->nodes()->create<luci::CircleFloorMod>(); } diff --git a/compiler/luci/service/src/Nodes/CircleFullyConnected.cpp b/compiler/luci/service/src/Nodes/CircleFullyConnected.cpp index 8acb35cbf..277107b06 100644 --- a/compiler/luci/service/src/Nodes/CircleFullyConnected.cpp +++ b/compiler/luci/service/src/Nodes/CircleFullyConnected.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleFullyConnected *node) +luci::CircleNode *CloneNodeLet<CN::DEF>::visit(const luci::CircleFullyConnected *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleGather.cpp b/compiler/luci/service/src/Nodes/CircleGather.cpp index 072bdeabc..66fc90dd7 100644 --- a/compiler/luci/service/src/Nodes/CircleGather.cpp +++ b/compiler/luci/service/src/Nodes/CircleGather.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleGather *node) +luci::CircleNode *CloneNodeLet<CN::GHIJ>::visit(const luci::CircleGather *node) { auto *cloned = _graph->nodes()->create<luci::CircleGather>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleGatherNd.cpp b/compiler/luci/service/src/Nodes/CircleGatherNd.cpp index df7ae6e79..4c017448d 100644 --- a/compiler/luci/service/src/Nodes/CircleGatherNd.cpp +++ b/compiler/luci/service/src/Nodes/CircleGatherNd.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleGatherNd *) +luci::CircleNode *CloneNodeLet<CN::GHIJ>::visit(const luci::CircleGatherNd *) { return _graph->nodes()->create<luci::CircleGatherNd>(); } diff --git a/compiler/luci/service/src/Nodes/CircleGreater.cpp b/compiler/luci/service/src/Nodes/CircleGreater.cpp index 366d955bf..95fdebae7 100644 --- a/compiler/luci/service/src/Nodes/CircleGreater.cpp +++ b/compiler/luci/service/src/Nodes/CircleGreater.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleGreater *) +luci::CircleNode *CloneNodeLet<CN::GHIJ>::visit(const luci::CircleGreater *) { return _graph->nodes()->create<luci::CircleGreater>(); } diff --git a/compiler/luci/service/src/Nodes/CircleGreaterEqual.cpp b/compiler/luci/service/src/Nodes/CircleGreaterEqual.cpp index 9705bbe1e..1fdd00196 100644 --- a/compiler/luci/service/src/Nodes/CircleGreaterEqual.cpp +++ b/compiler/luci/service/src/Nodes/CircleGreaterEqual.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleGreaterEqual *) +luci::CircleNode *CloneNodeLet<CN::GHIJ>::visit(const luci::CircleGreaterEqual *) { return _graph->nodes()->create<luci::CircleGreaterEqual>(); } diff --git a/compiler/luci/service/src/Nodes/CircleIf.cpp b/compiler/luci/service/src/Nodes/CircleIf.cpp new file mode 100644 index 000000000..6d45c9583 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleIf.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleCloneNode.h" + +namespace luci +{ + +luci::CircleNode *CloneNodeLet<CN::GHIJ>::visit(const luci::CircleIf *node) +{ + auto ic = node->input_count(); + auto oc = node->output_count(); + + return _graph->nodes()->create<luci::CircleIf>(ic, oc); +} + +} // namespace luci diff --git a/compiler/luci/service/src/Nodes/CircleIf.test.cpp b/compiler/luci/service/src/Nodes/CircleIf.test.cpp new file mode 100644 index 000000000..0dece100a --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleIf.test.cpp @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Service/CircleNodeClone.h" + +#include <gtest/gtest.h> + +TEST(CloneNodeTest, clone_If) +{ + auto g = loco::make_graph(); + auto node_if = g->nodes()->create<luci::CircleIf>(1, 1); + + auto gc = loco::make_graph(); + auto cloned = luci::clone_node(node_if, gc.get()); + ASSERT_NE(nullptr, cloned); + ASSERT_EQ(gc.get(), cloned->graph()); + + auto cloned_if = dynamic_cast<luci::CircleIf *>(cloned); + ASSERT_NE(nullptr, cloned_if); + ASSERT_EQ(-1, cloned_if->then_branch()); + ASSERT_EQ(-1, cloned_if->else_branch()); + ASSERT_EQ(nullptr, cloned_if->then_graph()); + ASSERT_EQ(nullptr, cloned_if->else_graph()); +} diff --git a/compiler/luci/service/src/Nodes/CircleIfOut.cpp b/compiler/luci/service/src/Nodes/CircleIfOut.cpp index 31ad7203f..b555a9538 100644 --- a/compiler/luci/service/src/Nodes/CircleIfOut.cpp +++ b/compiler/luci/service/src/Nodes/CircleIfOut.cpp @@ -17,6 +17,8 @@ #include <luci/Service/CircleShapeInference.h> #include <luci/Service/CircleTypeInference.h> +#include "CircleCloneNode.h" + namespace { @@ -87,3 +89,16 @@ loco::DataType tinf::Algorithm::visit(const luci::CircleIfOut *node) } } // namespace luci + +namespace luci +{ + +luci::CircleNode *CloneNode::visit(const luci::CircleIfOut *node) +{ + auto *cloned = _graph->nodes()->create<luci::CircleIfOut>(); + if (cloned != nullptr) + cloned->index(node->index()); + return cloned; +} + +} // namespace luci diff --git a/compiler/luci/service/src/Nodes/CircleIfOut.test.cpp b/compiler/luci/service/src/Nodes/CircleIfOut.test.cpp new file mode 100644 index 000000000..666a15105 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleIfOut.test.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Service/CircleNodeClone.h" + +#include <gtest/gtest.h> + +TEST(CloneNodeTest, clone_IfOut) +{ + auto g = loco::make_graph(); + auto node_iout = g->nodes()->create<luci::CircleIfOut>(); + node_iout->index(1); + + auto gc = loco::make_graph(); + auto cloned = luci::clone_node(node_iout, gc.get()); + ASSERT_NE(nullptr, cloned); + ASSERT_EQ(gc.get(), cloned->graph()); + + auto cloned_iout = dynamic_cast<luci::CircleIfOut *>(cloned); + ASSERT_NE(nullptr, cloned_iout); + ASSERT_EQ(node_iout->index(), cloned_iout->index()); +} diff --git a/compiler/luci/service/src/Nodes/CircleL2Normalize.cpp b/compiler/luci/service/src/Nodes/CircleL2Normalize.cpp index afa2a6acb..6a9f61f82 100644 --- a/compiler/luci/service/src/Nodes/CircleL2Normalize.cpp +++ b/compiler/luci/service/src/Nodes/CircleL2Normalize.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleL2Normalize *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleL2Normalize *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleL2Pool2D.cpp b/compiler/luci/service/src/Nodes/CircleL2Pool2D.cpp index 2d876c5bc..532b36237 100644 --- a/compiler/luci/service/src/Nodes/CircleL2Pool2D.cpp +++ b/compiler/luci/service/src/Nodes/CircleL2Pool2D.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleL2Pool2D *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleL2Pool2D *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleLeakyRelu.cpp b/compiler/luci/service/src/Nodes/CircleLeakyRelu.cpp index 91030618c..0432bbcfc 100644 --- a/compiler/luci/service/src/Nodes/CircleLeakyRelu.cpp +++ b/compiler/luci/service/src/Nodes/CircleLeakyRelu.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLeakyRelu *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLeakyRelu *node) { auto *cloned = _graph->nodes()->create<luci::CircleLeakyRelu>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleLess.cpp b/compiler/luci/service/src/Nodes/CircleLess.cpp index 33b70b735..d3e72d03a 100644 --- a/compiler/luci/service/src/Nodes/CircleLess.cpp +++ b/compiler/luci/service/src/Nodes/CircleLess.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLess *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLess *) { return _graph->nodes()->create<luci::CircleLess>(); } diff --git a/compiler/luci/service/src/Nodes/CircleLessEqual.cpp b/compiler/luci/service/src/Nodes/CircleLessEqual.cpp index 22491365a..ba3c80233 100644 --- a/compiler/luci/service/src/Nodes/CircleLessEqual.cpp +++ b/compiler/luci/service/src/Nodes/CircleLessEqual.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLessEqual *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLessEqual *) { return _graph->nodes()->create<luci::CircleLessEqual>(); } diff --git a/compiler/luci/service/src/Nodes/CircleLocalResponseNormalization.cpp b/compiler/luci/service/src/Nodes/CircleLocalResponseNormalization.cpp index bf69b5ef5..7e5f7a993 100644 --- a/compiler/luci/service/src/Nodes/CircleLocalResponseNormalization.cpp +++ b/compiler/luci/service/src/Nodes/CircleLocalResponseNormalization.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLocalResponseNormalization *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLocalResponseNormalization *node) { auto *cloned = _graph->nodes()->create<luci::CircleLocalResponseNormalization>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleLog.cpp b/compiler/luci/service/src/Nodes/CircleLog.cpp index 5788f129f..7c8562af3 100644 --- a/compiler/luci/service/src/Nodes/CircleLog.cpp +++ b/compiler/luci/service/src/Nodes/CircleLog.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLog *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLog *) { return _graph->nodes()->create<luci::CircleLog>(); } diff --git a/compiler/luci/service/src/Nodes/CircleLogSoftmax.cpp b/compiler/luci/service/src/Nodes/CircleLogSoftmax.cpp index 352160aff..5d843e13a 100644 --- a/compiler/luci/service/src/Nodes/CircleLogSoftmax.cpp +++ b/compiler/luci/service/src/Nodes/CircleLogSoftmax.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLogSoftmax *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLogSoftmax *) { return _graph->nodes()->create<luci::CircleLogSoftmax>(); } diff --git a/compiler/luci/service/src/Nodes/CircleLogicalAnd.cpp b/compiler/luci/service/src/Nodes/CircleLogicalAnd.cpp index 5df62b951..07e09d1d1 100644 --- a/compiler/luci/service/src/Nodes/CircleLogicalAnd.cpp +++ b/compiler/luci/service/src/Nodes/CircleLogicalAnd.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLogicalAnd *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLogicalAnd *) { return _graph->nodes()->create<luci::CircleLogicalAnd>(); } diff --git a/compiler/luci/service/src/Nodes/CircleLogicalNot.cpp b/compiler/luci/service/src/Nodes/CircleLogicalNot.cpp index ac982829d..13abad56d 100644 --- a/compiler/luci/service/src/Nodes/CircleLogicalNot.cpp +++ b/compiler/luci/service/src/Nodes/CircleLogicalNot.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLogicalNot *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLogicalNot *) { return _graph->nodes()->create<luci::CircleLogicalNot>(); } diff --git a/compiler/luci/service/src/Nodes/CircleLogicalOr.cpp b/compiler/luci/service/src/Nodes/CircleLogicalOr.cpp index 1201d6f34..7bd574527 100644 --- a/compiler/luci/service/src/Nodes/CircleLogicalOr.cpp +++ b/compiler/luci/service/src/Nodes/CircleLogicalOr.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLogicalOr *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLogicalOr *) { return _graph->nodes()->create<luci::CircleLogicalOr>(); } diff --git a/compiler/luci/service/src/Nodes/CircleLogistic.cpp b/compiler/luci/service/src/Nodes/CircleLogistic.cpp index b21b187e9..41cd99b92 100644 --- a/compiler/luci/service/src/Nodes/CircleLogistic.cpp +++ b/compiler/luci/service/src/Nodes/CircleLogistic.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleLogistic *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleLogistic *) { return _graph->nodes()->create<luci::CircleLogistic>(); } diff --git a/compiler/luci/service/src/Nodes/CircleMatrixDiag.cpp b/compiler/luci/service/src/Nodes/CircleMatrixDiag.cpp index 2bffa07b1..f1dd059eb 100644 --- a/compiler/luci/service/src/Nodes/CircleMatrixDiag.cpp +++ b/compiler/luci/service/src/Nodes/CircleMatrixDiag.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMatrixDiag *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMatrixDiag *) { return _graph->nodes()->create<luci::CircleMatrixDiag>(); } diff --git a/compiler/luci/service/src/Nodes/CircleMatrixSetDiag.cpp b/compiler/luci/service/src/Nodes/CircleMatrixSetDiag.cpp index 5ea2a5339..de5a85bcd 100644 --- a/compiler/luci/service/src/Nodes/CircleMatrixSetDiag.cpp +++ b/compiler/luci/service/src/Nodes/CircleMatrixSetDiag.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMatrixSetDiag *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMatrixSetDiag *) { return _graph->nodes()->create<luci::CircleMatrixSetDiag>(); } diff --git a/compiler/luci/service/src/Nodes/CircleMaxPool2D.cpp b/compiler/luci/service/src/Nodes/CircleMaxPool2D.cpp index b21610c7f..8b186094d 100644 --- a/compiler/luci/service/src/Nodes/CircleMaxPool2D.cpp +++ b/compiler/luci/service/src/Nodes/CircleMaxPool2D.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMaxPool2D *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMaxPool2D *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleMaximum.cpp b/compiler/luci/service/src/Nodes/CircleMaximum.cpp index 545f4ca21..72c00af8c 100644 --- a/compiler/luci/service/src/Nodes/CircleMaximum.cpp +++ b/compiler/luci/service/src/Nodes/CircleMaximum.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMaximum *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMaximum *) { return _graph->nodes()->create<luci::CircleMaximum>(); } diff --git a/compiler/luci/service/src/Nodes/CircleMean.cpp b/compiler/luci/service/src/Nodes/CircleMean.cpp index 95bc54532..217735693 100644 --- a/compiler/luci/service/src/Nodes/CircleMean.cpp +++ b/compiler/luci/service/src/Nodes/CircleMean.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMean *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMean *node) { auto *cloned = _graph->nodes()->create<luci::CircleMean>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleMinimum.cpp b/compiler/luci/service/src/Nodes/CircleMinimum.cpp index 2c2755c55..7a206e929 100644 --- a/compiler/luci/service/src/Nodes/CircleMinimum.cpp +++ b/compiler/luci/service/src/Nodes/CircleMinimum.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMinimum *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMinimum *) { return _graph->nodes()->create<luci::CircleMinimum>(); } diff --git a/compiler/luci/service/src/Nodes/CircleMirrorPad.cpp b/compiler/luci/service/src/Nodes/CircleMirrorPad.cpp index 919221a0b..8bc186a6b 100644 --- a/compiler/luci/service/src/Nodes/CircleMirrorPad.cpp +++ b/compiler/luci/service/src/Nodes/CircleMirrorPad.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMirrorPad *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMirrorPad *node) { if (node->mode() == luci::MirrorPadMode::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleMul.cpp b/compiler/luci/service/src/Nodes/CircleMul.cpp index 096aed196..f2ccfb899 100644 --- a/compiler/luci/service/src/Nodes/CircleMul.cpp +++ b/compiler/luci/service/src/Nodes/CircleMul.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleMul *node) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleMul *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleNeg.cpp b/compiler/luci/service/src/Nodes/CircleNeg.cpp index 312189e77..20190fd89 100644 --- a/compiler/luci/service/src/Nodes/CircleNeg.cpp +++ b/compiler/luci/service/src/Nodes/CircleNeg.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleNeg *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleNeg *) { return _graph->nodes()->create<luci::CircleNeg>(); } diff --git a/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV4.cpp b/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV4.cpp index 4757e8314..1024a2dfd 100644 --- a/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV4.cpp +++ b/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV4.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleNonMaxSuppressionV4 *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleNonMaxSuppressionV4 *) { return _graph->nodes()->create<luci::CircleNonMaxSuppressionV4>(); } diff --git a/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV5.cpp b/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV5.cpp index 34d128072..364c92749 100644 --- a/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV5.cpp +++ b/compiler/luci/service/src/Nodes/CircleNonMaxSuppressionV5.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleNonMaxSuppressionV5 *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleNonMaxSuppressionV5 *) { return _graph->nodes()->create<luci::CircleNonMaxSuppressionV5>(); } diff --git a/compiler/luci/service/src/Nodes/CircleNotEqual.cpp b/compiler/luci/service/src/Nodes/CircleNotEqual.cpp index 4cb5320e8..53ebe888d 100644 --- a/compiler/luci/service/src/Nodes/CircleNotEqual.cpp +++ b/compiler/luci/service/src/Nodes/CircleNotEqual.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleNotEqual *) +luci::CircleNode *CloneNodeLet<CN::KLMN>::visit(const luci::CircleNotEqual *) { return _graph->nodes()->create<luci::CircleNotEqual>(); } diff --git a/compiler/luci/service/src/Nodes/CircleOneHot.cpp b/compiler/luci/service/src/Nodes/CircleOneHot.cpp index a33c8ff26..c68ab9242 100644 --- a/compiler/luci/service/src/Nodes/CircleOneHot.cpp +++ b/compiler/luci/service/src/Nodes/CircleOneHot.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleOneHot *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleOneHot *node) { auto *cloned = _graph->nodes()->create<luci::CircleOneHot>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CirclePRelu.cpp b/compiler/luci/service/src/Nodes/CirclePRelu.cpp index 8a34e507e..b367e1968 100644 --- a/compiler/luci/service/src/Nodes/CirclePRelu.cpp +++ b/compiler/luci/service/src/Nodes/CirclePRelu.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CirclePRelu *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CirclePRelu *) { return _graph->nodes()->create<luci::CirclePRelu>(); } diff --git a/compiler/luci/service/src/Nodes/CirclePack.cpp b/compiler/luci/service/src/Nodes/CirclePack.cpp index a3cee0bfd..fccbd0f8c 100644 --- a/compiler/luci/service/src/Nodes/CirclePack.cpp +++ b/compiler/luci/service/src/Nodes/CirclePack.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CirclePack *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CirclePack *node) { auto *cloned = _graph->nodes()->create<luci::CirclePack>(node->values_count()); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CirclePad.cpp b/compiler/luci/service/src/Nodes/CirclePad.cpp index 425bdce4d..a8029e4e2 100644 --- a/compiler/luci/service/src/Nodes/CirclePad.cpp +++ b/compiler/luci/service/src/Nodes/CirclePad.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CirclePad *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CirclePad *) { return _graph->nodes()->create<luci::CirclePad>(); } diff --git a/compiler/luci/service/src/Nodes/CirclePadV2.cpp b/compiler/luci/service/src/Nodes/CirclePadV2.cpp index 0e93869b6..ad516a742 100644 --- a/compiler/luci/service/src/Nodes/CirclePadV2.cpp +++ b/compiler/luci/service/src/Nodes/CirclePadV2.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CirclePadV2 *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CirclePadV2 *) { return _graph->nodes()->create<luci::CirclePadV2>(); } diff --git a/compiler/luci/service/src/Nodes/CirclePow.cpp b/compiler/luci/service/src/Nodes/CirclePow.cpp index bf9388913..2cff58757 100644 --- a/compiler/luci/service/src/Nodes/CirclePow.cpp +++ b/compiler/luci/service/src/Nodes/CirclePow.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CirclePow *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CirclePow *) { return _graph->nodes()->create<luci::CirclePow>(); } diff --git a/compiler/luci/service/src/Nodes/CircleQuantize.cpp b/compiler/luci/service/src/Nodes/CircleQuantize.cpp new file mode 100644 index 000000000..a78eb3f02 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleQuantize.cpp @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleCloneNode.h" + +namespace luci +{ + +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleQuantize *) +{ + return _graph->nodes()->create<luci::CircleQuantize>(); +} + +} // namespace luci diff --git a/compiler/luci/service/src/Nodes/CircleQuantize.test.cpp b/compiler/luci/service/src/Nodes/CircleQuantize.test.cpp new file mode 100644 index 000000000..628dfa1e6 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleQuantize.test.cpp @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Service/CircleNodeClone.h" + +#include <gtest/gtest.h> + +TEST(CloneNodeTest, clone_Quantize) +{ + auto g = loco::make_graph(); + auto node_q = g->nodes()->create<luci::CircleQuantize>(); + + auto gc = loco::make_graph(); + auto cloned = luci::clone_node(node_q, gc.get()); + ASSERT_NE(nullptr, cloned); + ASSERT_EQ(gc.get(), cloned->graph()); + + auto cloned_q = dynamic_cast<luci::CircleQuantize *>(cloned); + ASSERT_NE(nullptr, cloned_q); +} diff --git a/compiler/luci/service/src/Nodes/CircleRange.cpp b/compiler/luci/service/src/Nodes/CircleRange.cpp index 9c6f7b494..ccb975d4e 100644 --- a/compiler/luci/service/src/Nodes/CircleRange.cpp +++ b/compiler/luci/service/src/Nodes/CircleRange.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleRange *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleRange *) { return _graph->nodes()->create<luci::CircleRange>(); } diff --git a/compiler/luci/service/src/Nodes/CircleRank.cpp b/compiler/luci/service/src/Nodes/CircleRank.cpp index db8171c51..168e76c70 100644 --- a/compiler/luci/service/src/Nodes/CircleRank.cpp +++ b/compiler/luci/service/src/Nodes/CircleRank.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleRank *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleRank *) { return _graph->nodes()->create<luci::CircleRank>(); } diff --git a/compiler/luci/service/src/Nodes/CircleReduceAny.cpp b/compiler/luci/service/src/Nodes/CircleReduceAny.cpp index 3ab0b3b59..c29755ec5 100644 --- a/compiler/luci/service/src/Nodes/CircleReduceAny.cpp +++ b/compiler/luci/service/src/Nodes/CircleReduceAny.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReduceAny *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReduceAny *node) { auto *cloned = _graph->nodes()->create<luci::CircleReduceAny>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleReduceMax.cpp b/compiler/luci/service/src/Nodes/CircleReduceMax.cpp index c026905ca..efe30491e 100644 --- a/compiler/luci/service/src/Nodes/CircleReduceMax.cpp +++ b/compiler/luci/service/src/Nodes/CircleReduceMax.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReduceMax *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReduceMax *node) { auto *cloned = _graph->nodes()->create<luci::CircleReduceMax>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleReduceMin.cpp b/compiler/luci/service/src/Nodes/CircleReduceMin.cpp index 3dfa19680..e1e38e38e 100644 --- a/compiler/luci/service/src/Nodes/CircleReduceMin.cpp +++ b/compiler/luci/service/src/Nodes/CircleReduceMin.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReduceMin *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReduceMin *node) { auto *cloned = _graph->nodes()->create<luci::CircleReduceMin>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleReduceProd.cpp b/compiler/luci/service/src/Nodes/CircleReduceProd.cpp index 418a8ce32..3d51c6929 100644 --- a/compiler/luci/service/src/Nodes/CircleReduceProd.cpp +++ b/compiler/luci/service/src/Nodes/CircleReduceProd.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReduceProd *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReduceProd *node) { auto *cloned = _graph->nodes()->create<luci::CircleReduceProd>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleRelu.cpp b/compiler/luci/service/src/Nodes/CircleRelu.cpp index 7447eea0c..952f1de4b 100644 --- a/compiler/luci/service/src/Nodes/CircleRelu.cpp +++ b/compiler/luci/service/src/Nodes/CircleRelu.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleRelu *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleRelu *) { return _graph->nodes()->create<luci::CircleRelu>(); } diff --git a/compiler/luci/service/src/Nodes/CircleRelu6.cpp b/compiler/luci/service/src/Nodes/CircleRelu6.cpp index 7b98311ed..1cef90cb5 100644 --- a/compiler/luci/service/src/Nodes/CircleRelu6.cpp +++ b/compiler/luci/service/src/Nodes/CircleRelu6.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleRelu6 *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleRelu6 *) { return _graph->nodes()->create<luci::CircleRelu6>(); } diff --git a/compiler/luci/service/src/Nodes/CircleReluN1To1.cpp b/compiler/luci/service/src/Nodes/CircleReluN1To1.cpp index 4efedb9fc..6e978566f 100644 --- a/compiler/luci/service/src/Nodes/CircleReluN1To1.cpp +++ b/compiler/luci/service/src/Nodes/CircleReluN1To1.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReluN1To1 *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReluN1To1 *) { return _graph->nodes()->create<luci::CircleReluN1To1>(); } diff --git a/compiler/luci/service/src/Nodes/CircleReshape.cpp b/compiler/luci/service/src/Nodes/CircleReshape.cpp index 07a81b306..8533f0873 100644 --- a/compiler/luci/service/src/Nodes/CircleReshape.cpp +++ b/compiler/luci/service/src/Nodes/CircleReshape.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReshape *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReshape *node) { auto *cloned = _graph->nodes()->create<luci::CircleReshape>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleResizeBilinear.cpp b/compiler/luci/service/src/Nodes/CircleResizeBilinear.cpp index 55d21af45..f77d3e216 100644 --- a/compiler/luci/service/src/Nodes/CircleResizeBilinear.cpp +++ b/compiler/luci/service/src/Nodes/CircleResizeBilinear.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleResizeBilinear *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleResizeBilinear *node) { auto *cloned = _graph->nodes()->create<luci::CircleResizeBilinear>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleResizeNearestNeighbor.cpp b/compiler/luci/service/src/Nodes/CircleResizeNearestNeighbor.cpp index 5727786a7..cfb396392 100644 --- a/compiler/luci/service/src/Nodes/CircleResizeNearestNeighbor.cpp +++ b/compiler/luci/service/src/Nodes/CircleResizeNearestNeighbor.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleResizeNearestNeighbor *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleResizeNearestNeighbor *node) { auto *cloned = _graph->nodes()->create<luci::CircleResizeNearestNeighbor>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleReverseSequence.cpp b/compiler/luci/service/src/Nodes/CircleReverseSequence.cpp index 6e6919b0c..682ad1105 100644 --- a/compiler/luci/service/src/Nodes/CircleReverseSequence.cpp +++ b/compiler/luci/service/src/Nodes/CircleReverseSequence.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReverseSequence *node) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReverseSequence *node) { auto *cloned = _graph->nodes()->create<luci::CircleReverseSequence>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleReverseV2.cpp b/compiler/luci/service/src/Nodes/CircleReverseV2.cpp index e8fee6c3e..f61e14e9d 100644 --- a/compiler/luci/service/src/Nodes/CircleReverseV2.cpp +++ b/compiler/luci/service/src/Nodes/CircleReverseV2.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleReverseV2 *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleReverseV2 *) { return _graph->nodes()->create<luci::CircleReverseV2>(); } diff --git a/compiler/luci/service/src/Nodes/CircleRound.cpp b/compiler/luci/service/src/Nodes/CircleRound.cpp index 2c23f2df6..6e9544c56 100644 --- a/compiler/luci/service/src/Nodes/CircleRound.cpp +++ b/compiler/luci/service/src/Nodes/CircleRound.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleRound *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleRound *) { return _graph->nodes()->create<luci::CircleRound>(); } diff --git a/compiler/luci/service/src/Nodes/CircleRsqrt.cpp b/compiler/luci/service/src/Nodes/CircleRsqrt.cpp index aca702fe1..1c50f5780 100644 --- a/compiler/luci/service/src/Nodes/CircleRsqrt.cpp +++ b/compiler/luci/service/src/Nodes/CircleRsqrt.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleRsqrt *) +luci::CircleNode *CloneNodeLet<CN::OPQR>::visit(const luci::CircleRsqrt *) { return _graph->nodes()->create<luci::CircleRsqrt>(); } diff --git a/compiler/luci/service/src/Nodes/CircleScatterNd.cpp b/compiler/luci/service/src/Nodes/CircleScatterNd.cpp index 6c477a598..d16524db2 100644 --- a/compiler/luci/service/src/Nodes/CircleScatterNd.cpp +++ b/compiler/luci/service/src/Nodes/CircleScatterNd.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleScatterNd *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleScatterNd *) { return _graph->nodes()->create<luci::CircleScatterNd>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSegmentSum.cpp b/compiler/luci/service/src/Nodes/CircleSegmentSum.cpp index aa4001f57..ea9eee27d 100644 --- a/compiler/luci/service/src/Nodes/CircleSegmentSum.cpp +++ b/compiler/luci/service/src/Nodes/CircleSegmentSum.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSegmentSum *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSegmentSum *) { return _graph->nodes()->create<luci::CircleSegmentSum>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSelect.cpp b/compiler/luci/service/src/Nodes/CircleSelect.cpp index 71b31d33f..609a67905 100644 --- a/compiler/luci/service/src/Nodes/CircleSelect.cpp +++ b/compiler/luci/service/src/Nodes/CircleSelect.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSelect *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSelect *) { return _graph->nodes()->create<luci::CircleSelect>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSelectV2.cpp b/compiler/luci/service/src/Nodes/CircleSelectV2.cpp index 07af40c40..027fbd9ee 100644 --- a/compiler/luci/service/src/Nodes/CircleSelectV2.cpp +++ b/compiler/luci/service/src/Nodes/CircleSelectV2.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSelectV2 *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSelectV2 *) { return _graph->nodes()->create<luci::CircleSelectV2>(); } diff --git a/compiler/luci/service/src/Nodes/CircleShape.cpp b/compiler/luci/service/src/Nodes/CircleShape.cpp index e5b5fa28f..9ae742091 100644 --- a/compiler/luci/service/src/Nodes/CircleShape.cpp +++ b/compiler/luci/service/src/Nodes/CircleShape.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleShape *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleShape *node) { auto *cloned = _graph->nodes()->create<luci::CircleShape>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleSin.cpp b/compiler/luci/service/src/Nodes/CircleSin.cpp index 46a07d21d..9cb35b0f2 100644 --- a/compiler/luci/service/src/Nodes/CircleSin.cpp +++ b/compiler/luci/service/src/Nodes/CircleSin.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSin *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSin *) { return _graph->nodes()->create<luci::CircleSin>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSlice.cpp b/compiler/luci/service/src/Nodes/CircleSlice.cpp index 6b2f4a591..341d37d75 100644 --- a/compiler/luci/service/src/Nodes/CircleSlice.cpp +++ b/compiler/luci/service/src/Nodes/CircleSlice.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSlice *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSlice *) { return _graph->nodes()->create<luci::CircleSlice>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSoftmax.cpp b/compiler/luci/service/src/Nodes/CircleSoftmax.cpp index 359d1000c..551bcbc90 100644 --- a/compiler/luci/service/src/Nodes/CircleSoftmax.cpp +++ b/compiler/luci/service/src/Nodes/CircleSoftmax.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSoftmax *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSoftmax *node) { auto *cloned = _graph->nodes()->create<luci::CircleSoftmax>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleSpaceToBatchND.cpp b/compiler/luci/service/src/Nodes/CircleSpaceToBatchND.cpp index feb4f3e37..00bb1d42b 100644 --- a/compiler/luci/service/src/Nodes/CircleSpaceToBatchND.cpp +++ b/compiler/luci/service/src/Nodes/CircleSpaceToBatchND.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSpaceToBatchND *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSpaceToBatchND *) { return _graph->nodes()->create<luci::CircleSpaceToBatchND>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSpaceToDepth.cpp b/compiler/luci/service/src/Nodes/CircleSpaceToDepth.cpp index 3a82f5c7a..7916acf6b 100644 --- a/compiler/luci/service/src/Nodes/CircleSpaceToDepth.cpp +++ b/compiler/luci/service/src/Nodes/CircleSpaceToDepth.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSpaceToDepth *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSpaceToDepth *node) { auto *cloned = _graph->nodes()->create<luci::CircleSpaceToDepth>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleSparseToDense.cpp b/compiler/luci/service/src/Nodes/CircleSparseToDense.cpp index 3dba1a542..8218e9244 100644 --- a/compiler/luci/service/src/Nodes/CircleSparseToDense.cpp +++ b/compiler/luci/service/src/Nodes/CircleSparseToDense.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSparseToDense *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSparseToDense *node) { auto *cloned = _graph->nodes()->create<luci::CircleSparseToDense>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleSplit.cpp b/compiler/luci/service/src/Nodes/CircleSplit.cpp index e68a24a1f..91aaa5547 100644 --- a/compiler/luci/service/src/Nodes/CircleSplit.cpp +++ b/compiler/luci/service/src/Nodes/CircleSplit.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSplit *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSplit *node) { auto *cloned = _graph->nodes()->create<luci::CircleSplit>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleSplitV.cpp b/compiler/luci/service/src/Nodes/CircleSplitV.cpp index de6c6cce6..18095b049 100644 --- a/compiler/luci/service/src/Nodes/CircleSplitV.cpp +++ b/compiler/luci/service/src/Nodes/CircleSplitV.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSplitV *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSplitV *node) { auto *cloned = _graph->nodes()->create<luci::CircleSplitV>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleSqrt.cpp b/compiler/luci/service/src/Nodes/CircleSqrt.cpp index a3e63684b..712ca457f 100644 --- a/compiler/luci/service/src/Nodes/CircleSqrt.cpp +++ b/compiler/luci/service/src/Nodes/CircleSqrt.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSqrt *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSqrt *) { return _graph->nodes()->create<luci::CircleSqrt>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSquare.cpp b/compiler/luci/service/src/Nodes/CircleSquare.cpp index 88bbed76c..a86f14a35 100644 --- a/compiler/luci/service/src/Nodes/CircleSquare.cpp +++ b/compiler/luci/service/src/Nodes/CircleSquare.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSquare *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSquare *) { return _graph->nodes()->create<luci::CircleSquare>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSquaredDifference.cpp b/compiler/luci/service/src/Nodes/CircleSquaredDifference.cpp index 6becdf1c9..c3dff271b 100644 --- a/compiler/luci/service/src/Nodes/CircleSquaredDifference.cpp +++ b/compiler/luci/service/src/Nodes/CircleSquaredDifference.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSquaredDifference *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSquaredDifference *) { return _graph->nodes()->create<luci::CircleSquaredDifference>(); } diff --git a/compiler/luci/service/src/Nodes/CircleSqueeze.cpp b/compiler/luci/service/src/Nodes/CircleSqueeze.cpp index 02ba5020c..384ee52c5 100644 --- a/compiler/luci/service/src/Nodes/CircleSqueeze.cpp +++ b/compiler/luci/service/src/Nodes/CircleSqueeze.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSqueeze *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSqueeze *node) { auto *cloned = _graph->nodes()->create<luci::CircleSqueeze>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleStridedSlice.cpp b/compiler/luci/service/src/Nodes/CircleStridedSlice.cpp index c4d199316..3298c92b5 100644 --- a/compiler/luci/service/src/Nodes/CircleStridedSlice.cpp +++ b/compiler/luci/service/src/Nodes/CircleStridedSlice.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleStridedSlice *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleStridedSlice *node) { auto *cloned = _graph->nodes()->create<luci::CircleStridedSlice>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleSub.cpp b/compiler/luci/service/src/Nodes/CircleSub.cpp index fb4bab19a..3084740cc 100644 --- a/compiler/luci/service/src/Nodes/CircleSub.cpp +++ b/compiler/luci/service/src/Nodes/CircleSub.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSub *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSub *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleSum.cpp b/compiler/luci/service/src/Nodes/CircleSum.cpp index 29e6ee5f1..1d5882afb 100644 --- a/compiler/luci/service/src/Nodes/CircleSum.cpp +++ b/compiler/luci/service/src/Nodes/CircleSum.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleSum *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleSum *node) { auto *cloned = _graph->nodes()->create<luci::CircleSum>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleTanh.cpp b/compiler/luci/service/src/Nodes/CircleTanh.cpp index 9cb35932f..56515314a 100644 --- a/compiler/luci/service/src/Nodes/CircleTanh.cpp +++ b/compiler/luci/service/src/Nodes/CircleTanh.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleTanh *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleTanh *) { return _graph->nodes()->create<luci::CircleTanh>(); } diff --git a/compiler/luci/service/src/Nodes/CircleTile.cpp b/compiler/luci/service/src/Nodes/CircleTile.cpp index 21c32e021..2cabd7818 100644 --- a/compiler/luci/service/src/Nodes/CircleTile.cpp +++ b/compiler/luci/service/src/Nodes/CircleTile.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleTile *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleTile *) { return _graph->nodes()->create<luci::CircleTile>(); } diff --git a/compiler/luci/service/src/Nodes/CircleTopKV2.cpp b/compiler/luci/service/src/Nodes/CircleTopKV2.cpp index e940c03dd..71dd5afbe 100644 --- a/compiler/luci/service/src/Nodes/CircleTopKV2.cpp +++ b/compiler/luci/service/src/Nodes/CircleTopKV2.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleTopKV2 *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleTopKV2 *) { return _graph->nodes()->create<luci::CircleTopKV2>(); } diff --git a/compiler/luci/service/src/Nodes/CircleTranspose.cpp b/compiler/luci/service/src/Nodes/CircleTranspose.cpp index 81db55269..bfbe116b4 100644 --- a/compiler/luci/service/src/Nodes/CircleTranspose.cpp +++ b/compiler/luci/service/src/Nodes/CircleTranspose.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleTranspose *) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleTranspose *) { return _graph->nodes()->create<luci::CircleTranspose>(); } diff --git a/compiler/luci/service/src/Nodes/CircleTransposeConv.cpp b/compiler/luci/service/src/Nodes/CircleTransposeConv.cpp index 1fe41bdb2..5d2fa482d 100644 --- a/compiler/luci/service/src/Nodes/CircleTransposeConv.cpp +++ b/compiler/luci/service/src/Nodes/CircleTransposeConv.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleTransposeConv *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleTransposeConv *node) { if (node->padding() == luci::Padding::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleUnidirectionalSequenceLSTM.cpp b/compiler/luci/service/src/Nodes/CircleUnidirectionalSequenceLSTM.cpp index 12205f3b0..7e03d9a1b 100644 --- a/compiler/luci/service/src/Nodes/CircleUnidirectionalSequenceLSTM.cpp +++ b/compiler/luci/service/src/Nodes/CircleUnidirectionalSequenceLSTM.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleUnidirectionalSequenceLSTM *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleUnidirectionalSequenceLSTM *node) { if (node->fusedActivationFunction() == luci::FusedActFunc::UNDEFINED) return nullptr; diff --git a/compiler/luci/service/src/Nodes/CircleUnique.cpp b/compiler/luci/service/src/Nodes/CircleUnique.cpp index bde2ea0dc..fb191e1c6 100644 --- a/compiler/luci/service/src/Nodes/CircleUnique.cpp +++ b/compiler/luci/service/src/Nodes/CircleUnique.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleUnique *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleUnique *node) { auto *cloned = _graph->nodes()->create<luci::CircleUnique>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleUnpack.cpp b/compiler/luci/service/src/Nodes/CircleUnpack.cpp index f9d61c426..4c90640c8 100644 --- a/compiler/luci/service/src/Nodes/CircleUnpack.cpp +++ b/compiler/luci/service/src/Nodes/CircleUnpack.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleUnpack *node) +luci::CircleNode *CloneNodeLet<CN::STUV>::visit(const luci::CircleUnpack *node) { auto *cloned = _graph->nodes()->create<luci::CircleUnpack>(); if (cloned != nullptr) diff --git a/compiler/luci/service/src/Nodes/CircleWhere.cpp b/compiler/luci/service/src/Nodes/CircleWhere.cpp index 73f4b64ac..7da48ed95 100644 --- a/compiler/luci/service/src/Nodes/CircleWhere.cpp +++ b/compiler/luci/service/src/Nodes/CircleWhere.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleWhere *) +luci::CircleNode *CloneNodeLet<CN::WXYZ>::visit(const luci::CircleWhere *) { return _graph->nodes()->create<luci::CircleWhere>(); } diff --git a/compiler/luci/service/src/Nodes/CircleWhile.cpp b/compiler/luci/service/src/Nodes/CircleWhile.cpp new file mode 100644 index 000000000..bdb6a4d16 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleWhile.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleCloneNode.h" + +namespace luci +{ + +luci::CircleNode *CloneNodeLet<CN::WXYZ>::visit(const luci::CircleWhile *node) +{ + auto ic = node->input_count(); + auto oc = node->output_count(); + + return _graph->nodes()->create<luci::CircleWhile>(ic, oc); +} + +} // namespace luci diff --git a/compiler/luci/service/src/Nodes/CircleWhile.test.cpp b/compiler/luci/service/src/Nodes/CircleWhile.test.cpp new file mode 100644 index 000000000..53b7c82f7 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleWhile.test.cpp @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Service/CircleNodeClone.h" + +#include <gtest/gtest.h> + +TEST(CloneNodeTest, clone_While) +{ + auto g = loco::make_graph(); + auto node_while = g->nodes()->create<luci::CircleWhile>(1, 1); + + auto gc = loco::make_graph(); + auto cloned = luci::clone_node(node_while, gc.get()); + ASSERT_NE(nullptr, cloned); + ASSERT_EQ(gc.get(), cloned->graph()); + + auto cloned_while = dynamic_cast<luci::CircleWhile *>(cloned); + ASSERT_NE(nullptr, cloned_while); + ASSERT_EQ(-1, cloned_while->cond_branch()); + ASSERT_EQ(-1, cloned_while->body_branch()); + ASSERT_EQ(nullptr, cloned_while->cond_graph()); + ASSERT_EQ(nullptr, cloned_while->body_graph()); +} diff --git a/compiler/luci/service/src/Nodes/CircleWhileOut.cpp b/compiler/luci/service/src/Nodes/CircleWhileOut.cpp new file mode 100644 index 000000000..52075a1b8 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleWhileOut.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleCloneNode.h" + +namespace luci +{ + +luci::CircleNode *CloneNode::visit(const luci::CircleWhileOut *node) +{ + auto *cloned = _graph->nodes()->create<luci::CircleWhileOut>(); + if (cloned != nullptr) + cloned->index(node->index()); + return cloned; +} + +} // namespace luci diff --git a/compiler/luci/service/src/Nodes/CircleWhileOut.test.cpp b/compiler/luci/service/src/Nodes/CircleWhileOut.test.cpp new file mode 100644 index 000000000..b16a99c51 --- /dev/null +++ b/compiler/luci/service/src/Nodes/CircleWhileOut.test.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Service/CircleNodeClone.h" + +#include <gtest/gtest.h> + +TEST(CloneNodeTest, clone_WhileOut) +{ + auto g = loco::make_graph(); + auto node_iout = g->nodes()->create<luci::CircleWhileOut>(); + node_iout->index(1); + + auto gc = loco::make_graph(); + auto cloned = luci::clone_node(node_iout, gc.get()); + ASSERT_NE(nullptr, cloned); + ASSERT_EQ(gc.get(), cloned->graph()); + + auto cloned_iout = dynamic_cast<luci::CircleWhileOut *>(cloned); + ASSERT_NE(nullptr, cloned_iout); + ASSERT_EQ(node_iout->index(), cloned_iout->index()); +} diff --git a/compiler/luci/service/src/Nodes/CircleZerosLike.cpp b/compiler/luci/service/src/Nodes/CircleZerosLike.cpp index 2ee455857..29db09eaf 100644 --- a/compiler/luci/service/src/Nodes/CircleZerosLike.cpp +++ b/compiler/luci/service/src/Nodes/CircleZerosLike.cpp @@ -19,7 +19,7 @@ namespace luci { -luci::CircleNode *CloneNode::visit(const luci::CircleZerosLike *) +luci::CircleNode *CloneNodeLet<CN::WXYZ>::visit(const luci::CircleZerosLike *) { return _graph->nodes()->create<luci::CircleZerosLike>(); } diff --git a/compiler/luci/service/src/Validate.cpp b/compiler/luci/service/src/Validate.cpp index 7ed14c356..fbd86dfa4 100644 --- a/compiler/luci/service/src/Validate.cpp +++ b/compiler/luci/service/src/Validate.cpp @@ -17,13 +17,16 @@ #include "luci/Service/Validate.h" #include <luci/IR/Nodes/CircleOutput.h> +#include <luci/IR/CircleNodeVisitor.h> #include <luci/Log.h> +#include <luci/LogHelper.h> #include <loco/IR/NodeShape.h> #include <cassert> #include <unordered_map> #include <vector> +#include <iostream> namespace { @@ -96,7 +99,7 @@ bool validate_shape_dtype(loco::Graph *g) assert(circle_output->from() != nullptr); auto circle_node = loco::must_cast<luci::CircleNode *>(circle_output->from()); - // Shape and dtype validation for CiecleOutputExclude is not needed + // Shape and dtype validation for CircleOutputExclude is not needed if (dynamic_cast<luci::CircleOutputExclude *>(circle_node)) continue; @@ -145,6 +148,30 @@ bool validate_shape_dtype(loco::Graph *g) return true; } +class VirtualNodeDetector final : public luci::CircleNodeVisitor<bool> +{ +public: + VirtualNodeDetector() {} + +public: + bool visit(const luci::CircleBidirectionalSequenceLSTMOut *) final { return true; } + bool visit(const luci::CircleCustomOut *) final { return true; } + bool visit(const luci::CircleIfOut *) final { return true; } + bool visit(const luci::CircleNonMaxSuppressionV4Out *) final { return true; } + bool visit(const luci::CircleNonMaxSuppressionV5Out *) final { return true; } + bool visit(const luci::CircleSplitOut *) final { return true; } + bool visit(const luci::CircleSplitVOut *) final { return true; } + bool visit(const luci::CircleTopKV2Out *) final { return true; } + bool visit(const luci::CircleUnpackOut *) final { return true; } + bool visit(const luci::CircleUniqueOut *) final { return true; } + bool visit(const luci::CircleWhileOut *) final { return true; } + bool visit(const luci::CircleOutputDummy *) final { return true; } + bool visit(const luci::CircleOutputExclude *) final { return true; } + + // Return false by default + bool visit(const luci::CircleNode *) final { return false; } +}; + } // namespace namespace luci @@ -169,6 +196,11 @@ bool validate_name(loco::Graph *g) for (uint32_t n = 0; n < nodes->size(); ++n) { auto node = loco::must_cast<luci::CircleNode *>(nodes->at(n)); + // skip virtual nodes + VirtualNodeDetector d; + if (node->accept(&d)) + continue; + auto name = node->name(); if (name.empty()) return false; @@ -179,6 +211,8 @@ bool validate_name(loco::Graph *g) bool validate_unique_name(luci::Module *m) { + LOGGER(l); + std::unordered_map<std::string, bool> names_col; for (size_t g = 0; g < m->size(); ++g) @@ -192,14 +226,58 @@ bool validate_unique_name(luci::Module *m) auto output = dynamic_cast<luci::CircleOutput *>(node); if (output != nullptr) continue; + // skip virtual nodes + VirtualNodeDetector d; + if (node->accept(&d)) + continue; auto name = node->name(); + INFO(l) << "Node: " << name << ", " << (uint32_t)(node->opcode()) << std::endl; auto it = names_col.find(name); if (it != names_col.end()) + { + INFO(l) << "validate_unique_name: found duplicate " << name << ", " << graph->name() + << std::endl; return false; + } names_col[name] = true; } + // There can exist same tensor name between different subgraphs. + names_col.clear(); + } + + return true; +} + +bool validate(luci::Module *module) +{ + LOGGER(l); + + INFO(l) << "--- validate Module -----------------------------------"; + + for (size_t g = 0; g < module->size(); ++g) + { + auto graph = module->graph(g); + + INFO(l) << luci::fmt(graph) << std::endl; + + if (!validate(graph)) + { + std::cerr << "ERROR: Invalid circle model" << std::endl; + return false; + } + if (!validate_name(graph)) + { + std::cerr << "ERROR: circle model has empty name" << std::endl; + return false; + } + } + + if (!validate_unique_name(module)) + { + std::cerr << "ERROR: circle model has duplicate names" << std::endl; + return false; } return true; diff --git a/compiler/luci/testhelper/include/luci/test/TestIOGraph.h b/compiler/luci/testhelper/include/luci/test/TestIOGraph.h index ae04f4dbc..68e834821 100644 --- a/compiler/luci/testhelper/include/luci/test/TestIOGraph.h +++ b/compiler/luci/testhelper/include/luci/test/TestIOGraph.h @@ -156,6 +156,7 @@ public: public: luci::CircleOutput *output(int idx) { return _outputs[idx]; } + uint32_t num_outputs(void) { return N; } protected: std::array<loco::GraphOutput *, N> _graph_outputs; diff --git a/compiler/luci/tests/test.lst b/compiler/luci/tests/test.lst index a278fa256..28ddcf672 100644 --- a/compiler/luci/tests/test.lst +++ b/compiler/luci/tests/test.lst @@ -2,6 +2,8 @@ addread(Abs_000) addread(Add_000) addread(Add_001) addread(Add_U8_000) +addread(Add_STR_000) +addread(Add_STR_001) addread(AddN_000) addread(ArgMax_000) addread(ArgMax_001) @@ -115,6 +117,7 @@ addread(Pad_U8_000) addread(PadV2_000) addread(Pow_000) addread(PRelu_000) +addread(Quantize_000) addread(Range_000) addread(Rank_000) addread(ReduceAny_000) @@ -223,6 +226,8 @@ addwrite(Abs_000) addwrite(Add_000) addwrite(Add_001) addwrite(Add_U8_000) +addwrite(Add_STR_000) +addwrite(Add_STR_001) addwrite(AddN_000) addwrite(ArgMax_000) addwrite(ArgMax_001) @@ -335,6 +340,7 @@ addwrite(Pad_000) addwrite(PadV2_000) addwrite(Pow_000) addwrite(PRelu_000) +addwrite(Quantize_000) addwrite(Range_000) addwrite(Rank_000) addwrite(ReduceAny_000) diff --git a/compiler/one-cmds/CMakeLists.txt b/compiler/one-cmds/CMakeLists.txt index 7d7a28fe1..fc89f4da5 100644 --- a/compiler/one-cmds/CMakeLists.txt +++ b/compiler/one-cmds/CMakeLists.txt @@ -8,8 +8,10 @@ set(ONE_COMMAND_FILES one-optimize one-quantize one-pack + one-profile one-codegen one-prepare-venv + onecc ) foreach(ONE_COMMAND IN ITEMS ${ONE_COMMAND_FILES}) @@ -37,7 +39,9 @@ endforeach(ONE_COMMAND) set(ONE_UTILITY_FILES one-build.template.cfg + onecc.template.cfg utils.py + conv_mixin_1.8.0.patch ) foreach(ONE_UTILITY IN ITEMS ${ONE_UTILITY_FILES}) diff --git a/compiler/one-cmds/conv_mixin_1.8.0.patch b/compiler/one-cmds/conv_mixin_1.8.0.patch new file mode 100644 index 000000000..96a0f41cf --- /dev/null +++ b/compiler/one-cmds/conv_mixin_1.8.0.patch @@ -0,0 +1,11 @@ +--- a/onnx_tf/handlers/backend/conv_mixin.py ++++ b/onnx_tf/handlers/backend/conv_mixin.py +@@ -98,7 +98,7 @@ + depthwise = (x_rank == 4 and len(weight_shape) == 4 and group != 1 and + not transpose and not (None in weight_shape)) + if depthwise and isinstance(x_shape, np.ndarray): +- depthwise = group == x_shape[1] ++ depthwise = bool(group == x_shape[1]) + + if depthwise is True: + # Depthwise convolution. diff --git a/compiler/one-cmds/dummy-driver/CMakeLists.txt b/compiler/one-cmds/dummy-driver/CMakeLists.txt index 8e122ccf3..690a60776 100644 --- a/compiler/one-cmds/dummy-driver/CMakeLists.txt +++ b/compiler/one-cmds/dummy-driver/CMakeLists.txt @@ -1,12 +1,18 @@ # dummy driver for interface test set(DUMMY_DRIVER_SRC src/dummy-compile.cpp) set(HELP_DRIVER_SRC src/help-compile.cpp) +set(DUMMY_PROFILE_SRC src/dummy-profile.cpp) +set(HELP_PROFILE_SRC src/help-profile.cpp) add_executable(dummy-compile ${DUMMY_DRIVER_SRC}) add_executable(help-compile ${HELP_DRIVER_SRC}) +add_executable(dummy-profile ${DUMMY_PROFILE_SRC}) +add_executable(help-profile ${HELP_PROFILE_SRC}) set(DUMMY_DRIVER "${CMAKE_CURRENT_BINARY_DIR}/dummy-compile") set(HELP_DRIVER "${CMAKE_CURRENT_BINARY_DIR}/help-compile") +set(DUMMY_PROFILE "${CMAKE_CURRENT_BINARY_DIR}/dummy-profile") +set(HELP_PROFILE "${CMAKE_CURRENT_BINARY_DIR}/help-profile") install(FILES ${DUMMY_DRIVER} PERMISSIONS OWNER_WRITE OWNER_READ OWNER_EXECUTE @@ -19,3 +25,15 @@ install(FILES ${HELP_DRIVER} GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE DESTINATION test) + +install(FILES ${DUMMY_PROFILE} + PERMISSIONS OWNER_WRITE OWNER_READ OWNER_EXECUTE + GROUP_READ GROUP_EXECUTE + WORLD_READ WORLD_EXECUTE + DESTINATION test) + +install(FILES ${HELP_PROFILE} + PERMISSIONS OWNER_WRITE OWNER_READ OWNER_EXECUTE + GROUP_READ GROUP_EXECUTE + WORLD_READ WORLD_EXECUTE + DESTINATION test) diff --git a/compiler/one-cmds/dummy-driver/src/dummy-profile.cpp b/compiler/one-cmds/dummy-driver/src/dummy-profile.cpp new file mode 100644 index 000000000..b997e0514 --- /dev/null +++ b/compiler/one-cmds/dummy-driver/src/dummy-profile.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * dummy-profile only tests its interface rather than its functionality. + * + * ./dummy-profile ${INPUT_NAME} + * dummy-profile dummy output!!! + */ + +#include <iostream> +#include <fstream> +#include <string> + +int main(int argc, char **argv) +{ + if (argc != 2) + return EXIT_FAILURE; + + std::cout << "dummy-profile dummy output!!!" << std::endl; + + return EXIT_SUCCESS; +} diff --git a/compiler/one-cmds/dummy-driver/src/help-profile.cpp b/compiler/one-cmds/dummy-driver/src/help-profile.cpp new file mode 100644 index 000000000..51425360c --- /dev/null +++ b/compiler/one-cmds/dummy-driver/src/help-profile.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * help-profile prints dummy help message. + * + * $ ./help-profile -h + * HELP MESSAGE!! + */ + +#include <iostream> +#include <fstream> +#include <string> + +int main(int argc, char **argv) +{ + if (argc != 2) + return EXIT_FAILURE; + + std::string opt_h{"-h"}; + std::string argv_1{argv[1]}; + + if (opt_h != argv_1) + return EXIT_FAILURE; + + std::cout << "HELP MESSAGE!!" << std::endl; + + return EXIT_SUCCESS; +} diff --git a/compiler/one-cmds/how-to-use-one-commands.txt b/compiler/one-cmds/how-to-use-one-commands.txt index d034fa9a2..f86709489 100644 --- a/compiler/one-cmds/how-to-use-one-commands.txt +++ b/compiler/one-cmds/how-to-use-one-commands.txt @@ -126,7 +126,7 @@ one-import-tf This will convert TensorFlow model (.pb) file to our circle model. You can also directly call this command. one-import-tf invokes tf2tfliteV2.py script that will internally use TensorFlow lite converter and then invoke tflite2circle -converter to convert tflite model to circle model. +converter to convert tflite model to circle model. As tf2tfliteV2.py runs TensorFlow lite converter, you need to have TensorFlow installed in your system. We recommand to use 2.3.0 for now. @@ -166,11 +166,15 @@ Current transformation options are execution. - fuse_preactivation_batchnorm: This fuses batch normalization operators of pre-activations to Conv operators. - fuse_activation_function: This fuses Activation function to a preceding operator. +- fuse_mean_with_mean: This fuses two consecutive ReduceMean operations into one. +- fuse_transpose_with_mean: This fuses ReduceMean with a preceding Transpose under certain conditions. - make_batchnorm_gamma_positive: This makes negative gamma of batch normalization into a small positive value (1e-10). Note that this pass can change the execution result of the model. So, use it only when the impact is known to be acceptable. - mute_warnings : This will turn off warning messages. - generate_profile_data : This will turn on profiling data generation. +- remove_fakequant : This will remove all fakequant operators. +- remove_quantdequant : This will remove all Quantize-Dequantize sequence. - remove_redundant_reshape : This fuses or removes redundant reshape operators. - remove_redundant_transpose : This fuses or removes redundant transpose operators. - remove_unnecessary_reshape : This removes unnecessary reshape operators. @@ -183,12 +187,16 @@ Current transformation options are normal BatchMatMul operator - resolve_customop_matmul: This will convert Custom(MatMul) to normal MatMul operator +- resolve_customop_max_pool_with_argmax: This will convert Custom(MaxPoolWithArgmax) + to net of builtin operators. - shuffle_weight_to_16x1float32 : This will convert weight format of FullyConnected to SHUFFLED16x1FLOAT32. Note that it only converts weights whose row is a multiple of 16. - substitute_pack_to_reshape : This will convert single input Pack to Reshape. - substitute_squeeze_to_reshape : This will convert certain condition Squeeze to Reshape. +- substitute_strided_slice_to_reshape : This will convert certain condition StridedSlice to Reshape. - substitute_transpose_to_reshape : This will convert certain condition Transpose to Reshape. - transform_min_max_to_relu6: This will transform Minimum-Maximum pattern to Relu6 operator. +- transform_min_relu_to_relu6: This will transform Minimum(6)-Relu pattern to Relu6 operator. There are options to enable multiple options at once for convenience. - O1: fuse_bcq, fuse_instnorm, resolve_customop_add, resolve_customop_batchmatmul, diff --git a/compiler/one-cmds/one-build b/compiler/one-cmds/one-build index 34ce42fca..79f32302a 100644 --- a/compiler/one-cmds/one-build +++ b/compiler/one-cmds/one-build @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -27,6 +27,10 @@ import sys import utils as _utils +# TODO Find better way to suppress trackback on error +# This suppression is applied only to `one-build` +sys.tracebacklimit = 0 + def _get_parser(): parser = argparse.ArgumentParser( @@ -68,6 +72,7 @@ def _get_driver_name(driver_name): def _parse_cfg(args): config = configparser.ConfigParser() + config.optionxform = str parsed = config.read(os.path.expanduser(getattr(args, 'config'))) if not parsed: raise FileNotFoundError('Not found given configuration file') @@ -127,13 +132,8 @@ def main(): for section in section_to_run: driver_path = os.path.join(dir_path, _get_driver_name(section)) cmd = [driver_path, '--config', getattr(args, 'config'), '--section', section] - with subprocess.Popen( - cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(cmd) if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-build.template.cfg b/compiler/one-cmds/one-build.template.cfg index 52d860813..e147896ef 100644 --- a/compiler/one-cmds/one-build.template.cfg +++ b/compiler/one-cmds/one-build.template.cfg @@ -15,6 +15,7 @@ input_arrays=input input_shapes=1,299,299,3 output_arrays=InceptionV3/Predictions/Reshape_1 converter_version=v1 +model_format=graph_def [one-optimize] input_path=inception_v3.circle diff --git a/compiler/one-cmds/one-codegen b/compiler/one-cmds/one-codegen index ebd8ad7e5..a496a54ec 100644 --- a/compiler/one-cmds/one-codegen +++ b/compiler/one-cmds/one-codegen @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -21,7 +21,9 @@ import argparse import copy +import glob import itertools +import ntpath import os import subprocess import sys @@ -29,19 +31,50 @@ import tempfile import utils as _utils +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + def _get_backends_list(): + """ + [one hierarchy] + one + ├── backends + ├── bin + ├── doc + ├── include + ├── lib + └── test + + The list where `one-codegen` finds its backends + - `bin` folder where `one-codegen` exists + - `backends` folder + + NOTE If there are backends of the same name in different places, + the closer to the top in the list, the higher the priority. + """ dir_path = os.path.dirname(os.path.realpath(__file__)) - files = [f for f in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, f))] + backend_set = set() + + # bin folder + files = [f for f in glob.glob(dir_path + '/*-compile')] + # backends folder + files += [ + f for f in glob.glob(dir_path + '/../backends/**/*-compile', recursive=True) + ] + # TODO find backends in `$PATH` + backends_list = [] for cand in files: - if cand.endswith('-compile'): - # 8 : length of '-compile' - backends_list.append(cand[:-8]) + base = ntpath.basename(cand) + if not base in backend_set and os.path.isfile(cand) and os.access(cand, os.X_OK): + backend_set.add(base) + backends_list.append(cand) + return backends_list -def _get_parser(): +def _get_parser(backends_list): codegen_usage = 'one-codegen [-h] [-v] [-C CONFIG] [-b BACKEND] [--] [COMMANDS FOR BACKEND]' parser = argparse.ArgumentParser( description='command line tool for code generation', usage=codegen_usage) @@ -49,13 +82,13 @@ def _get_parser(): _utils._add_default_arg(parser) # get backend list in the directory - backends_list = _get_backends_list() - if not backends_list: - backends_list_message = '(There is no available backend drivers)' + backends_name = [ntpath.basename(f) for f in backends_list] + if not backends_name: + backends_name_message = '(There is no available backend drivers)' else: - backends_list_message = '(available backend drivers: ' + '.'.join( - backends_list) + ')' - backend_help_message = 'backend name to use ' + backends_list_message + backends_name_message = '(available backend drivers: ' + ', '.join( + backends_name) + ')' + backend_help_message = 'backend name to use ' + backends_name_message parser.add_argument('-b', '--backend', type=str, help=backend_help_message) return parser @@ -98,8 +131,11 @@ def _parse_arg(parser): def main(): + # get backend list + backends_list = _get_backends_list() + # parse arguments - parser = _get_parser() + parser = _get_parser(backends_list) args, backend_args, unknown_args = _parse_arg(parser) # parse configuration file @@ -109,21 +145,20 @@ def main(): _verify_arg(parser, args) # make a command to run given backend driver - dir_path = os.path.dirname(os.path.realpath(__file__)) - codegen_path = os.path.join(dir_path, getattr(args, 'backend') + '-compile') + codegen_path = None + backend_base = getattr(args, 'backend') + '-compile' + for cand in backends_list: + if ntpath.basename(cand) == backend_base: + codegen_path = cand + if not codegen_path: + raise FileNotFoundError(backend_base + ' not found') codegen_cmd = [codegen_path] + backend_args + unknown_args if _utils._is_valid_attr(args, 'command'): codegen_cmd += getattr(args, 'command').split() # run backend driver - with subprocess.Popen( - codegen_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(codegen_cmd, err_prefix=backend_base) if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-import b/compiler/one-cmds/one-import index e8ce8817c..efd60bb6b 100644 --- a/compiler/one-cmds/one-import +++ b/compiler/one-cmds/one-import @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -37,7 +37,8 @@ def _get_parser(): # driver parser.add_argument( - 'driver', type=str, help='driver name to run (supported: tf, tflite, bcq)') + 'driver', type=str, help='driver name to run (supported: tf, tflite,' \ + ' bcq, onnx)') # version dir_path = os.path.dirname(os.path.realpath(__file__)) @@ -68,7 +69,8 @@ def _get_driver_name(driver_name): return { 'bcq': 'one-import-bcq', 'tf': 'one-import-tf', - 'tflite': 'one-import-tflite' + 'tflite': 'one-import-tflite', + 'onnx': 'one-import-onnx', }[driver_name] @@ -98,4 +100,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-import-bcq b/compiler/one-cmds/one-import-bcq index 50f587946..9aef6270e 100644 --- a/compiler/one-cmds/one-import-bcq +++ b/compiler/one-cmds/one-import-bcq @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -28,6 +28,9 @@ import tempfile import utils as _utils import generate_bcq_output_arrays as _bcq_info_gen +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + def _get_parser(): parser = argparse.ArgumentParser( @@ -140,16 +143,7 @@ def _convert(args): f.write((' '.join(generate_bcq_metadata_cmd) + '\n').encode()) # generate BCQ information metadata - with subprocess.Popen( - generate_bcq_metadata_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(generate_bcq_metadata_cmd, logfile=f) # get output_arrays with BCQ bcq_output_arrays = _bcq_info_gen.get_bcq_output_arrays( @@ -173,14 +167,7 @@ def _convert(args): f.write((' '.join(tf2tfliteV2_cmd) + '\n').encode()) # convert tf to tflite - with subprocess.Popen( - tf2tfliteV2_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(tf2tfliteV2_cmd, logfile=f) # make a command to convert from tflite to circle tflite2circle_path = os.path.join(dir_path, 'tflite2circle') @@ -191,16 +178,7 @@ def _convert(args): f.write((' '.join(tflite2circle_cmd) + '\n').encode()) # convert tflite to circle - with subprocess.Popen( - tflite2circle_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(tflite2circle_cmd, logfile=f) def main(): @@ -219,4 +197,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-import-onnx b/compiler/one-cmds/one-import-onnx index 1bcf2e838..1c0c5498e 100644 --- a/compiler/one-cmds/one-import-onnx +++ b/compiler/one-cmds/one-import-onnx @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -29,6 +29,9 @@ import onnx_tf import utils as _utils +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + def _get_parser(): parser = argparse.ArgumentParser( @@ -61,6 +64,12 @@ def _get_parser(): tf2tfliteV2_group.add_argument('--model_format', default='saved_model') tf2tfliteV2_group.add_argument('--converter_version', default='v2') + # save intermediate file(s) + parser.add_argument( + '--save_intermediate', + action='store_true', + help='Save intermediate files to output folder') + return parser @@ -85,12 +94,30 @@ def _parse_arg(parser): return args +def _apply_verbosity(verbosity): + # NOTE + # TF_CPP_MIN_LOG_LEVEL + # 0 : INFO + WARNING + ERROR + FATAL + # 1 : WARNING + ERROR + FATAL + # 2 : ERROR + FATAL + # 3 : FATAL + if verbosity: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' + else: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + + def _convert(args): + _apply_verbosity(args.verbose) + # get file path to log dir_path = os.path.dirname(os.path.realpath(__file__)) logfile_path = os.path.realpath(args.output_path) + '.log' with open(logfile_path, 'wb') as f, tempfile.TemporaryDirectory() as tmpdir: + # save intermediate + if _utils._is_valid_attr(args, 'save_intermediate'): + tmpdir = os.path.dirname(logfile_path) # convert onnx to tf saved model onnx_model = onnx.load(getattr(args, 'input_path')) tf_savedmodel = onnx_tf.backend.prepare(onnx_model) @@ -112,14 +139,7 @@ def _convert(args): f.write((' '.join(tf2tfliteV2_cmd) + '\n').encode()) # convert tf to tflite - with subprocess.Popen( - tf2tfliteV2_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(tf2tfliteV2_cmd, logfile=f) # make a command to convert from tflite to circle tflite2circle_path = os.path.join(dir_path, 'tflite2circle') @@ -130,16 +150,7 @@ def _convert(args): f.write((' '.join(tflite2circle_cmd) + '\n').encode()) # convert tflite to circle - with subprocess.Popen( - tflite2circle_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(tflite2circle_cmd, err_prefix="tflite2circle", logfile=f) def main(): @@ -158,4 +169,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-import-tf b/compiler/one-cmds/one-import-tf index 3a7c69af3..e2294caa6 100644 --- a/compiler/one-cmds/one-import-tf +++ b/compiler/one-cmds/one-import-tf @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -102,6 +102,12 @@ def _get_parser(): type=str, help='names of the output arrays, comma-separated') + # save intermediate file(s) + parser.add_argument( + '--save_intermediate', + action='store_true', + help='Save intermediate files to output folder') + return parser @@ -132,6 +138,9 @@ def _convert(args): logfile_path = os.path.realpath(args.output_path) + '.log' with open(logfile_path, 'wb') as f, tempfile.TemporaryDirectory() as tmpdir: + # save intermediate + if _utils._is_valid_attr(args, 'save_intermediate'): + tmpdir = os.path.dirname(logfile_path) # make a command to convert from tf to tflite tf2tfliteV2_path = os.path.join(dir_path, 'tf2tfliteV2.py') tf2tfliteV2_output_path = os.path.join( @@ -144,14 +153,7 @@ def _convert(args): f.write((' '.join(tf2tfliteV2_cmd) + '\n').encode()) # convert tf to tflite - with subprocess.Popen( - tf2tfliteV2_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(tf2tfliteV2_cmd, logfile=f) # make a command to convert from tflite to circle tflite2circle_path = os.path.join(dir_path, 'tflite2circle') @@ -162,16 +164,7 @@ def _convert(args): f.write((' '.join(tflite2circle_cmd) + '\n').encode()) # convert tflite to circle - with subprocess.Popen( - tflite2circle_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(tflite2circle_cmd, err_prefix="tflite2circle", logfile=f) def main(): @@ -190,4 +183,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-import-tflite b/compiler/one-cmds/one-import-tflite index 9b80f304b..7eee0484a 100644 --- a/compiler/one-cmds/one-import-tflite +++ b/compiler/one-cmds/one-import-tflite @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -23,10 +23,12 @@ import argparse import os import subprocess import sys -import tempfile import utils as _utils +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + def _get_parser(): parser = argparse.ArgumentParser( @@ -82,16 +84,7 @@ def _convert(args): f.write((' '.join(tflite2circle_cmd) + '\n').encode()) # convert tflite to circle - with subprocess.Popen( - tflite2circle_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(tflite2circle_cmd, err_prefix="tflite2circle", logfile=f) def main(): @@ -110,4 +103,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-optimize b/compiler/one-cmds/one-optimize index 8ce79d432..6ce973c37 100644 --- a/compiler/one-cmds/one-optimize +++ b/compiler/one-cmds/one-optimize @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -23,10 +23,12 @@ import argparse import os import subprocess import sys -import tempfile import utils as _utils +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + def _get_parser(): parser = argparse.ArgumentParser( @@ -43,6 +45,11 @@ def _get_parser(): action='store_true', help='generate profiling data') + utility_group.add_argument( + '--change_outputs', + type=str, + help='Experimental: Change first subgraph output nodes to CSV names') + ## circle2circle arguments circle2circle_group = parser.add_argument_group('arguments for optimization') @@ -93,19 +100,17 @@ def _optimize(args): getattr(args, 'input_path'), getattr(args, 'output_path')) + # verbose + if _utils._is_valid_attr(args, 'verbose'): + circle2circle_cmd.append('--verbose') + if _utils._is_valid_attr(args, 'change_outputs'): + circle2circle_cmd.append('--change_outputs') + circle2circle_cmd.append(getattr(args, 'change_outputs')) + f.write((' '.join(circle2circle_cmd) + '\n').encode()) # optimize - with subprocess.Popen( - circle2circle_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(circle2circle_cmd, err_prefix="circle2circle", logfile=f) def main(): @@ -124,4 +129,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-pack b/compiler/one-cmds/one-pack index 99df4a829..133207de0 100644 --- a/compiler/one-cmds/one-pack +++ b/compiler/one-cmds/one-pack @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -27,6 +27,9 @@ import tempfile import utils as _utils +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + def _get_parser(): parser = argparse.ArgumentParser( @@ -91,14 +94,7 @@ def _pack(args): f.write((' '.join(model2nnpkg_cmd) + '\n').encode()) # convert tflite to circle - with subprocess.Popen( - model2nnpkg_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(model2nnpkg_cmd, err_prefix="model2nnpkg.sh", logfile=f) def main(): @@ -117,4 +113,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/one-prepare-venv b/compiler/one-cmds/one-prepare-venv index bb3616574..fbc3a75de 100644 --- a/compiler/one-cmds/one-prepare-venv +++ b/compiler/one-cmds/one-prepare-venv @@ -19,53 +19,73 @@ set -e DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" VENV_ACTIVATE=${DRIVER_PATH}/venv/bin/activate - -function error_no_ensurepip () -{ - echo "ERROR: python3 'ensurepip' module is not found." - echo " On ubuntu, try following command:" - echo - echo " apt install python$(python3 --version | awk '{print $2}' | awk -F. '{print $1"."$2}')-venv" - echo - echo " You may need root privilege for this." - exit 1 -} +# NOTE please use venv's python instead of python after `source activation`. +# This script is called by debian maintainer script, i.e. `postinst`. +# Since debian maintainer script is called with sudo, `source activation` is ignored. +VENV_PYTHON=${DRIVER_PATH}/venv/bin/python if [ ! -f ${VENV_ACTIVATE} ]; then - # Install prerequisites - python3 -m ensurepip --version > /dev/null 2>&1 || error_no_ensurepip - python3 -m pip install --user -U virtualenv # Create python virtual enviornment python3 -m venv "${DRIVER_PATH}/venv" fi +# NOTE version +# - https://github.com/onnx/onnx/blob/master/docs/Versioning.md +# - https://github.com/onnx/onnx-tensorflow/blob/master/Versioning.md + +VER_TENSORFLOW=2.3.0 +VER_ONNX=1.8.0 +VER_ONNX_TF=1.8.0 + # Install tensorflow -source "${VENV_ACTIVATE}" + +PIP_TRUSTED_HOST="--trusted-host pypi.org " +PIP_TRUSTED_HOST+="--trusted-host files.pythonhost.org " +PIP_TRUSTED_HOST+="--trusted-host download.pytorch.org " + +PIP_TIMEOUT="--default-timeout=1000 " + +PIP_OPTIONS="${PIP_TIMEOUT} ${PIP_TRUSTED_HOST}" + +# NOTE $ONE_PREPVENV_PIP_OPTION is to provide additional PIP options +# such as ceritificate file behind firewall +# ex) ONE_PREPVENV_PIP_OPTION="--cert SomePrivateCetificate.crt" ./one-prepare-venv +if [[ ! -z "$ONE_PREPVENV_PIP_OPTION" ]]; then + PIP_OPTIONS+=" ${ONE_PREPVENV_PIP_OPTION} " +fi # TODO remove version number of 'pip==20.2.1 setuptools==49.3.0' # NOTE adding version is for temporary hotfix of setuptools 50.x.y version -python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ - install -U pip==20.2.1 setuptools==49.3.0 -python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ - install tensorflow-cpu==2.3.0 -python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ - install Pillow==6.2.2 +${VENV_PYTHON} -m pip ${PIP_OPTIONS} install -U pip==20.2.1 setuptools==49.3.0 +${VENV_PYTHON} -m pip ${PIP_OPTIONS} install tensorflow-cpu==${VER_TENSORFLOW} +${VENV_PYTHON} -m pip ${PIP_OPTIONS} install Pillow==6.2.2 # Install PyTorch and ONNX related -python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ - --trusted-host download.pytorch.org \ - install torch==1.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html +${VENV_PYTHON} -m pip ${PIP_OPTIONS} install torch==1.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html -# NOTE Latest onnx 1.8.1 has compatibility issue with onnx-tf 1.7.0 -# MUST install with onnx==1.8.0 # Provide install of custom onnx-tf if [ -n "${EXT_ONNX_TF_WHL}" ]; then - python -m pip --default-timeout=1000 install onnx==1.8.0 ${EXT_ONNX_TF_WHL} + ${VENV_PYTHON} -m pip ${PIP_OPTIONS} install onnx==${VER_ONNX} ${EXT_ONNX_TF_WHL} else - python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ - install onnx==1.8.0 onnx-tf==1.7.0 + ${VENV_PYTHON} -m pip ${PIP_OPTIONS} install onnx==${VER_ONNX} onnx-tf==${VER_ONNX_TF} fi -# Create python symoblic link -rm -f ${DRIVER_PATH}/python -ln -s venv/bin/python ${DRIVER_PATH}/python +# TODO remove this patch after onnx-tf next release +# apply patch for DWConv conversion bug: https://github.com/onnx/onnx-tensorflow/pull/905 +if [[ -z "${EXT_ONNX_TF_WHL}" ]]; then + PY_SITE_PACKAGES=$(${VENV_PYTHON} -c 'import sysconfig; print(sysconfig.get_paths()["purelib"])') + if [[ -d ${PY_SITE_PACKAGES} ]]; then + pushd ${PY_SITE_PACKAGES} > /dev/null + PATCH_TARGET_FILE=onnx_tf/handlers/backend/conv_mixin.py + if [[ -f "${PATCH_TARGET_FILE}" ]]; then + # if patch is already applied, error code is 1 + # catch error code and check if this is the case + set +e + patch -t -N -p1 < ${DRIVER_PATH}/conv_mixin_1.8.0.patch + ret_code=$? + [[ $ret_code -gt 1 ]] && exit $ret_code + set -e + fi + popd > /dev/null + fi +fi diff --git a/compiler/one-cmds/one-profile b/compiler/one-cmds/one-profile new file mode 100644 index 000000000..798cc756c --- /dev/null +++ b/compiler/one-cmds/one-profile @@ -0,0 +1,171 @@ +#!/usr/bin/env bash +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' +''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' +''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' +''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' +''''exit 255 # ''' + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import copy +import glob +import itertools +import ntpath +import os +import subprocess +import sys +import tempfile + +import utils as _utils + +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + + +def _get_backends_list(): + """ + [one hierarchy] + one + ├── backends + ├── bin + ├── doc + ├── include + ├── lib + └── test + + The list where `one-profile` finds its backends + - `bin` folder where `one-profile` exists + - `backends` folder + + NOTE If there are backends of the same name in different places, + the closer to the top in the list, the higher the priority. + """ + dir_path = os.path.dirname(os.path.realpath(__file__)) + backend_set = set() + + # bin folder + files = [f for f in glob.glob(dir_path + '/*-profile')] + # backends folder + files += [ + f for f in glob.glob(dir_path + '/../backends/**/*-profile', recursive=True) + ] + # TODO find backends in `$PATH` + + backends_list = [] + for cand in files: + base = ntpath.basename(cand) + if not base in backend_set and os.path.isfile(cand) and os.access(cand, os.X_OK): + backend_set.add(base) + backends_list.append(cand) + + return backends_list + + +def _get_parser(backends_list): + profile_usage = 'one-profile [-h] [-v] [-C CONFIG] [-b BACKEND] [--] [COMMANDS FOR BACKEND]' + parser = argparse.ArgumentParser( + description='command line tool for profiling backend model', usage=profile_usage) + + _utils._add_default_arg(parser) + + # get backend list in the directory + backends_name = [ntpath.basename(f) for f in backends_list] + if not backends_name: + backends_name_message = '(There is no available backend drivers)' + else: + backends_name_message = '(available backend drivers: ' + ', '.join( + backends_name) + ')' + backend_help_message = 'backend name to use ' + backends_name_message + parser.add_argument('-b', '--backend', type=str, help=backend_help_message) + + return parser + + +def _verify_arg(parser, args): + """verify given arguments""" + # check if required arguments is given + missing = [] + if not _utils._is_valid_attr(args, 'backend'): + missing.append('-b/--backend') + if len(missing): + parser.error('the following arguments are required: ' + ' '.join(missing)) + + +def _parse_arg(parser): + profile_args = [] + backend_args = [] + unknown_args = [] + argv = copy.deepcopy(sys.argv) + # delete file name + del argv[0] + # split by '--' + args = [list(y) for x, y in itertools.groupby(argv, lambda z: z == '--') if not x] + # one-profile has two interfaces + # 1. one-profile [-h] [-v] [-C CONFIG] [-b BACKEND] [COMMANDS FOR BACKEND] + if len(args) == 1: + profile_args = args[0] + profile_args, unknown_args = parser.parse_known_args(profile_args) + # 2. one-profile [-h] [-v] [-C CONFIG] [-b BACKEND] -- [COMMANDS FOR BACKEND] + if len(args) == 2: + profile_args = args[0] + backend_args = args[1] + profile_args = parser.parse_args(profile_args) + # print version + if len(args) and profile_args.version: + _utils._print_version_and_exit(__file__) + + return profile_args, backend_args, unknown_args + + +def main(): + # get backend list + backends_list = _get_backends_list() + + # parse arguments + parser = _get_parser(backends_list) + args, backend_args, unknown_args = _parse_arg(parser) + + # parse configuration file + _utils._parse_cfg(args, 'one-profile') + + # verify arguments + _verify_arg(parser, args) + + # make a command to run given backend driver + profile_path = None + backend_base = getattr(args, 'backend') + '-profile' + for cand in backends_list: + if ntpath.basename(cand) == backend_base: + profile_path = cand + if not profile_path: + raise FileNotFoundError(backend_base + ' not found') + profile_cmd = [profile_path] + backend_args + unknown_args + if _utils._is_valid_attr(args, 'command'): + profile_cmd += getattr(args, 'command').split() + + # run backend driver + with subprocess.Popen( + profile_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, + bufsize=1) as p: + for line in p.stdout: + sys.stdout.buffer.write(line) + sys.stdout.buffer.flush() + if p.returncode != 0: + sys.exit(p.returncode) + + +if __name__ == '__main__': + main() diff --git a/compiler/one-cmds/one-quantize b/compiler/one-cmds/one-quantize index 475f44a49..25ef17ab1 100644 --- a/compiler/one-cmds/one-quantize +++ b/compiler/one-cmds/one-quantize @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' @@ -27,6 +27,9 @@ import tempfile import utils as _utils +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + def _get_parser(): parser = argparse.ArgumentParser( @@ -45,6 +48,13 @@ def _get_parser(): 'full filepath of the input data file. if not specified, run with random input data.' ) parser.add_argument( + '-f', + '--input_data_format', + type=str, + help= + 'file format of input data. h5/hdf5 (default), list/filelist (a text file where a file path of input data is written in each line), or dir/directory (a directory where input data are saved)' + ) + parser.add_argument( '-o', '--output_path', type=str, help='full filepath of the output file') # argument for profiling @@ -129,6 +139,9 @@ def _quantize(args): ## make a command to quantize and dequantize the weights of the model circle_quantizer_cmd = [circle_quantizer_path] + # verbose + if _utils._is_valid_attr(args, 'verbose'): + circle_quantizer_cmd.append('--verbose') # quantize_dequantize_weights circle_quantizer_cmd.append('--quantize_dequantize_weights') if _utils._is_valid_attr(args, 'input_dtype'): @@ -151,19 +164,13 @@ def _quantize(args): f.write((' '.join(circle_quantizer_cmd) + '\n').encode()) # run circle-quantizer - with subprocess.Popen( - circle_quantizer_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(circle_quantizer_cmd, err_prefix="circle_quantizer", logfile=f) ## make a command to record min-max value of each tensor while running the representative dataset circle_record_minmax_cmd = [record_minmax_path] + # verbose + if _utils._is_valid_attr(args, 'verbose'): + circle_record_minmax_cmd.append('--verbose') # input and output path circle_record_minmax_cmd.append('--input_model') circle_record_minmax_cmd.append(tmp_output_path_1) @@ -176,6 +183,9 @@ def _quantize(args): if _utils._is_valid_attr(args, 'input_data'): circle_record_minmax_cmd.append('--input_data') circle_record_minmax_cmd.append(getattr(args, 'input_data')) + if _utils._is_valid_attr(args, 'input_data_format'): + circle_record_minmax_cmd.append('--input_data_format') + circle_record_minmax_cmd.append(getattr(args, 'input_data_format')) # min and max percentile if _utils._is_valid_attr(args, 'min_percentile'): circle_record_minmax_cmd.append('--min_percentile') @@ -194,19 +204,13 @@ def _quantize(args): f.write((' '.join(circle_record_minmax_cmd) + '\n').encode()) # run record-minmax - with subprocess.Popen( - circle_record_minmax_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(circle_record_minmax_cmd, err_prefix="record_minmax", logfile=f) ## make a second command to quantize the model using the embedded information circle_quantizer_cmd = [circle_quantizer_path] + # verbose + if _utils._is_valid_attr(args, 'verbose'): + circle_quantizer_cmd.append('--verbose') # quantize_dequantize_weights circle_quantizer_cmd.append('--quantize_with_minmax') if _utils._is_valid_attr(args, 'input_dtype'): @@ -226,16 +230,7 @@ def _quantize(args): f.write((' '.join(circle_quantizer_cmd) + '\n').encode()) # run circle-quantizer - with subprocess.Popen( - circle_quantizer_cmd, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - bufsize=1) as p: - for line in p.stdout: - sys.stdout.buffer.write(line) - f.write(line) - if p.returncode != 0: - sys.exit(p.returncode) + _utils._run(circle_quantizer_cmd, err_prefix="circle_quantizer", logfile=f) def main(): @@ -257,4 +252,4 @@ def main(): if __name__ == '__main__': - main() + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/onecc b/compiler/one-cmds/onecc new file mode 100644 index 000000000..ca440d852 --- /dev/null +++ b/compiler/one-cmds/onecc @@ -0,0 +1,196 @@ +#!/usr/bin/env bash +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' +''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' +''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' +''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' +''''exit 255 # ''' + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import configparser +import os +import subprocess +import sys + +import utils as _utils + +# TODO Find better way to suppress trackback on error +sys.tracebacklimit = 0 + +subtool_list = { + 'compile': { + 'import': 'Convert given model to circle', + 'optimize': 'Optimize circle model', + 'quantize': 'Quantize circle model', + }, + 'package': { + 'pack': 'Package circle and metadata into nnpackage', + }, + 'backend': { + 'codegen': 'Code generation tool', + 'profile': 'Profile backend model file', + }, +} + + +def _call_driver(driver_name, options): + dir_path = os.path.dirname(os.path.realpath(__file__)) + driver_path = os.path.join(dir_path, driver_name) + cmd = [driver_path] + options + _utils._run(cmd) + + +def _check_subtool_exists(): + """verify given arguments""" + subtool_keys = [n for k, v in subtool_list.items() for n in v.keys()] + if len(sys.argv) > 1 and sys.argv[1] in subtool_keys: + driver_name = 'one-' + sys.argv[1] + options = sys.argv[2:] + _call_driver(driver_name, options) + sys.exit(0) + + +def _get_parser(): + onecc_usage = 'onecc [-h] [-v] [-C CONFIG] [COMMAND <args>]' + onecc_desc = 'Run ONE driver via several commands or configuration file' + parser = argparse.ArgumentParser(description=onecc_desc, usage=onecc_usage) + + _utils._add_default_arg(parser) + + # just for help message + compile_group = parser.add_argument_group('compile to circle model') + for tool, desc in subtool_list['compile'].items(): + compile_group.add_argument(tool, action='store_true', help=desc) + + package_group = parser.add_argument_group('package circle model') + for tool, desc in subtool_list['package'].items(): + package_group.add_argument(tool, action='store_true', help=desc) + + backend_group = parser.add_argument_group('run backend tools') + for tool, desc in subtool_list['backend'].items(): + backend_group.add_argument(tool, action='store_true', help=desc) + + return parser + + +def _parse_arg(parser): + args = parser.parse_args() + # print version + if args.version: + _utils._print_version_and_exit(__file__) + + return args + + +def _verify_arg(parser, args): + """verify given arguments""" + # check if required arguments is given + if not _utils._is_valid_attr(args, 'config'): + parser.error('-C/--config argument is required') + + +def _get_driver_name(driver_name): + return { + 'one-import-bcq': 'one-import-bcq', + 'one-import-tf': 'one-import-tf', + 'one-import-tflite': 'one-import-tflite', + 'one-import-onnx': 'one-import-onnx', + 'one-optimize': 'one-optimize', + 'one-quantize': 'one-quantize', + 'one-pack': 'one-pack', + 'one-codegen': 'one-codegen', + 'one-profile': 'one-profile' + }[driver_name] + + +def _parse_cfg(args): + config = configparser.ConfigParser() + config.optionxform = str + parsed = config.read(os.path.expanduser(getattr(args, 'config'))) + if not parsed: + raise FileNotFoundError('Not found given configuration file') + return config + + +def _is_available_driver(config, driver_name): + return config.has_option('onecc', driver_name) and config.getboolean( + 'onecc', driver_name) + + +def _verify_cfg(driver_list, config): + if not config.has_section('onecc'): + raise ImportError('[onecc] section is required in configuration file') + + import_driver_cnt = 0 + if _is_available_driver(config, 'one-import-tf'): + import_driver_cnt += 1 + if _is_available_driver(config, 'one-import-tflite'): + import_driver_cnt += 1 + if _is_available_driver(config, 'one-import-bcq'): + import_driver_cnt += 1 + if _is_available_driver(config, 'one-import-onnx'): + import_driver_cnt += 1 + if import_driver_cnt > 1: + raise AssertionError('Only one import-* driver can be executed') + + +def main(): + # check if there is subtool argument + # if true, it executes subtool with argv + # NOTE: + # Why call subtool directly without using Argparse? + # Because if Argparse is used, options equivalent to onecc including + # '--help', '-C' are processed directly onecc itself. + # So options cannot be delivered to subtool. + _check_subtool_exists() + + # parse arguments + # since the configuration file path is required first, + # parsing of the configuration file proceeds after this. + parser = _get_parser() + args = _parse_arg(parser) + + # verify arguments + _verify_arg(parser, args) + + # parse configuration file + config = _parse_cfg(args) + + # verify configuration file + drivers = [ + 'one-import-tf', 'one-import-tflite', 'one-import-bcq', 'one-import-onnx', + 'one-optimize', 'one-quantize', 'one-pack', 'one-codegen', 'one-profile' + ] + _verify_cfg(drivers, config) + + # get sections to run + section_to_run = [] + for d in drivers: + if _is_available_driver(config, d): + section_to_run.append(d) + + # run + dir_path = os.path.dirname(os.path.realpath(__file__)) + for section in section_to_run: + driver_name = _get_driver_name(section) + options = ['--config', getattr(args, 'config'), '--section', section] + if _utils._is_valid_attr(args, 'verbose'): + options.append('--verbose') + _call_driver(driver_name, options) + + +if __name__ == '__main__': + _utils._safemain(main, __file__) diff --git a/compiler/one-cmds/onecc.template.cfg b/compiler/one-cmds/onecc.template.cfg new file mode 100644 index 000000000..a23d1cea9 --- /dev/null +++ b/compiler/one-cmds/onecc.template.cfg @@ -0,0 +1,28 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-import-onnx=False +one-optimize=True +one-quantize=False +one-pack=True +one-codegen=False +one-profile=False + +[one-import-tf] +input_path=/path/to/inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 +model_format=graph_def + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle +generate_profile_data=False + +[one-pack] +input_path=inception_v3.opt.circle +output_path=inception_v3_pack diff --git a/compiler/one-cmds/tests/one-build_001.test b/compiler/one-cmds/tests/one-build_001.test index ebbb3235b..30f48e174 100644 --- a/compiler/one-cmds/tests/one-build_001.test +++ b/compiler/one-cmds/tests/one-build_001.test @@ -31,7 +31,7 @@ configfile="one-build_001.cfg" outputfile="inception_v3.opt.circle" # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_002.test b/compiler/one-cmds/tests/one-build_002.test index 43fce4e6f..1485df9ae 100644 --- a/compiler/one-cmds/tests/one-build_002.test +++ b/compiler/one-cmds/tests/one-build_002.test @@ -31,7 +31,7 @@ configfile="one-build_002.cfg" outputfile="inception_v3_pkg" # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_003.test b/compiler/one-cmds/tests/one-build_003.test index d835be470..fd9585bef 100644 --- a/compiler/one-cmds/tests/one-build_003.test +++ b/compiler/one-cmds/tests/one-build_003.test @@ -33,7 +33,7 @@ outputfile="inception_v3.quantized.circle" rm -rf ${outputfile} # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_004.test b/compiler/one-cmds/tests/one-build_004.test index f4174bd73..3e729789d 100644 --- a/compiler/one-cmds/tests/one-build_004.test +++ b/compiler/one-cmds/tests/one-build_004.test @@ -37,7 +37,7 @@ rm -rf ${outputfile} cp dummy-compile ../bin/dummy-compile # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_005.test b/compiler/one-cmds/tests/one-build_005.test index 772483ddc..3a69cc3d9 100644 --- a/compiler/one-cmds/tests/one-build_005.test +++ b/compiler/one-cmds/tests/one-build_005.test @@ -37,7 +37,7 @@ rm -rf ${outputfile} cp dummy-compile ../bin/dummy-compile # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_006.test b/compiler/one-cmds/tests/one-build_006.test index caf8897b1..ff389a870 100644 --- a/compiler/one-cmds/tests/one-build_006.test +++ b/compiler/one-cmds/tests/one-build_006.test @@ -37,7 +37,7 @@ rm -rf ${outputfile} cp dummy-compile ../bin/dummy-compile # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_007.test b/compiler/one-cmds/tests/one-build_007.test index 086187013..4ad68b520 100644 --- a/compiler/one-cmds/tests/one-build_007.test +++ b/compiler/one-cmds/tests/one-build_007.test @@ -33,7 +33,7 @@ outputfile="inception_v3_pkg" rm -rf ${outputfile} # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_008.test b/compiler/one-cmds/tests/one-build_008.test index bfb7666db..2f8c4c261 100644 --- a/compiler/one-cmds/tests/one-build_008.test +++ b/compiler/one-cmds/tests/one-build_008.test @@ -37,7 +37,7 @@ rm -rf ${outputfile} cp dummy-compile ../bin/dummy-compile # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_009.test b/compiler/one-cmds/tests/one-build_009.test index 0d766261d..56c05057f 100644 --- a/compiler/one-cmds/tests/one-build_009.test +++ b/compiler/one-cmds/tests/one-build_009.test @@ -37,7 +37,7 @@ rm -rf ${outputfile} cp dummy-compile ../bin/dummy-compile # run test -one-build -C ${configfile} > /dev/null +one-build -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-build_010.cfg b/compiler/one-cmds/tests/one-build_010.cfg new file mode 100644 index 000000000..3be9ed3f9 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_010.cfg @@ -0,0 +1,17 @@ +[one-build] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.alt.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 +save_intermediate=True diff --git a/compiler/one-cmds/tests/one-build_010.test b/compiler/one-cmds/tests/one-build_010.test new file mode 100644 index 000000000..ec3163948 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_010.test @@ -0,0 +1,47 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf: intermediate file should exist + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="one-build_010.cfg" +outputfile="inception_v3.alt.circle" +intermfile="inception_v3.alt.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +one-build -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +if [[ ! -s "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-build_011.cfg b/compiler/one-cmds/tests/one-build_011.cfg new file mode 100644 index 000000000..15d2c103c --- /dev/null +++ b/compiler/one-cmds/tests/one-build_011.cfg @@ -0,0 +1,14 @@ +[one-build] +one-import-tf=False +one-import-tflite=False +one-import-bcq=False +one-import-onnx=True +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-onnx] +input_path=test_onnx_model.onnx +output_path=test_onnx_model.circle +save_intermediate=True diff --git a/compiler/one-cmds/tests/one-build_011.test b/compiler/one-cmds/tests/one-build_011.test new file mode 100644 index 000000000..29b42138d --- /dev/null +++ b/compiler/one-cmds/tests/one-build_011.test @@ -0,0 +1,47 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-onnx + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="one-build_011.cfg" +outputfile="test_onnx_model.circle" +intermfile="test_onnx_model.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +one-build -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +if [[ ! -s "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-build_012.cfg b/compiler/one-cmds/tests/one-build_012.cfg new file mode 100644 index 000000000..74b0a0ca0 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_012.cfg @@ -0,0 +1,22 @@ +[one-build] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=True +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 + +[one-quantize] +input_path=inception_v3.circle +output_path=inception_v3.list.quantized.circle +input_data=datalist.txt +input_data_format=list diff --git a/compiler/one-cmds/tests/one-build_012.test b/compiler/one-cmds/tests/one-build_012.test new file mode 100644 index 000000000..445a9480b --- /dev/null +++ b/compiler/one-cmds/tests/one-build_012.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-quantize + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="one-build_012.cfg" +outputfile="inception_v3.list.quantized.circle" + +rm -rf ${outputfile} + +# run test +one-build -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-build_013.cfg b/compiler/one-cmds/tests/one-build_013.cfg new file mode 100644 index 000000000..8e9e40e66 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_013.cfg @@ -0,0 +1,22 @@ +[one-build] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=True +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 + +[one-quantize] +input_path=inception_v3.circle +output_path=inception_v3.dir.quantized.circle +input_data=raw_files +input_data_format=directory diff --git a/compiler/one-cmds/tests/one-build_013.test b/compiler/one-cmds/tests/one-build_013.test new file mode 100644 index 000000000..1b405fbe1 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_013.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-quantize + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="one-build_013.cfg" +outputfile="inception_v3.dir.quantized.circle" + +rm -rf ${outputfile} + +# run test +one-build -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-build_neg_005.cfg b/compiler/one-cmds/tests/one-build_neg_005.cfg new file mode 100644 index 000000000..ad9efbca1 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_neg_005.cfg @@ -0,0 +1,16 @@ +[one-build] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.alt.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v2 diff --git a/compiler/one-cmds/tests/one-build_neg_005.test b/compiler/one-cmds/tests/one-build_neg_005.test new file mode 100644 index 000000000..b4e43d005 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_neg_005.test @@ -0,0 +1,49 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative one-import-tf intermediate file should not exist + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="one-build_neg_005.cfg" +outputfile="inception_v3.alt.circle" +intermfile="inception_v3.alt.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +one-build -C ${configfile} > ${filename}.log 2>&1 + +# output should exist +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +# intermediate file should not exist +if [[ -f "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-build_neg_006.cfg b/compiler/one-cmds/tests/one-build_neg_006.cfg new file mode 100644 index 000000000..abe4c7d77 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_neg_006.cfg @@ -0,0 +1,13 @@ +[one-build] +one-import-tf=False +one-import-tflite=False +one-import-bcq=False +one-import-onnx=True +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-onnx] +input_path=test_onnx_model.onnx +output_path=test_onnx_model.circle diff --git a/compiler/one-cmds/tests/one-build_neg_006.test b/compiler/one-cmds/tests/one-build_neg_006.test new file mode 100644 index 000000000..8723df297 --- /dev/null +++ b/compiler/one-cmds/tests/one-build_neg_006.test @@ -0,0 +1,49 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative one-import-tf intermediate file should not exist + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="one-build_neg_006.cfg" +outputfile="test_onnx_model.circle" +intermfile="test_onnx_model.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +one-build -C ${configfile} > ${filename}.log 2>&1 + +# output should exist +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +# intermediate file should not exist +if [[ -f "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-codegen_001.test b/compiler/one-cmds/tests/one-codegen_001.test index 7c679b38e..80d149e74 100644 --- a/compiler/one-cmds/tests/one-codegen_001.test +++ b/compiler/one-cmds/tests/one-codegen_001.test @@ -29,7 +29,7 @@ trap trap_err_onexit ERR cp help-compile ../bin/help-compile # run test -one-codegen -b help -- -h > ${filename}.log +one-codegen -b help -- -h > ${filename}.log 2>&1 rm -rf ../bin/help-compile diff --git a/compiler/one-cmds/tests/one-codegen_002.test b/compiler/one-cmds/tests/one-codegen_002.test index feb848919..8af539d43 100644 --- a/compiler/one-cmds/tests/one-codegen_002.test +++ b/compiler/one-cmds/tests/one-codegen_002.test @@ -36,7 +36,7 @@ rm -rf ${outputfile} cp dummy-compile ../bin/dummy-compile # run test -one-codegen -b dummy -o ${outputfile} "dummy.circle" +one-codegen -b dummy -o ${outputfile} "dummy.circle" > ${filename}.log 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-codegen_003.test b/compiler/one-cmds/tests/one-codegen_003.test index 47d12a4f1..889c38963 100644 --- a/compiler/one-cmds/tests/one-codegen_003.test +++ b/compiler/one-cmds/tests/one-codegen_003.test @@ -36,7 +36,7 @@ rm -rf ${outputfile} cp dummy-compile ../bin/dummy-compile # run test -one-codegen -b dummy -- -o ${outputfile} "dummy.circle" +one-codegen -b dummy -- -o ${outputfile} "dummy.circle" > ${filename}.log 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-codegen_004.test b/compiler/one-cmds/tests/one-codegen_004.test index 88f42338d..384689fa9 100644 --- a/compiler/one-cmds/tests/one-codegen_004.test +++ b/compiler/one-cmds/tests/one-codegen_004.test @@ -28,7 +28,7 @@ trap_err_onexit() trap trap_err_onexit ERR # run test -one-codegen -h > ${filename}.log +one-codegen -h > ${filename}.log 2>&1 if grep -q "command line tool for code generation" "${filename}.log"; then echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-import-bcq_001.test b/compiler/one-cmds/tests/one-import-bcq_001.test index 74b8bab32..fcca10eaf 100644 --- a/compiler/one-cmds/tests/one-import-bcq_001.test +++ b/compiler/one-cmds/tests/one-import-bcq_001.test @@ -29,14 +29,13 @@ inputfile="./bcq.pb" outputfile="./bcq.circle" rm -rf $outputfile -rm -rf $outputfile.log # run test one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder \ ---output_arrays MatMul >> /dev/null +--output_arrays MatMul > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_001.test b/compiler/one-cmds/tests/one-import-bcq_neg_001.test index 9a2b455ef..6d0d9b2b6 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_001.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_001.test @@ -43,7 +43,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder_null \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_002.test b/compiler/one-cmds/tests/one-import-bcq_neg_002.test index 5779aa03e..b50c72c9a 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_002.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_002.test @@ -43,7 +43,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder \ ---output_arrays MatMul_null > ${filename}.log +--output_arrays MatMul_null > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_003.test b/compiler/one-cmds/tests/one-import-bcq_neg_003.test index 9cc9bcb7f..0f75af82e 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_003.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_003.test @@ -43,7 +43,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_004.test b/compiler/one-cmds/tests/one-import-bcq_neg_004.test index dc83b98c8..62bfa4476 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_004.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_004.test @@ -43,7 +43,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_005.test b/compiler/one-cmds/tests/one-import-bcq_neg_005.test index ad1196a67..0e6d52c59 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_005.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_005.test @@ -42,7 +42,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_006.test b/compiler/one-cmds/tests/one-import-bcq_neg_006.test index f761aa7c0..df7116bab 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_006.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_006.test @@ -42,7 +42,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder --input_shapes "1,32,32" \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_007.test b/compiler/one-cmds/tests/one-import-bcq_neg_007.test index 5013254f5..521238b08 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_007.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_007.test @@ -42,7 +42,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder --input_shapes "30,30" \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_008.test b/compiler/one-cmds/tests/one-import-bcq_neg_008.test index e7d5d2e03..7729caf8b 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_008.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_008.test @@ -42,7 +42,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder --input_shapes "32,O" \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import-bcq_neg_009.test b/compiler/one-cmds/tests/one-import-bcq_neg_009.test index ef990438a..e48c86d09 100644 --- a/compiler/one-cmds/tests/one-import-bcq_neg_009.test +++ b/compiler/one-cmds/tests/one-import-bcq_neg_009.test @@ -42,7 +42,7 @@ one-import-bcq \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Placeholder --input_shapes "32,32:1" \ ---output_arrays MatMul > ${filename}.log +--output_arrays MatMul > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_001.test b/compiler/one-cmds/tests/one-import_001.test index 165f49193..35c9c3c77 100644 --- a/compiler/one-cmds/tests/one-import_001.test +++ b/compiler/one-cmds/tests/one-import_001.test @@ -35,7 +35,7 @@ one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "1,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_1 >> /dev/null +--output_arrays InceptionV3/Predictions/Reshape_1 > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-import_002.test b/compiler/one-cmds/tests/one-import_002.test index 0f8d69099..232becf55 100644 --- a/compiler/one-cmds/tests/one-import_002.test +++ b/compiler/one-cmds/tests/one-import_002.test @@ -32,7 +32,7 @@ outputfile="inception_v3_cmd.circle" # run test one-import tf -C ${configfile} \ ---output_path=inception_v3_cmd.circle > /dev/null +--output_path=inception_v3_cmd.circle > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-import_003.test b/compiler/one-cmds/tests/one-import_003.test index 6093f1422..cd1069855 100644 --- a/compiler/one-cmds/tests/one-import_003.test +++ b/compiler/one-cmds/tests/one-import_003.test @@ -33,7 +33,7 @@ outputfile="test_saved_model.circle" rm -f ${outputfile} # run test -one-import tf -C ${configfile} > /dev/null +one-import tf -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-import_004.test b/compiler/one-cmds/tests/one-import_004.test index 9d10c431a..fb30d4014 100644 --- a/compiler/one-cmds/tests/one-import_004.test +++ b/compiler/one-cmds/tests/one-import_004.test @@ -33,7 +33,7 @@ outputfile="test_keras_model.circle" rm -f ${outputfile} # run test -one-import tf -C ${configfile} > /dev/null +one-import tf -C ${configfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-import_005.test b/compiler/one-cmds/tests/one-import_005.test index ca49db189..385afb561 100644 --- a/compiler/one-cmds/tests/one-import_005.test +++ b/compiler/one-cmds/tests/one-import_005.test @@ -14,6 +14,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# import onnx model with cfg file + filename_ext="$(basename -- $0)" filename="${filename_ext%.*}" @@ -31,7 +33,7 @@ outputfile="test_onnx_model.circle" rm -f ${outputfile} # run test -one-build -C ${configfile} > ${filename}.log 2>&1 +one-import onnx -C ${configfile} > ${filename}.log 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-import_006.test b/compiler/one-cmds/tests/one-import_006.test new file mode 100644 index 000000000..88c6da593 --- /dev/null +++ b/compiler/one-cmds/tests/one-import_006.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# import onnx model + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="test_onnx_model.onnx" +outputfile="test_onnx_model.circle" + +rm -f ${outputfile} + +# run test +one-import onnx -i ${inputfile} -o ${outputfile} > ${filename}.log 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-import_neg_001.test b/compiler/one-cmds/tests/one-import_neg_001.test index 5a233d2f3..3986ee457 100644 --- a/compiler/one-cmds/tests/one-import_neg_001.test +++ b/compiler/one-cmds/tests/one-import_neg_001.test @@ -36,14 +36,14 @@ inputfile="./inception_v3.tflite" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "1,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_002.test b/compiler/one-cmds/tests/one-import_neg_002.test index 78b77511a..738c2cba9 100644 --- a/compiler/one-cmds/tests/one-import_neg_002.test +++ b/compiler/one-cmds/tests/one-import_neg_002.test @@ -36,14 +36,14 @@ inputfile="./while_3.pbtxt" outputfile="./while_3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays Hole,Hole_2 --input_shapes "1,1:1,1" \ ---output_arrays Output > ${filename}.log +--output_arrays Output > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_003.test b/compiler/one-cmds/tests/one-import_neg_003.test index b0e95b6d1..df8303375 100644 --- a/compiler/one-cmds/tests/one-import_neg_003.test +++ b/compiler/one-cmds/tests/one-import_neg_003.test @@ -21,11 +21,19 @@ filename="${filename_ext%.*}" trap_err_onexit() { + # TODO Error message depends on TF version. Find better way. + # TF 2.3.0 if grep -q "ValueError: Invalid tensors" "${filename}.log"; then echo "${filename_ext} SUCCESS" exit 0 fi + # TF 2.5.0 + if grep -q "ConverterError: <unknown>:0: error:" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + echo "${filename_ext} FAILED" exit 255 } @@ -36,14 +44,14 @@ inputfile="./inception_v3.pb" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "1,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_2 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_2 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_004.test b/compiler/one-cmds/tests/one-import_neg_004.test index afb206ad9..99badadc9 100644 --- a/compiler/one-cmds/tests/one-import_neg_004.test +++ b/compiler/one-cmds/tests/one-import_neg_004.test @@ -36,14 +36,14 @@ inputfile="./inception_v3.pb" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "1,299,299,1" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_005.test b/compiler/one-cmds/tests/one-import_neg_005.test index 045fb89b7..5e0b9a53d 100644 --- a/compiler/one-cmds/tests/one-import_neg_005.test +++ b/compiler/one-cmds/tests/one-import_neg_005.test @@ -36,14 +36,14 @@ inputfile="./inception_v3.pb" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "1,299,299" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_006.test b/compiler/one-cmds/tests/one-import_neg_006.test index bb3e2344c..7c63ee3e4 100644 --- a/compiler/one-cmds/tests/one-import_neg_006.test +++ b/compiler/one-cmds/tests/one-import_neg_006.test @@ -36,14 +36,14 @@ inputfile="./inception_v3.pb" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "0,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_007.test b/compiler/one-cmds/tests/one-import_neg_007.test index bcfc4bcc9..09e9ed8a8 100644 --- a/compiler/one-cmds/tests/one-import_neg_007.test +++ b/compiler/one-cmds/tests/one-import_neg_007.test @@ -36,14 +36,14 @@ inputfile="./inception_v3.pb" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "None,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_008.test b/compiler/one-cmds/tests/one-import_neg_008.test index 2f48fd708..7ad678b4e 100644 --- a/compiler/one-cmds/tests/one-import_neg_008.test +++ b/compiler/one-cmds/tests/one-import_neg_008.test @@ -36,14 +36,14 @@ inputfile="./inception_v3.pb" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input,InceptionV3/Predictions/Shape --input_shapes "1,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_009.test b/compiler/one-cmds/tests/one-import_neg_009.test index 79e463d64..0b5deb32f 100644 --- a/compiler/one-cmds/tests/one-import_neg_009.test +++ b/compiler/one-cmds/tests/one-import_neg_009.test @@ -35,14 +35,14 @@ trap trap_err_onexit ERR inputfile="./inception_v3.pb" outputfile="." -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input --input_shapes "1,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-import_neg_010.test b/compiler/one-cmds/tests/one-import_neg_010.test index 05677a6d4..ab039eed8 100644 --- a/compiler/one-cmds/tests/one-import_neg_010.test +++ b/compiler/one-cmds/tests/one-import_neg_010.test @@ -36,14 +36,14 @@ inputfile="./inception_v3.pb" outputfile="./inception_v3.circle" rm -rf ${outputfile} -rm -rf ${outputfile}.log +rm -rf ${filename}.log # run test one-import tf \ --input_path ${inputfile} \ --output_path ${outputfile} \ --input_arrays input2 --input_shapes "1,299,299,3" \ ---output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log +--output_arrays InceptionV3/Predictions/Reshape_1 > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-optimize_001.test b/compiler/one-cmds/tests/one-optimize_001.test index 0d58a6a9e..8eb58f4eb 100644 --- a/compiler/one-cmds/tests/one-optimize_001.test +++ b/compiler/one-cmds/tests/one-optimize_001.test @@ -32,7 +32,7 @@ rm -rf ${outputfile} # to create inception_v3.circle if [[ ! -s ${inputfile} ]]; then - /bin/bash one-import_001.test >> /dev/null + /bin/bash one-import_001.test > /dev/null 2>&1 return_code=$? if [[ ${return_code} != 0 ]]; then trap_err_onexit @@ -42,7 +42,7 @@ fi # run test one-optimize --O1 \ --input_path ${inputfile} \ ---output_path ${outputfile} >> /dev/null +--output_path ${outputfile} > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-optimize_002.test b/compiler/one-cmds/tests/one-optimize_002.test new file mode 100644 index 000000000..bd64494be --- /dev/null +++ b/compiler/one-cmds/tests/one-optimize_002.test @@ -0,0 +1,52 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +outputfile="./inception_v3-opt.circle" + +rm -rf ${outputfile} + +# to create inception_v3.circle +if [[ ! -s ${inputfile} ]]; then + /bin/bash one-import_001.test > /dev/null 2>&1 + return_code=$? + if [[ ${return_code} != 0 ]]; then + trap_err_onexit + fi +fi + +# run test +one-optimize --O1 \ +--change_outputs InceptionV3/Logits/SpatialSqueeze1 \ +--input_path ${inputfile} \ +--output_path ${outputfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-optimize_neg_001.test b/compiler/one-cmds/tests/one-optimize_neg_001.test index a30b4164d..f0b5563c7 100644 --- a/compiler/one-cmds/tests/one-optimize_neg_001.test +++ b/compiler/one-cmds/tests/one-optimize_neg_001.test @@ -41,7 +41,7 @@ rm -rf ${outputfile}.log # run test one-optimize --O1 \ --input_path ${inputfile} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-optimize_neg_002.test b/compiler/one-cmds/tests/one-optimize_neg_002.test index 7ccf4a89c..72f306e20 100644 --- a/compiler/one-cmds/tests/one-optimize_neg_002.test +++ b/compiler/one-cmds/tests/one-optimize_neg_002.test @@ -41,7 +41,7 @@ rm -rf ${outputfile}.log # run test one-optimize --O1 \ --input_path ${inputfile} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-optimize_neg_004.test b/compiler/one-cmds/tests/one-optimize_neg_004.test new file mode 100644 index 000000000..e73911b54 --- /dev/null +++ b/compiler/one-cmds/tests/one-optimize_neg_004.test @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# this test should fail + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "Change outputs failed" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +outputfile="./inception_v3-opt.circle" + +rm -rf ${outputfile} +rm -rf ${filename}.log + +# run test +one-optimize --O1 \ +--change_outputs non_existing_node_name \ +--input_path ${inputfile} \ +--output_path ${outputfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/one-pack_001.test b/compiler/one-cmds/tests/one-pack_001.test index 3d746dffd..858d3c752 100644 --- a/compiler/one-cmds/tests/one-pack_001.test +++ b/compiler/one-cmds/tests/one-pack_001.test @@ -32,7 +32,7 @@ rm -rf ${outputfolder} # to create inception_v3.circle if [[ ! -s ${inputfile} ]]; then - /bin/bash one-import_001.test >> /dev/null + /bin/bash one-import_001.test > /dev/null 2>&1 return_code=$? if [[ ${return_code} != 0 ]]; then trap_err_onexit @@ -42,7 +42,7 @@ fi # run test one-pack \ -i ${inputfile} \ --o ${outputfolder} >> /dev/null +-o ${outputfolder} > /dev/null 2>&1 if [[ ! -d "${outputfolder}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-pack_neg_001.test b/compiler/one-cmds/tests/one-pack_neg_001.test index 6b7411db1..9cf388004 100644 --- a/compiler/one-cmds/tests/one-pack_neg_001.test +++ b/compiler/one-cmds/tests/one-pack_neg_001.test @@ -37,7 +37,7 @@ rm -rf ${filename}.log # run test one-pack \ -i ./inception_v2.circle \ --o nnpack > ${filename}.log +-o nnpack > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-pack_neg_002.test b/compiler/one-cmds/tests/one-pack_neg_002.test index 6427c260e..9faa8d9d7 100644 --- a/compiler/one-cmds/tests/one-pack_neg_002.test +++ b/compiler/one-cmds/tests/one-pack_neg_002.test @@ -41,7 +41,7 @@ touch ./sample # run test one-pack \ -i ./sample \ --o nnpack > ${filename}.log +-o nnpack > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-profile_001.test b/compiler/one-cmds/tests/one-profile_001.test new file mode 100644 index 000000000..823a73154 --- /dev/null +++ b/compiler/one-cmds/tests/one-profile_001.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/help-profile + exit 255 +} + +trap trap_err_onexit ERR + +# copy help-profile to bin folder +cp help-profile ../bin/help-profile + +# run test +one-profile -b help -- -h > ${filename}.log + +rm -rf ../bin/help-profile + +if grep -q "HELP MESSAGE!!" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 +fi + +trap_err_onexit diff --git a/compiler/one-cmds/tests/one-profile_002.test b/compiler/one-cmds/tests/one-profile_002.test new file mode 100644 index 000000000..1365f9d68 --- /dev/null +++ b/compiler/one-cmds/tests/one-profile_002.test @@ -0,0 +1,50 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# run one-codegen with dummy-profile driver + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-profile + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="sample.tvn" + +if [[ ! -s "${inputfile}" ]]; then + touch ${inputfile} +fi + +# copy dummy-profile to bin folder +cp dummy-profile ../bin/dummy-profile + +# run test +one-profile -b dummy ${inputfile} > ${filename}.log + +rm -rf ../bin/dummy-profile + +if grep -q "dummy-profile dummy output!!!" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 +fi + +trap_err_onexit diff --git a/compiler/one-cmds/tests/one-profile_003.test b/compiler/one-cmds/tests/one-profile_003.test new file mode 100644 index 000000000..39e2f06a6 --- /dev/null +++ b/compiler/one-cmds/tests/one-profile_003.test @@ -0,0 +1,38 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# print one-profile's help message + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +# run test +one-profile -h > ${filename}.log + +if grep -q "command line tool for profiling backend model" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 +fi + +trap_err_onexit diff --git a/compiler/one-cmds/tests/one-profile_004.cfg b/compiler/one-cmds/tests/one-profile_004.cfg new file mode 100644 index 000000000..6d4387b0a --- /dev/null +++ b/compiler/one-cmds/tests/one-profile_004.cfg @@ -0,0 +1,3 @@ +[one-profile] +backend=dummy +command=sample.tvn diff --git a/compiler/one-cmds/tests/one-profile_004.test b/compiler/one-cmds/tests/one-profile_004.test new file mode 100644 index 000000000..0c9e15f73 --- /dev/null +++ b/compiler/one-cmds/tests/one-profile_004.test @@ -0,0 +1,51 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-profile with configuration input + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-profile + exit 255 +} + +trap trap_err_onexit ERR + +configfile="one-profile_004.cfg" +inputfile="sample.tvn" + +if [[ ! -s "${inputfile}" ]]; then + touch ${inputfile} +fi + +# copy dummy-profile to bin folder +cp dummy-profile ../bin/dummy-profile + +# run test +one-profile -C ${configfile} > ${filename}.log + +rm -rf ../bin/dummy-profile + +if grep -q "dummy-profile dummy output!!!" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 +fi + +trap_err_onexit diff --git a/compiler/one-cmds/tests/one-profile_neg_001.test b/compiler/one-cmds/tests/one-profile_neg_001.test new file mode 100644 index 000000000..0de1e3bb3 --- /dev/null +++ b/compiler/one-cmds/tests/one-profile_neg_001.test @@ -0,0 +1,39 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with no input + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "error: the following arguments are required: -b/--backend" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +# run test +one-profile > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_001.test b/compiler/one-cmds/tests/one-quantize_001.test index ef25ac800..60a9de2b3 100644 --- a/compiler/one-cmds/tests/one-quantize_001.test +++ b/compiler/one-cmds/tests/one-quantize_001.test @@ -32,7 +32,7 @@ rm -rf ${outputfile} # to create inception_v3.circle if [[ ! -s ${inputfile} ]]; then - /bin/bash one-import_001.test >> /dev/null + /bin/bash one-import_001.test > /dev/null 2>&1 return_code=$? if [[ ${return_code} != 0 ]]; then trap_err_onexit @@ -45,7 +45,7 @@ one-quantize \ --quantized_dtype uint8 \ --input_path ./inception_v3.circle \ --input_data ./inception_v3_test_data.h5 \ ---output_path ./inception_v3.quantized.circle >> /dev/null +--output_path ./inception_v3.quantized.circle > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-quantize_002.test b/compiler/one-cmds/tests/one-quantize_002.test index 3704425d4..08a830526 100644 --- a/compiler/one-cmds/tests/one-quantize_002.test +++ b/compiler/one-cmds/tests/one-quantize_002.test @@ -32,7 +32,7 @@ rm -rf ${outputfile} # to create inception_v3.circle if [[ ! -s ${inputfile} ]]; then - /bin/bash one-import_001.test >> /dev/null + /bin/bash one-import_001.test > /dev/null 2>&1 return_code=$? if [[ ${return_code} != 0 ]]; then trap_err_onexit @@ -44,7 +44,7 @@ one-quantize \ --input_dtype float32 \ --quantized_dtype uint8 \ --input_path ./inception_v3.circle \ ---output_path ./inception_v3.random.quantized.circle >> /dev/null +--output_path ./inception_v3.random.quantized.circle > /dev/null 2>&1 if [[ ! -s "${outputfile}" ]]; then trap_err_onexit diff --git a/compiler/one-cmds/tests/one-quantize_003.test b/compiler/one-cmds/tests/one-quantize_003.test new file mode 100644 index 000000000..a38711398 --- /dev/null +++ b/compiler/one-cmds/tests/one-quantize_003.test @@ -0,0 +1,46 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +outputfile="./inception_v3.list.quantized.circle" + +rm -rf ${outputfile} + +# run test with list-format input data (datalist.txt) +one-quantize \ +--input_dtype float32 \ +--quantized_dtype uint8 \ +--input_path ./inception_v3.circle \ +--input_data ./datalist.txt \ +--input_data_format list \ +--output_path ${outputfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-quantize_004.test b/compiler/one-cmds/tests/one-quantize_004.test new file mode 100644 index 000000000..a489e8ccf --- /dev/null +++ b/compiler/one-cmds/tests/one-quantize_004.test @@ -0,0 +1,46 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +outputfile="./inception_v3.directory.quantized.circle" + +rm -rf ${outputfile} + +# run test with directory-format input data (raw_files) +one-quantize \ +--input_dtype float32 \ +--quantized_dtype uint8 \ +--input_path ${inputfile} \ +--input_data ./raw_files \ +--input_data_format directory \ +--output_path ${outputfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/one-quantize_neg_001.test b/compiler/one-cmds/tests/one-quantize_neg_001.test index ccf16fbf8..4e42faa24 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_001.test +++ b/compiler/one-cmds/tests/one-quantize_neg_001.test @@ -55,7 +55,7 @@ one-quantize \ --quantized_dtype uint8 \ --input_path ${inputfile} \ --input_data ${inputdata} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_002.test b/compiler/one-cmds/tests/one-quantize_neg_002.test index 387e53637..3c0d67659 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_002.test +++ b/compiler/one-cmds/tests/one-quantize_neg_002.test @@ -54,7 +54,7 @@ one-quantize \ --quantized_dtype uint16 \ --input_path ${inputfile} \ --input_data ${inputdata} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_003.test b/compiler/one-cmds/tests/one-quantize_neg_003.test index 47cf0691f..156ed84f4 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_003.test +++ b/compiler/one-cmds/tests/one-quantize_neg_003.test @@ -53,7 +53,7 @@ one-quantize \ --quantized_dtype uint8 \ --input_path ${inputfile} \ --input_data ./mobilenet_test_data.h5 \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_004.test b/compiler/one-cmds/tests/one-quantize_neg_004.test index 8d53ca1d1..5a78a810f 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_004.test +++ b/compiler/one-cmds/tests/one-quantize_neg_004.test @@ -53,7 +53,7 @@ one-quantize \ --quantized_dtype uint8 \ --input_path ${inputfile} \ --input_data ${inputdata} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_005.test b/compiler/one-cmds/tests/one-quantize_neg_005.test index 4504730d0..adcaf6695 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_005.test +++ b/compiler/one-cmds/tests/one-quantize_neg_005.test @@ -44,7 +44,7 @@ one-quantize \ --quantized_dtype uint8 \ --input_path ${inputfile} \ --input_data ${inputdata} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_006.test b/compiler/one-cmds/tests/one-quantize_neg_006.test index 63e7aecef..42890a898 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_006.test +++ b/compiler/one-cmds/tests/one-quantize_neg_006.test @@ -44,7 +44,7 @@ one-quantize \ --quantized_dtype uint8 \ --input_path ${inputfile} \ --input_data ${inputdata} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_007.test b/compiler/one-cmds/tests/one-quantize_neg_007.test index 4796f9e89..f096dd606 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_007.test +++ b/compiler/one-cmds/tests/one-quantize_neg_007.test @@ -53,7 +53,7 @@ one-quantize \ --quantized_dtype uint8 \ --input_path ${inputfile} \ --input_data ${inputdata} \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_008.test b/compiler/one-cmds/tests/one-quantize_neg_008.test index 1656c6ba2..70752cf12 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_008.test +++ b/compiler/one-cmds/tests/one-quantize_neg_008.test @@ -54,7 +54,7 @@ one-quantize \ --input_path ${inputfile} \ --input_data ${inputdata} \ --mode average \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_009.test b/compiler/one-cmds/tests/one-quantize_neg_009.test index 6ecc5c32f..595dbdcc1 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_009.test +++ b/compiler/one-cmds/tests/one-quantize_neg_009.test @@ -54,7 +54,7 @@ one-quantize \ --input_path ${inputfile} \ --input_data ${inputdata} \ --max_percentile 101 \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_010.test b/compiler/one-cmds/tests/one-quantize_neg_010.test index 209645990..6236caf5b 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_010.test +++ b/compiler/one-cmds/tests/one-quantize_neg_010.test @@ -54,7 +54,7 @@ one-quantize \ --input_path ${inputfile} \ --input_data ${inputdata} \ --max_percentile -1 \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_011.test b/compiler/one-cmds/tests/one-quantize_neg_011.test index ea44c2ffe..cf6a3c035 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_011.test +++ b/compiler/one-cmds/tests/one-quantize_neg_011.test @@ -54,7 +54,7 @@ one-quantize \ --input_path ${inputfile} \ --input_data ${inputdata} \ --min_percentile 101 \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_012.test b/compiler/one-cmds/tests/one-quantize_neg_012.test index b744051ae..646c9d351 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_012.test +++ b/compiler/one-cmds/tests/one-quantize_neg_012.test @@ -54,7 +54,7 @@ one-quantize \ --input_path ${inputfile} \ --input_data ${inputdata} \ --min_percentile -1 \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_013.test b/compiler/one-cmds/tests/one-quantize_neg_013.test index dec1cbd06..768c0329b 100644 --- a/compiler/one-cmds/tests/one-quantize_neg_013.test +++ b/compiler/one-cmds/tests/one-quantize_neg_013.test @@ -54,7 +54,7 @@ one-quantize \ --input_path ${inputfile} \ --input_data ${inputdata} \ --granularity layered \ ---output_path ${outputfile} > ${filename}.log +--output_path ${outputfile} > ${filename}.log 2>&1 echo "${filename_ext} FAILED" exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_014.test b/compiler/one-cmds/tests/one-quantize_neg_014.test new file mode 100644 index 000000000..af1f8bbb7 --- /dev/null +++ b/compiler/one-cmds/tests/one-quantize_neg_014.test @@ -0,0 +1,52 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with invalid min_percentile + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "Given data file is not HDF5" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +inputdata="./datalist.txt" +outputfile="./inception_v3.quantized.circle" + +rm -rf ${outputfile}.log + +# run test +one-quantize \ +--input_dtype float32 \ +--quantized_dtype uint8 \ +--input_path ${inputfile} \ +--input_data ${inputdata} \ +--input_data_format h5 \ +--granularity channel \ +--output_path ${outputfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_015.test b/compiler/one-cmds/tests/one-quantize_neg_015.test new file mode 100644 index 000000000..73864cc4b --- /dev/null +++ b/compiler/one-cmds/tests/one-quantize_neg_015.test @@ -0,0 +1,52 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with invalid min_percentile + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "Cannot open file" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +inputdata="./inception_v3_test_data.h5" +outputfile="./inception_v3.quantized.circle" + +rm -rf ${outputfile}.log + +# run test +one-quantize \ +--input_dtype float32 \ +--quantized_dtype uint8 \ +--input_path ${inputfile} \ +--input_data ${inputdata} \ +--input_data_format list \ +--granularity channel \ +--output_path ${outputfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_016.test b/compiler/one-cmds/tests/one-quantize_neg_016.test new file mode 100644 index 000000000..4090b1502 --- /dev/null +++ b/compiler/one-cmds/tests/one-quantize_neg_016.test @@ -0,0 +1,52 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with invalid min_percentile + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "Unsupported input data format" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +inputdata="./datalist.txt" +outputfile="./inception_v3.quantized.circle" + +rm -rf ${outputfile}.log + +# run test +one-quantize \ +--input_dtype float32 \ +--quantized_dtype uint8 \ +--input_path ${inputfile} \ +--input_data ${inputdata} \ +--input_data_format h5list \ +--granularity channel \ +--output_path ${outputfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/one-quantize_neg_017.test b/compiler/one-cmds/tests/one-quantize_neg_017.test new file mode 100644 index 000000000..b666ace4c --- /dev/null +++ b/compiler/one-cmds/tests/one-quantize_neg_017.test @@ -0,0 +1,52 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with invalid min_percentile + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "Cannot open directory" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="./inception_v3.circle" +inputdata="./inception_v3_test_data.h5" +outputfile="./inception_v3.quantized.circle" + +rm -rf ${outputfile}.log + +# run test +one-quantize \ +--input_dtype float32 \ +--quantized_dtype uint8 \ +--input_path ${inputfile} \ +--input_data ${inputdata} \ +--input_data_format directory \ +--granularity channel \ +--output_path ${outputfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/onecc_001.cfg b/compiler/one-cmds/tests/onecc_001.cfg new file mode 100644 index 000000000..4776ea80e --- /dev/null +++ b/compiler/one-cmds/tests/onecc_001.cfg @@ -0,0 +1,20 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=True +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v2 + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle diff --git a/compiler/one-cmds/tests/onecc_001.test b/compiler/one-cmds/tests/onecc_001.test new file mode 100644 index 000000000..ece0aa2da --- /dev/null +++ b/compiler/one-cmds/tests/onecc_001.test @@ -0,0 +1,40 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-optimize + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_001.cfg" +outputfile="inception_v3.opt.circle" + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_002.cfg b/compiler/one-cmds/tests/onecc_002.cfg new file mode 100644 index 000000000..fedaf118b --- /dev/null +++ b/compiler/one-cmds/tests/onecc_002.cfg @@ -0,0 +1,24 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=True +one-quantize=False +one-pack=True +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v2 + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle + +[one-pack] +input_path=inception_v3.opt.circle +output_path=inception_v3_pkg diff --git a/compiler/one-cmds/tests/onecc_002.test b/compiler/one-cmds/tests/onecc_002.test new file mode 100644 index 000000000..4075b7951 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_002.test @@ -0,0 +1,40 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-optimize -> one-pack + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_002.cfg" +outputfile="inception_v3_pkg" + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_003.cfg b/compiler/one-cmds/tests/onecc_003.cfg new file mode 100644 index 000000000..d0b3a0cd6 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_003.cfg @@ -0,0 +1,21 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=True +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 + +[one-quantize] +input_path=inception_v3.circle +output_path=inception_v3.quantized.circle +input_data=inception_v3_test_data.h5 diff --git a/compiler/one-cmds/tests/onecc_003.test b/compiler/one-cmds/tests/onecc_003.test new file mode 100644 index 000000000..9ee6877be --- /dev/null +++ b/compiler/one-cmds/tests/onecc_003.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-quantize + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_003.cfg" +outputfile="inception_v3.quantized.circle" + +rm -rf ${outputfile} + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_004.cfg b/compiler/one-cmds/tests/onecc_004.cfg new file mode 100644 index 000000000..614ba134a --- /dev/null +++ b/compiler/one-cmds/tests/onecc_004.cfg @@ -0,0 +1,20 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=True + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 + +[one-codegen] +backend=dummy +command=-o sample.tvn inception_v3.circle diff --git a/compiler/one-cmds/tests/onecc_004.test b/compiler/one-cmds/tests/onecc_004.test new file mode 100644 index 000000000..2eaffa6a5 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_004.test @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-codegen + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-compile + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_004.cfg" +outputfile="sample.tvn" + +rm -rf ${outputfile} + +# copy dummy-compile to bin folder +cp dummy-compile ../bin/dummy-compile + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +rm -rf ../bin/dummy-compile + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_005.cfg b/compiler/one-cmds/tests/onecc_005.cfg new file mode 100644 index 000000000..83a8230ad --- /dev/null +++ b/compiler/one-cmds/tests/onecc_005.cfg @@ -0,0 +1,20 @@ +[onecc] +one-import-tf=False +one-import-tflite=True +one-import-bcq=False +one-optimize=True +one-quantize=False +one-pack=False +one-codegen=True + +[one-import-tflite] +input_path=inception_v3.tflite +output_path=inception_v3.circle + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle + +[one-codegen] +backend=dummy +command=-o sample.tvn inception_v3.opt.circle diff --git a/compiler/one-cmds/tests/onecc_005.test b/compiler/one-cmds/tests/onecc_005.test new file mode 100644 index 000000000..a98c3e438 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_005.test @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tflite -> one-optimize -> one-codgen + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-compile + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_005.cfg" +outputfile="sample.tvn" + +rm -rf ${outputfile} + +# copy dummy-compile to bin folder +cp dummy-compile ../bin/dummy-compile + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +rm -rf ../bin/dummy-compile + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_006.cfg b/compiler/one-cmds/tests/onecc_006.cfg new file mode 100644 index 000000000..a7c893894 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_006.cfg @@ -0,0 +1,29 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=True +one-quantize=True +one-pack=False +one-codegen=True + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle + +[one-quantize] +input_path=inception_v3.opt.circle +output_path=inception_v3.quantized.circle +input_data=inception_v3_test_data.h5 + +[one-codegen] +backend=dummy +command=-o sample.tvn inception_v3.quantized.circle diff --git a/compiler/one-cmds/tests/onecc_006.test b/compiler/one-cmds/tests/onecc_006.test new file mode 100644 index 000000000..096c86182 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_006.test @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-optimize -> one-quantize -> one-codegen + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-compile + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_006.cfg" +outputfile="sample.tvn" + +rm -rf ${outputfile} + +# copy dummy-compile to bin folder +cp dummy-compile ../bin/dummy-compile + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +rm -rf ../bin/dummy-compile + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_007.cfg b/compiler/one-cmds/tests/onecc_007.cfg new file mode 100644 index 000000000..8775f8eae --- /dev/null +++ b/compiler/one-cmds/tests/onecc_007.cfg @@ -0,0 +1,29 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=True +one-pack=True +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle + +[one-quantize] +input_path=inception_v3.opt.circle +output_path=inception_v3.quantized.circle +input_data=inception_v3_test_data.h5 + +[one-pack] +input_path=inception_v3.quantized.circle +output_path=inception_v3_pkg diff --git a/compiler/one-cmds/tests/onecc_007.test b/compiler/one-cmds/tests/onecc_007.test new file mode 100644 index 000000000..883a9a7c7 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_007.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-optimize -> one-quantize -> one-pack + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_007.cfg" +outputfile="inception_v3_pkg" + +rm -rf ${outputfile} + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_008.cfg b/compiler/one-cmds/tests/onecc_008.cfg new file mode 100644 index 000000000..0be026e6e --- /dev/null +++ b/compiler/one-cmds/tests/onecc_008.cfg @@ -0,0 +1,23 @@ +[onecc] +one-import-tf=False +one-import-tflite=False +one-import-bcq=False +one-import-onnx=True +one-optimize=True +one-quantize=False +one-pack=False +one-codegen=True + +[one-import-onnx] +input_path=test_onnx_model.onnx +output_path=test_onnx_model.circle + +[one-optimize] +input_path=test_onnx_model.circle +output_path=test_onnx_model.opt.circle +all=True +remove_redundant_transpose=True + +[one-codegen] +backend=dummy +command=-o test_onnx_model.bin test_onnx_model.opt.circle diff --git a/compiler/one-cmds/tests/onecc_008.test b/compiler/one-cmds/tests/onecc_008.test new file mode 100644 index 000000000..5bd783162 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_008.test @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-optimize -> one-quantize -> one-codegen + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-compile + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_008.cfg" +outputfile="test_onnx_model.bin" + +rm -rf ${outputfile} + +# copy dummy-compile to bin folder +cp dummy-compile ../bin/dummy-compile + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +rm -rf ../bin/dummy-compile + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_009.cfg b/compiler/one-cmds/tests/onecc_009.cfg new file mode 100644 index 000000000..a17ae59cb --- /dev/null +++ b/compiler/one-cmds/tests/onecc_009.cfg @@ -0,0 +1,24 @@ +[onecc] +one-import-tf=False +one-import-tflite=False +one-import-bcq=False +one-import-onnx=True +one-optimize=True +one-quantize=False +one-pack=False +one-codegen=True + +[one-import-onnx] +input_path=onnx_conv2d_conv2d.onnx +output_path=onnx_conv2d_conv2d.circle + +[one-optimize] +input_path=onnx_conv2d_conv2d.circle +output_path=onnx_conv2d_conv2d.opt.circle +all=True +remove_redundant_transpose=True +convert_nchw_to_nhwc=True + +[one-codegen] +backend=dummy +command=-o onnx_conv2d_conv2d.bin onnx_conv2d_conv2d.opt.circle diff --git a/compiler/one-cmds/tests/onecc_009.test b/compiler/one-cmds/tests/onecc_009.test new file mode 100644 index 000000000..921ebebb2 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_009.test @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-onnx -> one-optimize -> one-codegen + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-compile + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_009.cfg" +outputfile="onnx_conv2d_conv2d.bin" + +rm -rf ${outputfile} + +# copy dummy-compile to bin folder +cp dummy-compile ../bin/dummy-compile + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +rm -rf ../bin/dummy-compile + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_010.cfg b/compiler/one-cmds/tests/onecc_010.cfg new file mode 100644 index 000000000..d9f2a3d83 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_010.cfg @@ -0,0 +1,17 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.alt.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 +save_intermediate=True diff --git a/compiler/one-cmds/tests/onecc_010.test b/compiler/one-cmds/tests/onecc_010.test new file mode 100644 index 000000000..87d7436cc --- /dev/null +++ b/compiler/one-cmds/tests/onecc_010.test @@ -0,0 +1,47 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf: intermediate file should exist + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_010.cfg" +outputfile="inception_v3.alt.circle" +intermfile="inception_v3.alt.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +if [[ ! -s "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_011.cfg b/compiler/one-cmds/tests/onecc_011.cfg new file mode 100644 index 000000000..aa9457f09 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_011.cfg @@ -0,0 +1,14 @@ +[onecc] +one-import-tf=False +one-import-tflite=False +one-import-bcq=False +one-import-onnx=True +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-onnx] +input_path=test_onnx_model.onnx +output_path=test_onnx_model.circle +save_intermediate=True diff --git a/compiler/one-cmds/tests/onecc_011.test b/compiler/one-cmds/tests/onecc_011.test new file mode 100644 index 000000000..ff8c1095d --- /dev/null +++ b/compiler/one-cmds/tests/onecc_011.test @@ -0,0 +1,47 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-onnx + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_011.cfg" +outputfile="test_onnx_model.circle" +intermfile="test_onnx_model.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +if [[ ! -s "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_012.cfg b/compiler/one-cmds/tests/onecc_012.cfg new file mode 100644 index 000000000..498b602a1 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_012.cfg @@ -0,0 +1,22 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=True +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 + +[one-quantize] +input_path=inception_v3.circle +output_path=inception_v3.list.quantized.circle +input_data=datalist.txt +input_data_format=list diff --git a/compiler/one-cmds/tests/onecc_012.test b/compiler/one-cmds/tests/onecc_012.test new file mode 100644 index 000000000..2c71e30e6 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_012.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import-tf -> one-quantize + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_012.cfg" +outputfile="inception_v3.list.quantized.circle" + +rm -rf ${outputfile} + +# run test +onecc -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_013.cfg b/compiler/one-cmds/tests/onecc_013.cfg new file mode 100644 index 000000000..f909ab7ab --- /dev/null +++ b/compiler/one-cmds/tests/onecc_013.cfg @@ -0,0 +1,7 @@ +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 diff --git a/compiler/one-cmds/tests/onecc_013.test b/compiler/one-cmds/tests/onecc_013.test new file mode 100644 index 000000000..bcb272045 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_013.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import {tf} with config file + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_013.cfg" +outputfile="inception_v3.circle" + +rm -rf ${outputfile} + +# run test +onecc import tf -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_014.cfg b/compiler/one-cmds/tests/onecc_014.cfg new file mode 100644 index 000000000..c9a37f18f --- /dev/null +++ b/compiler/one-cmds/tests/onecc_014.cfg @@ -0,0 +1,7 @@ +[one-import-tflite] +input_path=inception_v3.tflite +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v1 diff --git a/compiler/one-cmds/tests/onecc_014.test b/compiler/one-cmds/tests/onecc_014.test new file mode 100644 index 000000000..9d42cc3b6 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_014.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import {tflite} with config file + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_014.cfg" +outputfile="inception_v3.circle" + +rm -rf ${outputfile} + +# run test +onecc import tflite -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_015.cfg b/compiler/one-cmds/tests/onecc_015.cfg new file mode 100644 index 000000000..7abfa7d86 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_015.cfg @@ -0,0 +1,5 @@ +[one-import-bcq] +input_path=bcq.pb +output_path=bcq.circle +input_arrays=Placeholder +output_arrays=MatMul diff --git a/compiler/one-cmds/tests/onecc_015.test b/compiler/one-cmds/tests/onecc_015.test new file mode 100644 index 000000000..d74ba32b7 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_015.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import {bcq} with config file + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_015.cfg" +outputfile="bcq.circle" + +rm -rf ${outputfile} + +# run test +onecc import bcq -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_016.cfg b/compiler/one-cmds/tests/onecc_016.cfg new file mode 100644 index 000000000..ea0110bf9 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_016.cfg @@ -0,0 +1,3 @@ +[one-import-onnx] +input_path=test_onnx_model.onnx +output_path=test_onnx_model.circle diff --git a/compiler/one-cmds/tests/onecc_016.test b/compiler/one-cmds/tests/onecc_016.test new file mode 100644 index 000000000..582cd0681 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_016.test @@ -0,0 +1,42 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-import {onnx} with config file + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_016.cfg" +outputfile="test_onnx_model.circle" + +rm -rf ${outputfile} + +# run test +onecc import onnx -C ${configfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_017.test b/compiler/one-cmds/tests/onecc_017.test new file mode 100644 index 000000000..d996a28ba --- /dev/null +++ b/compiler/one-cmds/tests/onecc_017.test @@ -0,0 +1,47 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-optimize + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="inception_v3.circle" +outputfile="inception_v3.opt.circle" + +if [[ ! -s "${inputfile}" ]]; then + echo "${filename_ext} ERROR: Missing inputfile" + trap_err_onexit +fi + +rm -rf ${outputfile} + +# run test +onecc optimize -i ${inputfile} -o ${outputfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_018.test b/compiler/one-cmds/tests/onecc_018.test new file mode 100644 index 000000000..ed97603d3 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_018.test @@ -0,0 +1,48 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-quantize + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="inception_v3.opt.circle" +inputdata="inception_v3_test_data.h5" +outputfile="inception_v3.quantized.circle" + +if [[ ! -s "${inputfile}" && ! -s "${inputdata}" ]]; then + echo "${filename_ext} ERROR: Missing inputfile" + trap_err_onexit +fi + +rm -rf ${outputfile} + +# run test +onecc quantize -i ${inputfile} -o ${outputfile} -d ${inputdata} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_019.test b/compiler/one-cmds/tests/onecc_019.test new file mode 100644 index 000000000..9d66e3abf --- /dev/null +++ b/compiler/one-cmds/tests/onecc_019.test @@ -0,0 +1,47 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-pack + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="inception_v3.quantized.circle" +outputfile="inception_v3_pkg" + +if [[ ! -s "${inputfile}" ]]; then + echo "${filename_ext} ERROR: Missing inputfile" + trap_err_onexit +fi + +rm -rf ${outputfile} + +# run test +onecc pack -i ${inputfile} -o ${outputfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_020.test b/compiler/one-cmds/tests/onecc_020.test new file mode 100644 index 000000000..9afa91003 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_020.test @@ -0,0 +1,53 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-codegen + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +inputfile="sample.circle" +outputfile="sample.dummy" + +# prepare dummy file +touch ${inputfile} + +if [[ ! -f "${inputfile}" ]]; then + echo "${filename_ext} ERROR: Missing inputfile" + trap_err_onexit +fi + +rm -rf ${outputfile} + +# copy dummy-compile to bin folder +cp dummy-compile ../bin/dummy-compile + +# run test +onecc codegen -b dummy -o ${outputfile} ${inputfile} > /dev/null 2>&1 + +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_021.cfg b/compiler/one-cmds/tests/onecc_021.cfg new file mode 100644 index 000000000..e2d3e6bd8 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_021.cfg @@ -0,0 +1,14 @@ +[onecc] +one-import-tf=False +one-import-tflite=False +one-import-bcq=False +one-import-onnx=False +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False +one-profile=True + +[one-profile] +backend=dummy +command=test_onnx_model.bin diff --git a/compiler/one-cmds/tests/onecc_021.test b/compiler/one-cmds/tests/onecc_021.test new file mode 100644 index 000000000..906ed1876 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_021.test @@ -0,0 +1,46 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# one-profile + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + rm -rf ../bin/dummy-profile + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_021.cfg" + +# copy dummy-profile to bin folder +cp dummy-profile ../bin/dummy-profile + +# run test +onecc -C ${configfile} > ${filename}.log + +rm -rf ../bin/dummy-profile + +if grep -q "dummy-profile dummy output!!!" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 +fi + +trap_err_onexit diff --git a/compiler/one-cmds/tests/onecc_neg_001.test b/compiler/one-cmds/tests/onecc_neg_001.test new file mode 100644 index 000000000..0456a4281 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_001.test @@ -0,0 +1,41 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with missing configuration file + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "Not found given configuration file" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_neg_001.cfg" + +# run test +onecc -C ${configfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/onecc_neg_002.cfg b/compiler/one-cmds/tests/onecc_neg_002.cfg new file mode 100644 index 000000000..46347ea4a --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_002.cfg @@ -0,0 +1,20 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=True +one-quantize=False +one-pack=True +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v2 + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle diff --git a/compiler/one-cmds/tests/onecc_neg_002.test b/compiler/one-cmds/tests/onecc_neg_002.test new file mode 100644 index 000000000..7c81bab0a --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_002.test @@ -0,0 +1,41 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with missing one-pack section in configuration file + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "configuration file must have 'one-pack' section" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_neg_002.cfg" + +# run test +onecc -C ${configfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/onecc_neg_003.cfg b/compiler/one-cmds/tests/onecc_neg_003.cfg new file mode 100644 index 000000000..fa027cb95 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_003.cfg @@ -0,0 +1,15 @@ +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v2 + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle + +[one-pack] +input_path=inception_v3.opt.circle +output_path=inception_v3_pkg diff --git a/compiler/one-cmds/tests/onecc_neg_003.test b/compiler/one-cmds/tests/onecc_neg_003.test new file mode 100644 index 000000000..b36bf8cc4 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_003.test @@ -0,0 +1,41 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with missing onecc section in configuration file + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "\[onecc\] section is required in configuration file" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_neg_003.cfg" + +# run test +onecc -C ${configfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/onecc_neg_004.cfg b/compiler/one-cmds/tests/onecc_neg_004.cfg new file mode 100644 index 000000000..dac8cb7d4 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_004.cfg @@ -0,0 +1,24 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=True +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v2 + +[one-optimize] +input_path=inception_v3.circle +output_path=inception_v3.opt.circle + +[one-optimize] +input_path=inception_v4.circle +output_path=inception_v4.opt.circle diff --git a/compiler/one-cmds/tests/onecc_neg_004.test b/compiler/one-cmds/tests/onecc_neg_004.test new file mode 100644 index 000000000..5efb7c66c --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_004.test @@ -0,0 +1,41 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative usage with duplicate section + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "section 'one-optimize' already exists" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_neg_004.cfg" + +# run test +onecc -C ${configfile} > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/onecc_neg_005.cfg b/compiler/one-cmds/tests/onecc_neg_005.cfg new file mode 100644 index 000000000..1b449c750 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_005.cfg @@ -0,0 +1,16 @@ +[onecc] +one-import-tf=True +one-import-tflite=False +one-import-bcq=False +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-tf] +input_path=inception_v3.pb +output_path=inception_v3.alt.circle +input_arrays=input +input_shapes=1,299,299,3 +output_arrays=InceptionV3/Predictions/Reshape_1 +converter_version=v2 diff --git a/compiler/one-cmds/tests/onecc_neg_005.test b/compiler/one-cmds/tests/onecc_neg_005.test new file mode 100644 index 000000000..684d94027 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_005.test @@ -0,0 +1,49 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative one-import-tf intermediate file should not exist + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_neg_005.cfg" +outputfile="inception_v3.alt.circle" +intermfile="inception_v3.alt.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +onecc -C ${configfile} > ${filename}.log 2>&1 + +# output should exist +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +# intermediate file should not exist +if [[ -f "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_neg_006.cfg b/compiler/one-cmds/tests/onecc_neg_006.cfg new file mode 100644 index 000000000..63ef42bfb --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_006.cfg @@ -0,0 +1,13 @@ +[onecc] +one-import-tf=False +one-import-tflite=False +one-import-bcq=False +one-import-onnx=True +one-optimize=False +one-quantize=False +one-pack=False +one-codegen=False + +[one-import-onnx] +input_path=test_onnx_model.onnx +output_path=test_onnx_model.circle diff --git a/compiler/one-cmds/tests/onecc_neg_006.test b/compiler/one-cmds/tests/onecc_neg_006.test new file mode 100644 index 000000000..27b4f502d --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_006.test @@ -0,0 +1,49 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative one-import-tf intermediate file should not exist + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +configfile="onecc_neg_006.cfg" +outputfile="test_onnx_model.circle" +intermfile="test_onnx_model.tflite" + +rm -rf ${outputfile} +rm -rf ${intermfile} + +# run test +onecc -C ${configfile} > ${filename}.log 2>&1 + +# output should exist +if [[ ! -s "${outputfile}" ]]; then + trap_err_onexit +fi +# intermediate file should not exist +if [[ -f "${intermfile}" ]]; then + trap_err_onexit +fi + +echo "${filename_ext} SUCCESS" diff --git a/compiler/one-cmds/tests/onecc_neg_007.test b/compiler/one-cmds/tests/onecc_neg_007.test new file mode 100644 index 000000000..bc32f2366 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_007.test @@ -0,0 +1,39 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative subcommand + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "unrecognized arguments" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +# run test +onecc wronginput > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/tests/onecc_neg_008.test b/compiler/one-cmds/tests/onecc_neg_008.test new file mode 100644 index 000000000..ff3930249 --- /dev/null +++ b/compiler/one-cmds/tests/onecc_neg_008.test @@ -0,0 +1,39 @@ +#!/bin/bash + +# Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# negative subcommand with empty argument + +filename_ext="$(basename -- $0)" +filename="${filename_ext%.*}" + +trap_err_onexit() +{ + if grep -q "error" "${filename}.log"; then + echo "${filename_ext} SUCCESS" + exit 0 + fi + + echo "${filename_ext} FAILED" + exit 255 +} + +trap trap_err_onexit ERR + +# run test +onecc > ${filename}.log 2>&1 + +echo "${filename_ext} FAILED" +exit 255 diff --git a/compiler/one-cmds/utils.py b/compiler/one-cmds/utils.py index 1d5c4d4fd..f18dc6f56 100644 --- a/compiler/one-cmds/utils.py +++ b/compiler/one-cmds/utils.py @@ -29,10 +29,10 @@ class _CONSTANT: ('convert_nchw_to_nhwc', 'Experimental: This will convert NCHW operators to NHWC under the assumption that input model is NCHW.' ), - ('nchw_to_nhwc_preserve_input_shape', - 'preserve the input shape of the model (argument for convert_nchw_to_nhwc)'), - ('nchw_to_nhwc_preserve_output_shape', - 'preserve the output shape of the model (argument for convert_nchw_to_nhwc)'), + ('nchw_to_nhwc_input_shape', + 'convert the input shape of the model (argument for convert_nchw_to_nhwc)'), + ('nchw_to_nhwc_output_shape', + 'convert the output shape of the model (argument for convert_nchw_to_nhwc)'), ('fold_add_v2', 'fold AddV2 op with constant inputs'), ('fold_cast', 'fold Cast op with constant input'), ('fold_dequantize', 'fold Dequantize op'), @@ -45,6 +45,9 @@ class _CONSTANT: ('fuse_bcq', 'apply Binary Coded Quantization'), ('fuse_preactivation_batchnorm', 'fuse BatchNorm operators of pre-activations to Convolution op'), + ('fuse_mean_with_mean', 'fuse two consecutive Mean ops'), + ('fuse_transpose_with_mean', + 'fuse Mean with a preceding Transpose under certain conditions'), ('make_batchnorm_gamma_positive', 'make negative gamma of BatchNorm to a small positive value (1e-10).' ' Note that this pass can change the execution result of the model.' @@ -53,6 +56,8 @@ class _CONSTANT: ('fuse_instnorm', 'fuse ops to InstanceNorm operator'), ('replace_cw_mul_add_with_depthwise_conv', 'replace channel-wise Mul/Add with DepthwiseConv2D'), + ('remove_fakequant', 'remove FakeQuant ops'), + ('remove_quantdequant', 'remove Quantize-Dequantize sequence'), ('remove_redundant_reshape', 'fuse or remove subsequent Reshape ops'), ('remove_redundant_transpose', 'fuse or remove subsequent Transpose ops'), ('remove_unnecessary_reshape', 'remove unnecessary reshape ops'), @@ -63,14 +68,19 @@ class _CONSTANT: ('resolve_customop_batchmatmul', 'convert Custom(BatchMatmul) op to BatchMatmul op'), ('resolve_customop_matmul', 'convert Custom(Matmul) op to Matmul op'), + ('resolve_customop_max_pool_with_argmax', + 'convert Custom(MaxPoolWithArgmax) to net of builtin operators'), ('shuffle_weight_to_16x1float32', 'convert weight format of FullyConnected op to SHUFFLED16x1FLOAT32.' ' Note that it only converts weights whose row is a multiple of 16'), ('substitute_pack_to_reshape', 'convert single input Pack op to Reshape op'), ('substitute_squeeze_to_reshape', 'convert certain condition Squeeze to Reshape'), + ('substitute_strided_slice_to_reshape', + 'convert certain condition StridedSlice to Reshape'), ('substitute_transpose_to_reshape', 'convert certain condition Transpose to Reshape'), - ('transform_min_max_to_relu6', 'transform Minimum-Maximum pattern to Relu6 op')) + ('transform_min_max_to_relu6', 'transform Minimum-Maximum pattern to Relu6 op'), + ('transform_min_relu_to_relu6', 'transform Minimum(6)-Relu pattern to Relu6 op')) _CONSTANT = _CONSTANT() @@ -84,6 +94,13 @@ def _add_default_arg(parser): action='store_true', help='show program\'s version number and exit') + # verbose + parser.add_argument( + '-V', + '--verbose', + action='store_true', + help='output additional information to stdout or stderr') + # configuration file parser.add_argument('-C', '--config', type=str, help='run with configuation file') # section name that you want to run in configuration file @@ -99,6 +116,7 @@ def _parse_cfg(args, driver_name): the option is processed prior to the configuration file.""" if _is_valid_attr(args, 'config'): config = configparser.ConfigParser() + config.optionxform = str config.read(args.config) # if section is given, verify given section if _is_valid_attr(args, 'section'): @@ -122,6 +140,9 @@ def _parse_cfg(args, driver_name): def _make_tf2tfliteV2_cmd(args, driver_path, input_path, output_path): """make a command for running tf2tfliteV2.py""" cmd = [sys.executable, os.path.expanduser(driver_path)] + # verbose + if _is_valid_attr(args, 'verbose'): + cmd.append('--verbose') # model_format if _is_valid_attr(args, 'model_format_cmd'): cmd.append(getattr(args, 'model_format_cmd')) @@ -172,10 +193,23 @@ def _make_circle2circle_cmd(args, driver_path, input_path, output_path): # profiling if _is_valid_attr(args, 'generate_profile_data'): cmd.append('--generate_profile_data') - # optimization pass + # optimization pass(only true/false options) + # TODO support options whose number of arguments is more than zero for opt in _CONSTANT.OPTIMIZATION_OPTS: if _is_valid_attr(args, opt[0]): - cmd.append('--' + opt[0]) + # ./driver --opt[0] + if type(getattr(args, opt[0])) is bool: + cmd.append('--' + opt[0]) + """ + This condition check is for config file interface, usually would be + SomeOption=True + but user can write as follows while development + SomeOption=False + instead of removing SomeOption option + """ + if type(getattr(args, opt[0])) is str and not getattr( + args, opt[0]).lower() in ['false', '0', 'n']: + cmd.append('--' + opt[0]) return cmd @@ -188,3 +222,53 @@ def _print_version_and_exit(file_path): # run one-version subprocess.call([os.path.join(dir_path, 'one-version'), script_name]) sys.exit() + + +def _safemain(main, mainpath): + """execute given method and print with program name for all uncaught exceptions""" + try: + main() + except Exception as e: + prog_name = os.path.basename(mainpath) + print(f"{prog_name}: {type(e).__name__}: " + str(e), file=sys.stderr) + sys.exit(255) + + +def _run(cmd, err_prefix=None, logfile=None): + """Execute command in subprocess + + Args: + cmd: command to be executed in subprocess + err_prefix: prefix to be put before every stderr lines + logfile: file stream to which both of stdout and stderr lines will be written + """ + if logfile == None: + with subprocess.Popen(cmd, stderr=subprocess.PIPE, bufsize=1) as p: + for line in p.stderr: + if err_prefix: + line = f"{err_prefix}: ".encode() + line + sys.stderr.buffer.write(line) + sys.stderr.buffer.flush() + else: + with subprocess.Popen( + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=1) as p: + import select + inputs = set([p.stdout, p.stderr]) + while inputs: + readable, _, _ = select.select(inputs, [], []) + for x in readable: + line = x.readline() + if len(line) == 0: + inputs.discard(x) + continue + if x == p.stdout: + out = sys.stdout + if x == p.stderr: + out = sys.stderr + if err_prefix: + line = f"{err_prefix}: ".encode() + line + out.buffer.write(line) + out.buffer.flush() + logfile.write(line) + if p.returncode != 0: + sys.exit(p.returncode) diff --git a/compiler/onnxkit/src/Support.cpp b/compiler/onnxkit/src/Support.cpp index 151290a00..7740fac6c 100644 --- a/compiler/onnxkit/src/Support.cpp +++ b/compiler/onnxkit/src/Support.cpp @@ -35,7 +35,7 @@ std::unique_ptr<T> open_fstream(const std::string &path, std::ios_base::openmode auto stream = std::make_unique<T>(path.c_str(), mode); if (!stream->is_open()) { - throw std::runtime_error{"ERROR: Failed to open " + path}; + throw std::runtime_error{"Failed to open " + path}; } return stream; } diff --git a/compiler/pepper-csv2vec/CMakeLists.txt b/compiler/pepper-csv2vec/CMakeLists.txt new file mode 100644 index 000000000..8c739aad9 --- /dev/null +++ b/compiler/pepper-csv2vec/CMakeLists.txt @@ -0,0 +1,19 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") +file(GLOB_RECURSE TESTS "src/*.test.cpp") +list(REMOVE_ITEM SOURCES ${TESTS}) + +add_library(pepper_csv2vec STATIC ${SOURCES}) +set_target_properties(pepper_csv2vec PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(pepper_csv2vec PUBLIC include) +target_link_libraries(pepper_csv2vec PRIVATE nncc_common) +target_link_libraries(pepper_csv2vec PUBLIC nncc_coverage) + +if(NOT ENABLE_TEST) + return() +endif(NOT ENABLE_TEST) + +# Google Test is mandatory for test +nnas_find_package(GTest REQUIRED) + +GTest_AddTest(pepper_csv2vec_test ${TESTS}) +target_link_libraries(pepper_csv2vec_test pepper_csv2vec) diff --git a/compiler/pepper-csv2vec/README.md b/compiler/pepper-csv2vec/README.md new file mode 100644 index 000000000..a42cb0b0e --- /dev/null +++ b/compiler/pepper-csv2vec/README.md @@ -0,0 +1,3 @@ +# pepper-csv2vec + +Returns `std::vector<T>` from CSV format string input. diff --git a/compiler/circle-partitioner/src/HelperStrings.h b/compiler/pepper-csv2vec/include/pepper/csv2vec.h index 2af14c1ff..7027b25b2 100644 --- a/compiler/circle-partitioner/src/HelperStrings.h +++ b/compiler/pepper-csv2vec/include/pepper/csv2vec.h @@ -14,19 +14,19 @@ * limitations under the License. */ -#ifndef __CIRCLE_HELPER_STRINGS_H__ -#define __CIRCLE_HELPER_STRINGS_H__ +#ifndef __PEPPER_CSV2VEC_H__ +#define __PEPPER_CSV2VEC_H__ #include <string> #include <vector> -namespace partee +namespace pepper { template <typename T> std::vector<T> csv_to_vector(const std::string &str); -bool is_one_of(const std::string &item, const std::vector<std::string> &items); +template <typename T> bool is_one_of(const T &item, const std::vector<T> &items); -} // namespace partee +} // namespace pepper -#endif // __CIRCLE_HELPER_STRINGS_H__ +#endif // __PEPPER_CSV2VEC_H__ diff --git a/compiler/circle-partitioner/src/HelperStrings.cpp b/compiler/pepper-csv2vec/src/pepper-csv2vec.cpp index 96d000c74..8b56ec883 100644 --- a/compiler/circle-partitioner/src/HelperStrings.cpp +++ b/compiler/pepper-csv2vec/src/pepper-csv2vec.cpp @@ -14,12 +14,13 @@ * limitations under the License. */ -#include "HelperStrings.h" +#include "pepper/csv2vec.h" #include <algorithm> #include <sstream> +#include <cassert> -namespace partee +namespace pepper { template <> std::vector<std::string> csv_to_vector(const std::string &str) @@ -33,9 +34,25 @@ template <> std::vector<std::string> csv_to_vector(const std::string &str) return ret; } -bool is_one_of(const std::string &item, const std::vector<std::string> &items) +// TODO merge std::string and int32_t type + +template <> std::vector<int32_t> csv_to_vector(const std::string &str) +{ + std::vector<int32_t> ret; + std::istringstream is(str); + for (int32_t i; is >> i;) + { + assert(i != ','); + ret.push_back(i); + if (is.peek() == ',') + is.ignore(); + } + return ret; +} + +template <> bool is_one_of(const std::string &item, const std::vector<std::string> &items) { return std::find(items.begin(), items.end(), item) != items.end(); } -} // namespace partee +} // namespace pepper diff --git a/compiler/pepper-csv2vec/src/pepper-csv2vec.test.cpp b/compiler/pepper-csv2vec/src/pepper-csv2vec.test.cpp new file mode 100644 index 000000000..0067f86ec --- /dev/null +++ b/compiler/pepper-csv2vec/src/pepper-csv2vec.test.cpp @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "pepper/csv2vec.h" + +#include <gtest/gtest.h> + +TEST(csv2vec, simple_string) +{ + auto ret = pepper::csv_to_vector<std::string>("hello,world"); + + ASSERT_EQ(2, ret.size()); + ASSERT_TRUE("hello" == ret.at(0)); + ASSERT_TRUE("world" == ret.at(1)); +} + +TEST(csv2vec, simple_int32) +{ + auto ret = pepper::csv_to_vector<int32_t>("1,2,3"); + + ASSERT_EQ(3, ret.size()); + ASSERT_EQ(1, ret.at(0)); + ASSERT_EQ(3, ret.at(2)); +} + +TEST(csv2vec, is_one_of) +{ + auto ret = pepper::csv_to_vector<std::string>("hello,world"); + + ASSERT_TRUE(pepper::is_one_of<std::string>("hello", ret)); + ASSERT_FALSE(pepper::is_one_of<std::string>("good", ret)); +} + +TEST(csv2vec, empty_string_NEG) +{ + // should not abort + EXPECT_NO_THROW(pepper::csv_to_vector<std::string>("")); +} + +TEST(csv2vec, invalid_int32_NEG) +{ + auto ret = pepper::csv_to_vector<int32_t>("hello,world"); + + ASSERT_EQ(0, ret.size()); +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm1.json b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm1.json index 239a3a46d..2fb0c68d8 100644 --- a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm1.json +++ b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm1.json @@ -1,4 +1,4 @@ { - "scale": 0.00014586378529202193, + "scale": 0.00014983004075475037, "zero_point": 0.0 } diff --git a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm2.json b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm2.json index b4422f49e..2fb0c68d8 100644 --- a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm2.json +++ b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/int16/quantization/ofm2.json @@ -1,4 +1,4 @@ { - "scale": 0.00014956798986531794, + "scale": 0.00014983004075475037, "zero_point": 0.0 } diff --git a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm1.json b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm1.json index 3c0134839..aaba6131c 100644 --- a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm1.json +++ b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm1.json @@ -1,4 +1,4 @@ { - "scale": 0.035256847739219666, - "zero_point": 123.0 + "scale": 0.038689617067575455, + "zero_point": 128.0 } diff --git a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm2.json b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm2.json index 20ebde60e..aaba6131c 100644 --- a/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm2.json +++ b/compiler/pota-quantization-value-test/expected_outputs/Split_000/channel/uint8/quantization/ofm2.json @@ -1,4 +1,4 @@ { - "scale": 0.0385618582367897, - "zero_point": 129.0 + "scale": 0.038689617067575455, + "zero_point": 128.0 } diff --git a/compiler/rawdata2hdf5/rawdata2hdf5 b/compiler/rawdata2hdf5/rawdata2hdf5 index db4296d73..bb46d3a39 100644 --- a/compiler/rawdata2hdf5/rawdata2hdf5 +++ b/compiler/rawdata2hdf5/rawdata2hdf5 @@ -1,5 +1,5 @@ #!/usr/bin/env bash -''''export SCRIPT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" # ''' +''''export SCRIPT_PATH="$(cd "$(dirname "$(readlink -f "${BASH_SOURCE[0]}")")" && pwd)" # ''' ''''export PY_PATH=${SCRIPT_PATH}/venv/bin/python # ''' ''''test -f ${PY_PATH} && exec ${PY_PATH} "$0" "$@" # ''' ''''echo "Error: Virtual environment not found. Please run 'one-prepare-venv' command." # ''' diff --git a/compiler/record-minmax/driver/Driver.cpp b/compiler/record-minmax/driver/Driver.cpp index 6dbb693b2..c9f1d0ca7 100644 --- a/compiler/record-minmax/driver/Driver.cpp +++ b/compiler/record-minmax/driver/Driver.cpp @@ -41,6 +41,12 @@ int entry(const int argc, char **argv) .help("Show version information and exit") .exit_with(print_version); + arser.add_argument("-V", "--verbose") + .nargs(0) + .required(false) + .default_value(false) + .help("output additional information to stdout or stderr"); + arser.add_argument("--input_model") .nargs(1) .type(arser::DataType::STR) @@ -76,6 +82,11 @@ int entry(const int argc, char **argv) .type(arser::DataType::STR) .help("Record mode. percentile (default) or moving_average"); + arser.add_argument("--input_data_format") + .nargs(1) + .type(arser::DataType::STR) + .help("Input data format. h5/hdf5 (default) or list/filelist"); + arser.add_argument("--generate_profile_data") .nargs(0) .required(false) @@ -93,6 +104,13 @@ int entry(const int argc, char **argv) return 255; } + if (arser.get<bool>("--verbose")) + { + // The third parameter of setenv means REPLACE. + // If REPLACE is zero, it does not overwrite an existing value. + setenv("LUCI_LOG", "100", 0); + } + auto settings = luci::UserSettings::settings(); auto input_model_path = arser.get<std::string>("--input_model"); @@ -102,6 +120,7 @@ int entry(const int argc, char **argv) std::string mode("percentile"); float min_percentile = 1.0; float max_percentile = 99.0; + std::string input_data_format("h5"); if (arser["--min_percentile"]) min_percentile = arser.get<float>("--min_percentile"); @@ -118,6 +137,9 @@ int entry(const int argc, char **argv) if (arser["--generate_profile_data"]) settings->set(luci::UserSettings::Key::ProfilingDataGen, true); + if (arser["--input_data_format"]) + input_data_format = arser.get<std::string>("--input_data_format"); + RecordMinMax rmm; // Initialize interpreter and observer @@ -127,8 +149,35 @@ int entry(const int argc, char **argv) { auto input_data_path = arser.get<std::string>("--input_data"); - // Profile min/max while executing the given input data - rmm.profileData(mode, input_data_path, min_percentile, max_percentile); + if (input_data_format == "h5" || input_data_format == "hdf5") + { + // Profile min/max while executing the H5 data + rmm.profileData(mode, input_data_path, min_percentile, max_percentile); + } + // input_data is a text file having a file path in each line. + // Each data file is composed of inputs of a model, concatenated in + // the same order with the input index of the model + // + // For example, for a model with n inputs, the contents of each data + // file can be visualized as below + // [input 1][input 2]...[input n] + // |start............end of file| + else if (input_data_format == "list" || input_data_format == "filelist") + { + // Profile min/max while executing the list of Raw data + rmm.profileRawData(mode, input_data_path, min_percentile, max_percentile); + } + else if (input_data_format == "directory" || input_data_format == "dir") + { + // Profile min/max while executing all files under the given directory + // The contents of each file is same as the raw data in the 'list' type + rmm.profileRawDataDirectory(mode, input_data_path, min_percentile, max_percentile); + } + else + { + throw std::runtime_error( + "Unsupported input data format (supported formats: h5/hdf5 (default), list/filelist)"); + } } else { diff --git a/compiler/record-minmax/include/RecordFunction.h b/compiler/record-minmax/include/RecordFunction.h index c34aee0e1..ba199d071 100644 --- a/compiler/record-minmax/include/RecordFunction.h +++ b/compiler/record-minmax/include/RecordFunction.h @@ -33,12 +33,6 @@ float getNthPercentile(std::vector<float> &vector, float percentile) if (percentile < 0 || percentile > 100) throw std::runtime_error("Percentile must be ranged from 0 to 100"); - if (percentile == 0.0) - return vector.front(); - - if (percentile == 100.0) - return vector.back(); - if (vector.empty()) throw std::runtime_error("Percentile must take a non-empty vector as an argument"); @@ -49,6 +43,12 @@ float getNthPercentile(std::vector<float> &vector, float percentile) copy.assign(vector.begin(), vector.end()); std::sort(copy.begin(), copy.end()); + if (percentile == 0.0) + return copy.front(); + + if (percentile == 100.0) + return copy.back(); + int index = static_cast<int>(std::floor((copy.size() - 1) * percentile / 100.0)); float percent_i = static_cast<float>(index) / static_cast<float>(copy.size() - 1); diff --git a/compiler/record-minmax/include/RecordMinMax.h b/compiler/record-minmax/include/RecordMinMax.h index 85ae4cdc7..aaf1161e0 100644 --- a/compiler/record-minmax/include/RecordMinMax.h +++ b/compiler/record-minmax/include/RecordMinMax.h @@ -39,6 +39,12 @@ public: void profileData(const std::string &mode, const std::string &input_data_path, float min_percentile, float max_percentile); + void profileRawData(const std::string &mode, const std::string &input_data_path, + float min_percentile, float max_percentile); + + void profileRawDataDirectory(const std::string &mode, const std::string &input_data_path, + float min_percentile, float max_percentile); + void profileDataWithRandomInputs(const std::string &mode, float min_percentile, float max_percentile); diff --git a/compiler/record-minmax/src/MinMaxObserver.cpp b/compiler/record-minmax/src/MinMaxObserver.cpp index 40c9b730d..28ae2b33b 100644 --- a/compiler/record-minmax/src/MinMaxObserver.cpp +++ b/compiler/record-minmax/src/MinMaxObserver.cpp @@ -46,13 +46,6 @@ void MinMaxObserver::postTensorWrite(const luci::CircleNode *node, return; } - if (node->opcode() == luci::CircleOpcode::ARG_MAX) - { - // Output of arg_max is the index of the largest value across axes of a tensor - // this should not be quantized - return; - } - if (node->dtype() == DataType::BOOL) { // Bool type tensor is not quantized @@ -61,7 +54,23 @@ void MinMaxObserver::postTensorWrite(const luci::CircleNode *node, // Only support recording of float32 values if (tensor->element_type() != DataType::FLOAT32) - throw std::runtime_error("Tensor's data type is not float"); + { + // Exceptions that should be processed in backends + switch (node->opcode()) + { + case luci::CircleOpcode::ARG_MAX: + // Output of arg_max is the index of the largest value across axes of a tensor. + // It always has integer type. + case luci::CircleOpcode::CAST: + // Cast is quantized only if it converts <type> -> float. + // Other cases should be processed in backends. + case luci::CircleOpcode::RESHAPE: + // Reshape changes only shape of input tensor, efficiently is it a no-op. + return; + default: + throw std::runtime_error("Tensor's data type is not float"); + } + } const auto data = tensor->data<float>(); const auto num_elements = tensor->shape().num_elements(); @@ -77,6 +86,10 @@ void MinMaxObserver::postTensorWrite(const luci::CircleNode *node, if (isnan(number)) continue; + // TODO use metadata hints to detect such cases + if (number == std::numeric_limits<float>::lowest()) + continue; + all_nan = false; if (number > max) diff --git a/compiler/record-minmax/src/RecordMinMax.cpp b/compiler/record-minmax/src/RecordMinMax.cpp index 333ff5e3b..c249960f8 100644 --- a/compiler/record-minmax/src/RecordMinMax.cpp +++ b/compiler/record-minmax/src/RecordMinMax.cpp @@ -24,6 +24,7 @@ #include <luci/CircleFileExpContract.h> #include <luci/IR/CircleQuantParam.h> +#include <dirent.h> #include <algorithm> #include <cmath> #include <fstream> @@ -38,6 +39,17 @@ using DataType = luci_interpreter::DataType; namespace { +void readDataFromFile(const std::string &filename, std::vector<char> &data, size_t data_size) +{ + assert(data.size() == data_size); // FIX_CALLER_UNLESS + + std::ifstream fs(filename, std::ifstream::binary); + if (fs.fail()) + throw std::runtime_error("Cannot open file \"" + filename + "\".\n"); + if (fs.read(data.data(), data_size).fail()) + throw std::runtime_error("Failed to read data from file \"" + filename + "\".\n"); +} + std::vector<uint8_t> genRandomBoolData(std::mt19937 &gen, uint32_t num_elements) { std::uniform_int_distribution<> dist(0, 1); @@ -134,14 +146,14 @@ void RecordMinMax::initialize(const std::string &input_model_path) model_data.size()}; if (!circle::VerifyModelBuffer(verifier)) { - throw std::runtime_error("ERROR: Failed to verify circle '" + input_model_path + "'"); + throw std::runtime_error("Failed to verify circle '" + input_model_path + "'"); } _module = luci::Importer().importModule(circle::GetModel(model_data.data())); if (_module == nullptr) { - throw std::runtime_error("ERROR: Failed to load '" + input_model_path + "'"); + throw std::runtime_error("Failed to load '" + input_model_path + "'"); } // Initialize interpreter @@ -152,6 +164,133 @@ void RecordMinMax::initialize(const std::string &input_model_path) _interpreter->attachObserver(_observer.get()); } +// input_data_path is a path to the directory +// The directory should contain binary files each of which is a raw data, +// ready to be consumed by the input circle model without any modification +// TODO reduce duplicate codes with profileRawData +void RecordMinMax::profileRawDataDirectory(const std::string &mode, + const std::string &input_data_path, float min_percentile, + float max_percentile) +{ + struct dirent *entry = nullptr; + DIR *dp = nullptr; + + dp = opendir(input_data_path.c_str()); + if (not dp) + throw std::runtime_error("Cannot open directory. Please check \"" + input_data_path + + "\" is a directory.\n"); + + uint32_t num_records = 0; + const auto input_nodes = loco::input_nodes(_module->graph()); + + // Get total input size + uint32_t total_input_size = 0; + for (auto input : input_nodes) + { + const auto *input_node = loco::must_cast<const luci::CircleInput *>(input); + total_input_size += getTensorSize(input_node); + } + + while (entry = readdir(dp)) + { + // Skip if the entry is not a regular file + if (entry->d_type != DT_REG) + continue; + + const std::string filename = entry->d_name; + std::cout << "Recording " << num_records << "'th data" << std::endl; + + // Read data from file to buffer + // Assumption: For a multi-input model, the binary file should have inputs concatenated in the + // same order with the input index. + std::vector<char> input_data(total_input_size); + readDataFromFile(input_data_path + "/" + filename, input_data, total_input_size); + + // Write data from buffer to interpreter + uint32_t offset = 0; + for (auto input : input_nodes) + { + const auto *input_node = loco::must_cast<const luci::CircleInput *>(input); + const auto input_size = getTensorSize(input_node); + _interpreter->writeInputTensor(input_node, input_data.data() + offset, input_size); + + offset += input_size; + } + + _interpreter->interpret(); + + num_records++; + } + + closedir(dp); + + if (num_records == 0) + throw std::runtime_error("The input data file does not contain any record."); + + std::cout << "Recording finished. Number of recorded data: " << num_records << std::endl; + + update_quantparam(_observer.get(), mode, min_percentile, max_percentile); +} + +// input_data_path is a text file which specifies the representative data +// The text file should contain absolute file path per line. +// The pointed file should be a binary file containing one representative data, +// ready to be consumed by the input circle model without any modification +// NOTE If a model has multiple inputs, the binary file should have inputs concatenated in the same +// order with the input index of the circle model. +void RecordMinMax::profileRawData(const std::string &mode, const std::string &input_data_path, + float min_percentile, float max_percentile) +{ + std::ifstream input_file(input_data_path); + if (input_file.fail()) + throw std::runtime_error("Cannot open file \"" + input_data_path + "\".\n"); + + std::string record; + uint32_t num_records = 0; + const auto input_nodes = loco::input_nodes(_module->graph()); + + // Get total input size + uint32_t total_input_size = 0; + for (auto input : input_nodes) + { + const auto *input_node = loco::must_cast<const luci::CircleInput *>(input); + total_input_size += getTensorSize(input_node); + } + + while (getline(input_file, record)) + { + std::cout << "Recording " << num_records << "'th data" << std::endl; + + // Read data from file to buffer + // Assumption: For a multi-input model, the binary file should have inputs concatenated in the + // same order with the input index. + std::vector<char> input_data(total_input_size); + readDataFromFile(record, input_data, total_input_size); + + // Write data from buffer to interpreter + uint32_t offset = 0; + for (auto input : input_nodes) + { + const auto *input_node = loco::must_cast<const luci::CircleInput *>(input); + const auto input_size = getTensorSize(input_node); + _interpreter->writeInputTensor(input_node, input_data.data() + offset, input_size); + + offset += input_size; + } + + _interpreter->interpret(); + + num_records++; + } + + if (num_records == 0) + throw std::runtime_error("The input data file does not contain any record."); + + std::cout << "Recording finished. Number of recorded data: " << num_records << std::endl; + + update_quantparam(_observer.get(), mode, min_percentile, max_percentile); +} + void RecordMinMax::profileData(const std::string &mode, const std::string &input_data_path, float min_percentile, float max_percentile) { @@ -174,8 +313,7 @@ void RecordMinMax::profileData(const std::string &mode, const std::string &input if (num_inputs != importer.numInputs(record_idx)) throw std::runtime_error("Wrong number of inputs."); - if (record_idx % 100 == 0) - std::cout << "Recording " << record_idx << "'th data" << std::endl; + std::cout << "Recording " << record_idx << "'th data" << std::endl; for (int32_t input_idx = 0; input_idx < num_inputs; input_idx++) { @@ -294,7 +432,7 @@ void RecordMinMax::saveModel(const std::string &output_model_path) if (!exporter.invoke(&contract)) { - throw std::runtime_error("ERROR: Failed to export '" + output_model_path + "'"); + throw std::runtime_error("Failed to export '" + output_model_path + "'"); } } diff --git a/compiler/souschef/include/souschef/Data/Explicit.h b/compiler/souschef/include/souschef/Data/Explicit.h index 6e5ee819e..7cbb773da 100644 --- a/compiler/souschef/include/souschef/Data/Explicit.h +++ b/compiler/souschef/include/souschef/Data/Explicit.h @@ -59,6 +59,27 @@ private: std::vector<T> _values; }; +template <> class ExplicitDataChef<std::string> final : public DataChef +{ +public: + ExplicitDataChef() + { + // DO NOTHING + } + +public: + std::vector<uint8_t> generate(int32_t count) const override; + +public: + void insert(const std::string &value) { _values.emplace_back(value); } + +private: + void write_value(std::vector<uint8_t> &res, int32_t value) const; + +private: + std::vector<std::string> _values; +}; + template <typename T> struct ExplicitDataChefFactory : public DataChefFactory { std::unique_ptr<DataChef> create(const Arguments &args) const diff --git a/compiler/souschef/src/Explicit.cpp b/compiler/souschef/src/Explicit.cpp new file mode 100644 index 000000000..eb36cb7c3 --- /dev/null +++ b/compiler/souschef/src/Explicit.cpp @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "souschef/Data/Explicit.h" + +#include <string> +#include <vector> + +namespace souschef +{ + +/** + * @note This emulates TensorFlow int DynamicBuffer::WriteToBuffer(char** buffer) method + * Memory structure: + * int32_t count + * int32_t offsets[count + 1] + * string values[count] + * where string is like std::string without ending null byte + */ +std::vector<uint8_t> ExplicitDataChef<std::string>::generate(int32_t count) const +{ + std::vector<uint8_t> res; + + // write count + write_value(res, count); + + // write first item offset + int32_t start = sizeof(int32_t) * (count + 2); + write_value(res, start); + + // write succeeding items offset (or the end) + int32_t offset = start; + for (uint32_t n = 0; n < count; ++n) + { + std::string const value = (n < _values.size()) ? _values.at(n) : std::string{}; + offset += value.length(); + write_value(res, offset); + } + + for (uint32_t n = 0; n < count; ++n) + { + std::string const value = (n < _values.size()) ? _values.at(n) : std::string{}; + const uint8_t *arr = reinterpret_cast<const uint8_t *>(value.c_str()); + + for (uint32_t b = 0; b < value.length(); ++b) + { + res.emplace_back(arr[b]); + } + } + + return res; +} + +void ExplicitDataChef<std::string>::write_value(std::vector<uint8_t> &res, int32_t value) const +{ + const uint8_t *arr = reinterpret_cast<const uint8_t *>(&value); + + for (uint32_t b = 0; b < sizeof(int32_t); ++b) + { + res.emplace_back(arr[b]); + } +} + +} // namespace souschef diff --git a/compiler/souschef/src/LexicalCast.cpp b/compiler/souschef/src/LexicalCast.cpp index 1af6e30f9..8ab877015 100644 --- a/compiler/souschef/src/LexicalCast.cpp +++ b/compiler/souschef/src/LexicalCast.cpp @@ -47,9 +47,12 @@ template <> uint8_t to_number(const std::string &s) } template <> bool to_number(const std::string &s) { - if (std::stoi(s) || s == "T" || s == "t" || s == "TRUE" || s == "true") + if (s == "T" || s == "t" || s == "TRUE" || s == "true" || s == "1") return true; - return false; + if (s == "F" || s == "f" || s == "FALSE" || s == "false" || s == "0") + return false; + throw std::invalid_argument("Unsupported boolean argument"); } +template <> std::string to_number(const std::string &s) { return s; } } // namespace souschef diff --git a/compiler/tf2tfliteV2/tf2tfliteV2.py b/compiler/tf2tfliteV2/tf2tfliteV2.py index c6973ff96..6b578ad53 100755 --- a/compiler/tf2tfliteV2/tf2tfliteV2.py +++ b/compiler/tf2tfliteV2/tf2tfliteV2.py @@ -15,6 +15,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os import tensorflow as tf import argparse import sys @@ -41,6 +42,13 @@ def _get_parser(): parser = argparse.ArgumentParser( description=("Command line tool to run TensorFlow Lite Converter.")) + # Verbose + parser.add_argument( + "-V", + "--verbose", + action="store_true", + help="output additional information to stdout or stderr") + # Converter version. converter_version = parser.add_mutually_exclusive_group(required=True) converter_version.add_argument( @@ -229,7 +237,29 @@ def _v2_convert(flags): open(flags.output_path, "wb").write(tflite_model) +def _apply_verbosity(verbosity): + # NOTE + # TF_CPP_MIN_LOG_LEVEL + # 0 : INFO + WARNING + ERROR + FATAL + # 1 : WARNING + ERROR + FATAL + # 2 : ERROR + FATAL + # 3 : FATAL + # + # TODO Find better way to suppress trackback on error + # tracebacklimit + # The default is 1000. + # When set to 0 or less, all traceback information is suppressed + if verbosity: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' + sys.tracebacklimit = 1000 + else: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + sys.tracebacklimit = 0 + + def _convert(flags): + _apply_verbosity(flags.verbose) + if (flags.v1): _v1_convert(flags) else: @@ -254,4 +284,9 @@ def main(): if __name__ == "__main__": - main() + try: + main() + except Exception as e: + prog_name = os.path.basename(__file__) + print(f"{prog_name}: {type(e).__name__}: " + str(e), file=sys.stderr) + sys.exit(255) diff --git a/compiler/tfkit/src/Support.cpp b/compiler/tfkit/src/Support.cpp index 1ce4c4680..1a713b58e 100644 --- a/compiler/tfkit/src/Support.cpp +++ b/compiler/tfkit/src/Support.cpp @@ -38,7 +38,7 @@ std::unique_ptr<T> open_fstream(const std::string &path, std::ios_base::openmode auto stream = std::make_unique<T>(path.c_str(), mode); if (!stream->is_open()) { - throw std::runtime_error{"ERROR: Failed to open " + path}; + throw std::runtime_error{"Failed to open " + path}; } return stream; } diff --git a/compiler/tflchef/core/src/Convert.cpp b/compiler/tflchef/core/src/Convert.cpp index de3ae4ed1..200c71eca 100644 --- a/compiler/tflchef/core/src/Convert.cpp +++ b/compiler/tflchef/core/src/Convert.cpp @@ -68,6 +68,8 @@ tflite::TensorType as_tflite_tensortype(const tflchef::TensorType &value) return tflite::TensorType_UINT8; case tflchef::INT64: return tflite::TensorType_INT64; + case tflchef::STRING: + return tflite::TensorType_STRING; case tflchef::BOOL: return tflite::TensorType_BOOL; case tflchef::INT16: diff --git a/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgMax.cpp b/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgmax.cpp index 13bf2e5e9..9dacf7bf6 100644 --- a/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgMax.cpp +++ b/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgmax.cpp @@ -15,21 +15,21 @@ * limitations under the License. */ -#include "MaxPoolWithArgMax.h" +#include "MaxPoolWithArgmax.h" #include "flatbuffers/flexbuffers.h" -flatbuffers::Offset<void> MaxPoolWithArgMaxChef::value(flatbuffers::FlatBufferBuilder &fbb) const +flatbuffers::Offset<void> MaxPoolWithArgmaxChef::value(flatbuffers::FlatBufferBuilder &fbb) const { return flatbuffers::Offset<void>(); } flatbuffers::Offset<flatbuffers::Vector<uint8_t>> -MaxPoolWithArgMaxChef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const +MaxPoolWithArgmaxChef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const { auto &operation = (*_operation); - assert(operation.type() == "MaxPoolWithArgMax"); + assert(operation.type() == "MaxPoolWithArgmax"); /** * REGISTER_OP("MaxPoolWithArgmax") @@ -80,7 +80,7 @@ MaxPoolWithArgMaxChef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const } std::unique_ptr<OpChef> -MaxPoolWithArgMaxChefFactory::create(const tflchef::Operation *operation) const +MaxPoolWithArgmaxChefFactory::create(const tflchef::Operation *operation) const { - return std::unique_ptr<OpChef>{new MaxPoolWithArgMaxChef{operation}}; + return std::unique_ptr<OpChef>{new MaxPoolWithArgmaxChef{operation}}; } diff --git a/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgMax.h b/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgmax.h index 2123a86bd..338ee8026 100644 --- a/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgMax.h +++ b/compiler/tflchef/core/src/CustomOp/MaxPoolWithArgmax.h @@ -19,10 +19,10 @@ #include "OpChef.h" -class MaxPoolWithArgMaxChef final : public OpChef +class MaxPoolWithArgmaxChef final : public OpChef { public: - explicit MaxPoolWithArgMaxChef(const tflchef::Operation *operation) : _operation{operation} + explicit MaxPoolWithArgmaxChef(const tflchef::Operation *operation) : _operation{operation} { // DO NOTHING } @@ -41,7 +41,7 @@ private: const tflchef::Operation *_operation; }; -struct MaxPoolWithArgMaxChefFactory final : public OpChefFactory +struct MaxPoolWithArgmaxChefFactory final : public OpChefFactory { std::unique_ptr<OpChef> create(const tflchef::Operation *operation) const override; }; diff --git a/compiler/tflchef/core/src/DataChef.def b/compiler/tflchef/core/src/DataChef.def new file mode 100644 index 000000000..c634c047e --- /dev/null +++ b/compiler/tflchef/core/src/DataChef.def @@ -0,0 +1,23 @@ +#ifndef DATA_CHEF +#error "Define DATA_CHEF first" +#endif // DATA_CHEF + +// DATA_CHEF(TYPE, NAME, FACTORY_CLASS) +// "TYPE" SHOULD BE an enum tag of tflchef::TensorType +DATA_CHEF(FLOAT32, constant, ConstantDataChefFactory<float>) +DATA_CHEF(BOOL, constant, ConstantDataChefFactory<bool>) +DATA_CHEF(UINT8, constant, ConstantDataChefFactory<uint8_t>) +DATA_CHEF(INT16, constant, ConstantDataChefFactory<int16_t>) +DATA_CHEF(INT32, constant, ConstantDataChefFactory<int32_t>) +DATA_CHEF(INT64, constant, ConstantDataChefFactory<int64_t>) +DATA_CHEF(INT64, explicit, ExplicitDataChefFactory<int64_t>) +DATA_CHEF(INT32, explicit, ExplicitDataChefFactory<int32_t>) +DATA_CHEF(INT16, explicit, ExplicitDataChefFactory<int16_t>) +DATA_CHEF(UINT8, explicit, ExplicitDataChefFactory<uint8_t>) +DATA_CHEF(BOOL, explicit, ExplicitDataChefFactory<bool>) +DATA_CHEF(FLOAT32, explicit, ExplicitDataChefFactory<float>) +DATA_CHEF(STRING, explicit, ExplicitDataChefFactory<std::string>) +DATA_CHEF(FLOAT32, gaussian, GaussianFloat32DataChefFactory) +DATA_CHEF(INT32, gaussian, GaussianInt32DataChefFactory) +DATA_CHEF(INT16, gaussian, GaussianInt16DataChefFactory) +DATA_CHEF(UINT8, gaussian, GaussianUint8DataChefFactory) diff --git a/compiler/tflchef/core/src/ModelChef.cpp b/compiler/tflchef/core/src/ModelChef.cpp index 467b0d300..aba20dcbf 100644 --- a/compiler/tflchef/core/src/ModelChef.cpp +++ b/compiler/tflchef/core/src/ModelChef.cpp @@ -89,6 +89,7 @@ DataChefRegistry &data_chef_registry(const tflchef::TensorType &type) static DataChefRegistry s64; static DataChefRegistry fp32; static DataChefRegistry u8; + static DataChefRegistry string; static DataChefRegistry boolean; static DataChefRegistry s16; @@ -102,6 +103,8 @@ DataChefRegistry &data_chef_registry(const tflchef::TensorType &type) return fp32; case tflchef::UINT8: return u8; + case tflchef::STRING: + return string; case tflchef::BOOL: return boolean; case tflchef::INT16: @@ -556,7 +559,7 @@ GeneratedModel cook(const ::tflchef::ModelRecipe &model_recipe) #define DATA_CHEF(TYPE, NAME, FACTORY_CLASS) \ data_chef_registry(::tflchef::TYPE) \ .add(#NAME, std::unique_ptr<FACTORY_CLASS>(new FACTORY_CLASS())); -#include <souschef/DataChef.def> +#include "DataChef.def" #undef DATA_CHEF // diff --git a/compiler/tflchef/core/src/Op/Quantize.cpp b/compiler/tflchef/core/src/Op/Quantize.cpp new file mode 100644 index 000000000..39b902805 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Quantize.cpp @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Quantize.h" + +flatbuffers::Offset<void> QuantizeChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset<void>(); +} + +std::unique_ptr<OpChef> QuantizeChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr<OpChef>{new QuantizeChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Quantize.h b/compiler/tflchef/core/src/Op/Quantize.h new file mode 100644 index 000000000..fe7a029bf --- /dev/null +++ b/compiler/tflchef/core/src/Op/Quantize.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_QUANTIZE_H__ +#define __OP_QUANTIZE_H__ + +#include "OpChef.h" + +class QuantizeChef final : public OpChef +{ +public: + explicit QuantizeChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_QUANTIZE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset<void> value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct QuantizeChefFactory final : public OpChefFactory +{ + std::unique_ptr<OpChef> create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_DEQUANTIZE_H__ diff --git a/compiler/tflchef/core/src/OpChef.def b/compiler/tflchef/core/src/OpChef.def index 714e8947b..b1e8a3829 100644 --- a/compiler/tflchef/core/src/OpChef.def +++ b/compiler/tflchef/core/src/OpChef.def @@ -67,6 +67,7 @@ OP_CHEF(Pad, PadChefFactory) OP_CHEF(PadV2, PadV2ChefFactory) OP_CHEF(Pow, PowChefFactory) OP_CHEF(PRelu, PReluChefFactory) +OP_CHEF(Quantize, QuantizeChefFactory) OP_CHEF(Range, RangeChefFactory) OP_CHEF(Rank, RankChefFactory) OP_CHEF(ReduceAny, ReduceAnyChefFactory) @@ -122,4 +123,4 @@ OP_CHEF(BatchMatMulV2, BatchMatMulV2ChefFactory) OP_CHEF(BroadcastTo, BroadcastToChefFactory) OP_CHEF(MatMul, MatMulChefFactory) OP_CHEF(MatrixBandPart, MatrixBandPartChefFactory) -OP_CHEF(MaxPoolWithArgMax, MaxPoolWithArgMaxChefFactory) +OP_CHEF(MaxPoolWithArgmax, MaxPoolWithArgmaxChefFactory) diff --git a/compiler/tflchef/core/src/OpChefs.h b/compiler/tflchef/core/src/OpChefs.h index 99f331e37..35688ba95 100644 --- a/compiler/tflchef/core/src/OpChefs.h +++ b/compiler/tflchef/core/src/OpChefs.h @@ -96,6 +96,7 @@ #include "Op/ReverseV2.h" #include "Op/Round.h" #include "Op/Rsqrt.h" +#include "Op/Quantize.h" #include "Op/ScatterNd.h" #include "Op/SegmentSum.h" #include "Op/Select.h" @@ -134,6 +135,6 @@ #include "CustomOp/BroadcastTo.h" #include "CustomOp/MatMul.h" #include "CustomOp/MatrixBandPart.h" -#include "CustomOp/MaxPoolWithArgMax.h" +#include "CustomOp/MaxPoolWithArgmax.h" #endif // __OP_CHEFS_H__ diff --git a/compiler/tflchef/proto/tflchef.proto b/compiler/tflchef/proto/tflchef.proto index c5e44f68c..34d50d985 100644 --- a/compiler/tflchef/proto/tflchef.proto +++ b/compiler/tflchef/proto/tflchef.proto @@ -18,6 +18,7 @@ enum TensorType { INT32 = 2; UINT8 = 3; INT64 = 4; + STRING = 5; BOOL = 6; INT16 = 7; } @@ -509,7 +510,7 @@ message DequantizeOptions { // NONE } -message MaxPoolWithArgMaxOptions { +message MaxPoolWithArgmaxOptions { optional Padding padding = 1 [default = VALID]; optional int32 stride_w = 2 [default = 1]; optional int32 stride_h = 3 [default = 1]; @@ -641,7 +642,7 @@ message Operation { optional SegmentSumOptions segment_sum_options = 206; optional AddNOptions add_n_options = 207; optional MatMulOptions matmul_options = 208; - optional MaxPoolWithArgMaxOptions max_pool_with_argmax_options = 209; + optional MaxPoolWithArgmaxOptions max_pool_with_argmax_options = 209; // NOTE if there are more than two options with same type of Options // use the number not listed in the above reserve list } diff --git a/compiler/tflchef/tests/explicit_bool/test.recipe b/compiler/tflchef/tests/explicit_bool/test.recipe new file mode 100644 index 000000000..8f09edd13 --- /dev/null +++ b/compiler/tflchef/tests/explicit_bool/test.recipe @@ -0,0 +1,31 @@ +operand { + name: "ifm1" + type: BOOL + shape { dim: 6 } +} +operand { + name: "ifm2" + type: BOOL + shape { dim: 6 } + filler { + tag: "explicit" + arg: "T" + arg: "f" + arg: "0" + arg: "1" + arg: "true" + arg: "FALSE" + } +} +operand { + name: "ofm" + type: BOOL + shape { dim: 6 } +} +operation { + type: "LogicalAnd" + input: "ifm1" + input: "ifm2" + output: "ofm" +} +output: "ofm" diff --git a/compiler/tflchef/tests/string_tensor/test.recipe b/compiler/tflchef/tests/string_tensor/test.recipe new file mode 100644 index 000000000..eecfbc6f7 --- /dev/null +++ b/compiler/tflchef/tests/string_tensor/test.recipe @@ -0,0 +1,30 @@ +operand { + name: "ifm" + type: STRING + shape { } +} +operand { + name: "suffix" + type: STRING + shape { } + filler { + tag: "explicit" + arg: "Hello" + } +} +operand { + name: "ofm" + type: STRING + shape { } +} +operation { + type: "Add" + input: "ifm" + input: "suffix" + output: "ofm" + add_options { + activation: NONE + } +} +input: "ifm" +output: "ofm" diff --git a/compiler/tflchef/tflite/src/Op/Quantize.cpp b/compiler/tflchef/tflite/src/Op/Quantize.cpp new file mode 100644 index 000000000..0808b9c3f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Quantize.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Quantize.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpQuantize::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpQuantize::build(const tflite::Operator *, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Quantize"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Quantize.h b/compiler/tflchef/tflite/src/Op/Quantize.h new file mode 100644 index 000000000..256ed5a5c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Quantize.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_QUANTIZE_H__ +#define __TFLITE_OP_QUANTIZE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Quantize + */ +class TFliteOpQuantize : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_QUANTIZE_H__ diff --git a/compiler/tflchef/tflite/src/TFliteOpChefs.h b/compiler/tflchef/tflite/src/TFliteOpChefs.h index 960ff6e36..26ada7d0a 100644 --- a/compiler/tflchef/tflite/src/TFliteOpChefs.h +++ b/compiler/tflchef/tflite/src/TFliteOpChefs.h @@ -80,6 +80,7 @@ #include "Op/PadV2.h" #include "Op/Pow.h" #include "Op/PRelu.h" +#include "Op/Quantize.h" #include "Op/Range.h" #include "Op/Rank.h" #include "Op/ReduceAny.h" diff --git a/compiler/tflchef/tflite/src/TFliteOpRegistry.h b/compiler/tflchef/tflite/src/TFliteOpRegistry.h index c240bcf52..06394ddfa 100644 --- a/compiler/tflchef/tflite/src/TFliteOpRegistry.h +++ b/compiler/tflchef/tflite/src/TFliteOpRegistry.h @@ -117,6 +117,7 @@ private: REG_TFL_OP(PADV2, TFliteOpPadV2); REG_TFL_OP(POW, TFliteOpPow); REG_TFL_OP(PRELU, TFliteOpPRelu); + REG_TFL_OP(QUANTIZE, TFliteOpQuantize); REG_TFL_OP(RANGE, TFliteOpRange); REG_TFL_OP(RANK, TFliteOpRank); REG_TFL_OP(REDUCE_ANY, TFliteOpReduceAny); diff --git a/compiler/tfldump/src/OpPrinter.cpp b/compiler/tfldump/src/OpPrinter.cpp index 4a417cef1..90cba7173 100644 --- a/compiler/tfldump/src/OpPrinter.cpp +++ b/compiler/tfldump/src/OpPrinter.cpp @@ -221,6 +221,7 @@ public: os << std::boolalpha; os << "align_corners(" << resize_params->align_corners() << ")"; os << "half_pixel_centers(" << resize_params->half_pixel_centers() << ")"; + os << std::noboolalpha; os << std::endl; } } @@ -236,6 +237,7 @@ public: os << " "; os << std::boolalpha; os << "align_corners(" << resize_params->align_corners() << ")"; + os << std::noboolalpha; os << std::endl; } } diff --git a/compiler/tflite2circle/driver/Driver.cpp b/compiler/tflite2circle/driver/Driver.cpp index ba7892179..fc7ee4042 100644 --- a/compiler/tflite2circle/driver/Driver.cpp +++ b/compiler/tflite2circle/driver/Driver.cpp @@ -43,6 +43,12 @@ int entry(int argc, char **argv) .help("Show version information and exit") .exit_with(print_version); + arser.add_argument("-V", "--verbose") + .nargs(0) + .required(false) + .default_value(false) + .help("output additional information to stdout or stderr"); + arser.add_argument("tflite") .nargs(1) .type(arser::DataType::STR) @@ -55,7 +61,7 @@ int entry(int argc, char **argv) } catch (const std::runtime_error &err) { - std::cout << err.what() << std::endl; + std::cerr << err.what() << std::endl; std::cout << arser; return 255; } diff --git a/compiler/tflite2circle/src/CircleModel.cpp b/compiler/tflite2circle/src/CircleModel.cpp index 9ab884e75..ab0b5b507 100644 --- a/compiler/tflite2circle/src/CircleModel.cpp +++ b/compiler/tflite2circle/src/CircleModel.cpp @@ -305,7 +305,7 @@ CircleModel::CircleModel(FlatBufBuilder &fb, TFLModel &model) model._data.size()}; if (!tflite::VerifyModelBuffer(verifier)) { - throw std::runtime_error("ERROR: Failed to verify tflite"); + throw std::runtime_error("Failed to verify tflite"); } _operator_codes_offset = diff --git a/compiler/vconone/CMakeLists.txt b/compiler/vconone/CMakeLists.txt index 882f6507b..1cf7c0c45 100644 --- a/compiler/vconone/CMakeLists.txt +++ b/compiler/vconone/CMakeLists.txt @@ -1,5 +1,5 @@ if (NOT VCONONE_VERSION) - set(VCONONE_VERSION 0x00000000000f0001) + set(VCONONE_VERSION 0x0000000100110000) # NOTE order is [build patch minor major] # if VCONONE_VERSION is set with -D option, it will be cached # you may have to remove cache file if you remove -D option diff --git a/compute/cker/include/cker/Types.h b/compute/cker/include/cker/Types.h index c5dd63b5b..495c89440 100644 --- a/compute/cker/include/cker/Types.h +++ b/compute/cker/include/cker/Types.h @@ -80,8 +80,6 @@ enum class BroadcastableOpCategory : uint8_t struct PoolParams { - FusedActivationFunctionType activation; - PaddingType padding_type; PaddingValues padding_values; int stride_height; int stride_width; diff --git a/compute/cker/include/cker/operation/FloorDiv.h b/compute/cker/include/cker/operation/FloorDiv.h new file mode 100644 index 000000000..cdb2c2a8b --- /dev/null +++ b/compute/cker/include/cker/operation/FloorDiv.h @@ -0,0 +1,82 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_FLOOR_DIV_H__ +#define __NNFW_CKER_FLOOR_DIV_H__ + +#include "cker/Shape.h" +#include "cker/Utils.h" + +namespace nnfw +{ +namespace cker +{ + +template <typename T> +inline void FloorDivBroadcast(const Shape &unextended_input1_shape, const T *input1_data, + const Shape &unextended_input2_shape, const T *input2_data, + const Shape &unextended_output_shape, T *output_data) +{ + assert(unextended_input1_shape.DimensionsCount() <= 4); + assert(unextended_input2_shape.DimensionsCount() <= 4); + assert(unextended_output_shape.DimensionsCount() <= 4); + const Shape output_shape = Shape::ExtendedShape(4, unextended_output_shape); + + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, unextended_input2_shape, &desc1, + &desc2); + + for (int b = 0; b < output_shape.Dims(0); ++b) + { + for (int y = 0; y < output_shape.Dims(1); ++y) + { + for (int x = 0; x < output_shape.Dims(2); ++x) + { + for (int c = 0; c < output_shape.Dims(3); ++c) + { + auto out_idx = Offset(output_shape, b, y, x, c); + auto in1_idx = SubscriptToIndex(desc1, b, y, x, c); + auto in2_idx = SubscriptToIndex(desc2, b, y, x, c); + auto in1_val = input1_data[in1_idx]; + auto in2_val = input2_data[in2_idx]; + output_data[out_idx] = std::floor( + std::divides<double>()(static_cast<double>(in1_val), static_cast<double>(in2_val))); + } + } + } + } +} + +template <typename T> +inline void FloorDivElementwise(const Shape &shape, const T *input1_data, const T *input2_data, + T *output_data) +{ + + int num_elements = shape.FlatSize(); + + for (int t = 0; t < num_elements; t++) + { + output_data[t] = std::floor(std::divides<double>()(static_cast<double>(input1_data[t]), + static_cast<double>(input2_data[t]))); + } +} + +} // namespace cker + +} // namespace nnfw +#endif diff --git a/compute/cker/include/cker/operation/optimized/Conv.h b/compute/cker/include/cker/operation/optimized/Conv.h index 26fc443db..6e0e129c6 100644 --- a/compute/cker/include/cker/operation/optimized/Conv.h +++ b/compute/cker/include/cker/operation/optimized/Conv.h @@ -42,6 +42,8 @@ namespace cker namespace optimized { +std::mutex _gemmlowp_mutex; + struct GemmlowpOutputPipeline { typedef gemmlowp::VectorMap<const int32_t, gemmlowp::VectorShape::Col> ColVectorMap; @@ -164,6 +166,8 @@ inline void Conv(const ConvParams ¶ms, const Shape &input_shape, const uint8 const auto &output_pipeline = GemmlowpOutputPipeline::MakeExp(bias_data, output_rows, output_offset, output_multiplier, output_shift, output_activation_min, output_activation_max); + + std::lock_guard<std::mutex> lock_guard(_gemmlowp_mutex); gemmlowp::GemmWithOutputPipeline<uint8_t, uint8_t, gemmlowp::L8R8WithLhsNonzeroBitDepthParams>( gemm_context, filter_matrix, input_matrix, &output_matrix, filter_offset, input_offset, output_pipeline); diff --git a/docs/conf.py b/docs/conf.py index cb5309565..ea17db054 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -21,7 +21,7 @@ copyright = '2020, Samsung Research & contributors' author = 'Samsung Research & contributors' # The full version, including alpha/beta/rc tags -release = '1.15.0' +release = '1.17.0' # -- General configuration --------------------------------------------------- diff --git a/docs/release/1.10/index.rst b/docs/release/1.10/index.rst index bc415fbda..28325e685 100644 --- a/docs/release/1.10/index.rst +++ b/docs/release/1.10/index.rst @@ -3,8 +3,8 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 -=== +1.10 +==== .. toctree:: :maxdepth: 2 diff --git a/docs/release/1.11/index.rst b/docs/release/1.11/index.rst index 2e4544af6..d08c88b29 100644 --- a/docs/release/1.11/index.rst +++ b/docs/release/1.11/index.rst @@ -3,8 +3,8 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 -=== +1.11 +==== .. toctree:: :maxdepth: 2 diff --git a/docs/release/1.12/index.rst b/docs/release/1.12/index.rst index 68b4c7347..61465ee7b 100644 --- a/docs/release/1.12/index.rst +++ b/docs/release/1.12/index.rst @@ -3,8 +3,8 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 -=== +1.12 +==== .. toctree:: :maxdepth: 2 diff --git a/docs/release/1.13/index.rst b/docs/release/1.13/index.rst index 04aa2b76b..5601f743b 100644 --- a/docs/release/1.13/index.rst +++ b/docs/release/1.13/index.rst @@ -3,8 +3,8 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 -=== +1.13 +==== .. toctree:: :maxdepth: 2 diff --git a/docs/release/1.14/index.rst b/docs/release/1.14/index.rst index c3d10bf33..558229e7f 100644 --- a/docs/release/1.14/index.rst +++ b/docs/release/1.14/index.rst @@ -3,8 +3,8 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 -=== +1.14 +==== .. toctree:: :maxdepth: 2 diff --git a/docs/release/1.15/index.rst b/docs/release/1.15/index.rst index 1680aaf1f..de2194742 100644 --- a/docs/release/1.15/index.rst +++ b/docs/release/1.15/index.rst @@ -3,8 +3,8 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 -=== +1.15 +==== .. toctree:: :maxdepth: 2 diff --git a/docs/release/1.16/index.rst b/docs/release/1.16/index.rst new file mode 100644 index 000000000..08f4c3e65 --- /dev/null +++ b/docs/release/1.16/index.rst @@ -0,0 +1,14 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 20 12:56:12 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +1.16 +==== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./release-note-1.16.0.md + ./release-note-1.16.1.md diff --git a/docs/release/1.16/release-note-1.16.0.md b/docs/release/1.16/release-note-1.16.0.md new file mode 100644 index 000000000..be492033c --- /dev/null +++ b/docs/release/1.16/release-note-1.16.0.md @@ -0,0 +1,17 @@ +# Release Note 1.16.0 + +## ONE Compiler + +### Compiler Frontend + +- Enable `PadV2` in luci-interpreter and quantization +- Provide `circle-tensordump`, `circledump` as a development tool +- Provide `luci-eval-driver` as test tool +- Enable `STRING` type as constant values in CircleConst +- Fix CircleCustom may have 0 input, 0 output +- Enable debian package generation +- More optimization pass + - Min(6)+ReLU to ReLU6 + - Remove FakeQuant Op +- Experimental support of ONNX upgraded to version 1.8.0 with additional patch +- Fix bugs where one-cmds' config file didn't evaluate boolean properly diff --git a/docs/release/1.16/release-note-1.16.1.md b/docs/release/1.16/release-note-1.16.1.md new file mode 100644 index 000000000..e357b9589 --- /dev/null +++ b/docs/release/1.16/release-note-1.16.1.md @@ -0,0 +1,7 @@ +# Release Note 1.16.1 + +## ONE Compiler + +### Compiler Frontend + +- Extends the point where `one-codegen` finds backends. diff --git a/docs/release/1.17/index.rst b/docs/release/1.17/index.rst new file mode 100644 index 000000000..c2eda6020 --- /dev/null +++ b/docs/release/1.17/index.rst @@ -0,0 +1,13 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 20 12:56:12 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +1.17 +==== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./release-note-1.17.0.md diff --git a/docs/release/1.17/release-note-1.17.0.md b/docs/release/1.17/release-note-1.17.0.md new file mode 100644 index 000000000..20f8f7ec5 --- /dev/null +++ b/docs/release/1.17/release-note-1.17.0.md @@ -0,0 +1,24 @@ +# Release Note 1.17.0 + +## ONE Compiler + +### Compiler Frontend + +- More optimization pass + - Remove Quant-Dequant sequence + - Replace Sub with Add + - Substitute StridedSlice to Reshape + - Fuse Mean with Mean + - Fuse Transpose with Mean + - Substitute PadV2 to Pad +- Add new InstanceNorm pattern in `FuseInstanceNormPass` +- Add verbose option +- Introduce `onecc` driver to `one-cmds` +- Introduce `one-profile` driver to `one-cmds` + +## ONE Runtime + +### gpu_cl backend added + +- New backend(gpu_cl) added. This backend exploits tensorflow lite's gpu delegate. +- This backend supports the following operations : Add, Convolution, Depthwise Convolution, Pooling, Reshape, Relu, Softmax diff --git a/docs/release/1.5/index.rst b/docs/release/1.5/index.rst index 0764bf230..6e7810280 100644 --- a/docs/release/1.5/index.rst +++ b/docs/release/1.5/index.rst @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 +1.5 === .. toctree:: diff --git a/docs/release/1.6/index.rst b/docs/release/1.6/index.rst index 79389cf08..3f380b5f8 100644 --- a/docs/release/1.6/index.rst +++ b/docs/release/1.6/index.rst @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 +1.6 === .. toctree:: diff --git a/docs/release/1.7/index.rst b/docs/release/1.7/index.rst index 65a839f13..234e4b6ec 100644 --- a/docs/release/1.7/index.rst +++ b/docs/release/1.7/index.rst @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 +1.7 === .. toctree:: diff --git a/docs/release/1.8/index.rst b/docs/release/1.8/index.rst index 4dc1d5be9..022e00ba5 100644 --- a/docs/release/1.8/index.rst +++ b/docs/release/1.8/index.rst @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 +1.8 === .. toctree:: diff --git a/docs/release/1.9/index.rst b/docs/release/1.9/index.rst index d77012c96..82f0e1210 100644 --- a/docs/release/1.9/index.rst +++ b/docs/release/1.9/index.rst @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -1.0 +1.9 === .. toctree:: diff --git a/docs/release/index.rst b/docs/release/index.rst index 31cd0d792..f49d3eac4 100644 --- a/docs/release/index.rst +++ b/docs/release/index.rst @@ -24,3 +24,6 @@ Release ./1.11/index ./1.12/index ./1.13/index + ./1.14/index + ./1.15/index + ./1.16/index diff --git a/infra/cmake/modules/ExternalSourceTools.cmake b/infra/cmake/modules/ExternalSourceTools.cmake index 87cb15270..0bfbaa33b 100644 --- a/infra/cmake/modules/ExternalSourceTools.cmake +++ b/infra/cmake/modules/ExternalSourceTools.cmake @@ -47,19 +47,36 @@ function(ExternalSource_Download PREFIX) file(MAKE_DIRECTORY "${TMP_DIR}") message(STATUS "Download ${PREFIX} from ${URL}") - file(DOWNLOAD ${URL} "${DOWNLOAD_PATH}" - STATUS status - LOG log) - list(GET status 0 status_code) - list(GET status 1 status_string) + foreach(retry_count RANGE 5) + message(STATUS "(Trial Count : ${retry_count})") - if(NOT status_code EQUAL 0) - message(FATAL_ERROR "error: downloading '${URL}' failed + file(DOWNLOAD ${URL} "${DOWNLOAD_PATH}" + STATUS status + LOG log) + + list(GET status 0 status_code) + list(GET status 1 status_string) + + # Download success + if(status_code EQUAL 0) + break() + endif() + + message(WARNING "error: downloading '${URL}' failed status_code: ${status_code} status_string: ${status_string} log: ${log}") - endif() + + # Retry limit exceed + if(retry_count EQUAL 5) + message(FATAL_ERROR "Download ${PREFIX} from ${URL} - failed") + endif() + + # Retry after 10 seconds when download fails + execute_process(COMMAND sleep 10) + endforeach() + message(STATUS "Download ${PREFIX} from ${URL} - done") # Verify checksum diff --git a/infra/cmake/packages/AbseilConfig.cmake b/infra/cmake/packages/AbseilConfig.cmake index e16dd94d7..6fae7211d 100644 --- a/infra/cmake/packages/AbseilConfig.cmake +++ b/infra/cmake/packages/AbseilConfig.cmake @@ -27,6 +27,7 @@ function(_Abseil_import) absl::numeric absl::random_random absl::strings + absl::status absl::synchronization absl::time absl::utility @@ -36,4 +37,9 @@ function(_Abseil_import) set(Abseil_FOUND TRUE PARENT_SCOPE) endfunction(_Abseil_import) +set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} -fPIC") +set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -fPIC") +set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -fPIC") +set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -fPIC") + _Abseil_import() diff --git a/infra/cmake/packages/Opencl_HeadersConfig.cmake b/infra/cmake/packages/Opencl_HeadersConfig.cmake new file mode 100644 index 000000000..8d65fe76e --- /dev/null +++ b/infra/cmake/packages/Opencl_HeadersConfig.cmake @@ -0,0 +1,22 @@ +function(_Opencl_Headers_import) + nnas_find_package(Opencl_HeadersSource QUIET) + + # NOTE This line prevents multiple definitions of target + if(TARGET Headers) + set(Opencl_HeadersSource_DIR ${Opencl_HeadersSource_DIR} PARENT_SCOPE) + set(Opencl_Headers_FOUND TRUE PARENT_SCOPE) + return() + endif(TARGET Headers) + + if(NOT Opencl_HeadersSource_FOUND) + message(STATUS "Opencl_Headers: Source not found") + set(Opencl_Headers_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT Opencl_HeadersSource_FOUND) + + add_extdirectory("${Opencl_HeadersSource_DIR}" OPENCL_HEADERS EXCLUDE_FROM_ALL) + set(Opencl_Headers_DIR ${Opencl_HeadersSource_DIR} PARENT_SCOPE) + set(Opencl_Headers_FOUND TRUE PARENT_SCOPE) +endfunction(_Opencl_Headers_import) + +_Opencl_Headers_import() diff --git a/infra/cmake/packages/Opencl_HeadersSourceConfig.cmake b/infra/cmake/packages/Opencl_HeadersSourceConfig.cmake new file mode 100644 index 000000000..04858aa41 --- /dev/null +++ b/infra/cmake/packages/Opencl_HeadersSourceConfig.cmake @@ -0,0 +1,22 @@ +function(_Opencl_HeadersSource_import) + if(NOT DOWNLOAD_OPENCL_HEADERS) + set(Opencl_HeadersSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_OPENCL_HEADERS) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + envoption(OPENCL_HEADERS_URL ${EXTERNAL_DOWNLOAD_SERVER}/KhronosGroup/OpenCL-Headers/archive/v2021.04.29.tar.gz) + + ExternalSource_Download(OPENCL_HEADERS + DIRNAME OPENCL_HEADERS + URL ${OPENCL_HEADERS_URL} + CHECKSUM MD5=5a7ea04265119aa76b4ecbd95f258219) + + set(Opencl_HeadersSource_DIR ${OPENCL_HEADERS_SOURCE_DIR} PARENT_SCOPE) + set(Opencl_HeadersSource_FOUND TRUE PARENT_SCOPE) +endfunction(_Opencl_HeadersSource_import) + +_Opencl_HeadersSource_import() diff --git a/infra/command/format b/infra/command/format index dc006639d..75e6184d3 100644 --- a/infra/command/format +++ b/infra/command/format @@ -109,7 +109,8 @@ function check_permission() { FILES_TO_CHECK_PERMISSION=() for f in ${FILES_TO_CHECK[@]}; do # Manually ignore permission checking - if [[ ${f} == !(nnas|nnfw|nncc|*.sh|*.py|*/gradlew) ]] || [[ ${f} == tests/nnapi/specs/**/*.py ]]; then + if [[ ${f} == !(nnas|nnfw|nncc|*.sh|*.py|*/gradlew|infra/debian/compiler/rules|infra/debian/runtime/rules) ]] \ + || [[ ${f} == tests/nnapi/specs/**/*.py ]]; then FILES_TO_CHECK_PERMISSION+=("${f}") fi done diff --git a/infra/command/gen-coverage-report b/infra/command/gen-coverage-report index bf65b1bfb..3058aee9a 100644 --- a/infra/command/gen-coverage-report +++ b/infra/command/gen-coverage-report @@ -66,10 +66,13 @@ done "${LCOV_PATH}" -e "${RAW_COVERAGE_INFO_PATH}" -o "${EXTRACTED_COVERAGE_INFO_PATH}" \ "${CANDIDATES[@]}" + +opencl_files=($(find ./runtime/onert/backend/gpu_cl/open_cl/ \( -name "*.cc" -o -name "*.h" \) -exec realpath {} \; )) + # Exclude *.test.cpp files from coverage report # Exclude flatbuffer generated files from coverage report "${LCOV_PATH}" -r "${EXTRACTED_COVERAGE_INFO_PATH}" -o "${EXCLUDED_COVERAGE_INFO_PATH}" \ - '*.test.cpp' '*_schema_generated.h' + '*.test.cpp' '*_schema_generated.h' "${opencl_files[@]}" # Final coverage data cp -v ${EXCLUDED_COVERAGE_INFO_PATH} ${COVERAGE_INFO_PATH} diff --git a/infra/debian/compiler/changelog b/infra/debian/compiler/changelog new file mode 100644 index 000000000..6859255ff --- /dev/null +++ b/infra/debian/compiler/changelog @@ -0,0 +1,21 @@ +one (1.17.0) bionic; urgency=medium + + * More optimization pass + * Add new InstanceNorm pattern in `FuseInstanceNormPass` + * Add verbose option + * Introduce `onecc` driver to `one-cmds` + * Introduce `one-profile` driver to `one-cmds` + + -- seongwoo <mhs4670go@naver.com> Fri, 20 Aug 2021 17:50:20 +0900 + +one (1.16.1) bionic; urgency=medium + + * Extends the point where `one-codegen` finds backends. + + -- seongwoo chae <mhs4670go@naver.com> Wed, 26 May 2021 18:06:53 +0900 + +one (1.16.0) bionic; urgency=low + + * Initial release. + + -- seongwoo chae <mhs4670go@naver.com> Mon, 26 Apr 2021 14:34:57 +0900 diff --git a/infra/debian/compiler/compat b/infra/debian/compiler/compat new file mode 100644 index 000000000..ec635144f --- /dev/null +++ b/infra/debian/compiler/compat @@ -0,0 +1 @@ +9 diff --git a/infra/debian/compiler/control b/infra/debian/compiler/control new file mode 100644 index 000000000..99fa479f2 --- /dev/null +++ b/infra/debian/compiler/control @@ -0,0 +1,25 @@ +Source: one +Section: devel +Priority: extra +Maintainer: Neural Network Acceleration Solution Developers <nnfw@samsung.com> +Build-Depends: cmake, debhelper (>=9), dh-python, python3-all +Standards-Version: 3.9.8 +Homepage: https://github.com/Samsung/ONE + +Package: one-compiler +Architecture: amd64 +Multi-Arch: foreign +Depends: ${misc:Depends}, ${shlibs:Depends}, python3-venv, python3-pip +Description: On-device Neural Engine compiler package + +Package: one-compiler-dev +Architecture: amd64 +Multi-Arch: same +Depends: one-compiler, ${shlibs:Depends}, ${misc:Depends} +Description: one-compiler development package + +Package: one-compiler-test +Architecture: amd64 +Multi-Arch: same +Depends: one-compiler, ${shlibs:Depends}, ${misc:Depends} +Description: one-compiler test package diff --git a/infra/debian/compiler/copyright b/infra/debian/compiler/copyright new file mode 100644 index 000000000..bb64695a4 --- /dev/null +++ b/infra/debian/compiler/copyright @@ -0,0 +1,3 @@ +Files: * +License: Proprietary +Copyright (c) <2018> <Samsung Electronics Co.,Ltd.> diff --git a/infra/debian/compiler/docs/one-build.1 b/infra/debian/compiler/docs/one-build.1 new file mode 100644 index 000000000..672d39f7f --- /dev/null +++ b/infra/debian/compiler/docs/one-build.1 @@ -0,0 +1,96 @@ +.TH ONE-BUILD "1" "August 2021" "one-build version 1.17.0" "User Commands" +.SH NAME +one-build \- run ONE drivers +.SH DESCRIPTION +usage: one\-build [\-h] [\-v] [\-V] [\-C CONFIG] +.PP +\fBone\-build\fR is a command line tool that runs ONE drivers in customized order. +.SS "Configuration file:" +\fBone\-build\fR takes input as a configuration file that supports ini format. +A configuration file consists of sections, each led by a [section] header. +Each section is the ONE driver you want to run, and consists of commands in a key/value combination to pass to the driver. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.SH EXAMPLES +Before you run \fBone\-build\fR, you must write a configuration file. +.PP +$ cat one-build.template.cfg +.PP +[one-build] +.br +one-import-tf=True +.br +one-import-tflite=False +.br +one-import-bcq=False +.br +one-import-onnx=False +.br +one-optimize=True +.br +one-quantize=False +.br +one-pack=True +.br +one-codegen=False +.PP +[one-import-tf] +.br +input_path=/path/to/inception_v3.pb +.br +output_path=inception_v3.circle +.br +input_arrays=input +.br +input_shapes=1,299,299,3 +.br +output_arrays=InceptionV3/Predictions/Reshape_1 +.br +converter_version=v1 +.br +model_format=graph_def +.PP +[one-optimize] +.br +input_path=inception_v3.circle +.br +output_path=inception_v3.opt.circle +.br +generate_profile_data=False +.PP +[one-pack] +.br +input_path=inception_v3.opt.circle +.br +output_path=inception_v3_pack +.PP +\fBone\-build\fR section decides whether to use each driver or not. +If the value is False, even if the corresponding section exists, the driver won't be executed. +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-build +is maintained as a Texinfo manual. If the +.B info +and +.B one-build +programs are properly installed at your site, the command +.IP +.B info one-build +.PP +should give you access to the complete manual. + diff --git a/infra/debian/compiler/docs/one-codegen.1 b/infra/debian/compiler/docs/one-codegen.1 new file mode 100644 index 000000000..b5296a018 --- /dev/null +++ b/infra/debian/compiler/docs/one-codegen.1 @@ -0,0 +1,39 @@ +.TH ONE-CODEGEN "1" "August 2021" "one-codegen version 1.17.0" "User Commands" +.SH NAME +one-codegen \- geneate codes +.SH DESCRIPTION +usage: one\-codegen [\-h] [\-v] [\-C CONFIG] [\-b BACKEND] [\-\-] [COMMANDS FOR BACKEND] +.PP +\fBone\-codegen\fR is a command line tool for code generation. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-b\fR BACKEND, \fB\-\-backend\fR BACKEND +backend name to use +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-codegen +is maintained as a Texinfo manual. If the +.B info +and +.B one-codegen +programs are properly installed at your site, the command +.IP +.B info one-codegen +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-import-bcq.1 b/infra/debian/compiler/docs/one-import-bcq.1 new file mode 100644 index 000000000..b8a85cee4 --- /dev/null +++ b/infra/debian/compiler/docs/one-import-bcq.1 @@ -0,0 +1,61 @@ +.TH ONE-IMPORT-BCQ "1" "August 2021" "one-import-bcq version 1.17.0" "User Commands" +.SH NAME +one-import-bcq \- convert TensorFlow with BCQ to circle +.SH DESCRIPTION +usage: one\-import\-bcq [\-h] [\-v] [\-V] [\-C CONFIG] [\-\-v1 | \-\-v2] [\-i INPUT_PATH] +.br +[\-o OUTPUT_PATH] [\-I INPUT_ARRAYS] [\-s INPUT_SHAPES] +.br +[\-O OUTPUT_ARRAYS] +.PP +\fBone\-import\-bcq\fR is a command line tool to convert TensorFlow with BCQ to circle. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-\-v1\fR +use TensorFlow Lite Converter 1.x +.TP +\fB\-\-v2\fR +use TensorFlow Lite Converter 2.x +.TP +\fB\-i\fR INPUT_PATH, \fB\-\-input_path\fR INPUT_PATH +full filepath of the input file +.TP +\fB\-o\fR OUTPUT_PATH, \fB\-\-output_path\fR OUTPUT_PATH +full filepath of the output file +.TP +\fB\-I\fR INPUT_ARRAYS, \fB\-\-input_arrays\fR INPUT_ARRAYS +names of the input arrays, comma\-separated +.TP +\fB\-s\fR INPUT_SHAPES, \fB\-\-input_shapes\fR INPUT_SHAPES +shapes corresponding to \fB\-\-input_arrays\fR, colon\-separated (ex:"1,4,4,3:1,20,20,3") +.TP +\fB\-O\fR OUTPUT_ARRAYS, \fB\-\-output_arrays\fR OUTPUT_ARRAYS +names of the output arrays, comma\-separated +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-import-bcq +is maintained as a Texinfo manual. If the +.B info +and +.B one-import-bcq +programs are properly installed at your site, the command +.IP +.B info one-import-bcq +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-import-onnx.1 b/infra/debian/compiler/docs/one-import-onnx.1 new file mode 100644 index 000000000..1953544dc --- /dev/null +++ b/infra/debian/compiler/docs/one-import-onnx.1 @@ -0,0 +1,63 @@ +.TH ONE-IMPORT-ONNX "1" "August 2021" "one-import-onnx version 1.17.0" "User Commands" +.SH NAME +one-import-onnx \- convert ONNX to circle +.SH DESCRIPTION +usage: one\-import\-onnx [\-h] [\-v] [\-V] [\-C CONFIG] [\-i INPUT_PATH] +.br +[\-o OUTPUT_PATH] [\-I INPUT_ARRAYS] [\-O OUTPUT_ARRAYS] +.br +[\-\-model_format MODEL_FORMAT] +.br +[\-\-converter_version CONVERTER_VERSION] +.br +[\-\-save_intermediate] +.PP +\fBone\-import\-onnx\fR is a command line tool to convert ONNX to circle. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-\-save_intermediate\fR +Save intermediate files to output folder +.TP +\fB\-i\fR INPUT_PATH, \fB\-\-input_path\fR INPUT_PATH +full filepath of the input file +.TP +\fB\-o\fR OUTPUT_PATH, \fB\-\-output_path\fR OUTPUT_PATH +full filepath of the output file +.TP +\fB\-I\fR INPUT_ARRAYS, \fB\-\-input_arrays\fR INPUT_ARRAYS +names of the input arrays, comma\-separated +.TP +\fB\-O\fR OUTPUT_ARRAYS, \fB\-\-output_arrays\fR OUTPUT_ARRAYS +names of the output arrays, comma\-separated +.HP +\fB\-\-model_format\fR MODEL_FORMAT +.HP +\fB\-\-converter_version\fR CONVERTER_VERSION +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-import-onnx +is maintained as a Texinfo manual. If the +.B info +and +.B one-import-onnx +programs are properly installed at your site, the command +.IP +.B info one-import-onnx +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-import-tf.1 b/infra/debian/compiler/docs/one-import-tf.1 new file mode 100644 index 000000000..9f05a888f --- /dev/null +++ b/infra/debian/compiler/docs/one-import-tf.1 @@ -0,0 +1,77 @@ +.TH ONE-IMPORT-TF "1" "August 2021" "one-import-tf version 1.17.0" "User Commands" +.SH NAME +one-import-tf \- convert TensorFlow to circle +.SH DESCRIPTION +usage: one\-import\-tf [\-h] [\-v] [\-V] [\-C CONFIG] [\-\-v1 | \-\-v2] +.br +[\-\-graph_def | \-\-saved_model | \-\-keras_model] +.br +[\-i INPUT_PATH] [\-o OUTPUT_PATH] [\-I INPUT_ARRAYS] +.br +[\-s INPUT_SHAPES] [\-O OUTPUT_ARRAYS] +.br +[\-\-save_intermediate] +.PP +\fBone\-import\-tf\fR is a command line tool to convert TensorFlow model to circle. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-\-save_intermediate\fR +Save intermediate files to output folder +.TP +\fB\-\-v1\fR +use TensorFlow Lite Converter 1.x +.TP +\fB\-\-v2\fR +use TensorFlow Lite Converter 2.x +.TP +\fB\-\-graph_def\fR +use graph def file(default) +.TP +\fB\-\-saved_model\fR +use saved model +.TP +\fB\-\-keras_model\fR +use keras model +.TP +\fB\-i\fR INPUT_PATH, \fB\-\-input_path\fR INPUT_PATH +full filepath of the input file +.TP +\fB\-o\fR OUTPUT_PATH, \fB\-\-output_path\fR OUTPUT_PATH +full filepath of the output file +.TP +\fB\-I\fR INPUT_ARRAYS, \fB\-\-input_arrays\fR INPUT_ARRAYS +names of the input arrays, comma\-separated +.TP +\fB\-s\fR INPUT_SHAPES, \fB\-\-input_shapes\fR INPUT_SHAPES +shapes corresponding to \fB\-\-input_arrays\fR, colon\-separated (ex:"1,4,4,3:1,20,20,3") +.TP +\fB\-O\fR OUTPUT_ARRAYS, \fB\-\-output_arrays\fR OUTPUT_ARRAYS +names of the output arrays, comma\-separated +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-import-tf +is maintained as a Texinfo manual. If the +.B info +and +.B one-import-tf +programs are properly installed at your site, the command +.IP +.B info one-import-tf +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-import-tflite.1 b/infra/debian/compiler/docs/one-import-tflite.1 new file mode 100644 index 000000000..ef63146ac --- /dev/null +++ b/infra/debian/compiler/docs/one-import-tflite.1 @@ -0,0 +1,44 @@ +.TH ONE-IMPORT-TFLITE "1" "August 2021" "one-import-tflite version 1.17.0" "User Commands" +.SH NAME +one-import-tflite \- convert TensorFlow lite to circle +.SH DESCRIPTION +usage: one\-import\-tflite [\-h] [\-v] [\-V] [\-C CONFIG] [\-i INPUT_PATH] +.br +[\-o OUTPUT_PATH] +.PP +\fBone\-import\-tflite\fR is a command line tool to convert TensorFlow lite to circle. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-i\fR INPUT_PATH, \fB\-\-input_path\fR INPUT_PATH +full filepath of the input file +.TP +\fB\-o\fR OUTPUT_PATH, \fB\-\-output_path\fR OUTPUT_PATH +full filepath of the output file +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-import-tflite +is maintained as a Texinfo manual. If the +.B info +and +.B one-import-tflite +programs are properly installed at your site, the command +.IP +.B info one-import-tflite +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-import.1 b/infra/debian/compiler/docs/one-import.1 new file mode 100644 index 000000000..674e9ada3 --- /dev/null +++ b/infra/debian/compiler/docs/one-import.1 @@ -0,0 +1,35 @@ +.TH ONE-IMPORT "1" "August 2021" "one-import version 1.17.0" "User Commands" +.SH NAME +one-import \- convert various format to circle +.SH SYNOPSIS +usage: one\-import [\-h] [\-C CONFIG] [\-v] driver +.SH DESCRIPTION +\fBone\-import\fR is a command line tool to convert various format to circle. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fBdriver\fR driver name to run (supported: tf, tflite, bcq, onnx) +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-import +is maintained as a Texinfo manual. If the +.B info +and +.B one-import +programs are properly installed at your site, the command +.IP +.B info one-import +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-optimize.1 b/infra/debian/compiler/docs/one-optimize.1 new file mode 100644 index 000000000..58b2c60bd --- /dev/null +++ b/infra/debian/compiler/docs/one-optimize.1 @@ -0,0 +1,222 @@ +.TH ONE-OPTIMIZE "1" "August 2021" "one-optimize version 1.17.0" "User Commands" +.SH NAME +one-optimize \- optimize circle model +.SH DESCRIPTION +usage: one\-optimize [\-h] [\-v] [\-V] [\-C CONFIG] [\-p] +.br +[\-\-change_outputs CHANGE_OUTPUTS] [\-i INPUT_PATH] +.br +[\-o OUTPUT_PATH] [\-\-O1] [\-\-convert_nchw_to_nhwc] +.br +[\-\-nchw_to_nhwc_input_shape] [\-\-nchw_to_nhwc_output_shape] +.br +[\-\-fold_add_v2] [\-\-fold_cast] [\-\-fold_dequantize] +.br +[\-\-fold_sparse_to_dense] [\-\-forward_reshape_to_unaryop] +.br +[\-\-fuse_add_with_tconv] [\-\-fuse_batchnorm_with_conv] +.br +[\-\-fuse_batchnorm_with_dwconv] +.br +[\-\-fuse_batchnorm_with_tconv] [\-\-fuse_bcq] +.br +[\-\-fuse_preactivation_batchnorm] +.br +[\-\-make_batchnorm_gamma_positive] +.br +[\-\-fuse_activation_function] [\-\-fuse_instnorm] +.br +[\-\-replace_cw_mul_add_with_depthwise_conv] +.br +[\-\-remove_fakequant] [\-\-remove_quantdequant] +.br +[\-\-remove_redundant_reshape] +.br +[\-\-remove_redundant_transpose] +.br +[\-\-remove_unnecessary_reshape] +.br +[\-\-remove_unnecessary_slice] +.br +[\-\-remove_unnecessary_strided_slice] +.br +[\-\-remove_unnecessary_split] [\-\-resolve_customop_add] +.br +[\-\-resolve_customop_batchmatmul] +.br +[\-\-resolve_customop_matmul] +.br +[\-\-shuffle_weight_to_16x1float32] +.br +[\-\-substitute_pack_to_reshape] +.br +[\-\-substitute_squeeze_to_reshape] +.br +[\-\-substitute_transpose_to_reshape] +.br +[\-\-transform_min_max_to_relu6] +.br +[\-\-transform_min_relu_to_relu6] +.PP +\fBone\-optimize\fR is a command line tool to optimize circle model. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.SS "arguments for utility:" +.TP +\fB\-p\fR, \fB\-\-generate_profile_data\fR +generate profiling data +.TP +\fB\-\-change_outputs\fR CHANGE_OUTPUTS +Experimental: Change first subgraph output nodes to +CSV names +.SS "arguments for optimization:" +.TP +\fB\-i\fR INPUT_PATH, \fB\-\-input_path\fR INPUT_PATH +full filepath of the input file +.TP +\fB\-o\fR OUTPUT_PATH, \fB\-\-output_path\fR OUTPUT_PATH +full filepath of the output file +.TP +\fB\-\-O1\fR +enable O1 optimization pass +.TP +\fB\-\-convert_nchw_to_nhwc\fR +Experimental: This will convert NCHW operators to NHWC +under the assumption that input model is NCHW. +.TP +\fB\-\-nchw_to_nhwc_input_shape\fR +convert the input shape of the model (argument for +convert_nchw_to_nhwc) +.TP +\fB\-\-nchw_to_nhwc_output_shape\fR +convert the output shape of the model (argument for +convert_nchw_to_nhwc) +.TP +\fB\-\-fold_add_v2\fR +fold AddV2 op with constant inputs +.TP +\fB\-\-fold_cast\fR +fold Cast op with constant input +.TP +\fB\-\-fold_dequantize\fR +fold Dequantize op +.TP +\fB\-\-fold_sparse_to_dense\fR +fold SparseToDense op +.TP +\fB\-\-forward_reshape_to_unaryop\fR +Forward Reshape op +.TP +\fB\-\-fuse_add_with_tconv\fR +fuse Add op to Transposed Convolution op +.TP +\fB\-\-fuse_batchnorm_with_conv\fR +fuse BatchNorm op to Convolution op +.TP +\fB\-\-fuse_batchnorm_with_dwconv\fR +fuse BatchNorm op to Depthwise Convolution op +.TP +\fB\-\-fuse_batchnorm_with_tconv\fR +fuse BatchNorm op to Transposed Convolution op +.TP +\fB\-\-fuse_bcq\fR +apply Binary Coded Quantization +.TP +\fB\-\-fuse_preactivation_batchnorm\fR +fuse BatchNorm operators of pre\-activations to +Convolution op +.TP +\fB\-\-make_batchnorm_gamma_positive\fR +make negative gamma of BatchNorm to a small positive +value (1e\-10). Note that this pass can change the +execution result of the model. So, use it only when +the impact is known to be acceptable. +.TP +\fB\-\-fuse_activation_function\fR +fuse Activation function to a preceding operator +.TP +\fB\-\-fuse_instnorm\fR +fuse ops to InstanceNorm operator +.TP +\fB\-\-replace_cw_mul_add_with_depthwise_conv\fR +replace channel\-wise Mul/Add with DepthwiseConv2D +.TP +\fB\-\-remove_fakequant\fR +remove FakeQuant ops +.TP +\fB\-\-remove_quantdequant\fR +remove Quantize\-Dequantize sequence +.TP +\fB\-\-remove_redundant_reshape\fR +fuse or remove subsequent Reshape ops +.TP +\fB\-\-remove_redundant_transpose\fR +fuse or remove subsequent Transpose ops +.TP +\fB\-\-remove_unnecessary_reshape\fR +remove unnecessary reshape ops +.TP +\fB\-\-remove_unnecessary_slice\fR +remove unnecessary slice ops +.TP +\fB\-\-remove_unnecessary_strided_slice\fR +remove unnecessary strided slice ops +.TP +\fB\-\-remove_unnecessary_split\fR +remove unnecessary split ops +.TP +\fB\-\-resolve_customop_add\fR +convert Custom(Add) op to Add op +.TP +\fB\-\-resolve_customop_batchmatmul\fR +convert Custom(BatchMatmul) op to BatchMatmul op +.TP +\fB\-\-resolve_customop_matmul\fR +convert Custom(Matmul) op to Matmul op +.TP +\fB\-\-shuffle_weight_to_16x1float32\fR +convert weight format of FullyConnected op to +SHUFFLED16x1FLOAT32. Note that it only converts +weights whose row is a multiple of 16 +.TP +\fB\-\-substitute_pack_to_reshape\fR +convert single input Pack op to Reshape op +.TP +\fB\-\-substitute_squeeze_to_reshape\fR +convert certain condition Squeeze to Reshape +.TP +\fB\-\-substitute_transpose_to_reshape\fR +convert certain condition Transpose to Reshape +.TP +\fB\-\-transform_min_max_to_relu6\fR +transform Minimum\-Maximum pattern to Relu6 op +.TP +\fB\-\-transform_min_relu_to_relu6\fR +transform Minimum(6)\-Relu pattern to Relu6 op +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-optimize +is maintained as a Texinfo manual. If the +.B info +and +.B one-optimize +programs are properly installed at your site, the command +.IP +.B info one-optimize +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-pack.1 b/infra/debian/compiler/docs/one-pack.1 new file mode 100644 index 000000000..dd0422146 --- /dev/null +++ b/infra/debian/compiler/docs/one-pack.1 @@ -0,0 +1,42 @@ +.TH ONE-PACK "1" "August 2021" "one-pack version 1.17.0" "User Commands" +.SH NAME +one-pack \- package circle and metadata into nnpackage +.SH DESCRIPTION +usage: one\-pack [\-h] [\-v] [\-V] [\-C CONFIG] [\-i INPUT_PATH] [\-o OUTPUT_PATH] +.PP +\fBone\-pack\fR is a command line tool to package circle and metadata into nnpackage. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-i\fR INPUT_PATH, \fB\-\-input_path\fR INPUT_PATH +full filepath of the input file +.TP +\fB\-o\fR OUTPUT_PATH, \fB\-\-output_path\fR OUTPUT_PATH +full filepath of the output file +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-pack +is maintained as a Texinfo manual. If the +.B info +and +.B one-pack +programs are properly installed at your site, the command +.IP +.B info one-pack +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-profile.1 b/infra/debian/compiler/docs/one-profile.1 new file mode 100644 index 000000000..3952c4484 --- /dev/null +++ b/infra/debian/compiler/docs/one-profile.1 @@ -0,0 +1,39 @@ +.TH ONE-PROFILE "1" "August 2021" "one-profile version 1.17.0" "User Commands" +.SH NAME +one-profile \- profile backend model file +.SH DESCRIPTION +usage: one\-profile [\-h] [\-v] [\-V] [\-C CONFIG] [\-b BACKEND] [\-\-] [COMMANDS FOR BACKEND] +.PP +\fBone\-profile\fR is a command line tool for profiling backend model. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-b\fR BACKEND, \fB\-\-backend\fR BACKEND +backend name to use +.SH COPYRIGHT +Copyright \(co 2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-profile +is maintained as a Texinfo manual. If the +.B info +and +.B one-profile +programs are properly installed at your site, the command +.IP +.B info one-profile +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/one-quantize.1 b/infra/debian/compiler/docs/one-quantize.1 new file mode 100644 index 000000000..43c4c0321 --- /dev/null +++ b/infra/debian/compiler/docs/one-quantize.1 @@ -0,0 +1,83 @@ +.TH ONE-QUANTIZE "1" "August 2021" "one-quantize version 1.17.0" "User Commands" +.SH NAME +one-quantize \- quantize circle model +.SH DESCRIPTION +usage: one\-quantize [\-h] [\-v] [\-V] [\-C CONFIG] [\-i INPUT_PATH] [\-d INPUT_DATA] +.br +[\-f INPUT_DATA_FORMAT] [\-o OUTPUT_PATH] [\-p] +.br +[\-\-input_dtype INPUT_DTYPE] +.br +[\-\-quantized_dtype QUANTIZED_DTYPE] +.br +[\-\-granularity GRANULARITY] +.br +[\-\-min_percentile MIN_PERCENTILE] +.br +[\-\-max_percentile MAX_PERCENTILE] [\-\-mode MODE] +.PP +\fBone\-quantize\fR is a command line tool to quantize circle model. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.TP +\fB\-i\fR INPUT_PATH, \fB\-\-input_path\fR INPUT_PATH +full filepath of the input file +.TP +\fB\-d\fR INPUT_DATA, \fB\-\-input_data\fR INPUT_DATA +full filepath of the input data file. if not +specified, run with random input data. +.TP +\fB\-o\fR OUTPUT_PATH, \fB\-\-output_path\fR OUTPUT_PATH +full filepath of the output file +.TP +\fB\-p\fR, \fB\-\-generate_profile_data\fR +generate profiling data +.SS "arguments for quantization:" +.TP +\fB\-\-input_dtype\fR INPUT_DTYPE +input data type (supported: float32, default=float32) +.TP +\fB\-\-quantized_dtype\fR QUANTIZED_DTYPE +output quantized data type (supported: uint8, int16, +default=uint8) +.TP +\fB\-\-granularity\fR GRANULARITY +quantize granularity (supported: layer, channel, +default=layer) +.TP +\fB\-\-min_percentile\fR MIN_PERCENTILE +minimum percentile (0.0~100.0, default=1.0) +.TP +\fB\-\-max_percentile\fR MAX_PERCENTILE +maximum percentile (0.0~100.0, default=99.0) +.TP +\fB\-\-mode\fR MODE +record mode (supported: percentile/moving_average, +default=percentile) +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B one-quantize +is maintained as a Texinfo manual. If the +.B info +and +.B one-quantize +programs are properly installed at your site, the command +.IP +.B info one-quantize +.PP +should give you access to the complete manual. diff --git a/infra/debian/compiler/docs/onecc.1 b/infra/debian/compiler/docs/onecc.1 new file mode 100644 index 000000000..352b30a00 --- /dev/null +++ b/infra/debian/compiler/docs/onecc.1 @@ -0,0 +1,170 @@ +.\" Manpage for onecc. +.\" Contact nnfw@samsung.com to correct errors or typos. +.TH ONECC "1" "August 2021" "onecc version 1.17.0" "User Commands" +.SH NAME +onecc \- run ONE driver via several commands or configuration file +.SH SYNOPSIS +\fBonecc\fR [\-h] [\-v] [\-C CONFIG] [COMMAND <args>] +.SH DESCRIPTION +\fBonecc\fR is a command line tool to execute ONE driver via several commands or configuration file. +.SS "Configuration file:" +\fBonecc\fR takes input as a configuration file that supports ini format. +A configuration file consists of sections, each led by a [section] header. +Each section is the ONE driver you want to run, and consists of commands in a key/value combination to pass to the driver. +.SH OPTIONS +.TP +\fB\-h\fR, \fB\-\-help\fR +show this help message and exit +.TP +\fB\-v\fR, \fB\-\-version\fR +show program's version number and exit +.TP +\fB\-V\fR, \fB\-\-verbose\fR +output additional information to stdout or stderr +.TP +\fB\-C\fR CONFIG, \fB\-\-config\fR CONFIG +run with configuation file +.SS compile to circle model +.TP +\fBimport\fR +Convert given model to circle. See one\-import(1) for details. +.TP +\fBoptimize\fR +Optimize circle model. See one-optimize(1) for details. +.TP +\fBquantize\fR +Quantize circle model. See one-quantize(1) for details. +.SS package circle model +.TP +\fBpack\fR +Package circle and metadata into nnpackage. See one-pack(1) for details. +.SS run backend tools +.TP +\fBcodegen\fR +Code generation tool. See one-codegen(1) for details. +.TP +\fBprofile\fR +Profile backend model file. See one-profile(1) for details. +.SH EXAMPLES +.SS Use command line interface +.TP +\fBonecc import tf --v1 -i\fR \fIinput_path\fR \fB-o\fR \fIoutput_path\fR \fB-I\fR \fIinput_arrays\fR \fB-s\fR \fIinput_shapes\fR \fB-O\fR \fIoutput_arrays\fR +import tf model +.TP +\fBonecc import tflite -i\fR \fIinput_path\fR \fB-o\fR \fIoutput_path\fR +import tflite model +.TP +\fBonecc import onnx -i\fR \fIinput_path\fR \fB-o\fR \fIoutput_path\fR +import onnx model +.TP +\fBonecc optimize -i\fR \fIinput_path\fR \fB-o\fR \fIoutput_path\fR \fIoptimize_arguments\fR +optimize circle model +.TP +\fBonecc quantize -i\fR \fIinput_path\fR \fB-o\fR \fIoutput_path\fR \fB-d\fR \fIinput_data\fR +quantize circle model +.TP +\fBonecc pack -i\fR \fIinput_path\fR \fB-o\fR \fIoutput_path\fR +package circle and metadata into nnpackage +.TP +\fBonecc codegen -b\fR \fIbackend\fR \fB--\fR \fIbackends_arguments\fR +generate backend code +.TP +\fBonecc profile -b\fR \fIbackend\fR \fB--\fR \fIbackends_arguments\fR +profile backend model +.PP +.SS Use configuration file +.PP +The configuration file should be written in the following format: +.IP +[onecc] +.br +one-import-tf=True +.br +one-import-tflite=False +.br +one-import-bcq=False +.br +one-import-onnx=False +.br +one-optimize=True +.br +one-quantize=True +.br +one-pack=True +.br +one-codegen=True +.br +one-profile=True +.IP +[one-import-tf] +.br +input_path=/path/to/inception_v3.pb +.br +output_path=inception_v3.circle +.br +input_arrays=input +.br +input_shapes=1,299,299,3 +.br +output_arrays=InceptionV3/Predictions/Reshape_1 +.br +converter_version=v1 +.br +model_format=graph_def +.IP +[one-optimize] +.br +input_path=inception_v3.circle +.br +output_path=inception_v3.opt.circle +.br +generate_profile_data=False +.IP +[one-quantize] +.br +input_path=inception_v3.opt.circle +.br +output_path=inception_v3.quantized.circle +.br +input_data=inception_v3_test_data.h5 +.IP +[one-pack] +.br +input_path=inception_v3.quantized.circle +.br +output_path=inception_v3_pack +.IP +[one-codegen] +.br +backend=dummy +.br +command=-o sample.out inception_v3.quantized.circle +.IP +[one-profile] +.br +backend=dummy +.br +command=sample.out +.TP +\fBonecc -C\fR \fIconfiguration file\fR +Run ONE driver according to configuration section parameter +.PP +\fBonecc\fR section decides whether to use each driver or not. +If the value is False, even if the corresponding section exists, the driver won't be executed. +.SH COPYRIGHT +Copyright \(co 2020\-2021 Samsung Electronics Co., Ltd. All Rights Reserved +Licensed under the Apache License, Version 2.0 +https://github.com/Samsung/ONE +.SH "SEE ALSO" +The full documentation for +.B onecc +is maintained as a Texinfo manual. If the +.B info +and +.B onecc +programs are properly installed at your site, the command +.IP +.B info onecc +.PP +should give you access to the complete manual. + diff --git a/infra/debian/compiler/one-compiler-dev.install b/infra/debian/compiler/one-compiler-dev.install new file mode 100644 index 000000000..b7a681dc0 --- /dev/null +++ b/infra/debian/compiler/one-compiler-dev.install @@ -0,0 +1,9 @@ +# {FILES_TO_INSTALL} {DEST_DIR} +# bin +usr/bin/circledump usr/share/one/bin/ +usr/bin/circle-tensordump usr/share/one/bin/ +usr/bin/tflchef usr/share/one/bin/ +usr/bin/tflchef-file usr/share/one/bin/ +usr/bin/tflchef-reverse usr/share/one/bin/ +# include +usr/include/* usr/share/one/include/ diff --git a/infra/debian/compiler/one-compiler-dev.links b/infra/debian/compiler/one-compiler-dev.links new file mode 100644 index 000000000..89a654db9 --- /dev/null +++ b/infra/debian/compiler/one-compiler-dev.links @@ -0,0 +1,6 @@ +# bin +usr/share/one/bin/circledump usr/bin/circledump +usr/share/one/bin/circle-tensordump usr/bin/circle-tensordump +usr/share/one/bin/tflchef usr/bin/tflchef +usr/share/one/bin/tflchef-file usr/bin/tflchef-file +usr/share/one/bin/tflchef-reverse usr/bin/tflchef-reverse diff --git a/infra/debian/compiler/one-compiler-test.install b/infra/debian/compiler/one-compiler-test.install new file mode 100644 index 000000000..fb9714da0 --- /dev/null +++ b/infra/debian/compiler/one-compiler-test.install @@ -0,0 +1,5 @@ +# {FILES_TO_INSTALL} {DEST_DIR} +# bin +usr/bin/luci_eval_driver usr/share/one/bin/ +# test +usr/test/* usr/share/one/test/ diff --git a/infra/debian/compiler/one-compiler.install b/infra/debian/compiler/one-compiler.install new file mode 100644 index 000000000..ba628545b --- /dev/null +++ b/infra/debian/compiler/one-compiler.install @@ -0,0 +1,35 @@ +# {FILES_TO_INSTALL} {DEST_DIR} +# bin +usr/bin/circle2circle usr/share/one/bin/ +usr/bin/circle_partitioner usr/share/one/bin/ +usr/bin/circle-quantizer usr/share/one/bin/ +usr/bin/conv_mixin_1.8.0.patch usr/share/one/bin/ +usr/bin/generate_bcq_metadata.py usr/share/one/bin/ +usr/bin/generate_bcq_output_arrays.py usr/share/one/bin/ +usr/bin/model2nnpkg.sh usr/share/one/bin/ +usr/bin/onecc usr/share/one/bin/ +usr/bin/onecc.template.cfg usr/share/one/bin/ +usr/bin/one-build usr/share/one/bin/ +usr/bin/one-build.template.cfg usr/share/one/bin/ +usr/bin/one-codegen usr/share/one/bin/ +usr/bin/one-import usr/share/one/bin/ +usr/bin/one-import-bcq usr/share/one/bin/ +usr/bin/one-import-onnx usr/share/one/bin/ +usr/bin/one-import-tf usr/share/one/bin/ +usr/bin/one-import-tflite usr/share/one/bin/ +usr/bin/one-optimize usr/share/one/bin/ +usr/bin/one-pack usr/share/one/bin/ +usr/bin/one-prepare-venv usr/share/one/bin/ +usr/bin/one-profile usr/share/one/bin/ +usr/bin/one-quantize usr/share/one/bin/ +usr/bin/one-version usr/share/one/bin/ +usr/bin/rawdata2hdf5 usr/share/one/bin/ +usr/bin/record-minmax usr/share/one/bin/ +usr/bin/tf2nnpkg usr/share/one/bin/ +usr/bin/tf2tfliteV2.py usr/share/one/bin/ +usr/bin/tflite2circle usr/share/one/bin/ +usr/bin/utils.py usr/share/one/bin/ +# lib +usr/lib/* usr/share/one/lib/ +# doc +usr/doc/* usr/share/one/doc/ diff --git a/infra/debian/compiler/one-compiler.links b/infra/debian/compiler/one-compiler.links new file mode 100644 index 000000000..8b6e542c1 --- /dev/null +++ b/infra/debian/compiler/one-compiler.links @@ -0,0 +1,16 @@ +# bin +usr/share/one/bin/one-build usr/bin/one-build +usr/share/one/bin/onecc usr/bin/onecc +# lib +usr/share/one/lib/libloco.so usr/lib/libloco.so +usr/share/one/lib/libluci_env.so usr/lib/libluci_env.so +usr/share/one/lib/libluci_export.so usr/lib/libluci_export.so +usr/share/one/lib/libluci_import.so usr/lib/libluci_import.so +usr/share/one/lib/libluci_interpreter.so usr/lib/libluci_interpreter.so +usr/share/one/lib/libluci_lang.so usr/lib/libluci_lang.so +usr/share/one/lib/libluci_logex.so usr/lib/libluci_logex.so +usr/share/one/lib/libluci_log.so usr/lib/libluci_log.so +usr/share/one/lib/libluci_partition.so usr/lib/libluci_partition.so +usr/share/one/lib/libluci_pass.so usr/lib/libluci_pass.so +usr/share/one/lib/libluci_profile.so usr/lib/libluci_profile.so +usr/share/one/lib/libluci_service.so usr/lib/libluci_service.so diff --git a/infra/debian/compiler/one-compiler.manpages b/infra/debian/compiler/one-compiler.manpages new file mode 100644 index 000000000..77f2f4e46 --- /dev/null +++ b/infra/debian/compiler/one-compiler.manpages @@ -0,0 +1,12 @@ +debian/docs/one-build.1 +debian/docs/one-codegen.1 +debian/docs/one-import.1 +debian/docs/one-import-bcq.1 +debian/docs/one-import-onnx.1 +debian/docs/one-import-tf.1 +debian/docs/one-import-tflite.1 +debian/docs/one-optimize.1 +debian/docs/one-pack.1 +debian/docs/one-profile.1 +debian/docs/one-quantize.1 +debian/docs/onecc.1 diff --git a/infra/debian/compiler/postinst b/infra/debian/compiler/postinst new file mode 100644 index 000000000..a339d06c1 --- /dev/null +++ b/infra/debian/compiler/postinst @@ -0,0 +1,12 @@ +#!/bin/bash + +# https://www.debian.org/doc/debian-policy/ch-maintainerscripts.html +# Boradly speaking, the `postinst` is called after a package is unpacked. + +set -e + +# This script is invoked as root except environmental variables, +# which causes invalid permission problem. +# e.g. When `pip` installs user packages, it proceeds based on $HOME. +# To proper installation, $HOME should be root. +su - $(whoami) -c '/usr/share/one/bin/one-prepare-venv' # $(whoami) = root diff --git a/infra/debian/compiler/postrm b/infra/debian/compiler/postrm new file mode 100644 index 000000000..2972f28db --- /dev/null +++ b/infra/debian/compiler/postrm @@ -0,0 +1,18 @@ +#!/bin/bash + +set -e + +case "$1" in + remove|purge) + rm -rf /usr/share/one/ + ;; + upgrade) + # DO NOTHING + ;; + failed-upgrade|abort-install|abort-upgrade) + # DO NOTHING + ;; + *) + # DO NOTHING + ;; +esac diff --git a/infra/debian/compiler/rules b/infra/debian/compiler/rules new file mode 100755 index 000000000..21b956b2f --- /dev/null +++ b/infra/debian/compiler/rules @@ -0,0 +1,22 @@ +#!/usr/bin/make -f +export DH_VERBOSE = 1 +export NNAS_BUILD_PREFIX = build +export PRESET = 20210706 +export _DESTDIR = debian/tmp/usr + +%: + dh $@ + +override_dh_auto_build: + ./nnas create-package --preset $(PRESET) --prefix "$(_DESTDIR)" + +override_dh_auto_install: + cmake --build "$(NNAS_BUILD_PREFIX)/nncc" -- install + +override_dh_install: + install -t "$(_DESTDIR)/bin" -D "tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh" + install -T -m 755 -D "infra/packaging/res/tf2nnpkg.${PRESET}" "$(_DESTDIR)/bin/tf2nnpkg" + dh_install + +override_dh_builddeb: + dh_builddeb --destdir=$(NNAS_BUILD_PREFIX) diff --git a/infra/debian/compiler/source/format b/infra/debian/compiler/source/format new file mode 100644 index 000000000..89ae9db8f --- /dev/null +++ b/infra/debian/compiler/source/format @@ -0,0 +1 @@ +3.0 (native) diff --git a/infra/debian/compiler/source/local-options b/infra/debian/compiler/source/local-options new file mode 100644 index 000000000..296a73032 --- /dev/null +++ b/infra/debian/compiler/source/local-options @@ -0,0 +1,2 @@ +# This is for reproducible building. Otherwise, `debuild` recognizes build artifacts as source files. +diff-ignore="build|externals" diff --git a/infra/debian/runtime/changelog b/infra/debian/runtime/changelog new file mode 100644 index 000000000..4a41d959c --- /dev/null +++ b/infra/debian/runtime/changelog @@ -0,0 +1,11 @@ +one (1.17.0) bionic; urgency=low + + * New gpu_gl backend supports the following operations : Add, Convolution, Depthwise Convolution, Pooling, Reshape, Relu, Softmax + + -- Chunseok Lee <chunseok.lee@samsung.com> Fri, 20 Aug 2021 17:00:00 +0900 + +one (1.16.0) bionic; urgency=low + + * Initial release. + + -- Chunseok Lee <chunseok.lee@samsung.com> Mon, 05 Jul 2021 17:11:00 +0900 diff --git a/infra/debian/runtime/compat b/infra/debian/runtime/compat new file mode 100644 index 000000000..ec635144f --- /dev/null +++ b/infra/debian/runtime/compat @@ -0,0 +1 @@ +9 diff --git a/infra/debian/runtime/control b/infra/debian/runtime/control new file mode 100644 index 000000000..20543baee --- /dev/null +++ b/infra/debian/runtime/control @@ -0,0 +1,19 @@ +Source: one +Section: devel +Priority: extra +Maintainer: Neural Network Acceleration Solution Developers <nnfw@samsung.com> +Build-Depends: cmake, debhelper (>=9), dh-python, python3-all +Standards-Version: 3.9.8 +Homepage: https://github.com/Samsung/ONE + +Package: nnfw +Architecture: amd64 +Multi-Arch: same +Depends: ${shlibs:Depends}, ${misc:Depends} +Description: one-runtime package + +Package: nnfw-dev +Architecture: amd64 +Multi-Arch: same +Depends: nnfw, ${shlibs:Depends}, ${misc:Depends} +Description: one-runtime development package diff --git a/infra/debian/runtime/copyright b/infra/debian/runtime/copyright new file mode 100644 index 000000000..bb64695a4 --- /dev/null +++ b/infra/debian/runtime/copyright @@ -0,0 +1,3 @@ +Files: * +License: Proprietary +Copyright (c) <2018> <Samsung Electronics Co.,Ltd.> diff --git a/infra/debian/runtime/nnfw-dev.install b/infra/debian/runtime/nnfw-dev.install new file mode 100644 index 000000000..f246e7c24 --- /dev/null +++ b/infra/debian/runtime/nnfw-dev.install @@ -0,0 +1,4 @@ +# {FILES_TO_INSTALL} {DEST_DIR} +# include +usr/include/nnfw usr/include/ +usr/lib/pkgconfig/*.pc usr/lib/pkgconfig/ diff --git a/infra/debian/runtime/nnfw.install b/infra/debian/runtime/nnfw.install new file mode 100644 index 000000000..44be07c9c --- /dev/null +++ b/infra/debian/runtime/nnfw.install @@ -0,0 +1,3 @@ +# {FILES_TO_INSTALL} {DEST_DIR} +# lib +usr/lib/*.so usr/lib/ diff --git a/infra/debian/runtime/rules b/infra/debian/runtime/rules new file mode 100755 index 000000000..dee87a9ed --- /dev/null +++ b/infra/debian/runtime/rules @@ -0,0 +1,22 @@ +#!/usr/bin/make -f +DEBVER := $(shell dpkg-parsechangelog -SVersion) +export DH_VERBOSE = 1 +export _DESTDIR = debian/tmp/ +export BUILD_TYPE=release +export OPTIONS=-DBUILD_LOGGING=0 -DBUILD_TFLITE_COMPARATOR_TEST_TOOL=0 -DBUILD_NNPACKAGE_RUN=0 -DBUILD_TFLITE_RUN=0 -DBUILD_NNAPI_TEST=0 -DBUILD_RUNTIME_NNAPI_TEST=0 -DBUILD_TFLITE_BENCHMARK_MODEL=0 -DBUILD_TFLITE_VANILLA_RUN=0 -DBUILD_TENSORFLOW_LITE_2_3_0=0 -DBUILD_TENSORFLOW_LITE=0 +export DEBIAN_BUILD=1 +export INSTALL_PATH=debian/tmp/usr/ +%: + dh $@ + +override_dh_auto_build: + make -f Makefile.template +override_dh_auto_install: + make -f Makefile.template install +override_dh_install: + install -d debian/tmp/usr/lib/pkgconfig + sed -i 's:@libdir@:\/usr\/lib:g' ./packaging/nnfw.pc.in + sed -i 's:@includedir@:\/usr\/include:g' ./packaging/nnfw.pc.in + sed -i 's:@version@:${DEBVER}:g' ./packaging/nnfw.pc.in + install -m 0644 packaging/nnfw.pc.in -T debian/tmp/usr/lib/pkgconfig/nnfw.pc + dh_install diff --git a/infra/debian/runtime/source/format b/infra/debian/runtime/source/format new file mode 100644 index 000000000..89ae9db8f --- /dev/null +++ b/infra/debian/runtime/source/format @@ -0,0 +1 @@ +3.0 (native) diff --git a/infra/debian/runtime/source/local-options b/infra/debian/runtime/source/local-options new file mode 100644 index 000000000..296a73032 --- /dev/null +++ b/infra/debian/runtime/source/local-options @@ -0,0 +1,2 @@ +# This is for reproducible building. Otherwise, `debuild` recognizes build artifacts as source files. +diff-ignore="build|externals" diff --git a/infra/docker/bionic/Dockerfile b/infra/docker/bionic/Dockerfile index 15a91d7f3..c3d5b3e95 100644 --- a/infra/docker/bionic/Dockerfile +++ b/infra/docker/bionic/Dockerfile @@ -20,9 +20,18 @@ ARG UBUNTU_MIRROR # Install 'add-apt-repository' RUN apt-get update && apt-get -qqy install software-properties-common +# Git repo for latest version (github checkout@v2 action requires v2.18) +RUN add-apt-repository ppa:git-core/ppa -y + # Build tool RUN apt-get update && apt-get -qqy install build-essential cmake scons git g++-arm-linux-gnueabihf g++-aarch64-linux-gnu +# ARM none eabi build tool +RUN apt-get update && apt-get -qqy install gcc-arm-none-eabi + +# Debian build tool +RUN apt-get update && apt-get -qqy install fakeroot devscripts debhelper python3-all + # Install extra dependencies (Caffe, nnkit) RUN apt-get update && apt-get -qqy install libboost-all-dev libgflags-dev libgoogle-glog-dev libatlas-base-dev libhdf5-dev diff --git a/infra/docker/focal/Dockerfile b/infra/docker/focal/Dockerfile index cccf3042d..45faa6975 100644 --- a/infra/docker/focal/Dockerfile +++ b/infra/docker/focal/Dockerfile @@ -22,6 +22,9 @@ RUN apt-get update && apt-get -qqy install software-properties-common # Build tool RUN apt-get update && apt-get -qqy install build-essential cmake scons git lcov g++-arm-linux-gnueabihf g++-aarch64-linux-gnu +# Debian build tool +RUN apt-get update && apt-get -qqy install fakeroot devscripts debhelper python3-all dh-python + # Install extra dependencies (Caffe, nnkit) RUN apt-get update && apt-get -qqy install libboost-all-dev libgflags-dev libgoogle-glog-dev libatlas-base-dev libhdf5-dev diff --git a/infra/nncc/CMakeLists.txt b/infra/nncc/CMakeLists.txt index d416db2fd..eb279902e 100644 --- a/infra/nncc/CMakeLists.txt +++ b/infra/nncc/CMakeLists.txt @@ -99,6 +99,7 @@ option(DOWNLOAD_CAFFE "Download Caffe source" ON) option(DOWNLOAD_PYTORCH "Download Pytorch source" ON) option(DOWNLOAD_ONNX "Download ONNX source" ON) option(DOWNLOAD_ABSEIL "Download Abseil-cpp source" ON) +option(DOWNLOAD_OPENCL_HEADERS "Download OpenCl Header source" ON) option(DOWNLOAD_PYBIND11 "Download Pybind11 source" ON) option(DOWNLOAD_GTEST "Download Google Test source" ON) diff --git a/infra/nnfw/cmake/CfgOptionFlags.cmake b/infra/nnfw/cmake/CfgOptionFlags.cmake index 87c2c86f6..438d6b275 100644 --- a/infra/nnfw/cmake/CfgOptionFlags.cmake +++ b/infra/nnfw/cmake/CfgOptionFlags.cmake @@ -29,6 +29,7 @@ option(BUILD_WITH_HDF5 "Build test tool with HDF5 library" ON) option(GENERATE_RUNTIME_NNAPI_TESTS "Generate NNAPI operation gtest" ON) option(ENVVAR_ONERT_CONFIG "Use environment variable for onert configuration" ON) option(INSTALL_TEST_SCRIPTS "Install test scripts" ON) +option(BUILD_GPU_CL "Build gpu_cl backend" ON) # # Default build configuration for contrib # @@ -54,6 +55,7 @@ option(BUILD_TFLITE_ACCURACY "Build tflite accuracy tool" OFF) # option(DOWNLOAD_TENSORFLOW "Download Tensorflow source" ON) option(DOWNLOAD_ABSEIL "Download Abseil source" ON) +option(DOWNLOAD_OPENCL_HEADERS "Download Opencl_headers source" ON) option(DOWNLOAD_EIGEN "Download Eigen source" ON) option(DOWNLOAD_FARMHASH "Download farmhash source" ON) option(DOWNLOAD_GEMMLOWP "Download GEMM low precesion library source" ON) diff --git a/infra/nnfw/cmake/options/options_x86_64-darwin.cmake b/infra/nnfw/cmake/options/options_x86_64-darwin.cmake index 135cfbf6e..5dbcf7e08 100644 --- a/infra/nnfw/cmake/options/options_x86_64-darwin.cmake +++ b/infra/nnfw/cmake/options/options_x86_64-darwin.cmake @@ -4,3 +4,5 @@ option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" OFF) option(DOWNLOAD_ARMCOMPUTE "Download ARM Compute source" OFF) option(BUILD_XNNPACK "Build XNNPACK" OFF) +option(DOWNLOAD_OPENCL_HEADERS "Download opencl headers" OFF) +option(BUILD_GPU_CL "Build gpu_cl backend" OFF) diff --git a/infra/nnfw/cmake/options/options_x86_64-linux.cmake b/infra/nnfw/cmake/options/options_x86_64-linux.cmake index 135cfbf6e..5dbcf7e08 100644 --- a/infra/nnfw/cmake/options/options_x86_64-linux.cmake +++ b/infra/nnfw/cmake/options/options_x86_64-linux.cmake @@ -4,3 +4,5 @@ option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" OFF) option(DOWNLOAD_ARMCOMPUTE "Download ARM Compute source" OFF) option(BUILD_XNNPACK "Build XNNPACK" OFF) +option(DOWNLOAD_OPENCL_HEADERS "Download opencl headers" OFF) +option(BUILD_GPU_CL "Build gpu_cl backend" OFF) diff --git a/infra/nnfw/cmake/options/options_x86_64-tizen.cmake b/infra/nnfw/cmake/options/options_x86_64-tizen.cmake index 1e83e4ee0..0f1c86d23 100644 --- a/infra/nnfw/cmake/options/options_x86_64-tizen.cmake +++ b/infra/nnfw/cmake/options/options_x86_64-tizen.cmake @@ -10,3 +10,5 @@ option(GENERATE_RUNTIME_NNAPI_TESTS "Generate NNAPI operation gtest" OFF) option(ENVVAR_ONERT_CONFIG "Use environment variable for onert configuration" OFF) option(BUILD_XNNPACK "Build XNNPACK" OFF) +option(DOWNLOAD_OPENCL_HEADERS "Download opencl headers" OFF) +option(BUILD_GPU_CL "Build gpu_cl backend" OFF) diff --git a/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLite/CMakeLists.txt b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLite/CMakeLists.txt index 2c9618d68..9a7b240e9 100644 --- a/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLite/CMakeLists.txt +++ b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLite/CMakeLists.txt @@ -23,8 +23,6 @@ file(GLOB TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/*.cc") file(GLOB TFLITE_PROFILING_TESTS "${TENSORFLOW_LITE_BASE}/profiling/*test*.cc") list(REMOVE_ITEM TFLITE_PROFILING_SRCS ${TFLITE_PROFILING_TESTS}) -# We will use our own BuiltinOpResolver -list(REMOVE_ITEM TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/register.cc") # We will use our own summarizer list(REMOVE_ITEM TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/profile_summarizer.cc") list(APPEND TFLITE_SRCS ${TFLITE_CORE_SRCS}) @@ -35,6 +33,10 @@ list(APPEND TFLITE_SRCS ${TFLITE_PROFILING_SRCS}) list(APPEND TFLITE_SRCS "${FarmhashSource_DIR}/src/farmhash.cc") +# externals for spectrogram +list(APPEND TFLITE_SRCS "${OouraFFTSource_DIR}/fftsg.c") +list(APPEND TFLITE_SRCS "${OouraFFTSource_DIR}/fftsg2d.c") + list(APPEND TFLITE_INCLUDES "${TensorFlowSource_DIR}") list(APPEND TFLITE_INCLUDES "${AbseilSource_DIR}") list(APPEND TFLITE_INCLUDES "${GEMMLowpSource_DIR}") @@ -44,12 +46,6 @@ if(NEON2SSESource_FOUND) list(APPEND TFLITE_INCLUDES "${NEON2SSESource_DIR}") endif(NEON2SSESource_FOUND) -# This kernels are not used on nnfw -## spectrogram -list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/audio_spectrogram.cc") -list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/audio_spectrogram_test.cc") -list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/internal/spectrogram.cc") - add_library(tensorflow-lite STATIC ${TFLITE_SRCS}) target_include_directories(tensorflow-lite SYSTEM PUBLIC ${TFLITE_INCLUDES}) target_compile_definitions(tensorflow-lite PUBLIC "GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK") diff --git a/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake index e4fbc3ad3..b03d59375 100644 --- a/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake +++ b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake @@ -25,6 +25,8 @@ if(BUILD_TENSORFLOW_LITE) return_unless(GEMMLowpSource_FOUND) nnas_find_package(TensorFlowSource EXACT 1.13.1 QUIET) return_unless(TensorFlowSource_FOUND) + nnas_find_package(OouraFFTSource QUIET) + return_unless(OouraFFTSource_FOUND) # Optional packages nnas_find_package(NEON2SSESource QUIET) diff --git a/infra/packaging/build b/infra/packaging/build index 667857f53..8d3230010 100644 --- a/infra/packaging/build +++ b/infra/packaging/build @@ -8,7 +8,7 @@ if [[ -z "${NNAS_PROJECT_PATH}" ]]; then fi # The default preset -PRESET="20210406" +PRESET="20210706" EXTRA_OPTIONS=() while [ "$#" -ne 0 ]; do diff --git a/infra/packaging/preset/20210406 b/infra/packaging/preset/20210406 index 3da09705c..caddb0a53 100644 --- a/infra/packaging/preset/20210406 +++ b/infra/packaging/preset/20210406 @@ -25,7 +25,9 @@ function preset_configure() REQUIRED_UNITS+=("luci") # Tools REQUIRED_UNITS+=("tflite2circle" "circle2circle" "tflchef" "circlechef") + REQUIRED_UNITS+=("circle-tensordump" "circledump") REQUIRED_UNITS+=("tf2tfliteV2" "luci-interpreter" "circle-verify") + REQUIRED_UNITS+=("luci-eval-driver") REQUIRED_UNITS+=("record-minmax" "circle-quantizer" "rawdata2hdf5") REQUIRED_UNITS+=("circle-partitioner") REQUIRED_UNITS+=("one-cmds") diff --git a/infra/packaging/preset/20210406_windows b/infra/packaging/preset/20210406_windows index 5a250ca6c..5d4bd8d5f 100644 --- a/infra/packaging/preset/20210406_windows +++ b/infra/packaging/preset/20210406_windows @@ -21,6 +21,7 @@ function preset_configure() # Tools REQUIRED_UNITS+=("tflite2circle" "circle2circle" "tflchef" "circlechef") REQUIRED_UNITS+=("tf2tfliteV2" "luci-interpreter" "circle-verify") + REQUIRED_UNITS+=("luci-eval-driver") REQUIRED_UNITS+=("record-minmax" "circle-quantizer" "rawdata2hdf5") REQUIRED_UNITS+=("circle-partitioner") REQUIRED_UNITS+=("one-cmds") diff --git a/infra/packaging/preset/20210706 b/infra/packaging/preset/20210706 new file mode 100644 index 000000000..ef6b6e521 --- /dev/null +++ b/infra/packaging/preset/20210706 @@ -0,0 +1,55 @@ +#!/bin/bash + +# NOTE purpose of this file is static analysis only +# new official preset will be added when new programs are ready + +PRESET="20210706" + +function preset_configure() +{ + REQUIRED_UNITS=() + # Common Libraries + REQUIRED_UNITS+=("angkor" "cwrap" "pepper-str" "pepper-strcast" "pp") + REQUIRED_UNITS+=("oops" "pepper-assert" "pepper-csv2vec" "foder" "crew") + REQUIRED_UNITS+=("souschef") + REQUIRED_UNITS+=("safemain") + REQUIRED_UNITS+=("arser") + REQUIRED_UNITS+=("vconone") + # Hermes Logging Framework + REQUIRED_UNITS+=("hermes" "hermes-std") + # loco IR and related utilities + REQUIRED_UNITS+=("loco" "locop" "locomotiv" "logo-core" "logo") + # Flatbuffer I/O + REQUIRED_UNITS+=("mio-tflite" "mio-circle") + # Circle compiler library (.circle -> .circle) + REQUIRED_UNITS+=("luci") + # Tools + REQUIRED_UNITS+=("tflite2circle" "circle2circle" "tflchef" "circlechef") + REQUIRED_UNITS+=("circle-tensordump" "circledump") + REQUIRED_UNITS+=("tf2tfliteV2" "luci-interpreter" "circle-verify") + REQUIRED_UNITS+=("luci-eval-driver") + REQUIRED_UNITS+=("record-minmax" "circle-quantizer" "rawdata2hdf5") + REQUIRED_UNITS+=("circle-partitioner") + REQUIRED_UNITS+=("one-cmds") + REQUIRED_UNITS+=("bcq-tools") + + NPROC=${NPROC:-$(cat /proc/cpuinfo | grep -c processor)} + + # TODO Use "nncc configure" and "nncc build" + cmake \ + -DCMAKE_INSTALL_PREFIX="${NNCC_INSTALL_PREFIX}" \ + -DCMAKE_BUILD_TYPE=release \ + -DBUILD_WHITELIST=$(join_by ";" "${REQUIRED_UNITS[@]}") \ + -DEXTERNALS_BUILD_THREADS=$((NPROC/2)) \ + ${EXTRA_OPTIONS[@]} \ + "${NNAS_PROJECT_PATH}/infra/nncc" +} + +function preset_install() +{ + install -t "${NNPKG_INSTALL_PREFIX}/bin" -D \ + "${NNAS_PROJECT_PATH}/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh" + + # Install tf2nnpkg + install -T -m 755 -D "${SCRIPT_PATH}/res/tf2nnpkg.${PRESET}" "${NNAS_INSTALL_PREFIX}/bin/tf2nnpkg" +} diff --git a/infra/packaging/preset/20210706_windows b/infra/packaging/preset/20210706_windows new file mode 100644 index 000000000..857540870 --- /dev/null +++ b/infra/packaging/preset/20210706_windows @@ -0,0 +1,67 @@ +#!/bin/bash + +function preset_configure() +{ + REQUIRED_UNITS=() + # Common Libraries + REQUIRED_UNITS+=("angkor" "cwrap" "pepper-str" "pepper-strcast" "pp") + REQUIRED_UNITS+=("oops" "pepper-assert" "pepper-csv2vec" "foder" "crew") + REQUIRED_UNITS+=("souschef") + REQUIRED_UNITS+=("safemain") + REQUIRED_UNITS+=("arser") + REQUIRED_UNITS+=("vconone") + # Hermes Logging Framework + REQUIRED_UNITS+=("hermes" "hermes-std") + # loco IR and related utilities + REQUIRED_UNITS+=("loco" "locop" "locomotiv" "logo-core" "logo") + # Flatbuffer I/O + REQUIRED_UNITS+=("mio-tflite" "mio-circle") + # Circle compiler library (.circle -> .circle) + REQUIRED_UNITS+=("luci") + # Tools + REQUIRED_UNITS+=("tflite2circle" "circle2circle" "tflchef" "circlechef") + REQUIRED_UNITS+=("tf2tfliteV2" "luci-interpreter" "circle-verify") + REQUIRED_UNITS+=("luci-eval-driver") + REQUIRED_UNITS+=("record-minmax" "circle-quantizer" "rawdata2hdf5") + REQUIRED_UNITS+=("circle-partitioner") + REQUIRED_UNITS+=("one-cmds") + REQUIRED_UNITS+=("bcq-tools") + + NPROC=$(cat /proc/cpuinfo | grep -c processor) + + # TODO Use "nncc configure" and "nncc build" + cmake \ + -G "MSYS Makefiles" \ + -DUSE_PROTOBUF_LEGACY_IMPORT=ON \ + -DCMAKE_EXE_LINKER_FLAGS="-Wl,--allow-multiple-definition" \ + -DCMAKE_SHARED_LINKER_FLAGS="-Wl,--allow-multiple-definition" \ + -DENABLE_TEST=OFF \ + -DDOWNLOAD_GTEST=OFF \ + -DBUILD_GTEST=OFF \ + -DCMAKE_C_COMPILER=gcc \ + -DCMAKE_CXX_COMPILER=g++ \ + -DCMAKE_INSTALL_PREFIX="${NNCC_INSTALL_PREFIX}" \ + -DCMAKE_BUILD_TYPE=release \ + -DBUILD_WHITELIST=$(join_by ";" "${REQUIRED_UNITS[@]}") \ + -DEXTERNALS_BUILD_THREADS=$((NPROC/2)) \ + ${EXTRA_OPTIONS[@]} \ + "${NNAS_PROJECT_PATH}/infra/nncc" +} + +function preset_install() +{ + # Install libraries to bin/ for Windows release + mv ${NNCC_INSTALL_PREFIX}/lib/*.dll ${NNCC_INSTALL_PREFIX}/bin + rm -rf ${NNCC_INSTALL_PREFIX}/lib + + install -t "${NNPKG_INSTALL_PREFIX}/bin" -D \ + "${NNAS_PROJECT_PATH}/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh" + + # Install tf2nnpkg + install -T -m 755 -D "${SCRIPT_PATH}/res/tf2nnpkg.20210706" "${NNAS_INSTALL_PREFIX}/bin/tf2nnpkg" + + # Though you have to install tensorflow to run 'tf2tfliteV2', + # tensorflow can't be installed in mingw. First, You can install tensorflow + # from Window native CMD(run as administrator) with python virtual environment. + # And, you must copy it to "${NNAS_INSTALL_PREFIX}/bin/venv" +} diff --git a/infra/packaging/res/tf2nnpkg.20210406 b/infra/packaging/res/tf2nnpkg.20210406 index b7091541a..0d44818a1 100644 --- a/infra/packaging/res/tf2nnpkg.20210406 +++ b/infra/packaging/res/tf2nnpkg.20210406 @@ -92,37 +92,16 @@ OUTPUT=$(awk -F, '/^output/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' INPUT_SHAPES=$(grep ^input ${INFO_FILE} | cut -d "[" -f2 | cut -d "]" -f1 | tr -d ' ' | xargs | tr ' ' ':') -# Generate BCQ information metadata -# If model has no BCQ information or invalid information, pb file is not changed. -"${ROOT}/bin/generate_bcq_metadata" \ ---input_path "${GRAPHDEF_FILE}" \ ---output_path "${TMPDIR}/${MODEL_NAME}_withmeta.pb" \ ---output_arrays "${OUTPUT}" - -# Generate BCQ information nodes as output_arrays -# If model has no BCQ information, output_arrays would be empty. -"${ROOT}/bin/generate_bcq_output_arrays" \ ---input_path "${TMPDIR}/${MODEL_NAME}_withmeta.pb" \ ---metadata_path "${TMPDIR}/${MODEL_NAME}_metadata_arrays.txt" \ ---output_arrays_path "${TMPDIR}/${MODEL_NAME}_output_arrays.txt" - -# generate tflite file -TF2TFLITE_CONVERT_SCRIPT="python ${ROOT}/bin/tf2tfliteV2.py ${TF_INTERFACE} " -TF2TFLITE_CONVERT_SCRIPT+="--input_path ${TMPDIR}/${MODEL_NAME}_withmeta.pb " -TF2TFLITE_CONVERT_SCRIPT+="--input_arrays ${INPUT} " -TF2TFLITE_CONVERT_SCRIPT+="--output_path ${TMPDIR}/${MODEL_NAME}.tflite " -TF2TFLITE_CONVERT_SCRIPT+="--output_arrays " -TF2TFLITE_CONVERT_SCRIPT+="$(cat ${TMPDIR}/${MODEL_NAME}_metadata_arrays.txt)" -TF2TFLITE_CONVERT_SCRIPT+="${OUTPUT}" -TF2TFLITE_CONVERT_SCRIPT+="$(cat ${TMPDIR}/${MODEL_NAME}_output_arrays.txt) " +ONE_IMPORT_BCQ_SCRIPT="${ROOT}/bin/one-import-bcq ${TF_INTERFACE} " +ONE_IMPORT_BCQ_SCRIPT+="-i ${GRAPHDEF_FILE} " +ONE_IMPORT_BCQ_SCRIPT+="-o ${TMPDIR}/${MODEL_NAME}.tmp.circle " +ONE_IMPORT_BCQ_SCRIPT+="-I ${INPUT} " +ONE_IMPORT_BCQ_SCRIPT+="-O ${OUTPUT} " if [ ! -z ${INPUT_SHAPES} ]; then - TF2TFLITE_CONVERT_SCRIPT+="--input_shapes ${INPUT_SHAPES} " + ONE_IMPORT_BCQ_SCRIPT+="-s ${INPUT_SHAPES} " fi -${TF2TFLITE_CONVERT_SCRIPT} - -# convert .tflite to .circle -"${ROOT}/bin/tflite2circle" "${TMPDIR}/${MODEL_NAME}.tflite" "${TMPDIR}/${MODEL_NAME}.tmp.circle" +${ONE_IMPORT_BCQ_SCRIPT} # optimize "${ROOT}/bin/circle2circle" --O1 "${TMPDIR}/${MODEL_NAME}.tmp.circle" "${TMPDIR}/${MODEL_NAME}.circle" diff --git a/infra/packaging/res/tf2nnpkg.20210706 b/infra/packaging/res/tf2nnpkg.20210706 new file mode 100644 index 000000000..0d44818a1 --- /dev/null +++ b/infra/packaging/res/tf2nnpkg.20210706 @@ -0,0 +1,109 @@ +#!/bin/bash + +set -e + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +command_exists() { + if [ "$#" -le 0 ]; then + return 1 + fi + command -v "$@" > /dev/null 2>&1 +} + +usage() +{ + echo "Convert TensorFlow model to nnpackage." + echo "Usage: tf2nnpkg" + echo " --info <path/to/info>" + echo " --graphdef <path/to/pb>" + echo " -o <path/to/nnpkg/directory>" + echo " --v2 (optional) Use TF 2.x interface" + exit 255 +} + +TF_INTERFACE="--v1" + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '--info') + export INFO_FILE="$2" + shift 2 + ;; + '--graphdef') + export GRAPHDEF_FILE="$2" + shift 2 + ;; + '-o') + export OUTPUT_DIR="$2" + shift 2 + ;; + '--v2') + TF_INTERFACE="--v2" + shift + ;; + *) + echo "${CUR}" + shift + ;; + esac +done + +if [ -z ${GRAPHDEF_FILE} ] || [ ! -e ${GRAPHDEF_FILE} ]; then + echo "pb is not found. Please check --graphdef is correct." + exit 2 +fi + +if [ -z ${INFO_FILE} ] || [ ! -e ${INFO_FILE} ]; then + echo "info is not found. Please check --info is correct." + exit 2 +fi + +if [ -z ${OUTPUT_DIR} ]; then + echo "output directory is not specifed. Please check -o is correct.." + exit 2 +fi + +FILE_BASE=$(basename ${GRAPHDEF_FILE}) +MODEL_NAME="${FILE_BASE%.*}" +TMPDIR=$(mktemp -d) +trap "{ rm -rf $TMPDIR; }" EXIT + +# activate python virtual environment +VIRTUALENV_LINUX="${ROOT}/bin/venv/bin/activate" +VIRTUALENV_WINDOWS="${ROOT}/bin/venv/Scripts/activate" + +if [ -e ${VIRTUALENV_LINUX} ]; then + source ${VIRTUALENV_LINUX} +elif [ -e ${VIRTUALENV_WINDOWS} ]; then + source ${VIRTUALENV_WINDOWS} +fi + +# parse inputs, outputs from info file +INPUT=$(awk -F, '/^input/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) +OUTPUT=$(awk -F, '/^output/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) + +INPUT_SHAPES=$(grep ^input ${INFO_FILE} | cut -d "[" -f2 | cut -d "]" -f1 | tr -d ' ' | xargs | tr ' ' ':') + +ONE_IMPORT_BCQ_SCRIPT="${ROOT}/bin/one-import-bcq ${TF_INTERFACE} " +ONE_IMPORT_BCQ_SCRIPT+="-i ${GRAPHDEF_FILE} " +ONE_IMPORT_BCQ_SCRIPT+="-o ${TMPDIR}/${MODEL_NAME}.tmp.circle " +ONE_IMPORT_BCQ_SCRIPT+="-I ${INPUT} " +ONE_IMPORT_BCQ_SCRIPT+="-O ${OUTPUT} " +if [ ! -z ${INPUT_SHAPES} ]; then + ONE_IMPORT_BCQ_SCRIPT+="-s ${INPUT_SHAPES} " +fi + +${ONE_IMPORT_BCQ_SCRIPT} + +# optimize +"${ROOT}/bin/circle2circle" --O1 "${TMPDIR}/${MODEL_NAME}.tmp.circle" "${TMPDIR}/${MODEL_NAME}.circle" + +"${ROOT}/bin/model2nnpkg.sh" -o "${OUTPUT_DIR}" "${TMPDIR}/${MODEL_NAME}.circle" diff --git a/infra/scripts/build-tcm.sh b/infra/scripts/build-tcm.sh index 38533c1f9..768cff762 100755 --- a/infra/scripts/build-tcm.sh +++ b/infra/scripts/build-tcm.sh @@ -2,13 +2,16 @@ # # STEP 1 # Download latest TCM tool from -# https://github.sec.samsung.net/RS-TCM/tca-standalone/releases/download/v0.0.8/tca-standalone-0.0.8.jar +# https://github.sec.samsung.net/RS-TCM/tca-standalone/releases/download/1.0.2/tca-standalone-1.0.2.jar # # STEP 2 # Create symbolic link `./src` for source directory to be analyzed which has `.ahub` configuration. # # STEP 3 -# run this `build-tcm.sh` script. +# run this script in `build-tcm.sh [test_target]` format. +# ex) $ build_tcm.sh # to analyze both NN Runtime and NN Compiler +# ex) $ build_tcm.sh NN_Runtime # to analyze NN Runtime only +# ex) $ build_tcm.sh NN_Compiler # to analyze NN Compiler only # # See the following link for additional details. # https://github.sec.samsung.net/RS-TCM/tca-standalone/wiki/Tutorials-CPP-Gtest @@ -16,9 +19,10 @@ echo ${PROJECT_DIR:=${PWD}} -java -jar $PROJECT_DIR/tca-standalone-0.0.8.jar \ +java -jar $PROJECT_DIR/tca-standalone-1.0.2.jar \ --outdir=$PROJECT_DIR/tcm-output \ --config=$PROJECT_DIR/src/.ahub/tcchecker-tca/config.yaml \ --local=$PROJECT_DIR/src \ --logfile=$PROJECT_DIR/tcm-output/tcm.log \ --debug + $@ diff --git a/infra/scripts/build_android_runtime_release.sh b/infra/scripts/build_android_runtime_release.sh index a131fbe40..99858ebf9 100755 --- a/infra/scripts/build_android_runtime_release.sh +++ b/infra/scripts/build_android_runtime_release.sh @@ -3,15 +3,6 @@ CURRENT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" ROOT_PATH="$CURRENT_PATH/../../" -# prepare pre-built armcompute library -# android build requires pre-built armcompute library -# if [ ! -n "$EXT_ACL_FOLDER" ]; then -# echo "Please set EXT_ACL_FOLDER to use pre-built armcompute library" -# exit 1 -# fi - -unset EXT_ACL_FOLDER - # prepare ndk if [ ! -n "$NDK_DIR" ]; then export NDK_DIR=$ROOT_PATH/tools/cross/ndk/r20/ndk diff --git a/infra/scripts/compiler_modules.sh b/infra/scripts/compiler_modules.sh index 133af3f69..a63140eaf 100644 --- a/infra/scripts/compiler_modules.sh +++ b/infra/scripts/compiler_modules.sh @@ -4,7 +4,7 @@ [[ "${BASH_SOURCE[0]}" == "${0}" ]] && echo "Please don't execute ${BASH_SOURCE[0]}, source it" && return DEBUG_BUILD_ITEMS="angkor;cwrap;pepper-str;pepper-strcast;pp" -DEBUG_BUILD_ITEMS+=";oops;pepper-assert" +DEBUG_BUILD_ITEMS+=";oops;pepper-assert;pepper-csv2vec" DEBUG_BUILD_ITEMS+=";hermes;hermes-std" DEBUG_BUILD_ITEMS+=";loco;locop;locomotiv;logo-core;logo" DEBUG_BUILD_ITEMS+=";foder;crew;souschef;arser;vconone" diff --git a/infra/scripts/docker_build_nncc.sh b/infra/scripts/docker_build_nncc.sh index 96a00fa05..7146141bb 100755 --- a/infra/scripts/docker_build_nncc.sh +++ b/infra/scripts/docker_build_nncc.sh @@ -2,6 +2,10 @@ [[ "${BASH_SOURCE[0]}" != "${0}" ]] && echo "Please don't source ${BASH_SOURCE[0]}, execute it" && return +unset RELEASE_VERSION +# TODO need more better argument parsing +RELEASE_VERSION="$1" + CURRENT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" ROOT_PATH="$CURRENT_PATH/../../" @@ -57,7 +61,17 @@ mkdir -p ${NNCC_INSTALL_PREFIX} ./nncc docker-run ./nnas create-package --prefix "${PWD}/${NNCC_INSTALL_PREFIX}" -- "${CONFIG_OPTIONS}" mkdir -p ${ARCHIVE_PATH} -tar -zcf ${ARCHIVE_PATH}/nncc-package.tar.gz -C ${NNCC_INSTALL_PREFIX} --exclude test --exclude tflchef* ./ +tar -zcf ${ARCHIVE_PATH}/nncc-package.tar.gz -C ${NNCC_INSTALL_PREFIX} \ + --exclude test --exclude tflchef* --exclude circle-tensordump --exclude circledump ./ tar -zcf ${ARCHIVE_PATH}/nncc-test-package.tar.gz -C ${NNCC_INSTALL_PREFIX} ./test +if [ -z ${RELEASE_VERSION} ] || [ ${RELEASE_VERSION} == "nightly" ]; then + ./nncc docker-run /bin/bash -c \ + 'dch -v $($(pwd)/tools/release_tool/onert_version.sh)~$(date "+%y%m%d%H") "nightly release" -D $(lsb_release --short --codename)' + ./nncc docker-run dch -r '' +fi + +./nncc docker-run debuild --preserve-env --no-lintian -us -uc \ + -b --buildinfo-option=-ubuild --changes-option=-ubuild + popd > /dev/null diff --git a/infra/scripts/docker_collect_nnpkg_resources.sh b/infra/scripts/docker_collect_nnpkg_resources.sh index 5608c7800..65963f4b8 100755 --- a/infra/scripts/docker_collect_nnpkg_resources.sh +++ b/infra/scripts/docker_collect_nnpkg_resources.sh @@ -62,6 +62,7 @@ pushd $ROOT_PATH > /dev/null REQUIRED_UNITS=() # Common Libraries REQUIRED_UNITS+=("angkor" "cwrap" "pepper-str" "pepper-strcast" "pp") +REQUIRED_UNITS+=("pepper-csv2vec") REQUIRED_UNITS+=("oops" "safemain" "foder" "crew" "arser" "vconone") # Hermes Logging Framework REQUIRED_UNITS+=("hermes" "hermes-std") diff --git a/packaging/ABSEIL.tar.gz b/packaging/ABSEIL.tar.gz Binary files differnew file mode 100644 index 000000000..e05654156 --- /dev/null +++ b/packaging/ABSEIL.tar.gz diff --git a/packaging/FARMHASH.tar.gz b/packaging/FARMHASH.tar.gz Binary files differnew file mode 100644 index 000000000..4bf98d891 --- /dev/null +++ b/packaging/FARMHASH.tar.gz diff --git a/packaging/FLATBUFFERS-1.12.tar.gz b/packaging/FLATBUFFERS-1.12.tar.gz Binary files differnew file mode 100644 index 000000000..166983b1a --- /dev/null +++ b/packaging/FLATBUFFERS-1.12.tar.gz diff --git a/packaging/OPENCL_HEADERS.tar.gz b/packaging/OPENCL_HEADERS.tar.gz Binary files differnew file mode 100644 index 000000000..7bc3656e8 --- /dev/null +++ b/packaging/OPENCL_HEADERS.tar.gz diff --git a/packaging/nnfw.spec b/packaging/nnfw.spec index 2eba073fd..0d170e7ed 100644 --- a/packaging/nnfw.spec +++ b/packaging/nnfw.spec @@ -1,6 +1,6 @@ Name: nnfw Summary: nnfw -Version: 1.15.0 +Version: 1.17.0 Release: 1 Group: Development License: Apache-2.0 and MIT and BSD-2-Clause @@ -18,6 +18,10 @@ Source1008: FXDIV.tar.gz Source1009: PTHREADPOOL.tar.gz Source1010: PSIMD.tar.gz Source1011: FP16.tar.gz +Source1012: OPENCL_HEADERS.tar.gz +Source1013: FARMHASH.tar.gz +Source1014: ABSEIL.tar.gz +Source1015: oourafft.tar.gz Source2001: nnfw.pc.in Source2002: nnfw-plugin.pc.in @@ -36,7 +40,7 @@ BuildRequires: flatbuffers-devel %ifarch %{arm} aarch64 # Require python for acl-ex library build pre-process BuildRequires: python -BuildRequires: libarmcl-devel >= v20.05 +BuildRequires: libarmcl-devel >= v21.02 %endif Requires(post): /sbin/ldconfig @@ -129,6 +133,10 @@ tar -xf %{SOURCE1008} -C ./externals tar -xf %{SOURCE1009} -C ./externals tar -xf %{SOURCE1010} -C ./externals tar -xf %{SOURCE1011} -C ./externals +tar -xf %{SOURCE1012} -C ./externals +tar -xf %{SOURCE1013} -C ./externals +tar -xf %{SOURCE1014} -C ./externals +tar -xf %{SOURCE1015} -C ./externals %build %ifarch arm armv7l aarch64 x86_64 %ix86 @@ -198,6 +206,7 @@ install -m 0644 ./tests/scripts/build_path.txt %{buildroot}%{test_install_dir}/t %defattr(-,root,root,-) %ifarch arm armv7l aarch64 x86_64 %ix86 %{_libdir}/*.so +%exclude %{_includedir}/CL/* %endif %files devel diff --git a/packaging/oourafft.tar.gz b/packaging/oourafft.tar.gz Binary files differnew file mode 100644 index 000000000..85cf7fd6b --- /dev/null +++ b/packaging/oourafft.tar.gz diff --git a/res/TensorFlowLiteRecipes/Add_STR_000/test.recipe b/res/TensorFlowLiteRecipes/Add_STR_000/test.recipe new file mode 100644 index 000000000..32f77dd36 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Add_STR_000/test.recipe @@ -0,0 +1,33 @@ +# This is test for import/export of STRING tensortype +# interpreter or runtime may fail as Add won't support this + +operand { + name: "ifm" + type: STRING + shape { } +} +operand { + name: "suffix" + type: STRING + shape { } + filler { + tag: "explicit" + arg: "Hello" + } +} +operand { + name: "ofm" + type: STRING + shape { } +} +operation { + type: "Add" + input: "ifm" + input: "suffix" + output: "ofm" + add_options { + activation: NONE + } +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Add_STR_001/test.recipe b/res/TensorFlowLiteRecipes/Add_STR_001/test.recipe new file mode 100644 index 000000000..b3310650c --- /dev/null +++ b/res/TensorFlowLiteRecipes/Add_STR_001/test.recipe @@ -0,0 +1,34 @@ +# This is test for import/export of STRING tensortype +# interpreter or runtime may fail as Add won't support this + +operand { + name: "ifm" + type: STRING + shape { } +} +operand { + name: "suffix" + type: STRING + shape { dim: 2 } + filler { + tag: "explicit" + arg: "Hello" + arg: "World" + } +} +operand { + name: "ofm" + type: STRING + shape { } +} +operation { + type: "Add" + input: "ifm" + input: "suffix" + output: "ofm" + add_options { + activation: NONE + } +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/MaxPoolWithArgMax_000/test.recipe b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_000/test.recipe index 948448b66..9218c2010 100644 --- a/res/TensorFlowLiteRecipes/MaxPoolWithArgMax_000/test.recipe +++ b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_000/test.recipe @@ -14,7 +14,7 @@ operand { shape { dim: 1 dim: 9 dim: 9 dim: 1 } } operation { - type: "MaxPoolWithArgMax" + type: "MaxPoolWithArgmax" input: "ifm" output: "ofm" output: "argmax" diff --git a/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_000/test.rule b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_000/test.rule new file mode 100644 index 000000000..05aa5f7ac --- /dev/null +++ b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_000/test.rule @@ -0,0 +1,17 @@ +# To check if MaxPoolWithArgmax is transformed to MaxPool, ArgMax and index computation network + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "ARG_MAX_COUNT" $(op_count ARG_MAX) '=' 1 +RULE "ARG_MAX_COUNT" $(op_count MAX_POOL_2D) '=' 1 +RULE "CONV_COUNT" $(op_count CONV_2D) '=' 1 +RULE "SPLIT_COUNT" $(op_count SPLIT) '=' 1 +RULE "RESHAPE_COUNT" $(op_count RESHAPE) '=' 1 +RULE "CAST_COUNT" $(op_count CAST) '=' 2 +RULE "ADD_COUNT" $(op_count ADD) '=' 3 +RULE "MUL_COUNT" $(op_count MUL) '=' 5 +RULE "FLOOR_COUNT" $(op_count FLOOR) '=' 1 +RULE "NEG_COUNT" $(op_count NEG) '=' 1 +RULE "CONCATENATION_COUNT" $(op_count CONCATENATION) '=' 1 +RULE "PADV2_COUNT" $(op_count PADV2) '=' 1 +RULE "CUSTOM_COUNT" $(op_count 'CUSTOM(MaxPoolWithArgmax)') '=' 0 diff --git a/res/TensorFlowLiteRecipes/MaxPoolWithArgMax_001/test.recipe b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_001/test.recipe index dd883ad30..9c15a7c63 100644 --- a/res/TensorFlowLiteRecipes/MaxPoolWithArgMax_001/test.recipe +++ b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_001/test.recipe @@ -14,7 +14,7 @@ operand { shape { dim: 1 dim: 9 dim: 9 dim: 1 } } operation { - type: "MaxPoolWithArgMax" + type: "MaxPoolWithArgmax" input: "ifm" output: "ofm" output: "argmax" diff --git a/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_001/test.rule b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_001/test.rule new file mode 100644 index 000000000..05aa5f7ac --- /dev/null +++ b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_001/test.rule @@ -0,0 +1,17 @@ +# To check if MaxPoolWithArgmax is transformed to MaxPool, ArgMax and index computation network + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "ARG_MAX_COUNT" $(op_count ARG_MAX) '=' 1 +RULE "ARG_MAX_COUNT" $(op_count MAX_POOL_2D) '=' 1 +RULE "CONV_COUNT" $(op_count CONV_2D) '=' 1 +RULE "SPLIT_COUNT" $(op_count SPLIT) '=' 1 +RULE "RESHAPE_COUNT" $(op_count RESHAPE) '=' 1 +RULE "CAST_COUNT" $(op_count CAST) '=' 2 +RULE "ADD_COUNT" $(op_count ADD) '=' 3 +RULE "MUL_COUNT" $(op_count MUL) '=' 5 +RULE "FLOOR_COUNT" $(op_count FLOOR) '=' 1 +RULE "NEG_COUNT" $(op_count NEG) '=' 1 +RULE "CONCATENATION_COUNT" $(op_count CONCATENATION) '=' 1 +RULE "PADV2_COUNT" $(op_count PADV2) '=' 1 +RULE "CUSTOM_COUNT" $(op_count 'CUSTOM(MaxPoolWithArgmax)') '=' 0 diff --git a/res/TensorFlowLiteRecipes/MaxPoolWithArgMax_002/test.recipe b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_002/test.recipe index ddc1c4f49..702e01634 100644 --- a/res/TensorFlowLiteRecipes/MaxPoolWithArgMax_002/test.recipe +++ b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_002/test.recipe @@ -1,20 +1,20 @@ operand { name: "ifm" type: FLOAT32 - shape { dim: 1 dim: 18 dim: 18 dim: 1 } + shape { dim: 1 dim: 18 dim: 18 dim: 2 } } operand { name: "ofm" type: FLOAT32 - shape { dim: 1 dim: 8 dim: 8 dim: 1 } + shape { dim: 1 dim: 8 dim: 8 dim: 2 } } operand { name: "argmax" type: INT64 - shape { dim: 1 dim: 8 dim: 8 dim: 1 } + shape { dim: 1 dim: 8 dim: 8 dim: 2 } } operation { - type: "MaxPoolWithArgMax" + type: "MaxPoolWithArgmax" input: "ifm" output: "ofm" output: "argmax" diff --git a/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_002/test.rule b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_002/test.rule new file mode 100644 index 000000000..a388374c2 --- /dev/null +++ b/res/TensorFlowLiteRecipes/MaxPoolWithArgmax_002/test.rule @@ -0,0 +1,16 @@ +# To check if MaxPoolWithArgmax is transformed to MaxPool, ArgMax and index computation network + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "ARG_MAX_COUNT" $(op_count ARG_MAX) '=' 2 +RULE "ARG_MAX_COUNT" $(op_count MAX_POOL_2D) '=' 1 +RULE "CONV_COUNT" $(op_count CONV_2D) '=' 2 +RULE "SPLIT_COUNT" $(op_count SPLIT) '=' 1 +RULE "RESHAPE_COUNT" $(op_count RESHAPE) '=' 2 +RULE "CAST_COUNT" $(op_count CAST) '=' 3 +RULE "ADD_COUNT" $(op_count ADD) '=' 7 +RULE "MUL_COUNT" $(op_count MUL) '=' 8 +RULE "FLOOR_COUNT" $(op_count FLOOR) '=' 2 +RULE "NEG_COUNT" $(op_count NEG) '=' 2 +RULE "CONCATENATION_COUNT" $(op_count CONCATENATION) '=' 1 +RULE "CUSTOM_COUNT" $(op_count 'CUSTOM(MaxPoolWithArgmax)') '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_Conv_FakeQuant_000/test.recipe b/res/TensorFlowLiteRecipes/Net_Conv_FakeQuant_000/test.recipe new file mode 100644 index 000000000..e666e95e7 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Conv_FakeQuant_000/test.recipe @@ -0,0 +1,61 @@ +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "filter" + type: FLOAT32 + shape { dim: 8 dim: 1 dim: 1 dim: 3 } + filler { + tag: "gaussian" + arg: "0.0" + arg: "1.0" + } +} +operand { + name: "bias" + type: FLOAT32 + shape { dim: 8 } + filler { + tag: "gaussian" + arg: "0.0" + arg: "1.0" + } +} +operand { + name: "conv" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 8 } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 8 } +} +operation { + type: "Conv2D" + conv2d_options { + padding: VALID + stride_w: 1 + stride_h: 1 + } + input: "ifm" + input: "filter" + input: "bias" + output: "conv" +} +operation { + type: "FakeQuant" + fakequant_options { + min: 0.0 + max: 1.0 + num_bits: 8 + narrow_range: false + } + input: "conv" + output: "ofm" +} + +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Net_Conv_FakeQuant_000/test.rule b/res/TensorFlowLiteRecipes/Net_Conv_FakeQuant_000/test.rule new file mode 100644 index 000000000..7fd354611 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Conv_FakeQuant_000/test.rule @@ -0,0 +1,7 @@ +# To check if FakeQuant is removed by remove_fakequant +# + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "CONV_EXIST" $(op_count CONV_2D) '=' 1 +RULE "NO_FAKE_QUANT" $(op_count FAKE_QUANT) '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_Conv_Min_Relu_000/test.recipe b/res/TensorFlowLiteRecipes/Net_Conv_Min_Relu_000/test.recipe new file mode 100644 index 000000000..18c12472e --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Conv_Min_Relu_000/test.recipe @@ -0,0 +1,113 @@ +operand { + name: "Placeholder" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "Const_4" + type: FLOAT32 + shape { } + filler { tag: "explicit" arg: "6" } +} +operand { + name: "Conv2D_1" + type: FLOAT32 + shape { dim: 3 dim: 3 dim: 3 dim: 3 } + filler { tag: "gaussian" arg: "0.0" arg: "0.1" } +} +operand { + name: "Conv2D_2" + type: FLOAT32 + shape { dim: 3 } + filler { tag: "gaussian" arg: "0.0" arg: "0.1" } +} +operand { + name: "Conv2D_21" + type: FLOAT32 + shape { dim: 3 dim: 3 dim: 3 dim: 3 } + filler { tag: "gaussian" arg: "0.0" arg: "0.1" } +} +operand { + name: "Conv2D_11" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "Minimum" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "Relu" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "Conv2D_22" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "Minimum_1" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "Relu_1" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operation { + type: "Conv2D" + input: "Placeholder" + input: "Conv2D_1" + input: "Conv2D_2" + output: "Conv2D_11" + conv2d_options { + padding: SAME + stride_w: 1 + stride_h: 1 + activation: NONE + dilation_w_factor: 1 + dilation_h_factor: 1 + } +} +operation { + type: "Minimum" + input: "Conv2D_11" + input: "Const_4" + output: "Minimum" +} +operation { + type: "ReLU" + input: "Minimum" + output: "Relu" +} +operation { + type: "Conv2D" + input: "Relu" + input: "Conv2D_21" + input: "Conv2D_2" + output: "Conv2D_22" + conv2d_options { + padding: SAME + stride_w: 1 + stride_h: 1 + activation: NONE + dilation_w_factor: 1 + dilation_h_factor: 1 + } +} +operation { + type: "Minimum" + input: "Conv2D_22" + input: "Const_4" + output: "Minimum_1" +} +operation { + type: "ReLU" + input: "Minimum_1" + output: "Relu_1" +} +input: "Placeholder" +output: "Relu_1" diff --git a/res/TensorFlowLiteRecipes/Net_Conv_Min_Relu_000/test.rule b/res/TensorFlowLiteRecipes/Net_Conv_Min_Relu_000/test.rule new file mode 100644 index 000000000..1256227ac --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Conv_Min_Relu_000/test.rule @@ -0,0 +1,8 @@ +# To check if Minumum and ReLU are converte to Relu6 op + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "CONV_EXIST" $(op_count CONV_2D) '=' 2 +RULE "RELU6_EXIST" $(op_count RELU6) '=' 2 +RULE "MIN_NOT_EXIST" $(op_count MINUMUM) '=' 0 +RULE "RELU_NOT_EXIST" $(op_count RELU) '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_Conv_QuantDequant_000/test.recipe b/res/TensorFlowLiteRecipes/Net_Conv_QuantDequant_000/test.recipe new file mode 100644 index 000000000..fa7fa7df7 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Conv_QuantDequant_000/test.recipe @@ -0,0 +1,65 @@ +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 3 } +} +operand { + name: "filter" + type: FLOAT32 + shape { dim: 8 dim: 1 dim: 1 dim: 3 } + filler { + tag: "gaussian" + arg: "0.0" + arg: "1.0" + } +} +operand { + name: "bias" + type: FLOAT32 + shape { dim: 8 } + filler { + tag: "gaussian" + arg: "0.0" + arg: "1.0" + } +} +operand { + name: "conv" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 8 } +} +operand { + name: "quantize" + type: UINT8 + shape { dim: 1 dim: 16 dim: 16 dim: 8 } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 1 dim: 16 dim: 16 dim: 8 } +} +operation { + type: "Conv2D" + conv2d_options { + padding: VALID + stride_w: 1 + stride_h: 1 + } + input: "ifm" + input: "filter" + input: "bias" + output: "conv" +} +operation { + type: "Quantize" + input: "conv" + output: "quantize" +} +operation { + type: "Dequantize" + input: "quantize" + output: "ofm" +} + +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Net_Conv_QuantDequant_000/test.rule b/res/TensorFlowLiteRecipes/Net_Conv_QuantDequant_000/test.rule new file mode 100644 index 000000000..4bf309491 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Conv_QuantDequant_000/test.rule @@ -0,0 +1,8 @@ +# To check if FakeQuant is removed by remove_quantdequant +# + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "CONV_EXIST" $(op_count CONV_2D) '=' 1 +RULE "NO_QUANTIZE" $(op_count QUANTIZE) '=' 0 +RULE "NO_DEQUANTIZE" $(op_count DEQUANTIZE) '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_001/test.rule b/res/TensorFlowLiteRecipes/Net_InstanceNorm_001/test.rule index c31145aa6..85b4aee47 100644 --- a/res/TensorFlowLiteRecipes/Net_InstanceNorm_001/test.rule +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_001/test.rule @@ -1,4 +1,4 @@ -# To check if custom op BatchMatMulV2 is converted to circle builtin op +# To check if this network is converted to circle InstanceNorm op RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_002/test.rule b/res/TensorFlowLiteRecipes/Net_InstanceNorm_002/test.rule index d6e47712f..f926e75dd 100644 --- a/res/TensorFlowLiteRecipes/Net_InstanceNorm_002/test.rule +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_002/test.rule @@ -1,4 +1,4 @@ -# To check if custom op InstanceNorm is converted to circle builtin op +# To check if this network is converted to circle InstanceNorm op RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_003/test.rule b/res/TensorFlowLiteRecipes/Net_InstanceNorm_003/test.rule index f435b1d8b..ccdc9e6da 100644 --- a/res/TensorFlowLiteRecipes/Net_InstanceNorm_003/test.rule +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_003/test.rule @@ -1,4 +1,4 @@ -# To check if custom op BatchMatMulV2 is converted to circle builtin op +# To check if this network is converted to circle InstanceNorm op RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_004/test.recipe b/res/TensorFlowLiteRecipes/Net_InstanceNorm_004/test.recipe new file mode 100644 index 000000000..0892f3e77 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_004/test.recipe @@ -0,0 +1,294 @@ +# generated using tflchef-reverse +# with tflite from https://github.com/Samsung/ONE/issues/7067#issuecomment-867203553 + +operand { + name: "input_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/Mean" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/Mean/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/Reshape" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } + filler { + tag: "explicit" + arg: "1" + arg: "1" + arg: "1" + } +} +operand { + name: "instance_normalization/Reshape_1" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } + filler { + tag: "explicit" + arg: "0" + arg: "0" + arg: "0" + } +} +operand { + name: "instance_normalization/add" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/add/y" + type: FLOAT32 + shape { + } + filler { + tag: "explicit" + arg: "1e-09" + } +} +operand { + name: "instance_normalization/add_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/mul" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/Sqrt" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean_1" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean_1/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Square" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/truediv" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operation { + type: "Mean" + input: "input_1" + input: "instance_normalization/Mean/reduction_indices" + output: "instance_normalization/Mean" + mean_options { + keep_dims: true + } +} +operation { + type: "Mean" + input: "input_1" + input: "instance_normalization/reduce_std/reduce_variance/Mean/reduction_indices" + output: "instance_normalization/reduce_std/reduce_variance/Mean" + mean_options { + keep_dims: true + } +} +operation { + type: "Sub" + input: "input_1" + input: "instance_normalization/Mean" + output: "instance_normalization/sub" + sub_options { + activation: NONE + } +} +operation { + type: "Sub" + input: "input_1" + input: "instance_normalization/reduce_std/reduce_variance/Mean" + output: "instance_normalization/reduce_std/reduce_variance/sub" + sub_options { + activation: NONE + } +} +operation { + type: "Square" + input: "instance_normalization/reduce_std/reduce_variance/sub" + output: "instance_normalization/reduce_std/reduce_variance/Square" +} +operation { + type: "Mean" + input: "instance_normalization/reduce_std/reduce_variance/Square" + input: "instance_normalization/reduce_std/reduce_variance/Mean_1/reduction_indices" + output: "instance_normalization/reduce_std/reduce_variance/Mean_1" + mean_options { + keep_dims: true + } +} +operation { + type: "Sqrt" + input: "instance_normalization/reduce_std/reduce_variance/Mean_1" + output: "instance_normalization/reduce_std/Sqrt" +} +operation { + type: "Add" + input: "instance_normalization/reduce_std/Sqrt" + input: "instance_normalization/add/y" + output: "instance_normalization/add" + add_options { + activation: NONE + } +} +operation { + type: "Div" + input: "instance_normalization/sub" + input: "instance_normalization/add" + output: "instance_normalization/truediv" + div_options { + activation: NONE + } +} +operation { + type: "Mul" + input: "instance_normalization/truediv" + input: "instance_normalization/Reshape" + output: "instance_normalization/mul" + mul_options { + activation: NONE + } +} +operation { + type: "Add" + input: "instance_normalization/mul" + input: "instance_normalization/Reshape_1" + output: "instance_normalization/add_1" + add_options { + activation: NONE + } +} +input: "input_1" +output: "instance_normalization/add_1" diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_004/test.rule b/res/TensorFlowLiteRecipes/Net_InstanceNorm_004/test.rule new file mode 100644 index 000000000..1a65b1af6 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_004/test.rule @@ -0,0 +1,12 @@ +# To check if this network is converted to circle InstanceNorm op + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "INSTANCE_NORM_EXIST" $(op_count INSTANCE_NORM) '=' 1 +RULE "NO_ADD" $(op_count ADD) '=' 0 +RULE "NO_MUL" $(op_count MUL) '=' 0 +RULE "NO_SQRT" $(op_count SQRT) '=' 0 +RULE "NO_DIV" $(op_count DIV) '=' 0 +RULE "NO_SUB" $(op_count SUB) '=' 0 +RULE "NO_SQUARE" $(op_count SQUARE) '=' 0 +RULE "NO_MEAN" $(op_count MEAN) '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_005/test.recipe b/res/TensorFlowLiteRecipes/Net_InstanceNorm_005/test.recipe new file mode 100644 index 000000000..70952f232 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_005/test.recipe @@ -0,0 +1,224 @@ +# generated using tflchef-reverse +# with tflite from https://github.com/Samsung/ONE/issues/7067#issuecomment-867203553 + +operand { + name: "input_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/Mean" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/Mean/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/add" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/add/y" + type: FLOAT32 + shape { + } + filler { + tag: "explicit" + arg: "1e-09" + } +} +operand { + name: "instance_normalization/reduce_std/Sqrt" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean_1" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean_1/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Square" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/truediv" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operation { + type: "Mean" + input: "input_1" + input: "instance_normalization/Mean/reduction_indices" + output: "instance_normalization/Mean" + mean_options { + keep_dims: true + } +} +operation { + type: "Mean" + input: "input_1" + input: "instance_normalization/reduce_std/reduce_variance/Mean/reduction_indices" + output: "instance_normalization/reduce_std/reduce_variance/Mean" + mean_options { + keep_dims: true + } +} +operation { + type: "Sub" + input: "input_1" + input: "instance_normalization/Mean" + output: "instance_normalization/sub" + sub_options { + activation: NONE + } +} +operation { + type: "Sub" + input: "input_1" + input: "instance_normalization/reduce_std/reduce_variance/Mean" + output: "instance_normalization/reduce_std/reduce_variance/sub" + sub_options { + activation: NONE + } +} +operation { + type: "Square" + input: "instance_normalization/reduce_std/reduce_variance/sub" + output: "instance_normalization/reduce_std/reduce_variance/Square" +} +operation { + type: "Mean" + input: "instance_normalization/reduce_std/reduce_variance/Square" + input: "instance_normalization/reduce_std/reduce_variance/Mean_1/reduction_indices" + output: "instance_normalization/reduce_std/reduce_variance/Mean_1" + mean_options { + keep_dims: true + } +} +operation { + type: "Sqrt" + input: "instance_normalization/reduce_std/reduce_variance/Mean_1" + output: "instance_normalization/reduce_std/Sqrt" +} +operation { + type: "Add" + input: "instance_normalization/reduce_std/Sqrt" + input: "instance_normalization/add/y" + output: "instance_normalization/add" + add_options { + activation: NONE + } +} +operation { + type: "Div" + input: "instance_normalization/sub" + input: "instance_normalization/add" + output: "instance_normalization/truediv" + div_options { + activation: NONE + } +} +input: "input_1" +output: "instance_normalization/truediv" diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_005/test.rule b/res/TensorFlowLiteRecipes/Net_InstanceNorm_005/test.rule new file mode 100644 index 000000000..219fbce88 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_005/test.rule @@ -0,0 +1,11 @@ +# To check if this network is converted to circle InstanceNorm op + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "INSTANCE_NORM_EXIST" $(op_count INSTANCE_NORM) '=' 1 +RULE "NO_ADD" $(op_count ADD) '=' 0 +RULE "NO_SQRT" $(op_count SQRT) '=' 0 +RULE "NO_DIV" $(op_count DIV) '=' 0 +RULE "NO_SUB" $(op_count SUB) '=' 0 +RULE "NO_SQUARE" $(op_count SQUARE) '=' 0 +RULE "NO_MEAN" $(op_count MEAN) '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_006/test.recipe b/res/TensorFlowLiteRecipes/Net_InstanceNorm_006/test.recipe new file mode 100644 index 000000000..b0cafd4d5 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_006/test.recipe @@ -0,0 +1,283 @@ +# InstanceNorm network with one element for gamma, beta + +operand { + name: "input_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/Mean" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/Mean/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/Reshape" + type: FLOAT32 + shape { + dim: 1 + } + filler { + tag: "explicit" + arg: "1" + } +} +operand { + name: "instance_normalization/Reshape_1" + type: FLOAT32 + shape { + dim: 1 + } + filler { + tag: "explicit" + arg: "0" + } +} +operand { + name: "instance_normalization/add" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/add/y" + type: FLOAT32 + shape { + } + filler { + tag: "explicit" + arg: "1e-09" + } +} +operand { + name: "instance_normalization/add_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/mul" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/Sqrt" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean_1" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Mean_1/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/Square" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/reduce_std/reduce_variance/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operand { + name: "instance_normalization/truediv" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 3 + } +} +operation { + type: "Mean" + input: "input_1" + input: "instance_normalization/Mean/reduction_indices" + output: "instance_normalization/Mean" + mean_options { + keep_dims: true + } +} +operation { + type: "Mean" + input: "input_1" + input: "instance_normalization/reduce_std/reduce_variance/Mean/reduction_indices" + output: "instance_normalization/reduce_std/reduce_variance/Mean" + mean_options { + keep_dims: true + } +} +operation { + type: "Sub" + input: "input_1" + input: "instance_normalization/Mean" + output: "instance_normalization/sub" + sub_options { + activation: NONE + } +} +operation { + type: "Sub" + input: "input_1" + input: "instance_normalization/reduce_std/reduce_variance/Mean" + output: "instance_normalization/reduce_std/reduce_variance/sub" + sub_options { + activation: NONE + } +} +operation { + type: "Square" + input: "instance_normalization/reduce_std/reduce_variance/sub" + output: "instance_normalization/reduce_std/reduce_variance/Square" +} +operation { + type: "Mean" + input: "instance_normalization/reduce_std/reduce_variance/Square" + input: "instance_normalization/reduce_std/reduce_variance/Mean_1/reduction_indices" + output: "instance_normalization/reduce_std/reduce_variance/Mean_1" + mean_options { + keep_dims: true + } +} +operation { + type: "Sqrt" + input: "instance_normalization/reduce_std/reduce_variance/Mean_1" + output: "instance_normalization/reduce_std/Sqrt" +} +operation { + type: "Add" + input: "instance_normalization/reduce_std/Sqrt" + input: "instance_normalization/add/y" + output: "instance_normalization/add" + add_options { + activation: NONE + } +} +operation { + type: "Div" + input: "instance_normalization/sub" + input: "instance_normalization/add" + output: "instance_normalization/truediv" + div_options { + activation: NONE + } +} +operation { + type: "Mul" + input: "instance_normalization/truediv" + input: "instance_normalization/Reshape" + output: "instance_normalization/mul" + mul_options { + activation: NONE + } +} +operation { + type: "Add" + input: "instance_normalization/mul" + input: "instance_normalization/Reshape_1" + output: "instance_normalization/add_1" + add_options { + activation: NONE + } +} +input: "input_1" +output: "instance_normalization/add_1" diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_006/test.rule b/res/TensorFlowLiteRecipes/Net_InstanceNorm_006/test.rule new file mode 100644 index 000000000..1a65b1af6 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_006/test.rule @@ -0,0 +1,12 @@ +# To check if this network is converted to circle InstanceNorm op + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "INSTANCE_NORM_EXIST" $(op_count INSTANCE_NORM) '=' 1 +RULE "NO_ADD" $(op_count ADD) '=' 0 +RULE "NO_MUL" $(op_count MUL) '=' 0 +RULE "NO_SQRT" $(op_count SQRT) '=' 0 +RULE "NO_DIV" $(op_count DIV) '=' 0 +RULE "NO_SUB" $(op_count SUB) '=' 0 +RULE "NO_SQUARE" $(op_count SQUARE) '=' 0 +RULE "NO_MEAN" $(op_count MEAN) '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_007/test.recipe b/res/TensorFlowLiteRecipes/Net_InstanceNorm_007/test.recipe new file mode 100644 index 000000000..b8e3924ed --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_007/test.recipe @@ -0,0 +1,184 @@ +# +# This was generated from https://github.com/Samsung/ONE/issues/7032#issuecomment-862238083 +# And some modification +# + +operand { + name: "Hole" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/beta" + type: FLOAT32 + shape { + dim: 32 + } + filler { + tag: "constant" + arg: "0" + } +} +operand { + name: "InstanceNorm/instancenorm/add/y" + type: FLOAT32 + shape { + } + filler { + tag: "explicit" + arg: "1e-06" + } +} +operand { + name: "InstanceNorm/moments/variance/reduction_indices" + type: INT32 + shape { + dim: 2 + } + filler { + tag: "explicit" + arg: "1" + arg: "2" + } +} +operand { + name: "InstanceNorm/moments/mean" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/moments/SquaredDifference" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/moments/variance" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/instancenorm/add" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/instancenorm/Rsqrt" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/instancenorm/mul_1" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/instancenorm/mul_2" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/instancenorm/sub" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operand { + name: "InstanceNorm/instancenorm/add_1" + type: FLOAT32 + shape { + dim: 1 dim: 1 dim: 1 dim: 32 + } +} +operation { + type: "Mean" + input: "Hole" + input: "InstanceNorm/moments/variance/reduction_indices" + output: "InstanceNorm/moments/mean" + mean_options { + keep_dims: true + } +} +operation { + type: "SquaredDifference" + input: "Hole" + input: "InstanceNorm/moments/mean" + output: "InstanceNorm/moments/SquaredDifference" +} +operation { + type: "Mean" + input: "InstanceNorm/moments/SquaredDifference" + input: "InstanceNorm/moments/variance/reduction_indices" + output: "InstanceNorm/moments/variance" + mean_options { + keep_dims: true + } +} +operation { + type: "Add" + input: "InstanceNorm/moments/variance" + input: "InstanceNorm/instancenorm/add/y" + output: "InstanceNorm/instancenorm/add" + add_options { + activation: NONE + } +} +operation { + type: "Rsqrt" + input: "InstanceNorm/instancenorm/add" + output: "InstanceNorm/instancenorm/Rsqrt" +} +operation { + type: "Mul" + input: "Hole" + input: "InstanceNorm/instancenorm/Rsqrt" + output: "InstanceNorm/instancenorm/mul_1" + mul_options { + activation: NONE + } +} +operation { + type: "Mul" + input: "InstanceNorm/moments/mean" + input: "InstanceNorm/instancenorm/Rsqrt" + output: "InstanceNorm/instancenorm/mul_2" + mul_options { + activation: NONE + } +} +operation { + type: "Sub" + input: "InstanceNorm/beta" + input: "InstanceNorm/instancenorm/mul_2" + output: "InstanceNorm/instancenorm/sub" + sub_options { + activation: NONE + } +} +operation { + type: "Add" + input: "InstanceNorm/instancenorm/mul_1" + input: "InstanceNorm/instancenorm/sub" + output: "InstanceNorm/instancenorm/add_1" + add_options { + activation: NONE + } +} +input: "Hole" +output: "InstanceNorm/instancenorm/add_1" diff --git a/res/TensorFlowLiteRecipes/Net_InstanceNorm_007/test.rule b/res/TensorFlowLiteRecipes/Net_InstanceNorm_007/test.rule new file mode 100644 index 000000000..e8af35f05 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_InstanceNorm_007/test.rule @@ -0,0 +1,13 @@ +# To check if this network is converted to circle InstanceNorm op + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "INSTANCE_NORM_EXIST" $(op_count INSTANCE_NORM) '=' 1 +RULE "NO_ADD" $(op_count ADD) '=' 0 +RULE "NO_MUL" $(op_count MUL) '=' 0 +RULE "NO_POW" $(op_count POW) '=' 0 +RULE "NO_DIV" $(op_count DIV) '=' 0 +RULE "NO_SQUARED_DIFF" $(op_count SQUARED_DIFFERENCE) '=' 0 +RULE "NO_MEAN" $(op_count MEAN) '=' 0 +RULE "NO_RSQRT" $(op_count RSQRT) '=' 0 +RULE "NO_SUB" $(op_count SUB) '=' 0 diff --git a/res/TensorFlowLiteRecipes/Net_Mean_Mean_000/test.recipe b/res/TensorFlowLiteRecipes/Net_Mean_Mean_000/test.recipe new file mode 100644 index 000000000..b2cd68f9f --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Mean_Mean_000/test.recipe @@ -0,0 +1,47 @@ +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 3 dim: 8 dim: 8 dim: 4 } +} +operand { + name: "inner" + type: FLOAT32 + shape { dim: 3 dim: 4 } +} +operand { + name: "reduction_indices1" + type: INT32 + shape { dim: 2 } + filler { tag: "explicit" arg: "1" arg: "2" } +} +operand { + name: "reduction_indices2" + type: INT32 + shape { dim: 1 } + filler { tag: "explicit" arg: "1"} +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 3 } +} +operation { + type: "Mean" + mean_options { + keep_dims: false + } + input: "ifm" + input: "reduction_indices1" + output: "inner" +} +operation { + type: "Mean" + mean_options { + keep_dims: false + } + input: "inner" + input: "reduction_indices2" + output: "ofm" +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Net_Mean_Mean_000/test.rule b/res/TensorFlowLiteRecipes/Net_Mean_Mean_000/test.rule new file mode 100644 index 000000000..36520da1b --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Mean_Mean_000/test.rule @@ -0,0 +1,5 @@ +# To check if Maximum and Minimum is fused to Relu6. + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "MEAN_SINGLE" $(op_count MEAN) '=' 1 diff --git a/res/TensorFlowLiteRecipes/Net_Mean_Mean_001/test.recipe b/res/TensorFlowLiteRecipes/Net_Mean_Mean_001/test.recipe new file mode 100644 index 000000000..2dfc07107 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Mean_Mean_001/test.recipe @@ -0,0 +1,47 @@ +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 3 dim: 8 dim: 8 dim: 4 } +} +operand { + name: "inner" + type: FLOAT32 + shape { dim: 3 dim: 8 dim: 1 dim: 4 } +} +operand { + name: "reduction_indices1" + type: INT32 + shape { dim: 1 } + filler { tag: "explicit" arg: "2" } +} +operand { + name: "reduction_indices2" + type: INT32 + shape { dim: 1 } + filler { tag: "explicit" arg: "1"} +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 3 dim: 1 dim: 1 dim: 4} +} +operation { + type: "Mean" + mean_options { + keep_dims: true + } + input: "ifm" + input: "reduction_indices1" + output: "inner" +} +operation { + type: "Mean" + mean_options { + keep_dims: true + } + input: "inner" + input: "reduction_indices2" + output: "ofm" +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Net_Mean_Mean_001/test.rule b/res/TensorFlowLiteRecipes/Net_Mean_Mean_001/test.rule new file mode 100644 index 000000000..36520da1b --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Mean_Mean_001/test.rule @@ -0,0 +1,5 @@ +# To check if Maximum and Minimum is fused to Relu6. + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "MEAN_SINGLE" $(op_count MEAN) '=' 1 diff --git a/res/TensorFlowLiteRecipes/Net_Mean_Transpose_Mean_000/test.recipe b/res/TensorFlowLiteRecipes/Net_Mean_Transpose_Mean_000/test.recipe new file mode 100644 index 000000000..b4bec1486 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Mean_Transpose_Mean_000/test.recipe @@ -0,0 +1,66 @@ +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 3 dim: 8 dim: 8 dim: 4 } +} +operand { + name: "inner1" + type: FLOAT32 + shape { dim: 3 dim: 8 dim: 1 dim: 4 } +} +operand { + name: "inner2" + type: FLOAT32 + shape { dim: 3 dim: 1 dim: 4 dim: 8 } +} +operand { + name: "reduction_indices1" + type: INT32 + shape { dim: 1 } + filler { tag: "explicit" arg: "2" } +} +operand { + name: "reduction_indices2" + type: INT32 + shape { dim: 1 } + filler { tag: "explicit" arg: "3"} +} +operand { + name: "perm" + type: INT32 + shape { dim: 4 } + filler { tag: "explicit" arg: "0" arg: "2" arg: "3" arg: "1" } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 3 dim: 1 dim: 4 dim: 1 } +} +operation { + type: "Mean" + mean_options { + keep_dims: true + } + input: "ifm" + input: "reduction_indices1" + output: "inner1" +} +operation { + type: "Transpose" + transpose_options { + } + input: "inner1" + input: "perm" + output: "inner2" +} +operation { + type: "Mean" + mean_options { + keep_dims: true + } + input: "inner2" + input: "reduction_indices2" + output: "ofm" +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Net_Mean_Transpose_Mean_000/test.rule b/res/TensorFlowLiteRecipes/Net_Mean_Transpose_Mean_000/test.rule new file mode 100644 index 000000000..36520da1b --- /dev/null +++ b/res/TensorFlowLiteRecipes/Net_Mean_Transpose_Mean_000/test.rule @@ -0,0 +1,5 @@ +# To check if Maximum and Minimum is fused to Relu6. + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "MEAN_SINGLE" $(op_count MEAN) '=' 1 diff --git a/res/TensorFlowLiteRecipes/Part_Add_Sub_001/test.recipe b/res/TensorFlowLiteRecipes/Part_Add_Sub_001/test.recipe new file mode 100644 index 000000000..3d765bfaf --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_Add_Sub_001/test.recipe @@ -0,0 +1,67 @@ +operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "ifm3" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "ifm4" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "some/node/add1;and/another" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "some/node/add2;and/another" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operation { + type: "Add" + add_options { + activation: NONE + } + input: "ifm1" + input: "ifm2" + output: "some/node/add1;and/another" +} +operation { + type: "Add" + add_options { + activation: NONE + } + input: "some/node/add1;and/another" + input: "ifm3" + output: "some/node/add2;and/another" +} +operation { + type: "Sub" + sub_options { + activation: NONE + } + input: "some/node/add2;and/another" + input: "ifm4" + output: "ofm" +} +input: "ifm1" +input: "ifm2" +input: "ifm3" +input: "ifm4" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Part_Add_Sub_002/test.recipe b/res/TensorFlowLiteRecipes/Part_Add_Sub_002/test.recipe new file mode 100644 index 000000000..25cce49f3 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_Add_Sub_002/test.recipe @@ -0,0 +1,59 @@ +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "const" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } + filler { + tag: "gaussian" + arg: "0.0" + arg: "1.0" + } +} +operand { + name: "add1" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "add2" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 1 dim: 3 dim: 3 dim: 2 } +} +operation { + type: "Add" + add_options { + activation: NONE + } + input: "ifm" + input: "const" + output: "add1" +} +operation { + type: "Add" + add_options { + activation: NONE + } + input: "add1" + input: "const" + output: "add2" +} +operation { + type: "Sub" + sub_options { + activation: NONE + } + input: "add2" + input: "const" + output: "ofm" +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Part_If_Add_Sub_000/test.recipe b/res/TensorFlowLiteRecipes/Part_If_Add_Sub_000/test.recipe new file mode 100644 index 000000000..e946d6509 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_If_Add_Sub_000/test.recipe @@ -0,0 +1,128 @@ +version: 1 + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operation { + type: "Add" + input: "ifm1" + input: "ifm2" + output: "ofm" + add_options { + activation: NONE + } + } + input: "ifm1" + input: "ifm2" + output: "ofm" + name: "IF_ELSE" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operation { + type: "Mul" + input: "ifm1" + input: "ifm2" + output: "ofm" + mul_options { + activation: NONE + } + } + input: "ifm1" + input: "ifm2" + output: "ofm" + name: "IF_THEN" +} + +operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "cond" + type: BOOL + shape { } +} +operand { + name: "add" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "sub" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operation { + type: "Add" + input: "ifm1" + input: "ifm2" + output: "add" + add_options { + activation: NONE + } +} +operation { + type: "Sub" + input: "ifm1" + input: "ifm2" + output: "sub" + sub_options { + activation: NONE + } +} +operation { + type: "If" + input: "cond" + input: "add" + input: "sub" + output: "ofm" + if_options { + then_subgraph_index: 2 + else_subgraph_index: 1 + } +} +input: "cond" +input: "ifm1" +input: "ifm2" +output: "ofm" +name: "Main" diff --git a/res/TensorFlowLiteRecipes/Part_If_Add_Sub_001/test.recipe b/res/TensorFlowLiteRecipes/Part_If_Add_Sub_001/test.recipe new file mode 100644 index 000000000..6a3bfab75 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_If_Add_Sub_001/test.recipe @@ -0,0 +1,204 @@ +version: 1 + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operation { + type: "Add" + input: "ifm1" + input: "ifm2" + output: "ofm" + add_options { + activation: NONE + } + } + input: "ifm1" + input: "ifm2" + output: "ofm" + name: "IF_THEN_THEN" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operation { + type: "Mul" + input: "ifm1" + input: "ifm2" + output: "ofm" + mul_options { + activation: NONE + } + } + input: "ifm1" + input: "ifm2" + output: "ofm" + name: "IF_THEN_ELSE" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operation { + type: "Add" + input: "ifm1" + input: "ifm2" + output: "ofm" + add_options { + activation: NONE + } + } + input: "ifm1" + input: "ifm2" + output: "ofm" + name: "IF_ELSE" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operand { + name: "cond" + type: BOOL + shape { dim: 1 } + filler { + tag: "explicit" + arg: "T" + } + } + operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } + } + operation { + type: "If" + input: "cond" + input: "ifm1" + input: "ifm2" + output: "ofm" + if_options { + then_subgraph_index: 1 + else_subgraph_index: 2 + } + } + input: "ifm1" + input: "ifm2" + output: "ofm" + name: "IF_THEN" +} + +operand { + name: "ifm1" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "ifm2" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "cond" + type: BOOL + shape { dim: 1 } + filler { + tag: "explicit" + arg: "T" + } +} +operand { + name: "add" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "sub" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim: 2 dim: 3 } +} +operation { + type: "Add" + input: "ifm1" + input: "ifm2" + output: "add" + add_options { + activation: NONE + } +} +operation { + type: "Sub" + input: "ifm1" + input: "ifm2" + output: "sub" + sub_options { + activation: NONE + } +} +operation { + type: "If" + input: "cond" + input: "add" + input: "sub" + output: "ofm" + if_options { + then_subgraph_index: 4 + else_subgraph_index: 3 + } +} +input: "ifm1" +input: "ifm2" +output: "ofm" +name: "Main" diff --git a/res/TensorFlowLiteRecipes/Part_While_000/test.readme b/res/TensorFlowLiteRecipes/Part_While_000/test.readme new file mode 100644 index 000000000..7e2684c38 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_While_000/test.readme @@ -0,0 +1,4 @@ +test.readme of Part_While_000 + +MAXIMUM and MINIMUM Op exist to make random input to 0 +to make this model loop from 0 to 10. diff --git a/res/TensorFlowLiteRecipes/Part_While_000/test.recipe b/res/TensorFlowLiteRecipes/Part_While_000/test.recipe new file mode 100644 index 000000000..02324dfdc --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_While_000/test.recipe @@ -0,0 +1,124 @@ +version: 1 + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim:1 } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { dim:1 } + filler { + tag: "explicit" + arg: "10" + } + } + operand { + name: "ofm" + type: BOOL + shape { dim:1 } + } + operation { + type: "Less" + input: "ifm1" + input: "ifm2" + output: "ofm" + } + input: "ifm1" + output: "ofm" + name: "WHILE_COND" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { dim:1 } + } + operand { + name: "ifm3" + type: FLOAT32 + shape { dim:1 } + filler { + tag: "explicit" + arg: "1" + } + } + operand { + name: "ofm" + type: FLOAT32 + shape { dim:1 } + } + operation { + type: "Add" + input: "ifm1" + input: "ifm3" + output: "ofm" + add_options { + activation: NONE + } + } + input: "ifm1" + output: "ofm" + name: "WHILE_BODY" +} + +operand { + name: "ifm1" + type: FLOAT32 + shape { dim:1 } +} +operand { + name: "zero" + type: FLOAT32 + shape { dim:1 } + filler { + tag: "explicit" + arg: "0" + } +} +operand { + name: "min" + type: FLOAT32 + shape { dim:1 } +} +operand { + name: "max" + type: FLOAT32 + shape { dim:1 } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim:1 } +} +operation { + type: "Minimum" + maximum_options { + } + input: "ifm1" + input: "zero" + output: "min" +} +operation { + type: "Maximum" + maximum_options { + } + input: "min" + input: "zero" + output: "max" +} +operation { + type: "While" + input: "max" + output: "ofm" + while_options { + body_subgraph_index: 2 + cond_subgraph_index: 1 + } +} +input: "ifm1" +output: "ofm" +name: "Main" diff --git a/res/TensorFlowLiteRecipes/Part_While_000/test.rule b/res/TensorFlowLiteRecipes/Part_While_000/test.rule new file mode 100644 index 000000000..dee60deab --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_While_000/test.rule @@ -0,0 +1,5 @@ +# To check if this network is converted to circle InstanceNorm op + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "WHILE_EXIST" $(op_count WHILE) '=' 1 diff --git a/res/TensorFlowLiteRecipes/Part_While_001/test.readme b/res/TensorFlowLiteRecipes/Part_While_001/test.readme new file mode 100644 index 000000000..304c49483 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_While_001/test.readme @@ -0,0 +1,5 @@ +test.readme of Part_While_001 + +This has WHILE Op inside WHILE_BODY subgraph. +MAXIMUM and MINIMUM Op exist to make random input to 0 +to make this model loop from 0 to 10. diff --git a/res/TensorFlowLiteRecipes/Part_While_001/test.recipe b/res/TensorFlowLiteRecipes/Part_While_001/test.recipe new file mode 100644 index 000000000..c088053b3 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Part_While_001/test.recipe @@ -0,0 +1,203 @@ +version: 1 + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { } + } + operand { + name: "ifm2" + type: FLOAT32 + shape { } + filler { + tag: "explicit" + arg: "10" + } + } + operand { + name: "ofm" + type: BOOL + shape { } + } + operation { + type: "Less" + input: "ifm1" + input: "ifm2" + output: "ofm" + } + input: "ifm1" + output: "ofm" + name: "WHILE_WHILE_COND" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { } + } + operand { + name: "ifm3" + type: FLOAT32 + shape { } + filler { + tag: "explicit" + arg: "1" + } + } + operand { + name: "ofm" + type: FLOAT32 + shape { } + } + operation { + type: "Add" + input: "ifm1" + input: "ifm3" + output: "ofm" + add_options { + activation: NONE + } + } + input: "ifm1" + output: "ofm" + name: "WHILE_WHILE_BODY" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { } + } + operand { + name: "ifm3" + type: FLOAT32 + shape { } + filler { + tag: "explicit" + arg: "10" + } + } + operand { + name: "ofm" + type: BOOL + shape { } + } + operation { + type: "Less" + input: "ifm1" + input: "ifm3" + output: "ofm" + } + input: "ifm1" + output: "ofm" + name: "WHILE_COND" +} + +graph { + operand { + name: "ifm1" + type: FLOAT32 + shape { } + } + operand { + name: "ifm3" + type: FLOAT32 + shape { } + filler { + tag: "explicit" + arg: "1" + } + } + operand { + name: "add" + type: FLOAT32 + shape { } + } + operand { + name: "ofm1" + type: FLOAT32 + shape { } + } + operation { + type: "Add" + input: "ifm1" + input: "ifm3" + output: "add" + add_options { + activation: NONE + } + } + operation { + type: "While" + input: "add" + output: "ofm1" + while_options { + cond_subgraph_index: 1 + body_subgraph_index: 2 + } + } + input: "ifm1" + output: "ofm1" + name: "WHILE_BODY" +} + +operand { + name: "ifm1" + type: FLOAT32 + shape { } +} +operand { + name: "zero" + type: FLOAT32 + shape { } + filler { + tag: "explicit" + arg: "0" + } +} +operand { + name: "min" + type: FLOAT32 + shape { } +} +operand { + name: "max" + type: FLOAT32 + shape { } +} +operand { + name: "ofm1" + type: FLOAT32 + shape { } +} +operation { + type: "Minimum" + maximum_options { + } + input: "ifm1" + input: "zero" + output: "min" +} +operation { + type: "Maximum" + maximum_options { + } + input: "min" + input: "zero" + output: "max" +} +operation { + type: "While" + input: "max" + output: "ofm1" + while_options { + cond_subgraph_index: 3 + body_subgraph_index: 4 + } +} +input: "ifm1" +output: "ofm1" +name: "Main" diff --git a/res/TensorFlowLiteRecipes/Quantize_000/test.recipe b/res/TensorFlowLiteRecipes/Quantize_000/test.recipe new file mode 100644 index 000000000..061551a87 --- /dev/null +++ b/res/TensorFlowLiteRecipes/Quantize_000/test.recipe @@ -0,0 +1,18 @@ +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 4 } +} +operand { + name: "ofm" + type: UINT8 + shape { dim: 4 } + quant { min: 0 max: 255 scale: 1.0 zero_point: 0 } +} +operation { + type: "Quantize" + input: "ifm" + output: "ofm" +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/Quantize_000/test.reverse b/res/TensorFlowLiteRecipes/Quantize_000/test.reverse new file mode 100644 index 000000000..e69de29bb --- /dev/null +++ b/res/TensorFlowLiteRecipes/Quantize_000/test.reverse diff --git a/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_001/test.recipe b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_001/test.recipe new file mode 100644 index 000000000..1c6e8000c --- /dev/null +++ b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_001/test.recipe @@ -0,0 +1,421 @@ +operand { + name: "Const_8" + type: INT32 + shape { + dim: 4 + dim: 2 + } + filler { + tag: "explicit" + arg: "0" + arg: "0" + arg: "0" + arg: "0" + arg: "1" + arg: "1" + arg: "1" + arg: "1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_8/perm" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "0" + arg: "3" + arg: "1" + arg: "2" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_9/perm" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "0" + arg: "2" + arg: "3" + arg: "1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "batchnorm/mul" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 1 + } + filler { + tag: "explicit" + arg: "0.00498116" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "batchnorm/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 1 + dim: 1 + } + filler { + tag: "explicit" + arg: "0.0332279" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_1" + type: FLOAT32 + shape { + dim: 1 + dim: 3 + dim: 3 + dim: 16 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_2" + type: FLOAT32 + shape { + dim: 8 + dim: 3 + dim: 3 + dim: 1 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_1_weight" + type: FLOAT32 + shape { + dim: 1 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_2_weight" + type: FLOAT32 + shape { + dim: 8 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_5;PartitionedCall/transpose_5" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "1" + arg: "1" + arg: "128" + arg: "128" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_7" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "1" + arg: "130" + arg: "130" + arg: "1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "Pad_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 130 + dim: 130 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_4" + type: FLOAT32 + shape { + dim: 1 + dim: 130 + dim: 130 + dim: 16 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_1_out" + type: FLOAT32 + shape { + dim: 1 + dim: 128 + dim: 128 + dim: 1 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_51" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 128 + dim: 128 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "batchnorm/mul_1" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 128 + dim: 128 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "batchnorm/add_1" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 128 + dim: 128 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "Pad_2" + type: FLOAT32 + shape { + dim: 1 + dim: 1 + dim: 130 + dim: 130 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_71" + type: FLOAT32 + shape { + dim: 1 + dim: 130 + dim: 130 + dim: 1 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_2_out" + type: FLOAT32 + shape { + dim: 1 + dim: 128 + dim: 128 + dim: 8 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_8" + type: FLOAT32 + shape { + dim: 1 + dim: 8 + dim: 128 + dim: 128 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operation { + type: "Transpose" + input: "Pad_1" + input: "transpose_9/perm" + output: "transpose_4" +} +operation { + type: "Conv2D" + input: "transpose_4" + input: "convolution_1" + input: "convolution_1_weight" + output: "convolution_1_out" + conv2d_options { + padding: VALID + stride_w: 1 + stride_h: 1 + activation: NONE + dilation_w_factor: 1 + dilation_h_factor: 1 + } +} +operation { + type: "Reshape" + input: "convolution_1_out" + input: "transpose_5;PartitionedCall/transpose_5" + output: "transpose_51" +} +operation { + type: "Mul" + input: "transpose_51" + input: "batchnorm/mul" + output: "batchnorm/mul_1" + mul_options { + activation: NONE + } +} +operation { + type: "Add" + input: "batchnorm/mul_1" + input: "batchnorm/sub" + output: "batchnorm/add_1" + add_options { + activation: NONE + } +} +operation { + type: "Pad" + input: "batchnorm/add_1" + input: "Const_8" + output: "Pad_2" +} +operation { + type: "Reshape" + input: "Pad_2" + input: "transpose_7" + output: "transpose_71" +} +operation { + type: "Conv2D" + input: "transpose_71" + input: "convolution_2" + input: "convolution_2_weight" + output: "convolution_2_out" + conv2d_options { + padding: VALID + stride_w: 1 + stride_h: 1 + activation: NONE + dilation_w_factor: 1 + dilation_h_factor: 1 + } +} +operation { + type: "Transpose" + input: "convolution_2_out" + input: "transpose_8/perm" + output: "transpose_8" +} +input: "Pad_1" +output: "transpose_8" diff --git a/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_001/test.rule b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_001/test.rule new file mode 100644 index 000000000..5aa380447 --- /dev/null +++ b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_001/test.rule @@ -0,0 +1,9 @@ +# To check ONNX conversion is OK + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "CONV_EXIST" $(op_count CONV_2D) '=' 2 +RULE "NO_MUL" $(op_count MUL) '=' 0 +RULE "NO_ADD" $(op_count ADD) '=' 0 +RULE "NO_RESHAPE" $(op_count RESHAPE) '=' 0 +RULE "NO_TRANSPOSE" $(op_count TRANSPOSE) '=' 0 diff --git a/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_MeanMean_001/test.recipe b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_MeanMean_001/test.recipe new file mode 100644 index 000000000..8acba41bf --- /dev/null +++ b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_MeanMean_001/test.recipe @@ -0,0 +1,309 @@ +operand { + name: "Mean_4/reduction_indices" + type: INT32 + shape { + } + filler { + tag: "explicit" + arg: "3" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "Mean_5/reduction_indices" + type: INT32 + shape { + } + filler { + tag: "explicit" + arg: "2" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_73/perm" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "0" + arg: "2" + arg: "3" + arg: "1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_8/perm" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "0" + arg: "3" + arg: "1" + arg: "2" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "batchnorm_24/mul" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + dim: 1 + dim: 1 + } + filler { + tag: "explicit" + arg: "1.0" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "batchnorm_24/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + dim: 1 + dim: 1 + } + filler { + tag: "explicit" + arg: "0.0" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_12" + type: FLOAT32 + shape { + dim: 256 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_121" + type: FLOAT32 + shape { + dim: 256 + dim: 1 + dim: 1 + dim: 256 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "Relu_23" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + dim: 5 + dim: 5 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_73" + type: FLOAT32 + shape { + dim: 1 + dim: 5 + dim: 5 + dim: 256 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "convolution_122" + type: FLOAT32 + shape { + dim: 1 + dim: 5 + dim: 5 + dim: 256 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "transpose_74" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + dim: 5 + dim: 5 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "batchnorm_24/mul_1" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + dim: 5 + dim: 5 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "Relu_24" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + dim: 5 + dim: 5 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "Mean_4" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + dim: 5 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operand { + name: "Mean_5" + type: FLOAT32 + shape { + dim: 1 + dim: 256 + } + quant { + quantized_dimension: 0 + } + is_variable: false +} +operation { + type: "Transpose" + input: "Relu_23" + input: "transpose_73/perm" + output: "transpose_73" +} +operation { + type: "Conv2D" + input: "transpose_73" + input: "convolution_121" + input: "convolution_12" + output: "convolution_122" + conv2d_options { + padding: VALID + stride_w: 1 + stride_h: 1 + activation: NONE + dilation_w_factor: 1 + dilation_h_factor: 1 + } +} +operation { + type: "Transpose" + input: "convolution_122" + input: "transpose_8/perm" + output: "transpose_74" +} +operation { + type: "Mul" + input: "transpose_74" + input: "batchnorm_24/mul" + output: "batchnorm_24/mul_1" + mul_options { + activation: NONE + } +} +operation { + type: "Add" + input: "batchnorm_24/mul_1" + input: "batchnorm_24/sub" + output: "Relu_24" + add_options { + activation: RELU + } +} +operation { + type: "Mean" + input: "Relu_24" + input: "Mean_4/reduction_indices" + output: "Mean_4" + mean_options { + keep_dims: false + } +} +operation { + type: "Mean" + input: "Mean_4" + input: "Mean_5/reduction_indices" + output: "Mean_5" + mean_options { + keep_dims: false + } +} +input: "Relu_23" +output: "Mean_5" diff --git a/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_MeanMean_001/test.rule b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_MeanMean_001/test.rule new file mode 100644 index 000000000..f1ac77cfb --- /dev/null +++ b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_MeanMean_001/test.rule @@ -0,0 +1,9 @@ +# To check ONNX conversion is OK + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "CONV_EXIST" $(op_count CONV_2D) '=' 1 +RULE "ONE_MEAN" $(op_count MEAN) '=' 1 +RULE "NO_TRANSPOSE" $(op_count TRANSPOSE) '=' 0 +RULE "NO_ADD" $(op_count ADD) '=' 0 +RULE "NO_MUL" $(op_count MUL) '=' 0 diff --git a/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_Relu6_001/test.recipe b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_Relu6_001/test.recipe new file mode 100644 index 000000000..e36e4e806 --- /dev/null +++ b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_Relu6_001/test.recipe @@ -0,0 +1,277 @@ +operand { + name: "input0" + type: FLOAT32 + shape { + dim: 1 + dim: 3 + dim: 32 + dim: 32 + } +} +operand { + name: "Const_95" + type: INT32 + shape { + dim: 4 + dim: 2 + } + filler { + tag: "explicit" + arg: "0" + arg: "0" + arg: "0" + arg: "0" + arg: "1" + arg: "1" + arg: "1" + arg: "1" + } +} +operand { + name: "Pad" + type: FLOAT32 + shape { + dim: 1 + dim: 3 + dim: 34 + dim: 34 + } +} +operand { + name: "transpose_158/perm" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "0" + arg: "2" + arg: "3" + arg: "1" + } +} +operand { + name: "transpose_1" + type: FLOAT32 + shape { + dim: 1 + dim: 34 + dim: 34 + dim: 3 + } +} +operand { + name: "convolution" + type: FLOAT32 + shape { + dim: 16 + dim: 3 + dim: 3 + dim: 3 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } +} +operand { + name: "convolution_41" + type: FLOAT32 + shape { + dim: 16 + } + filler { + tag: "gaussian" + arg: "0.0" + arg: "0.1" + } +} +operand { + name: "convolution1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 16 + } +} +operand { + name: "transpose_159/perm" + type: INT32 + shape { + dim: 4 + } + filler { + tag: "explicit" + arg: "0" + arg: "3" + arg: "1" + arg: "2" + } +} +operand { + name: "transpose_2" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 16 + } +} +operand { + name: "batchnorm/mul" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 1 + dim: 1 + } + filler { + tag: "explicit" + arg: "0.001" + } +} +operand { + name: "batchnorm/mul_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 16 + } +} +operand { + name: "batchnorm/sub" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 1 + dim: 1 + } + filler { + tag: "explicit" + arg: "0.0" + } +} +operand { + name: "batchnorm/add_1" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 16 + } +} +operand { + name: "clip_by_value_9/Minimum/y" + type: FLOAT32 + shape { + } + filler { + tag: "explicit" + arg: "6" + } +} +operand { + name: "clip_by_value/Minimum" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 16 + } +} +operand { + name: "clip_by_value_9/y" + type: FLOAT32 + shape { + } + filler { + tag: "explicit" + arg: "0" + } +} +operand { + name: "clip_by_value" + type: FLOAT32 + shape { + dim: 1 + dim: 16 + dim: 16 + dim: 16 + } +} +operation { + type: "Pad" + input: "input0" + input: "Const_95" + output: "Pad" +} +operation { + type: "Transpose" + input: "Pad" + input: "transpose_158/perm" + output: "transpose_1" +} +operation { + type: "Conv2D" + input: "transpose_1" + input: "convolution" + input: "convolution_41" + output: "convolution1" + conv2d_options { + padding: VALID + stride_w: 2 + stride_h: 2 + activation: NONE + dilation_w_factor: 1 + dilation_h_factor: 1 + } +} +operation { + type: "Transpose" + input: "convolution1" + input: "transpose_159/perm" + output: "transpose_2" +} +operation { + type: "Mul" + input: "transpose_2" + input: "batchnorm/mul" + output: "batchnorm/mul_1" + mul_options { + activation: NONE + } +} +operation { + type: "Add" + input: "batchnorm/mul_1" + input: "batchnorm/sub" + output: "batchnorm/add_1" + add_options { + activation: NONE + } +} +operation { + type: "Minimum" + input: "batchnorm/add_1" + input: "clip_by_value_9/Minimum/y" + output: "clip_by_value/Minimum" +} +operation { + type: "Maximum" + input: "clip_by_value/Minimum" + input: "clip_by_value_9/y" + output: "clip_by_value" +} +input: "input0" +output: "clip_by_value" diff --git a/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_Relu6_001/test.rule b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_Relu6_001/test.rule new file mode 100644 index 000000000..ac81f4326 --- /dev/null +++ b/res/TensorFlowLiteRecipes/REGRESS_ONNX_Conv_BN_Relu6_001/test.rule @@ -0,0 +1,11 @@ +# To check ONNX conversion is OK + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "CONV_EXIST" $(op_count CONV_2D) '=' 1 +RULE "NO_TRANSPOSE" $(op_count TRANSPOSE) '=' 0 +RULE "NO_ADD" $(op_count ADD) '=' 0 +RULE "NO_MUL" $(op_count MUL) '=' 0 +RULE "NO_RELU6" $(op_count RELU6) '=' 0 +RULE "NO_MINIMUM" $(op_count MINIMUM) '=' 0 +RULE "NO_MAXIMUM" $(op_count MAXIMUM) '=' 0 diff --git a/res/TensorFlowLiteRecipes/StridedSlice_003/test.recipe b/res/TensorFlowLiteRecipes/StridedSlice_003/test.recipe new file mode 100644 index 000000000..c5e408f1f --- /dev/null +++ b/res/TensorFlowLiteRecipes/StridedSlice_003/test.recipe @@ -0,0 +1,58 @@ +# Recipe for StridedSlice that will converted to Reshape by SubstituteStridedSliceToReshapePass +# +# shrink_axis_mask will remove axis 0 + +operand { + name: "ifm" + type: FLOAT32 + shape { dim: 1 dim: 10 dim: 1 dim: 4 } +} +operand { + name: "begin" + type: INT32 + shape { dim: 4 } + filler { + tag: "explicit" + arg: "0" arg: "0" arg: "0" arg: "0" + } +} +operand { + name: "end" + type: INT32 + shape { dim: 4 } + filler { + tag: "explicit" + arg: "1" arg: "10" arg: "1" arg: "100" + } +} +operand { + name: "strides" + type: INT32 + shape { dim: 4 } + filler { + tag: "explicit" + arg: "1" arg: "1" arg: "1" arg: "1" + } +} +operand { + name: "ofm" + type: FLOAT32 + shape { dim:10 dim: 1 dim: 4} +} +operation { + type: "StridedSlice" + strided_slice_options { + begin_mask: 0 + end_mask: 0 + ellipsis_mask: 0 + new_axis_mask: 0 + shrink_axis_mask: 1 + } + input: "ifm" + input: "begin" + input: "end" + input: "strides" + output: "ofm" +} +input: "ifm" +output: "ofm" diff --git a/res/TensorFlowLiteRecipes/StridedSlice_003/test.rule b/res/TensorFlowLiteRecipes/StridedSlice_003/test.rule new file mode 100644 index 000000000..d65d43c9c --- /dev/null +++ b/res/TensorFlowLiteRecipes/StridedSlice_003/test.rule @@ -0,0 +1,6 @@ +# To check if Add and Mul are fused to Convolution op + +RULE "VERIFY_FILE_FORMAT" $(verify_file_format) '=' 1 + +RULE "RESHAPE_EXIST" $(op_count RESHAPE) '=' 1 +RULE "NO_STRIDEDSLICE" $(op_count STRIDEDSLICE) '=' 0 diff --git a/res/TensorFlowPythonExamples/examples/cond_1/__init__.py b/res/TensorFlowPythonExamples/examples/cond_1/__init__.py new file mode 100644 index 000000000..fed192018 --- /dev/null +++ b/res/TensorFlowPythonExamples/examples/cond_1/__init__.py @@ -0,0 +1,26 @@ +import tensorflow as tf + +x_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=[], name="HoleX") +y_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=[], name="HoleY") +z_ = tf.compat.v1.placeholder(dtype=tf.float32, shape=[], name="HoleZ") + + +def fn01(a, b): + return tf.math.multiply(a, b, name="Hole0M") + + +def fn02(a, b): + return tf.math.add(a, b, name="Hole0A") + + +def fn1(c, x, y, z): + return tf.cond(c, lambda: fn01(x, y), lambda: fn02(y, z), name="Cond0") + + +def fn2(a, b): + return tf.math.add(a, b, name="HoleA") + + +pr_ = tf.compat.v1.placeholder(tf.bool, shape=[], name="HoleC") +op_ = tf.cond(pr_, lambda: fn1(pr_, x_, y_, z_), lambda: fn2(y_, z_), name="Cond") +re_ = tf.identity(op_, name="HoleR") diff --git a/runtime/contrib/android/api/build.gradle b/runtime/contrib/android/api/build.gradle index 8f0d61c47..2e3955c3a 100644 --- a/runtime/contrib/android/api/build.gradle +++ b/runtime/contrib/android/api/build.gradle @@ -8,7 +8,7 @@ android { minSdkVersion 26 targetSdkVersion 29 versionCode 1 - versionName "1.15.0" + versionName "1.17.0" externalNativeBuild { ndkBuild { diff --git a/runtime/libs/tflite/CMakeLists.txt b/runtime/libs/tflite/CMakeLists.txt index f02c93aa6..d2694b8b9 100644 --- a/runtime/libs/tflite/CMakeLists.txt +++ b/runtime/libs/tflite/CMakeLists.txt @@ -4,8 +4,6 @@ if(NOT TensorFlowLite_FOUND) return() endif(NOT TensorFlowLite_FOUND) -add_subdirectory(port) - file(GLOB_RECURSE SOURCES "src/*.cpp") file(GLOB_RECURSE TESTS "src/*.test.cpp") list(REMOVE_ITEM SOURCES ${TESTS}) @@ -13,7 +11,7 @@ list(REMOVE_ITEM SOURCES ${TESTS}) add_library(nnfw_lib_tflite STATIC ${SOURCES}) set_target_properties(nnfw_lib_tflite PROPERTIES POSITION_INDEPENDENT_CODE ON) target_include_directories(nnfw_lib_tflite PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) -target_link_libraries(nnfw_lib_tflite PUBLIC tensorflow-lite-ex) +target_link_libraries(nnfw_lib_tflite PUBLIC tensorflow-lite) target_link_libraries(nnfw_lib_tflite PUBLIC nnfw_lib_misc) target_link_libraries(nnfw_lib_tflite PRIVATE ${LIB_PTHREAD} dl) target_link_libraries(nnfw_lib_tflite PRIVATE nnfw_common) diff --git a/runtime/libs/tflite/include/tflite/NNAPISession.h b/runtime/libs/tflite/include/tflite/NNAPISession.h index f430e86d3..681dda8b0 100644 --- a/runtime/libs/tflite/include/tflite/NNAPISession.h +++ b/runtime/libs/tflite/include/tflite/NNAPISession.h @@ -24,7 +24,6 @@ #define __NNFW_TFLITE_NNAPI_SESSION_H__ #include "Session.h" -#include "tflite/ext/nnapi_delegate.h" namespace nnfw { @@ -44,9 +43,7 @@ public: */ NNAPISession(::tflite::Interpreter *interp) : _interp{interp} { - // Construct Graph from Interpreter - // primary_subgraph: Experimental interface. Return 1st sugbraph - _delegate.BuildGraph(&interp->primary_subgraph()); + // DO NOTHING } public: @@ -65,7 +62,7 @@ public: { // Explicitly turn off T/F lite internal NNAPI delegation in order to use locally defined // NNAPI delegation. - _interp->UseNNAPI(false); + _interp->UseNNAPI(true); if (kTfLiteOk != _interp->AllocateTensors()) { @@ -79,7 +76,7 @@ public: * @brief Run the Invoke function of NNAPI delegate * @return @c true if Invoke() is successful, otherwise @c false */ - bool run(void) override { return kTfLiteOk == _delegate.Invoke(&_interp->primary_subgraph()); } + bool run(void) override { return kTfLiteOk == _interp->Invoke(); } /** * @brief Tear down TfLite interpreter session @@ -93,7 +90,6 @@ public: private: ::tflite::Interpreter *const _interp; - nnfw::tflite::NNAPIDelegate _delegate; }; } // namespace tflite diff --git a/runtime/libs/tflite/port/1.13.1/CMakeLists.txt b/runtime/libs/tflite/port/1.13.1/CMakeLists.txt deleted file mode 100644 index e3cf97569..000000000 --- a/runtime/libs/tflite/port/1.13.1/CMakeLists.txt +++ /dev/null @@ -1,14 +0,0 @@ -if(NOT SUPPORT_TFLITE_VERSION VERSION_EQUAL 1.13.1) - return() -endif(NOT SUPPORT_TFLITE_VERSION VERSION_EQUAL 1.13.1) - -file(GLOB_RECURSE SOURCES "src/*.cpp") - -add_library(tensorflow-lite-ex STATIC ${SOURCES}) -set_target_properties(tensorflow-lite-ex PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(tensorflow-lite-ex PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include) -target_link_libraries(tensorflow-lite-ex PUBLIC tensorflow-lite) -target_link_libraries(tensorflow-lite-ex PUBLIC nnfw_lib_misc nnfw_lib_rua_shim) -target_link_libraries(tensorflow-lite-ex PRIVATE ${LIB_PTHREAD} dl) -target_link_libraries(tensorflow-lite-ex PRIVATE nnfw_common) -target_link_libraries(tensorflow-lite-ex PRIVATE nnfw_coverage) diff --git a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/CustomOps.h b/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/CustomOps.h deleted file mode 100644 index c073ad58e..000000000 --- a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/CustomOps.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file CustomOps.h - * @brief This file contains registration of custom operands - * @ingroup COM_AI_RUNTIME - */ - -#ifndef __NNFW_TFLITE_EXT_KERNELS_CUSTOM_OP_H__ -#define __NNFW_TFLITE_EXT_KERNELS_CUSTOM_OP_H__ - -#include "tensorflow/lite/context.h" -#include "tflite/ext/kernels/SquaredDifference.h" - -namespace nnfw -{ -namespace tflite -{ -namespace custom -{ - -#define REGISTER_FUNCTION(Name) \ - TfLiteRegistration *Register_##Name(void) \ - { \ - static TfLiteRegistration r = {}; \ - r.init = Name::Init##Name; \ - r.free = Name::Free##Name; \ - r.prepare = Name::Prepare##Name; \ - r.invoke = Name::Eval##Name; \ - r.custom_name = #Name; \ - return &r; \ - } - -REGISTER_FUNCTION(SquaredDifference) - -#undef REGISTER_FUNCTION - -} // namespace custom -} // namespace tflite -} // namespace nnfw - -#endif // __NNFW_TFLITE_EXT_KERNELS_CUSTOM_OP_H__ diff --git a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/SquaredDifference.h b/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/SquaredDifference.h deleted file mode 100644 index 5512ead78..000000000 --- a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/SquaredDifference.h +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file SquaredDifference.h - * @brief This file contains SquaredDifference namespace and SquaredDifference function - * definitions - * @ingroup COM_AI_RUNTIME - */ - -#ifndef __NNFW_TFLITE_EXT_KERNELS_SQUARED_DIFFERENCE_H__ -#define __NNFW_TFLITE_EXT_KERNELS_SQUARED_DIFFERENCE_H__ - -#include "tensorflow/lite/context.h" - -namespace nnfw -{ -namespace tflite -{ -namespace custom -{ -namespace SquaredDifference -{ - -/** - * @brief Initialize SquaredDifference operand using the contents of buffer - * @param[in] context The TfLite context - * @param[in] buffer The buffer with contents - * @param[in] length The buffer length - * @return The void pointer for user data - */ -void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length); - -/** - * @brief Release any memory it might have allocated via 'InitSquaredDifference' - * @param[in] context The TfLite context - * @param[in] buffer The buffer with contents - * @return N/A - */ -void FreeSquaredDifference(TfLiteContext *context, void *buffer); - -/** - * @brief Prepare the SquaredDifference operand for execution - * @param[in] context The TfLite context - * @param[in] node The operand node - * @return The TfLite status - */ -TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node); - -/** - * @brief Evaluation the SquaredDifference operand for execution - * @param[in] context The TfLite context - * @param[in] node The operand node - * @return The TfLite status - */ -TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node); - -} // namespace SquaredDifference -} // namespace custom -} // namespace tflite -} // namespace nnfw - -#endif // __NNFW_TFLITE_EXT_KERNELS_SQUARED_DIFFERENCE_H__ diff --git a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/register.h b/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/register.h deleted file mode 100644 index 6e32b35fb..000000000 --- a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/kernels/register.h +++ /dev/null @@ -1,46 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This header is derived from the following file (in TensorFlow v1.13.1) -// 'externals/tensorflow/tensorflow/lite/kernels/register.h' -#ifndef __NNFW_TFLITE_EXT_KERNELS_REGISTER_H__ -#define __NNFW_TFLITE_EXT_KERNELS_REGISTER_H__ - -#include <unordered_map> -#include "tensorflow/lite/context.h" -#include "tensorflow/lite/model.h" - -namespace nnfw { -namespace tflite { - -class BuiltinOpResolver : public ::tflite::MutableOpResolver { - public: - BuiltinOpResolver(); - - const TfLiteRegistration* FindOp(::tflite::BuiltinOperator op, - int version) const override; - const TfLiteRegistration* FindOp(const char* op, int version) const override; -}; - -} // namespace tflite -} // namespace nnfw - -#endif // __NNFW_TFLITE_EXT_KERNELS_REGISTER_H__ - -// clang-format on diff --git a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/nnapi_delegate.h b/runtime/libs/tflite/port/1.13.1/include/tflite/ext/nnapi_delegate.h deleted file mode 100644 index 231baa25c..000000000 --- a/runtime/libs/tflite/port/1.13.1/include/tflite/ext/nnapi_delegate.h +++ /dev/null @@ -1,92 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This header is derived from the following file (in TensorFlow v1.13.1) -// 'externals/tensorflow/tensorflow/lite/nnapi_delegate.h' -#ifndef __NNFW_TFLITE_EXT_NNAPI_DELEGATE_H__ -#define __NNFW_TFLITE_EXT_NNAPI_DELEGATE_H__ - -#include "tensorflow/lite/allocation.h" -#include "tensorflow/lite/c/c_api_internal.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/core/subgraph.h" -#include "tensorflow/lite/interpreter.h" - -struct ANeuralNetworksModel; -struct ANeuralNetworksMemory; -struct ANeuralNetworksCompilation; - -namespace nnfw { -namespace tflite { - -class NNAPIAllocation : public ::tflite::MMAPAllocation { - public: - NNAPIAllocation(const char* filename, ::tflite::ErrorReporter* error_reporter); - ~NNAPIAllocation(); - - size_t offset(const void* ptr) const { - auto signed_offset = reinterpret_cast<const uint8_t*>(ptr) - - reinterpret_cast<const uint8_t*>(mmapped_buffer_); - - return static_cast<size_t>(signed_offset); - } - - ANeuralNetworksMemory* memory() const { return handle_; } - bool valid() const override { return handle_ != nullptr; } - - private: - mutable ANeuralNetworksMemory* handle_ = nullptr; -}; - -class NNAPIDelegate { - public: - ~NNAPIDelegate(); - - // Convert a tflite graph to NNAPI - TfLiteStatus BuildGraph(::tflite::Subgraph* subgraph); - - // Run - TfLiteStatus Invoke(::tflite::Subgraph* subgraph); - - // Whether the current platform supports NNAPI delegation. - static bool IsSupported(); - - private: - // The NN API model handle - ANeuralNetworksModel* nn_model_ = nullptr; - // The NN API compilation handle - ANeuralNetworksCompilation* nn_compiled_model_ = nullptr; - // Model status - TfLiteStatus model_status_ = kTfLiteOk; - - // List of state tensors for LSTM, RNN, SVDF. - // NN API does not allow ops to maintain states across multiple - // invocations. We need to manually create state input tensors from - // corresponding state output tensors of TFLite operations, and map them - // correctly. - std::vector<int> model_states_inputs_; // holds NNAPI operand ids - std::vector<int> model_states_outputs_; // holds TFLite tensor ids -}; - -} // namespace tflite -} // namespace nnfw - -#endif // __NNFW_TFLITE_EXT_NNAPI_DELEGATE_H__ - -// clang-format on diff --git a/runtime/libs/tflite/port/1.13.1/src/kernels/SquaredDifference.cpp b/runtime/libs/tflite/port/1.13.1/src/kernels/SquaredDifference.cpp deleted file mode 100644 index 615878513..000000000 --- a/runtime/libs/tflite/port/1.13.1/src/kernels/SquaredDifference.cpp +++ /dev/null @@ -1,109 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "tflite/ext/kernels/SquaredDifference.h" -#include "tensorflow/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace nnfw -{ -namespace tflite -{ -namespace custom -{ -namespace SquaredDifference -{ - -void *InitSquaredDifference(TfLiteContext *, const char *, size_t) { return nullptr; } - -void FreeSquaredDifference(TfLiteContext *, void *) {} - -TfLiteStatus PrepareSquaredDifference(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); - - const TfLiteTensor *input1 = ::tflite::GetInput(context, node, 0); - const TfLiteTensor *input2 = ::tflite::GetInput(context, node, 1); - TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); - - TF_LITE_ENSURE_EQ(context, input1->type, input2->type); - TF_LITE_ENSURE_EQ(context, input1->type, output->type); - - return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input1->dims)); -} - -TfLiteStatus EvalSquaredDifference(TfLiteContext *context, TfLiteNode *node) -{ - - const TfLiteTensor *input1 = ::tflite::GetInput(context, node, 0); - const TfLiteTensor *input2 = ::tflite::GetInput(context, node, 1); - - TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); - - size_t elements = ::tflite::NumElements(input1); - - switch (input1->type) - { - case kTfLiteFloat32: - { - const float *in1 = input1->data.f; - const float *in2 = input2->data.f; - const float *in_end1 = in1 + elements; - float *out = output->data.f; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - case kTfLiteInt32: - { - const int *in1 = input1->data.i32; - const int *in2 = input2->data.i32; - const int *in_end1 = in1 + elements; - int *out = output->data.i32; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - case kTfLiteInt64: - { - const int64_t *in1 = input1->data.i64; - const int64_t *in2 = input1->data.i64; - const int64_t *in_end1 = in1 + elements; - int64_t *out = output->data.i64; - - for (; in1 < in_end1; in1++, in2++, out++) - *out = ((*in1 - *in2) * (*in1 - *in2)); - - return kTfLiteOk; - } - default: - { - context->ReportError(context, "InputType is %d Unsupported", input1->type); - return kTfLiteError; - } - } -} - -} // namespace SquaredDifference -} // namespace custom -} // namespace tflite -} // namespace nnfw diff --git a/runtime/libs/tflite/port/1.13.1/src/kernels/register.cpp b/runtime/libs/tflite/port/1.13.1/src/kernels/register.cpp deleted file mode 100644 index 89f81b612..000000000 --- a/runtime/libs/tflite/port/1.13.1/src/kernels/register.cpp +++ /dev/null @@ -1,314 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This code is derived from the following file (in TensorFlow v1.13.1) -// 'externals/tensorflow/tensorflow/lite/kernels/register.cc' -#include "tflite/ext/kernels/register.h" -#include "tensorflow/lite/util.h" -#include "tflite/ext/kernels/CustomOps.h" - -namespace tflite { -namespace ops { - -namespace custom { - -// Need additional external library for AUDIO_SPECTROGRAM -//TfLiteRegistration* Register_AUDIO_SPECTROGRAM(); -TfLiteRegistration* Register_LAYER_NORM_LSTM(); -TfLiteRegistration* Register_MFCC(); -TfLiteRegistration* Register_DETECTION_POSTPROCESS(); -TfLiteRegistration* Register_RELU_1(); - -} // namespace custom -} -} - -namespace tflite { -namespace ops { -namespace builtin { - -TfLiteRegistration* Register_ABS(); -TfLiteRegistration* Register_RELU(); -TfLiteRegistration* Register_RELU_N1_TO_1(); -TfLiteRegistration* Register_RELU6(); -TfLiteRegistration* Register_TANH(); -TfLiteRegistration* Register_LOGISTIC(); -TfLiteRegistration* Register_AVERAGE_POOL_2D(); -TfLiteRegistration* Register_MAX_POOL_2D(); -TfLiteRegistration* Register_L2_POOL_2D(); -TfLiteRegistration* Register_CONV_2D(); -TfLiteRegistration* Register_DEPTHWISE_CONV_2D(); -TfLiteRegistration* Register_SVDF(); -TfLiteRegistration* Register_RNN(); -TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_RNN(); -TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_RNN(); -TfLiteRegistration* Register_EMBEDDING_LOOKUP(); -TfLiteRegistration* Register_EMBEDDING_LOOKUP_SPARSE(); -TfLiteRegistration* Register_FULLY_CONNECTED(); -TfLiteRegistration* Register_LSH_PROJECTION(); -TfLiteRegistration* Register_HASHTABLE_LOOKUP(); -TfLiteRegistration* Register_SOFTMAX(); -TfLiteRegistration* Register_CONCATENATION(); -TfLiteRegistration* Register_ADD(); -TfLiteRegistration* Register_SPACE_TO_BATCH_ND(); -TfLiteRegistration* Register_DIV(); -TfLiteRegistration* Register_SUB(); -TfLiteRegistration* Register_BATCH_TO_SPACE_ND(); -TfLiteRegistration* Register_MUL(); -TfLiteRegistration* Register_L2_NORMALIZATION(); -TfLiteRegistration* Register_LOCAL_RESPONSE_NORMALIZATION(); -TfLiteRegistration* Register_LSTM(); -TfLiteRegistration* Register_BIDIRECTIONAL_SEQUENCE_LSTM(); -TfLiteRegistration* Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); -TfLiteRegistration* Register_PAD(); -TfLiteRegistration* Register_PADV2(); -TfLiteRegistration* Register_RESHAPE(); -TfLiteRegistration* Register_RESIZE_BILINEAR(); -TfLiteRegistration* Register_RESIZE_NEAREST_NEIGHBOR(); -TfLiteRegistration* Register_SKIP_GRAM(); -TfLiteRegistration* Register_SPACE_TO_DEPTH(); -TfLiteRegistration* Register_GATHER(); -TfLiteRegistration* Register_TRANSPOSE(); -TfLiteRegistration* Register_MEAN(); -TfLiteRegistration* Register_SPLIT(); -TfLiteRegistration* Register_SPLIT_V(); -TfLiteRegistration* Register_SQUEEZE(); -TfLiteRegistration* Register_STRIDED_SLICE(); -TfLiteRegistration* Register_EXP(); -TfLiteRegistration* Register_TOPK_V2(); -TfLiteRegistration* Register_LOG(); -TfLiteRegistration* Register_LOG_SOFTMAX(); -TfLiteRegistration* Register_CAST(); -TfLiteRegistration* Register_DEQUANTIZE(); -TfLiteRegistration* Register_PRELU(); -TfLiteRegistration* Register_MAXIMUM(); -TfLiteRegistration* Register_MINIMUM(); -TfLiteRegistration* Register_ARG_MAX(); -TfLiteRegistration* Register_ARG_MIN(); -TfLiteRegistration* Register_GREATER(); -TfLiteRegistration* Register_GREATER_EQUAL(); -TfLiteRegistration* Register_LESS(); -TfLiteRegistration* Register_LESS_EQUAL(); -TfLiteRegistration* Register_FLOOR(); -TfLiteRegistration* Register_TILE(); -TfLiteRegistration* Register_NEG(); -TfLiteRegistration* Register_SUM(); -TfLiteRegistration* Register_REDUCE_PROD(); -TfLiteRegistration* Register_REDUCE_MAX(); -TfLiteRegistration* Register_REDUCE_MIN(); -TfLiteRegistration* Register_REDUCE_ANY(); -TfLiteRegistration* Register_SELECT(); -TfLiteRegistration* Register_SLICE(); -TfLiteRegistration* Register_SIN(); -TfLiteRegistration* Register_TRANSPOSE_CONV(); -TfLiteRegistration* Register_EXPAND_DIMS(); -TfLiteRegistration* Register_SPARSE_TO_DENSE(); -TfLiteRegistration* Register_EQUAL(); -TfLiteRegistration* Register_NOT_EQUAL(); -TfLiteRegistration* Register_SQRT(); -TfLiteRegistration* Register_RSQRT(); -TfLiteRegistration* Register_SHAPE(); -TfLiteRegistration* Register_POW(); -TfLiteRegistration* Register_FAKE_QUANT(); -TfLiteRegistration* Register_PACK(); -TfLiteRegistration* Register_ONE_HOT(); -TfLiteRegistration* Register_LOGICAL_OR(); -TfLiteRegistration* Register_LOGICAL_AND(); -TfLiteRegistration* Register_LOGICAL_NOT(); -TfLiteRegistration* Register_UNPACK(); -TfLiteRegistration* Register_FLOOR_DIV(); -TfLiteRegistration* Register_SQUARE(); -TfLiteRegistration* Register_ZEROS_LIKE(); -TfLiteRegistration* Register_FLOOR_MOD(); -TfLiteRegistration* Register_RANGE(); -TfLiteRegistration* Register_LEAKY_RELU(); -TfLiteRegistration* Register_SQUARED_DIFFERENCE(); -TfLiteRegistration* Register_FILL(); -TfLiteRegistration* Register_MIRROR_PAD(); - -} // namespace builtin -} // namespace ops -} // namespace tflite - -namespace nnfw { -namespace tflite { - -// Using namespace directive to minimize diff with upstream tensorflow -using namespace ::tflite::ops::custom; -using namespace ::tflite::ops::builtin; -using namespace ::tflite; - -// Fix to use strict build option -TfLiteStatus UnsupportedTensorFlowOp(TfLiteContext* context, TfLiteNode* /*node*/) { - context->ReportError( - context, - "Regular TensorFlow ops are not supported by this interpreter. Make sure " - "you invoke the Flex delegate before inference."); - return kTfLiteError; -} - -const TfLiteRegistration* BuiltinOpResolver::FindOp(tflite::BuiltinOperator op, - int version) const { - return MutableOpResolver::FindOp(op, version); -} - -const TfLiteRegistration* BuiltinOpResolver::FindOp(const char* op, - int version) const { - // Return the NULL Op for all ops whose name start with "Flex", allowing - // the interpreter to delegate their execution. - if (IsFlexOp(op)) { - static TfLiteRegistration null_op{ - nullptr, nullptr, &UnsupportedTensorFlowOp, - nullptr, nullptr, BuiltinOperator_CUSTOM, - "Flex", 1}; - return &null_op; - } - return MutableOpResolver::FindOp(op, version); -} - -BuiltinOpResolver::BuiltinOpResolver() { - AddBuiltin(BuiltinOperator_ABS, Register_ABS()); - AddBuiltin(BuiltinOperator_RELU, Register_RELU()); - AddBuiltin(BuiltinOperator_RELU_N1_TO_1, Register_RELU_N1_TO_1()); - AddBuiltin(BuiltinOperator_RELU6, Register_RELU6()); - AddBuiltin(BuiltinOperator_TANH, Register_TANH()); - AddBuiltin(BuiltinOperator_LOGISTIC, Register_LOGISTIC()); - AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, Register_AVERAGE_POOL_2D()); - AddBuiltin(BuiltinOperator_MAX_POOL_2D, Register_MAX_POOL_2D()); - AddBuiltin(BuiltinOperator_L2_POOL_2D, Register_L2_POOL_2D()); - AddBuiltin(BuiltinOperator_CONV_2D, Register_CONV_2D()); - AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, Register_DEPTHWISE_CONV_2D(), - /* min_version */ 1, - /* max_version */ 2); - AddBuiltin(BuiltinOperator_SVDF, Register_SVDF()); - AddBuiltin(BuiltinOperator_RNN, Register_RNN()); - AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN, - Register_BIDIRECTIONAL_SEQUENCE_RNN()); - AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN, - Register_UNIDIRECTIONAL_SEQUENCE_RNN()); - AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP, Register_EMBEDDING_LOOKUP()); - AddBuiltin(BuiltinOperator_EMBEDDING_LOOKUP_SPARSE, - Register_EMBEDDING_LOOKUP_SPARSE()); - AddBuiltin(BuiltinOperator_FULLY_CONNECTED, Register_FULLY_CONNECTED(), - /* min_version */ 1, - /* max_version */ 2); - AddBuiltin(BuiltinOperator_LSH_PROJECTION, Register_LSH_PROJECTION()); - AddBuiltin(BuiltinOperator_HASHTABLE_LOOKUP, Register_HASHTABLE_LOOKUP()); - AddBuiltin(BuiltinOperator_SOFTMAX, Register_SOFTMAX()); - AddBuiltin(BuiltinOperator_CONCATENATION, Register_CONCATENATION()); - AddBuiltin(BuiltinOperator_ADD, Register_ADD()); - AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND, Register_SPACE_TO_BATCH_ND()); - AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND, Register_BATCH_TO_SPACE_ND()); - AddBuiltin(BuiltinOperator_MUL, Register_MUL()); - AddBuiltin(BuiltinOperator_L2_NORMALIZATION, Register_L2_NORMALIZATION()); - AddBuiltin(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, - Register_LOCAL_RESPONSE_NORMALIZATION()); - AddBuiltin(BuiltinOperator_LSTM, Register_LSTM(), /* min_version */ 1, - /* max_version */ 2); - AddBuiltin(BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM, - Register_BIDIRECTIONAL_SEQUENCE_LSTM()); - AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - Register_UNIDIRECTIONAL_SEQUENCE_LSTM()); - AddBuiltin(BuiltinOperator_PAD, Register_PAD()); - AddBuiltin(BuiltinOperator_PADV2, Register_PADV2()); - AddBuiltin(BuiltinOperator_RESHAPE, Register_RESHAPE()); - AddBuiltin(BuiltinOperator_RESIZE_BILINEAR, Register_RESIZE_BILINEAR()); - AddBuiltin(BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - Register_RESIZE_NEAREST_NEIGHBOR()); - AddBuiltin(BuiltinOperator_SKIP_GRAM, Register_SKIP_GRAM()); - AddBuiltin(BuiltinOperator_SPACE_TO_DEPTH, Register_SPACE_TO_DEPTH()); - AddBuiltin(BuiltinOperator_GATHER, Register_GATHER()); - AddBuiltin(BuiltinOperator_TRANSPOSE, Register_TRANSPOSE()); - AddBuiltin(BuiltinOperator_MEAN, Register_MEAN()); - AddBuiltin(BuiltinOperator_DIV, Register_DIV()); - AddBuiltin(BuiltinOperator_SUB, Register_SUB()); - AddBuiltin(BuiltinOperator_SPLIT, Register_SPLIT()); - AddBuiltin(BuiltinOperator_SPLIT_V, Register_SPLIT_V()); - AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE()); - AddBuiltin(BuiltinOperator_STRIDED_SLICE, Register_STRIDED_SLICE()); - AddBuiltin(BuiltinOperator_EXP, Register_EXP()); - AddBuiltin(BuiltinOperator_TOPK_V2, Register_TOPK_V2()); - AddBuiltin(BuiltinOperator_LOG, Register_LOG()); - AddBuiltin(BuiltinOperator_LOG_SOFTMAX, Register_LOG_SOFTMAX()); - AddBuiltin(BuiltinOperator_CAST, Register_CAST()); - AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE(), - /* min_version */ 1, - /* max_version */ 2); - AddBuiltin(BuiltinOperator_PRELU, Register_PRELU()); - AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM()); - AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); - AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX()); - AddBuiltin(BuiltinOperator_ARG_MIN, Register_ARG_MIN()); - AddBuiltin(BuiltinOperator_GREATER, Register_GREATER()); - AddBuiltin(BuiltinOperator_GREATER_EQUAL, Register_GREATER_EQUAL()); - AddBuiltin(BuiltinOperator_LESS, Register_LESS()); - AddBuiltin(BuiltinOperator_LESS_EQUAL, Register_LESS_EQUAL()); - AddBuiltin(BuiltinOperator_FLOOR, Register_FLOOR()); - AddBuiltin(BuiltinOperator_NEG, Register_NEG()); - AddBuiltin(BuiltinOperator_SELECT, Register_SELECT()); - AddBuiltin(BuiltinOperator_SLICE, Register_SLICE()); - AddBuiltin(BuiltinOperator_SIN, Register_SIN()); - AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); - AddBuiltin(BuiltinOperator_TILE, Register_TILE()); - AddBuiltin(BuiltinOperator_SUM, Register_SUM()); - AddBuiltin(BuiltinOperator_REDUCE_PROD, Register_REDUCE_PROD()); - AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX()); - AddBuiltin(BuiltinOperator_REDUCE_MIN, Register_REDUCE_MIN()); - AddBuiltin(BuiltinOperator_REDUCE_ANY, Register_REDUCE_ANY()); - AddBuiltin(BuiltinOperator_EXPAND_DIMS, Register_EXPAND_DIMS()); - AddBuiltin(BuiltinOperator_SPARSE_TO_DENSE, Register_SPARSE_TO_DENSE()); - AddBuiltin(BuiltinOperator_EQUAL, Register_EQUAL()); - AddBuiltin(BuiltinOperator_NOT_EQUAL, Register_NOT_EQUAL()); - AddBuiltin(BuiltinOperator_SQRT, Register_SQRT()); - AddBuiltin(BuiltinOperator_RSQRT, Register_RSQRT()); - AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE()); - AddBuiltin(BuiltinOperator_POW, Register_POW()); - AddBuiltin(BuiltinOperator_FAKE_QUANT, Register_FAKE_QUANT(), 1, 2); - AddBuiltin(BuiltinOperator_PACK, Register_PACK()); - AddBuiltin(BuiltinOperator_ONE_HOT, Register_ONE_HOT()); - AddBuiltin(BuiltinOperator_LOGICAL_OR, Register_LOGICAL_OR()); - AddBuiltin(BuiltinOperator_LOGICAL_AND, Register_LOGICAL_AND()); - AddBuiltin(BuiltinOperator_LOGICAL_NOT, Register_LOGICAL_NOT()); - AddBuiltin(BuiltinOperator_UNPACK, Register_UNPACK()); - AddBuiltin(BuiltinOperator_FLOOR_DIV, Register_FLOOR_DIV()); - AddBuiltin(BuiltinOperator_SQUARE, Register_SQUARE()); - AddBuiltin(BuiltinOperator_ZEROS_LIKE, Register_ZEROS_LIKE()); - AddBuiltin(BuiltinOperator_FLOOR_MOD, Register_FLOOR_MOD()); - AddBuiltin(BuiltinOperator_RANGE, Register_RANGE()); - AddBuiltin(BuiltinOperator_LEAKY_RELU, Register_LEAKY_RELU()); - AddBuiltin(BuiltinOperator_SQUARED_DIFFERENCE, Register_SQUARED_DIFFERENCE()); - AddBuiltin(BuiltinOperator_FILL, Register_FILL()); - AddBuiltin(BuiltinOperator_MIRROR_PAD, Register_MIRROR_PAD()); - - AddCustom("SquaredDifference", nnfw::tflite::custom::Register_SquaredDifference()); - - // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that - // custom ops aren't always included by default. - AddCustom("Mfcc", tflite::ops::custom::Register_MFCC()); - // Need additional external library for audio spectrogram - //AddCustom("AudioSpectrogram", - // tflite::ops::custom::Register_AUDIO_SPECTROGRAM()); - AddCustom("LayerNormLstm", tflite::ops::custom::Register_LAYER_NORM_LSTM()); - AddCustom("Relu1", tflite::ops::custom::Register_RELU_1()); - AddCustom("TFLite_Detection_PostProcess", - tflite::ops::custom::Register_DETECTION_POSTPROCESS()); -} - -} // namespace tflite -} // namespace nnfw diff --git a/runtime/libs/tflite/port/1.13.1/src/nnapi_delegate.cpp b/runtime/libs/tflite/port/1.13.1/src/nnapi_delegate.cpp deleted file mode 100644 index 9675570ad..000000000 --- a/runtime/libs/tflite/port/1.13.1/src/nnapi_delegate.cpp +++ /dev/null @@ -1,1262 +0,0 @@ -/* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// NOTE To minimize diff with upstream tensorflow, disable clang-format -// clang-format off - -// NOTE This code is derived from the following file (in TensorFlow v1.13.1) -// 'externals/tensorflow/tensorflow/lite/nnapi_delegate.cc' -#include "tflite/ext/nnapi_delegate.h" -#include <fcntl.h> -#include <sys/mman.h> -#include <sys/stat.h> -#include <sys/types.h> -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/model.h" -#include <rua/Shim.h> -#include "NeuralNetworksExShim.h" - -#ifdef __ANDROID__ -#include <android/log.h> -#include <sys/system_properties.h> -#endif - -#include <memory> - -namespace nnfw { -namespace tflite { - -void logError(const char* format, ...) { - // stderr is convenient for native tests, but is not captured for apps - va_list args_for_stderr; - va_start(args_for_stderr, format); - vfprintf(stderr, format, args_for_stderr); - va_end(args_for_stderr); - fprintf(stderr, "\n"); - fflush(stderr); -#ifdef __ANDROID__ - // produce logcat output for general consumption - va_list args_for_log; - va_start(args_for_log, format); - __android_log_vprint(ANDROID_LOG_ERROR, "tflite", format, args_for_log); - va_end(args_for_log); -#endif -} - -#define FATAL(...) \ - logError(__VA_ARGS__); \ - exit(1); - -// TODO(aselle): Change the error model to use status codes. -#define CHECK_TFLITE_SUCCESS(x) \ - if (x != kTfLiteOk) { \ - FATAL("Aborting since tflite returned failure nnapi_delegate.cc:%d.", \ - __LINE__); \ - } - -#define CHECK_NN(x) \ - if (x != ANEURALNETWORKS_NO_ERROR) { \ - FATAL("Aborting since NNAPI returned failure nnapi_delegate.cc:%d", \ - __LINE__); \ - } - -#define RETURN_ERROR_IF_TFLITE_FAILED(x) \ - if (x != kTfLiteOk) { \ - logError( \ - "Returning error since TFLite returned failure nnapi_delegate.cc:%d.", \ - __LINE__); \ - return kTfLiteError; \ - } - -#define RETURN_ERROR_IF_NN_FAILED(x) \ - if (x != ANEURALNETWORKS_NO_ERROR) { \ - logError( \ - "Returning error since NNAPI returned failure nnapi_delegate.cc:%d.", \ - __LINE__); \ - return kTfLiteError; \ - } - -// Tracking of NNAPI operand ids -static const int64_t kOperandIdNotSet = -1; -static const int64_t kOperandNotNeeded = -2; - -namespace { - -int32_t GetAndroidSdkVersion() { -#ifdef __ANDROID__ - const char* sdkProp = "ro.build.version.sdk"; - char sdkVersion[PROP_VALUE_MAX]; - int length = __system_property_get(sdkProp, sdkVersion); - if (length != 0) { - for (int i = 0; i < length; ++i) { - int digit = sdkVersion[i] - '0'; - if (digit < 0 || digit > 9) { - // Non-numeric SDK version, assume it's higher then expected; - return 0xFFFF; - } - } - // NOTE use std::strtol instead of atoi: security issue - return std::strtol(sdkVersion, NULL, 0); - } - FATAL("No %s prop", sdkProp); -#endif // __ANDROID__ - return 0; -} - -int32_t GetAndroidSdkVersionCached() { - static int32_t androidSdkVersion = GetAndroidSdkVersion(); - return androidSdkVersion; -} - -// WORKAROUND Some model have dimension zero -// Consider scalar as vector size 1 -static const uint32_t dimension_for_scalar[1] = {1}; - -} // namespace - -NNAPIAllocation::NNAPIAllocation(const char* filename, - ::tflite::ErrorReporter* error_reporter) - : MMAPAllocation(filename, error_reporter) { - if (mmapped_buffer_ != MAP_FAILED) - CHECK_NN(ANeuralNetworksMemory_createFromFd(buffer_size_bytes_, PROT_READ, - mmap_fd_, 0, &handle_)); -} - -NNAPIAllocation::~NNAPIAllocation() { - if (handle_) { - ANeuralNetworksMemory_free(handle_); - } -} - -NNAPIDelegate::~NNAPIDelegate() { - if (nn_compiled_model_) { - ANeuralNetworksCompilation_free(nn_compiled_model_); - nn_compiled_model_ = nullptr; - } - if (nn_model_) { - ANeuralNetworksModel_free(nn_model_); - nn_model_ = nullptr; - // TODO(aselle): Is this thread-safe and callable multiple times? - } - // ANeuralNetworksShutdown(); -} - -// Adds the tensors of the subgraph to the NN API model. -TfLiteStatus addTensorOperands(::tflite::Subgraph* subgraph, - ANeuralNetworksModel* nn_model, - uint32_t* no_of_operands_added, - std::vector<int64_t>* nnapi_ids) { - uint32_t next_id = 0; - // Allocate temporary buffer to save casted boolean tensor - std::unordered_map<size_t, std::unique_ptr<uint8_t[]>> const_boolean_tensors; - - for (size_t i = 0; i < subgraph->tensors_size(); i++) { - // Skip temporaries and RNN back-edges. - if ((*nnapi_ids)[i] == kOperandNotNeeded) continue; - - (*nnapi_ids)[i] = int64_t(next_id); - - int32_t nn_type = 0; - // NNAPI requires 32-bit float scale to be zero, tflite doesn't care - float scale = 0.0f; - int32_t zeroPoint = 0; - TfLiteTensor* tensor = subgraph->tensor(i); - switch (tensor->type) { - case kTfLiteNoType: - // Tensors added during initialization of Ops don't have a type yet and - // should not be registered with the NNAPI. - continue; - case kTfLiteFloat32: - nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; - break; - case kTfLiteUInt8: - // NNAPI uses ANEURALNETWORKS_TENSOR_QUANT8_ASYMM to represent uint8 type - // ex. ANEURALNETWORKS_CAST - nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; - scale = tensor->params.scale; - // ANEURALNETWORKS_TENSOR_QUANT8_ASYMM type requires scale > 0, - // zeroPoint >= 0 and zeroPoint <= 255 - scale = (scale == 0.0f) ? 1.0f : scale; - zeroPoint = tensor->params.zero_point; - break; - case kTfLiteInt32: - nn_type = ANEURALNETWORKS_TENSOR_INT32; - scale = tensor->params.scale; - zeroPoint = tensor->params.zero_point; - break; - case kTfLiteBool: - // Workaround to pass bool type under NNAPI - // Use bool type using ANEURALNETWORKS_TENSOR_QUANT8_ASYMM with scale = 1.0f and zero_point = 0 - nn_type = ANEURALNETWORKS_TENSOR_BOOL8; - break; - default: - logError("Unsupported tensor type %d", tensor->type); - return kTfLiteError; - } - if (tensor->dims->size == 0) { - // WORKAROUND Some model have dimension zero - switch (tensor->type) { - case kTfLiteFloat32: - nn_type = ANEURALNETWORKS_TENSOR_FLOAT32; - break; - case kTfLiteInt32: - nn_type = ANEURALNETWORKS_TENSOR_INT32; - break; - default: - logError("NNAPI doesn't support tensors with rank 0 (index %d name %s)", - i, tensor->name); - return kTfLiteError; - } - } - if (tensor->dims->size > 4) { - logError("NNAPI doesn't support tensors with rank > 4 (index %d name %s)", - i, tensor->name); - return kTfLiteError; - } - // TODO(aselle): Note, many of these are intermediate results. Do I need - // to ever specify these sizes. I am currently below doing setValue - // on all of them, but I shouldn't in the future. - // Answer(jeanluc): If all the operators can set the dimension correctly, - // you won't need to. - ANeuralNetworksOperandType operand_type{ - nn_type, static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), scale, zeroPoint}; - if (tensor->dims->size == 0) { - // WORKAROUND Some model have dimension zero - // Consider scalar as vector size 1 - operand_type.dimensions = dimension_for_scalar; - operand_type.dimensionCount = 1; - } - RETURN_ERROR_IF_NN_FAILED( - ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - // TODO(aselle): Based on Michael's suggestion, limiting this to read - // only memory - if (tensor->allocation_type == kTfLiteMmapRo) { - if (tensor->type == kTfLiteBool) - { - // ANEURALNETWORKS_TENSOR_BOOL8 tensor element size is 8 bits - size_t elements = tensor->bytes / sizeof(bool); - const_boolean_tensors[i] = std::make_unique<uint8_t[]>(elements); - for (size_t idx = 0; idx < elements; idx++) - { - const_boolean_tensors[i].get()[idx] = (tensor->data.b[idx] ? 0x00 : 0xff); - } - RETURN_ERROR_IF_NN_FAILED(ANeuralNetworksModel_setOperandValue( - nn_model, next_id, const_boolean_tensors[i].get(), tensor->bytes)); - } - else if (const NNAPIAllocation* alloc = dynamic_cast<const NNAPIAllocation*>( - static_cast<const ::tflite::Allocation*>(tensor->allocation))) { - RETURN_ERROR_IF_NN_FAILED( - ANeuralNetworksModel_setOperandValueFromMemory( - nn_model, next_id, alloc->memory(), - alloc->offset(tensor->data.raw), tensor->bytes)); - } else { - RETURN_ERROR_IF_NN_FAILED(ANeuralNetworksModel_setOperandValue( - nn_model, next_id, tensor->data.raw, tensor->bytes)); - } - } else if (tensor->bytes == 0) { - // These size 0 tensors are optional tensors reserved. - RETURN_ERROR_IF_NN_FAILED( - ANeuralNetworksModel_setOperandValue(nn_model, next_id, nullptr, 0)); - } - - ++next_id; - } - *no_of_operands_added = next_id; - return kTfLiteOk; -} - -void MapAndAddTensorIds(const int* from_ids_buf, size_t from_ids_count, - std::vector<uint32_t>* into, - const std::vector<int64_t>& map) { - for (size_t i = 0; i < from_ids_count; i++) { - int from_id = from_ids_buf[i]; - if (from_id == kOptionalTensor) { - into->push_back(from_id); - } else { - into->push_back(map[from_id]); - } - } -} - -// Adds the operations and their parameters to the NN API model. -// 'next-id' is the operand ID of the next operand of the model. -TfLiteStatus AddOpsAndParams( - ::tflite::Subgraph* subgraph, ANeuralNetworksModel* nn_model, - uint32_t next_id, std::vector<int>* model_state_inputs, - std::vector<int>* model_state_outputs, - const std::vector<int64_t>& tensor_id_to_nnapi_id) { - for (size_t i = 0; i < subgraph->nodes_size(); i++) { - const auto* node_and_registration = subgraph->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - const TfLiteRegistration& registration = node_and_registration->second; - ::tflite::BuiltinOperator builtin = - static_cast<::tflite::BuiltinOperator>(registration.builtin_code); - - // Add the parameters. - std::vector<uint32_t> augmented_inputs, augmented_outputs; - MapAndAddTensorIds(node.inputs->data, node.inputs->size, &augmented_inputs, - tensor_id_to_nnapi_id); - MapAndAddTensorIds(node.outputs->data, node.outputs->size, - &augmented_outputs, tensor_id_to_nnapi_id); - - auto add_scalar_int32 = [&nn_model, &augmented_inputs, - &next_id](int value) { - // Fix to use strict build option - ANeuralNetworksOperandType operand_type{}; operand_type.type = ANEURALNETWORKS_INT32; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(int32_t))) - augmented_inputs.push_back(next_id++); - }; - - auto add_scalar_float32 = [&nn_model, &augmented_inputs, - &next_id](float value) { - // Fix to use strict build option - ANeuralNetworksOperandType operand_type{}; operand_type.type = ANEURALNETWORKS_FLOAT32; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &value, - sizeof(float))) - augmented_inputs.push_back(next_id++); - }; - - auto add_vector_int32 = [&](const int* values, uint32_t num_values) { - // Fix to use strict build option - ANeuralNetworksOperandType operand_type{}; - operand_type.type = ANEURALNETWORKS_TENSOR_INT32; - operand_type.dimensionCount = 1; - operand_type.dimensions = &num_values; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue( - nn_model, next_id, values, sizeof(int32_t) * num_values)); - augmented_inputs.push_back(next_id++); - }; - - // Handle state tensors of RNN, LSTM, SVDF. - // For each state_out tensor, a corresponding state_in operand needs to be - // created for NNAPI. - auto duplicate_state_tensor_float32 = - [subgraph, &nn_model, &next_id, &augmented_inputs, &model_state_inputs, - &model_state_outputs](int tensor_id) { - const TfLiteTensor* tensor = subgraph->tensor(tensor_id); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), - tensor->params.scale, tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_inputs.push_back(next_id); - model_state_inputs->push_back(next_id); - model_state_outputs->push_back(tensor_id); - next_id++; - }; - auto check_and_add_activation = [&add_scalar_int32](int activation) { - if (activation > kTfLiteActRelu6) { - logError("NNAPI only supports RELU, RELU1 and RELU6 activations"); - return kTfLiteError; - } - add_scalar_int32(activation); - return kTfLiteOk; - }; - - auto add_add_params = [&add_scalar_int32](void* data) { - auto* builtin = reinterpret_cast<TfLiteAddParams*>(data); - if (builtin->activation > kTfLiteActRelu6) { - logError("NNAPI only supports RELU, RELU1 and RELU6 activations"); - return kTfLiteError; - } - add_scalar_int32(builtin->activation); - return kTfLiteOk; - }; - - auto add_pooling_params = [&add_scalar_int32, - &check_and_add_activation](void* data) { - auto builtin = reinterpret_cast<TfLitePoolParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->filter_width); - add_scalar_int32(builtin->filter_height); - return check_and_add_activation(builtin->activation); - }; - - auto add_convolution_params = [&add_scalar_int32, - &check_and_add_activation](void* data) { - auto builtin = reinterpret_cast<TfLiteConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - return check_and_add_activation(builtin->activation); - }; - - auto add_depthwise_conv_params = [&add_scalar_int32, - &check_and_add_activation](void* data) { - auto builtin = reinterpret_cast<TfLiteDepthwiseConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - add_scalar_int32(builtin->depth_multiplier); - return check_and_add_activation(builtin->activation); - }; - - auto add_fully_connected_params = [&check_and_add_activation](void* data) { - auto builtin = reinterpret_cast<TfLiteFullyConnectedParams*>(data); - return check_and_add_activation(builtin->activation); - }; - - auto add_concatenation_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteConcatenationParams*>(data); - add_scalar_int32(builtin->axis); - if (builtin->activation != kTfLiteActNone) { - logError("Concatenation does not support fused activation in NNAPI"); - return kTfLiteError; - } - return kTfLiteOk; - }; - - auto add_softmax_params = [&add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteSoftmaxParams*>(data); - add_scalar_float32(builtin->beta); - }; - - auto add_space_to_depth_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSpaceToDepthParams*>(data); - add_scalar_int32(builtin->block_size); - }; - - auto add_lstm_params = [&add_scalar_int32, - &add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteLSTMParams*>(data); - add_scalar_int32(builtin->activation); - add_scalar_float32(builtin->cell_clip); - add_scalar_float32(builtin->proj_clip); - }; - - // LSTM in NNAPI requires scratch tensor as an output operand. - auto add_lstm_scratch_tensor_float32 = [subgraph, &node, &nn_model, - &next_id, &augmented_outputs]() { - if (node.temporaries->size == 0) return; - int scratch_buffer_index = node.temporaries->data[0]; - const TfLiteTensor* tensor = subgraph->tensor(scratch_buffer_index); - ANeuralNetworksOperandType operand_type{ - ANEURALNETWORKS_TENSOR_FLOAT32, - static_cast<uint32_t>(tensor->dims->size), - reinterpret_cast<uint32_t*>(tensor->dims->data), tensor->params.scale, - tensor->params.zero_point}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)); - augmented_outputs.insert(augmented_outputs.begin(), next_id++); - }; - - auto add_mean_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteReducerParams*>(data); - add_scalar_int32(builtin->keep_dims); - }; - - auto add_svdf_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteSVDFParams*>(data); - add_scalar_int32(builtin->rank); - add_scalar_int32(builtin->activation); - }; - - auto add_rnn_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteRNNParams*>(data); - add_scalar_int32(builtin->activation); - }; - - auto add_squeeze_params = [&](void* data) { - const auto* builtin = reinterpret_cast<TfLiteSqueezeParams*>(data); - // Note that we add the squeeze dimensions even if the dimensions were - // unspecified (empty), as NNAPI requires the operand. - add_vector_int32(builtin->squeeze_dims, - static_cast<uint32_t>(builtin->num_squeeze_dims)); - }; - - // Handle optional input tensors. - auto add_optional_tensors = [&nn_model, &augmented_inputs, - &next_id](int nn_type) { - for (size_t idx = 0; idx < augmented_inputs.size(); idx++) { - // Fix to use strict build option - if (augmented_inputs[idx] == static_cast<uint32_t>(kOptionalTensor)) { - const std::vector<uint32_t> dim = {0, 0}; - ANeuralNetworksOperandType operand_type{nn_type, 2, dim.data(), 0, 0}; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, - nullptr, 0)) - augmented_inputs[idx] = next_id++; - } - } - }; - - int nnapi_version = 10; -#include "nnapi_delegate_ex_AddOpsAndParams_lambda.inc" - - // Fix to use strict build option - ANeuralNetworksOperationType nn_op_type = -1; - - // Using namespace directive to minimize diff with upstream tensorflow - namespace tflite = ::tflite; - - switch (builtin) { - case tflite::BuiltinOperator_ADD: - nn_op_type = ANEURALNETWORKS_ADD; - RETURN_ERROR_IF_TFLITE_FAILED(add_add_params(node.builtin_data)); - break; - case tflite::BuiltinOperator_MUL: - nn_op_type = ANEURALNETWORKS_MUL; - RETURN_ERROR_IF_TFLITE_FAILED(add_add_params(node.builtin_data)); - break; - case tflite::BuiltinOperator_AVERAGE_POOL_2D: - RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); - nn_op_type = ANEURALNETWORKS_AVERAGE_POOL_2D; - break; - case tflite::BuiltinOperator_MAX_POOL_2D: - RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); - nn_op_type = ANEURALNETWORKS_MAX_POOL_2D; - break; - case tflite::BuiltinOperator_L2_POOL_2D: - RETURN_ERROR_IF_TFLITE_FAILED(add_pooling_params(node.builtin_data)); - nn_op_type = ANEURALNETWORKS_L2_POOL_2D; - break; - case tflite::BuiltinOperator_CONV_2D: { - auto builtin = reinterpret_cast<TfLiteConvParams*>(node.builtin_data); - if (builtin->dilation_width_factor != 1 || - builtin->dilation_height_factor != 1 || node.inputs->size != 3) { - logError("NNAPI does not support dilated Conv2D."); - return kTfLiteError; - } - } - RETURN_ERROR_IF_TFLITE_FAILED( - add_convolution_params(node.builtin_data)); - nn_op_type = ANEURALNETWORKS_CONV_2D; - break; - case tflite::BuiltinOperator_RELU: - nn_op_type = ANEURALNETWORKS_RELU; - break; - case tflite::BuiltinOperator_RELU_N1_TO_1: - nn_op_type = ANEURALNETWORKS_RELU1; - break; - case tflite::BuiltinOperator_RELU6: - nn_op_type = ANEURALNETWORKS_RELU6; - break; - case tflite::BuiltinOperator_TANH: - nn_op_type = ANEURALNETWORKS_TANH; - break; - case tflite::BuiltinOperator_FLOOR: - nn_op_type = ANEURALNETWORKS_FLOOR; - break; - case tflite::BuiltinOperator_LOGISTIC: - nn_op_type = ANEURALNETWORKS_LOGISTIC; - break; - case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: - RETURN_ERROR_IF_TFLITE_FAILED( - add_depthwise_conv_params(node.builtin_data)); - nn_op_type = ANEURALNETWORKS_DEPTHWISE_CONV_2D; - break; - case tflite::BuiltinOperator_CONCATENATION: - RETURN_ERROR_IF_TFLITE_FAILED( - add_concatenation_params(node.builtin_data)); - nn_op_type = ANEURALNETWORKS_CONCATENATION; - break; - case tflite::BuiltinOperator_SOFTMAX: - add_softmax_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SOFTMAX; - break; - case tflite::BuiltinOperator_FULLY_CONNECTED: - RETURN_ERROR_IF_TFLITE_FAILED( - add_fully_connected_params(node.builtin_data)); - nn_op_type = ANEURALNETWORKS_FULLY_CONNECTED; - break; - case tflite::BuiltinOperator_RESHAPE: - if (node.inputs->size != 2) { - logError("NNAPI only supports 2-input RESHAPE"); - return kTfLiteError; - } - nn_op_type = ANEURALNETWORKS_RESHAPE; - // add_reshape_params(node.builtin_data); - break; - case tflite::BuiltinOperator_RESIZE_BILINEAR: - add_resize_bilinear_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RESIZE_BILINEAR; - break; - case tflite::BuiltinOperator_SPACE_TO_DEPTH: - add_space_to_depth_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SPACE_TO_DEPTH; - break; - case tflite::BuiltinOperator_LSTM: { - if (node.inputs->size + /* no of params */ 3 != 21) { - logError("NNAPI only supports 21-input LSTMs"); - return kTfLiteError; - } - duplicate_state_tensor_float32( - node.outputs->data[/*kOutputStateTensor*/ 0]); - duplicate_state_tensor_float32( - node.outputs->data[/*kCellStateTensor*/ 1]); - add_lstm_params(node.builtin_data); - add_lstm_scratch_tensor_float32(); - add_optional_tensors(ANEURALNETWORKS_TENSOR_FLOAT32); - nn_op_type = ANEURALNETWORKS_LSTM; - break; - } - case tflite::BuiltinOperator_DEQUANTIZE: - nn_op_type = ANEURALNETWORKS_DEQUANTIZE; - break; - case tflite::BuiltinOperator_SVDF: { - duplicate_state_tensor_float32(node.outputs->data[/*kStateTensor*/ 0]); - add_svdf_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SVDF; - break; - } - case tflite::BuiltinOperator_RNN: { - duplicate_state_tensor_float32( - node.outputs->data[/*kHiddenStateTensor*/ 0]); - add_rnn_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_RNN; - break; - } - case tflite::BuiltinOperator_EMBEDDING_LOOKUP: - nn_op_type = ANEURALNETWORKS_EMBEDDING_LOOKUP; - break; - case tflite::BuiltinOperator_PAD: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_PAD; - break; - case tflite::BuiltinOperator_MEAN: - nnapi_version = 11; // require NNAPI 1.1 - add_mean_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_MEAN; - break; - case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: - nn_op_type = ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION; - add_lrn_params(node.builtin_data); - break; - case tflite::BuiltinOperator_DIV: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_DIV; - RETURN_ERROR_IF_TFLITE_FAILED(check_and_add_activation( - reinterpret_cast<TfLiteDivParams*>(node.builtin_data)->activation)); - break; - case tflite::BuiltinOperator_SUB: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_SUB; - RETURN_ERROR_IF_TFLITE_FAILED(check_and_add_activation( - reinterpret_cast<TfLiteSubParams*>(node.builtin_data)->activation)); - break; - case tflite::BuiltinOperator_SQUEEZE: - nnapi_version = 11; // requires NNAPI 1.1 - add_squeeze_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_SQUEEZE; - break; - case tflite::BuiltinOperator_TRANSPOSE: - // The permutation input tensor value dictates the output dimensions. - // TODO(b/110888333): Support dynamically-sized tensors in delegates. - if ((node.inputs->size > 1) && - (subgraph->tensor(node.inputs->data[1])->allocation_type != - kTfLiteMmapRo)) { - logError("NNAPI does not yet support dynamic tensors."); - return kTfLiteError; - } - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_TRANSPOSE; - break; - case tflite::BuiltinOperator_L2_NORMALIZATION: - nn_op_type = ANEURALNETWORKS_L2_NORMALIZATION; - if (reinterpret_cast<TfLiteL2NormParams*>(node.builtin_data) - ->activation != kTfLiteActNone) { - logError( - "NNAPI does not support L2Normalization with fused activations"); - return kTfLiteError; - } - if ((node.inputs->size > 0) && - (subgraph->tensor(node.inputs->data[0])->dims->size != 4)) { - logError("NNAPI only supports input rank 4 for L2Normalization"); - return kTfLiteError; - } - break; - case tflite::BuiltinOperator_HASHTABLE_LOOKUP: - if (subgraph->tensor(node.outputs->data[0])->type != kTfLiteFloat32) { - logError("NNAPI only support HASHTABLE_LOOKUP with float32 output", - builtin); - return kTfLiteError; - } - nn_op_type = ANEURALNETWORKS_HASHTABLE_LOOKUP; - break; - case tflite::BuiltinOperator_SLICE: - nn_op_type = ANEURALNETWORKS_SLICE; - break; - case tflite::BuiltinOperator_STRIDED_SLICE: - add_strided_slice_params(node.builtin_data); - nn_op_type = ANEURALNETWORKS_STRIDED_SLICE; - break; - case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_SPACE_TO_BATCH_ND; - break; - case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: - nnapi_version = 11; // require NNAPI 1.1 - nn_op_type = ANEURALNETWORKS_BATCH_TO_SPACE_ND; - check_batch_to_space_params(); - break; - case tflite::BuiltinOperator_CAST: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_CAST; - break; - case tflite::BuiltinOperator_TOPK_V2: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_TOPK_V2; - break; - case tflite::BuiltinOperator_GREATER: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_GREATER; - break; - case tflite::BuiltinOperator_GREATER_EQUAL: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_GREATER_EQUAL; - break; - case tflite::BuiltinOperator_LESS: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_LESS; - break; - case tflite::BuiltinOperator_LESS_EQUAL: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_LESS_EQUAL; - break; - case tflite::BuiltinOperator_GATHER: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_GATHER; - add_gather_params(node.builtin_data); - break; - case tflite::BuiltinOperator_SPLIT: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_SPLIT; - add_split_params(node.builtin_data); - break; - case tflite::BuiltinOperator_NEG: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_NEG; - break; - case tflite::BuiltinOperator_EXP: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_EXP; - break; - case tflite::BuiltinOperator_TRANSPOSE_CONV: - add_transpose_conv_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TRANSPOSE_CONV_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_PRELU: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_PRELU; - break; - case tflite::BuiltinOperator_ARG_MAX: - check_arg_max_input(node.builtin_data); - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_ARGMAX; - break; - case tflite::BuiltinOperator_PACK: - add_pack_ex_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_PACK_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_UNPACK: - add_unpack_ex_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_UNPACK_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_SQRT: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_SQRT; - break; - case tflite::BuiltinOperator_RSQRT: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_RSQRT; - break; - case tflite::BuiltinOperator_EQUAL: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_EQUAL; - break; - case tflite::BuiltinOperator_NOT_EQUAL: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_NOT_EQUAL; - break; - case tflite::BuiltinOperator_SUM: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_REDUCE_SUM; - add_reducer_params(node.builtin_data); - break; - case tflite::BuiltinOperator_REDUCE_ANY: - add_reducer_params(node.builtin_data); - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_REDUCE_ANY; - break; - case tflite::BuiltinOperator_REDUCE_MAX: - add_reducer_params(node.builtin_data); - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_REDUCE_MAX; - break; - case tflite::BuiltinOperator_REDUCE_MIN: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_REDUCE_MIN; - add_reducer_params(node.builtin_data); - break; - case tflite::BuiltinOperator_LOG: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_LOG; - break; - case tflite::BuiltinOperator_LOGICAL_AND: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_LOGICAL_AND; - break; - case tflite::BuiltinOperator_LOGICAL_OR: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_LOGICAL_OR; - break; - case tflite::BuiltinOperator_LOGICAL_NOT: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_LOGICAL_NOT; - break; - case tflite::BuiltinOperator_SQUARED_DIFFERENCE: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SQUARED_DIFFERENCE_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - case tflite::BuiltinOperator_MAXIMUM: - nn_op_type = ANEURALNETWORKS_MAXIMUM; - break; - case tflite::BuiltinOperator_MINIMUM: - nn_op_type = ANEURALNETWORKS_MINIMUM; - break; - case tflite::BuiltinOperator_ABS: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_ABS; - break; - case tflite::BuiltinOperator_ONE_HOT: - add_one_hot_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_ONE_HOT_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; // _EX operator should use `continue` to skip addOperanation. - case tflite::BuiltinOperator_SIN: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_SIN; - break; - case tflite::BuiltinOperator_SHAPE: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SHAPE_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; // _EX operator should use `continue` to skip addOperanation. - case tflite::BuiltinOperator_REDUCE_PROD: - add_reducer_params(node.builtin_data); - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_REDUCE_PROD; - break; - case tflite::BuiltinOperator_EXPAND_DIMS: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_EXPAND_DIMS; - break; - case tflite::BuiltinOperator_POW: - if (!(subgraph->tensor(node.inputs->data[0])->type == kTfLiteFloat32 && - subgraph->tensor(node.inputs->data[1])->type == kTfLiteFloat32)) { - logError("NNAPI delegate for Pow supports only float32.", builtin); - return kTfLiteError; - } - nn_op_type = ANEURALNETWORKS_POW; - break; - case tflite::BuiltinOperator_SELECT: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_SELECT; - break; - case tflite::BuiltinOperator_ZEROS_LIKE: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_ZEROS_LIKE_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; // _EX operator should use `continue` to skip addOperanation. - case tflite::BuiltinOperator_TILE: - nnapi_version = 12; // require NNAPI 1.2 - nn_op_type = ANEURALNETWORKS_TILE; - break; - case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: - case tflite::BuiltinOperator_LSH_PROJECTION: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: - case tflite::BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: - case tflite::BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: - case tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: - //case tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: - case tflite::BuiltinOperator_PADV2: - //case tflite::BuiltinOperator_RESIZE_BILINEAR: - case tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: - case tflite::BuiltinOperator_CALL: - case tflite::BuiltinOperator_SKIP_GRAM: - //case tflite::BuiltinOperator_RELU_N1_TO_1: - //case tflite::BuiltinOperator_GATHER: - //case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: - //case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: - //case tflite::BuiltinOperator_TOPK_V2: - //case tflite::BuiltinOperator_SPLIT: - //case tflite::BuiltinOperator_STRIDED_SLICE: - //case tflite::BuiltinOperator_EXP: - case tflite::BuiltinOperator_LOG_SOFTMAX: - //case tflite::BuiltinOperator_DEQUANTIZE: - case tflite::BuiltinOperator_DELEGATE: - //case tflite::BuiltinOperator_CAST: - //case tflite::BuiltinOperator_PRELU: - //case tflite::BuiltinOperator_MAXIMUM: - //case tflite::BuiltinOperator_MINIMUM: - //case tflite::BuiltinOperator_ARG_MAX: - case tflite::BuiltinOperator_ARG_MIN: - //case tflite::BuiltinOperator_GREATER: - //case tflite::BuiltinOperator_GREATER_EQUAL: - //case tflite::BuiltinOperator_LESS: - //case tflite::BuiltinOperator_LESS_EQUAL: - //case tflite::BuiltinOperator_NEG: - //case tflite::BuiltinOperator_SELECT: - // case tflite::BuiltinOperator_SLICE: - //case tflite::BuiltinOperator_SIN: - //case tflite::BuiltinOperator_LOG: - //case tflite::BuiltinOperator_TRANSPOSE_CONV: - //case tflite::BuiltinOperator_TILE: - //case tflite::BuiltinOperator_EXPAND_DIMS: - case tflite::BuiltinOperator_SPARSE_TO_DENSE: - //case tflite::BuiltinOperator_EQUAL: - //case tflite::BuiltinOperator_NOT_EQUAL: - //case tflite::BuiltinOperator_SUM: - //case tflite::BuiltinOperator_REDUCE_MAX: - //case tflite::BuiltinOperator_REDUCE_MIN: - //case tflite::BuiltinOperator_REDUCE_PROD: - //case tflite::BuiltinOperator_SQRT: - //case tflite::BuiltinOperator_RSQRT: - //case tflite::BuiltinOperator_SHAPE: - //case tflite::BuiltinOperator_POW: - case tflite::BuiltinOperator_FAKE_QUANT: - //case tflite::BuiltinOperator_PACK: - //case tflite::BuiltinOperator_LOGICAL_OR: - //case tflite::BuiltinOperator_ONE_HOT: - //case tflite::BuiltinOperator_LOGICAL_AND: - //case tflite::BuiltinOperator_LOGICAL_NOT: - //case tflite::BuiltinOperator_UNPACK: - case tflite::BuiltinOperator_FLOOR_DIV: - //case tflite::BuiltinOperator_REDUCE_ANY: - case tflite::BuiltinOperator_SQUARE: - //case tflite::BuiltinOperator_ZEROS_LIKE: - case tflite::BuiltinOperator_FILL: - case tflite::BuiltinOperator_FLOOR_MOD: - case tflite::BuiltinOperator_RANGE: - case tflite::BuiltinOperator_LEAKY_RELU: - //case tflite::BuiltinOperator_SQUARED_DIFFERENCE: - case tflite::BuiltinOperator_MIRROR_PAD: - //case tflite::BuiltinOperator_ABS: - case tflite::BuiltinOperator_SPLIT_V: - logError("Op code %d is currently not delegated to NNAPI", builtin); - return kTfLiteError; - break; - case tflite::BuiltinOperator_CUSTOM: { - std::string custom_name(registration.custom_name); - if (custom_name.compare("SquaredDifference") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SQUARED_DIFFERENCE_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - else if (custom_name.compare("MatrixBandPart") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_MATRIX_BAND_PART_EX, - static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(node.outputs->size), - reinterpret_cast<uint32_t*>(node.outputs->data))); - continue; - } - logError("Custom operations are not supported when using NNAPI."); - return kTfLiteError; - break; - } - default: - // Fix to use strict build option - logError("Op code %d is currently not delegated to NNAPI", builtin); - return kTfLiteError; - break; - } - - if (nnapi_version == 11 && GetAndroidSdkVersionCached() < 28) { - //logError("Op %d needs NNAPI1.1", builtin); - //return kTfLiteError; - } - - // Add the operation. - RETURN_ERROR_IF_NN_FAILED(ANeuralNetworksModel_addOperation( - nn_model, nn_op_type, static_cast<uint32_t>(augmented_inputs.size()), - augmented_inputs.data(), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<uint32_t*>(augmented_outputs.data()))); - } - return kTfLiteOk; -} - -TfLiteStatus NNAPIDelegate::BuildGraph(::tflite::Subgraph* subgraph) { - if (nn_model_ && nn_compiled_model_) return model_status_; - - // TODO(aselle): This is not correct. need to handle resize invalidation. - if (!nn_model_) { - CHECK_NN(ANeuralNetworksModel_create(&nn_model_)); - - // Find which tensors should be added to NNAPI. TFLite has temporaries - // and RNN back-edges which are are not valid for NNAPI. We look through all - // inputs and outputs and mark the mapping in tensor_id_to_nnapi_id with - // kOperandIdNotSet. addTensorOperands will replace those with the - // corresponding NNAPI operand ids and skip kOperandNotNeeded entries. - std::vector<int64_t> tensor_id_to_nnapi_id(subgraph->tensors_size(), - kOperandNotNeeded); - // Fix to use strict build option - auto set_ids_to_not_set = [&tensor_id_to_nnapi_id](const int* buf, - int count) { - for (int j = 0; j < count; j++) { - auto tensor_id = buf[j]; - if (tensor_id != kOptionalTensor) { - tensor_id_to_nnapi_id[tensor_id] = kOperandIdNotSet; - } - } - }; - for (size_t i = 0; i < subgraph->nodes_size(); i++) { - const auto* node_and_registration = subgraph->node_and_registration(i); - const TfLiteNode& node = node_and_registration->first; - set_ids_to_not_set(node.inputs->data, node.inputs->size); - set_ids_to_not_set(node.outputs->data, node.outputs->size); - } - set_ids_to_not_set(subgraph->inputs().data(), subgraph->inputs().size()); - set_ids_to_not_set(subgraph->outputs().data(), subgraph->outputs().size()); - - uint32_t next_id = 0; - RETURN_ERROR_IF_TFLITE_FAILED(addTensorOperands( - subgraph, nn_model_, &next_id, &tensor_id_to_nnapi_id)); - RETURN_ERROR_IF_TFLITE_FAILED( - AddOpsAndParams(subgraph, nn_model_, next_id, &model_states_inputs_, - &model_states_outputs_, tensor_id_to_nnapi_id)); - - std::vector<uint32_t> augmented_inputs; - MapAndAddTensorIds(subgraph->inputs().data(), subgraph->inputs().size(), - &augmented_inputs, tensor_id_to_nnapi_id); - augmented_inputs.insert(augmented_inputs.end(), - model_states_inputs_.begin(), - model_states_inputs_.end()); - std::vector<uint32_t> augmented_outputs; - MapAndAddTensorIds(subgraph->outputs().data(), subgraph->outputs().size(), - &augmented_outputs, tensor_id_to_nnapi_id); - MapAndAddTensorIds(model_states_outputs_.data(), - model_states_outputs_.size(), &augmented_outputs, - tensor_id_to_nnapi_id); - - CHECK_NN(ANeuralNetworksModel_identifyInputsAndOutputs( - nn_model_, static_cast<uint32_t>(augmented_inputs.size()), - reinterpret_cast<const uint32_t*>(augmented_inputs.data()), - static_cast<uint32_t>(augmented_outputs.size()), - reinterpret_cast<const uint32_t*>(augmented_outputs.data()))); - - // TODO Support ANeuralNetworksModel_relaxComputationFloat32toFloat16 - /*if (GetAndroidSdkVersionCached() >= 28) { - CHECK_NN(ANeuralNetworksModel_relaxComputationFloat32toFloat16( - nn_model_, subgraph->GetAllowFp16PrecisionForFp32())); - }*/ - CHECK_NN(ANeuralNetworksModel_finish(nn_model_)); - } - if (!nn_compiled_model_) { - CHECK_NN(ANeuralNetworksCompilation_create(nn_model_, &nn_compiled_model_)); - CHECK_NN(ANeuralNetworksCompilation_finish(nn_compiled_model_)); - } - return kTfLiteOk; -} - -// Use unordered_map for temporary buffer -#include <unordered_map> - -TfLiteStatus NNAPIDelegate::Invoke(::tflite::Subgraph* subgraph) { - if (!nn_model_) { - model_status_ = BuildGraph(subgraph); - if (model_status_ != kTfLiteOk) { - logError("Failed to build graph for NNAPI"); - } - } - if (model_status_ != kTfLiteOk) { - return model_status_; - } - - ANeuralNetworksExecution* execution = nullptr; - CHECK_NN(ANeuralNetworksExecution_create(nn_compiled_model_, &execution)); - - // Allocate temporary buffer to save casted boolean tensor - std::unordered_map<size_t, uint8_t*> input_boolean_tensors; - std::unordered_map<size_t, uint8_t*> output_boolean_tensors; - for (size_t i = 0; i < subgraph->inputs().size(); i++) - { - int input = subgraph->inputs()[i]; - TfLiteTensor* tensor = subgraph->tensor(input); - if (tensor->type == kTfLiteBool) - { - size_t elements = tensor->bytes / sizeof(bool); - uint8_t* temp_tensor = new uint8_t[tensor->bytes / sizeof(bool)]; - input_boolean_tensors[i] = temp_tensor; - for (size_t idx = 0; idx < elements; idx++) - { - temp_tensor[idx] = (tensor->data.b[idx] ? 0x00 : 0xff); - } - } - } - for (size_t i = 0; i < subgraph->outputs().size(); i++) - { - int output = subgraph->outputs()[i]; - TfLiteTensor* tensor = subgraph->tensor(output); - if (tensor->type == kTfLiteBool) - { - uint8_t* temp_tensor = new uint8_t[tensor->bytes / sizeof(bool)]; - output_boolean_tensors[i] = temp_tensor; - } - } - - // Currently perform deep copy of input buffer - for (size_t i = 0; i < subgraph->inputs().size(); i++) { - int input = subgraph->inputs()[i]; - // TODO(aselle): Is this what we want or do we want input instead? - // TODO(aselle): This should be called setInputValue maybe to be cons. - TfLiteTensor* tensor = subgraph->tensor(input); - // Workaround to pass bool type under NNAPI - // ANEURALNETWORKS_TENSOR_BOOL8 tensor element size is 8 bits - if (tensor->type == kTfLiteBool) - { - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i, nullptr, input_boolean_tensors[i], tensor->bytes * sizeof(uint8_t) / sizeof(bool))); - } - else - { - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - } - - // Tell nn api where to place final data. - for (size_t i = 0; i < subgraph->outputs().size(); i++) { - int output = subgraph->outputs()[i]; - TfLiteTensor* tensor = subgraph->tensor(output); - - // Workaround to pass bool type under NNAPI - // ANEURALNETWORKS_TENSOR_BOOL8 tensor element size is 8 bits - if (tensor->type == kTfLiteBool) - { - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i, nullptr, output_boolean_tensors[i], tensor->bytes * sizeof(uint8_t) / sizeof(bool))); - } - else - { - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i, nullptr, tensor->data.raw, tensor->bytes)); - } - } - - // The state_out of previous invocation need to be mapped to state_in of - // current invocation. - for (size_t i = 0; i < model_states_outputs_.size(); i++) { - int state_tensor_idx = model_states_outputs_[i]; - TfLiteTensor* tensor = subgraph->tensor(state_tensor_idx); - // Here we are using a deep copy for state_in tensors so that we are not - // reading and writing into the same buffer during a invocation. - // TODO(miaowang): using double shared buffer to minimize the copies. - CHECK_NN(ANeuralNetworksExecution_setInput( - execution, i + subgraph->inputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - // Tell NNAPI where to output the state_out. - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i + subgraph->outputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - } - - // Currently use blocking compute. - ANeuralNetworksEvent* event = nullptr; - CHECK_NN(ANeuralNetworksExecution_startCompute(execution, &event)); - CHECK_NN(ANeuralNetworksEvent_wait(event)); - ANeuralNetworksEvent_free(event); - ANeuralNetworksExecution_free(execution); - - // Tell nn api where to place final data. - for (size_t i = 0; i < subgraph->inputs().size(); i++) { - int input = subgraph->inputs()[i]; - TfLiteTensor* tensor = subgraph->tensor(input); - - if (tensor->type == kTfLiteBool) - { - uint8_t* temp_tensor = input_boolean_tensors[i]; - input_boolean_tensors[i] = nullptr; - delete temp_tensor; - } - } - for (size_t i = 0; i < subgraph->outputs().size(); i++) { - int output = subgraph->outputs()[i]; - TfLiteTensor* tensor = subgraph->tensor(output); - - if (tensor->type == kTfLiteBool) - { - uint8_t* temp_tensor = output_boolean_tensors[i]; - size_t elements = tensor->bytes / sizeof(bool); - for (size_t idx = 0; idx < elements; idx++) - { - tensor->data.b[idx] = ((temp_tensor[idx] == 0x00) ? false : true); - } - output_boolean_tensors[i] = nullptr; - delete temp_tensor; - } - } - -#if 0 - printf("From the NN API:\n"); - TfLiteTensor* tensor = subgraph->tensor(subgraph->outputs()[0]); - if (float* data = - subgraph->typed_tensor<float>(subgraph->outputs()[0])) { - size_t num = tensor->bytes / sizeof(float); - for (float* p = data; p < data + num; p++) { - printf(" %f", *p); - } - printf("\n"); - } -#endif - - return kTfLiteOk; -} - -bool NNAPIDelegate::IsSupported() { return nnfw::NNAPIExists(); } - -} // namespace tflite -} // namespace nnfw - -// clang-format on diff --git a/runtime/libs/tflite/port/1.13.1/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc b/runtime/libs/tflite/port/1.13.1/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc deleted file mode 100644 index 39355b106..000000000 --- a/runtime/libs/tflite/port/1.13.1/src/nnapi_delegate_ex_AddOpsAndParams_lambda.inc +++ /dev/null @@ -1,153 +0,0 @@ -// This file is included from AddOpsAndParams defined in nnapi_delegate.cc -// and contains lambda for extened implementation to original Tensorflow Lite. - auto add_scalar_bool8 = [&nn_model, &augmented_inputs, - &next_id](bool value) { - // Fix to use strict build option - int8_t casted_value = (value ? 1 : 0); - ANeuralNetworksOperandType operand_type{}; operand_type.type = ANEURALNETWORKS_BOOL; - CHECK_NN(ANeuralNetworksModel_addOperand(nn_model, &operand_type)) - CHECK_NN(ANeuralNetworksModel_setOperandValue(nn_model, next_id, &casted_value, - sizeof(int8_t))) - augmented_inputs.push_back(next_id++); - }; - - auto add_resize_bilinear_params = [&add_scalar_int32, &subgraph, &augmented_inputs](void* data) { - auto builtin = reinterpret_cast<TfLiteResizeBilinearParams*>(data); - if (builtin->align_corners) { - FATAL("Resize bilinear does not support align corners in NNAPI"); - } - - TfLiteTensor* tensor = subgraph->tensor(augmented_inputs.back()); - assert(tensor->type == kTfLiteInt32); - assert(tensor->bytes == sizeof(int)*2); - augmented_inputs.pop_back(); - - int height = ((int*)(tensor->data.raw))[1]; - int width = ((int*)(tensor->data.raw))[0]; - add_scalar_int32(height); - add_scalar_int32(width); - }; - - auto add_transpose_conv_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteTransposeConvParams*>(data); - add_scalar_int32(builtin->padding); - add_scalar_int32(builtin->stride_width); - add_scalar_int32(builtin->stride_height); - }; - - auto add_lrn_params = [&add_scalar_int32, - &add_scalar_float32](void* data) { - auto builtin = reinterpret_cast<TfLiteLocalResponseNormParams*>(data); - add_scalar_int32(builtin->radius); - add_scalar_float32(builtin->bias); - add_scalar_float32(builtin->alpha); - add_scalar_float32(builtin->beta); - }; - - auto add_strided_slice_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteStridedSliceParams*>(data); - add_scalar_int32(builtin->begin_mask); - add_scalar_int32(builtin->end_mask); - // ellipsis_mask and new_axis_mask are not supported on nn runtime - // cf) tflite interpreter supports both operations - if (builtin->ellipsis_mask) { - FATAL("STRIDE_SLICE does not support ellipsis_mask in NNAPI"); - } - if (builtin->new_axis_mask) { - FATAL("STRIDE_SLICE does not support new_axis_mask in NNAPI"); - } - add_scalar_int32(builtin->shrink_axis_mask); - }; - - auto add_gather_params = [&add_scalar_int32, &augmented_inputs](void* data) { - auto builtin = reinterpret_cast<TfLiteGatherParams*>(data); - if (builtin->axis != 0) { - FATAL("GATHER does not support axis>0 in NNAPI"); - } - - auto indices_index = augmented_inputs.back(); - augmented_inputs.pop_back(); - add_scalar_int32(builtin->axis); - augmented_inputs.push_back(indices_index); - }; - - auto add_pack_ex_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLitePackParams*>(data); - add_scalar_int32(builtin->values_count); - add_scalar_int32(builtin->axis); - }; - - auto add_unpack_ex_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteUnpackParams*>(data); - add_scalar_int32(builtin->num); - add_scalar_int32(builtin->axis); - }; - - auto check_batch_to_space_params = [subgraph, &node, &augmented_inputs]() { - - //If there are 3 inputs, check if crops is having default values {0, 0, 0, 0} - //Else unsupported by NNAPI - - if(augmented_inputs.size() == 3) - { - const uint32_t crops_buffer_index = node.inputs->data[2]; - const TfLiteTensor* crops = subgraph->tensor(crops_buffer_index); - const int *crops_value = crops->data.i32; - - //Check if crops is having default values {0, 0, 0, 0} - if(crops_value[0] != 0 || crops_value[1] != 0 || crops_value[2] != 0 || crops_value[3] != 0) - { - FATAL("BATCH_TO_SPACE_ND does not support Explicit crops in NNAPI"); - } - else - { - //Restrict crops input and pass only other two inputs - augmented_inputs.pop_back(); - } - } - }; - - auto add_split_params = [&add_scalar_int32, &augmented_inputs](void* data) { - // swap 1st and 2nd operand order - auto input_tensor = augmented_inputs[1]; - auto axis = augmented_inputs[0]; - augmented_inputs[0] = input_tensor; - augmented_inputs[1] = axis; - - auto builtin = reinterpret_cast<TfLiteSplitParams*>(data); - add_scalar_int32(builtin->num_splits); - }; - - auto check_arg_max_input = [&subgraph, &augmented_inputs](void *data) { - auto params = reinterpret_cast<TfLiteArgMaxParams*>(data); - if (params->output_type != kTfLiteInt32) - { - FATAL("Cannot handle output type in NNAPI"); - } - - TfLiteTensor* axis_tensor = subgraph->tensor(augmented_inputs.back()); - assert(axis_tensor->type == kTfLiteInt32); - - int64_t count = 1; - for (int i = 0; i < axis_tensor->dims->size; ++i) { - count *= axis_tensor->dims->data[i]; - } - assert(count == 1); - }; - - auto add_reducer_params = [&add_scalar_bool8](void* data) { - auto builtin = reinterpret_cast<TfLiteReducerParams*>(data); - if (builtin == nullptr) - { - add_scalar_bool8(0); - } - else - { - add_scalar_bool8(builtin->keep_dims); - } - }; - - auto add_one_hot_params = [&add_scalar_int32](void* data) { - const auto* builtin = reinterpret_cast<TfLiteOneHotParams*>(data); - add_scalar_int32(builtin->axis); - }; diff --git a/runtime/libs/tflite/port/CMakeLists.txt b/runtime/libs/tflite/port/CMakeLists.txt deleted file mode 100644 index 82c83f722..000000000 --- a/runtime/libs/tflite/port/CMakeLists.txt +++ /dev/null @@ -1,7 +0,0 @@ -# We may need to support multiple tensorflow version -# Example) -# For ubuntu: tensorflow lite v1.13.1 -# For tizen: tensorflow lite v1.9 -set(SUPPORT_TFLITE_VERSION "1.13.1" CACHE STRING "Supporting TensorFlow lite version") - -add_subdirectories() diff --git a/runtime/libs/tflite/src/RandomTestRunner.cpp b/runtime/libs/tflite/src/RandomTestRunner.cpp index ae834e79e..48e7f1187 100644 --- a/runtime/libs/tflite/src/RandomTestRunner.cpp +++ b/runtime/libs/tflite/src/RandomTestRunner.cpp @@ -20,7 +20,6 @@ #include "tflite/RandomTestRunner.h" #include "tflite/Diff.h" #include "tflite/TensorLogger.h" -#include "tflite/ext/nnapi_delegate.h" #include <misc/tensor/IndexIterator.h> #include <misc/tensor/Object.h> @@ -66,8 +65,6 @@ int RandomTestRunner::run(size_t running_count) std::cout << "[NNAPI TEST] Run T/F Lite Interpreter without NNAPI" << std::endl; _tfl_interp->Invoke(); - nnfw::tflite::NNAPIDelegate d; - for (size_t i = 1; i <= running_count; ++i) { resetter.run(*(_nnapi.get())); @@ -77,21 +74,9 @@ int RandomTestRunner::run(size_t running_count) std::cout << "[NNAPI TEST #" << i << "] Run T/F Lite Interpreter with NNAPI" << std::endl; - char *env = getenv("UPSTREAM_DELEGATE"); - - if (env && !std::string(env).compare("1")) - { - _nnapi->Invoke(); - } - else + if (_nnapi->Invoke() != kTfLiteOk) { - // WARNING - // primary_subgraph: Experimental interface. Return 1st sugbraph - // Invoke() will call BuildGraph() internally - if (d.Invoke(&_nnapi.get()->primary_subgraph())) - { - throw std::runtime_error{"Failed to BuildGraph"}; - } + throw std::runtime_error{"Failed to Run T/F Lite Interpreter with NNAPI"}; } // Compare OFM diff --git a/runtime/libs/tflite/src/interp/FlatBufferBuilder.cpp b/runtime/libs/tflite/src/interp/FlatBufferBuilder.cpp index f54e67202..70dbb2714 100644 --- a/runtime/libs/tflite/src/interp/FlatBufferBuilder.cpp +++ b/runtime/libs/tflite/src/interp/FlatBufferBuilder.cpp @@ -16,7 +16,7 @@ #include "tflite/interp/FlatBufferBuilder.h" -#include "tflite/ext/kernels/register.h" +#include <tensorflow/lite/kernels/register.h> namespace nnfw { @@ -27,7 +27,7 @@ std::unique_ptr<::tflite::Interpreter> FlatBufferBuilder::build(void) const { std::unique_ptr<::tflite::Interpreter> interpreter; - nnfw::tflite::BuiltinOpResolver resolver; + ::tflite::ops::builtin::BuiltinOpResolver resolver; ::tflite::InterpreterBuilder builder(_model, resolver); diff --git a/runtime/onert/api/include/nnfw_experimental.h b/runtime/onert/api/include/nnfw_experimental.h index 94f781988..b20447e9e 100644 --- a/runtime/onert/api/include/nnfw_experimental.h +++ b/runtime/onert/api/include/nnfw_experimental.h @@ -96,4 +96,60 @@ NNFW_STATUS nnfw_input_tensorindex(nnfw_session *session, const char *tensorname */ NNFW_STATUS nnfw_output_tensorindex(nnfw_session *session, const char *tensorname, uint32_t *index); +/** + * @brief Set the backend for each operation in the session + * + * This function assigns backends (acl_cl, acl_neon, cpu) to each operation in the session. + * If successful,the function returns @c NNFW_STATUS_NO_ERROR. Otherwise, the function returns + * @c NNFW_STATUS_ERROR. + * + * @note The argument specifying backends must be in the format + * "OP_BACKEND_MAP=\"0=acl_cl;1=cpu;2=acl_cl\"". + * + * @param[in] session the session object + * @param[in] backend_settings String containing backend assignments indexed by operation sequence + * @return @c NNFW_STATUS_NO_ERROR if successful + */ +NNFW_STATUS nnfw_set_backends_per_operation(nnfw_session *session, const char *backend_settings); + +/* + * Prepare session to be ready for inference + * This phase may finalize model compilation, scheduling, and additional settings. + * + * @param session the session to be prepared + * @return NNFW_STATUS_NO_ERROR if successful + */ +NNFW_STATUS nnfw_prepare_pipeline(nnfw_session *session, const char *map_file_path = nullptr); + +/** + * @brief Set input buffer + * + * This function must be called after {@link nnfw_prepare_pipeline}, \p inputs given to this + * function can be reused for many inferences. \p lengths must be greater or equal than the operand + * requires. if you give empty \p inputs to this function, then this function will join all threads. + * + * @param[in] session Session to the input is to be set + * @param[in] inputs Raw buffers for input, it must be \p std::vector<void *> type pointer for + * multiple input model + * @param[in] lengths Size of bytes of input buffers, it must be \p std::vector<uint32_t> type + * pointer for multiple input model + * + * @return @c NNFW_STATUS_NO_ERROR if successful + */ +NNFW_STATUS nnfw_push_pipeline_input(nnfw_session *session, void *inputs, void *lengths); + +/** + * @brief Get last outputs of partitioned model in session + * + * This function must be called after {@link nnfw_prepare_pipeline}, \p outputs given to this + * function must be cleared for memory management. + * + * @param[in] session Session from last outputs is to be extracted + * @param[out] outputs Raw buffer for outputs, it must be \p std::vector<void *> type pointer for + * multiple output model + * + * @return @c NNFW_STATUS_NO_ERROR if successful + */ +NNFW_STATUS nnfw_pop_pipeline_output(nnfw_session *session, void *outputs); + #endif // __NNFW_EXPERIMENTAL_H__ diff --git a/runtime/onert/api/include/nnfw_version.h b/runtime/onert/api/include/nnfw_version.h index 1210e274f..6624ae676 100644 --- a/runtime/onert/api/include/nnfw_version.h +++ b/runtime/onert/api/include/nnfw_version.h @@ -21,6 +21,6 @@ * NNFW_VERSION is a uint32 value representing nnfw runtime version * in 0xMMmmmmPP, where MM = major, mmmm = minor, PP = patch */ -#define NNFW_VERSION 0x01000f00 +#define NNFW_VERSION 0x01001100 #endif // __NNFW_VERSION_H__ diff --git a/runtime/onert/api/src/nnfw_api.cc b/runtime/onert/api/src/nnfw_api.cc index 4eba4ecec..b69dd83e4 100644 --- a/runtime/onert/api/src/nnfw_api.cc +++ b/runtime/onert/api/src/nnfw_api.cc @@ -367,3 +367,28 @@ NNFW_STATUS nnfw_output_tensorindex(nnfw_session *session, const char *tensornam NNFW_RETURN_ERROR_IF_NULL(session); return session->output_tensorindex(tensorname, index); } + +NNFW_STATUS nnfw_set_backends_per_operation(nnfw_session *session, const char *backend_settings) +{ + NNFW_RETURN_ERROR_IF_NULL(session); + return session->set_backends_per_operation(backend_settings); +} + +NNFW_STATUS nnfw_prepare_pipeline(nnfw_session *session, const char *map_file_path) +{ + NNFW_RETURN_ERROR_IF_NULL(session); + return session->prepare_pipeline(map_file_path); +} + +NNFW_STATUS nnfw_push_pipeline_input(nnfw_session *session, void *inputs, void *lengths) +{ + NNFW_RETURN_ERROR_IF_NULL(session); + return session->push_pipeline_input((std::vector<void *> *)inputs, + (std::vector<uint32_t> *)lengths); +} + +NNFW_STATUS nnfw_pop_pipeline_output(nnfw_session *session, void *outputs) +{ + NNFW_RETURN_ERROR_IF_NULL(session); + return session->pop_pipeline_output((std::vector<void *> *)outputs); +} diff --git a/runtime/onert/api/src/nnfw_api_internal.cc b/runtime/onert/api/src/nnfw_api_internal.cc index 316bafb52..1a3aaf9e9 100644 --- a/runtime/onert/api/src/nnfw_api_internal.cc +++ b/runtime/onert/api/src/nnfw_api_internal.cc @@ -158,7 +158,7 @@ void setConfigKeyValues(const CfgKeyValues &keyValues) } // namespace nnfw_session::nnfw_session() - : _subgraphs{nullptr}, _execution{nullptr}, + : _subgraphs{nullptr}, _compiler{nullptr}, _execution{nullptr}, _kernel_registry{std::make_shared<onert::api::CustomKernelRegistry>()}, _tracing_ctx{nullptr} { // DO NOTHING @@ -277,6 +277,7 @@ NNFW_STATUS nnfw_session::load_model_from_nnpackage(const char *package_dir) std::string manifest_file_name = package_path + "/metadata/MANIFEST"; std::ifstream mfs(manifest_file_name); + _package_file_path = package_path; // extract the filename of the first(index 0) model // e.g. In MANIFEST file, { "models" : [ "firstmodel.tflite", "2nd.tflite" ] } Json::Value root; @@ -361,6 +362,51 @@ NNFW_STATUS nnfw_session::prepare() return NNFW_STATUS_NO_ERROR; } +NNFW_STATUS nnfw_session::prepare_pipeline(const char *map_file_path) +{ + // NOTE. If users want to run prepare_pipeline() more than one time, this could be removed. + if (!isStateModelLoaded()) + { + std::cerr << "Error during model prepare pipeline : "; + if (isStateInitialized()) + { + std::cerr << "prepare_pipeline should be run once"; + } + else + { + std::cerr << "invalid state"; + } + std::cerr << std::endl; + return NNFW_STATUS_INVALID_STATE; + } + + try + { + _subgraphs.reset(); + std::vector<std::shared_ptr<onert::exec::ExecutorMap>> executor_maps = + _compiler->compile(_package_file_path.c_str(), map_file_path); + + for (auto it = executor_maps.begin(); it != executor_maps.end(); ++it) + { + _executions.push_back(std::make_shared<onert::exec::Execution>(*it)); + } + make_dependency(); + _threads.resize(_executions.size()); + for (uint32_t i = 0; i < _threads.size(); i++) + { + _threads[i] = std::thread(&onert::exec::Execution::runInference, _executions[i].get()); + } + } + catch (const std::exception &e) + { + std::cerr << "Error during model prepare : " << e.what() << std::endl; + return NNFW_STATUS_ERROR; + } + + _state = State::PREPARED; + return NNFW_STATUS_NO_ERROR; +} + NNFW_STATUS nnfw_session::run() { if (!isStatePreparedOrFinishedRun()) @@ -370,6 +416,12 @@ NNFW_STATUS nnfw_session::run() return NNFW_STATUS_INVALID_STATE; } + if (!_executions.empty()) + { + std::cerr << "Error during nnfw_session::run : not supported for pipeline run" << std::endl; + return NNFW_STATUS_ERROR; + } + try { _execution->execute(); @@ -399,6 +451,13 @@ NNFW_STATUS nnfw_session::run_async() return NNFW_STATUS_INVALID_STATE; } + if (!_executions.empty()) + { + std::cerr << "Error during nnfw_session::run_async : not supported for pipeline run" + << std::endl; + return NNFW_STATUS_ERROR; + } + _execution->startExecute(); _state = State::RUNNING; @@ -414,6 +473,12 @@ NNFW_STATUS nnfw_session::await() return NNFW_STATUS_ERROR; } + if (!_executions.empty()) + { + std::cerr << "Error during nnfw_session::await : not supported for pipeline run" << std::endl; + return NNFW_STATUS_ERROR; + } + _execution->waitFinish(); _state = State::FINISHED_RUN; @@ -437,6 +502,13 @@ NNFW_STATUS nnfw_session::set_input(uint32_t index, NNFW_TYPE /*type*/, const vo return NNFW_STATUS_ERROR; } + if (!_executions.empty()) + { + std::cerr << "Error during nnfw_session::set_input : not supported for pipeline run" + << std::endl; + return NNFW_STATUS_ERROR; + } + try { _execution->setInput(onert::ir::IOIndex(index), buffer, length); @@ -466,6 +538,13 @@ NNFW_STATUS nnfw_session::set_output(uint32_t index, NNFW_TYPE /*type*/, void *b return NNFW_STATUS_ERROR; } + if (!_executions.empty()) + { + std::cerr << "Error during nnfw_session::set_output : not supported for pipeline run" + << std::endl; + return NNFW_STATUS_ERROR; + } + try { _execution->setOutput(onert::ir::IOIndex(index), buffer, length); @@ -532,7 +611,14 @@ NNFW_STATUS nnfw_session::set_input_layout(uint32_t index, NNFW_LAYOUT layout) std::cerr << "Error during nnfw_session::set_input_layout, not supported layout" << std::endl; return NNFW_STATUS_ERROR; } - _execution->setInputLayout(onert::ir::IOIndex(index), convertLayout(layout)); + if (_execution) + { + _execution->setInputLayout(onert::ir::IOIndex(index), convertLayout(layout)); + } + else + { + _executions.at(0)->setInputLayout(onert::ir::IOIndex(index), convertLayout(layout)); + } } catch (const std::exception &e) { @@ -553,7 +639,15 @@ NNFW_STATUS nnfw_session::set_output_layout(uint32_t index, NNFW_LAYOUT layout) << std::endl; return NNFW_STATUS_ERROR; } - _execution->setOutputLayout(onert::ir::IOIndex(index), convertLayout(layout)); + if (_execution) + { + _execution->setOutputLayout(onert::ir::IOIndex(index), convertLayout(layout)); + } + else + { + _executions.at(_executions.size() - 1) + ->setOutputLayout(onert::ir::IOIndex(index), convertLayout(layout)); + } } catch (const std::exception &e) { @@ -633,7 +727,14 @@ NNFW_STATUS nnfw_session::apply_tensorinfo(uint32_t index, nnfw_tensorinfo ti) } else // when called after nnfw_session::prepare() { - _execution->changeInputShape(onert::ir::IOIndex(index), new_shape); + if (_execution) + { + _execution->changeInputShape(onert::ir::IOIndex(index), new_shape); + } + else + { + _executions.at(0)->changeInputShape(onert::ir::IOIndex(index), new_shape); + } } return NNFW_STATUS_NO_ERROR; @@ -667,7 +768,17 @@ NNFW_STATUS nnfw_session::input_tensorinfo(uint32_t index, nnfw_tensorinfo *ti) auto opidx = primary_subgraph()->getInputs().at(index); auto shape = primary_subgraph()->operands().at(opidx).shape(); if (isStatePreparedOrFinishedRun()) - shape = _execution->getInputShape(onert::ir::IOIndex{index}); + { + if (_execution) + { + shape = _execution->getInputShape(onert::ir::IOIndex{index}); + } + else + { + shape = _executions.at(0)->getInputShape(onert::ir::IOIndex{index}); + } + } + ti->rank = shape.rank(); for (int j = 0; j < ti->rank; ++j) { @@ -708,7 +819,16 @@ NNFW_STATUS nnfw_session::output_tensorinfo(uint32_t index, nnfw_tensorinfo *ti) auto shape = primary_subgraph()->operands().at(opidx).shape(); // If it is called after `nnfw_run` then get the shape from Execution, not from the graph if (isStateFinishedRun()) - shape = _execution->getOutputShape(onert::ir::IOIndex{index}); + { + if (_execution) + { + shape = _execution->getOutputShape(onert::ir::IOIndex{index}); + } + else + { + shape = _executions.at(_executions.size() - 1)->getOutputShape(onert::ir::IOIndex{index}); + } + } ti->rank = shape.rank(); for (int j = 0; j < ti->rank; ++j) { @@ -724,6 +844,89 @@ NNFW_STATUS nnfw_session::output_tensorinfo(uint32_t index, nnfw_tensorinfo *ti) return NNFW_STATUS_NO_ERROR; } + +void nnfw_session::make_dependency() +{ + for (uint32_t out_exe = 0; out_exe < _executions.size(); out_exe++) + { + auto out_graph = _executions[out_exe]->primary_subgraph(); + for (uint32_t in_exe = 0; in_exe < _executions.size(); in_exe++) + { + if (out_exe == in_exe) + continue; + auto in_graph = _executions[in_exe]->primary_subgraph(); + for (auto out = out_graph._name_to_output_begin(); out != out_graph._name_to_output_end(); + out++) + { + auto out_opidx = out_graph.getOutputs().at(out->second); + auto out_shape = out_graph.operands().at(out_opidx).shape(); + for (auto in = in_graph._name_to_input_begin(); in != in_graph._name_to_input_end(); in++) + { + if (out->first != in->first) + continue; + + auto in_opidx = in_graph.getInputs().at(in->second); + auto in_shape = in_graph.operands().at(in_opidx).shape(); + if (out_shape.rank() != in_shape.rank()) + continue; + + bool is_same = true; + for (int32_t i = 0; i < out_shape.rank(); i++) + { + if (out_shape.dim(i) != in_shape.dim(i)) + { + is_same = false; + break; + } + } + + if (is_same) + _executions[out_exe]->pushNextExe(_executions[in_exe], out->second, in->second); + } + } + } + } +} + +NNFW_STATUS nnfw_session::push_pipeline_input(std::vector<void *> *inputs, + std::vector<uint32_t> *lengths) +{ + static uint32_t count = 0; + if (inputs->empty()) + { + _executions[0]->setFinish(); + for (uint32_t i = 0; i < _threads.size(); i++) + { + _threads[i].join(); + } + return NNFW_STATUS_NO_ERROR; + } + _executions[0]->asyncIoDescSemWait(); + _executions[0]->createNewAsyncDesc(count++); + for (uint32_t i = 0; i < inputs->size(); i++) + { + _executions[0]->executeAsyncInput(onert::ir::IOIndex(i), inputs->at(i), lengths->at(i)); + } + _executions[0]->asyncIoDescSemPost(); + return NNFW_STATUS_NO_ERROR; +} + +NNFW_STATUS nnfw_session::pop_pipeline_output(std::vector<void *> *outputs) +{ + auto results = _executions[_executions.size() - 1]->getAsyncResults(); + while (results->empty()) + { + if (_executions[_executions.size() - 1]->stopWait()) + return NNFW_STATUS_ERROR; + } + + auto result = results->front(); + results->pop_front(); + for (uint32_t i = 0; i < result.size(); i++) + outputs->push_back(result[i]); + return NNFW_STATUS_NO_ERROR; +} + NNFW_STATUS nnfw_session::register_custom_operation(const std::string &id, nnfw_custom_eval eval_func) { @@ -864,14 +1067,19 @@ const onert::ir::Graph *nnfw_session::primary_subgraph() { if (_subgraphs) { - assert(!_execution); + assert(!_execution && _executions.empty()); return _subgraphs->primary().get(); } else { - assert(_execution); + assert(_execution || !_executions.empty()); // TODO Remove const_cast // We assumed the graph will not change after compilation, but shape could change + if (!_executions.empty()) + { + return &_executions[0]->primary_parentgraph(); + } + return &_execution->primary_subgraph(); } } @@ -930,7 +1138,7 @@ bool nnfw_session::isStateInitialized() { assert(!_subgraphs); assert(!_compiler); - assert(!_execution); + assert(!_execution && _executions.empty()); return true; } else @@ -945,7 +1153,7 @@ bool nnfw_session::isStateModelLoaded() { assert(_subgraphs); assert(_compiler); - assert(!_execution); + assert(!_execution && _executions.empty()); return true; } else @@ -960,7 +1168,7 @@ bool nnfw_session::isStatePrepared() { assert(!_subgraphs); assert(_compiler); - assert(_execution); + assert(_execution || !_executions.empty()); return true; } else @@ -975,7 +1183,7 @@ bool nnfw_session::isStateRunning() { assert(!_subgraphs); assert(_compiler); - assert(_execution); + assert(_execution || !_executions.empty()); return true; } return false; @@ -987,7 +1195,7 @@ bool nnfw_session::isStateFinishedRun() { assert(!_subgraphs); assert(_compiler); - assert(_execution); + assert(_execution || !_executions.empty()); return true; } else @@ -1010,3 +1218,13 @@ NNFW_STATUS nnfw_session::output_tensorindex(const char *tensorname, uint32_t *i { return getTensorIndexImpl(*primary_subgraph(), tensorname, index, false); } + +NNFW_STATUS nnfw_session::set_backends_per_operation(const char *backend_settings) +{ + if (backend_settings == NULL) + { + return NNFW_STATUS_ERROR; + } + _compiler->set_backend_from_str(backend_settings); + return NNFW_STATUS_NO_ERROR; +} diff --git a/runtime/onert/api/src/nnfw_api_internal.h b/runtime/onert/api/src/nnfw_api_internal.h index b13962907..6d75d894f 100644 --- a/runtime/onert/api/src/nnfw_api_internal.h +++ b/runtime/onert/api/src/nnfw_api_internal.h @@ -25,6 +25,8 @@ #include <string> #include <memory> +#include <thread> +#include <vector> namespace onert { @@ -100,6 +102,7 @@ public: NNFW_STATUS load_model_from_nnpackage(const char *package_file_path); NNFW_STATUS prepare(); + NNFW_STATUS prepare_pipeline(const char *map_file_path); NNFW_STATUS run(); NNFW_STATUS run_async(); @@ -123,6 +126,9 @@ public: NNFW_STATUS set_available_backends(const char *backends); NNFW_STATUS set_op_backend(const char *op, const char *backend); + // accessor + std::vector<std::shared_ptr<onert::exec::Execution>> *get_executions() { return &_executions; } + // // Internal-only API // @@ -135,10 +141,14 @@ public: // // Experimental API // + void make_dependency(); + NNFW_STATUS push_pipeline_input(std::vector<void *> *inputs, std::vector<uint32_t> *lengths); + NNFW_STATUS pop_pipeline_output(std::vector<void *> *outputs); NNFW_STATUS register_custom_operation(const std::string &id, nnfw_custom_eval eval_func); NNFW_STATUS input_tensorindex(const char *tensorname, uint32_t *index); NNFW_STATUS output_tensorindex(const char *tensorname, uint32_t *index); + NNFW_STATUS set_backends_per_operation(const char *backend_settings); private: const onert::ir::Graph *primary_subgraph(); @@ -155,6 +165,9 @@ private: std::unique_ptr<onert::compiler::Compiler> _compiler; std::unique_ptr<onert::exec::Execution> _execution; std::shared_ptr<onert::api::CustomKernelRegistry> _kernel_registry; + std::vector<std::thread> _threads; + std::vector<std::shared_ptr<onert::exec::Execution>> _executions; + std::string _package_file_path; std::unique_ptr<onert::util::TracingCtx> _tracing_ctx; }; diff --git a/runtime/onert/backend/CMakeLists.txt b/runtime/onert/backend/CMakeLists.txt index dc038c975..4b21e0ace 100644 --- a/runtime/onert/backend/CMakeLists.txt +++ b/runtime/onert/backend/CMakeLists.txt @@ -5,4 +5,5 @@ add_subdirectory(acl_cl) add_subdirectory(acl_neon) add_subdirectory(acl_common) add_subdirectory(ruy) +add_subdirectory(gpu_cl) add_subdirectory(xnnpack) diff --git a/runtime/onert/backend/cpu/KernelGenerator.cc b/runtime/onert/backend/cpu/KernelGenerator.cc index d5096ff09..59fb68d55 100644 --- a/runtime/onert/backend/cpu/KernelGenerator.cc +++ b/runtime/onert/backend/cpu/KernelGenerator.cc @@ -130,6 +130,8 @@ convertElementwiseBinaryType(ir::operation::ElementwiseBinary::ElementwiseBinary { switch (type_ir) { + case ir::operation::ElementwiseBinary::ElementwiseBinaryType::FLOOR_DIV: + return ops::ElementwiseBinaryType::kFloorDiv; case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_AND: return ops::ElementwiseBinaryType::kLogicalAnd; case ir::operation::ElementwiseBinary::ElementwiseBinaryType::LOGICAL_OR: diff --git a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.cc b/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.cc index 1704c7cc6..391bf512c 100644 --- a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.cc +++ b/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.cc @@ -18,6 +18,7 @@ #include "OperationUtils.h" +#include <cker/operation/FloorDiv.h> #include <cker/operation/LogicalAnd.h> #include <cker/operation/LogicalOr.h> #include <cker/operation/MaxMin.h> @@ -34,6 +35,22 @@ namespace ops namespace { template <typename T> +void FloorDivGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs, + IPortableTensor *output) +{ + if (!HaveSameShapes(lhs, rhs)) + { + nnfw::cker::FloorDivBroadcast<T>(getShape(lhs), getBuffer<T>(lhs), getShape(rhs), + getBuffer<T>(rhs), getShape(output), getBuffer<T>(output)); + } + else + { + nnfw::cker::FloorDivElementwise<T>(getShape(lhs), getBuffer<T>(lhs), getBuffer<T>(rhs), + getBuffer<T>(output)); + } +} + +template <typename T> void logicalAndGeneric(const IPortableTensor *lhs, const IPortableTensor *rhs, IPortableTensor *output) { @@ -101,6 +118,20 @@ void ElementwiseBinaryLayer::configure(const IPortableTensor *lhs, const IPortab switch (op_type) { + case ElementwiseBinaryType::kFloorDiv: + if (_lhs->data_type() == OperandType::FLOAT32) + { + _kernel = FloorDivGeneric<float>; + } + else if (_lhs->data_type() == OperandType::INT32) + { + _kernel = FloorDivGeneric<int32_t>; + } + else + { + throw std::runtime_error{"Max: unsupported data type"}; + } + break; case ElementwiseBinaryType::kLogicalAnd: if ((_lhs->data_type() == OperandType::BOOL8) && (_rhs->data_type() == OperandType::BOOL8)) { diff --git a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.h b/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.h index 052747a4c..af3bb63c7 100644 --- a/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.h +++ b/runtime/onert/backend/cpu/ops/ElementwiseBinaryLayer.h @@ -32,6 +32,7 @@ namespace ops enum class ElementwiseBinaryType { + kFloorDiv, kLogicalAnd, kLogicalOr, kMax, diff --git a/runtime/onert/backend/cpu/ops/PoolLayer.cc b/runtime/onert/backend/cpu/ops/PoolLayer.cc index 101b6f266..088ca5fd7 100644 --- a/runtime/onert/backend/cpu/ops/PoolLayer.cc +++ b/runtime/onert/backend/cpu/ops/PoolLayer.cc @@ -79,7 +79,11 @@ PoolLayer::PoolLayer() : _input(nullptr), _output(nullptr), _kernel() op_params.filter_height = kernelHeight; \ op_params.filter_width = kernelWidth; \ op_params.padding_values.height = (int8_t)paddingTop; \ - op_params.padding_values.width = (int8_t)paddingLeft; + op_params.padding_values.width = (int8_t)paddingLeft; \ + op_params.float_activation_min = 0; \ + op_params.float_activation_max = 0; \ + op_params.quantized_activation_min = 0; \ + op_params.quantized_activation_max = 0; void PoolLayer::configure(const IPortableTensor *input, const uint32_t paddingLeft, const uint32_t, const uint32_t paddingTop, const uint32_t, const uint32_t strideWidth, diff --git a/runtime/onert/backend/gpu_cl/Backend.h b/runtime/onert/backend/gpu_cl/Backend.h new file mode 100644 index 000000000..dc0b8596c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/Backend.h @@ -0,0 +1,91 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_BACKEND_H__ +#define __ONERT_BACKEND_GPU_CL_BACKEND_H__ + +#include <backend/Backend.h> +#include <memory> + +#include "BackendContext.h" +#include "Config.h" +#include "ClTensorRegistry.h" +#include "KernelGenerator.h" +#include "TensorManager.h" +#include "TensorBuilder.h" + +#include "open_cl/Environment.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class Backend : public ::onert::backend::Backend +{ +public: + Backend() : _config{std::make_shared<Config>()} {} + + std::shared_ptr<IConfig> config() const override { return _config; } + + std::unique_ptr<onert::backend::BackendContext> newContext(ContextData &&data) const override + { + const auto &graph = *data.graph; + const auto &operands = data.graph->operands(); + auto context = std::make_unique<gpu_cl::BackendContext>(this, std::move(data)); + + auto environment = std::make_shared<Environment>(); + if (!CreateEnvironment(environment.get()).ok()) + { + return nullptr; + } + auto tm = createTensorManager(&environment->context()); + + auto tr = std::make_shared<ClTensorRegistry<TensorManager>>(tm); + + InferenceContext::CreateInferenceInfo create_info; + create_info.precision = CalculationsPrecision::F32; + create_info.storage_type = + GetStorageTypeWithMinimalMemoryConsumption(environment->device().GetInfo()); + create_info.hints.Add(ModelHints::kFastestInference); + + auto cc = std::make_shared<CreationContext>(); + cc->device = environment->GetDevicePtr(); + cc->context = &environment->context(); + cc->queue = environment->queue(); + cc->cache = environment->program_cache(); + + auto tb = std::make_shared<TensorBuilder>(operands, tm, create_info, environment); + context->tensor_registry = tr; + context->tensor_builder = tb; + + context->kernel_gen = std::make_shared<KernelGenerator>(graph, tb, tr, cc); + context->constant_initializer = std::make_shared<ConstantInitializer>(operands, tr); + return context; + } + +private: + std::shared_ptr<IConfig> _config; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_BACKEND_H__ diff --git a/runtime/onert/backend/gpu_cl/BackendContext.cc b/runtime/onert/backend/gpu_cl/BackendContext.cc new file mode 100644 index 000000000..6c3ac81a2 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/BackendContext.cc @@ -0,0 +1,242 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BackendContext.h" + +#include "ConstantInitializer.h" +#include "TensorBuilder.h" +#include "KernelGenerator.h" + +#include "util/logging.h" +#include "ir/Index.h" +#include "ir/Operations.h" +#include "ir/OperandIndexMap.h" +#include "ir/OperandIndexSequence.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +void BackendContext::initConsts() +{ + _data.graph->operations().iterate([&](const ir::OperationIndex &, const ir::Operation &op) { + constant_initializer->setLayout(graph()->layout()); + op.accept(*constant_initializer); + }); + _data.graph->operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &operand) { + if (_data.external_operands.contains(ind) || !operand.isConstant()) + return; + const auto &obj = graph()->operands().at(ind); + if (obj.isConstant() && !constant_initializer->exist(ind)) + { + constant_initializer->registerDefaultInitializer(ind, obj); + } + }); + + constant_initializer->run(); +} + +void BackendContext::planTensors() +{ + ir::OperandIndexMap<uint32_t> uses_map; + ir::OperandIndexMap<uint32_t> def_map; + ir::OperandIndexSequence constants; + + // Prepare scanning + _data.graph->operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) { + if (_data.external_operands.contains(ind)) + return; + uses_map[ind] = obj.getUses().size(); + def_map[ind] = obj.getDef().valid() ? 1 : 0; + + if (obj.isConstant()) + constants.append(ind); + + if (!tensor_builder->isRegistered(ind)) + { + // These tensors do not exist in any operation (No use and def) + const auto info = obj.info(); + const auto layout = _data.operand_layouts.at(ind); + // TODO Change tensor info to have permuted shape + tensor_builder->registerTensorInfo(ind, info, layout); + } + }); + + // Start scanning to do notify{First|Last}Use for each tensor + + // If a tensor is a constant, increase the use of the tensor and allocate it first. + // Increasing use count here makes the tensor never be deallocated, i.e it they will be + // deallocated last. + VERBOSE(planTensors) << "TENSORS as CONSTANT" << std::endl; + for (const auto &ind : constants) + { + uses_map[ind]++; + tensor_builder->notifyFirstUse(ind); + } + + // At each operation, + // 1. Scan DEF of outputs. If the DEF, allocate it + // 2. Scan DEF of inputs. If variable tensor, allocate it + // 3. Scan USE of inputs. Decrease the USE and deallocate if the USE is 0 + for (const auto op_ind : _data.op_order) + { + const auto &op = graph()->operations().at(op_ind); + auto op_inputs = op.getInputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED; + auto op_outputs = op.getOutputs() | ir::Remove::DUPLICATED | ir::Remove::UNDEFINED; + + // Define outputs + for (const auto &ind : op_outputs) + { + if (!tensor_builder->isRegistered(ind)) + continue; + assert(def_map.find(ind) != def_map.end()); + if (def_map[ind]) + { + def_map[ind] = 0; + tensor_builder->notifyFirstUse(ind); + } + } + + // Scan variable tensors + // This tensor has features like constant. But OperandInfo and LowerInfo treat them as + // non-constant because of less memory usage by memory planning in here + for (const auto &ind : op_inputs) + { + if (!tensor_builder->isRegistered(ind)) + continue; + const auto &operand = graph()->operands().at(ind); + if (operand.info().isVariable()) + { + // The variable tensor with buffer is not supported yet + assert(operand.data() == nullptr); + assert(operand.getUses().size() == 1 && !operand.getDef().valid()); + assert(uses_map[ind] == 1 && def_map[ind] == 0); + tensor_builder->notifyFirstUse(ind); + } + } + + for (const auto &ind : op_inputs) + { + if (!tensor_builder->isRegistered(ind)) + continue; + assert(uses_map.find(ind) != uses_map.end()); + assert(uses_map[ind] > 0); + uses_map[ind]--; + if (uses_map[ind] == 0) + { + // plan for deallocation of static tensornode + tensor_builder->notifyLastUse(ind); + } + } + } + + _data.graph->operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &) { + if (uses_map[ind] == 0) + { + tensor_builder->notifyLastUse(ind); + } + }); + + // Dispose and validate + for (const auto &ind : constants) + { + --uses_map[ind]; + if (uses_map[ind] == 0) // To prevent notifyLastUse from being called twice + { + tensor_builder->notifyLastUse(ind); + } + } + + assert( + std::all_of(uses_map.begin(), uses_map.end(), + [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; })); + + assert( + std::all_of(def_map.begin(), def_map.end(), + [](std::pair<const ir::OperandIndex, uint32_t> it) { return it.second == 0; })); +} + +ITensorRegistry *BackendContext::genTensors() +{ + graph()->operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &obj) { + if (external_operands().contains(ind)) + return; + + const auto frontend_layout = graph()->layout(); + const auto backend_layout = operand_layouts().at(ind); + ir::OperandInfo backend_info{permuteShape(obj.shape(), frontend_layout, backend_layout), + obj.typeInfo(), obj.info().memAllocType(), obj.isConstant()}; + tensor_builder->registerTensorInfo(ind, backend_info, backend_layout); + }); + + // TODO Get compiler options from compiler, and use it rather than getting it from Env + if (util::getConfigString(util::config::EXECUTOR) == "Linear") + { + planTensors(); + } + else + { + // For the executors that does not have fixed linear execution order: + // To make tensors never be deallocated, this is a workaround to use static memory planner + graph()->operands().iterate([&](const ir::OperandIndex &ind, const ir::Operand &) { + if (tensor_builder->isRegistered(ind)) + tensor_builder->notifyFirstUse(ind); + }); + } + + tensor_builder->prepare(); + + return tensor_registry.get(); +} + +FunctionMap BackendContext::genKernels() +{ + FunctionMap ret; + + // kernel_gen + for (auto op_ind : _data.op_order) + { + auto fn_seq = kernel_gen->generate(op_ind); + ret.emplace_back(op_ind, std::move(fn_seq)); + } + + tensor_builder->allocate(); + + initConsts(); + + // NOTE For memory optimization, we want to free some operand data + const_cast<ir::Graph &>(*_data.graph) + .operands() + .iterate([&](const ir::OperandIndex &, ir::Operand &obj) { obj.releaseData(); }); + + for (auto &it : ret) + { + auto &fn_seq = it.second; + fn_seq->iterate([&](exec::IFunction &ifunc) { + ifunc.prepare(); + tensor_builder->postFunctionPrepare(); + }); + } + + return ret; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/BackendContext.h b/runtime/onert/backend/gpu_cl/BackendContext.h new file mode 100644 index 000000000..f17489e7a --- /dev/null +++ b/runtime/onert/backend/gpu_cl/BackendContext.h @@ -0,0 +1,66 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_BACKEND_CONTEXT_H__ +#define __ONERT_BACKEND_GPU_CL_BACKEND_CONTEXT_H__ + +#include <backend/BackendContext.h> +#include <util/ConfigSource.h> + +#include "ConstantInitializer.h" +#include "KernelGenerator.h" +#include "TensorBuilder.h" +#include "open_cl/InferenceContext.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class BackendContext : public onert::backend::BackendContext +{ +public: + BackendContext(const Backend *backend, ContextData &&data, + std::shared_ptr<ITensorRegistry> tensor_registry = nullptr, + std::shared_ptr<TensorBuilder> tensor_builder = nullptr, + std::shared_ptr<ConstantInitializer> constant_initializer = nullptr, + std::shared_ptr<KernelGenerator> kernel_gen = nullptr) + : onert::backend::BackendContext(backend, std::move(data), tensor_registry), + tensor_builder{tensor_builder}, constant_initializer{constant_initializer}, kernel_gen{ + kernel_gen} + { + } + + ITensorRegistry *genTensors() override; + FunctionMap genKernels() override; + +private: + void initConsts(); + void planTensors(); + +public: + std::shared_ptr<TensorBuilder> tensor_builder; + std::shared_ptr<ConstantInitializer> constant_initializer; + std::shared_ptr<KernelGenerator> kernel_gen; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_BACKEND_CONTEXT_H__ diff --git a/runtime/onert/backend/gpu_cl/CMakeLists.txt b/runtime/onert/backend/gpu_cl/CMakeLists.txt new file mode 100644 index 000000000..49bae37f8 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/CMakeLists.txt @@ -0,0 +1,44 @@ +set(LIB_ONERT_BACKEND_GPU_CL onert_backend_gpu_cl) + +nnas_find_package(Opencl_Headers QUIET) +if(NOT Opencl_Headers_FOUND) + return() +endif(NOT Opencl_Headers_FOUND) + +if(NOT BUILD_GPU_CL) + return() +endif(NOT BUILD_GPU_CL) + +nnas_find_package(Farmhash QUIET) +if(NOT Farmhash_FOUND) + return() +endif(NOT Farmhash_FOUND) + +nnas_find_package(Abseil QUIET) +if(NOT Abseil_FOUND) + return() +endif(NOT Abseil_FOUND) + +file(GLOB_RECURSE SOURCES "*.cc") + + +add_library(${LIB_ONERT_BACKEND_GPU_CL} SHARED ${SOURCES}) + +target_include_directories(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) + +target_link_libraries(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE abseil) +target_link_libraries(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE dl) +target_link_libraries(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE farmhash) +target_link_libraries(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE Headers) +target_link_libraries(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE onert_core) +target_link_libraries(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE nnfw_common) +target_link_libraries(${LIB_ONERT_BACKEND_GPU_CL} PRIVATE nnfw_coverage) + +set_target_properties(${LIB_ONERT_BACKEND_GPU_CL} PROPERTIES OUTPUT_NAME backend_gpu_cl) + +if(CMAKE_BUILD_TYPE_LC STREQUAL "release") + add_custom_command(TARGET ${LIB_ONERT_BACKEND_GPU_CL} POST_BUILD + COMMAND ${CMAKE_STRIP} "--strip-unneeded" $<TARGET_FILE_NAME:${LIB_ONERT_BACKEND_GPU_CL}>) +endif() + +install(TARGETS ${LIB_ONERT_BACKEND_GPU_CL} DESTINATION lib) diff --git a/runtime/onert/backend/gpu_cl/ClConstantInitializer.cc b/runtime/onert/backend/gpu_cl/ClConstantInitializer.cc new file mode 100644 index 000000000..b3ef2f560 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ClConstantInitializer.cc @@ -0,0 +1,104 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClConstantInitializer.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +ClConstantInitializer::ClConstantInitializer(const ir::Operands &operands, + const std::shared_ptr<ITensorRegistry> &tensor_reg) + : _operands{operands}, _tensor_reg{tensor_reg}, _current_layout{ir::Layout::UNKNOWN} +{ + // DO NOTHING +} + +void ClConstantInitializer::copyInputInitialize(const ir::Operation &node, uint32_t index) +{ + assert(node.getInputs().size() > index); + + const auto &input_index = node.getInputs().at(index); + if (input_index.valid()) + { + const auto &input_obj = _operands.at(input_index); + registerCopyInitializer(input_index, input_obj); + } +} + +void ClConstantInitializer::permuteInputInitialize(const ir::Operation &node, uint32_t index) +{ + assert(node.getInputs().size() > index); + + const auto &input_index = node.getInputs().at(index); + const auto &input_obj = _operands.at(input_index); + registerPermuteInitializer(input_index, input_obj); +} + +// NOTE Workaround for 16b float type. Here, this is enough since only the size of bytes matters. +using float16 = uint16_t; + +void ClConstantInitializer::registerCopyInitializer(const ir::OperandIndex &index, + const ir::Operand &obj) +{ + // For only CONSTANTS + // TODO Add to check if tensor has been allocated + if (!obj.isConstant()) + return; + + const auto type = obj.typeInfo().type(); + using ir::DataType; + + switch (type) + { + case DataType::FLOAT32: + _init_map[index] = copyInit<float>; + break; + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} + +void ClConstantInitializer::registerPermuteInitializer(const ir::OperandIndex &index, + const ir::Operand &obj) +{ + // For only CONSTANTS + // TODO Add to check if tensor has been allocated + if (!obj.isConstant()) + return; + + const auto type = obj.typeInfo().type(); + using ir::DataType; + using namespace std::placeholders; + + switch (type) + { + case DataType::FLOAT32: + _init_map[index] = std::bind(permuteInit<float>, _1, _2, _current_layout); + break; + default: + throw std::runtime_error("Not supported, yet"); + break; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/ClConstantInitializer.h b/runtime/onert/backend/gpu_cl/ClConstantInitializer.h new file mode 100644 index 000000000..d7d21e847 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ClConstantInitializer.h @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_COMPILER_GPU_CL_CLCONSTANT_INITIALIZER_H__ +#define __ONERT_COMPILER_GPU_CL_CLCONSTANT_INITIALIZER_H__ + +#include "ClTensorRegistry.h" + +#include <unordered_map> +#include <functional> + +#include <ir/Coordinates.h> +#include <ir/Layout.h> +#include <ir/Operand.h> +#include <ir/Operands.h> +#include <ir/OperationVisitor.h> +#include <backend/ITensorRegistry.h> +#include <util/logging.h> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <typename T> +static void Init(const onert::ir::Operand &model_obj, onert::backend::ITensor &obj, const bool copy, + const onert::ir::Layout frontend_layout = onert::ir::Layout::UNKNOWN) +{ + const auto shape = model_obj.shape(); + assert(model_obj.data()); + obj.access([&](::onert::backend::ITensor &tensor) { + switch (shape.rank()) + { + case 0: + case 1: + case 2: + case 3: + case 4: + if (copy) + { + tensor.enqueueWriteBuffer(model_obj.data()->base(), true); + } + else + { + // NYI + (void)frontend_layout; + throw std::runtime_error{"Not yet supported"}; + } + break; + default: + throw std::runtime_error{"Not yet supported"}; + } + }); +} + +template <typename T> +void copyInit(const onert::ir::Operand &model_obj, onert::backend::ITensor &obj) +{ + Init<T>(model_obj, obj, true); +} + +template <typename T> +void permuteInit(const onert::ir::Operand &model_obj, onert::backend::ITensor &obj, + const onert::ir::Layout frontend_layout) +{ + const bool copy = frontend_layout == obj.layout(); + Init<T>(model_obj, obj, copy, frontend_layout); +} + +class ClConstantInitializer : public ir::OperationVisitor +{ +public: + void run() + { + assert(_tensor_reg); + for (const auto &it : _init_map) + { + const auto &ind = it.first; + const auto &fn = it.second; + + const auto &model_obj = _operands.at(ind); + auto tensor_obj = _tensor_reg->getNativeITensor(ind); + assert(tensor_obj != nullptr); + fn(model_obj, *tensor_obj); + VERBOSE(FillOperandData) << "Fill data for operand " << ind << std::endl; + } + _init_map.clear(); + } + +public: + ClConstantInitializer(const ir::Operands &operands, + const std::shared_ptr<ITensorRegistry> &tensor_reg); + +public: + using Initializer = std::function<void(const ir::Operand &, backend::ITensor &)>; + +public: + void registerDefaultInitializer(const ir::OperandIndex &index, const ir::Operand &obj) + { + registerPermuteInitializer(index, obj); + } + void registerCopyInitializer(const ir::OperandIndex &index, const ir::Operand &obj); + void registerPermuteInitializer(const ir::OperandIndex &index, const ir::Operand &obj); + +public: + void setLayout(ir::Layout layout) { _current_layout = layout; } + bool exist(const ir::OperandIndex &ind) { return _init_map.find(ind) != _init_map.end(); } + +public: +protected: + void copyInputInitialize(const ir::Operation &node, uint32_t index); + void permuteInputInitialize(const ir::Operation &node, uint32_t index); + +protected: + const ir::Operands &_operands; + std::shared_ptr<ITensorRegistry> _tensor_reg; + std::unordered_map<ir::OperandIndex, Initializer> _init_map; + ir::Layout _current_layout; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_COMPILER_GPU_CL_CLCONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/backend/gpu_cl/ClFunction.h b/runtime/onert/backend/gpu_cl/ClFunction.h new file mode 100644 index 000000000..9d3d69092 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ClFunction.h @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_GPU_CL_OPEN_CL_FUNCTION_H__ +#define __ONERT_GPU_CL_OPEN_CL_FUNCTION_H__ + +#include <exec/IFunction.h> + +#include <vector> +#include <memory> + +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/ClCommandQueue.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class ClFunction : public ::onert::exec::IFunction +{ +public: + ClFunction() : _gpu_operations(), _creation_context() {} + +public: + void configure(std::shared_ptr<CreationContext> creation_context) + { + _creation_context = creation_context; + } + + void add_operation(std::unique_ptr<GPUOperation> gpu_operation) + { + _gpu_operations.push_back(std::move(gpu_operation)); + } + + void run() override + { + for (const auto &gpu_operation : _gpu_operations) + { + if (!gpu_operation->AddToQueue(_creation_context->queue).ok()) + { + throw std::runtime_error("Failed to AddToQueue."); + } + } + } + + void prepare() override + { + for (const auto &gpu_operation : _gpu_operations) + { + if (!gpu_operation->Compile(*_creation_context).ok()) + { + throw std::runtime_error("Failed to Compile."); + } + + if (!gpu_operation->UpdateParams().ok()) + { + throw std::runtime_error("Failed to UpdateParams."); + } + } + } + +private: + std::vector<std::unique_ptr<GPUOperation>> _gpu_operations; + std::shared_ptr<CreationContext> _creation_context; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_GPU_CL_OPEN_CL_FUNCTION_H__ diff --git a/runtime/onert/backend/gpu_cl/ClMemoryManager.h b/runtime/onert/backend/gpu_cl/ClMemoryManager.h new file mode 100644 index 000000000..3bac0d51d --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ClMemoryManager.h @@ -0,0 +1,135 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__ +#define __ONERT_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__ + +#include <cassert> + +#include "ir/OperandIndexMap.h" +#include "ir/Shape.h" +#include "open_cl/ClContext.h" +#include "open_cl/InferenceContext.h" +#include "open_cl/Status.h" +#include "open_cl/StorageTypeUtil.h" +#include "open_cl/TensorType.h" +#include "util/logging.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <typename T_ITensor, typename T_Tensor> class ClMemoryManager +{ +public: + ClMemoryManager(CLContext *context) : _context{context} {} + + virtual ~ClMemoryManager() = default; + + virtual void allocate(void) + { + for (const auto &tensor_entry : _tensors) + { + auto tensor = tensor_entry.second; + const auto &t = tensor_reserver_.Get(tensor_entry.first.value()); + const auto &shape = t->shape; + const auto &descriptor = t->descriptor; + if (!CreateTensor(*_context, shape, descriptor, tensor->handle()).ok()) + { + return; + } + } + } + + virtual void deallocate(void) + { + // NYI + } + + virtual void startLifetime(const ir::OperandIndex &) + { /* DO NOTHING */ + } + virtual void finishLifetime(const ir::OperandIndex &) + { /* DO NOTHING */ + } + + void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &info, + InferenceContext::CreateInferenceInfo create_info, + std::shared_ptr<Environment> environment, DeviceInfo &device_info) + { + ValueId max_id = 0; + auto data_type = DeduceDataTypeFromPrecision(create_info.precision); + const auto shape = info.shape(); + + auto tensor = std::make_shared<T_Tensor>(shape.rank(), shape, environment); + _tensors[ind] = tensor; + + BHWC t_shape; + switch (shape.rank()) + { + case 1: + // B layout + t_shape = BHWC(shape.dim(0), 1, 1, 1); + break; + case 2: + // BC layout + t_shape = BHWC(shape.dim(0), 1, 1, shape.dim(1)); + break; + case 3: + // BWC layout + t_shape = BHWC(shape.dim(0), 1, shape.dim(1), shape.dim(2)); + break; + case 4: + // BHWC layout + t_shape = BHWC(shape.dim(0), shape.dim(1), shape.dim(2), shape.dim(3)); + break; + default: + break; + } + + TensorStorageType storage_type = create_info.storage_type; + Layout layout = t_shape.b == 1 ? Layout::HWC : Layout::BHWC; + + ValueId id = ind.value(); + storage_type = SelectBestStorageType(device_info, t_shape, storage_type, data_type, layout); + auto dummy = std::make_shared<InferenceContext::DummyTensor>(); + dummy->shape = t_shape; + dummy->descriptor = TensorDescriptor{data_type, storage_type, layout}; + tensor_reserver_.Add(id, dummy); + + max_id = std::max(max_id, id); + + tensor_reserver_.SetNext(max_id + 1); + } + + ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &tensors(void) { return _tensors; } + + InferenceContext::TensorReserver &tensorReservers(void) { return tensor_reserver_; } + +private: + ir::OperandIndexMap<std::shared_ptr<T_Tensor>> _tensors; + InferenceContext::TensorReserver tensor_reserver_; + CLContext *_context; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_ACL_COMMON_MEMORY_MANAGER_H__ diff --git a/runtime/onert/backend/gpu_cl/ClTensorBuilder.h b/runtime/onert/backend/gpu_cl/ClTensorBuilder.h new file mode 100644 index 000000000..951bbd844 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ClTensorBuilder.h @@ -0,0 +1,289 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_CL_TENSOR_BUILDER_H__ +#define __ONERT_BACKEND_CL_TENSOR_BUILDER_H__ + +#include <memory> +#include <queue> + +#include "ClTensorManager.h" +#include "ClTensorRegistry.h" +#include "ParentInfo.h" + +#include "open_cl/TensorType.h" +#include "open_cl/TensorTypeUtil.h" +#include "open_cl/ClDevice.h" +#include "open_cl/InferenceContext.h" + +#include "ir/OperandIndexMap.h" +#include "ir/OperandIndexSequence.h" +#include <ir/Operands.h> +#include <util/Utils.h> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class UsesType +{ + FIRST, + LAST +}; + +template <typename T_ITensor, typename T_Tensor> class ClTensorBuilder +{ +public: + using T_ClTensorManager = ClTensorManager<T_ITensor, T_Tensor>; + + ClTensorBuilder(const ir::Operands &operands, T_ClTensorManager *tensor_mgr, + InferenceContext::CreateInferenceInfo create_info, + const std::shared_ptr<Environment> &environment); + + /** + * @brief Register tensor information to allocate on ACL-CL backend + * @param[in] ind Operand index + * @param[in] info Tensor information + * @param[in] layout Tensor data layout + */ + void registerTensorInfo(const ir::OperandIndex &ind, const ir::OperandInfo &info, + ir::Layout backend_layout); + + void notifyFirstUse(const ir::OperandIndex &); + void notifyLastUse(const ir::OperandIndex &); + + bool isRegistered(const ir::OperandIndex &) const; + + void prepare(); + void allocate(); + void postFunctionPrepare(); + + T_ClTensorManager *cl_tensor_manager(void) { return _tensor_mgr.get(); } + + void setUsesCount(const ir::OperandIndex &index, size_t num_uses) + { + assert(_uses_count_map.find(index) != _uses_count_map.end() ? _uses_count_map[index] == num_uses + : true); + _uses_count_map[index] = num_uses; + } + + void parent_map(std::unordered_map<ir::OperandIndex, ParentInfo> &&parent_map) + { + _parent_map = std::move(parent_map); + } + + bool areSubTensorsOf(const ir::OperandIndex &parent, const ir::OperandIndexSequence &seq); + + /** + * @brief Check child tensor is allocated as subtensor of parent tensor + * @param[in] parent Index of parent + * @param[in] child Index of child + * @return @c true if child is allocated as subtensor of parent, otherwise @c false + */ + bool isSubTensorOf(const ir::OperandIndex &parent, const ir::OperandIndex &child); + +private: + void buildTensors(void); + ir::OperandIndex findRootParent(ir::OperandIndex index); + +private: + const ir::Operands &_operands; + ir::OperandIndexMap<ir::OperandInfo> _tensor_info_map; + ir::OperandIndexMap<ir::Layout> _tensor_layout_map; + ir::OperandIndexMap<size_t> _uses_count_map; + + std::unique_ptr<T_ClTensorManager> _tensor_mgr; + InferenceContext::CreateInferenceInfo _create_info; + std::shared_ptr<Environment> _environment; + + // for linear executor + std::vector<std::pair<UsesType, ir::OperandIndex>> _lifetime_seq; + + // Extra info for concat elimination + ir::OperandIndexMap<ParentInfo> _parent_map; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#include <cassert> +#include <stack> + +#include "util/logging.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <typename T_ITensor, typename T_Tensor> +ClTensorBuilder<T_ITensor, T_Tensor>::ClTensorBuilder( + const ir::Operands &operands, T_ClTensorManager *tensor_mgr, + InferenceContext::CreateInferenceInfo create_info, + const std::shared_ptr<Environment> &environment) + : _operands{operands}, _tensor_mgr{tensor_mgr}, _create_info{create_info}, _environment{ + environment} +{ + assert(_tensor_mgr); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorBuilder<T_ITensor, T_Tensor>::registerTensorInfo(const ir::OperandIndex &ind, + const ir::OperandInfo &info, + ir::Layout backend_layout) +{ + assert(_tensor_mgr->constTensors().size() == 0); + assert(_tensor_mgr->nonconstTensors().size() == 0); + + _uses_count_map[ind] = _operands.at(ind).getUses().size(); + + _tensor_info_map.emplace(ind, info); + _tensor_layout_map.insert({ind, backend_layout}); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorBuilder<T_ITensor, T_Tensor>::notifyFirstUse(const ir::OperandIndex &ind) +{ + _lifetime_seq.emplace_back(UsesType::FIRST, ind); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorBuilder<T_ITensor, T_Tensor>::notifyLastUse(const ir::OperandIndex &ind) +{ + _lifetime_seq.emplace_back(UsesType::LAST, ind); +} + +template <typename T_ITensor, typename T_Tensor> +bool ClTensorBuilder<T_ITensor, T_Tensor>::isRegistered(const ir::OperandIndex &ind) const +{ + return _tensor_info_map.find(ind) != _tensor_info_map.end(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorBuilder<T_ITensor, T_Tensor>::prepare(void) +{ + buildTensors(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorBuilder<T_ITensor, T_Tensor>::allocate(void) +{ + // Update lifetime sequence to apply subtensor optimization + + std::unordered_map<ir::OperandIndex, ir::OperandIndex> root_map; + std::function<ir::OperandIndex &(ir::OperandIndex)> find_root = + [&](ir::OperandIndex ind) -> ir::OperandIndex & { + ir::OperandIndex &ret = root_map[ind]; + + // We know the root parent value already + if (ret.valid()) + return ret; + + auto itr = _parent_map.find(ind); + if (itr == _parent_map.end()) + { + // If there is no parent, let's store the value of itself + return ret = ind; + } + else + { + return ret = find_root(itr->second.parent); + } + }; + + ir::OperandIndexMap<bool> first_use_check; + ir::OperandIndexMap<bool> last_use_check; + std::map<size_t, std::pair<UsesType, ir::OperandIndex>> lifetime_map; + for (size_t i = 0; i < _lifetime_seq.size(); i++) + { + auto &entry = _lifetime_seq[i]; + if (entry.first != UsesType::FIRST) + continue; + auto root_ind = find_root(entry.second); + if (first_use_check[root_ind]) + continue; + first_use_check[root_ind] = true; + lifetime_map[i] = {UsesType::FIRST, root_ind}; + } + + for (int i = _lifetime_seq.size() - 1; i >= 0; i--) + { + auto &entry = _lifetime_seq[i]; + if (entry.first != UsesType::LAST) + continue; + auto root_ind = find_root(entry.second); + if (last_use_check[root_ind]) + continue; + last_use_check[root_ind] = true; + lifetime_map[i] = {UsesType::LAST, root_ind}; + } + + for (auto &entry : lifetime_map) + { + auto &use = entry.second; + auto use_type = use.first; + auto use_index = use.second; + assert(use_index.valid()); + if (use_type == UsesType::FIRST) + _tensor_mgr->startLifetime(use_index); + else + _tensor_mgr->finishLifetime(use_index); + } + + _tensor_mgr->allocateConsts(); + + // TODO Since `_parent_map` is filled for all Concat nodes even if the node this backend uses + // After refactoring BackendContext we can uncomment this + // assert(_tensor_info_map.size() == + // _tensor_mgr->nonconstTensors().size() + num of constants of _tensor_info_map + + // _parent_map.size()); + _tensor_mgr->allocateNonconsts(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorBuilder<T_ITensor, T_Tensor>::postFunctionPrepare(void) +{ + _tensor_mgr->tryDeallocConstants(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorBuilder<T_ITensor, T_Tensor>::buildTensors(void) +{ + assert(_tensor_mgr->constTensors().size() == 0); + assert(_tensor_mgr->nonconstTensors().size() == 0); + // Normal tensors + for (auto &entry : _tensor_info_map) + { + auto ind = entry.first; + if (_parent_map.count(ind) > 0) + continue; + + const auto &info = entry.second; + _tensor_mgr->buildTensor(ind, info, _create_info, _environment, _environment->device().info_); + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_ACL_COMMON_TEMPL_TENSOR_BUILDER_H__ diff --git a/runtime/onert/backend/gpu_cl/ClTensorManager.h b/runtime/onert/backend/gpu_cl/ClTensorManager.h new file mode 100644 index 000000000..49a11730f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ClTensorManager.h @@ -0,0 +1,235 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__ +#define __ONERT_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__ + +#include "ClMemoryManager.h" + +#include "open_cl/InferenceContext.h" +#include "open_cl/TensorType.h" + +#include "ir/OperandInfo.h" +#include "ir/OperandIndexMap.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <typename T_ITensor, typename T_Tensor> class ClTensorManager +{ +public: + using T_ClMemoryManager = ClMemoryManager<T_ITensor, T_Tensor>; + + ClTensorManager(T_ClMemoryManager *const_mgr, T_ClMemoryManager *nonconst_mgr); + + virtual ~ClTensorManager() = default; + + void allocateConsts(void); + void allocateNonconsts(void); + void deallocateConsts(void); + void deallocateNonconsts(void); + + void buildTensor(const ir::OperandIndex &ind, const ir::OperandInfo &info, + InferenceContext::CreateInferenceInfo create_info, + std::shared_ptr<Environment> environment, DeviceInfo &device_info); + + std::shared_ptr<T_ITensor> findTensorAsParent(const ir::OperandIndex &ind); + + void startLifetime(const ir::OperandIndex &ind); + void finishLifetime(const ir::OperandIndex &ind); + + std::shared_ptr<T_ITensor> at(const ir::OperandIndex &ind); + std::shared_ptr<InferenceContext::DummyTensor> atR(const ir::OperandIndex &ind); + + InferenceContext::TensorReserver &constTensorReservers(void); + InferenceContext::TensorReserver &nonconstTensorReservers(void); + + ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &constTensors(void); + ir::OperandIndexMap<std::shared_ptr<T_Tensor>> &nonconstTensors(void); + + void iterate(const std::function<void(const ir::OperandIndex &)> &fn); + + void tryDeallocConstants(void); + +private: + std::unique_ptr<T_ClMemoryManager> _const_mgr; + std::unique_ptr<T_ClMemoryManager> _nonconst_mgr; + ir::OperandIndexMap<T_ClMemoryManager &> _ind_to_mgr; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#include <cassert> +#include "util/logging.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <typename T_ITensor, typename T_Tensor> +ClTensorManager<T_ITensor, T_Tensor>::ClTensorManager(T_ClMemoryManager *const_mgr, + T_ClMemoryManager *nonconst_mgr) + : _const_mgr{const_mgr}, _nonconst_mgr{nonconst_mgr} +{ + // DO NOTHING +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::allocateConsts(void) +{ + _const_mgr->allocate(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::allocateNonconsts(void) +{ + _nonconst_mgr->allocate(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::deallocateConsts(void) +{ + _const_mgr->deallocate(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::deallocateNonconsts(void) +{ + _nonconst_mgr->deallocate(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::buildTensor( + const ir::OperandIndex &ind, const ir::OperandInfo &info, + InferenceContext::CreateInferenceInfo create_info, std::shared_ptr<Environment> environment, + DeviceInfo &device_info) +{ + assert(_ind_to_mgr.find(ind) == _ind_to_mgr.end()); + + if (info.isConstant()) + { + _const_mgr->buildTensor(ind, info, create_info, environment, device_info); + _ind_to_mgr.insert({ind, *_const_mgr}); + } + else + { + _nonconst_mgr->buildTensor(ind, info, create_info, environment, device_info); + _ind_to_mgr.insert({ind, *_nonconst_mgr}); + } +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::startLifetime(const ir::OperandIndex &ind) +{ + assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end()); + _ind_to_mgr.at(ind).startLifetime(ind); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::finishLifetime(const ir::OperandIndex &ind) +{ + assert(_ind_to_mgr.find(ind) != _ind_to_mgr.end()); + _ind_to_mgr.at(ind).finishLifetime(ind); +} + +template <typename T_ITensor, typename T_Tensor> +std::shared_ptr<T_ITensor> ClTensorManager<T_ITensor, T_Tensor>::at(const ir::OperandIndex &ind) +{ + if (_ind_to_mgr.find(ind) == _ind_to_mgr.end()) + return nullptr; + + auto &tensors = _ind_to_mgr.at(ind).tensors(); + if (tensors.find(ind) != tensors.end()) + { + return tensors.at(ind); + } + + return nullptr; +} + +template <typename T_ITensor, typename T_Tensor> +ir::OperandIndexMap<std::shared_ptr<T_Tensor>> & +ClTensorManager<T_ITensor, T_Tensor>::constTensors(void) +{ + return _const_mgr->tensors(); +} + +template <typename T_ITensor, typename T_Tensor> +ir::OperandIndexMap<std::shared_ptr<T_Tensor>> & +ClTensorManager<T_ITensor, T_Tensor>::nonconstTensors(void) +{ + return _nonconst_mgr->tensors(); +} + +template <typename T_ITensor, typename T_Tensor> +std::shared_ptr<InferenceContext::DummyTensor> +ClTensorManager<T_ITensor, T_Tensor>::atR(const ir::OperandIndex &ind) +{ + if (_nonconst_mgr->tensorReservers().HaveTensor(ind.value())) + { + return _nonconst_mgr->tensorReservers().Get(ind.value()); + } + else if (_const_mgr->tensorReservers().HaveTensor(ind.value())) + { + return _const_mgr->tensorReservers().Get(ind.value()); + } + return nullptr; +} + +template <typename T_ITensor, typename T_Tensor> +InferenceContext::TensorReserver &ClTensorManager<T_ITensor, T_Tensor>::constTensorReservers(void) +{ + return _const_mgr->tensorReservers(); +} + +template <typename T_ITensor, typename T_Tensor> +InferenceContext::TensorReserver & +ClTensorManager<T_ITensor, T_Tensor>::nonconstTensorReservers(void) +{ + return _nonconst_mgr->tensorReservers(); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::iterate( + const std::function<void(const ir::OperandIndex &)> &fn) +{ + for (auto it : _nonconst_mgr->tensors()) + fn(it.first); + + for (auto it : _const_mgr->tensors()) + fn(it.first); +} + +template <typename T_ITensor, typename T_Tensor> +void ClTensorManager<T_ITensor, T_Tensor>::tryDeallocConstants(void) +{ + // NYI +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_ACL_COMMON_TENSOR_MANAGER_H__ diff --git a/runtime/onert/backend/gpu_cl/ClTensorRegistry.h b/runtime/onert/backend/gpu_cl/ClTensorRegistry.h new file mode 100644 index 000000000..1f0018bd1 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ClTensorRegistry.h @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_TENSOR_REGISTRY_H__ +#define __ONERT_BACKEND_GPU_CL_TENSOR_REGISTRY_H__ + +#include "backend/ITensorRegistry.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +/** + * @brief Tensor registry class for acl backends + * + * This is implemented as a wrapper of AclTensorManager. + */ +template <typename T_ClTensorManager> class ClTensorRegistry : public ITensorRegistry +{ +public: + ClTensorRegistry(T_ClTensorManager *tensor_mgr) : _tensor_mgr{tensor_mgr} {} + + ITensor *getITensor(const ir::OperandIndex &ind) override { return _tensor_mgr->at(ind).get(); } + + ITensor *getNativeITensor(const ir::OperandIndex &ind) override { return getITensor(ind); } + + auto getClTensor(const ir::OperandIndex &ind) { return _tensor_mgr->at(ind).get(); } + + auto getClTensorReserver(const ir::OperandIndex &ind) { return _tensor_mgr->atR(ind); } + +private: + T_ClTensorManager *_tensor_mgr; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_TENSOR_REGISTRY_H__ diff --git a/runtime/onert/backend/gpu_cl/Config.cc b/runtime/onert/backend/gpu_cl/Config.cc new file mode 100644 index 000000000..067a2070f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/Config.cc @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Config.h" + +#include <dlfcn.h> +#include "open_cl/OpenclWrapper.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +Config::~Config() { UnloadOpenCL(_handle); } + +bool Config::initialize() +{ + if (LoadOpenCL(&_handle).ok()) + { + return true; + } + else + { + return false; + } +} + +ir::Layout Config::supportLayout(const ir::Operation &, ir::Layout) { return ir::Layout::NHWC; } + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/Config.h b/runtime/onert/backend/gpu_cl/Config.h new file mode 100644 index 000000000..aa5a51a15 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/Config.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_CONFIG_H__ +#define __ONERT_BACKEND_GPU_CL_CONFIG_H__ + +#include <backend/IConfig.h> +#include <memory> +#include <util/ITimer.h> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class Config : public IConfig +{ +public: + virtual ~Config(); + +public: + std::string id() override { return "gpu_cl"; } + bool initialize() override; + ir::Layout supportLayout(const ir::Operation &node, ir::Layout frontend_layout) override; + bool supportPermutation() override { return true; } + bool supportDynamicTensor() override { return false; } + bool supportFP16() override { return true; } + std::unique_ptr<util::ITimer> timer() override { return std::make_unique<util::CPUTimer>(); } + +private: + void *_handle = nullptr; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_CONFIG_H__ diff --git a/runtime/onert/backend/gpu_cl/ConstantInitializer.cc b/runtime/onert/backend/gpu_cl/ConstantInitializer.cc new file mode 100644 index 000000000..7976abea9 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ConstantInitializer.cc @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ConstantInitializer.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +ConstantInitializer::ConstantInitializer(const ir::Operands &operands, + const std::shared_ptr<ITensorRegistry> &tensor_reg) + : ClConstantInitializer{operands, tensor_reg} +{ + // DO NOTHING +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/ConstantInitializer.h b/runtime/onert/backend/gpu_cl/ConstantInitializer.h new file mode 100644 index 000000000..ce8131af2 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ConstantInitializer.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_CONSTANT_INITIALIZER_H__ +#define __ONERT_BACKEND_GPU_CL_CONSTANT_INITIALIZER_H__ + +#include "ClConstantInitializer.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class ConstantInitializer : public ClConstantInitializer +{ +public: + ConstantInitializer(const ir::Operands &operands, + const std::shared_ptr<ITensorRegistry> &tensor_reg); + +public: + using ClConstantInitializer::visit; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_CONSTANT_INITIALIZER_H__ diff --git a/runtime/onert/backend/gpu_cl/KernelGenerator.cc b/runtime/onert/backend/gpu_cl/KernelGenerator.cc new file mode 100644 index 000000000..a84867f8c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/KernelGenerator.cc @@ -0,0 +1,593 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include <stdexcept> + +#include <backend/basic/KernelGeneratorBase.h> + +#include "KernelGenerator.h" + +#include "ClTensorRegistry.h" +#include "ClFunction.h" +#include "TensorManager.h" + +#include "open_cl/selectors/ConvolutionSelector.h" +#include "open_cl/selectors/DwConvolutionSelector.h" +#include "open_cl/selectors/SimpleSelectors.h" + +#include "ir/Operations.h" +#include "ir/Operations.Include.h" +#include "ir/Index.h" +#include "ir/DataType.h" +#include "ir/InternalType.h" +#include "exec/NopFunction.h" +#include "exec/FunctionSequence.h" +#include "util/logging.h" +#include "util/Utils.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +HW ToHW(int32_t h, int32_t w) { return HW(h > 0 ? h : 1, w > 0 ? w : 1); } + +template <typename AttrT> +void UpdatePadding(const ir::PaddingType type, const BHWC &input_shape, AttrT *attr) +{ + if (type == ir::PaddingType::SAME) + { + attr->padding = CalculateSamePadding(input_shape, *attr); + } + else + { + attr->padding.prepended = HW(0, 0); + attr->padding.appended = HW(0, 0); + } +} + +gpu_cl::PoolingType convertPoolType(ir::operation::Pool2D::PoolType type_ir) +{ + switch (type_ir) + { + case ir::operation::Pool2D::PoolType::AVG: + return gpu_cl::PoolingType::AVERAGE; + case ir::operation::Pool2D::PoolType::MAX: + return gpu_cl::PoolingType::MAX; + default: + throw std::runtime_error("gpu_Cl KernelGenerator : Not supported operation yet"); + } +} + +KernelGenerator::KernelGenerator(const ir::Graph &graph, + const std::shared_ptr<TensorBuilder> &tensor_builder, + const std::shared_ptr<ClTensorRegistry<TensorManager>> &tensor_reg, + const std::shared_ptr<CreationContext> &creation_context) + : basic::KernelGeneratorBase{graph}, _ctx(graph.operands()), + _operations_ctx(graph.operations()), _current_layout{graph.layout()}, + _tensor_builder(tensor_builder), _tensor_reg(tensor_reg), _creation_context(creation_context) +{ +} + +std::unique_ptr<exec::FunctionSequence> KernelGenerator::generate(ir::OperationIndex ind) +{ + auto ret = std::make_unique<exec::FunctionSequence>(); + ret->enableDynamicShapeInferer(false); + + const auto &op = _graph.operations().at(ind); + op.accept(*this); + ret->append(releaseFunction()); + return ret; +} + +void KernelGenerator::visit(const ir::operation::BinaryArithmetic &node) +{ + const auto ofm_index{node.getOutputs().at(0)}; + const auto lhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::LHS)}; + const auto rhs_index{node.getInputs().at(ir::operation::BinaryArithmetic::Input::RHS)}; + + // const auto activation = node.param().activation; + + OperationDef op_def; + op_def.precision = CalculationsPrecision::F32; + + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(lhs_index)->descriptor); + auto lhs_shape = _tensor_reg->getClTensorReserver(lhs_index)->shape; + + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(rhs_index)->descriptor); + auto rhs_shape = _tensor_reg->getClTensorReserver(rhs_index)->shape; + + op_def.dst_tensors.push_back(_tensor_reg->getClTensorReserver(ofm_index)->descriptor); + auto out_shape = _tensor_reg->getClTensorReserver(ofm_index)->shape; + + auto fn = std::make_unique<ClFunction>(); + + std::unique_ptr<GPUOperation> gpu_op; + switch (node.param().arithmetic_type) + { + case ir::operation::BinaryArithmetic::ArithmeticType::ADD: + { + std::vector<int> channels(2); + channels[0] = lhs_shape.c; + channels[1] = rhs_shape.c; + SelectAdd(op_def, channels, out_shape.c, &gpu_op); + + auto ofm_tensor = _tensor_reg->getClTensor(ofm_index); + auto lhs_tensor = _tensor_reg->getClTensor(lhs_index); + auto rhs_tensor = _tensor_reg->getClTensor(rhs_index); + gpu_op->SetSrc(lhs_tensor->handle(), ir::operation::BinaryArithmetic::Input::LHS); + gpu_op->SetSrc(rhs_tensor->handle(), ir::operation::BinaryArithmetic::Input::RHS); + gpu_op->SetDst(ofm_tensor->handle(), 0); + + fn->configure(_creation_context); + fn->add_operation(std::move(gpu_op)); + break; + } + case ir::operation::BinaryArithmetic::ArithmeticType::SUB: + { + // NYI + break; + } + case ir::operation::BinaryArithmetic::ArithmeticType::MUL: + { + // NYI + break; + } + case ir::operation::BinaryArithmetic::ArithmeticType::DIV: + { + // NYI + break; + } + default: + assert(false && "The BinaryArithmetic operation supports only binary arithmetic operations"); + break; + } + + _return_fn = std::move(fn); +} + +void KernelGenerator::visit(const ir::operation::Conv2D &node) +{ + auto output{node.getOutputs().at(0)}; + + auto input{node.getInputs().at(ir::operation::Conv2D::INPUT)}; + auto kernel{node.getInputs().at(ir::operation::Conv2D::KERNEL)}; + auto bias{node.getInputs().at(ir::operation::Conv2D::BIAS)}; + + const auto param = node.param(); + + OperationDef op_def; + op_def.precision = CalculationsPrecision::F32; + + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(input)->descriptor); + + auto input_shape = _tensor_reg->getClTensorReserver(input)->shape; + auto kernel_shape = _tensor_reg->getClTensorReserver(kernel)->shape; + auto output_shape = _tensor_reg->getClTensorReserver(output)->shape; + auto bias_shape = _tensor_reg->getClTensorReserver(bias)->shape; + + op_def.dst_tensors.push_back(_tensor_reg->getClTensorReserver(output)->descriptor); + + ModelHints hints; + std::unique_ptr<GPUOperation> gpu_op; // = InitSingleOpSubgraph(inputs, outputs, gpu_subgraph); + + auto input_tensor = _tensor_reg->getClTensor(input); + auto kernel_tensor = _tensor_reg->getClTensor(kernel); + auto bias_tensor = _tensor_reg->getClTensor(bias); + auto output_tensor = _tensor_reg->getClTensor(output); + + gpu_cl::Convolution2DAttributes attr; + attr.strides = ToHW(param.stride.vertical, param.stride.horizontal); + attr.dilations = HW(std::max(static_cast<u_int32_t>(1), param.dilation.height_factor), + std::max(static_cast<u_int32_t>(1), param.dilation.width_factor)); + + bool is_weight = (_ctx.at(kernel).isConstant() ? true : false); + + if (is_weight) + { + attr.weights.id = kernel.value(); + attr.weights.shape.o = kernel_shape.b; + attr.weights.shape.h = kernel_shape.h; + attr.weights.shape.w = kernel_shape.w; + attr.weights.shape.i = kernel_shape.c; + attr.weights.data.resize(kernel_shape.DimensionsProduct()); + memcpy(attr.weights.data.data(), _ctx.at(kernel).data()->base(), kernel_tensor->total_size()); + } + + attr.bias.id = bias.value(); + // TODO Modify + attr.bias.shape.v = bias_shape.b != 1 ? bias_shape.b : bias_shape.c; + attr.bias.data.resize(bias_shape.DimensionsProduct()); + memcpy(attr.bias.data.data(), _ctx.at(bias).data()->base(), bias_tensor->total_size()); + + UpdatePadding(param.padding.type, input_shape, &attr); + + gpu_op = SelectConvolution(attr, output_shape, _creation_context->GetDeviceInfo(), op_def, hints); + gpu_op->SetSrc(input_tensor->handle(), ir::operation::Conv2D::INPUT); + + auto fn = std::make_unique<ClFunction>(); + + fn->configure(_creation_context); + + const auto activation = node.param().activation; + + switch (activation) + { + case ir::Activation::NONE: + { + gpu_op->SetDst(output_tensor->handle(), 0); + fn->add_operation(std::move(gpu_op)); + break; + } + case ir::Activation::RELU6: + { + std::unique_ptr<GPUOperation> gpu_op_1; + OperationDef op_def_1; + std::shared_ptr<Tensor> new_tensor = std::make_shared<Tensor>(); + + _new_tensors[output] = new_tensor; + if (!CreateTensor(*_creation_context->context, output_shape, + _tensor_reg->getClTensorReserver(output)->descriptor, new_tensor.get()) + .ok()) + { + throw std::runtime_error("Error CreateTensor."); + } + + gpu_op->SetDst(new_tensor.get(), 0); + fn->add_operation(std::move(gpu_op)); + op_def_1.precision = CalculationsPrecision::F32; + op_def_1.src_tensors.push_back(_tensor_reg->getClTensorReserver(output)->descriptor); + op_def_1.dst_tensors.push_back(_tensor_reg->getClTensorReserver(output)->descriptor); + + // - ReLU6: clip = 6, alpha = 0 + ReLUAttributes attr_1; + attr_1.clip = 6; + attr_1.alpha = 0; + gpu_op_1 = SelectReLU(attr_1, op_def_1); + + gpu_op_1->SetSrc(new_tensor.get(), 0); + gpu_op_1->SetDst(output_tensor->handle(), 0); + fn->add_operation(std::move(gpu_op_1)); + break; + } + default: + { + throw std::runtime_error("gpu_cl KernelGenerator : Not supported operation yet"); + } + } + + _return_fn = std::move(fn); +} + +void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node) +{ + using ir::operation::DepthwiseConv2D; + + const auto ofm_index{node.getOutputs().at(0)}; + const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)}; + const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)}; + const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)}; + + const auto stride = node.param().stride; + const auto dilation = node.param().dilation; + const auto padding = node.param().padding; + + const auto multiplier = node.param().multiplier; + + auto ofm_tensor = _tensor_reg->getClTensor(ofm_index); + auto ifm_tensor = _tensor_reg->getClTensor(ifm_index); + auto ker_tensor = _tensor_reg->getClTensor(ker_index); + auto bias_tensor = _tensor_reg->getClTensor(bias_index); + + bool is_weight = (_ctx.at(ker_index).isConstant() ? true : false); + OperationDef op_def; + op_def.precision = CalculationsPrecision::F32; + + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(ifm_index)->descriptor); + auto input_shape = _tensor_reg->getClTensorReserver(ifm_index)->shape; + + auto ker_shape = _tensor_reg->getClTensorReserver(ker_index)->shape; + + op_def.dst_tensors.push_back(_tensor_reg->getClTensorReserver(ofm_index)->descriptor); + auto out_shape = _tensor_reg->getClTensorReserver(ofm_index)->shape; + auto bias_shape = _tensor_reg->getClTensorReserver(bias_index)->shape; + + DepthwiseConvolution2DAttributes attr; + attr.strides = ToHW(stride.vertical, stride.horizontal); + attr.dilations = HW(std::max(static_cast<u_int32_t>(1), dilation.height_factor), + std::max(static_cast<u_int32_t>(1), dilation.width_factor)); + + if (is_weight) + { + attr.weights.id = ker_index.value(); + attr.weights.shape.o = ker_shape.b; + attr.weights.shape.h = ker_shape.h; + attr.weights.shape.w = ker_shape.w; + attr.weights.shape.i = ker_shape.c; + attr.weights.data.resize(ker_shape.DimensionsProduct()); + memcpy(attr.weights.data.data(), _ctx.at(ker_index).data()->base(), ker_tensor->total_size()); + } + attr.bias.id = bias_index.value(); + attr.bias.shape.v = bias_shape.b != 1 ? bias_shape.b : bias_shape.c; + attr.bias.data.resize(bias_shape.DimensionsProduct()); + memcpy(attr.bias.data.data(), _ctx.at(bias_index).data()->base(), bias_tensor->total_size()); + UpdatePadding(padding.type, input_shape, &attr); + + if (multiplier != 1) + { + const int input_depth = input_shape.c; + const int filter_height = ker_shape.h; + const int filter_width = ker_shape.w; + const int output_depth = out_shape.c; + + InternalTensor<OHWI, DataType::FLOAT32> weights; + weights.id = attr.weights.id; + weights.shape = OHWI(output_depth, filter_height, filter_width, input_depth); + weights.data.resize(weights.shape.DimensionsProduct()); + float *dst = &weights.data[0]; + for (int j = 0; j < output_depth; ++j) + { + const float *src = attr.weights.data.data() + j; + for (int i = 0; i < filter_height * filter_width; ++i) + { + *dst = *src; + dst++; + src += output_depth; + } + } + attr.weights = std::move(weights); + } + + auto fn = std::make_unique<ClFunction>(); + std::unique_ptr<GPUOperation> gpu_op; + + if (is_weight) + { + gpu_op = SelectDWConvolution(attr, _creation_context->GetDeviceInfo(), op_def); + } + else + { + if (ker_shape.b != 1) + { + throw std::runtime_error( + "No support of depthwise runtime weights with channel multiplier != 1"); + } + gpu_op = SelectDWConvolutionDynamicWeights(attr, _creation_context->GetDeviceInfo(), op_def); + } + + gpu_op->SetSrc(ifm_tensor->handle(), ir::operation::DepthwiseConv2D::Input::INPUT); + + fn->configure(_creation_context); + + const auto activation = node.param().activation; + + switch (activation) + { + case ir::Activation::NONE: + { + gpu_op->SetDst(ofm_tensor->handle(), 0); + fn->add_operation(std::move(gpu_op)); + break; + } + case ir::Activation::RELU6: + { + std::unique_ptr<GPUOperation> gpu_op_1; + OperationDef op_def_1; + std::shared_ptr<Tensor> new_tensor = std::make_shared<Tensor>(); + + _new_tensors[ofm_index] = new_tensor; + if (!CreateTensor(*_creation_context->context, out_shape, + _tensor_reg->getClTensorReserver(ofm_index)->descriptor, new_tensor.get()) + .ok()) + { + throw std::runtime_error("Error CreateTensor."); + } + + gpu_op->SetDst(new_tensor.get(), 0); + fn->add_operation(std::move(gpu_op)); + op_def_1.precision = CalculationsPrecision::F32; + op_def_1.src_tensors.push_back(_tensor_reg->getClTensorReserver(ofm_index)->descriptor); + op_def_1.dst_tensors.push_back(_tensor_reg->getClTensorReserver(ofm_index)->descriptor); + + // - ReLU6: clip = 6, alpha = 0 + ReLUAttributes attr_1; + attr_1.clip = 6; + attr_1.alpha = 0; + gpu_op_1 = SelectReLU(attr_1, op_def_1); + + gpu_op_1->SetSrc(new_tensor.get(), 0); + gpu_op_1->SetDst(ofm_tensor->handle(), 0); + fn->add_operation(std::move(gpu_op_1)); + break; + } + default: + { + throw std::runtime_error("gpu_cl KernelGenerator : Not supported operation yet"); + } + } + + _return_fn = std::move(fn); +} + +void KernelGenerator::visit(const ir::operation::ElementwiseActivation &node) +{ + std::unique_ptr<GPUOperation> gpu_op; + auto fn = std::make_unique<ClFunction>(); + + switch (node.param().op_type) + { + case ir::operation::ElementwiseActivation::Type::LEAKY_RELU: + case ir::operation::ElementwiseActivation::Type::RELU: + { + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{ + node.getInputs().at(ir::operation::ElementwiseActivation::Input::INPUT)}; + + OperationDef op_def; + op_def.precision = CalculationsPrecision::F32; + auto output_tensor = _tensor_reg->getClTensor(output_index); + auto input_tensor = _tensor_reg->getClTensor(input_index); + op_def.dst_tensors.push_back(_tensor_reg->getClTensorReserver(output_index)->descriptor); + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(input_index)->descriptor); + + ReLUAttributes attr; + if (ir::operation::ElementwiseActivation::Type::LEAKY_RELU == node.param().op_type) + { + attr.alpha = node.param().alpha; + attr.clip = 0; + } + else + { + attr.alpha = node.param().beta; + attr.clip = node.param().alpha; + } + gpu_op = SelectReLU(attr, op_def); + gpu_op->SetSrc(input_tensor->handle(), ir::operation::ElementwiseActivation::Input::INPUT); + gpu_op->SetDst(output_tensor->handle(), 0); + fn->configure(_creation_context); + fn->add_operation(std::move(gpu_op)); + + _return_fn = std::move(fn); + break; + } + default: + throw std::runtime_error("gpu_cl KernelGenerator : Not supported operation yet"); + } +} + +void KernelGenerator::visit(const ir::operation::Pool2D &node) +{ + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(ir::operation::Pool2D::Input::INPUT)}; + + OperationDef op_def; + op_def.precision = CalculationsPrecision::F32; + + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(input_index)->descriptor); + auto input_shape = _tensor_reg->getClTensorReserver(input_index)->shape; + + op_def.dst_tensors.push_back(_tensor_reg->getClTensorReserver(output_index)->descriptor); + + const auto kh = node.param().kh; + const auto kw = node.param().kw; + const auto stride = node.param().stride; + const auto op_type = convertPoolType(node.param().op_type); + + Pooling2DAttributes attributes; + attributes.type = op_type; + attributes.kernel = HW(kh > 0 ? kh : 1, kw > 0 ? kw : 1); + attributes.strides = + HW(stride.vertical > 0 ? stride.vertical : 1, stride.horizontal > 0 ? stride.horizontal : 1); + + if (node.param().padding.type == ir::PaddingType::SAME) + { + attributes.padding = CalculateSamePadding(input_shape, attributes); + } + else + { + attributes.padding.prepended = HW(0, 0); + attributes.padding.appended = HW(0, 0); + } + + auto fn = std::make_unique<ClFunction>(); + std::unique_ptr<GPUOperation> gpu_op; + gpu_op = SelectPooling(attributes, op_def); + + auto input_tensor = _tensor_reg->getClTensor(input_index); + auto output_tensor = _tensor_reg->getClTensor(output_index); + + gpu_op->SetSrc(input_tensor->handle(), ir::operation::Pool2D::Input::INPUT); + gpu_op->SetDst(output_tensor->handle(), 0); + + fn->configure(_creation_context); + fn->add_operation(std::move(gpu_op)); + + _return_fn = std::move(fn); +} + +void KernelGenerator::visit(const ir::operation::Reshape &node) +{ + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(ir::operation::Reshape::Input::INPUT)}; + + OperationDef op_def; + op_def.precision = CalculationsPrecision::F32; + + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(input_index)->descriptor); + auto input_shape = _tensor_reg->getClTensorReserver(input_index)->shape; + + op_def.dst_tensors.push_back(_tensor_reg->getClTensorReserver(output_index)->descriptor); + auto output_shape = _tensor_reg->getClTensorReserver(output_index)->shape; + + ReshapeAttributes attr; + attr.new_shape = output_shape; + + auto fn = std::make_unique<ClFunction>(); + std::unique_ptr<GPUOperation> gpu_op; + const int src_channels = input_shape.c; + SelectReshape(src_channels, attr.new_shape.c, op_def, &gpu_op); + + auto input_tensor = _tensor_reg->getClTensor(input_index); + auto output_tensor = _tensor_reg->getClTensor(output_index); + gpu_op->SetSrc(input_tensor->handle(), ir::operation::Reshape::Input::INPUT); + gpu_op->SetDst(output_tensor->handle(), 0); + + fn->configure(_creation_context); + fn->add_operation(std::move(gpu_op)); + + _return_fn = std::move(fn); +} + +void KernelGenerator::visit(const ir::operation::Softmax &node) +{ + const auto output_index{node.getOutputs().at(0)}; + const auto input_index{node.getInputs().at(ir::operation::Softmax::Input::INPUT)}; + + const auto beta = node.param().beta; + + if (beta != 1.0) + { + throw std::runtime_error("Softmax.beta != 1 is not supported in gpu_cl"); + } + + OperationDef op_def; + op_def.precision = CalculationsPrecision::F32; + + op_def.dst_tensors.push_back(_tensor_reg->getClTensorReserver(output_index)->descriptor); + + op_def.src_tensors.push_back(_tensor_reg->getClTensorReserver(input_index)->descriptor); + auto input_shape = _tensor_reg->getClTensorReserver(input_index)->shape; + + auto fn = std::make_unique<ClFunction>(); + + std::unique_ptr<GPUOperation> gpu_op; + SelectSoftmax(input_shape, op_def, &gpu_op); + auto output_tensor = _tensor_reg->getClTensor(output_index); + auto input_tensor = _tensor_reg->getClTensor(input_index); + + gpu_op->SetSrc(input_tensor->handle(), ir::operation::Softmax::Input::INPUT); + gpu_op->SetDst(output_tensor->handle(), 0); + + fn->configure(_creation_context); + fn->add_operation(std::move(gpu_op)); + + _return_fn = std::move(fn); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/KernelGenerator.h b/runtime/onert/backend/gpu_cl/KernelGenerator.h new file mode 100644 index 000000000..3e341b111 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/KernelGenerator.h @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_KERNEL_GENERATOR_H__ +#define __ONERT_BACKEND_GPU_CL_KERNEL_GENERATOR_H__ + +#include "ClTensorRegistry.h" +#include "backend/basic/TensorRegistry.h" +#include "TensorBuilder.h" +#include "TensorManager.h" + +#include <backend/CustomKernelBuilder.h> +#include <backend/basic/KernelGeneratorBase.h> +#include <ir/Operands.h> +#include <ir/Operations.h> +#include <ir/Operations.Include.h> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class KernelGenerator : public basic::KernelGeneratorBase +{ +public: + KernelGenerator(const ir::Graph &graph, const std::shared_ptr<TensorBuilder> &tensor_builder, + const std::shared_ptr<ClTensorRegistry<TensorManager>> &tensor_reg, + const std::shared_ptr<CreationContext> &creation_context); + + std::unique_ptr<exec::FunctionSequence> generate(ir::OperationIndex ind) override; + +private: + void visit(const ir::operation::BinaryArithmetic &) override; + void visit(const ir::operation::Conv2D &) override; + void visit(const ir::operation::DepthwiseConv2D &) override; + void visit(const ir::operation::ElementwiseActivation &) override; + void visit(const ir::operation::Pool2D &) override; + void visit(const ir::operation::Reshape &) override; + void visit(const ir::operation::Softmax &) override; + +private: + const ir::Operands &_ctx; + const ir::Operations &_operations_ctx; + ir::Layout _current_layout; + std::shared_ptr<TensorBuilder> _tensor_builder; + std::shared_ptr<ClTensorRegistry<TensorManager>> _tensor_reg; + std::shared_ptr<CreationContext> _creation_context; + ir::OperandIndexMap<std::shared_ptr<Tensor>> _new_tensors; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_KERNEL_GENERATOR_H__ diff --git a/runtime/onert/backend/gpu_cl/ParentInfo.h b/runtime/onert/backend/gpu_cl/ParentInfo.h new file mode 100644 index 000000000..d7cb2d4fb --- /dev/null +++ b/runtime/onert/backend/gpu_cl/ParentInfo.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_PARENT_INFO_H__ +#define __ONERT_BACKEND_PARENT_INFO_H__ + +#include <ir/Index.h> +#include <ir/Coordinates.h> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +/** + * @brief Struct to represent parent operand in child operand + */ +struct ParentInfo +{ + ir::OperandIndex parent; + ir::Layout frontend_layout; + ir::Coordinates coordinates; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_ACL_COMMON_PARENT_INFO_H__ diff --git a/runtime/onert/backend/gpu_cl/TensorBuilder.h b/runtime/onert/backend/gpu_cl/TensorBuilder.h new file mode 100644 index 000000000..d55358191 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/TensorBuilder.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_TENSOR_BUILDER_H__ +#define __ONERT_BACKEND_GPU_CL_TENSOR_BUILDER_H__ + +#include <backend/basic/TensorBuilder.h> +#include "operand/ICLTensor.h" +#include "operand/CLTensor.h" +#include "ClTensorBuilder.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +using TensorBuilder = ClTensorBuilder<operand::ICLTensor, operand::CLTensor>; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_TENSOR_BUILDER_H__ diff --git a/runtime/onert/backend/gpu_cl/TensorManager.h b/runtime/onert/backend/gpu_cl/TensorManager.h new file mode 100644 index 000000000..111b5f8a7 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/TensorManager.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_CL_TENSOR_MANAGER_H__ +#define __ONERT_BACKEND_CL_TENSOR_MANAGER_H__ + +#include "ClMemoryManager.h" +#include "ClTensorManager.h" +#include "open_cl/ClContext.h" +#include "operand/CLTensor.h" +#include "operand/ICLTensor.h" +#include "util/logging.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +using MemoryManager = ClMemoryManager<operand::ICLTensor, operand::CLTensor>; + +using TensorManager = ClTensorManager<operand::ICLTensor, operand::CLTensor>; + +inline TensorManager *createTensorManager(CLContext *context) +{ + VERBOSE(createTensorManager) << "ClTensorManager" << std::endl; + return new TensorManager(new MemoryManager(context), new MemoryManager(context)); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_ACL_CL_TENSOR_MANAGER_H__ diff --git a/runtime/onert/backend/gpu_cl/gpu_cl.cc b/runtime/onert/backend/gpu_cl/gpu_cl.cc new file mode 100644 index 000000000..b771d6d29 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/gpu_cl.cc @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Backend.h" + +#include <util/logging.h> + +extern "C" { +onert::backend::Backend *onert_backend_create() +{ + VERBOSE(onert_backend_create) << "'gpu_cl' loaded\n"; + return new onert::backend::gpu_cl::Backend; +} + +void onert_backend_destroy(onert::backend::Backend *backend) +{ + VERBOSE(onert_backend_destroy) << "'gpu_cl' unloaded\n"; + delete backend; +} +} diff --git a/runtime/onert/backend/gpu_cl/open_cl/AccessType.h b/runtime/onert/backend/gpu_cl/open_cl/AccessType.h new file mode 100644 index 000000000..81efd666f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/AccessType.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_ACCESS_TYPE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_ACCESS_TYPE_H__ + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +enum class AccessType +{ + UNKNOWN, + READ, + WRITE, + READ_WRITE, +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_ACCESS_TYPE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Api.cc b/runtime/onert/backend/gpu_cl/open_cl/Api.cc new file mode 100644 index 000000000..10bf87c38 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Api.cc @@ -0,0 +1,202 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Api.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +struct ObjectTypeGetter +{ + ObjectType operator()(absl::monostate) const { return ObjectType::UNKNOWN; } + ObjectType operator()(OpenClBuffer) const { return ObjectType::OPENCL_BUFFER; } + ObjectType operator()(OpenClTexture) const { return ObjectType::OPENCL_TEXTURE; } + ObjectType operator()(CpuMemory) const { return ObjectType::CPU_MEMORY; } +}; + +struct ObjectValidityChecker +{ + bool operator()(absl::monostate) const { return false; } + bool operator()(OpenClBuffer obj) const { return obj.memobj; } + bool operator()(OpenClTexture obj) const { return obj.memobj; } + bool operator()(CpuMemory obj) const + { + return obj.data != nullptr && obj.size_bytes > 0 && + (data_type == DataType::UNKNOWN || obj.size_bytes % SizeOf(data_type) == 0); + } + DataType data_type; +}; + +} // namespace + +bool IsValid(const ObjectDef &def) +{ + return def.data_type != DataType::UNKNOWN && def.data_layout != DataLayout::UNKNOWN && + def.object_type != ObjectType::UNKNOWN; +} + +ObjectType GetType(const TensorObject &object) { return absl::visit(ObjectTypeGetter{}, object); } + +bool IsValid(const TensorObjectDef &def) { return IsValid(def.object_def); } + +bool IsValid(const TensorObjectDef &def, const TensorObject &object) +{ + return GetType(object) == def.object_def.object_type && + absl::visit(ObjectValidityChecker{def.object_def.data_type}, object); +} + +bool IsObjectPresent(ObjectType type, const TensorObject &obj) +{ + switch (type) + { + case ObjectType::CPU_MEMORY: + return absl::holds_alternative<CpuMemory>(obj); + case ObjectType::OPENCL_BUFFER: + return absl::holds_alternative<OpenClBuffer>(obj); + case ObjectType::OPENCL_TEXTURE: + return absl::holds_alternative<OpenClTexture>(obj); + case ObjectType::UNKNOWN: + return false; + } + return false; +} + +uint32_t NumElements(const TensorObjectDef &def) +{ + const auto &d = def.dimensions; + switch (def.object_def.data_layout) + { + case DataLayout::BHWC: + return d.product(); + case DataLayout::HWDC4: + case DataLayout::HDWC4: + case DataLayout::DHWC4: + return d.b * d.h * d.w * AlignByN(d.c, 4); + case DataLayout::UNKNOWN: + return 0; + } + return 0; +} + +int GetPosition(const InferenceOptions &options, InferencePriority p) +{ + if (options.priority1 == p) + return 1; + if (options.priority2 == p) + return 2; + if (options.priority3 == p) + return 3; + return 4; // least important +} + +PriorityImportance GetRelativeImportance(const InferenceOptions &options, InferencePriority p1, + InferencePriority p2) +{ + int p1_position = GetPosition(options, p1); + int p2_position = GetPosition(options, p2); + if (p1_position == p2_position) + return PriorityImportance::UNKNOWN; + return p1_position < p2_position ? PriorityImportance::HIGHER : PriorityImportance::LOWER; +} + +bool IsValid(const InferenceOptions &options) +{ + if (options.usage == InferenceUsage::UNKNOWN) + { + return false; + } + if (options.priority1 == InferencePriority::UNKNOWN || + options.priority2 == InferencePriority::UNKNOWN || + options.priority3 == InferencePriority::UNKNOWN) + { + return false; + } + if (options.priority1 == InferencePriority::AUTO) + { + return false; + } + if (options.priority2 == InferencePriority::AUTO && options.priority3 != InferencePriority::AUTO) + { + return false; + } + if (options.priority1 == options.priority2 || options.priority1 == options.priority3) + { + return false; + } + if (options.priority2 == options.priority3 && options.priority2 != InferencePriority::AUTO) + { + return false; + } + return true; +} + +// Implementation note: this resolution logic is shared between GL and CL +// backends, but they might have own logic. Thus, the function is defined +// here just for code re-use purposes. +void ResolveAutoPriority(InferenceOptions *options) +{ + // priority1 can not be AUTO as it would make options invalid. + if (options->priority2 == InferencePriority::AUTO) + { + switch (options->priority1) + { + case InferencePriority::MIN_LATENCY: + options->priority2 = InferencePriority::MIN_MEMORY_USAGE; + options->priority3 = InferencePriority::MAX_PRECISION; + return; + case InferencePriority::MIN_MEMORY_USAGE: + options->priority2 = InferencePriority::MAX_PRECISION; + options->priority3 = InferencePriority::MIN_LATENCY; + return; + case InferencePriority::MAX_PRECISION: + options->priority2 = InferencePriority::MIN_LATENCY; + options->priority3 = InferencePriority::MIN_MEMORY_USAGE; + return; + case InferencePriority::UNKNOWN: + case InferencePriority::AUTO: + // Invalid and unreachable option. + return; + } + } + + if (options->priority3 == InferencePriority::AUTO) + { + // Simply add missing priority + if (GetPosition(*options, InferencePriority::MIN_LATENCY) == 4) + { + options->priority3 = InferencePriority::MIN_LATENCY; + } + else if (GetPosition(*options, InferencePriority::MAX_PRECISION) == 4) + { + options->priority3 = InferencePriority::MAX_PRECISION; + } + else if (GetPosition(*options, InferencePriority::MIN_MEMORY_USAGE) == 4) + { + options->priority3 = InferencePriority::MIN_MEMORY_USAGE; + } + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Api.h b/runtime/onert/backend/gpu_cl/open_cl/Api.h new file mode 100644 index 000000000..35be3d99c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Api.h @@ -0,0 +1,359 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_API_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_API_H__ + +// Usage example: +// +// // Builder is created from a model using GPU-specific parameters. +// std::unique_ptr<InferenceBuilder> builder = ...; +// +// // input data is coming from a texture +// // output data goes to CPU +// builder->SetInputObjectDef(0, {DataType::FLOAT16, DataLayout::PHWC4, +// ObjectType::OPENGL_TEXTURE, true}); +// builder->SetOutputObjectDef(0, {DataType::FLOAT32, DataLayout::BHWC, +// ObjectType::CPU_MEMORY, false}); +// std::unique_ptr<InferenceRunner> runner; +// RETURN_IF_ERROR(builder->Build(&runner)); // may take significant time. +// RETURN_IF_ERROR( +// runner->SetInputObject(0, OpenGlTexture{texture_ud, texture_format})); +// RETURN_IF_ERROR(runner->Run()); + +#include <cstdint> +#include <memory> +#include <vector> + +#include "absl/types/span.h" +#include "absl/types/variant.h" +#include "DataType.h" +#include "Status.h" +#include "Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// Common abbreviations: +// B - batch +// H - height +// W - width +// C - channels +// D - depth := DivideRoundUp(C, 4) +// C4 - is the constant = 4. +enum class DataLayout +{ + UNKNOWN, + BHWC, + DHWC4, + HWDC4, + HDWC4, +}; + +enum class ObjectType +{ + UNKNOWN, + CPU_MEMORY, + OPENCL_TEXTURE, + OPENCL_BUFFER, +}; + +struct OpenClBuffer +{ + OpenClBuffer() = default; + explicit OpenClBuffer(cl_mem new_memobj) : memobj(new_memobj) {} + + cl_mem memobj = nullptr; +}; + +struct OpenClTexture +{ + OpenClTexture() = default; + explicit OpenClTexture(cl_mem new_memobj) : memobj(new_memobj) {} + + cl_mem memobj = nullptr; + // TODO(akulik): should it specify texture format? +}; + +struct CpuMemory +{ + CpuMemory() = default; + CpuMemory(void *new_data, size_t new_size_bytes) : data(new_data), size_bytes(new_size_bytes) {} + + void *data = nullptr; + size_t size_bytes = 0; +}; + +template <typename T> inline CpuMemory MakeCpuMemory(absl::Span<T> t) +{ + CpuMemory m; + m.data = t.data(); + m.size_bytes = t.size() * sizeof(T); + return m; +} + +template <typename T> inline CpuMemory MakeReadableCpuMemory(absl::Span<const T> t) +{ + CpuMemory m; + m.data = const_cast<T *>(t.data()); + m.size_bytes = t.size() * sizeof(T); + return m; +} + +// Defines object representation. +struct ObjectDef +{ + DataType data_type = DataType::UNKNOWN; + DataLayout data_layout = DataLayout::UNKNOWN; + ObjectType object_type = ObjectType::UNKNOWN; + + // If true, then object is managed externally and needs to be provided to + // InferenceRunner by a user before running inference. + // + // User-provided objects will not be re-used internally for any purpose to + // lower overall memory usage. + bool user_provided = false; + + bool operator==(const ObjectDef &other) const + { + return data_type == other.data_type && data_layout == other.data_layout && + object_type == other.object_type && user_provided == other.user_provided; + } +}; + +bool IsValid(const ObjectDef &def); + +struct Dimensions +{ + Dimensions() : b(1), h(1), w(1), c(1) {} + + Dimensions(int32_t batch, int32_t height, int32_t width, int32_t channels) + : b(batch), h(height), w(width), c(channels) + { + } + + int32_t d() const { return DivideRoundUp(c, 4); } + + int32_t product() const { return b * h * w * c; } + + bool operator==(const Dimensions &other) const + { + return b == other.b && h == other.h && w == other.w && c == other.c; + } + + int32_t b; + int32_t h; + int32_t w; + int32_t c; +}; + +// Connects tensor shape with corresponding object definition. +struct TensorObjectDef +{ + // Dimensions semantic is defined by corresponding DataLayout. + Dimensions dimensions; + ObjectDef object_def; + + bool operator==(const TensorObjectDef &other) const + { + return dimensions == other.dimensions && object_def == other.object_def; + } +}; + +// @return true if tensor object def is defined. +bool IsValid(const TensorObjectDef &def); + +// @return the number of elements in a tensor object. +uint32_t NumElements(const TensorObjectDef &def); + +using TensorObject = absl::variant<absl::monostate, CpuMemory, OpenClBuffer, OpenClTexture>; + +// @return true if object is set and corresponding values are defined. +bool IsValid(const TensorObjectDef &def, const TensorObject &object); + +ObjectType GetType(const TensorObject &object); + +// @return true if corresponding object is set for the given type +bool IsObjectPresent(ObjectType type, const TensorObject &obj); + +class InferenceRunner; + +// Allows to inspect and change input and output definitions before a graph is +// prepared for the inference. +class InferenceBuilder +{ +public: + virtual ~InferenceBuilder() {} + + // Returns inference graph inputs and outputs definitions. + virtual std::vector<TensorObjectDef> inputs() const = 0; + virtual std::vector<TensorObjectDef> outputs() const = 0; + + // Sets new shape for the input if underlying implementation and graph + // structure allows dynamic tensors. + virtual absl::Status SetInputShape(int index, const Dimensions &dimensions) = 0; + + // Updates object definitions for the given index. Implementation may allow + // to use different layouts and/or data type conversions between objects + // defined in a graph and given objects, for example: + // input '0' is DataType::FLOAT32, DataLayout::BHWC. + // A user, however, has an input in DataType::FLOAT16, DataLayout::PHWC4. + // An implementation may allow this transformation to happen automatically + // under the hood. + virtual absl::Status SetInputObjectDef(int index, ObjectDef def) = 0; + virtual absl::Status SetOutputObjectDef(int index, ObjectDef def) = 0; + virtual absl::Status SetAllInputObjectDefsTo(ObjectDef def) + { + auto input_defs = inputs(); + for (size_t i = 0; i < input_defs.size(); ++i) + { + RETURN_IF_ERROR(SetInputObjectDef(i, def)); + } + return absl::OkStatus(); + } + virtual absl::Status SetAllOutputObjectDefsTo(ObjectDef def) + { + auto output_defs = outputs(); + for (size_t i = 0; i < output_defs.size(); ++i) + { + RETURN_IF_ERROR(SetOutputObjectDef(i, def)); + } + return absl::OkStatus(); + } + + // Creates new instance of the inference runner. InferenceBuilder stays valid + // and could be used to create another inference runner if needed. + // + // This method may take significant time to prepare new inference runner. For + // example, it may require to compile OpenGL shaders. + virtual absl::Status Build(std::unique_ptr<InferenceRunner> *runner) = 0; +}; + +// Runs prepared inference. Every object marked as external needs to be set +// prior calling Run method. +class InferenceRunner +{ +public: + virtual ~InferenceRunner() {} + + // Returns inference graph inputs and outputs definitions. + virtual std::vector<TensorObjectDef> inputs() const = 0; + virtual std::vector<TensorObjectDef> outputs() const = 0; + + // Getters provide access to underlying objects for the given index. + // Setters allow to set or change external object for the given index. Note, + // object need to match object definition set before in InferenceBuilder. + + virtual absl::Status GetInputObject(int index, TensorObject *object) = 0; + virtual absl::Status GetOutputObject(int index, TensorObject *object) = 0; + virtual absl::Status SetInputObject(int index, TensorObject object) = 0; + virtual absl::Status SetOutputObject(int index, TensorObject object) = 0; + + virtual absl::Status Run() = 0; +}; + +// Encapsulated compilation/runtime tradeoffs. +enum class InferenceUsage +{ + UNKNOWN, + + // InferenceRunner will be used only once. Therefore, it is important to + // minimize bootstrap time as well. + FAST_SINGLE_ANSWER, + + // Prefer maximizing the throughput. Same inference runner will be used + // repeatedly on different inputs. + SUSTAINED_SPEED, +}; + +// Defines aspects to control while instantiating a runner. +enum class InferencePriority +{ + UNKNOWN, + + AUTO, + + MIN_LATENCY, + + MAX_PRECISION, + + MIN_MEMORY_USAGE, +}; + +struct InferenceOptions +{ + InferenceUsage usage = InferenceUsage::SUSTAINED_SPEED; + + // Ordered priorities provide better understanding of desired semantics, + // where priority(n) is more important than priority(n+1). + // AUTO priority is needed when a single priority is the most important + // factor. For example, priority1 = InferencePriority::MIN_LATENCY and leaving + // everything else to AUTO would result in configuration that achieves maximum + // performance. + // + // AUTO priority can only be used when higher priorities are fully specified. + // For example: + // VALID: priority1 = MIN_LATENCY, priority2 = AUTO, priority3 = AUTO + // VALID: priority1 = MIN_LATENCY, priority2 = MAX_PRECISION, + // priority3 = AUTO + // INVALID: priority1 = AUTO, priority2 = MIN_LATENCY, priority3 = AUTO + // INVALID: priority1 = MIN_LATENCY, priority2 = AUTO, + // priority3 = MAX_PRECISION + // Invalid priorities will result in error. + InferencePriority priority1 = InferencePriority::MAX_PRECISION; + + InferencePriority priority2 = InferencePriority::AUTO; + + InferencePriority priority3 = InferencePriority::AUTO; +}; + +// Returns a position number for the priority. If priority is missing, +// then it it would return 'max num priorities + 1'. +int GetPosition(const InferenceOptions &options, InferencePriority p); + +// Return true if options are valid. +bool IsValid(const InferenceOptions &options); + +// Resolves AUTO priorities and specifies them explicitly. +// Note, no-one should assume that these mappings will not change. +// Technically this function is declared here for code re-use purposes and +// by no means it should be treated as canonical way to resolve AUTO. +void ResolveAutoPriority(InferenceOptions *options); + +enum class PriorityImportance +{ + UNKNOWN, + HIGHER, + LOWER, +}; + +// If both p1 and p2 are not present in options, return UNKNOWN +// If p1 is present, but p2 is not, return HIGHER +// If p2 is present, but p1 is not, return LOWER +// If both are present, and p1 is more important, return HIGHER, otherwise, +// LOWER. +PriorityImportance GetRelativeImportance(const InferenceOptions &options, InferencePriority p1, + InferencePriority p2); +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_API_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Arguments.cc b/runtime/onert/backend/gpu_cl/open_cl/Arguments.cc new file mode 100644 index 000000000..a7f86bffc --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Arguments.cc @@ -0,0 +1,926 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Arguments.h" + +#include "absl/strings/ascii.h" +#include "absl/strings/str_cat.h" +#include "absl/strings/str_replace.h" +#include "absl/strings/str_split.h" +#include "absl/strings/substitute.h" + +#include "AccessType.h" +#include "TensorType.h" +#include "DataType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +namespace +{ + +bool IsWordSymbol(char symbol) { return absl::ascii_isalnum(symbol) || symbol == '_'; } + +std::string GetNextWord(const std::string &code, size_t first_position) +{ + size_t pos = first_position; + char t = code[pos]; + while (IsWordSymbol(t)) + { + pos++; + t = code[pos]; + } + return code.substr(first_position, pos - first_position); +} + +size_t FindEnclosingBracket(const std::string &text, size_t first_pos, char bracket) +{ + const std::map<char, char> brackets = { + {'(', ')'}, + {'{', '}'}, + {'[', ']'}, + {'<', '>'}, + }; + char b_open = bracket; + auto it = brackets.find(b_open); + if (it == brackets.end()) + { + return -1; + } + char b_close = it->second; + size_t pos = first_pos; + int opened = 1; + int closed = 0; + while (opened != closed && pos < text.size()) + { + if (text[pos] == b_open) + { + opened++; + } + else if (text[pos] == b_close) + { + closed++; + } + pos++; + } + if (opened == closed) + { + return pos; + } + else + { + return -1; + } +} + +absl::Status ParseArgsInsideBrackets(const std::string &text, size_t open_bracket_pos, + size_t *close_bracket_pos, std::vector<std::string> *args) +{ + *close_bracket_pos = FindEnclosingBracket(text, open_bracket_pos + 1, text[open_bracket_pos]); + if (*close_bracket_pos == static_cast<size_t>(-1)) + { + return absl::NotFoundError("Not found enclosing bracket"); + } + std::string str_args = + text.substr(open_bracket_pos + 1, *close_bracket_pos - open_bracket_pos - 2); + std::vector<absl::string_view> words = absl::StrSplit(str_args, ','); + args->reserve(words.size()); + for (const auto &word : words) + { + absl::string_view arg = absl::StripAsciiWhitespace(word); + if (!arg.empty()) + { + args->push_back(std::string(arg)); + } + } + return absl::OkStatus(); +} + +void ReplaceAllWords(const std::string &old_word, const std::string &new_word, std::string *str) +{ + size_t position = str->find(old_word); + while (position != std::string::npos) + { + char prev = position == 0 ? '.' : (*str)[position - 1]; + char next = position + old_word.size() < str->size() ? (*str)[position + old_word.size()] : '.'; + if (IsWordSymbol(prev) || IsWordSymbol(next)) + { + position = str->find(old_word, position + 1); + continue; + } + str->replace(position, old_word.size(), new_word); + position = str->find(old_word, position + new_word.size()); + } +} + +std::string RenameArg(const std::vector<std::string> &object_names, const std::string &postfix, + const std::string &arg_name) +{ + for (const auto &object_name : object_names) + { + if (absl::StartsWith(arg_name, object_name) && arg_name.size() > object_name.size() && + arg_name[object_name.size()] == '_') + { + return object_name + postfix + + arg_name.substr(object_name.size(), arg_name.size() - object_name.size()); + } + } + return arg_name + postfix; +} + +void AppendArgument(const std::string &arg, std::string *args) +{ + if (!args->empty()) + { + absl::StrAppend(args, ",\n "); + } + absl::StrAppend(args, arg); +} + +std::string GetImageModifier(AccessType access) +{ + switch (access) + { + case AccessType::READ: + return "__read_only"; + case AccessType::WRITE: + return "__write_only"; + case AccessType::READ_WRITE: + return "__read_write"; + default: + throw std::runtime_error("Invalid AccessType"); + } +} + +std::string GetDefaultSamplers(const DeviceInfo &device_info) +{ + std::string result; + result += "__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | " + "CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;\n"; + if (device_info.IsAdreno3xx()) + { + // Unfortunately, CLK_ADDRESS_CLAMP is very slow on Adreno3xx and + // we can observe huge register overhead when compared to other modes. + + // While using CLK_ADDRESS_NONE with out-of-range image coordinates is + // undefined in the OpenCL specification, we have observed that + // CLK_ADDRESS_NONE works like CLK_ADDRESS_CLAMP for out-of-range image + // coordinates for RGBA F16/F32 textures on Adreno3xx devices. Using + // CLK_ADDRESS_NONE is significantly faster than CLK_ADDRESS_CLAMP on Adreno + // 3xx. + result += "__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | " + "CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;\n"; + } + else + { + result += "__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | " + "CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;\n"; + } + + return result; +} + +} // namespace + +// Static +constexpr char Arguments::kArgsPrefix[]; + +Arguments::Arguments(Arguments &&args) + : int_values_(std::move(args.int_values_)), + shared_int4s_data_(std::move(args.shared_int4s_data_)), + float_values_(std::move(args.float_values_)), + shared_float4s_data_(std::move(args.shared_float4s_data_)), buffers_(std::move(args.buffers_)), + images2d_(std::move(args.images2d_)), image2d_arrays_(std::move(args.image2d_arrays_)), + images3d_(std::move(args.images3d_)), image_buffers_(std::move(args.image_buffers_)), + custom_memories_(std::move(args.custom_memories_)), object_refs_(std::move(args.object_refs_)), + objects_(std::move(args.objects_)) +{ +} +Arguments &Arguments::operator=(Arguments &&args) +{ + if (this != &args) + { + int_values_ = std::move(args.int_values_); + shared_int4s_data_ = std::move(args.shared_int4s_data_); + float_values_ = std::move(args.float_values_); + shared_float4s_data_ = std::move(args.shared_float4s_data_); + buffers_ = std::move(args.buffers_); + images2d_ = std::move(args.images2d_); + image2d_arrays_ = std::move(args.image2d_arrays_); + images3d_ = std::move(args.images3d_); + image_buffers_ = std::move(args.image_buffers_); + custom_memories_ = std::move(args.custom_memories_); + object_refs_ = std::move(args.object_refs_); + objects_ = std::move(args.objects_); + } + return *this; +} + +void Arguments::AddFloat(const std::string &name, float value) +{ + float_values_[name].value = value; +} +void Arguments::AddInt(const std::string &name, int value) { int_values_[name].value = value; } +void Arguments::AddBuffer(const std::string &name, const GPUBufferDescriptor &desc) +{ + buffers_[name] = desc; +} +void Arguments::AddImage2D(const std::string &name, const GPUImage2DDescriptor &desc) +{ + images2d_[name] = desc; +} + +void Arguments::AddImage2DArray(const std::string &name, const GPUImage2DArrayDescriptor &desc) +{ + image2d_arrays_[name] = desc; +} + +void Arguments::AddImage3D(const std::string &name, const GPUImage3DDescriptor &desc) +{ + images3d_[name] = desc; +} + +void Arguments::AddImageBuffer(const std::string &name, const GPUImageBufferDescriptor &desc) +{ + image_buffers_[name] = desc; +} + +void Arguments::AddCustomMemory(const std::string &name, const GPUCustomMemoryDescriptor &desc) +{ + custom_memories_[name] = desc; +} + +void Arguments::AddObjectRef(const std::string &name, AccessType access_type, + GPUObjectDescriptorPtr &&descriptor_ptr) +{ + descriptor_ptr->SetAccess(access_type); + object_refs_[name] = {std::move(descriptor_ptr)}; +} + +void Arguments::AddObject(const std::string &name, GPUObjectDescriptorPtr &&descriptor_ptr) +{ + descriptor_ptr->SetAccess(AccessType::READ); + objects_[name] = {nullptr, std::move(descriptor_ptr)}; +} + +void Arguments::AddGPUResources(const std::string &name, const GPUResources &resources) +{ + for (const auto &r : resources.ints) + { + AddInt(absl::StrCat(name, "_", r)); + } + for (const auto &r : resources.floats) + { + AddFloat(absl::StrCat(name, "_", r)); + } + for (const auto &r : resources.buffers) + { + AddBuffer(absl::StrCat(name, "_", r.first), r.second); + } + for (const auto &r : resources.images2d) + { + AddImage2D(absl::StrCat(name, "_", r.first), r.second); + } + for (const auto &r : resources.image2d_arrays) + { + AddImage2DArray(absl::StrCat(name, "_", r.first), r.second); + } + for (const auto &r : resources.images3d) + { + AddImage3D(absl::StrCat(name, "_", r.first), r.second); + } + for (const auto &r : resources.image_buffers) + { + AddImageBuffer(absl::StrCat(name, "_", r.first), r.second); + } + for (const auto &r : resources.custom_memories) + { + AddCustomMemory(absl::StrCat(name, "_", r.first), r.second); + } +} + +absl::Status Arguments::SetInt(const std::string &name, int value) +{ + auto it = int_values_.find(name); + if (it == int_values_.end()) + { + return absl::NotFoundError(absl::StrCat("No int argument with name - ", name)); + } + it->second.value = value; + if (it->second.active) + { + shared_int4s_data_[it->second.offset] = value; + } + return absl::OkStatus(); +} + +absl::Status Arguments::SetFloat(const std::string &name, float value) +{ + auto it = float_values_.find(name); + if (it == float_values_.end()) + { + return absl::NotFoundError(absl::StrCat("No float argument with name - ", name)); + } + it->second.value = value; + if (it->second.active) + { + shared_float4s_data_[it->second.offset] = value; + } + return absl::OkStatus(); +} + +absl::Status Arguments::SetImage2D(const std::string &name, cl_mem memory) +{ + auto it = images2d_.find(name); + if (it == images2d_.end()) + { + return absl::NotFoundError(absl::StrCat("No image2D argument with name - ", name)); + } + it->second.memory = memory; + return absl::OkStatus(); +} + +absl::Status Arguments::SetBuffer(const std::string &name, cl_mem memory) +{ + auto it = buffers_.find(name); + if (it == buffers_.end()) + { + return absl::NotFoundError(absl::StrCat("No buffer argument with name - ", name)); + } + it->second.memory = memory; + return absl::OkStatus(); +} + +absl::Status Arguments::SetImage2DArray(const std::string &name, cl_mem memory) +{ + auto it = image2d_arrays_.find(name); + if (it == image2d_arrays_.end()) + { + return absl::NotFoundError(absl::StrCat("No image2D array argument with name - ", name)); + } + it->second.memory = memory; + return absl::OkStatus(); +} + +absl::Status Arguments::SetImage3D(const std::string &name, cl_mem memory) +{ + auto it = images3d_.find(name); + if (it == images3d_.end()) + { + return absl::NotFoundError(absl::StrCat("No image3D argument with name - ", name)); + } + it->second.memory = memory; + return absl::OkStatus(); +} + +absl::Status Arguments::SetImageBuffer(const std::string &name, cl_mem memory) +{ + auto it = image_buffers_.find(name); + if (it == image_buffers_.end()) + { + return absl::NotFoundError(absl::StrCat("No image buffer argument with name - ", name)); + } + it->second.memory = memory; + return absl::OkStatus(); +} + +absl::Status Arguments::SetCustomMemory(const std::string &name, cl_mem memory) +{ + auto it = custom_memories_.find(name); + if (it == custom_memories_.end()) + { + return absl::NotFoundError(absl::StrCat("No custom memory argument with name - ", name)); + } + it->second.memory = memory; + return absl::OkStatus(); +} + +absl::Status Arguments::SetObjectRef(const std::string &name, const GPUObject *object) +{ + auto it = object_refs_.find(name); + if (it == object_refs_.end()) + { + return absl::NotFoundError(absl::StrCat("No object ref with name - ", name)); + } + GPUResourcesWithValue resources; + RETURN_IF_ERROR(object->GetGPUResources(it->second.descriptor.get(), &resources)); + return SetGPUResources(name, resources); +} + +absl::Status Arguments::SetGPUResources(const std::string &name, + const GPUResourcesWithValue &resources) +{ + for (const auto &r : resources.ints) + { + RETURN_IF_ERROR(SetInt(absl::StrCat(name, "_", r.first), r.second)); + } + for (const auto &r : resources.floats) + { + RETURN_IF_ERROR(SetFloat(absl::StrCat(name, "_", r.first), r.second)); + } + for (const auto &r : resources.buffers) + { + RETURN_IF_ERROR(SetBuffer(absl::StrCat(name, "_", r.first), r.second)); + } + for (const auto &r : resources.images2d) + { + RETURN_IF_ERROR(SetImage2D(absl::StrCat(name, "_", r.first), r.second)); + } + for (const auto &r : resources.image2d_arrays) + { + RETURN_IF_ERROR(SetImage2DArray(absl::StrCat(name, "_", r.first), r.second)); + } + for (const auto &r : resources.images3d) + { + RETURN_IF_ERROR(SetImage3D(absl::StrCat(name, "_", r.first), r.second)); + } + for (const auto &r : resources.image_buffers) + { + RETURN_IF_ERROR(SetImageBuffer(absl::StrCat(name, "_", r.first), r.second)); + } + for (const auto &r : resources.custom_memories) + { + RETURN_IF_ERROR(SetCustomMemory(absl::StrCat(name, "_", r.first), r.second)); + } + return absl::OkStatus(); +} +void Arguments::RenameArgs(const std::string &postfix, std::string *code) const +{ + size_t next_position = code->find(kArgsPrefix); + while (next_position != std::string::npos) + { + size_t arg_pos = next_position + strlen(kArgsPrefix); + std::string arg_name = GetNextWord(*code, arg_pos); + code->replace(arg_pos, arg_name.size(), arg_name + postfix); + next_position = code->find(kArgsPrefix, arg_pos + arg_name.size()); + } +} + +absl::Status Arguments::Merge(Arguments &&args, const std::string &postfix) +{ + std::vector<std::string> object_names; + object_names.reserve(args.object_refs_.size() + args.objects_.size()); + for (auto &v : args.object_refs_) + { + object_names.push_back(v.first); + const std::string name = v.first + postfix; + if (object_refs_.find(name) != object_refs_.end()) + { + return absl::InvalidArgumentError( + absl::StrCat("Object reference name collision. Name - ", name)); + } + object_refs_[name] = {std::move(v.second.descriptor)}; + } + for (auto &v : args.objects_) + { + object_names.push_back(v.first); + const std::string name = v.first + postfix; + if (objects_.find(name) != objects_.end()) + { + return absl::InvalidArgumentError(absl::StrCat("Object name collision. Name - ", name)); + } + objects_[name] = {std::move(v.second.obj_ptr), std::move(v.second.descriptor)}; + } + for (const auto &v : args.int_values_) + { + AddInt(RenameArg(object_names, postfix, v.first), v.second.value); + } + for (const auto &v : args.float_values_) + { + AddFloat(RenameArg(object_names, postfix, v.first), v.second.value); + } + for (const auto &v : args.buffers_) + { + AddBuffer(RenameArg(object_names, postfix, v.first), v.second); + } + for (const auto &v : args.images2d_) + { + AddImage2D(RenameArg(object_names, postfix, v.first), v.second); + } + for (const auto &v : args.image2d_arrays_) + { + AddImage2DArray(RenameArg(object_names, postfix, v.first), v.second); + } + for (const auto &v : args.images3d_) + { + AddImage3D(RenameArg(object_names, postfix, v.first), v.second); + } + for (const auto &v : args.image_buffers_) + { + AddImageBuffer(RenameArg(object_names, postfix, v.first), v.second); + } + for (const auto &v : args.custom_memories_) + { + AddCustomMemory(RenameArg(object_names, postfix, v.first), v.second); + } + return absl::OkStatus(); +} + +absl::Status Arguments::TransformToCLCode(const DeviceInfo &device_info, + const std::map<std::string, std::string> &linkables, + std::string *code) +{ + RETURN_IF_ERROR(AddObjectArgs()); + RETURN_IF_ERROR(ResolveSelectorsPass(linkables, code)); + ResolveArgsPass(device_info, code); + *code = absl::Substitute(*code, GetListOfArgs()); + *code = GetDefaultSamplers(device_info) + *code; + return absl::OkStatus(); +} + +std::string Arguments::GetListOfArgs() +{ + std::string result; + for (auto &t : buffers_) + { + const std::string type_name = t.second.data_type == DataType::FLOAT32 ? "float" : "half"; + std::string attributes; + for (const auto &attr : t.second.attributes) + { + attributes += absl::StrCat(" __attribute__((", attr, "))"); + } + AppendArgument(absl::StrCat(MemoryTypeToCLType(t.second.memory_type), " ", + ToCLDataType(t.second.data_type, t.second.element_size), "* ", + t.first, attributes), + &result); + } + for (auto &t : image_buffers_) + { + AppendArgument( + absl::StrCat(GetImageModifier(t.second.access_type), " image1d_buffer_t ", t.first), &result); + } + for (auto &t : images2d_) + { + AppendArgument(absl::StrCat(GetImageModifier(t.second.access_type), " image2d_t ", t.first), + &result); + } + for (auto &t : image2d_arrays_) + { + AppendArgument( + absl::StrCat(GetImageModifier(t.second.access_type), " image2d_array_t ", t.first), &result); + } + for (auto &t : images3d_) + { + AppendArgument(absl::StrCat(GetImageModifier(t.second.access_type), " image3d_t ", t.first), + &result); + } + for (auto &t : custom_memories_) + { + AppendArgument(absl::StrCat(t.second.type_name, " ", t.first), &result); + } + for (uint32_t i = 0; i < shared_int4s_data_.size() / 4; ++i) + { + AppendArgument(absl::StrCat("int4 shared_int4_", i), &result); + } + for (uint32_t i = 0; i < shared_float4s_data_.size() / 4; ++i) + { + AppendArgument(absl::StrCat("float4 shared_float4_", i), &result); + } + return result; +} + +absl::Status Arguments::Bind(cl_kernel kernel, int offset) +{ + for (auto &t : buffers_) + { + const int error_code = clSetKernelArg(kernel, offset, sizeof(cl_mem), &t.second.memory); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + for (auto &t : image_buffers_) + { + const int error_code = clSetKernelArg(kernel, offset, sizeof(cl_mem), &t.second.memory); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + for (auto &t : images2d_) + { + const int error_code = clSetKernelArg(kernel, offset, sizeof(cl_mem), &t.second.memory); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + for (auto &t : image2d_arrays_) + { + const int error_code = clSetKernelArg(kernel, offset, sizeof(cl_mem), &t.second.memory); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + for (auto &t : images3d_) + { + const int error_code = clSetKernelArg(kernel, offset, sizeof(cl_mem), &t.second.memory); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + for (auto &t : custom_memories_) + { + const int error_code = clSetKernelArg(kernel, offset, sizeof(cl_mem), &t.second.memory); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + for (size_t i = 0; i < shared_int4s_data_.size() / 4; ++i) + { + const int error_code = + clSetKernelArg(kernel, offset, sizeof(int32_t) * 4, &shared_int4s_data_[i * 4]); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + for (size_t i = 0; i < shared_float4s_data_.size() / 4; ++i) + { + const int error_code = + clSetKernelArg(kernel, offset, sizeof(int32_t) * 4, &shared_float4s_data_[i * 4]); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + offset, ")")); + } + offset++; + } + return absl::OkStatus(); +} + +std::string Arguments::AddActiveArgument(const std::string &arg_name, bool) +{ + { + auto it = int_values_.find(arg_name); + if (it != int_values_.end()) + { + int int_index; + if (it->second.active) + { + int_index = it->second.offset; + } + else + { + it->second.active = true; + it->second.offset = shared_int4s_data_.size(); + int_index = it->second.offset; + shared_int4s_data_.push_back(it->second.value); + } + std::string index = std::to_string(int_index / 4); + std::string postfixes[4] = {"x", "y", "z", "w"}; + return "shared_int4_" + index + "." + postfixes[int_index % 4]; + } + } + { + auto it = float_values_.find(arg_name); + if (it != float_values_.end()) + { + int float_index; + if (it->second.active) + { + float_index = it->second.offset; + } + else + { + it->second.active = true; + it->second.offset = shared_float4s_data_.size(); + float_index = it->second.offset; + shared_float4s_data_.push_back(it->second.value); + } + std::string index = std::to_string(float_index / 4); + std::string postfixes[4] = {"x", "y", "z", "w"}; + return "shared_float4_" + index + "." + postfixes[float_index % 4]; + } + } + return arg_name; +} + +void Arguments::ResolveArgsPass(const DeviceInfo &device_info, std::string *code) +{ + bool use_f32_for_half_arguments = device_info.IsPowerVR(); + size_t position = 0; + size_t next_position = code->find(kArgsPrefix); + while (next_position != std::string::npos) + { + size_t arg_pos = next_position; + next_position += strlen(kArgsPrefix); + std::string object_name = GetNextWord(*code, next_position); + std::string new_name = AddActiveArgument(object_name, use_f32_for_half_arguments); + code->replace(arg_pos, object_name.size() + strlen(kArgsPrefix), new_name); + position = arg_pos + new_name.size(); + next_position = code->find(kArgsPrefix, position); + } + + int shared_int4s_aligned_size = AlignByN(shared_int4s_data_.size(), 4); + shared_int4s_data_.resize(shared_int4s_aligned_size); + int shared_float4s_aligned_size = AlignByN(shared_float4s_data_.size(), 4); + shared_float4s_data_.resize(shared_float4s_aligned_size); +} + +void Arguments::ResolveObjectNames(const std::string &object_name, + const std::vector<std::string> &member_names, std::string *code) +{ + for (const auto &member_name : member_names) + { + const std::string new_name = kArgsPrefix + object_name + "_" + member_name; + ReplaceAllWords(member_name, new_name, code); + } +} + +GPUObjectDescriptor *Arguments::GetObjectDescriptor(const std::string &object_name) const +{ + { + auto it = object_refs_.find(object_name); + if (it != object_refs_.end()) + { + return it->second.descriptor.get(); + } + } + { + auto it = objects_.find(object_name); + if (it != objects_.end()) + { + return it->second.descriptor.get(); + } + } + return nullptr; +} + +absl::Status Arguments::ResolveSelector(const std::map<std::string, std::string> &linkables, + const std::string &object_name, const std::string &selector, + const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) +{ + const GPUObjectDescriptor *desc_ptr = GetObjectDescriptor(object_name); + if (!desc_ptr) + { + return absl::NotFoundError(absl::StrCat("No object with name - ", object_name)); + } + auto names = desc_ptr->GetGPUResources().GetNames(); + const auto *tensor_desc = dynamic_cast<const TensorDescriptor *>(desc_ptr); + if (tensor_desc && selector == "Write") + { + auto it = linkables.find(object_name); + if (it != linkables.end()) + { + if (desc_ptr->GetAccess() != AccessType::WRITE && + desc_ptr->GetAccess() != AccessType::READ_WRITE) + { + return absl::FailedPreconditionError( + absl::StrCat("Object with name - ", object_name, " should have Write access.")); + } + std::string value_name, x_coord, y_coord, s_coord; + RETURN_IF_ERROR(tensor_desc->GetLinkingContextFromWriteSelector(args, &value_name, &x_coord, + &y_coord, &s_coord)); + // x_coord can have batch size property of link_object + ResolveObjectNames(object_name, names, &x_coord); + *result = it->second; + ReplaceAllWords("in_out_value", value_name, result); + ReplaceAllWords("X_COORD", x_coord, result); + ReplaceAllWords("Y_COORD", y_coord, result); + ReplaceAllWords("S_COORD", s_coord, result); + RETURN_IF_ERROR(ResolveSelectorsPass({}, result)); + } + } + std::string patch; + RETURN_IF_ERROR(desc_ptr->PerformSelector(selector, args, template_args, &patch)); + ResolveObjectNames(object_name, names, &patch); + *result += patch; + return absl::OkStatus(); +} + +absl::Status Arguments::ResolveSelectorsPass(const std::map<std::string, std::string> &linkables, + std::string *code) +{ + std::string result; + size_t position = 0; + size_t next_position = code->find(kArgsPrefix); + while (next_position != std::string::npos) + { + size_t arg_pos = next_position; + next_position += strlen(kArgsPrefix); + std::string object_name = GetNextWord(*code, next_position); + char next = (*code)[next_position + object_name.size()]; + if (next == '.') + { + next_position += object_name.size() + 1; + std::string selector_name = GetNextWord(*code, next_position); + next_position += selector_name.size(); + next = (*code)[next_position]; + std::vector<std::string> template_args; + if (next == '<') + { + size_t close_bracket_pos; + RETURN_IF_ERROR( + ParseArgsInsideBrackets(*code, next_position, &close_bracket_pos, &template_args)); + next_position = close_bracket_pos; + next = (*code)[next_position]; + } + if (next != '(') + { + return absl::NotFoundError( + absl::StrCat("Expected ( after ", object_name, ".", selector_name, " call")); + } + std::vector<std::string> args; + size_t close_bracket_pos; + RETURN_IF_ERROR(ParseArgsInsideBrackets(*code, next_position, &close_bracket_pos, &args)); + for (auto &arg : args) + { + RETURN_IF_ERROR(ResolveSelectorsPass({}, &arg)); + } + std::string patch; + RETURN_IF_ERROR( + ResolveSelector(linkables, object_name, selector_name, args, template_args, &patch)); + code->replace(arg_pos, close_bracket_pos - arg_pos, patch); + position = arg_pos + patch.size(); + } + else + { + position = arg_pos + strlen(kArgsPrefix); + } + next_position = code->find(kArgsPrefix, position); + } + return absl::OkStatus(); +} + +absl::Status Arguments::AllocateObjects(CLContext *context) +{ + for (auto &t : objects_) + { + RETURN_IF_ERROR(t.second.descriptor->CreateGPUObject(context, &t.second.obj_ptr)); + } + return absl::OkStatus(); +} + +void Arguments::ReleaseCPURepresentation() +{ + for (auto &t : objects_) + { + t.second.descriptor->Release(); + } +} + +absl::Status Arguments::AddObjectArgs() +{ + for (auto &t : objects_) + { + AddGPUResources(t.first, t.second.descriptor->GetGPUResources()); + GPUResourcesWithValue resources; + RETURN_IF_ERROR(t.second.obj_ptr->GetGPUResources(t.second.descriptor.get(), &resources)); + RETURN_IF_ERROR(SetGPUResources(t.first, resources)); + } + for (auto &t : object_refs_) + { + AddGPUResources(t.first, t.second.descriptor->GetGPUResources()); + } + return absl::OkStatus(); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Arguments.h b/runtime/onert/backend/gpu_cl/open_cl/Arguments.h new file mode 100644 index 000000000..0c6ce1edf --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Arguments.h @@ -0,0 +1,175 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_ARGUMENTS_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_ARGUMENTS_H__ + +#include <map> +#include <string> +#include <vector> + +#include "ClDevice.h" +#include "GpuObject.h" +#include "OpenclWrapper.h" + +#include "AccessType.h" +#include "Types.h" +#include "Util.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class ArgumentsBinder +{ +public: + virtual absl::Status SetInt(const std::string &name, int value) = 0; + virtual absl::Status SetFloat(const std::string &name, float value) = 0; + virtual ~ArgumentsBinder() = default; +}; + +class Arguments : public ArgumentsBinder +{ +public: + Arguments() = default; + void AddFloat(const std::string &name, float value = 0.0f); + void AddInt(const std::string &name, int value = 0); + void AddObjectRef(const std::string &name, AccessType access_type, + GPUObjectDescriptorPtr &&descriptor_ptr); + void AddObject(const std::string &name, GPUObjectDescriptorPtr &&descriptor_ptr); + + absl::Status SetInt(const std::string &name, int value) override; + absl::Status SetFloat(const std::string &name, float value) override; + absl::Status SetObjectRef(const std::string &name, const GPUObject *object); + + absl::Status Bind(cl_kernel kernel, int offset = 0); + + void RenameArgs(const std::string &postfix, std::string *code) const; + absl::Status Merge(Arguments &&args, const std::string &postfix); + + absl::Status AllocateObjects(CLContext *context); + void ReleaseCPURepresentation(); + absl::Status TransformToCLCode(const DeviceInfo &device_info, + const std::map<std::string, std::string> &linkables, + std::string *code); + + // Move only + Arguments(Arguments &&args); + Arguments &operator=(Arguments &&args); + Arguments(const Arguments &) = delete; + Arguments &operator=(const Arguments &) = delete; + + ~Arguments() override = default; + +private: + void AddBuffer(const std::string &name, const GPUBufferDescriptor &desc); + void AddImage2D(const std::string &name, const GPUImage2DDescriptor &desc); + void AddImage2DArray(const std::string &name, const GPUImage2DArrayDescriptor &desc); + void AddImage3D(const std::string &name, const GPUImage3DDescriptor &desc); + void AddImageBuffer(const std::string &name, const GPUImageBufferDescriptor &desc); + void AddCustomMemory(const std::string &name, const GPUCustomMemoryDescriptor &desc); + + absl::Status SetImage2D(const std::string &name, cl_mem memory); + absl::Status SetBuffer(const std::string &name, cl_mem memory); + absl::Status SetImage2DArray(const std::string &name, cl_mem memory); + absl::Status SetImage3D(const std::string &name, cl_mem memory); + absl::Status SetImageBuffer(const std::string &name, cl_mem memory); + absl::Status SetCustomMemory(const std::string &name, cl_mem memory); + + std::string GetListOfArgs(); + + std::string AddActiveArgument(const std::string &arg_name, bool use_f32_for_halfs); + void AddGPUResources(const std::string &name, const GPUResources &resources); + + absl::Status SetGPUResources(const std::string &name, const GPUResourcesWithValue &resources); + + absl::Status AddObjectArgs(); + + void ResolveArgsPass(const DeviceInfo &device_info, std::string *code); + absl::Status ResolveSelectorsPass(const std::map<std::string, std::string> &linkables, + std::string *code); + + absl::Status ResolveSelector(const std::map<std::string, std::string> &linkables, + const std::string &object_name, const std::string &selector, + const std::vector<std::string> &args, + const std::vector<std::string> &template_args, std::string *result); + + void ResolveObjectNames(const std::string &object_name, + const std::vector<std::string> &member_names, std::string *code); + + GPUObjectDescriptor *GetObjectDescriptor(const std::string &object_name) const; + + static constexpr char kArgsPrefix[] = "args."; + + struct IntValue + { + int value; + + // many uniforms generated automatically and not used + // to reduce amount of data transferred we adding this optimization + bool active = false; + + // offset to shared uniform storage. + uint32_t offset = -1; + }; + std::map<std::string, IntValue> int_values_; + std::vector<int32_t> shared_int4s_data_; + + struct FloatValue + { + float value; + + // many uniforms generated automatically and not used + // to reduce amount of data transferred we adding this optimization + bool active = false; + + // offset to shared uniform storage. + uint32_t offset = -1; + }; + std::map<std::string, FloatValue> float_values_; + std::vector<float> shared_float4s_data_; + + std::map<std::string, GPUBufferDescriptor> buffers_; + std::map<std::string, GPUImage2DDescriptor> images2d_; + std::map<std::string, GPUImage2DArrayDescriptor> image2d_arrays_; + std::map<std::string, GPUImage3DDescriptor> images3d_; + std::map<std::string, GPUImageBufferDescriptor> image_buffers_; + std::map<std::string, GPUCustomMemoryDescriptor> custom_memories_; + + struct ObjectRefArg + { + GPUObjectDescriptorPtr descriptor; + }; + std::map<std::string, ObjectRefArg> object_refs_; + + struct ObjectArg + { + GPUObjectPtr obj_ptr; + GPUObjectDescriptorPtr descriptor; + }; + std::map<std::string, ObjectArg> objects_; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_ARGUMENTS_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Buffer.cc b/runtime/onert/backend/gpu_cl/open_cl/Buffer.cc new file mode 100644 index 000000000..64c071921 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Buffer.cc @@ -0,0 +1,234 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Buffer.h" + +#include <string> + +#include "ClContext.h" +#include "DataType.h" +#include "GpuObject.h" +#include "Util.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +absl::Status CreateBuffer(size_t size_in_bytes, bool gpu_read_only, const void *data, + CLContext *context, Buffer *result) +{ + cl_mem buffer; + RETURN_IF_ERROR(CreateCLBuffer(context->context(), size_in_bytes, gpu_read_only, + const_cast<void *>(data), &buffer)); + *result = Buffer(buffer, size_in_bytes); + + return absl::OkStatus(); +} + +} // namespace + +BufferDescriptor::BufferDescriptor(BufferDescriptor &&desc) + : GPUObjectDescriptor(std::move(desc)), element_type(desc.element_type), + element_size(desc.element_size), memory_type(desc.memory_type), + attributes(std::move(desc.attributes)), size(desc.size), data(std::move(desc.data)) +{ +} + +BufferDescriptor &BufferDescriptor::operator=(BufferDescriptor &&desc) +{ + if (this != &desc) + { + std::swap(element_type, desc.element_type); + std::swap(element_size, desc.element_size); + std::swap(memory_type, desc.memory_type); + attributes = std::move(desc.attributes); + std::swap(size, desc.size); + data = std::move(desc.data); + GPUObjectDescriptor::operator=(std::move(desc)); + } + return *this; +} + +void BufferDescriptor::Release() { data.clear(); } + +GPUResources BufferDescriptor::GetGPUResources() const +{ + GPUResources resources; + GPUBufferDescriptor desc; + desc.data_type = element_type; + desc.access_type = access_type_; + desc.element_size = element_size; + desc.memory_type = memory_type; + desc.attributes = attributes; + resources.buffers.push_back({"buffer", desc}); + return resources; +} + +absl::Status BufferDescriptor::PerformSelector(const std::string &selector, + const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const +{ + if (selector == "Read") + { + return PerformReadSelector(args, result); + } + else if (selector == "GetPtr") + { + return PerformGetPtrSelector(args, template_args, result); + } + else + { + return absl::NotFoundError( + absl::StrCat("BufferDescriptor don't have selector with name - ", selector)); + } +} + +absl::Status BufferDescriptor::PerformReadSelector(const std::vector<std::string> &args, + std::string *result) const +{ + if (args.size() != 1) + { + return absl::NotFoundError( + absl::StrCat("BufferDescriptor Read require one argument, but ", args.size(), " was passed")); + } + *result = absl::StrCat("buffer[", args[0], "]"); + return absl::OkStatus(); +} + +absl::Status BufferDescriptor::PerformGetPtrSelector(const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const +{ + if (args.size() > 1) + { + return absl::NotFoundError(absl::StrCat( + "BufferDescriptor GetPtr require one or zero arguments, but ", args.size(), " was passed")); + } + if (template_args.size() > 1) + { + return absl::NotFoundError(absl::StrCat("BufferDescriptor GetPtr require one or zero teemplate " + "arguments, but ", + template_args.size(), " was passed")); + } + std::string conversion; + if (template_args.size() == 1) + { + const std::string type_name = ToCLDataType(element_type, element_size); + if (type_name != template_args[0]) + { + conversion = absl::StrCat("(", MemoryTypeToCLType(memory_type), " ", template_args[0], "*)&"); + } + } + if (args.empty()) + { + *result = absl::StrCat(conversion, "buffer"); + } + else if (conversion.empty()) + { + *result = absl::StrCat("(buffer + ", args[0], ")"); + } + else + { + *result = absl::StrCat(conversion, "buffer[", args[0], "]"); + } + return absl::OkStatus(); +} + +absl::Status BufferDescriptor::CreateGPUObject(CLContext *context, GPUObjectPtr *result) const +{ + Buffer gpu_buffer; + RETURN_IF_ERROR(gpu_buffer.CreateFromBufferDescriptor(*this, context)); + *result = absl::make_unique<Buffer>(std::move(gpu_buffer)); + return absl::OkStatus(); +} + +Buffer::Buffer(cl_mem buffer, size_t size_in_bytes) : buffer_(buffer), size_(size_in_bytes) {} + +Buffer::Buffer(Buffer &&buffer) : buffer_(buffer.buffer_), size_(buffer.size_) +{ + buffer.buffer_ = nullptr; + buffer.size_ = 0; +} + +Buffer &Buffer::operator=(Buffer &&buffer) +{ + if (this != &buffer) + { + Release(); + std::swap(size_, buffer.size_); + std::swap(buffer_, buffer.buffer_); + } + return *this; +} + +void Buffer::Release() +{ + if (buffer_) + { + clReleaseMemObject(buffer_); + buffer_ = nullptr; + size_ = 0; + } +} + +absl::Status Buffer::GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const +{ + const auto *buffer_desc = dynamic_cast<const BufferDescriptor *>(obj_ptr); + if (!buffer_desc) + { + return absl::InvalidArgumentError("Expected BufferDescriptor on input."); + } + + resources->buffers.push_back({"buffer", buffer_}); + return absl::OkStatus(); +} + +absl::Status Buffer::CreateFromBufferDescriptor(const BufferDescriptor &desc, CLContext *context) +{ + bool read_only = desc.memory_type == MemoryType::CONSTANT; + uint8_t *data_ptr = desc.data.empty() ? nullptr : const_cast<unsigned char *>(desc.data.data()); + size_ = desc.size; + return CreateCLBuffer(context->context(), desc.size, read_only, data_ptr, &buffer_); +} + +absl::Status CreateReadOnlyBuffer(size_t size_in_bytes, CLContext *context, Buffer *result) +{ + return CreateBuffer(size_in_bytes, true, nullptr, context, result); +} + +absl::Status CreateReadOnlyBuffer(size_t size_in_bytes, const void *data, CLContext *context, + Buffer *result) +{ + return CreateBuffer(size_in_bytes, true, data, context, result); +} + +absl::Status CreateReadWriteBuffer(size_t size_in_bytes, CLContext *context, Buffer *result) +{ + return CreateBuffer(size_in_bytes, false, nullptr, context, result); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Buffer.h b/runtime/onert/backend/gpu_cl/open_cl/Buffer.h new file mode 100644 index 000000000..39e97be6d --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Buffer.h @@ -0,0 +1,121 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_BUFFER_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_BUFFER_H__ + +#include "absl/strings/str_cat.h" +#include "absl/types/span.h" + +#include "ClCommandQueue.h" +#include "ClContext.h" +#include "GpuObject.h" +#include "OpenclWrapper.h" +#include "DataType.h" +#include "Util.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +struct BufferDescriptor : public GPUObjectDescriptor +{ + DataType element_type; + int element_size; + MemoryType memory_type = MemoryType::GLOBAL; + std::vector<std::string> attributes; + + // optional + int size = 0; + std::vector<uint8_t> data; + + BufferDescriptor() = default; + BufferDescriptor(const BufferDescriptor &) = default; + BufferDescriptor &operator=(const BufferDescriptor &) = default; + BufferDescriptor(BufferDescriptor &&desc); + BufferDescriptor &operator=(BufferDescriptor &&desc); + + absl::Status PerformSelector(const std::string &selector, const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const override; + + GPUResources GetGPUResources() const override; + absl::Status PerformReadSelector(const std::vector<std::string> &args, std::string *result) const; + absl::Status PerformGetPtrSelector(const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const; + + absl::Status CreateGPUObject(CLContext *context, GPUObjectPtr *result) const override; + void Release() override; +}; + +// Buffer represent linear GPU data storage with arbitrary data format. +// Buffer is moveable but not copyable. +class Buffer : public GPUObject +{ +public: + Buffer() {} // just for using Buffer as a class members + Buffer(cl_mem buffer, size_t size_in_bytes); + + // Move only + Buffer(Buffer &&buffer); + Buffer &operator=(Buffer &&buffer); + Buffer(const Buffer &) = delete; + Buffer &operator=(const Buffer &) = delete; + + virtual ~Buffer() { Release(); } + + // for profiling and memory statistics + uint64_t GetMemorySizeInBytes() const { return size_; } + + cl_mem GetMemoryPtr() const { return buffer_; } + + // Writes data to a buffer. Data should point to a region that + // has exact size in bytes as size_in_bytes(constructor parameter). + template <typename T> absl::Status WriteData(CLCommandQueue *queue, const std::vector<T> *data); + + // Reads data from Buffer into CPU memory. + template <typename T> absl::Status ReadData(CLCommandQueue *queue, std::vector<T> *result) const; + + absl::Status GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const override; + + absl::Status CreateFromBufferDescriptor(const BufferDescriptor &desc, CLContext *context); + +private: + void Release(); + + cl_mem buffer_ = nullptr; + size_t size_ = 0; +}; + +absl::Status CreateReadOnlyBuffer(size_t size_in_bytes, CLContext *context, Buffer *result); + +absl::Status CreateReadOnlyBuffer(size_t size_in_bytes, const void *data, CLContext *context, + Buffer *result); + +absl::Status CreateReadWriteBuffer(size_t size_in_bytes, CLContext *context, Buffer *result); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_BUFFER_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClCommandQueue.cc b/runtime/onert/backend/gpu_cl/open_cl/ClCommandQueue.cc new file mode 100644 index 000000000..d147b7b13 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClCommandQueue.cc @@ -0,0 +1,359 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClCommandQueue.h" + +#include <algorithm> +#include <map> +#include <string> +#include <vector> +#include <limits> + +#include "absl/strings/str_cat.h" +#include "ClDevice.h" +#include "ClEvent.h" +#include "Util.h" +#include "Types.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +using namespace std; + +CLCommandQueue::CLCommandQueue(cl_command_queue queue, bool has_ownership) + : queue_(queue), has_ownership_(has_ownership) +{ +} + +CLCommandQueue::CLCommandQueue(CLCommandQueue &&queue) + : queue_(queue.queue_), has_ownership_(queue.has_ownership_) +{ + queue.queue_ = nullptr; +} + +CLCommandQueue &CLCommandQueue::operator=(CLCommandQueue &&queue) +{ + if (this != &queue) + { + Release(); + std::swap(queue_, queue.queue_); + has_ownership_ = queue.has_ownership_; + } + return *this; +} + +CLCommandQueue::~CLCommandQueue() { Release(); } + +void CLCommandQueue::Release() +{ + if (has_ownership_ && queue_) + { + clReleaseCommandQueue(queue_); + queue_ = nullptr; + } +} + +absl::Status CLCommandQueue::Dispatch(const CLKernel &kernel, const int3 &work_groups_count, + const int3 &work_group_size, CLEvent *event) +{ + std::vector<size_t> local(3); + std::vector<size_t> global(3); + for (int i = 0; i < 3; ++i) + { + local[i] = work_group_size[i]; + global[i] = work_groups_count[i] * work_group_size[i]; + } + cl_event resulting_event; + const int error_code = + clEnqueueNDRangeKernel(queue_, kernel.kernel(), 3, nullptr, global.data(), local.data(), 0, + nullptr, event ? &resulting_event : nullptr); + if (event) + { + *event = CLEvent(resulting_event); + } + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to clEnqueueNDRangeKernel - ", CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status CLCommandQueue::Dispatch(const CLKernel &kernel, const int3 &work_groups_count, + const int3 &work_group_size) +{ + return Dispatch(kernel, work_groups_count, work_group_size, nullptr); +} + +absl::Status CLCommandQueue::EnqueueEvent(CLEvent *event) +{ + cl_event resulting_event; + const int error_code = clEnqueueMarker(queue_, &resulting_event); + *event = CLEvent(resulting_event); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to clEnqueueMarker - ", CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status CLCommandQueue::EnqueueWriteImage(cl_mem memory, int3 region, const void *data) +{ + const size_t origin[] = {0, 0, 0}; + const size_t r[] = {static_cast<size_t>(region.x), static_cast<size_t>(region.y), + static_cast<size_t>(region.z)}; + auto error_code = + clEnqueueWriteImage(queue_, memory, CL_TRUE, origin, r, 0, 0, data, 0, nullptr, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to upload data to GPU (clEnqueueWriteImage) - ", + CLErrorCodeToString(error_code))); + } + + return absl::OkStatus(); +} + +absl::Status CLCommandQueue::EnqueueReadImage(cl_mem memory, int3 region, void *data) +{ + const size_t origin[] = {0, 0, 0}; + const size_t r[] = {static_cast<size_t>(region.x), static_cast<size_t>(region.y), + static_cast<size_t>(region.z)}; + auto error_code = + clEnqueueReadImage(queue_, memory, CL_TRUE, origin, r, 0, 0, data, 0, nullptr, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to read data from GPU (clEnqueueReadImage) - ", + CLErrorCodeToString(error_code))); + } + + return absl::OkStatus(); +} + +absl::Status CLCommandQueue::EnqueueWriteBuffer(cl_mem memory, size_t size_in_bytes, + const void *data) +{ + auto error_code = + clEnqueueWriteBuffer(queue_, memory, CL_TRUE, 0, size_in_bytes, data, 0, nullptr, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to upload data to GPU (clEnqueueWriteBuffer) - ", + CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status CLCommandQueue::EnqueueReadBuffer(cl_mem memory, size_t size_in_bytes, void *data) +{ + auto error_code = + clEnqueueReadBuffer(queue_, memory, CL_TRUE, 0, size_in_bytes, data, 0, nullptr, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to read data from GPU (clEnqueueReadBuffer) - ", + CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status CLCommandQueue::WaitForCompletion() +{ + auto error_code = clFinish(queue_); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to clFinish - ", CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +ProfilingCommandQueue::ProfilingCommandQueue(cl_command_queue queue) : CLCommandQueue(queue, true) +{ + events_.reserve(128); +} + +ProfilingCommandQueue::ProfilingCommandQueue(ProfilingCommandQueue &&queue) + : CLCommandQueue(std::move(queue)), events_(std::move(queue.events_)), + current_label_(std::move(queue.current_label_)) +{ +} + +ProfilingCommandQueue &ProfilingCommandQueue::operator=(ProfilingCommandQueue &&queue) +{ + if (this != &queue) + { + events_ = std::move(queue.events_); + current_label_ = std::move(queue.current_label_); + CLCommandQueue::operator=(std::move(queue)); + } + return *this; +} + +void ProfilingCommandQueue::SetEventsLabel(const std::string &name) { current_label_ = name; } + +void ProfilingCommandQueue::ResetMeasurements() { events_.clear(); } + +absl::Status ProfilingCommandQueue::Dispatch(const CLKernel &kernel, const int3 &work_groups_count, + const int3 &work_group_size) +{ + events_.push_back(CLEvent()); + RETURN_IF_ERROR(CLCommandQueue::Dispatch(kernel, work_groups_count, work_group_size, + &events_[events_.size() - 1])); + events_.back().SetName(current_label_); + return absl::OkStatus(); +} + +absl::Status +ProfilingCommandQueue::GetBestWorkGroupIndex(const CLKernel &kernel, const DeviceInfo &device_info, + const std::vector<int3> &work_groups_count, + const std::vector<int3> &work_group_sizes, int *index) +{ + // Some Adreno 3xx can have wrong numbers for some events + const bool possible_bug_with_events = device_info.IsAdreno3xx(); + events_.resize(work_group_sizes.size()); + for (size_t i = 0; i < work_group_sizes.size(); ++i) + { + RETURN_IF_ERROR( + CLCommandQueue::Dispatch(kernel, work_groups_count[i], work_group_sizes[i], &events_[i])); + + // reducing the speed of memory leak on Mali for some kernels + if (device_info.IsMali() && i % 8 == 7) + { + events_[i - 7].Wait(); + } + if (possible_bug_with_events) + { + // We are trying to increase probability for correct result. + RETURN_IF_ERROR(WaitForCompletion()); + } + } + + RETURN_IF_ERROR(WaitForCompletion()); + + // To release memory of some kernel pool on Mali. + if (device_info.IsMali()) + { + RETURN_IF_ERROR(kernel.ReInit()); + } + + int minimum_index = 0; + double minimum_time = std::numeric_limits<double>::max(); + if (possible_bug_with_events) + { // we will try to cut out suspicious results + double average_time = 0.0; + int average_samples_count = 0; + for (size_t i = 0; i < work_group_sizes.size(); ++i) + { + if (events_[i].GetEventTimeMs() < 100 * 1000) + { // 100 sec + average_time += events_[i].GetEventTimeMs(); + average_samples_count++; + } + } + if (average_samples_count == 0) + { + throw std::runtime_error("It cannot be divided by zero"); + } + else + { + average_time /= average_samples_count; + } + + for (size_t i = 0; i < work_group_sizes.size(); ++i) + { + double time = events_[i].GetEventTimeMs(); + if (time < minimum_time && time >= 0.1 * average_time) + { + minimum_index = i; + minimum_time = time; + } + } + } + else + { + for (size_t i = 0; i < work_group_sizes.size(); ++i) + { + double time = events_[i].GetEventTimeMs(); + if (time < minimum_time) + { + minimum_index = i; + minimum_time = time; + } + } + } + + *index = minimum_index; + + return absl::OkStatus(); +} + +absl::Status CreateCLCommandQueue(const CLDevice &device, const CLContext &context, + CLCommandQueue *result) +{ + int error_code; + cl_command_queue queue = clCreateCommandQueue(context.context(), device.id(), 0, &error_code); + if (!queue) + { + return absl::UnknownError( + absl::StrCat("Failed to create a command queue - ", CLErrorCodeToString(error_code))); + } + *result = CLCommandQueue(queue, true); + return absl::OkStatus(); +} + +double ProfilingCommandQueue::GetQueueExecutionTimeMs() const +{ + const uint64_t start = events_.front().GetStartedTimeNs(); + const uint64_t end = events_.back().GetFinishedTimeNs(); + const uint64_t time_ns = (end - start); + + return static_cast<double>(time_ns) / 1000000.0; +} + +double ProfilingCommandQueue::GetSumOfEventsTimeMs() const +{ + double sum = 0.0; + for (uint32_t i = 0; i < events_.size(); ++i) + { + sum += events_[i].GetEventTimeMs(); + } + return sum; +} + +absl::Status CreateProfilingCommandQueue(const CLDevice &device, const CLContext &context, + ProfilingCommandQueue *result) +{ + int error_code; + cl_command_queue queue = + clCreateCommandQueue(context.context(), device.id(), CL_QUEUE_PROFILING_ENABLE, &error_code); + if (!queue) + { + return absl::UnknownError( + absl::StrCat("Failed to create a command queue - ", CLErrorCodeToString(error_code))); + } + + *result = ProfilingCommandQueue(queue); + return absl::OkStatus(); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClCommandQueue.h b/runtime/onert/backend/gpu_cl/open_cl/ClCommandQueue.h new file mode 100644 index 000000000..81f93fd23 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClCommandQueue.h @@ -0,0 +1,157 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_COMMAND_QUEUE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_COMMAND_QUEUE_H__ + +#include <cstdint> +#include <string> +#include <vector> + +#include "absl/time/time.h" +#include "ClContext.h" +#include "ClDevice.h" +#include "ClEvent.h" +#include "ClKernel.h" +#include "OpenclWrapper.h" +#include "Types.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +struct ProfilingInfo +{ + struct DispatchInfo + { + std::string label; + absl::Duration duration; + }; + + std::vector<DispatchInfo> dispatches; + + absl::Duration GetTotalTime() const; + + // Returns report (string of lines delimited by \n) + // This method uses GPU counters and measure GPU time only. + // Report has next structure: + // Per kernel timing(K kernels): + // conv2d 3.2ms + // ... + // -------------------- + // Accumulated time per operation type: + // conv2d - 14.5ms + // .... + // -------------------- + // Ideal total time: 23.4ms // Total time for all kernels + std::string GetDetailedReport() const; +}; + +// A wrapper around opencl command queue +class CLCommandQueue +{ +public: + CLCommandQueue() {} + CLCommandQueue(cl_command_queue queue, bool has_ownership); + + // Move only + CLCommandQueue(CLCommandQueue &&queue); + CLCommandQueue &operator=(CLCommandQueue &&queue); + CLCommandQueue(const CLCommandQueue &) = delete; + CLCommandQueue &operator=(const CLCommandQueue &) = delete; + + virtual ~CLCommandQueue(); + + cl_command_queue queue() const { return queue_; } + + virtual absl::Status Dispatch(const CLKernel &kernel, const int3 &work_groups_count, + const int3 &work_group_size); + + absl::Status Dispatch(const CLKernel &kernel, const int3 &work_groups_count, + const int3 &work_group_size, CLEvent *event); + + absl::Status EnqueueEvent(CLEvent *event); + + absl::Status EnqueueWriteImage(cl_mem memory, int3 region, const void *data); + absl::Status EnqueueReadImage(cl_mem memory, int3 region, void *data); + + absl::Status EnqueueWriteBuffer(cl_mem memory, size_t size_in_bytes, const void *data); + absl::Status EnqueueReadBuffer(cl_mem memory, size_t size_in_bytes, void *data); + + absl::Status WaitForCompletion(); + +protected: + void Release(); + + cl_command_queue queue_ = nullptr; + bool has_ownership_ = false; +}; + +class ProfilingCommandQueue : public CLCommandQueue +{ +public: + ProfilingCommandQueue() {} + explicit ProfilingCommandQueue(cl_command_queue queue); + + // Move only + ProfilingCommandQueue(ProfilingCommandQueue &&queue); + ProfilingCommandQueue &operator=(ProfilingCommandQueue &&queue); + ProfilingCommandQueue(const ProfilingCommandQueue &) = delete; + ProfilingCommandQueue &operator=(const ProfilingCommandQueue &) = delete; + + absl::Status Dispatch(const CLKernel &kernel, const int3 &work_groups_count, + const int3 &work_group_size) override; + + // will write index for fastest work_group among work_group_sizes + absl::Status GetBestWorkGroupIndex(const CLKernel &kernel, const DeviceInfo &device_info, + const std::vector<int3> &work_groups_count, + const std::vector<int3> &work_group_sizes, int *index); + + // call ResetMeasurements() to start new seriese of measurements + void ResetMeasurements(); + + double GetQueueExecutionTimeMs() const; + + // Difference from GetQueueExecutionTimeMs is that this number doesn't include + // time between kernels(kernels launches or preparing) on GPU. Usually, this + // time should be 5-10% better than GetQueueExecutionTimeMs, because 5-10% + // spend on something else(maybe kernels launches or preparing) + double GetSumOfEventsTimeMs() const; + + // This label will be used for all subsequent dispatches. + void SetEventsLabel(const std::string &name); + +private: + std::vector<CLEvent> events_; + std::string current_label_; +}; + +absl::Status CreateCLCommandQueue(const CLDevice &device, const CLContext &context, + CLCommandQueue *result); + +absl::Status CreateProfilingCommandQueue(const CLDevice &device, const CLContext &context, + ProfilingCommandQueue *result); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_COMMAND_QUEUE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClContext.cc b/runtime/onert/backend/gpu_cl/open_cl/ClContext.cc new file mode 100644 index 000000000..3289ff914 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClContext.cc @@ -0,0 +1,177 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClContext.h" + +#include "absl/strings/str_cat.h" +#include "ClImageFormat.h" +#include "Util.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::vector<cl_image_format> GetSupportedImage2DFormats(cl_context context, cl_mem_flags flags) +{ + cl_uint num_image_formats; + cl_int error = clGetSupportedImageFormats(context, flags, CL_MEM_OBJECT_IMAGE2D, 0, nullptr, + &num_image_formats); + if (error != CL_SUCCESS) + { + return {}; + } + + std::vector<cl_image_format> result(num_image_formats); + error = clGetSupportedImageFormats(context, flags, CL_MEM_OBJECT_IMAGE2D, num_image_formats, + &result[0], nullptr); + if (error != CL_SUCCESS) + { + return {}; + } + return result; +} + +bool IsEqualToImageFormat(cl_image_format image_format, DataType data_type, int num_channels) +{ + return image_format.image_channel_data_type == ToImageChannelType(data_type) && + image_format.image_channel_order == ToChannelOrder(num_channels); +} + +void AddSupportedImageFormats(cl_context context, DeviceInfo *info) +{ + auto supported_formats = GetSupportedImage2DFormats(context, CL_MEM_READ_WRITE); + for (auto format : supported_formats) + { + info->supports_r_f16_tex2d = + info->supports_r_f16_tex2d || IsEqualToImageFormat(format, DataType::FLOAT16, 1); + info->supports_rg_f16_tex2d = + info->supports_rg_f16_tex2d || IsEqualToImageFormat(format, DataType::FLOAT16, 2); + info->supports_rgb_f16_tex2d = + info->supports_rgb_f16_tex2d || IsEqualToImageFormat(format, DataType::FLOAT16, 3); + info->supports_rgba_f16_tex2d = + info->supports_rgba_f16_tex2d || IsEqualToImageFormat(format, DataType::FLOAT16, 4); + info->supports_r_f32_tex2d = + info->supports_r_f32_tex2d || IsEqualToImageFormat(format, DataType::FLOAT32, 1); + info->supports_rg_f32_tex2d = + info->supports_rg_f32_tex2d || IsEqualToImageFormat(format, DataType::FLOAT32, 2); + info->supports_rgb_f32_tex2d = + info->supports_rgb_f32_tex2d || IsEqualToImageFormat(format, DataType::FLOAT32, 3); + info->supports_rgba_f32_tex2d = + info->supports_rgba_f32_tex2d || IsEqualToImageFormat(format, DataType::FLOAT32, 4); + } +} + +absl::Status CreateCLContext(const CLDevice &device, cl_context_properties *properties, + CLContext *result) +{ + int error_code; + cl_device_id device_id = device.id(); + cl_context context = clCreateContext(properties, 1, &device_id, nullptr, nullptr, &error_code); + if (!context) + { + return absl::UnknownError( + absl::StrCat("Failed to create a compute context - ", CLErrorCodeToString(error_code))); + } + AddSupportedImageFormats(context, &device.info_); + + *result = CLContext(context, true); + return absl::OkStatus(); +} + +} // namespace + +CLContext::CLContext(cl_context context, bool has_ownership) + : context_(context), has_ownership_(has_ownership) +{ +} + +CLContext::CLContext(CLContext &&context) + : context_(context.context_), has_ownership_(context.has_ownership_) +{ + context.context_ = nullptr; +} + +CLContext &CLContext::operator=(CLContext &&context) +{ + if (this != &context) + { + Release(); + std::swap(context_, context.context_); + has_ownership_ = context.has_ownership_; + } + return *this; +} + +CLContext::~CLContext() { Release(); } + +void CLContext::Release() +{ + if (has_ownership_ && context_) + { + clReleaseContext(context_); + context_ = nullptr; + } +} + +bool CLContext::IsFloatTexture2DSupported(int num_channels, DataType data_type, + cl_mem_flags flags) const +{ + auto supported_formats = GetSupportedImage2DFormats(context_, flags); + for (auto format : supported_formats) + { + if (format.image_channel_data_type == ToImageChannelType(data_type) && + format.image_channel_order == ToChannelOrder(num_channels)) + { + return true; + } + } + + return false; +} + +absl::Status CreateCLContext(const CLDevice &device, CLContext *result) +{ + return CreateCLContext(device, nullptr, result); +} + +absl::Status CreateCLGLContext(const CLDevice &device, cl_context_properties egl_context, + cl_context_properties egl_display, CLContext *result) +{ + if (!device.SupportsExtension("cl_khr_gl_sharing")) + { + return absl::UnavailableError("Device doesn't support CL-GL sharing."); + } + cl_context_properties platform = reinterpret_cast<cl_context_properties>(device.platform()); + cl_context_properties props[] = {CL_GL_CONTEXT_KHR, + egl_context, + CL_EGL_DISPLAY_KHR, + egl_display, + CL_CONTEXT_PLATFORM, + platform, + 0}; + return CreateCLContext(device, props, result); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClContext.h b/runtime/onert/backend/gpu_cl/open_cl/ClContext.h new file mode 100644 index 000000000..cf1d0d2d2 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClContext.h @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_CONTEXT_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_CONTEXT_H__ + +#include "ClDevice.h" +#include "OpenclWrapper.h" +#include "DataType.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// A RAII wrapper around opencl context +class CLContext +{ +public: + CLContext() {} + CLContext(cl_context context, bool has_ownership); + + // Move only + CLContext(CLContext &&context); + CLContext &operator=(CLContext &&context); + CLContext(const CLContext &) = delete; + CLContext &operator=(const CLContext &) = delete; + + ~CLContext(); + + cl_context context() const { return context_; } + + bool IsFloatTexture2DSupported(int num_channels, DataType data_type, + cl_mem_flags flags = CL_MEM_READ_WRITE) const; + +private: + void Release(); + + cl_context context_ = nullptr; + bool has_ownership_ = false; +}; + +absl::Status CreateCLContext(const CLDevice &device, CLContext *result); +absl::Status CreateCLGLContext(const CLDevice &device, cl_context_properties egl_context, + cl_context_properties egl_display, CLContext *result); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_CONTEXT_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClDevice.cc b/runtime/onert/backend/gpu_cl/open_cl/ClDevice.cc new file mode 100644 index 000000000..8dede139c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClDevice.cc @@ -0,0 +1,448 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClDevice.h" + +#include <algorithm> +#include <string> +#include <vector> + +#include "Util.h" +#include "Status.h" + +#include "absl/strings/numbers.h" +#include "absl/strings/str_split.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <> std::string GetDeviceInfo<std::string>(cl_device_id id, cl_device_info info) +{ + size_t size; + cl_int error = clGetDeviceInfo(id, info, 0, nullptr, &size); + if (error != CL_SUCCESS) + { + return ""; + } + + std::string result(size - 1, 0); + error = clGetDeviceInfo(id, info, size, &result[0], nullptr); + if (error != CL_SUCCESS) + { + return ""; + } + return result; +} + +namespace +{ +template <typename T> T GetPlatformInfo(cl_platform_id id, cl_platform_info info) +{ + T result; + cl_int error = clGetPlatformInfo(id, info, sizeof(T), &result, nullptr); + if (error != CL_SUCCESS) + { + return -1; + } + return result; +} + +std::string GetPlatformInfo(cl_platform_id id, cl_platform_info info) +{ + size_t size; + cl_int error = clGetPlatformInfo(id, info, 0, nullptr, &size); + if (error != CL_SUCCESS) + { + return ""; + } + + std::string result(size - 1, 0); + error = clGetPlatformInfo(id, info, size, &result[0], nullptr); + if (error != CL_SUCCESS) + { + return ""; + } + return result; +} + +void GetDeviceWorkDimsSizes(cl_device_id id, int3 *result) +{ + int dims_count = GetDeviceInfo<cl_uint>(id, CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS); + if (dims_count < 3) + { + return; + } + std::vector<size_t> limits(dims_count); + cl_int error = clGetDeviceInfo(id, CL_DEVICE_MAX_WORK_ITEM_SIZES, sizeof(size_t) * dims_count, + limits.data(), nullptr); + if (error != CL_SUCCESS) + { + return; + } + // dims_count must be at least 3 according to spec + result->x = limits[0]; + result->y = limits[1]; + result->z = limits[2]; +} + +OpenCLVersion ParseCLVersion(const std::string &version) +{ + const auto first_dot_pos = version.find_first_of('.'); + if (first_dot_pos == std::string::npos) + { + return OpenCLVersion::CL_1_0; + } + const int major = version[first_dot_pos - 1] - '0'; + const int minor = version[first_dot_pos + 1] - '0'; + + if (major == 1) + { + if (minor == 2) + { + return OpenCLVersion::CL_1_2; + } + else if (minor == 1) + { + return OpenCLVersion::CL_1_1; + } + else + { + return OpenCLVersion::CL_1_0; + } + } + else if (major == 2) + { + if (minor == 2) + { + return OpenCLVersion::CL_2_2; + } + else if (minor == 1) + { + return OpenCLVersion::CL_2_1; + } + else + { + return OpenCLVersion::CL_2_0; + } + } + else if (major == 3) + { + return OpenCLVersion::CL_3_0; + } + else + { + return OpenCLVersion::CL_1_0; + } +} + +Vendor ParseVendor(const std::string &device_name, const std::string &vendor_name) +{ + std::string d_name = device_name; + std::string v_name = vendor_name; + std::transform(d_name.begin(), d_name.end(), d_name.begin(), ::tolower); + std::transform(v_name.begin(), v_name.end(), v_name.begin(), ::tolower); + if (d_name.find("qualcomm") != std::string::npos || v_name.find("qualcomm") != std::string::npos) + { + return Vendor::kQualcomm; + } + else if (d_name.find("mali") != std::string::npos || v_name.find("mali") != std::string::npos) + { + return Vendor::kMali; + } + else if (d_name.find("power") != std::string::npos || v_name.find("power") != std::string::npos) + { + return Vendor::kPowerVR; + } + else if (d_name.find("nvidia") != std::string::npos || v_name.find("nvidia") != std::string::npos) + { + return Vendor::kNvidia; + } + else if (d_name.find("advanced micro devices") != std::string::npos || + v_name.find("advanced micro devices") != std::string::npos) + { + return Vendor::kAMD; + } + else if (d_name.find("intel") != std::string::npos || v_name.find("intel") != std::string::npos) + { + return Vendor::kIntel; + } + else + { + return Vendor::kUnknown; + } +} + +// check that gpu_version belong to range min_version-max_version +// min_version is included and max_version is excluded. +bool IsGPUVersionInRange(int gpu_version, int min_version, int max_version) +{ + return gpu_version >= min_version && gpu_version < max_version; +} +} // namespace + +DeviceInfo DeviceInfoFromDeviceID(cl_device_id id) +{ + DeviceInfo info; + const auto device_name = GetDeviceInfo<std::string>(id, CL_DEVICE_NAME); + const auto vendor_name = GetDeviceInfo<std::string>(id, CL_DEVICE_VENDOR); + const auto opencl_c_version = GetDeviceInfo<std::string>(id, CL_DEVICE_OPENCL_C_VERSION); + info.vendor = ParseVendor(device_name, vendor_name); + if (info.vendor == Vendor::kQualcomm) + { + info.adreno_info = AdrenoInfo(opencl_c_version); + } + else if (info.vendor == Vendor::kMali) + { + info.mali_info = MaliInfo(device_name); + } + info.cl_version = ParseCLVersion(opencl_c_version); + info.extensions = absl::StrSplit(GetDeviceInfo<std::string>(id, CL_DEVICE_EXTENSIONS), ' '); + + info.supports_fp16 = false; + info.supports_image3d_writes = false; + for (const auto &ext : info.extensions) + { + if (ext == "cl_khr_fp16") + { + info.supports_fp16 = true; + } + if (ext == "cl_khr_3d_image_writes") + { + info.supports_image3d_writes = true; + } + } + + cl_device_fp_config f32_config = + GetDeviceInfo<cl_device_fp_config>(id, CL_DEVICE_SINGLE_FP_CONFIG); + info.supports_fp32_rtn = f32_config & CL_FP_ROUND_TO_NEAREST; + + if (info.supports_fp16) + { + cl_device_fp_config f16_config; + auto status = GetDeviceInfo<cl_device_fp_config>(id, CL_DEVICE_HALF_FP_CONFIG, &f16_config); + // AMD supports cl_khr_fp16 but CL_DEVICE_HALF_FP_CONFIG is empty. + if (status.ok() && info.vendor != Vendor::kAMD) + { + info.supports_fp16_rtn = f16_config & CL_FP_ROUND_TO_NEAREST; + } + else + { // happens on PowerVR + f16_config = f32_config; + info.supports_fp16_rtn = info.supports_fp32_rtn; + } + } + else + { + info.supports_fp16_rtn = false; + } + + if (info.vendor == Vendor::kPowerVR && !info.supports_fp16) + { + // PowerVR doesn't have full support of fp16 and so doesn't list this + // extension. But it can support fp16 in MADs and as buffers/textures types, + // so we will use it. + info.supports_fp16 = true; + info.supports_fp16_rtn = info.supports_fp32_rtn; + } + + if (!info.supports_image3d_writes && + ((info.vendor == Vendor::kQualcomm && + IsGPUVersionInRange(info.adreno_info.gpu_version, 400, 500)) || + info.vendor == Vendor::kNvidia)) + { + // in local tests Adreno 430 can write in image 3d, at least on small sizes, + // but it doesn't have cl_khr_3d_image_writes in list of available + // extensions + // The same for NVidia + info.supports_image3d_writes = true; + } + info.compute_units_count = GetDeviceInfo<cl_uint>(id, CL_DEVICE_MAX_COMPUTE_UNITS); + info.image2d_max_width = GetDeviceInfo<size_t>(id, CL_DEVICE_IMAGE2D_MAX_WIDTH); + info.image2d_max_height = GetDeviceInfo<size_t>(id, CL_DEVICE_IMAGE2D_MAX_HEIGHT); + info.buffer_max_size = GetDeviceInfo<cl_ulong>(id, CL_DEVICE_MAX_MEM_ALLOC_SIZE); + if (info.cl_version >= OpenCLVersion::CL_1_2) + { + info.image_buffer_max_size = GetDeviceInfo<size_t>(id, CL_DEVICE_IMAGE_MAX_BUFFER_SIZE); + info.image_array_max_layers = GetDeviceInfo<size_t>(id, CL_DEVICE_IMAGE_MAX_ARRAY_SIZE); + } + info.image3d_max_width = GetDeviceInfo<size_t>(id, CL_DEVICE_IMAGE3D_MAX_WIDTH); + info.image3d_max_height = GetDeviceInfo<size_t>(id, CL_DEVICE_IMAGE2D_MAX_HEIGHT); + info.image3d_max_depth = GetDeviceInfo<size_t>(id, CL_DEVICE_IMAGE3D_MAX_DEPTH); + int3 max_work_group_sizes; + GetDeviceWorkDimsSizes(id, &max_work_group_sizes); + info.max_work_group_size_x = max_work_group_sizes.x; + info.max_work_group_size_y = max_work_group_sizes.y; + info.max_work_group_size_z = max_work_group_sizes.z; + + if (info.IsIntel()) + { + if (info.SupportsExtension("cl_intel_required_subgroup_size")) + { + size_t sub_groups_count; + cl_int status = clGetDeviceInfo(id, 0x4108 /*CL_DEVICE_SUB_GROUP_SIZES_INTEL*/, 0, nullptr, + &sub_groups_count); + if (status == CL_SUCCESS) + { + std::vector<size_t> sub_group_sizes(sub_groups_count); + status = + clGetDeviceInfo(id, 0x4108 /*CL_DEVICE_SUB_GROUP_SIZES_INTEL*/, + sizeof(size_t) * sub_groups_count, sub_group_sizes.data(), nullptr); + if (status == CL_SUCCESS) + { + for (size_t i = 0; i < sub_groups_count; ++i) + { + info.supported_subgroup_sizes.push_back(sub_group_sizes[i]); + } + } + } + } + } + return info; +} + +CLDevice::CLDevice(cl_device_id id, cl_platform_id platform_id) + : info_(DeviceInfoFromDeviceID(id)), id_(id), platform_id_(platform_id) +{ +} + +CLDevice::CLDevice(const CLDevice &device) + : info_(device.info_), id_(device.id_), platform_id_(device.platform_id_) +{ +} + +CLDevice &CLDevice::operator=(const CLDevice &device) +{ + if (this != &device) + { + info_ = device.info_; + id_ = device.id_; + platform_id_ = device.platform_id_; + } + return *this; +} + +CLDevice::CLDevice(CLDevice &&device) + : info_(std::move(device.info_)), id_(device.id_), platform_id_(device.platform_id_) +{ + device.id_ = nullptr; + device.platform_id_ = nullptr; +} + +CLDevice &CLDevice::operator=(CLDevice &&device) +{ + if (this != &device) + { + id_ = nullptr; + platform_id_ = nullptr; + info_ = std::move(device.info_); + std::swap(id_, device.id_); + std::swap(platform_id_, device.platform_id_); + } + return *this; +} + +bool CLDevice::SupportsFP16() const { return info_.supports_fp16; } + +bool CLDevice::SupportsExtension(const std::string &extension) const +{ + return info_.SupportsExtension(extension); +} + +bool CLDevice::SupportsTextureArray() const { return info_.SupportsTextureArray(); } + +bool CLDevice::SupportsImageBuffer() const { return info_.SupportsImageBuffer(); } + +bool CLDevice::SupportsImage3D() const { return info_.SupportsImage3D(); } + +bool CLDevice::SupportsFP32RTN() const { return info_.supports_fp32_rtn; } + +bool CLDevice::SupportsFP16RTN() const { return info_.supports_fp16_rtn; } + +std::string CLDevice::GetPlatformVersion() const +{ + return GetPlatformInfo(platform_id_, CL_PLATFORM_VERSION); +} + +bool CLDevice::IsCL20OrHigher() const { return info_.IsCL20OrHigher(); } + +bool CLDevice::SupportsSubGroupWithSize(int sub_group_size) const +{ + return info_.SupportsSubGroupWithSize(sub_group_size); +} + +bool CLDevice::IsAdreno() const { return info_.IsAdreno(); } + +bool CLDevice::IsAdreno3xx() const { return info_.IsAdreno3xx(); } + +bool CLDevice::IsAdreno4xx() const { return info_.IsAdreno4xx(); } + +bool CLDevice::IsAdreno5xx() const { return info_.IsAdreno5xx(); } + +bool CLDevice::IsAdreno6xx() const { return info_.IsAdreno6xx(); } + +bool CLDevice::IsAdreno6xxOrHigher() const { return info_.IsAdreno6xxOrHigher(); } + +bool CLDevice::IsPowerVR() const { return info_.IsPowerVR(); } + +bool CLDevice::IsNvidia() const { return info_.IsNvidia(); } + +bool CLDevice::IsMali() const { return info_.IsMali(); } + +bool CLDevice::IsAMD() const { return info_.IsAMD(); } + +bool CLDevice::IsIntel() const { return info_.IsIntel(); } + +bool CLDevice::SupportsOneLayerTextureArray() const { return info_.SupportsOneLayerTextureArray(); } + +void CLDevice::DisableOneLayerTextureArray() +{ + info_.adreno_info.support_one_layer_texture_array = false; +} + +absl::Status CreateDefaultGPUDevice(CLDevice *result) +{ + cl_uint num_platforms; + clGetPlatformIDs(0, nullptr, &num_platforms); + if (num_platforms == 0) + { + return absl::UnknownError("No supported OpenCL platform."); + } + std::vector<cl_platform_id> platforms(num_platforms); + clGetPlatformIDs(num_platforms, platforms.data(), nullptr); + + cl_platform_id platform_id = platforms[0]; + cl_uint num_devices; + clGetDeviceIDs(platform_id, CL_DEVICE_TYPE_GPU, 0, nullptr, &num_devices); + if (num_devices == 0) + { + return absl::UnknownError("No GPU on current platform."); + } + + std::vector<cl_device_id> devices(num_devices); + clGetDeviceIDs(platform_id, CL_DEVICE_TYPE_GPU, num_devices, devices.data(), nullptr); + + *result = CLDevice(devices[0], platform_id); + return absl::OkStatus(); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClDevice.h b/runtime/onert/backend/gpu_cl/open_cl/ClDevice.h new file mode 100644 index 000000000..6e740fe97 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClDevice.h @@ -0,0 +1,119 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_DEVICE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_DEVICE_H__ + +#include <string> +#include <vector> + +#include "DeviceInfo.h" +#include "OpenclWrapper.h" +#include "Util.h" +#include "Types.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// A wrapper around opencl device id +class CLDevice +{ +public: + CLDevice() = default; + CLDevice(cl_device_id id, cl_platform_id platform_id); + + CLDevice(CLDevice &&device); + CLDevice &operator=(CLDevice &&device); + CLDevice(const CLDevice &); + CLDevice &operator=(const CLDevice &); + + ~CLDevice() {} + + cl_device_id id() const { return id_; } + cl_platform_id platform() const { return platform_id_; } + std::string GetPlatformVersion() const; + + Vendor vendor() const { return info_.vendor; } + OpenCLVersion cl_version() const { return info_.cl_version; } + bool SupportsFP16() const; + bool SupportsTextureArray() const; + bool SupportsImageBuffer() const; + bool SupportsImage3D() const; + bool SupportsExtension(const std::string &extension) const; + bool SupportsFP32RTN() const; + bool SupportsFP16RTN() const; + bool IsCL20OrHigher() const; + bool SupportsSubGroupWithSize(int sub_group_size) const; + bool IsAdreno() const; + bool IsAdreno3xx() const; + bool IsAdreno4xx() const; + bool IsAdreno5xx() const; + bool IsAdreno6xx() const; + bool IsAdreno6xxOrHigher() const; + bool IsPowerVR() const; + bool IsNvidia() const; + bool IsMali() const; + bool IsAMD() const; + bool IsIntel() const; + + // To track bug on some Adreno. b/131099086 + bool SupportsOneLayerTextureArray() const; + void DisableOneLayerTextureArray(); + + const DeviceInfo &GetInfo() const { return info_; } + // We update device info during context creation, so as supported texture + // formats can be requested from context only. + mutable DeviceInfo info_; + +private: + cl_device_id id_ = nullptr; + cl_platform_id platform_id_ = nullptr; +}; + +absl::Status CreateDefaultGPUDevice(CLDevice *result); + +template <typename T> T GetDeviceInfo(cl_device_id id, cl_device_info info) +{ + T result; + cl_int error = clGetDeviceInfo(id, info, sizeof(T), &result, nullptr); + if (error != CL_SUCCESS) + { + return -1; + } + return result; +} + +template <typename T> absl::Status GetDeviceInfo(cl_device_id id, cl_device_info info, T *result) +{ + cl_int error = clGetDeviceInfo(id, info, sizeof(T), result, nullptr); + if (error != CL_SUCCESS) + { + return absl::InvalidArgumentError(CLErrorCodeToString(error)); + } + return absl::OkStatus(); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_DEVICE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClErrors.h b/runtime/onert/backend/gpu_cl/open_cl/ClErrors.h new file mode 100644 index 000000000..48cd2fb00 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClErrors.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_ERRORS_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_ERRORS_H__ + +#include <string> + +#include "Util.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// @return if error_code is success, then return OK status. Otherwise translates +// error code into a message. +inline absl::Status GetOpenCLError(cl_int error_code) +{ + if (error_code == CL_SUCCESS) + { + return absl::OkStatus(); + } + return absl::InternalError("OpenCL error: " + CLErrorCodeToString(error_code)); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_ERRORS_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClEvent.cc b/runtime/onert/backend/gpu_cl/open_cl/ClEvent.cc new file mode 100644 index 000000000..beb64a9a8 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClEvent.cc @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClEvent.h" + +#include "OpenclWrapper.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +CLEvent::CLEvent(cl_event event) : event_(event) {} + +CLEvent::CLEvent(CLEvent &&event) : event_(event.event_), name_(std::move(event.name_)) +{ + event.event_ = nullptr; +} + +CLEvent &CLEvent::operator=(CLEvent &&event) +{ + if (this != &event) + { + Release(); + std::swap(event_, event.event_); + name_ = std::move(event.name_); + } + return *this; +} + +uint64_t CLEvent::GetStartedTimeNs() const +{ + cl_ulong time_ns; + clGetEventProfilingInfo(event_, CL_PROFILING_COMMAND_START, sizeof(cl_ulong), &time_ns, nullptr); + return time_ns; +} + +uint64_t CLEvent::GetFinishedTimeNs() const +{ + cl_ulong time_ns; + clGetEventProfilingInfo(event_, CL_PROFILING_COMMAND_END, sizeof(cl_ulong), &time_ns, nullptr); + return time_ns; +} + +double CLEvent::GetEventTimeMs() const +{ + const uint64_t start = GetStartedTimeNs(); + const uint64_t end = GetFinishedTimeNs(); + const uint64_t time_ns = (end - start); + + return static_cast<double>(time_ns) * 1e-6; +} + +uint64_t CLEvent::GetEventTimeNs() const { return GetFinishedTimeNs() - GetStartedTimeNs(); } + +void CLEvent::SetName(const std::string &name) { name_ = name; } + +void CLEvent::Wait() const { clWaitForEvents(1, &event_); } + +CLEvent::~CLEvent() { Release(); } + +void CLEvent::Release() +{ + if (event_) + { + clReleaseEvent(event_); + event_ = nullptr; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClEvent.h b/runtime/onert/backend/gpu_cl/open_cl/ClEvent.h new file mode 100644 index 000000000..265409ffe --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClEvent.h @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_EVENT_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_EVENT_H__ + +#include <cstdint> +#include <string> + +#include "OpenclWrapper.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// A RAII wrapper around opencl event +class CLEvent +{ +public: + CLEvent() {} + explicit CLEvent(cl_event event); + + // Move only + CLEvent(CLEvent &&event); + CLEvent &operator=(CLEvent &&event); + CLEvent(const CLEvent &) = delete; + CLEvent &operator=(const CLEvent &) = delete; + + ~CLEvent(); + + uint64_t GetStartedTimeNs() const; + uint64_t GetFinishedTimeNs() const; + + double GetEventTimeMs() const; + uint64_t GetEventTimeNs() const; + + void Wait() const; + + cl_event event() const { return event_; } + + bool is_valid() const { return event_ != nullptr; } + + void SetName(const std::string &name); + std::string GetName() const { return name_; } + +private: + void Release(); + + cl_event event_ = nullptr; + + std::string name_; // optional, for profiling mostly +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_EVENT_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClImageFormat.cc b/runtime/onert/backend/gpu_cl/open_cl/ClImageFormat.cc new file mode 100644 index 000000000..247a63d39 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClImageFormat.cc @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClImageFormat.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +cl_channel_order ToChannelOrder(int num_channels) +{ + switch (num_channels) + { + case 1: + return CL_R; + case 2: + return CL_RG; + case 3: + return CL_RGB; + case 4: + return CL_RGBA; + default: + return -1; + } +} + +cl_channel_type ToImageChannelType(DataType data_type) +{ + switch (data_type) + { + case DataType::FLOAT32: + return CL_FLOAT; + case DataType::FLOAT16: + return CL_HALF_FLOAT; + default: + return -1; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClImageFormat.h b/runtime/onert/backend/gpu_cl/open_cl/ClImageFormat.h new file mode 100644 index 000000000..a763746bd --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClImageFormat.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_IMAGE_FORMAT_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_IMAGE_FORMAT_H__ + +#include "OpenclWrapper.h" +#include "DataType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +cl_channel_order ToChannelOrder(int num_channels); + +cl_channel_type ToImageChannelType(DataType data_type); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_IMAGE_FORMAT_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClKernel.cc b/runtime/onert/backend/gpu_cl/open_cl/ClKernel.cc new file mode 100644 index 000000000..f7745b9ac --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClKernel.cc @@ -0,0 +1,171 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClKernel.h" + +#include "absl/strings/str_cat.h" +#include "ClProgram.h" +#include "Util.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +absl::Status GetKernelMaxWorkGroupSize(cl_kernel kernel, cl_device_id device_id, int *result) +{ + size_t max_work_group_size; + cl_int error_code = clGetKernelWorkGroupInfo(kernel, device_id, CL_KERNEL_WORK_GROUP_SIZE, + sizeof(size_t), &max_work_group_size, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to get info CL_KERNEL_WORK_GROUP_SIZE ", + CLErrorCodeToString(error_code))); + } + *result = static_cast<int>(max_work_group_size); + return absl::OkStatus(); +} + +absl::Status GetKernelPrivateMemorySize(cl_kernel kernel, cl_device_id device_id, int *result) +{ + cl_ulong private_mem_size; + cl_int error_code = clGetKernelWorkGroupInfo(kernel, device_id, CL_KERNEL_PRIVATE_MEM_SIZE, + sizeof(cl_ulong), &private_mem_size, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to get info CL_KERNEL_PRIVATE_MEM_SIZE ", + CLErrorCodeToString(error_code))); + } + *result = static_cast<int>(private_mem_size); + return absl::OkStatus(); +} + +} // namespace + +CLKernel::CLKernel(CLKernel &&kernel) + : info_(kernel.info_), binding_counter_(kernel.binding_counter_), + function_name_(std::move(kernel.function_name_)), program_(kernel.program_), + kernel_(kernel.kernel_) +{ + kernel.kernel_ = nullptr; +} + +CLKernel &CLKernel::operator=(CLKernel &&kernel) +{ + if (this != &kernel) + { + Release(); + std::swap(info_, kernel.info_); + std::swap(binding_counter_, kernel.binding_counter_); + function_name_ = std::move(kernel.function_name_); + std::swap(program_, kernel.program_); + std::swap(kernel_, kernel.kernel_); + } + return *this; +} + +CLKernel::~CLKernel() { Release(); } + +absl::Status CLKernel::ReInit() const +{ + clReleaseKernel(kernel_); + cl_kernel *kern_ptr = const_cast<cl_kernel *>(&kernel_); + int error_code; + *kern_ptr = clCreateKernel(program_, function_name_.c_str(), &error_code); + if (!kernel_ || error_code != CL_SUCCESS) + { + *kern_ptr = nullptr; + return absl::UnknownError( + absl::StrCat("Failed to create ", function_name_, CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +void CLKernel::Release() +{ + if (kernel_) + { + clReleaseKernel(kernel_); + clReleaseProgram(program_); + kernel_ = nullptr; + } +} + +absl::Status CLKernel::CreateFromProgram(const CLProgram &program, const std::string &function_name) +{ + int error_code; + function_name_ = function_name; + kernel_ = clCreateKernel(program.program(), function_name.c_str(), &error_code); + if (!kernel_ || error_code != CL_SUCCESS) + { + kernel_ = nullptr; + return absl::UnknownError( + absl::StrCat("Failed to create ", function_name, CLErrorCodeToString(error_code))); + } + + program_ = program.program(); + clRetainProgram(program_); + + RETURN_IF_ERROR( + GetKernelPrivateMemorySize(kernel_, program.GetDeviceId(), &info_.private_memory_size)); + RETURN_IF_ERROR( + GetKernelMaxWorkGroupSize(kernel_, program.GetDeviceId(), &info_.max_work_group_size)); + return absl::OkStatus(); +} + +absl::Status CLKernel::SetMemory(int index, cl_mem memory) +{ + return SetBytes(index, &memory, sizeof(cl_mem)); +} + +absl::Status CLKernel::SetMemoryAuto(cl_mem memory) +{ + return SetBytesAuto(&memory, sizeof(cl_mem)); +} + +absl::Status CLKernel::SetBytes(int index, const void *ptr, int length) const +{ + const int error_code = clSetKernelArg(kernel_, index, length, ptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to set kernel arguments - ", CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status CLKernel::SetBytesAuto(const void *ptr, int length) +{ + const int error_code = clSetKernelArg(kernel_, binding_counter_, length, ptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to set kernel arguments - ", + CLErrorCodeToString(error_code), "(at index - ", + binding_counter_, ")")); + } + binding_counter_++; + return absl::OkStatus(); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClKernel.h b/runtime/onert/backend/gpu_cl/open_cl/ClKernel.h new file mode 100644 index 000000000..9575b7946 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClKernel.h @@ -0,0 +1,101 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_KERNEL_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_KERNEL_H__ + +#include <string> + +#include "ClContext.h" +#include "ClDevice.h" +#include "ClProgram.h" +#include "OpenclWrapper.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +struct KernelInfo +{ + int private_memory_size = 0; + int max_work_group_size = 0; +}; + +// Arguments binding to CLKernel can be manual or automatic +// In manual you specify binding index explicitly +// In automatic binding, index auto-incremented with every binding call +// Also, if you use automatic mode you must call ResetBindingCounter +// before parameters binding +class CLKernel +{ +public: + CLKernel() {} + + // Move only + CLKernel(CLKernel &&kernel); + CLKernel &operator=(CLKernel &&kernel); + CLKernel(const CLKernel &) = delete; + CLKernel &operator=(const CLKernel &) = delete; + + ~CLKernel(); + + cl_kernel kernel() const { return kernel_; } + + absl::Status CreateFromProgram(const CLProgram &program, const std::string &function_name); + + absl::Status SetMemory(int index, cl_mem memory); + absl::Status SetMemoryAuto(cl_mem memory); + template <typename T> absl::Status SetBytes(int index, const T &value) const + { + return SetBytes(index, static_cast<const void *>(&value), sizeof(T)); + } + template <typename T> absl::Status SetBytesAuto(const T &value) + { + return SetBytesAuto(static_cast<const void *>(&value), sizeof(T)); + } + + int GetBindingCounter() const { return binding_counter_; } + void ResetBindingCounter() { binding_counter_ = 0; } + + // Do not use this function + // workaround for Mali memory leak + absl::Status ReInit() const; + + KernelInfo info_; + +private: + void Release(); + absl::Status SetBytes(int index, const void *ptr, int length) const; + absl::Status SetBytesAuto(const void *ptr, int length); + + int binding_counter_ = -1; + + std::string function_name_ = ""; + // reference to program from which kernel was created + cl_program program_ = nullptr; + cl_kernel kernel_ = nullptr; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_KERNEL_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClMemory.cc b/runtime/onert/backend/gpu_cl/open_cl/ClMemory.cc new file mode 100644 index 000000000..fd3bc5579 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClMemory.cc @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClMemory.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +cl_mem_flags ToClMemFlags(AccessType access_type) +{ + switch (access_type) + { + case AccessType::READ: + return CL_MEM_READ_ONLY; + case AccessType::WRITE: + return CL_MEM_WRITE_ONLY; + case AccessType::READ_WRITE: + return CL_MEM_READ_WRITE; + default: + throw std::runtime_error("Invalid AccessType"); + } + + return CL_MEM_READ_ONLY; // unreachable +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClMemory.h b/runtime/onert/backend/gpu_cl/open_cl/ClMemory.h new file mode 100644 index 000000000..c704ec71f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClMemory.h @@ -0,0 +1,100 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_MEMORY_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_MEMORY_H__ + +#include <algorithm> + +#include "OpenclWrapper.h" +#include "AccessType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// RAII wrapper for OpenCL memory object. +// +// Image is moveable but not copyable. +class CLMemory +{ +public: + // Creates invalid object. + CLMemory() : CLMemory(nullptr, false) {} + + CLMemory(cl_mem memory, bool has_ownership) : memory_(memory), has_ownership_(has_ownership) {} + + // Move-only + CLMemory(const CLMemory &) = delete; + CLMemory &operator=(const CLMemory &) = delete; + CLMemory(CLMemory &&image) : memory_(image.memory_), has_ownership_(image.has_ownership_) + { + image.memory_ = nullptr; + } + + ~CLMemory() { Invalidate(); } + + CLMemory &operator=(CLMemory &&image) + { + if (this != &image) + { + Invalidate(); + std::swap(memory_, image.memory_); + has_ownership_ = image.has_ownership_; + } + return *this; + } + + cl_mem memory() const { return memory_; } + + bool is_valid() const { return memory_ != nullptr; } + + // @return true if this object actually owns corresponding CL memory + // and manages it's lifetime. + bool has_ownership() const { return has_ownership_; } + + cl_mem Release() + { + cl_mem to_return = memory_; + memory_ = nullptr; + return to_return; + } + +private: + void Invalidate() + { + if (memory_ && has_ownership_) + { + clReleaseMemObject(memory_); + } + memory_ = nullptr; + } + + cl_mem memory_ = nullptr; + bool has_ownership_ = false; +}; + +cl_mem_flags ToClMemFlags(AccessType access_type); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_MEMORY_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClProgram.cc b/runtime/onert/backend/gpu_cl/open_cl/ClProgram.cc new file mode 100644 index 000000000..c72b01a73 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClProgram.cc @@ -0,0 +1,224 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ClProgram.h" + +#include <cstdint> +#include <cstring> +#include <vector> + +#include "absl/strings/str_cat.h" +#include "absl/types/span.h" +#include "Util.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::string GetProgramBuildInfo(cl_program program, cl_device_id id, cl_program_build_info info) +{ + size_t size; + cl_int error_code = clGetProgramBuildInfo(program, id, info, 0, nullptr, &size); + if (error_code != CL_SUCCESS) + { + return absl::StrCat("Failed to GetProgramBuildInfo - ", CLErrorCodeToString(error_code)); + } + + std::string result(size - 1, 0); + error_code = clGetProgramBuildInfo(program, id, info, size, &result[0], nullptr); + if (error_code != CL_SUCCESS) + { + return absl::StrCat("Failed to GetProgramBuildInfo - ", CLErrorCodeToString(error_code)); + } + return result; +} + +absl::Status GetBinarySize(cl_program program, size_t *binary_size) +{ + cl_int error_code = + clGetProgramInfo(program, CL_PROGRAM_BINARY_SIZES, sizeof(size_t), binary_size, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to get program binary size - ", CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status BuildProgram(cl_program program, const CLDevice &device, + const std::string &compiler_options) +{ + const int error_code = + clBuildProgram(program, 0, nullptr, compiler_options.c_str(), nullptr, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to build program executable - ", CLErrorCodeToString(error_code), + GetProgramBuildInfo(program, device.id(), CL_PROGRAM_BUILD_LOG))); + } + + return absl::OkStatus(); +} + +std::string CompilerOptionToString(const CLDevice &device, CompilerOptions option) +{ + switch (option) + { + case CompilerOptions::ADRENO_FULL_SIMD_LINE: + if (device.info_.adreno_info.gpu_version < 500) + { + return "-qcom-accelerate-16-bit"; + } + else + { + return "-qcom-accelerate-16-bit=true"; + } + case CompilerOptions::ADRENO_MORE_WAVES: + if (device.info_.adreno_info.gpu_version >= 500) + { + return "-qcom-accelerate-16-bit=false"; + } + else + { + return ""; + } + case CompilerOptions::POWERVR_FP16: + return "-cl-fast-relaxed-math"; + case CompilerOptions::CL_OPT_DISABLE: + return "-cl-opt-disable"; + case CompilerOptions::CL_2_0: + return "-cl-std=CL2.0"; + case CompilerOptions::CL_3_0: + return "-cl-std=CL3.0"; + } + return ""; +} + +} // namespace + +std::string CompilerOptionsToString(const CLDevice &device, + const std::vector<CompilerOptions> &compiler_options) +{ + std::string result; + for (auto option : compiler_options) + { + absl::StrAppend(&result, CompilerOptionToString(device, option), " "); + } + return result; +} + +CLProgram::CLProgram(cl_program program, cl_device_id device_id) + : program_(program), device_id_(device_id) +{ +} + +CLProgram::CLProgram(CLProgram &&program) + : program_(program.program_), device_id_(program.device_id_) +{ + program.program_ = nullptr; +} + +CLProgram &CLProgram::operator=(CLProgram &&program) +{ + if (this != &program) + { + Release(); + std::swap(program_, program.program_); + std::swap(device_id_, program.device_id_); + } + return *this; +} + +CLProgram::~CLProgram() { Release(); } + +void CLProgram::Release() +{ + if (program_) + { + clReleaseProgram(program_); + program_ = nullptr; + } +} + +absl::Status CLProgram::GetBinary(std::vector<uint8_t> *result) const +{ + size_t binary_size; + RETURN_IF_ERROR(GetBinarySize(program_, &binary_size)); + result->resize(result->size() + binary_size); + uint8_t *binary_ptr = result->data() + result->size() - binary_size; + cl_int error_code = + clGetProgramInfo(program_, CL_PROGRAM_BINARIES, binary_size, &binary_ptr, nullptr); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to get program binary - ", CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status CreateCLProgram(const std::string &code, const std::string &compiler_options, + const CLContext &context, const CLDevice &device, CLProgram *result) +{ + int error_code; + const char *source = code.c_str(); + + cl_program program = + clCreateProgramWithSource(context.context(), 1, &source, nullptr, &error_code); + if (!program || error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to create compute program - ", CLErrorCodeToString(error_code))); + } + + *result = CLProgram(program, device.id()); + RETURN_IF_ERROR(BuildProgram(program, device, compiler_options)); + return absl::OkStatus(); +} + +absl::Status CreateCLProgramFromBinary(const CLContext &context, const CLDevice &device, + absl::Span<const uint8_t> binary, CLProgram *result) +{ + cl_int binary_status; + cl_int error_code; + cl_device_id devices_list[] = {device.id()}; + size_t binary_size = binary.size(); + const uint8_t *binary_pointer = binary.data(); + cl_program program = clCreateProgramWithBinary(context.context(), 1, devices_list, &binary_size, + &binary_pointer, &binary_status, &error_code); + if (binary_status != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat( + "Something wrong with binary after clCreateProgramWithBinary - ", binary_status)); + } + if (error_code != CL_SUCCESS) + { + return absl::UnknownError( + absl::StrCat("Failed to create program - ", CLErrorCodeToString(error_code))); + } + *result = CLProgram(program, device.id()); + return BuildProgram(program, device, ""); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ClProgram.h b/runtime/onert/backend/gpu_cl/open_cl/ClProgram.h new file mode 100644 index 000000000..d039ff698 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ClProgram.h @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_CL_PROGRAM_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_CL_PROGRAM_H__ + +#include <cstdint> +#include <vector> + +#include "ClContext.h" +#include "ClDevice.h" +#include "OpenclWrapper.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class CompilerOptions +{ + // ADRENO_FULL_SIMD_LINE: + // Adreno can have 2 sizes for SIMD size. + // On Adreno 4xx/5xx it is 32/64, on Adreno6xx it is 64/128. + // Some our algorithms actually rely on exact size, for example on full + // SIMD size, so we need this define. + // This define is actually -qcom-accelerate-16-bit, but it controls SIMD + // size. + ADRENO_FULL_SIMD_LINE, + ADRENO_MORE_WAVES, + POWERVR_FP16, + CL_OPT_DISABLE, + CL_2_0, + CL_3_0, +}; + +std::string CompilerOptionsToString(const CLDevice &device, + const std::vector<CompilerOptions> &compiler_options); + +class CLProgram +{ +public: + CLProgram() {} + CLProgram(cl_program program, cl_device_id device_id); + + // Move only + CLProgram(CLProgram &&program); + CLProgram &operator=(CLProgram &&program); + CLProgram(const CLProgram &) = delete; + CLProgram &operator=(const CLProgram &) = delete; + + ~CLProgram(); + + cl_program program() const { return program_; } + + // Return the cl_device_id associated with the program object. + // This can be the device associated with context on which the program object + // has been created or can be device that was specified when a program object + // was created using clCreateProgramWithBinary. + cl_device_id GetDeviceId() const { return device_id_; } + + absl::Status GetBinary(std::vector<uint8_t> *result) const; + +private: + void Release(); + + cl_program program_ = nullptr; + + // reference + cl_device_id device_id_ = nullptr; +}; + +absl::Status CreateCLProgram(const std::string &code, const std::string &compiler_options, + const CLContext &context, const CLDevice &device, CLProgram *result); + +absl::Status CreateCLProgramFromBinary(const CLContext &context, const CLDevice &device, + absl::Span<const uint8_t> binary, CLProgram *result); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_CL_PROGRAM_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/DataType.cc b/runtime/onert/backend/gpu_cl/open_cl/DataType.cc new file mode 100644 index 000000000..ce2aa8298 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/DataType.cc @@ -0,0 +1,122 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DataType.h" + +#include <stddef.h> +#include <string> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +size_t SizeOf(DataType data_type) +{ + switch (data_type) + { + case DataType::UINT8: + case DataType::INT8: + return 1; + case DataType::FLOAT16: + case DataType::INT16: + case DataType::UINT16: + return 2; + case DataType::FLOAT32: + case DataType::INT32: + case DataType::UINT32: + return 4; + case DataType::FLOAT64: + case DataType::INT64: + case DataType::UINT64: + return 8; + case DataType::UNKNOWN: + return 0; + } + return 0; +} + +std::string ToString(DataType data_type) +{ + switch (data_type) + { + case DataType::FLOAT16: + return "float16"; + case DataType::FLOAT32: + return "float32"; + case DataType::FLOAT64: + return "float64"; + case DataType::INT16: + return "int16"; + case DataType::INT32: + return "int32"; + case DataType::INT64: + return "int64"; + case DataType::INT8: + return "int8"; + case DataType::UINT16: + return "uint16"; + case DataType::UINT32: + return "uint32"; + case DataType::UINT64: + return "uint64"; + case DataType::UINT8: + return "uint8"; + case DataType::UNKNOWN: + return "unknown"; + } + return "undefined"; +} + +std::string ToCLDataType(DataType data_type, int vec_size) +{ + const std::string postfix = vec_size == 1 ? "" : std::to_string(vec_size); + switch (data_type) + { + case DataType::FLOAT16: + return "half" + postfix; + case DataType::FLOAT32: + return "float" + postfix; + case DataType::FLOAT64: + return "double" + postfix; + case DataType::INT16: + return "short" + postfix; + case DataType::INT32: + return "int" + postfix; + case DataType::INT64: + return "long" + postfix; + case DataType::INT8: + return "char" + postfix; + case DataType::UINT16: + return "ushort" + postfix; + case DataType::UINT32: + return "uint" + postfix; + case DataType::UINT64: + return "ulong" + postfix; + case DataType::UINT8: + return "uchar" + postfix; + case DataType::UNKNOWN: + return "unknown"; + } + return "undefined"; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/DataType.h b/runtime/onert/backend/gpu_cl/open_cl/DataType.h new file mode 100644 index 000000000..2a5afd551 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/DataType.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_DATA_TYPE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_DATA_TYPE_H__ + +#include <stddef.h> +#include <string> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class DataType +{ + UNKNOWN = 0, + FLOAT16 = 1, + FLOAT32 = 2, + FLOAT64 = 3, + UINT8 = 4, + INT8 = 5, + UINT16 = 6, + INT16 = 7, + UINT32 = 8, + INT32 = 9, + UINT64 = 10, + INT64 = 11, +}; + +size_t SizeOf(DataType type); + +std::string ToString(DataType t); + +std::string ToCLDataType(DataType data_type, int vec_size = 1); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_DATA_TYPE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/DeviceInfo.cc b/runtime/onert/backend/gpu_cl/open_cl/DeviceInfo.cc new file mode 100644 index 000000000..2966fad75 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/DeviceInfo.cc @@ -0,0 +1,383 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DeviceInfo.h" + +#include <algorithm> +#include <map> +#include <string> +#include <vector> + +#include "absl/strings/numbers.h" +#include "absl/strings/str_split.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +namespace +{ +// check that gpu_version belong to range min_version-max_version +// min_version is included and max_version is excluded. +bool IsGPUVersionInRange(int gpu_version, int min_version, int max_version) +{ + return gpu_version >= min_version && gpu_version < max_version; +} + +MaliGPU GetMaliGPUVersion(const std::string &device_name) +{ + const std::map<std::string, MaliGPU> kMapping = { + {"T604", MaliGPU::T604}, {"T622", MaliGPU::T622}, {"T624", MaliGPU::T624}, + {"T628", MaliGPU::T628}, {"T658", MaliGPU::T658}, {"T678", MaliGPU::T678}, + {"T720", MaliGPU::T720}, {"T760", MaliGPU::T760}, {"T820", MaliGPU::T820}, + {"T830", MaliGPU::T830}, {"T860", MaliGPU::T860}, {"T880", MaliGPU::T880}, + {"G31", MaliGPU::G31}, {"G51", MaliGPU::G51}, {"G71", MaliGPU::G71}, + {"G52", MaliGPU::G52}, {"G72", MaliGPU::G72}, {"G76", MaliGPU::G76}, + {"G57", MaliGPU::G57}, {"G77", MaliGPU::G77}, {"G68", MaliGPU::G68}, + {"G78", MaliGPU::G78}, + }; + for (const auto &v : kMapping) + { + if (device_name.find(v.first) != std::string::npos) + { + return v.second; + } + } + return MaliGPU::UNKNOWN; +} + +} // namespace + +// There is no rule for gpu version encoding, but we found these samples: +// Version: OpenCL C 2.0 Adreno(TM) 540 // Pixel 2 +// Version: OpenCL C 2.0 Adreno(TM) 630 // Sony Compact XZ2 +// Version: OpenCL C 2.0 Adreno(TM) 630 // Pixel 3 +// Version: OpenCL C 2.0 Adreno(TM) 540 // Samsung S8 +// Version: OpenCL C 1.2 Adreno(TM) 430 // HTC One M9 +// Version: OpenCL C 2.0 Adreno(TM) 530 // Samsung S7 Edge +// Version: OpenCL C 1.2 Adreno(TM) 405 // Motorola Moto G(4) +// After the number string ends. +// It is assumed that the <vendor-specific information> for Adreno GPUs has +// the following format: +// <text?><space?>Adreno(TM)<space><text?><version> +// Returns -1 if vendor-specific information cannot be parsed +int GetAdrenoGPUVersion(const std::string &gpu_version) +{ + const std::string gpu = absl::AsciiStrToLower(gpu_version); + const std::vector<absl::string_view> words = absl::StrSplit(gpu, ' '); + size_t i = 0; + for (; i < words.size(); ++i) + { + if (words[i].find("adreno") != words[i].npos) + { + break; + } + } + i += 1; + for (; i < words.size(); ++i) + { + int number; + bool is_number = absl::SimpleAtoi(words[i], &number); + // Adreno GPUs starts from 2xx, but opencl support should be only from 3xx + if (is_number && number >= 300) + { + return number; + } + } + return -1; +} + +std::string VendorToString(Vendor v) +{ + switch (v) + { + case Vendor::kQualcomm: + return "Qualcomm"; + case Vendor::kMali: + return "Mali"; + case Vendor::kPowerVR: + return "PowerVR"; + case Vendor::kNvidia: + return "NVIDIA"; + case Vendor::kAMD: + return "AMD"; + case Vendor::kIntel: + return "Intel"; + case Vendor::kUnknown: + return "unknown vendor"; + default: + return "Error"; + } +} + +std::string OpenCLVersionToString(OpenCLVersion version) +{ + switch (version) + { + case OpenCLVersion::CL_1_0: + return "1.0"; + case OpenCLVersion::CL_1_1: + return "1.1"; + case OpenCLVersion::CL_1_2: + return "1.2"; + case OpenCLVersion::CL_2_0: + return "2.0"; + case OpenCLVersion::CL_2_1: + return "2.1"; + case OpenCLVersion::CL_2_2: + return "2.2"; + case OpenCLVersion::CL_3_0: + return "3.0"; + default: + return "Error"; + } +} + +AdrenoInfo::AdrenoInfo(const std::string &device_version) + : gpu_version(GetAdrenoGPUVersion(device_version)) +{ +} + +int AdrenoInfo::GetMaximumWavesCount() const +{ + if (gpu_version < 400) + { + return -1; // Adreno 3xx does not support it currently + } + else if (gpu_version >= 400 && gpu_version < 500) + { + return -1; // Adreno 4xx does not support it currently + } + else if (gpu_version >= 500 && gpu_version < 600) + { + return -1; // Adreno 5xx does not support it currently + } + else if (gpu_version >= 600 && gpu_version < 700) + { + return gpu_version == 640 ? 30 : 16; + } + else + { + return -1; // Adreno 7xx and higher does not exist yet + } +} + +int AdrenoInfo::GetRegisterMemorySizePerComputeUnit() const +{ + if (gpu_version < 400) + { + return -1; // Adreno 3xx does not support it currently + } + else if (gpu_version >= 400 && gpu_version < 500) + { + return -1; // Adreno 4xx does not support it currently + } + else if (gpu_version >= 500 && gpu_version < 600) + { + return -1; // Adreno 5xx does not support it currently + } + else if (gpu_version >= 600 && gpu_version < 700) + { + return gpu_version == 640 ? 128 * 144 * 16 : 128 * 96 * 16; + } + else + { + return -1; // Adreno 7xx and higher does not exist yet + } +} + +int AdrenoInfo::GetMaximumWavesCount(int register_footprint_per_tread, bool full_wave) const +{ + const int register_usage_per_wave = GetWaveSize(full_wave) * register_footprint_per_tread; + const int possible_waves_count = GetRegisterMemorySizePerComputeUnit() / register_usage_per_wave; + return std::min(possible_waves_count, GetMaximumWavesCount()); +} + +int AdrenoInfo::GetWaveSize(bool full_wave) const +{ + if (gpu_version < 400) + { + return -1; // Adreno 3xx does not support it currently + } + else if (gpu_version < 600) + { + return full_wave ? 64 : 32; + } + else + { + return full_wave ? 128 : 64; + } +} + +MaliInfo::MaliInfo(const std::string &device_name) : gpu_version(GetMaliGPUVersion(device_name)) {} + +bool MaliInfo::IsMaliT6xx() const +{ + return gpu_version == MaliGPU::T604 || gpu_version == MaliGPU::T622 || + gpu_version == MaliGPU::T624 || gpu_version == MaliGPU::T628 || + gpu_version == MaliGPU::T658 || gpu_version == MaliGPU::T678; +} + +bool MaliInfo::IsMaliT7xx() const +{ + return gpu_version == MaliGPU::T720 || gpu_version == MaliGPU::T760; +} + +bool MaliInfo::IsMaliT8xx() const +{ + return gpu_version == MaliGPU::T820 || gpu_version == MaliGPU::T830 || + gpu_version == MaliGPU::T860 || gpu_version == MaliGPU::T880; +} + +bool MaliInfo::IsMidgard() const { return IsMaliT6xx() || IsMaliT7xx() || IsMaliT8xx(); } + +bool MaliInfo::IsBifrostGen1() const +{ + return gpu_version == MaliGPU::G31 || gpu_version == MaliGPU::G51 || gpu_version == MaliGPU::G71; +} + +bool MaliInfo::IsBifrostGen2() const +{ + return gpu_version == MaliGPU::G52 || gpu_version == MaliGPU::G72; +} + +bool MaliInfo::IsBifrostGen3() const { return gpu_version == MaliGPU::G76; } + +bool MaliInfo::IsBifrost() const { return IsBifrostGen1() || IsBifrostGen2() || IsBifrostGen3(); } + +bool MaliInfo::IsValhall() const +{ + return gpu_version == MaliGPU::G57 || gpu_version == MaliGPU::G77 || + gpu_version == MaliGPU::G68 || gpu_version == MaliGPU::G78; +} + +bool DeviceInfo::SupportsTextureArray() const { return cl_version >= OpenCLVersion::CL_1_2; } + +bool DeviceInfo::SupportsImageBuffer() const { return cl_version >= OpenCLVersion::CL_1_2; } + +bool DeviceInfo::SupportsImage3D() const +{ + if (vendor == Vendor::kMali) + { + // On Mali T880 read_imageh doesn't compile with image3d_t + return false; + } + return supports_image3d_writes; +} + +bool DeviceInfo::SupportsFloatImage2D(DataType data_type, int channels) const +{ + if (channels == 1) + { + return data_type == DataType::FLOAT32 ? supports_r_f32_tex2d : supports_r_f16_tex2d; + } + else if (channels == 2) + { + return data_type == DataType::FLOAT32 ? supports_rg_f32_tex2d : supports_rg_f16_tex2d; + } + else if (channels == 3) + { + return data_type == DataType::FLOAT32 ? supports_rgb_f32_tex2d : supports_rgb_f16_tex2d; + } + else if (channels == 4) + { + return data_type == DataType::FLOAT32 ? supports_rgba_f32_tex2d : supports_rgba_f16_tex2d; + } + else + { + return false; + } +} + +bool DeviceInfo::SupportsOneLayerTextureArray() const +{ + return !IsAdreno() || adreno_info.support_one_layer_texture_array; +} + +bool DeviceInfo::SupportsExtension(const std::string &extension) const +{ + for (const auto &ext : extensions) + { + if (ext == extension) + { + return true; + } + } + return false; +} + +bool DeviceInfo::IsCL20OrHigher() const +{ + return cl_version != OpenCLVersion::CL_1_0 && cl_version != OpenCLVersion::CL_1_1 && + cl_version != OpenCLVersion::CL_1_2; +} + +bool DeviceInfo::SupportsSubGroupWithSize(int sub_group_size) const +{ + for (auto subgroup_size : supported_subgroup_sizes) + { + if (sub_group_size == subgroup_size) + { + return true; + } + } + return false; +} + +bool DeviceInfo::IsAdreno() const { return vendor == Vendor::kQualcomm; } + +bool DeviceInfo::IsAdreno3xx() const +{ + return IsAdreno() && IsGPUVersionInRange(adreno_info.gpu_version, 300, 400); +} + +bool DeviceInfo::IsAdreno4xx() const +{ + return IsAdreno() && IsGPUVersionInRange(adreno_info.gpu_version, 400, 500); +} + +bool DeviceInfo::IsAdreno5xx() const +{ + return IsAdreno() && IsGPUVersionInRange(adreno_info.gpu_version, 500, 600); +} + +bool DeviceInfo::IsAdreno6xx() const +{ + return IsAdreno() && IsGPUVersionInRange(adreno_info.gpu_version, 600, 700); +} + +bool DeviceInfo::IsAdreno6xxOrHigher() const +{ + return IsAdreno() && adreno_info.gpu_version >= 600; +} + +bool DeviceInfo::IsPowerVR() const { return vendor == Vendor::kPowerVR; } + +bool DeviceInfo::IsNvidia() const { return vendor == Vendor::kNvidia; } + +bool DeviceInfo::IsMali() const { return vendor == Vendor::kMali; } + +bool DeviceInfo::IsAMD() const { return vendor == Vendor::kAMD; } + +bool DeviceInfo::IsIntel() const { return vendor == Vendor::kIntel; } + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/DeviceInfo.h b/runtime/onert/backend/gpu_cl/open_cl/DeviceInfo.h new file mode 100644 index 000000000..85d7d4c80 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/DeviceInfo.h @@ -0,0 +1,203 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_DEVICE_INFO_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_DEVICE_INFO_H__ + +#include <string> +#include <vector> + +#include "DataType.h" + +// for use only in device_info.cc, but keep here to make tests +int GetAdrenoGPUVersion(const std::string &gpu_version); + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class Vendor +{ + kQualcomm, + kMali, + kPowerVR, + kNvidia, + kAMD, + kIntel, + kUnknown +}; +std::string VendorToString(Vendor v); + +enum class OpenCLVersion +{ + UNKNOWN, + CL_1_0, + CL_1_1, + CL_1_2, + CL_2_0, + CL_2_1, + CL_2_2, + CL_3_0 +}; +std::string OpenCLVersionToString(OpenCLVersion version); + +struct AdrenoInfo +{ + AdrenoInfo() = default; + explicit AdrenoInfo(const std::string &device_version); + int gpu_version = -1; // can be, for example, 405/430/540/530/630 etc. + + // This function returns some not very documented physical parameter of + // Adreno6xx GPU. + // We obtained it using Snapdragon Profiler. + int GetMaximumWavesCount() const; + + // returns amount of register memory per CU(Compute Unit) in bytes. + int GetRegisterMemorySizePerComputeUnit() const; + + // returns maximum possible amount of waves based on register usage. + int GetMaximumWavesCount(int register_footprint_per_tread, bool full_wave = true) const; + + int GetWaveSize(bool full_wave) const; + + // Not supported on some Adreno devices with specific driver version. + // b/131099086 + bool support_one_layer_texture_array = true; +}; + +enum class MaliGPU +{ + T604, + T622, + T624, + T628, + T658, + T678, + T720, + T760, + T820, + T830, + T860, + T880, + G31, + G51, + G71, + G52, + G72, + G76, + G57, + G77, + G68, + G78, + UNKNOWN +}; + +struct MaliInfo +{ + MaliInfo() = default; + explicit MaliInfo(const std::string &device_name); + MaliGPU gpu_version = MaliGPU::UNKNOWN; + + bool IsMaliT6xx() const; + bool IsMaliT7xx() const; + bool IsMaliT8xx() const; + bool IsMidgard() const; + bool IsBifrostGen1() const; + bool IsBifrostGen2() const; + bool IsBifrostGen3() const; + bool IsBifrost() const; + bool IsValhall() const; +}; + +struct DeviceInfo +{ + DeviceInfo() = default; + + bool IsAdreno() const; + bool IsAdreno3xx() const; + bool IsAdreno4xx() const; + bool IsAdreno5xx() const; + bool IsAdreno6xx() const; + bool IsAdreno6xxOrHigher() const; + bool IsPowerVR() const; + bool IsNvidia() const; + bool IsMali() const; + bool IsAMD() const; + bool IsIntel() const; + + bool SupportsTextureArray() const; + bool SupportsImageBuffer() const; + bool SupportsImage3D() const; + + bool SupportsFloatImage2D(DataType data_type, int channels) const; + + // To track bug on some Adreno. b/131099086 + bool SupportsOneLayerTextureArray() const; + + bool SupportsExtension(const std::string &extension) const; + bool IsCL20OrHigher() const; + bool SupportsSubGroupWithSize(int sub_group_size) const; + + std::vector<std::string> extensions; + bool supports_fp16 = false; + bool supports_image3d_writes = false; + Vendor vendor = Vendor::kUnknown; + OpenCLVersion cl_version = OpenCLVersion::UNKNOWN; + int compute_units_count = 0; + uint64_t buffer_max_size = 0; + uint64_t image2d_max_width = 0; + uint64_t image2d_max_height = 0; + uint64_t image_buffer_max_size = 0; + uint64_t image_array_max_layers = 0; + uint64_t image3d_max_width = 0; + uint64_t image3d_max_height = 0; + uint64_t image3d_max_depth = 0; + int max_work_group_size_x = 0; + int max_work_group_size_y = 0; + int max_work_group_size_z = 0; + std::vector<int> supported_subgroup_sizes; + + // rtn is ROUND_TO_NEAREST + // with rtn precision is much better then with rtz (ROUND_TO_ZERO) + // Adreno 3xx supports only rtz, Adreno 4xx and more support rtn + // Mali from T6xx supports rtn + // PowerVR supports only rtz + bool supports_fp32_rtn = false; + bool supports_fp16_rtn = false; + + bool supports_r_f16_tex2d = false; + bool supports_rg_f16_tex2d = false; + bool supports_rgb_f16_tex2d = false; + bool supports_rgba_f16_tex2d = false; + + bool supports_r_f32_tex2d = false; + bool supports_rg_f32_tex2d = false; + bool supports_rgb_f32_tex2d = false; + bool supports_rgba_f32_tex2d = false; + + AdrenoInfo adreno_info; + MaliInfo mali_info; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_DEVICE_INFO_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Environment.cc b/runtime/onert/backend/gpu_cl/open_cl/Environment.cc new file mode 100644 index 000000000..b558f0377 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Environment.cc @@ -0,0 +1,276 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Environment.h" + +#include <string> +#include <vector> + +#include "Util.h" +#include "Shape.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +Environment::Environment(CLDevice &&device, CLContext &&context, CLCommandQueue &&queue, + ProfilingCommandQueue &&profiling_queue) + : device_(std::move(device)), context_(std::move(context)), queue_(std::move(queue)), + profiling_queue_(std::move(profiling_queue)) +{ +} + +Environment::Environment(Environment &&environment) + : device_(std::move(environment.device_)), context_(std::move(environment.context_)), + queue_(std::move(environment.queue_)), + profiling_queue_(std::move(environment.profiling_queue_)), + program_cache_(std::move(environment.program_cache_)) +{ +} + +Environment &Environment::operator=(Environment &&environment) +{ + if (this != &environment) + { + device_ = std::move(environment.device_); + context_ = std::move(environment.context_); + queue_ = std::move(environment.queue_); + profiling_queue_ = std::move(environment.profiling_queue_); + program_cache_ = std::move(environment.program_cache_); + } + return *this; +} + +absl::Status Environment::Init() +{ + if (device().IsAdreno() && device().SupportsTextureArray()) + { + // Some Adreno < 600 have bug with one layer texture array. b/131099086 + // If we have one layer texture array and will write smt from kernel to this + // texture, we will get zeroes instead of actual values. + // The same kernel will work, if we use texture array with more than one + // layer. + if (device().info_.adreno_info.gpu_version < 600) + { + GetDevicePtr()->DisableOneLayerTextureArray(); + } + } + return absl::OkStatus(); +} + +void Environment::SetHighPerformance() const +{ + // TODO(sorokin) use cl_perf_hint if available +} + +void Environment::SetDefaultPerformance() const +{ + // TODO(sorokin) use cl_perf_hint if available +} + +void Environment::SetLowPerformance() const +{ + // TODO(sorokin) use cl_perf_hint if available +} + +std::vector<CalculationsPrecision> Environment::GetSupportedPrecisions() const +{ + std::vector<CalculationsPrecision> precisions; + for (CalculationsPrecision precision : + {CalculationsPrecision::F32, CalculationsPrecision::F32_F16, CalculationsPrecision::F16}) + { + if (IsSupported(precision)) + { + precisions.push_back(precision); + } + } + return precisions; +} + +bool Environment::IsSupported(CalculationsPrecision precision) const +{ + switch (precision) + { + case CalculationsPrecision::F32_F16: + case CalculationsPrecision::F16: + return device_.SupportsFP16(); + case CalculationsPrecision::F32: + return true; + } + return false; +} + +std::vector<TensorStorageType> Environment::GetSupportedStorages() const +{ + std::vector<TensorStorageType> storage_types; + for (auto storage_type : + {TensorStorageType::TEXTURE_2D, TensorStorageType::BUFFER, TensorStorageType::TEXTURE_ARRAY, + TensorStorageType::IMAGE_BUFFER, TensorStorageType::TEXTURE_3D}) + { + if (IsSupported(storage_type)) + { + storage_types.push_back(storage_type); + } + } + return storage_types; +} + +std::vector<TensorStorageType> Environment::GetSupportedStoragesWithHWZeroClampSupport() const +{ + std::vector<TensorStorageType> storage_types; + for (auto storage_type : {TensorStorageType::TEXTURE_2D, TensorStorageType::TEXTURE_ARRAY, + TensorStorageType::TEXTURE_3D}) + { + if (IsSupported(storage_type)) + { + storage_types.push_back(storage_type); + } + } + return storage_types; +} + +bool Environment::IsSupported(TensorStorageType storage_type) const +{ + switch (storage_type) + { + case TensorStorageType::TEXTURE_2D: + return !device_.IsAMD(); + case TensorStorageType::BUFFER: + return true; + case TensorStorageType::TEXTURE_ARRAY: + return !device_.IsAMD() && device_.SupportsTextureArray(); + case TensorStorageType::IMAGE_BUFFER: + return (device_.IsAdreno() || device_.IsAMD() || device_.IsNvidia()) && + device_.SupportsImageBuffer(); + case TensorStorageType::TEXTURE_3D: + return !device_.IsAMD() && device_.SupportsImage3D(); + case TensorStorageType::SINGLE_TEXTURE_2D: + return false; + case TensorStorageType::UNKNOWN: + return false; + } + return false; +} + +TensorStorageType GetFastestStorageType(const DeviceInfo &gpu_info) +{ + if (gpu_info.IsAdreno()) + { + if (gpu_info.IsAdreno6xxOrHigher()) + { + return TensorStorageType::TEXTURE_ARRAY; + } + else + { + return TensorStorageType::TEXTURE_2D; + } + } + else if (gpu_info.IsPowerVR()) + { + return TensorStorageType::TEXTURE_2D; + } + else if (gpu_info.IsMali()) + { + const MaliInfo mali_info = gpu_info.mali_info; + if (mali_info.IsMaliT8xx() || mali_info.IsBifrostGen3() || mali_info.IsValhall()) + { + return TensorStorageType::TEXTURE_2D; + } + else + { + return TensorStorageType::BUFFER; + } + } + else if (gpu_info.IsNvidia()) + { + return gpu_info.SupportsImageBuffer() ? TensorStorageType::IMAGE_BUFFER + : TensorStorageType::BUFFER; + } + else if (gpu_info.IsAMD()) + { + return gpu_info.SupportsImageBuffer() ? TensorStorageType::IMAGE_BUFFER + : TensorStorageType::BUFFER; + } + else if (gpu_info.IsIntel()) + { + return TensorStorageType::BUFFER; + } + return TensorStorageType::BUFFER; +} + +TensorStorageType GetStorageTypeWithMinimalMemoryConsumption(const DeviceInfo &gpu_info) +{ + if (gpu_info.IsAdreno()) + { + if (gpu_info.IsAdreno3xx() || gpu_info.IsAdreno4xx()) + { + return TensorStorageType::BUFFER; + } + else + { + return TensorStorageType::IMAGE_BUFFER; + } + } + else if (gpu_info.IsPowerVR()) + { + return TensorStorageType::BUFFER; + } + else if (gpu_info.IsMali()) + { + return TensorStorageType::BUFFER; + } + else if (gpu_info.IsNvidia()) + { + return gpu_info.SupportsImageBuffer() ? TensorStorageType::IMAGE_BUFFER + : TensorStorageType::BUFFER; + } + else if (gpu_info.IsAMD()) + { + return gpu_info.SupportsImageBuffer() ? TensorStorageType::IMAGE_BUFFER + : TensorStorageType::BUFFER; + } + else if (gpu_info.IsIntel()) + { + return TensorStorageType::BUFFER; + } + return TensorStorageType::BUFFER; +} + +absl::Status CreateEnvironment(Environment *result) +{ + CLDevice gpu; + RETURN_IF_ERROR(CreateDefaultGPUDevice(&gpu)); + + CLContext context; + RETURN_IF_ERROR(CreateCLContext(gpu, &context)); + CLCommandQueue queue; + RETURN_IF_ERROR(CreateCLCommandQueue(gpu, context, &queue)); + ProfilingCommandQueue profiling_queue; + RETURN_IF_ERROR(CreateProfilingCommandQueue(gpu, context, &profiling_queue)); + + *result = + Environment(std::move(gpu), std::move(context), std::move(queue), std::move(profiling_queue)); + return result->Init(); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Environment.h b/runtime/onert/backend/gpu_cl/open_cl/Environment.h new file mode 100644 index 000000000..47866b563 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Environment.h @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_ENVIRONMENT_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_ENVIRONMENT_H__ + +#include "ClCommandQueue.h" +#include "ClContext.h" +#include "ClDevice.h" +#include "DeviceInfo.h" +#include "Precision.h" +#include "TensorType.h" +#include "DataType.h" +#include "ProgramCache.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class Environment +{ +public: + Environment() = default; + explicit Environment(CLDevice &&device, CLContext &&context, CLCommandQueue &&queue, + ProfilingCommandQueue &&profiling_queue); + // Move only + Environment(Environment &&environment); + Environment &operator=(Environment &&environment); + Environment(const Environment &) = delete; + Environment &operator=(const Environment &) = delete; + + const CLDevice &device() const { return device_; } + CLDevice *GetDevicePtr() { return &device_; } + const CLDevice *GetDevicePtr() const { return &device_; } + CLContext &context() { return context_; } + CLCommandQueue *queue() { return &queue_; } + ProfilingCommandQueue *profiling_queue() { return &profiling_queue_; } + ProgramCache *program_cache() { return &program_cache_; } + const ProgramCache *program_cache() const { return &program_cache_; } + + std::vector<CalculationsPrecision> GetSupportedPrecisions() const; + bool IsSupported(CalculationsPrecision precision) const; + std::vector<TensorStorageType> GetSupportedStorages() const; + // returns storage types that support zero clamping when reading OOB in HW + // (Height/Width) dimensions. + std::vector<TensorStorageType> GetSupportedStoragesWithHWZeroClampSupport() const; + bool IsSupported(TensorStorageType storage_type) const; + + absl::Status Init(); + + void SetHighPerformance() const; + void SetDefaultPerformance() const; + void SetLowPerformance() const; // for energy saving + +private: + CLDevice device_; + CLContext context_; + CLCommandQueue queue_; + ProfilingCommandQueue profiling_queue_; + ProgramCache program_cache_; +}; + +TensorStorageType GetFastestStorageType(const DeviceInfo &gpu_info); +TensorStorageType GetStorageTypeWithMinimalMemoryConsumption(const DeviceInfo &gpu_info); + +absl::Status CreateEnvironment(Environment *result); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_ENVIRONMENT_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/GpuObject.cc b/runtime/onert/backend/gpu_cl/open_cl/GpuObject.cc new file mode 100644 index 000000000..774f8151f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/GpuObject.cc @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GpuObject.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +std::string MemoryTypeToCLType(MemoryType type) +{ + switch (type) + { + case MemoryType::GLOBAL: + return "__global"; + case MemoryType::CONSTANT: + return "__constant"; + break; + case MemoryType::LOCAL: + return "__local"; + } + return ""; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/GpuObject.h b/runtime/onert/backend/gpu_cl/open_cl/GpuObject.h new file mode 100644 index 000000000..a31630235 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/GpuObject.h @@ -0,0 +1,222 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_GPU_OBJECT_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_GPU_OBJECT_H__ + +#include <map> +#include <memory> +#include <string> +#include <vector> + +#include "ClContext.h" +#include "OpenclWrapper.h" +#include "AccessType.h" +#include "DataType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +struct GPUImage2DDescriptor +{ + DataType data_type = DataType::UNKNOWN; + AccessType access_type = AccessType::UNKNOWN; + cl_mem memory = nullptr; +}; + +struct GPUImage3DDescriptor +{ + DataType data_type = DataType::UNKNOWN; + AccessType access_type = AccessType::UNKNOWN; + cl_mem memory = nullptr; +}; + +struct GPUImage2DArrayDescriptor +{ + DataType data_type = DataType::UNKNOWN; + AccessType access_type = AccessType::UNKNOWN; + cl_mem memory = nullptr; +}; + +struct GPUImageBufferDescriptor +{ + DataType data_type = DataType::UNKNOWN; + AccessType access_type = AccessType::UNKNOWN; + cl_mem memory = nullptr; +}; + +struct GPUCustomMemoryDescriptor +{ + std::string type_name = ""; + cl_mem memory = nullptr; +}; + +enum class MemoryType +{ + GLOBAL, + CONSTANT, + LOCAL +}; + +std::string MemoryTypeToCLType(MemoryType type); + +struct GPUBufferDescriptor +{ + DataType data_type = DataType::UNKNOWN; + AccessType access_type = AccessType::UNKNOWN; + int element_size = 0; + MemoryType memory_type = MemoryType::GLOBAL; + std::vector<std::string> attributes; + cl_mem memory = nullptr; +}; + +struct GPUResources +{ + std::vector<std::string> ints; + std::vector<std::string> floats; + std::vector<std::pair<std::string, GPUBufferDescriptor>> buffers; + std::vector<std::pair<std::string, GPUImage2DDescriptor>> images2d; + std::vector<std::pair<std::string, GPUImage2DArrayDescriptor>> image2d_arrays; + std::vector<std::pair<std::string, GPUImage3DDescriptor>> images3d; + std::vector<std::pair<std::string, GPUImageBufferDescriptor>> image_buffers; + std::vector<std::pair<std::string, GPUCustomMemoryDescriptor>> custom_memories; + + std::vector<std::string> GetNames() const + { + std::vector<std::string> names = ints; + names.insert(names.end(), floats.begin(), floats.end()); + for (const auto &obj : buffers) + { + names.push_back(obj.first); + } + for (const auto &obj : images2d) + { + names.push_back(obj.first); + } + for (const auto &obj : image2d_arrays) + { + names.push_back(obj.first); + } + for (const auto &obj : images3d) + { + names.push_back(obj.first); + } + for (const auto &obj : image_buffers) + { + names.push_back(obj.first); + } + for (const auto &obj : custom_memories) + { + names.push_back(obj.first); + } + return names; + } +}; + +struct GPUResourcesWithValue +{ + std::vector<std::pair<std::string, int>> ints; + std::vector<std::pair<std::string, float>> floats; + std::vector<std::pair<std::string, cl_mem>> buffers; + std::vector<std::pair<std::string, cl_mem>> images2d; + std::vector<std::pair<std::string, cl_mem>> image2d_arrays; + std::vector<std::pair<std::string, cl_mem>> images3d; + std::vector<std::pair<std::string, cl_mem>> image_buffers; + std::vector<std::pair<std::string, cl_mem>> custom_memories; +}; + +class GPUObject; + +class GPUObjectDescriptor +{ +public: + GPUObjectDescriptor() = default; + GPUObjectDescriptor(const GPUObjectDescriptor &) = default; + GPUObjectDescriptor &operator=(const GPUObjectDescriptor &) = default; + GPUObjectDescriptor(GPUObjectDescriptor &&obj_desc) : state_vars_(std::move(obj_desc.state_vars_)) + { + } + GPUObjectDescriptor &operator=(GPUObjectDescriptor &&obj_desc) + { + if (this != &obj_desc) + { + state_vars_ = std::move(obj_desc.state_vars_); + } + return *this; + } + virtual ~GPUObjectDescriptor() = default; + + void SetStateVar(const std::string &key, const std::string &value) const + { + state_vars_[key] = value; + } + + virtual std::string PerformConstExpr(const std::string &) const { return ""; } + + virtual absl::Status PerformSelector(const std::string &, const std::vector<std::string> &, + const std::vector<std::string> &, std::string *result) const + { + *result = ""; + return absl::OkStatus(); + } + virtual GPUResources GetGPUResources() const { return GPUResources(); } + + virtual absl::Status CreateGPUObject(CLContext *, std::unique_ptr<GPUObject> *) const + { + return absl::OkStatus(); + } + virtual void Release() {} + + void SetAccess(AccessType access_type) { access_type_ = access_type; } + AccessType GetAccess() const { return access_type_; } + +protected: + // friend flatbuffers::Offset<data::GPUObjectDescriptor> Encode( + // const GPUObjectDescriptor& desc, flatbuffers::FlatBufferBuilder* builder); + // friend void Decode(const data::GPUObjectDescriptor* fb_obj, + // GPUObjectDescriptor* obj); + mutable std::map<std::string, std::string> state_vars_; + AccessType access_type_ = AccessType::UNKNOWN; +}; + +using GPUObjectDescriptorPtr = std::unique_ptr<GPUObjectDescriptor>; + +class GPUObject +{ +public: + GPUObject() = default; + // Move only + GPUObject(GPUObject &&obj_desc) = default; + GPUObject &operator=(GPUObject &&obj_desc) = default; + GPUObject(const GPUObject &) = delete; + GPUObject &operator=(const GPUObject &) = delete; + virtual ~GPUObject() = default; + virtual absl::Status GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const = 0; +}; + +using GPUObjectPtr = std::unique_ptr<GPUObject>; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_GPU_OBJECT_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/InferenceContext.cc b/runtime/onert/backend/gpu_cl/open_cl/InferenceContext.cc new file mode 100644 index 000000000..afb7e2950 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/InferenceContext.cc @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "InferenceContext.h" + +#include <algorithm> +#include <cmath> +#include <cstdint> +#include <map> +#include <memory> +#include <string> +#include <vector> +#include <unordered_map> + +#include "Buffer.h" +#include "ClDevice.h" + +#include "kernels/GpuOperation.h" +#include "ModelHints.h" +#include "Precision.h" +#include "StorageTypeUtil.h" +#include "TensorType.h" +#include "DataType.h" +#include "Model.h" +#include "Operations.h" +#include "Shape.h" +#include "Types.h" +#include "Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +CLNode::CLNode(CLNode &&node) + : operation(std::move(node.operation)), inputs(std::move(node.inputs)), + outputs(std::move(node.outputs)), name(std::move(node.name)) +{ +} + +CLNode &CLNode::operator=(CLNode &&node) +{ + if (this != &node) + { + operation = std::move(node.operation); + inputs = std::move(node.inputs); + outputs = std::move(node.outputs); + name = std::move(node.name); + } + return *this; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/InferenceContext.h b/runtime/onert/backend/gpu_cl/open_cl/InferenceContext.h new file mode 100644 index 000000000..ebe2c5313 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/InferenceContext.h @@ -0,0 +1,143 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_INFERENCE_CONTEXT_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_INFERENCE_CONTEXT_H__ + +#include <cstdint> +#include <functional> +#include <map> +#include <memory> +#include <vector> +#include <unordered_map> + +#include "Buffer.h" +#include "ClCommandQueue.h" +#include "Environment.h" +#include "GpuObject.h" +#include "kernels/GpuOperation.h" +#include "ModelHints.h" +#include "OpenclWrapper.h" +#include "Precision.h" +#include "TensorType.h" +#include "Model.h" +#include "InternalTensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +struct CLNode +{ + std::unique_ptr<GPUOperation> operation; + std::vector<ValueId> inputs; + std::vector<ValueId> outputs; + + // Mostly for debug purposes. + std::string name; + + CLNode() = default; + + CLNode(CLNode &&node); + CLNode &operator=(CLNode &&node); + CLNode(const CLNode &) = delete; + CLNode &operator=(const CLNode &) = delete; +}; + +class InferenceContext +{ +public: + struct CreateInferenceInfo + { + CalculationsPrecision precision; + TensorStorageType storage_type; + ModelHints hints; + }; + + struct DummyTensor + { + BHWC shape; + TensorDescriptor descriptor; + + bool operator==(const DummyTensor &b) const + { + return shape == b.shape && descriptor == b.descriptor; + } + }; + + class TensorReserver + { + public: + ValueId Add(const std::shared_ptr<DummyTensor> dummy) + { + reservations_[next_] = std::move(dummy); + return next_++; + } + void Add(ValueId id, const std::shared_ptr<DummyTensor> dummy) + { + reservations_[id] = std::move(dummy); + } + void SetNext(ValueId id) { next_ = id; } + bool HaveTensor(ValueId id) { return reservations_.find(id) != reservations_.end(); } + std::shared_ptr<DummyTensor> Get(ValueId id) { return reservations_[id]; } + + std::vector<std::pair<ValueId, TensorDescriptor>> GetTensorDescs() const + { + std::vector<std::pair<ValueId, TensorDescriptor>> result; + for (auto &v : reservations_) + { + TensorDescriptor desc = v.second->descriptor; + desc.shape.b = v.second->shape.b; + desc.shape.h = v.second->shape.h; + desc.shape.w = v.second->shape.w; + desc.shape.d = 1; + desc.shape.c = v.second->shape.c; + result.push_back({v.first, desc}); + } + return result; + } + + void Add(const std::vector<std::pair<ValueId, TensorDescriptor>> &tensors) + { + for (auto &v : tensors) + { + auto dummy = std::make_shared<DummyTensor>(); + dummy->descriptor = v.second; + dummy->shape.b = v.second.shape.b; + dummy->shape.h = v.second.shape.h; + dummy->shape.w = v.second.shape.w; + dummy->shape.c = v.second.shape.c; + Add(v.first, dummy); + } + } + + private: + std::unordered_map<ValueId, std::shared_ptr<DummyTensor>> reservations_; + ValueId next_ = 0; + }; + +private: +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_INFERENCE_CONTEXT_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/InternalTensor.h b/runtime/onert/backend/gpu_cl/open_cl/InternalTensor.h new file mode 100644 index 000000000..f0423db86 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/InternalTensor.h @@ -0,0 +1,106 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_INTERNAL_TENSOR_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_INTERNAL_TENSOR_H__ + +#include <stdint.h> + +#include <vector> + +#include "DataType.h" +#include "Shape.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace internal_tensor +{ + +// Meta function given element type returns a type for Tensor data container. +template <DataType Type> struct StorageType; + +template <> struct StorageType<DataType::FLOAT32> +{ + using value = std::vector<float>; +}; + +template <> struct StorageType<DataType::INT32> +{ + using value = std::vector<int32_t>; +}; + +} // namespace internal_tensor + +template <typename ShapeT, DataType Type> struct InternalTensor +{ + using ShapeType = ShapeT; + + constexpr static DataType kType = Type; + + using TensorStorageType = typename internal_tensor::StorageType<Type>::value; + + // Opaque id of a tensor. + int64_t id = -1; + + ShapeType shape; + + TensorStorageType data; +}; + +// TensorRef is a reference to another tensor. If an object should never hold +// tensor data, then TensorRef should be used instead. +template <typename ShapeT> struct TensorRef +{ + using ShapeType = ShapeT; + + DataType type = DataType::UNKNOWN; + + ShapeT shape; + + // Opaque reference to a tensor. Upstream component is responsible for + // resolving this reference into an actual tensor. + int64_t ref = -1; + + // Specifies if the tensor should be a variable input tensor that must be an + // output as well as an input to the graph. + bool is_variable_input = false; +}; + +template <typename ShapeT, DataType Type> constexpr DataType InternalTensor<ShapeT, Type>::kType; + +template <typename ShapeT, DataType Type> +InternalTensor<ShapeT, Type> MakeZeroTensor(const ShapeT &shape) +{ + InternalTensor<ShapeT, Type> tensor; + tensor.shape = shape; + tensor.data = + typename InternalTensor<ShapeT, Type>::TensorStorageType(shape.DimensionsProduct(), 0); + return tensor; +} + +using TensorFloat32 = InternalTensor<BHWC, DataType::FLOAT32>; +using Tensor5DFloat32 = InternalTensor<BHWDC, DataType::FLOAT32>; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_INTERNAL_TENSOR_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/LinearStorage.cc b/runtime/onert/backend/gpu_cl/open_cl/LinearStorage.cc new file mode 100644 index 000000000..3889d4369 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/LinearStorage.cc @@ -0,0 +1,265 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LinearStorage.h" + +#include "absl/strings/str_cat.h" +#include "DataType.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +TensorLinearDescriptor::TensorLinearDescriptor(TensorLinearDescriptor &&desc) + : GPUObjectDescriptor(std::move(desc)), storage_type(desc.storage_type), + element_type(desc.element_type), memory_type(desc.memory_type), size(desc.size), + data(std::move(desc.data)) +{ +} + +TensorLinearDescriptor &TensorLinearDescriptor::operator=(TensorLinearDescriptor &&desc) +{ + if (this != &desc) + { + std::swap(storage_type, desc.storage_type); + std::swap(element_type, desc.element_type); + std::swap(memory_type, desc.memory_type); + std::swap(size, desc.size); + data = std::move(desc.data); + GPUObjectDescriptor::operator=(std::move(desc)); + } + return *this; +} + +void TensorLinearDescriptor::Release() { data.clear(); } + +GPUResources TensorLinearDescriptor::GetGPUResources() const +{ + GPUResources resources; + resources.ints.push_back("length"); + if (storage_type == LinearStorageType::BUFFER) + { + GPUBufferDescriptor desc; + desc.data_type = element_type; + desc.access_type = access_type_; + desc.element_size = 4; + desc.memory_type = memory_type; + resources.buffers.push_back({"buffer", desc}); + } + else + { + GPUImage2DDescriptor desc; + desc.data_type = element_type; + desc.access_type = access_type_; + resources.images2d.push_back({"tex2d", desc}); + } + return resources; +} + +absl::Status TensorLinearDescriptor::PerformSelector(const std::string &selector, + const std::vector<std::string> &args, + const std::vector<std::string> &, + std::string *result) const +{ + if (selector == "Length") + { + *result = "length"; + return absl::OkStatus(); + } + else if (selector == "Read") + { + return PerformReadSelector(args, result); + } + else if (selector == "GetPtr") + { + if (storage_type != LinearStorageType::BUFFER) + { + return absl::InvalidArgumentError( + "GetPtr selector supported for LinearStorageType::BUFFER only."); + } + *result = "buffer"; + return absl::OkStatus(); + } + else + { + return absl::NotFoundError( + absl::StrCat("TensorLinearDescriptor don't have selector with name - ", selector)); + } +} + +absl::Status TensorLinearDescriptor::PerformReadSelector(const std::vector<std::string> &args, + std::string *result) const +{ + if (args.size() != 1) + { + return absl::NotFoundError(absl::StrCat( + "TensorLinearDescriptor Read require one argument, but ", args.size(), " was passed")); + } + if (storage_type == LinearStorageType::BUFFER) + { + *result = absl::StrCat("buffer[", args[0], "]"); + return absl::OkStatus(); + } + else + { + const std::string read = element_type == DataType::FLOAT16 ? "read_imageh" : "read_imagef"; + *result = absl::StrCat(read, "(tex2d, smp_none, (int2)(", args[0], ", 0))"); + return absl::OkStatus(); + } +} + +absl::Status TensorLinearDescriptor::CreateGPUObject(CLContext *context, GPUObjectPtr *result) const +{ + LinearStorage gpu_storage; + RETURN_IF_ERROR(gpu_storage.CreateFromTensorLinearDescriptor(*this, context)); + *result = absl::make_unique<LinearStorage>(std::move(gpu_storage)); + return absl::OkStatus(); +} + +void TensorLinearDescriptor::UploadLinearData(const InternalTensor<Linear, DataType::FLOAT32> &src, + int aligned_size) +{ + size = aligned_size == 0 ? DivideRoundUp(src.shape.v, 4) : aligned_size; + if (element_type == DataType::FLOAT32) + { + data.resize(size * sizeof(float) * 4); + float *gpu_data = reinterpret_cast<float *>(data.data()); + for (int i = 0; i < size * 4; ++i) + { + if (i < src.shape.v) + { + gpu_data[i] = src.data[i]; + } + else + { + gpu_data[i] = 0.0f; + } + } + } + // TODO + // It doesn't support F16 yet. I will try to add it later. + // + // else { + // data.resize(size * sizeof(half) * 4); + // half* gpu_data = reinterpret_cast<half*>(data.data()); + // for (int i = 0; i < size * 4; ++i) { + // if (i < src.shape.v) { + // gpu_data[i] = src.data[i]; + // } else { + // gpu_data[i] = 0.0f; + // } + // } + // } +} + +void LinearStorage::Release() +{ + if (memory_) + { + clReleaseMemObject(memory_); + memory_ = nullptr; + } +} + +LinearStorage::LinearStorage(LinearStorage &&storage) + : GPUObject(std::move(storage)), memory_(storage.memory_), depth_(storage.depth_), + storage_type_(storage.storage_type_) +{ + storage.memory_ = nullptr; +} + +LinearStorage &LinearStorage::operator=(LinearStorage &&storage) +{ + if (this != &storage) + { + Release(); + std::swap(memory_, storage.memory_); + std::swap(depth_, storage.depth_); + std::swap(storage_type_, storage.storage_type_); + GPUObject::operator=(std::move(storage)); + } + return *this; +} + +absl::Status LinearStorage::GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const +{ + const auto *linear_desc = dynamic_cast<const TensorLinearDescriptor *>(obj_ptr); + if (!linear_desc) + { + return absl::InvalidArgumentError("Expected TensorLinearDescriptor on input."); + } + + resources->ints.push_back({"length", depth_}); + + if (storage_type_ == LinearStorageType::BUFFER) + { + resources->buffers.push_back({"buffer", memory_}); + } + else + { + resources->images2d.push_back({"tex2d", memory_}); + } + + return absl::OkStatus(); +} + +absl::Status LinearStorage::CreateFromTensorLinearDescriptor(const TensorLinearDescriptor &desc, + CLContext *context) +{ + storage_type_ = desc.storage_type; + depth_ = desc.size; + uint8_t *data_ptr = desc.data.empty() ? nullptr : const_cast<unsigned char *>(desc.data.data()); + if (storage_type_ == LinearStorageType::BUFFER) + { + bool read_only = desc.memory_type == MemoryType::CONSTANT; + uint8_t *data_ptr = desc.data.empty() ? nullptr : const_cast<unsigned char *>(desc.data.data()); + // TODO + // It doesn't support F16 yet. I will try to add it later. + // + // const int float4_size = desc.element_type == DataType::FLOAT32 + // ? sizeof(float) * 4 + // : sizeof(half) * 4; + const int float4_size = sizeof(float) * 4; + return CreateCLBuffer(context->context(), depth_ * float4_size, read_only, data_ptr, &memory_); + } + else + { + return CreateRGBAImage2D(context->context(), depth_, 1, + DataTypeToChannelType(desc.element_type), data_ptr, &memory_); + } +} + +LinearStorageType DeduceLinearStorageType(TensorStorageType tensor_storage_type) +{ + if (tensor_storage_type == TensorStorageType::BUFFER) + { + return LinearStorageType::BUFFER; + } + else + { + return LinearStorageType::TEXTURE_2D; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/LinearStorage.h b/runtime/onert/backend/gpu_cl/open_cl/LinearStorage.h new file mode 100644 index 000000000..f6c3ac82f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/LinearStorage.h @@ -0,0 +1,110 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_LINEAR_STORAGE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_LINEAR_STORAGE_H__ + +#include <string> +#include <utility> + +#include "absl/strings/str_cat.h" +#include "absl/types/span.h" +#include "GpuObject.h" +#include "OpenclWrapper.h" +#include "TensorType.h" +#include "Util.h" +#include "DataType.h" +#include "Status.h" +#include "Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class LinearStorageType +{ + BUFFER, + TEXTURE_2D +}; + +struct TensorLinearDescriptor : public GPUObjectDescriptor +{ + LinearStorageType storage_type; + DataType element_type; // FLOAT32 or FLOAT16 + MemoryType memory_type = MemoryType::GLOBAL; // applicable for BUFFER + + // optional + int size = 0; + std::vector<uint8_t> data; + + TensorLinearDescriptor() = default; + TensorLinearDescriptor(const TensorLinearDescriptor &) = default; + TensorLinearDescriptor &operator=(const TensorLinearDescriptor &) = default; + TensorLinearDescriptor(TensorLinearDescriptor &&desc); + TensorLinearDescriptor &operator=(TensorLinearDescriptor &&desc); + + void UploadLinearData(const InternalTensor<Linear, DataType::FLOAT32> &src, int aligned_size = 0); + + absl::Status PerformSelector(const std::string &selector, const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const override; + + GPUResources GetGPUResources() const override; + absl::Status PerformReadSelector(const std::vector<std::string> &args, std::string *result) const; + + absl::Status CreateGPUObject(CLContext *context, GPUObjectPtr *result) const override; + void Release() override; +}; + +LinearStorageType DeduceLinearStorageType(TensorStorageType tensor_storage_type); + +// Represent GPU 1D-array of FLT4(float4/half4) values +// Can use inside texture2d or buffer +class LinearStorage : public GPUObject +{ +public: + LinearStorage() {} + ~LinearStorage() override { Release(); } + + // Move only + LinearStorage(LinearStorage &&storage); + LinearStorage &operator=(LinearStorage &&storage); + LinearStorage(const LinearStorage &) = delete; + LinearStorage &operator=(const LinearStorage &) = delete; + + absl::Status GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const override; + + absl::Status CreateFromTensorLinearDescriptor(const TensorLinearDescriptor &desc, + CLContext *context); + +private: + void Release(); + + cl_mem memory_ = nullptr; + int depth_; + LinearStorageType storage_type_; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_LINEAR_STORAGE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Model.h b/runtime/onert/backend/gpu_cl/open_cl/Model.h new file mode 100644 index 000000000..f434bb22f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Model.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_MODEL_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_MODEL_H__ + +#include <string> + +#include "absl/types/any.h" +#include "InternalTensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// There is yet another representation of CNN graph. The primary purpose of this +// representation is to simplify graph manipulation. + +using ValueId = uint32_t; + +// Used to emulate quantized behavior. +struct QuantizationParams +{ + float min = 0; + float max = 0; + float scale = 0; +}; + +struct Operation +{ + std::string type; + absl::any attributes; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_MODEL_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ModelHints.h b/runtime/onert/backend/gpu_cl/open_cl/ModelHints.h new file mode 100644 index 000000000..474c56b2a --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ModelHints.h @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_MODEL_HINTS_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_MODEL_HINTS_H__ + +#include <cstdint> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +struct ModelHints +{ + using ModelHint = uint64_t; + + // By default we want the fastest inference. + static constexpr ModelHint kFastestInference = 0x00000000; + // Can improve compilation time, but inference can be slower. + static constexpr ModelHint kReduceKernelsCount = 0x00000001; + // Can improve tuning time, but inference can be slower. + static constexpr ModelHint kFastTuning = 0x00000002; + + // Experimental. + // Can improve performance and memory consumption, but slow down + // initialization a lot and create more kernels. + static constexpr ModelHint kAllowSpecialKernels = 0x00000004; + + void Add(ModelHint hint) + { + if (hint == kFastestInference) + { + hints = kFastestInference; + } + else + { + hints |= hint; + } + } + + bool Check(ModelHint hint) const { return hints & hint; } + + uint64_t hints = kFastestInference; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_MODEL_HINTS_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/OpenclWrapper.cc b/runtime/onert/backend/gpu_cl/open_cl/OpenclWrapper.cc new file mode 100644 index 000000000..dbaf6faf6 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/OpenclWrapper.cc @@ -0,0 +1,407 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#if defined(_WIN32) +#define __WINDOWS__ +#endif + +#include "OpenclWrapper.h" + +#ifdef __WINDOWS__ +#include <windows.h> +#else +#include <dlfcn.h> +#endif + +#include <string> + +#include "absl/strings/str_cat.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +#ifdef __ANDROID__ +#define LoadFunction(function) \ + if (use_wrapper) \ + { \ + function = reinterpret_cast<PFN_##function>(loadOpenCLPointer(#function)); \ + } \ + else \ + { \ + function = reinterpret_cast<PFN_##function>(dlsym(*libopencl, #function)); \ + } +#elif defined(__WINDOWS__) +#define LoadFunction(function) \ + function = reinterpret_cast<PFN_##function>(GetProcAddress(libopencl, #function)); +#else +#define LoadFunction(function) \ + function = reinterpret_cast<PFN_##function>(dlsym(*libopencl, #function)); +#endif + +#ifdef __WINDOWS__ +void LoadOpenCLFunctions(HMODULE libopencl); +#else +void LoadOpenCLFunctions(void **libopencl, bool use_wrapper); +#endif + +absl::Status LoadOpenCL(void **libopencl) +{ +#ifdef __WINDOWS__ + HMODULE libopencl = LoadLibraryA("OpenCL.dll"); + if (libopencl) + { + LoadOpenCLFunctions(libopencl); + return absl::OkStatus(); + } + else + { + DWORD error_code = GetLastError(); + return absl::UnknownError( + absl::StrCat("Can not open OpenCL library on this device, error code - ", error_code)); + } +#else + *libopencl = dlopen("libOpenCL.so", RTLD_NOW | RTLD_LOCAL); + if (*libopencl) + { + LoadOpenCLFunctions(libopencl, false); + return absl::OkStatus(); + } + // record error + std::string error(dlerror()); +#ifdef __ANDROID__ + // Pixel phone or auto? + *libopencl = dlopen("libOpenCL-pixel.so", RTLD_NOW | RTLD_LOCAL); + if (!*libopencl) + { + *libopencl = dlopen("libOpenCL-car.so", RTLD_NOW | RTLD_LOCAL); + } + if (*libopencl) + { + typedef void (*enableOpenCL_t)(); + enableOpenCL_t enableOpenCL = + reinterpret_cast<enableOpenCL_t>(dlsym(*libopencl, "enableOpenCL")); + enableOpenCL(); + LoadOpenCLFunctions(libopencl, true); + return absl::OkStatus(); + } +#endif + return absl::UnknownError(absl::StrCat("Can not open OpenCL library on this device - ", error)); +#endif +} + +void UnloadOpenCL(void *libopencl) +{ + if (libopencl) + { + dlclose(libopencl); + } +} + +#ifdef __WINDOWS__ +void LoadOpenCLFunctions(HMODULE libopencl) +{ +#else +#ifdef __ANDROID__ +void LoadOpenCLFunctions(void **libopencl, bool use_wrapper) +{ + typedef void *(*loadOpenCLPointer_t)(const char *name); + loadOpenCLPointer_t loadOpenCLPointer; + if (use_wrapper) + { + loadOpenCLPointer = + reinterpret_cast<loadOpenCLPointer_t>(dlsym(*libopencl, "loadOpenCLPointer")); + } +#else +void LoadOpenCLFunctions(void **libopencl, bool) +{ +#endif // __ANDROID__ +#endif // __WINDOWS__ + + LoadFunction(clGetPlatformIDs); + LoadFunction(clGetPlatformInfo); + LoadFunction(clGetDeviceIDs); + LoadFunction(clGetDeviceInfo); + LoadFunction(clCreateSubDevices); + LoadFunction(clRetainDevice); + LoadFunction(clReleaseDevice); + LoadFunction(clCreateContext); + LoadFunction(clCreateContextFromType); + LoadFunction(clRetainContext); + LoadFunction(clReleaseContext); + LoadFunction(clGetContextInfo); + LoadFunction(clCreateCommandQueueWithProperties); + LoadFunction(clRetainCommandQueue); + LoadFunction(clReleaseCommandQueue); + LoadFunction(clGetCommandQueueInfo); + LoadFunction(clCreateBuffer); + LoadFunction(clCreateSubBuffer); + LoadFunction(clCreateImage); + LoadFunction(clCreatePipe); + LoadFunction(clRetainMemObject); + LoadFunction(clReleaseMemObject); + LoadFunction(clGetSupportedImageFormats); + LoadFunction(clGetMemObjectInfo); + LoadFunction(clGetImageInfo); + LoadFunction(clGetPipeInfo); + LoadFunction(clSetMemObjectDestructorCallback); + LoadFunction(clSVMAlloc); + LoadFunction(clSVMFree); + LoadFunction(clCreateSamplerWithProperties); + LoadFunction(clRetainSampler); + LoadFunction(clReleaseSampler); + LoadFunction(clGetSamplerInfo); + LoadFunction(clCreateProgramWithSource); + LoadFunction(clCreateProgramWithBinary); + LoadFunction(clCreateProgramWithBuiltInKernels); + LoadFunction(clRetainProgram); + LoadFunction(clReleaseProgram); + LoadFunction(clBuildProgram); + LoadFunction(clCompileProgram); + LoadFunction(clLinkProgram); + LoadFunction(clUnloadPlatformCompiler); + LoadFunction(clGetProgramInfo); + LoadFunction(clGetProgramBuildInfo); + LoadFunction(clCreateKernel); + LoadFunction(clCreateKernelsInProgram); + LoadFunction(clRetainKernel); + LoadFunction(clReleaseKernel); + LoadFunction(clSetKernelArg); + LoadFunction(clSetKernelArgSVMPointer); + LoadFunction(clSetKernelExecInfo); + LoadFunction(clGetKernelInfo); + LoadFunction(clGetKernelArgInfo); + LoadFunction(clGetKernelWorkGroupInfo); + LoadFunction(clWaitForEvents); + LoadFunction(clGetEventInfo); + LoadFunction(clCreateUserEvent); + LoadFunction(clRetainEvent); + LoadFunction(clReleaseEvent); + LoadFunction(clSetUserEventStatus); + LoadFunction(clSetEventCallback); + LoadFunction(clGetEventProfilingInfo); + LoadFunction(clFlush); + LoadFunction(clFinish); + LoadFunction(clEnqueueReadBuffer); + LoadFunction(clEnqueueReadBufferRect); + LoadFunction(clEnqueueWriteBuffer); + LoadFunction(clEnqueueWriteBufferRect); + LoadFunction(clEnqueueFillBuffer); + LoadFunction(clEnqueueCopyBuffer); + LoadFunction(clEnqueueCopyBufferRect); + LoadFunction(clEnqueueReadImage); + LoadFunction(clEnqueueWriteImage); + LoadFunction(clEnqueueFillImage); + LoadFunction(clEnqueueCopyImage); + LoadFunction(clEnqueueCopyImageToBuffer); + LoadFunction(clEnqueueCopyBufferToImage); + LoadFunction(clEnqueueMapBuffer); + LoadFunction(clEnqueueMapImage); + LoadFunction(clEnqueueUnmapMemObject); + LoadFunction(clEnqueueMigrateMemObjects); + LoadFunction(clEnqueueNDRangeKernel); + LoadFunction(clEnqueueNativeKernel); + LoadFunction(clEnqueueMarkerWithWaitList); + LoadFunction(clEnqueueBarrierWithWaitList); + LoadFunction(clEnqueueSVMFree); + LoadFunction(clEnqueueSVMMemcpy); + LoadFunction(clEnqueueSVMMemFill); + LoadFunction(clEnqueueSVMMap); + LoadFunction(clEnqueueSVMUnmap); + LoadFunction(clGetExtensionFunctionAddressForPlatform); + LoadFunction(clCreateImage2D); + LoadFunction(clCreateImage3D); + LoadFunction(clEnqueueMarker); + LoadFunction(clEnqueueWaitForEvents); + LoadFunction(clEnqueueBarrier); + LoadFunction(clUnloadCompiler); + LoadFunction(clGetExtensionFunctionAddress); + LoadFunction(clCreateCommandQueue); + LoadFunction(clCreateSampler); + LoadFunction(clEnqueueTask); + + // OpenGL sharing + LoadFunction(clCreateFromGLBuffer); + LoadFunction(clCreateFromGLTexture); + LoadFunction(clEnqueueAcquireGLObjects); + LoadFunction(clEnqueueReleaseGLObjects); + + // cl_khr_egl_event extension + LoadFunction(clCreateEventFromEGLSyncKHR); + + // EGL sharing + LoadFunction(clCreateFromEGLImageKHR); + LoadFunction(clEnqueueAcquireEGLObjectsKHR); + LoadFunction(clEnqueueReleaseEGLObjectsKHR); +} // namespace gpu_cl + +// No OpenCL support, do not set function addresses +PFN_clGetPlatformIDs clGetPlatformIDs; +PFN_clGetPlatformInfo clGetPlatformInfo; +PFN_clGetDeviceIDs clGetDeviceIDs; +PFN_clGetDeviceInfo clGetDeviceInfo; +PFN_clCreateSubDevices clCreateSubDevices; +PFN_clRetainDevice clRetainDevice; +PFN_clReleaseDevice clReleaseDevice; +PFN_clCreateContext clCreateContext; +PFN_clCreateContextFromType clCreateContextFromType; +PFN_clRetainContext clRetainContext; +PFN_clReleaseContext clReleaseContext; +PFN_clGetContextInfo clGetContextInfo; +PFN_clCreateCommandQueueWithProperties clCreateCommandQueueWithProperties; +PFN_clRetainCommandQueue clRetainCommandQueue; +PFN_clReleaseCommandQueue clReleaseCommandQueue; +PFN_clGetCommandQueueInfo clGetCommandQueueInfo; +PFN_clCreateBuffer clCreateBuffer; +PFN_clCreateSubBuffer clCreateSubBuffer; +PFN_clCreateImage clCreateImage; +PFN_clCreatePipe clCreatePipe; +PFN_clRetainMemObject clRetainMemObject; +PFN_clReleaseMemObject clReleaseMemObject; +PFN_clGetSupportedImageFormats clGetSupportedImageFormats; +PFN_clGetMemObjectInfo clGetMemObjectInfo; +PFN_clGetImageInfo clGetImageInfo; +PFN_clGetPipeInfo clGetPipeInfo; +PFN_clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback; +PFN_clSVMAlloc clSVMAlloc; +PFN_clSVMFree clSVMFree; +PFN_clCreateSamplerWithProperties clCreateSamplerWithProperties; +PFN_clRetainSampler clRetainSampler; +PFN_clReleaseSampler clReleaseSampler; +PFN_clGetSamplerInfo clGetSamplerInfo; +PFN_clCreateProgramWithSource clCreateProgramWithSource; +PFN_clCreateProgramWithBinary clCreateProgramWithBinary; +PFN_clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels; +PFN_clRetainProgram clRetainProgram; +PFN_clReleaseProgram clReleaseProgram; +PFN_clBuildProgram clBuildProgram; +PFN_clCompileProgram clCompileProgram; +PFN_clLinkProgram clLinkProgram; +PFN_clUnloadPlatformCompiler clUnloadPlatformCompiler; +PFN_clGetProgramInfo clGetProgramInfo; +PFN_clGetProgramBuildInfo clGetProgramBuildInfo; +PFN_clCreateKernel clCreateKernel; +PFN_clCreateKernelsInProgram clCreateKernelsInProgram; +PFN_clRetainKernel clRetainKernel; +PFN_clReleaseKernel clReleaseKernel; +PFN_clSetKernelArg clSetKernelArg; +PFN_clSetKernelArgSVMPointer clSetKernelArgSVMPointer; +PFN_clSetKernelExecInfo clSetKernelExecInfo; +PFN_clGetKernelInfo clGetKernelInfo; +PFN_clGetKernelArgInfo clGetKernelArgInfo; +PFN_clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo; +PFN_clWaitForEvents clWaitForEvents; +PFN_clGetEventInfo clGetEventInfo; +PFN_clCreateUserEvent clCreateUserEvent; +PFN_clRetainEvent clRetainEvent; +PFN_clReleaseEvent clReleaseEvent; +PFN_clSetUserEventStatus clSetUserEventStatus; +PFN_clSetEventCallback clSetEventCallback; +PFN_clGetEventProfilingInfo clGetEventProfilingInfo; +PFN_clFlush clFlush; +PFN_clFinish clFinish; +PFN_clEnqueueReadBuffer clEnqueueReadBuffer; +PFN_clEnqueueReadBufferRect clEnqueueReadBufferRect; +PFN_clEnqueueWriteBuffer clEnqueueWriteBuffer; +PFN_clEnqueueWriteBufferRect clEnqueueWriteBufferRect; +PFN_clEnqueueFillBuffer clEnqueueFillBuffer; +PFN_clEnqueueCopyBuffer clEnqueueCopyBuffer; +PFN_clEnqueueCopyBufferRect clEnqueueCopyBufferRect; +PFN_clEnqueueReadImage clEnqueueReadImage; +PFN_clEnqueueWriteImage clEnqueueWriteImage; +PFN_clEnqueueFillImage clEnqueueFillImage; +PFN_clEnqueueCopyImage clEnqueueCopyImage; +PFN_clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer; +PFN_clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage; +PFN_clEnqueueMapBuffer clEnqueueMapBuffer; +PFN_clEnqueueMapImage clEnqueueMapImage; +PFN_clEnqueueUnmapMemObject clEnqueueUnmapMemObject; +PFN_clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects; +PFN_clEnqueueNDRangeKernel clEnqueueNDRangeKernel; +PFN_clEnqueueNativeKernel clEnqueueNativeKernel; +PFN_clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList; +PFN_clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList; +PFN_clEnqueueSVMFree clEnqueueSVMFree; +PFN_clEnqueueSVMMemcpy clEnqueueSVMMemcpy; +PFN_clEnqueueSVMMemFill clEnqueueSVMMemFill; +PFN_clEnqueueSVMMap clEnqueueSVMMap; +PFN_clEnqueueSVMUnmap clEnqueueSVMUnmap; +PFN_clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform; +PFN_clCreateImage2D clCreateImage2D; +PFN_clCreateImage3D clCreateImage3D; +PFN_clEnqueueMarker clEnqueueMarker; +PFN_clEnqueueWaitForEvents clEnqueueWaitForEvents; +PFN_clEnqueueBarrier clEnqueueBarrier; +PFN_clUnloadCompiler clUnloadCompiler; +PFN_clGetExtensionFunctionAddress clGetExtensionFunctionAddress; +PFN_clCreateCommandQueue clCreateCommandQueue; +PFN_clCreateSampler clCreateSampler; +PFN_clEnqueueTask clEnqueueTask; + +// OpenGL sharing +PFN_clCreateFromGLBuffer clCreateFromGLBuffer; +PFN_clCreateFromGLTexture clCreateFromGLTexture; +PFN_clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects; +PFN_clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects; + +// cl_khr_egl_event extension +PFN_clCreateEventFromEGLSyncKHR clCreateEventFromEGLSyncKHR; + +// EGL sharing +PFN_clCreateFromEGLImageKHR clCreateFromEGLImageKHR; +PFN_clEnqueueAcquireEGLObjectsKHR clEnqueueAcquireEGLObjectsKHR; +PFN_clEnqueueReleaseEGLObjectsKHR clEnqueueReleaseEGLObjectsKHR; + +cl_mem CreateImage2DLegacy(cl_context context, cl_mem_flags flags, + const cl_image_format *image_format, const cl_image_desc *image_desc, + void *host_ptr, cl_int *errcode_ret) +{ + if (clCreateImage) + { // clCreateImage available since OpenCL 1.2 + return clCreateImage(context, flags, image_format, image_desc, host_ptr, errcode_ret); + } + else + { + return clCreateImage2D(context, flags, image_format, image_desc->image_width, + image_desc->image_height, image_desc->image_row_pitch, host_ptr, + errcode_ret); + } +} + +cl_mem CreateImage3DLegacy(cl_context context, cl_mem_flags flags, + const cl_image_format *image_format, const cl_image_desc *image_desc, + void *host_ptr, cl_int *errcode_ret) +{ + if (clCreateImage) + { // clCreateImage available since OpenCL 1.2 + return clCreateImage(context, flags, image_format, image_desc, host_ptr, errcode_ret); + } + else + { + return clCreateImage3D(context, flags, image_format, image_desc->image_width, + image_desc->image_height, image_desc->image_depth, + image_desc->image_row_pitch, image_desc->image_slice_pitch, host_ptr, + errcode_ret); + } +} +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/OpenclWrapper.h b/runtime/onert/backend/gpu_cl/open_cl/OpenclWrapper.h new file mode 100644 index 000000000..021f8735a --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/OpenclWrapper.h @@ -0,0 +1,560 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_WRAPPERE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_WRAPPERE_H__ + +#include "CL/cl.h" +#include "CL/cl_egl.h" +#include "CL/cl_ext.h" +#include "CL/cl_gl.h" +#include "CL/cl_platform.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +absl::Status LoadOpenCL(void **libopencl); +void UnloadOpenCL(void *libopencl); + +typedef cl_int(CL_API_CALL *PFN_clGetPlatformIDs)( + cl_uint /* num_entries */, cl_platform_id * /* platforms */, + cl_uint * /* num_platforms */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetPlatformInfo)( + cl_platform_id /* platform */, cl_platform_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetDeviceIDs)( + cl_platform_id /* platform */, cl_device_type /* device_type */, cl_uint /* num_entries */, + cl_device_id * /* devices */, cl_uint * /* num_devices */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetDeviceInfo)( + cl_device_id /* device */, cl_device_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clCreateSubDevices)( + cl_device_id /* in_device */, const cl_device_partition_property * /* properties */, + cl_uint /* num_devices */, cl_device_id * /* out_devices */, + cl_uint * /* num_devices_ret */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clRetainDevice)(cl_device_id /* device */) + CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clReleaseDevice)(cl_device_id /* device */) + CL_API_SUFFIX__VERSION_1_2; +typedef cl_context(CL_API_CALL *PFN_clCreateContext)( + const cl_context_properties * /* properties */, cl_uint /* num_devices */, + const cl_device_id * /* devices */, + void(CL_CALLBACK * /* pfn_notify */)(const char *, const void *, size_t, void *), + void * /* user_data */, cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_context(CL_API_CALL *PFN_clCreateContextFromType)( + const cl_context_properties * /* properties */, cl_device_type /* device_type */, + void(CL_CALLBACK * /* pfn_notify*/)(const char *, const void *, size_t, void *), + void * /* user_data */, cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clRetainContext)(cl_context /* context */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clReleaseContext)(cl_context /* context */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetContextInfo)( + cl_context /* context */, cl_context_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_command_queue(CL_API_CALL *PFN_clCreateCommandQueueWithProperties)( + cl_context /* context */, cl_device_id /* device */, const cl_queue_properties * /* properties */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clRetainCommandQueue)(cl_command_queue /* command_queue */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clReleaseCommandQueue)(cl_command_queue /* command_queue */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetCommandQueueInfo)( + cl_command_queue /* command_queue */, cl_command_queue_info /* param_name */, + size_t /* param_value_size */, void * /* param_value */, + size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_mem(CL_API_CALL *PFN_clCreateBuffer)( + cl_context /* context */, cl_mem_flags /* flags */, size_t /* size */, void * /* host_ptr */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_mem(CL_API_CALL *PFN_clCreateSubBuffer)( + cl_mem /* buffer */, cl_mem_flags /* flags */, cl_buffer_create_type /* buffer_create_type */, + const void * /* buffer_create_info */, cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_1; +typedef cl_mem(CL_API_CALL *PFN_clCreateImage)( + cl_context /* context */, cl_mem_flags /* flags */, const cl_image_format * /* image_format */, + const cl_image_desc * /* image_desc */, void * /* host_ptr */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_mem(CL_API_CALL *PFN_clCreatePipe)( + cl_context /* context */, cl_mem_flags /* flags */, cl_uint /* pipe_packet_size */, + cl_uint /* pipe_max_packets */, const cl_pipe_properties * /* properties */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clRetainMemObject)(cl_mem /* memobj */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clReleaseMemObject)(cl_mem /* memobj */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetSupportedImageFormats)( + cl_context /* context */, cl_mem_flags /* flags */, cl_mem_object_type /* image_type */, + cl_uint /* num_entries */, cl_image_format * /* image_formats */, + cl_uint * /* num_image_formats */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetMemObjectInfo)( + cl_mem /* memobj */, cl_mem_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetImageInfo)( + cl_mem /* image */, cl_image_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetPipeInfo)( + cl_mem /* pipe */, cl_pipe_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clSetMemObjectDestructorCallback)( + cl_mem /* memobj */, + void(CL_CALLBACK * /*pfn_notify*/)(cl_mem /* memobj */, void * /*user_data*/), + void * /*user_data */) CL_API_SUFFIX__VERSION_1_1; +typedef void *(CL_API_CALL *PFN_clSVMAlloc)(cl_context /* context */, cl_svm_mem_flags /* flags */, + size_t /* size */, + cl_uint /* alignment */)CL_API_SUFFIX__VERSION_2_0; +typedef void(CL_API_CALL *PFN_clSVMFree)(cl_context /* context */, + void * /* svm_pointer */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_sampler(CL_API_CALL *PFN_clCreateSamplerWithProperties)( + cl_context /* context */, const cl_sampler_properties * /* normalized_coords */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clRetainSampler)(cl_sampler /* sampler */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clReleaseSampler)(cl_sampler /* sampler */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetSamplerInfo)( + cl_sampler /* sampler */, cl_sampler_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_program(CL_API_CALL *PFN_clCreateProgramWithSource)( + cl_context /* context */, cl_uint /* count */, const char ** /* strings */, + const size_t * /* lengths */, cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_program(CL_API_CALL *PFN_clCreateProgramWithBinary)( + cl_context /* context */, cl_uint /* num_devices */, const cl_device_id * /* device_list */, + const size_t * /* lengths */, const unsigned char ** /* binaries */, cl_int * /* binary_status */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_program(CL_API_CALL *PFN_clCreateProgramWithBuiltInKernels)( + cl_context /* context */, cl_uint /* num_devices */, const cl_device_id * /* device_list */, + const char * /* kernel_names */, cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clRetainProgram)(cl_program /* program */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clReleaseProgram)(cl_program /* program */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clBuildProgram)( + cl_program /* program */, cl_uint /* num_devices */, const cl_device_id * /* device_list */, + const char * /* options */, + void(CL_CALLBACK * /* pfn_notify */)(cl_program /* program */, void * /* user_data */), + void * /* user_data */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clCompileProgram)( + cl_program /* program */, cl_uint /* num_devices */, const cl_device_id * /* device_list */, + const char * /* options */, cl_uint /* num_input_headers */, + const cl_program * /* input_headers */, const char ** /* header_include_names */, + void(CL_CALLBACK * /* pfn_notify */)(cl_program /* program */, void * /* user_data */), + void * /* user_data */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_program(CL_API_CALL *PFN_clLinkProgram)( + cl_context /* context */, cl_uint /* num_devices */, const cl_device_id * /* device_list */, + const char * /* options */, cl_uint /* num_input_programs */, + const cl_program * /* input_programs */, + void(CL_CALLBACK * /* pfn_notify */)(cl_program /* program */, void * /* user_data */), + void * /* user_data */, cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clUnloadPlatformCompiler)(cl_platform_id /* platform */) + CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clGetProgramInfo)( + cl_program /* program */, cl_program_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetProgramBuildInfo)( + cl_program /* program */, cl_device_id /* device */, cl_program_build_info /* param_name */, + size_t /* param_value_size */, void * /* param_value */, + size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_kernel(CL_API_CALL *PFN_clCreateKernel)( + cl_program /* program */, const char * /* kernel_name */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clCreateKernelsInProgram)( + cl_program /* program */, cl_uint /* num_kernels */, cl_kernel * /* kernels */, + cl_uint * /* num_kernels_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clRetainKernel)(cl_kernel /* kernel */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clReleaseKernel)(cl_kernel /* kernel */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clSetKernelArg)(cl_kernel /* kernel */, cl_uint /* arg_index */, + size_t /* arg_size */, const void * /* arg_value */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clSetKernelArgSVMPointer)( + cl_kernel /* kernel */, cl_uint /* arg_index */, + const void * /* arg_value */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clSetKernelExecInfo)( + cl_kernel /* kernel */, cl_kernel_exec_info /* param_name */, size_t /* param_value_size */, + const void * /* param_value */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clGetKernelInfo)( + cl_kernel /* kernel */, cl_kernel_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetKernelArgInfo)( + cl_kernel /* kernel */, cl_uint /* arg_indx */, cl_kernel_arg_info /* param_name */, + size_t /* param_value_size */, void * /* param_value */, + size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clGetKernelWorkGroupInfo)( + cl_kernel /* kernel */, cl_device_id /* device */, cl_kernel_work_group_info /* param_name */, + size_t /* param_value_size */, void * /* param_value */, + size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clWaitForEvents)( + cl_uint /* num_events */, const cl_event * /* event_list */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clGetEventInfo)( + cl_event /* event */, cl_event_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_event(CL_API_CALL *PFN_clCreateUserEvent)( + cl_context /* context */, cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_1; +typedef cl_int(CL_API_CALL *PFN_clRetainEvent)(cl_event /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clReleaseEvent)(cl_event /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clSetUserEventStatus)( + cl_event /* event */, cl_int /* execution_status */) CL_API_SUFFIX__VERSION_1_1; +typedef cl_int(CL_API_CALL *PFN_clSetEventCallback)( + cl_event /* event */, cl_int /* command_exec_callback_type */, + void(CL_CALLBACK * /* pfn_notify */)(cl_event, cl_int, void *), + void * /* user_data */) CL_API_SUFFIX__VERSION_1_1; +typedef cl_int(CL_API_CALL *PFN_clGetEventProfilingInfo)( + cl_event /* event */, cl_profiling_info /* param_name */, size_t /* param_value_size */, + void * /* param_value */, size_t * /* param_value_size_ret */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clFlush)(cl_command_queue /* command_queue */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clFinish)(cl_command_queue /* command_queue */) + CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueReadBuffer)( + cl_command_queue /* command_queue */, cl_mem /* buffer */, cl_bool /* blocking_read */, + size_t /* offset */, size_t /* size */, void * /* ptr */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueReadBufferRect)( + cl_command_queue /* command_queue */, cl_mem /* buffer */, cl_bool /* blocking_read */, + const size_t * /* buffer_offset */, const size_t * /* host_offset */, const size_t * /* region */, + size_t /* buffer_row_pitch */, size_t /* buffer_slice_pitch */, size_t /* host_row_pitch */, + size_t /* host_slice_pitch */, void * /* ptr */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_1; +typedef cl_int(CL_API_CALL *PFN_clEnqueueWriteBuffer)( + cl_command_queue /* command_queue */, cl_mem /* buffer */, cl_bool /* blocking_write */, + size_t /* offset */, size_t /* size */, const void * /* ptr */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueWriteBufferRect)( + cl_command_queue /* command_queue */, cl_mem /* buffer */, cl_bool /* blocking_write */, + const size_t * /* buffer_offset */, const size_t * /* host_offset */, const size_t * /* region */, + size_t /* buffer_row_pitch */, size_t /* buffer_slice_pitch */, size_t /* host_row_pitch */, + size_t /* host_slice_pitch */, const void * /* ptr */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_1; +typedef cl_int(CL_API_CALL *PFN_clEnqueueFillBuffer)( + cl_command_queue /* command_queue */, cl_mem /* buffer */, const void * /* pattern */, + size_t /* pattern_size */, size_t /* offset */, size_t /* size */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clEnqueueCopyBuffer)( + cl_command_queue /* command_queue */, cl_mem /* src_buffer */, cl_mem /* dst_buffer */, + size_t /* src_offset */, size_t /* dst_offset */, size_t /* size */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueCopyBufferRect)( + cl_command_queue /* command_queue */, cl_mem /* src_buffer */, cl_mem /* dst_buffer */, + const size_t * /* src_origin */, const size_t * /* dst_origin */, const size_t * /* region */, + size_t /* src_row_pitch */, size_t /* src_slice_pitch */, size_t /* dst_row_pitch */, + size_t /* dst_slice_pitch */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_1; +typedef cl_int(CL_API_CALL *PFN_clEnqueueReadImage)( + cl_command_queue /* command_queue */, cl_mem /* image */, cl_bool /* blocking_read */, + const size_t * /* origin[3] */, const size_t * /* region[3] */, size_t /* row_pitch */, + size_t /* slice_pitch */, void * /* ptr */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueWriteImage)( + cl_command_queue /* command_queue */, cl_mem /* image */, cl_bool /* blocking_write */, + const size_t * /* origin[3] */, const size_t * /* region[3] */, size_t /* input_row_pitch */, + size_t /* input_slice_pitch */, const void * /* ptr */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueFillImage)( + cl_command_queue /* command_queue */, cl_mem /* image */, const void * /* fill_color */, + const size_t * /* origin[3] */, const size_t * /* region[3] */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clEnqueueCopyImage)( + cl_command_queue /* command_queue */, cl_mem /* src_image */, cl_mem /* dst_image */, + const size_t * /* src_origin[3] */, const size_t * /* dst_origin[3] */, + const size_t * /* region[3] */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueCopyImageToBuffer)( + cl_command_queue /* command_queue */, cl_mem /* src_image */, cl_mem /* dst_buffer */, + const size_t * /* src_origin[3] */, const size_t * /* region[3] */, size_t /* dst_offset */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueCopyBufferToImage)( + cl_command_queue /* command_queue */, cl_mem /* src_buffer */, cl_mem /* dst_image */, + size_t /* src_offset */, const size_t * /* dst_origin[3] */, const size_t * /* region[3] */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef void *(CL_API_CALL *PFN_clEnqueueMapBuffer)( + cl_command_queue /* command_queue */, cl_mem /* buffer */, cl_bool /* blocking_map */, + cl_map_flags /* map_flags */, size_t /* offset */, size_t /* size */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */, cl_int * /* errcode_ret */)CL_API_SUFFIX__VERSION_1_0; +typedef void *(CL_API_CALL *PFN_clEnqueueMapImage)( + cl_command_queue /* command_queue */, cl_mem /* image */, cl_bool /* blocking_map */, + cl_map_flags /* map_flags */, const size_t * /* origin[3] */, const size_t * /* region[3] */, + size_t * /* image_row_pitch */, size_t * /* image_slice_pitch */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */, cl_int * /* errcode_ret */)CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueUnmapMemObject)( + cl_command_queue /* command_queue */, cl_mem /* memobj */, void * /* mapped_ptr */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueMigrateMemObjects)( + cl_command_queue /* command_queue */, cl_uint /* num_mem_objects */, + const cl_mem * /* mem_objects */, cl_mem_migration_flags /* flags */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clEnqueueNDRangeKernel)( + cl_command_queue /* command_queue */, cl_kernel /* kernel */, cl_uint /* work_dim */, + const size_t * /* global_work_offset */, const size_t * /* global_work_size */, + const size_t * /* local_work_size */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueNativeKernel)( + cl_command_queue /* command_queue */, void(CL_CALLBACK * /*user_func*/)(void *), + void * /* args */, size_t /* cb_args */, cl_uint /* num_mem_objects */, + const cl_mem * /* mem_list */, const void ** /* args_mem_loc */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueMarkerWithWaitList)( + cl_command_queue /* command_queue */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clEnqueueBarrierWithWaitList)( + cl_command_queue /* command_queue */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clEnqueueSVMFree)( + cl_command_queue /* command_queue */, cl_uint /* num_svm_pointers */, + void *[] /* svm_pointers[] */, + void(CL_CALLBACK * /*pfn_free_func*/)(cl_command_queue /* queue */, + cl_uint /* num_svm_pointers */, + void *[] /* svm_pointers[] */, void * /* user_data */), + void * /* user_data */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueSVMMemcpy)( + cl_command_queue /* command_queue */, cl_bool /* blocking_copy */, void * /* dst_ptr */, + const void * /* src_ptr */, size_t /* size */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueSVMMemFill)( + cl_command_queue /* command_queue */, void * /* svm_ptr */, const void * /* pattern */, + size_t /* pattern_size */, size_t /* size */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueSVMMap)( + cl_command_queue /* command_queue */, cl_bool /* blocking_map */, cl_map_flags /* flags */, + void * /* svm_ptr */, size_t /* size */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; +typedef cl_int(CL_API_CALL *PFN_clEnqueueSVMUnmap)( + cl_command_queue /* command_queue */, void * /* svm_ptr */, cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, cl_event * /* event */) CL_API_SUFFIX__VERSION_2_0; +typedef void *(CL_API_CALL *PFN_clGetExtensionFunctionAddressForPlatform)( + cl_platform_id /* platform */, const char * /* func_name */)CL_API_SUFFIX__VERSION_1_2; +typedef cl_mem(CL_API_CALL *PFN_clCreateImage2D)(cl_context /* context */, cl_mem_flags /* flags */, + const cl_image_format * /* image_format */, + size_t /* image_width */, + size_t /* image_height */, + size_t /* image_row_pitch */, + void * /* host_ptr */, cl_int * /* errcode_ret */); +typedef cl_mem(CL_API_CALL *PFN_clCreateImage3D)( + cl_context /* context */, cl_mem_flags /* flags */, const cl_image_format * /* image_format */, + size_t /* image_width */, size_t /* image_height */, size_t /* image_depth */, + size_t /* image_row_pitch */, size_t /* image_slice_pitch */, void * /* host_ptr */, + cl_int * /* errcode_ret */); +typedef cl_int(CL_API_CALL *PFN_clEnqueueMarker)(cl_command_queue /* command_queue */, + cl_event * /* event */); +typedef cl_int(CL_API_CALL *PFN_clEnqueueWaitForEvents)(cl_command_queue /* command_queue */, + cl_uint /* num_events */, + const cl_event * /* event_list */); +typedef cl_int(CL_API_CALL *PFN_clEnqueueBarrier)(cl_command_queue /* command_queue */); +typedef cl_int(CL_API_CALL *PFN_clUnloadCompiler)(); +typedef void *(CL_API_CALL *PFN_clGetExtensionFunctionAddress)(const char * /* func_name */); +typedef cl_command_queue(CL_API_CALL *PFN_clCreateCommandQueue)( + cl_context /* context */, cl_device_id /* device */, cl_command_queue_properties /* properties */, + cl_int * /* errcode_ret */); +typedef cl_sampler(CL_API_CALL *PFN_clCreateSampler)(cl_context /* context */, + cl_bool /* normalized_coords */, + cl_addressing_mode /* addressing_mode */, + cl_filter_mode /* filter_mode */, + cl_int * /* errcode_ret */); +typedef cl_int(CL_API_CALL *PFN_clEnqueueTask)(cl_command_queue /* command_queue */, + cl_kernel /* kernel */, + cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, + cl_event * /* event */); + +// OpenGL sharing +typedef cl_mem(CL_API_CALL *PFN_clCreateFromGLBuffer)(cl_context, cl_mem_flags, cl_GLuint, int *); +typedef cl_mem(CL_API_CALL *PFN_clCreateFromGLTexture)( + cl_context /* context */, cl_mem_flags /* flags */, cl_GLenum /* target */, + cl_GLint /* miplevel */, cl_GLuint /* texture */, + cl_int * /* errcode_ret */) CL_API_SUFFIX__VERSION_1_2; +typedef cl_int(CL_API_CALL *PFN_clEnqueueAcquireGLObjects)(cl_command_queue /* command_queue */, + cl_uint /* num_objects */, + const cl_mem * /* mem_objects */, + cl_uint /* num_events_in_wait_list */, + const cl_event * /* event_wait_list */, + cl_event * /* event */); +typedef cl_int(CL_API_CALL *PFN_clEnqueueReleaseGLObjects)( + cl_command_queue /* command_queue */, cl_uint /* num_objects */, const cl_mem * /* mem_objects */, + cl_uint /* num_events_in_wait_list */, const cl_event * /* event_wait_list */, + cl_event * /* event */) CL_API_SUFFIX__VERSION_1_0; + +// cl_khr_egl_event extension + +// CLeglDisplayKHR is an opaque handle to an EGLDisplay +typedef void *CLeglDisplayKHR; + +// CLeglSyncKHR is an opaque handle to an EGLSync object +typedef void *CLeglSyncKHR; + +typedef cl_event(CL_API_CALL *PFN_clCreateEventFromEGLSyncKHR)(cl_context /* context */, + CLeglSyncKHR /* sync */, + CLeglDisplayKHR /* display */, + cl_int * /* errcode_ret */); + +// EGL sharing +typedef cl_mem(CL_API_CALL *PFN_clCreateFromEGLImageKHR)( + cl_context /*context*/, CLeglDisplayKHR /*display*/, CLeglImageKHR /*image*/, + cl_mem_flags /*flags*/, const cl_egl_image_properties_khr * /*properties*/, + cl_int * /*errcode_ret*/); +typedef cl_int(CL_API_CALL *PFN_clEnqueueAcquireEGLObjectsKHR)( + cl_command_queue /*command_queue*/, cl_uint /*num_objects*/, const cl_mem * /*mem_objects*/, + cl_uint /*num_events_in_wait_list*/, const cl_event * /*event_wait_list*/, cl_event * /*event*/); +typedef cl_int(CL_API_CALL *PFN_clEnqueueReleaseEGLObjectsKHR)( + cl_command_queue /*command_queue*/, cl_uint /*num_objects*/, const cl_mem * /*mem_objects*/, + cl_uint /*num_events_in_wait_list*/, const cl_event * /*event_wait_list*/, cl_event * /*event*/); + +extern PFN_clGetPlatformIDs clGetPlatformIDs; +extern PFN_clGetPlatformInfo clGetPlatformInfo; +extern PFN_clGetDeviceIDs clGetDeviceIDs; +extern PFN_clGetDeviceInfo clGetDeviceInfo; +extern PFN_clCreateSubDevices clCreateSubDevices; +extern PFN_clRetainDevice clRetainDevice; +extern PFN_clReleaseDevice clReleaseDevice; +extern PFN_clCreateContext clCreateContext; +extern PFN_clCreateContextFromType clCreateContextFromType; +extern PFN_clRetainContext clRetainContext; +extern PFN_clReleaseContext clReleaseContext; +extern PFN_clGetContextInfo clGetContextInfo; +extern PFN_clCreateCommandQueueWithProperties clCreateCommandQueueWithProperties; +extern PFN_clRetainCommandQueue clRetainCommandQueue; +extern PFN_clReleaseCommandQueue clReleaseCommandQueue; +extern PFN_clGetCommandQueueInfo clGetCommandQueueInfo; +extern PFN_clCreateBuffer clCreateBuffer; +extern PFN_clCreateSubBuffer clCreateSubBuffer; +extern PFN_clCreateImage clCreateImage; +extern PFN_clCreatePipe clCreatePipe; +extern PFN_clRetainMemObject clRetainMemObject; +extern PFN_clReleaseMemObject clReleaseMemObject; +extern PFN_clGetSupportedImageFormats clGetSupportedImageFormats; +extern PFN_clGetMemObjectInfo clGetMemObjectInfo; +extern PFN_clGetImageInfo clGetImageInfo; +extern PFN_clGetPipeInfo clGetPipeInfo; +extern PFN_clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback; +extern PFN_clSVMAlloc clSVMAlloc; +extern PFN_clSVMFree clSVMFree; +extern PFN_clCreateSamplerWithProperties clCreateSamplerWithProperties; +extern PFN_clRetainSampler clRetainSampler; +extern PFN_clReleaseSampler clReleaseSampler; +extern PFN_clGetSamplerInfo clGetSamplerInfo; +extern PFN_clCreateProgramWithSource clCreateProgramWithSource; +extern PFN_clCreateProgramWithBinary clCreateProgramWithBinary; +extern PFN_clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels; +extern PFN_clRetainProgram clRetainProgram; +extern PFN_clReleaseProgram clReleaseProgram; +extern PFN_clBuildProgram clBuildProgram; +extern PFN_clCompileProgram clCompileProgram; +extern PFN_clLinkProgram clLinkProgram; +extern PFN_clUnloadPlatformCompiler clUnloadPlatformCompiler; +extern PFN_clGetProgramInfo clGetProgramInfo; +extern PFN_clGetProgramBuildInfo clGetProgramBuildInfo; +extern PFN_clCreateKernel clCreateKernel; +extern PFN_clCreateKernelsInProgram clCreateKernelsInProgram; +extern PFN_clRetainKernel clRetainKernel; +extern PFN_clReleaseKernel clReleaseKernel; +extern PFN_clSetKernelArg clSetKernelArg; +extern PFN_clSetKernelArgSVMPointer clSetKernelArgSVMPointer; +extern PFN_clSetKernelExecInfo clSetKernelExecInfo; +extern PFN_clGetKernelInfo clGetKernelInfo; +extern PFN_clGetKernelArgInfo clGetKernelArgInfo; +extern PFN_clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo; +extern PFN_clWaitForEvents clWaitForEvents; +extern PFN_clGetEventInfo clGetEventInfo; +extern PFN_clCreateUserEvent clCreateUserEvent; +extern PFN_clRetainEvent clRetainEvent; +extern PFN_clReleaseEvent clReleaseEvent; +extern PFN_clSetUserEventStatus clSetUserEventStatus; +extern PFN_clSetEventCallback clSetEventCallback; +extern PFN_clGetEventProfilingInfo clGetEventProfilingInfo; +extern PFN_clFlush clFlush; +extern PFN_clFinish clFinish; +extern PFN_clEnqueueReadBuffer clEnqueueReadBuffer; +extern PFN_clEnqueueReadBufferRect clEnqueueReadBufferRect; +extern PFN_clEnqueueWriteBuffer clEnqueueWriteBuffer; +extern PFN_clEnqueueWriteBufferRect clEnqueueWriteBufferRect; +extern PFN_clEnqueueFillBuffer clEnqueueFillBuffer; +extern PFN_clEnqueueCopyBuffer clEnqueueCopyBuffer; +extern PFN_clEnqueueCopyBufferRect clEnqueueCopyBufferRect; +extern PFN_clEnqueueReadImage clEnqueueReadImage; +extern PFN_clEnqueueWriteImage clEnqueueWriteImage; +extern PFN_clEnqueueFillImage clEnqueueFillImage; +extern PFN_clEnqueueCopyImage clEnqueueCopyImage; +extern PFN_clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer; +extern PFN_clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage; +extern PFN_clEnqueueMapBuffer clEnqueueMapBuffer; +extern PFN_clEnqueueMapImage clEnqueueMapImage; +extern PFN_clEnqueueUnmapMemObject clEnqueueUnmapMemObject; +extern PFN_clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects; +extern PFN_clEnqueueNDRangeKernel clEnqueueNDRangeKernel; +extern PFN_clEnqueueNativeKernel clEnqueueNativeKernel; +extern PFN_clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList; +extern PFN_clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList; +extern PFN_clEnqueueSVMFree clEnqueueSVMFree; +extern PFN_clEnqueueSVMMemcpy clEnqueueSVMMemcpy; +extern PFN_clEnqueueSVMMemFill clEnqueueSVMMemFill; +extern PFN_clEnqueueSVMMap clEnqueueSVMMap; +extern PFN_clEnqueueSVMUnmap clEnqueueSVMUnmap; +extern PFN_clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform; +extern PFN_clCreateImage2D clCreateImage2D; +extern PFN_clCreateImage3D clCreateImage3D; +extern PFN_clEnqueueMarker clEnqueueMarker; +extern PFN_clEnqueueWaitForEvents clEnqueueWaitForEvents; +extern PFN_clEnqueueBarrier clEnqueueBarrier; +extern PFN_clUnloadCompiler clUnloadCompiler; +extern PFN_clGetExtensionFunctionAddress clGetExtensionFunctionAddress; +extern PFN_clCreateCommandQueue clCreateCommandQueue; +extern PFN_clCreateSampler clCreateSampler; +extern PFN_clEnqueueTask clEnqueueTask; + +// OpenGL sharing +extern PFN_clCreateFromGLBuffer clCreateFromGLBuffer; +extern PFN_clCreateFromGLTexture clCreateFromGLTexture; +extern PFN_clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects; +extern PFN_clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects; + +// cl_khr_egl_event extension +extern PFN_clCreateEventFromEGLSyncKHR clCreateEventFromEGLSyncKHR; + +// EGL sharing +extern PFN_clCreateFromEGLImageKHR clCreateFromEGLImageKHR; +extern PFN_clEnqueueAcquireEGLObjectsKHR clEnqueueAcquireEGLObjectsKHR; +extern PFN_clEnqueueReleaseEGLObjectsKHR clEnqueueReleaseEGLObjectsKHR; + +// For convenient image creation +// It uses clCreateImage if it available (clCreateImage available since cl 1.2) +// otherwise it will use legacy clCreateImage2D +cl_mem CreateImage2DLegacy(cl_context context, cl_mem_flags flags, + const cl_image_format *image_format, const cl_image_desc *image_desc, + void *host_ptr, cl_int *errcode_ret); + +// It uses clCreateImage if it available (clCreateImage available since cl 1.2) +// otherwise it will use legacy clCreateImage3D +cl_mem CreateImage3DLegacy(cl_context context, cl_mem_flags flags, + const cl_image_format *image_format, const cl_image_desc *image_desc, + void *host_ptr, cl_int *errcode_ret); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_WRAPPERE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Operations.cc b/runtime/onert/backend/gpu_cl/open_cl/Operations.cc new file mode 100644 index 000000000..2608b5364 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Operations.cc @@ -0,0 +1,704 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Operations.h" +#include "open_cl/Operations.h" + +#include <algorithm> +#include <cstdint> +#include <set> +#include <string> +#include <utility> +#include <vector> +#include <unordered_map> + +#include "absl/container/flat_hash_map.h" + +#include "Shape.h" +#include "Status.h" +#include "InternalTensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +Padding2D &Padding2D::operator=(const Padding2D &value) +{ + prepended = value.prepended; + appended = value.appended; + return *this; +} + +bool Padding2D::operator==(const Padding2D &value) +{ + return this->prepended == value.prepended && this->appended == value.appended; +} + +bool Padding2D::operator!=(const Padding2D &value) { return !(*this == value); } + +Padding2D &Padding2D::operator-(const Padding2D &value) +{ + prepended.h -= value.prepended.h; + prepended.w -= value.prepended.w; + appended.h -= value.appended.h; + appended.w -= value.appended.w; + return *this; +} + +Padding3D &Padding3D::operator=(const Padding3D &value) +{ + prepended = value.prepended; + appended = value.appended; + return *this; +} + +bool Padding3D::operator==(const Padding3D &value) +{ + return this->prepended == value.prepended && this->appended == value.appended; +} + +bool Padding3D::operator!=(const Padding3D &value) { return !(*this == value); } + +Padding3D &Padding3D::operator-(const Padding3D &value) +{ + prepended.h -= value.prepended.h; + prepended.w -= value.prepended.w; + prepended.d -= value.prepended.d; + appended.h -= value.appended.h; + appended.w -= value.appended.w; + appended.d -= value.appended.d; + return *this; +} + +std::string ToString(enum OperationType op) +{ + switch (op) + { + // case OperationType::ABS: + // return "abs"; + case OperationType::ADD: + return "add"; + // case OperationType::CONCAT: + // return "concat"; + // case OperationType::COS: + // return "cos"; + // case OperationType::EXP: + // return "exp"; + // case OperationType::LOG: + // return "log"; + // case OperationType::NEG: + // return "neg"; + // case OperationType::POOLING_2D: + // return "pooling_2d"; + // case OperationType::REDUCE_MAXIMUM: + // return "reduce_maximum"; + // case OperationType::REDUCE_MINIMUM: + // return "reduce_minimum"; + // case OperationType::REDUCE_PRODUCT: + // return "reduce_product"; + // case OperationType::REDUCE_SUM: + // return "reduce_sum"; + // case OperationType::RESIZE: + // return "resize"; + // case OperationType::RELU: + // return "relu"; + // case OperationType::RSQRT: + // return "rsqrt"; + // case OperationType::SQRT: + // return "sqrt"; + // case OperationType::SQUARE: + // return "square"; + case OperationType::UNKNOWN: + return "unknown_operation"; + } + return ""; +} + +OperationType OperationTypeFromString(const std::string &name) +{ + static const auto operations = new std::unordered_map<std::string, OperationType>({ + // {"abs", OperationType::ABS}, + {"add", OperationType::ADD}, + // {"concat", OperationType::CONCAT}, + // {"cos", OperationType::COS}, + // {"exp", OperationType::EXP}, + // {"log", OperationType::LOG}, + // {"neg", OperationType::NEG}, + // {"pooling_2d", OperationType::POOLING_2D}, + // {"reduce_maximum", OperationType::REDUCE_MAXIMUM}, + // {"reduce_minimum", OperationType::REDUCE_MINIMUM}, + // {"reduce_product", OperationType::REDUCE_PRODUCT}, + // {"reduce_sum", OperationType::REDUCE_SUM}, + // {"relu", OperationType::RELU}, + // {"resize", OperationType::RESIZE}, + // {"rsqrt", OperationType::RSQRT}, + // {"sqrt", OperationType::SQRT}, + // {"square", OperationType::SQUARE}, + }); + auto op = operations->find(name); + return op == operations->end() ? OperationType::UNKNOWN : op->second; +} + +namespace +{ + +template <typename T> T DivideRoundUp(T n, T divisor) { return (n - 1) / divisor + 1; } + +int32_t CalculateOutputSizeBeforeStrides(int32_t input, int32_t kernel, int32_t padding, + int32_t dilation) +{ + const int32_t dilated_kernel = (kernel - 1) * dilation + 1; + return input + padding - dilated_kernel + 1; +} + +template <Axis T> +int32_t CalculateOutputWithoutStrides(const BHWC &input, const Convolution2DAttributes &attr) +{ + return CalculateOutputSizeBeforeStrides( + input.get<T>(), attr.weights.shape.get<T>(), + attr.padding.prepended.get<T>() + attr.padding.appended.get<T>(), attr.dilations.get<T>()); +} + +template <Axis T> +int32_t CalculateOutputWithoutStrides(const BHWDC &input, const Convolution3DAttributes &attr) +{ + return CalculateOutputSizeBeforeStrides( + input.get<T>(), attr.weights.shape.get<T>(), + attr.padding.prepended.get<T>() + attr.padding.appended.get<T>(), attr.dilations.get<T>()); +} + +template <Axis T> +int32_t CalculateOutputWithoutStrides(const BHWC &input, const Pooling2DAttributes &attr) +{ + return CalculateOutputSizeBeforeStrides(input.get<T>(), attr.kernel.get<T>(), + attr.padding.prepended.get<T>() + + attr.padding.appended.get<T>(), + /*dilation=*/1); +} + +template <Axis T> +int32_t CalculateOutputWithoutStrides(const BHWDC &input, const Pooling3DAttributes &attr) +{ + return CalculateOutputSizeBeforeStrides(input.get<T>(), attr.kernel.get<T>(), + attr.padding.prepended.get<T>() + + attr.padding.appended.get<T>(), + /*dilation=*/1); +} + +template <Axis T> +int32_t CalculateOutput(const BHWC &input, const ConvolutionTransposedAttributes &attr) +{ + return (input.get<T>() - 1) * attr.stride.get<T>() - + (attr.padding.prepended.get<T>() + attr.padding.appended.get<T>()) + + attr.weights.shape.get<T>() + attr.adjacent.get<T>(); +} + +template <Axis T> +int32_t CalculateOutput(const BHWDC &input, const ConvolutionTransposed3DAttributes &attr) +{ + return (input.get<T>() - 1) * attr.stride.get<T>() - + (attr.padding.prepended.get<T>() + attr.padding.appended.get<T>()) + + attr.weights.shape.get<T>(); +} + +inline int32_t StridedSize(int32_t size, int32_t stride) +{ + return stride == 0 ? -1 : DivideRoundUp(size, stride); +} + +template <Axis AxisT, typename AttrT> int32_t CalculateOutput(const BHWC &input, const AttrT &attr) +{ + return StridedSize(CalculateOutputWithoutStrides<AxisT>(input, attr), + attr.strides.template get<AxisT>()); +} + +template <Axis AxisT, typename AttrT> int32_t CalculateOutput(const BHWDC &input, const AttrT &attr) +{ + return StridedSize(CalculateOutputWithoutStrides<AxisT>(input, attr), + attr.strides.template get<AxisT>()); +} + +int32_t CalculateSamePadding(int32_t input, int32_t kernel, int32_t dilation, int32_t stride) +{ + const int32_t dilated_kernel = (kernel - 1) * dilation + 1; + return std::max(0, dilated_kernel - (input - 1) % stride - 1); +} + +// Returns a padding that should be present to make sure image size stays +// the same. +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWC &input, const Convolution2DAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.weights.shape.get<AxisT>(), + attr.dilations.get<AxisT>(), attr.strides.get<AxisT>()); +} + +// Returns a padding that should be present to make sure image size stays +// the same. +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWDC &input, const Convolution3DAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.weights.shape.get<AxisT>(), + attr.dilations.get<AxisT>(), attr.strides.get<AxisT>()); +} + +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWC &input, const ConvolutionTransposedAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.weights.shape.get<AxisT>(), + /*dilation=*/1, attr.stride.get<AxisT>()); +} + +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWDC &input, const ConvolutionTransposed3DAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.weights.shape.get<AxisT>(), + /*dilation=*/1, attr.stride.get<AxisT>()); +} + +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWC &input, const Pooling2DAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.kernel.get<AxisT>(), + /*dilation=*/1, attr.strides.get<AxisT>()); +} + +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWDC &input, const Pooling3DAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.kernel.get<AxisT>(), + /*dilation=*/1, attr.strides.get<AxisT>()); +} + +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWC &input, const MaxUnpooling2DAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.kernel.get<AxisT>(), + /*dilation=*/1, attr.strides.get<AxisT>()); +} + +template <Axis AxisT> +int32_t CalculateSamePadding(const BHWDC &input, const MaxUnpooling3DAttributes &attr) +{ + return CalculateSamePadding(input.get<AxisT>(), attr.kernel.get<AxisT>(), + /*dilation=*/1, attr.strides.get<AxisT>()); +} + +Padding2D MakeSamePadding(const BHWC &input, const ConvolutionTransposedAttributes &attr) +{ + int32_t padding_height = CalculateSamePadding<Axis::HEIGHT>(input, attr); + int32_t padding_width = CalculateSamePadding<Axis::WIDTH>(input, attr); + Padding2D padding; + padding.prepended = HW(padding_height / 2, padding_width / 2); + padding.appended = HW(padding_height - padding_height / 2, padding_width - padding_width / 2); + return padding; +} + +Padding3D MakeSamePadding(const BHWDC &input, const ConvolutionTransposed3DAttributes &attr) +{ + int32_t padding_height = CalculateSamePadding<Axis::HEIGHT>(input, attr); + int32_t padding_width = CalculateSamePadding<Axis::WIDTH>(input, attr); + int32_t padding_depth = CalculateSamePadding<Axis::DEPTH>(input, attr); + Padding3D padding; + padding.prepended = HWD(padding_height / 2, padding_width / 2, padding_depth / 2); + padding.appended = HWD(padding_height - padding_height / 2, padding_width - padding_width / 2, + padding_depth - padding_depth / 2); + return padding; +} + +// If padding depends on input, convert it into fixed padding. +template <class AttrT> Padding2D MakeSamePadding(const BHWC &input, const AttrT &attr) +{ + int32_t padding_height = CalculateSamePadding<Axis::HEIGHT>(input, attr); + int32_t padding_width = CalculateSamePadding<Axis::WIDTH>(input, attr); + Padding2D padding; + padding.prepended = HW(padding_height / 2, padding_width / 2); + padding.appended = HW(padding_height - padding_height / 2, padding_width - padding_width / 2); + return padding; +} + +// If padding depends on input, convert it into fixed padding. +template <class AttrT> Padding3D MakeSamePadding(const BHWDC &input, const AttrT &attr) +{ + int32_t padding_height = CalculateSamePadding<Axis::HEIGHT>(input, attr); + int32_t padding_width = CalculateSamePadding<Axis::WIDTH>(input, attr); + int32_t padding_depth = CalculateSamePadding<Axis::DEPTH>(input, attr); + Padding3D padding; + padding.prepended = HWD(padding_height / 2, padding_width / 2, padding_depth / 2); + padding.appended = HWD(padding_height - padding_height / 2, padding_width - padding_width / 2, + padding_depth - padding_depth / 2); + return padding; +} + +} // namespace + +BHWC CalculateOutputShape(const BHWC &input, const MaxUnpooling2DAttributes &attr) +{ + return BHWC( + input.b, input.h * attr.strides.h - attr.padding.prepended.h - attr.padding.appended.h, + input.w * attr.strides.w - attr.padding.prepended.w - attr.padding.appended.w, input.c); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const MaxUnpooling3DAttributes &attr) +{ + return BHWDC( + input.b, input.h * attr.strides.h - attr.padding.prepended.h - attr.padding.appended.h, + input.w * attr.strides.w - attr.padding.prepended.w - attr.padding.appended.w, + input.d * attr.strides.d - attr.padding.prepended.d - attr.padding.appended.d, input.c); +} + +BHWC CalculateOutputShape(const BHWC &input, const Pooling2DAttributes &attr) +{ + return BHWC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), input.c); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const Pooling3DAttributes &attr) +{ + return BHWDC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), CalculateOutput<Axis::DEPTH>(input, attr), + input.c); +} + +BHWC CalculateOutputShape(const BHWC &input, const Convolution2DAttributes &attr) +{ + return BHWC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), + attr.weights.shape.get<Axis::OUTPUT_CHANNELS>()); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const Convolution3DAttributes &attr) +{ + return BHWDC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), CalculateOutput<Axis::DEPTH>(input, attr), + attr.weights.shape.get<Axis::OUTPUT_CHANNELS>()); +} + +BHWC CalculateOutputShape(const BHWC &input, const ConvolutionTransposedAttributes &attr) +{ + return BHWC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), + attr.weights.shape.get<Axis::OUTPUT_CHANNELS>()); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const ConvolutionTransposed3DAttributes &attr) +{ + return BHWDC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), CalculateOutput<Axis::DEPTH>(input, attr), + attr.weights.shape.get<Axis::OUTPUT_CHANNELS>()); +} + +BHWC CalculateOutputShape(const BHWC &input, const DepthwiseConvolution2DAttributes &attr) +{ + return BHWC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), + attr.weights.shape.get<Axis::OUTPUT_CHANNELS>() * + attr.weights.shape.get<Axis::INPUT_CHANNELS>()); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const DepthwiseConvolution3DAttributes &attr) +{ + return BHWDC(input.b, CalculateOutput<Axis::HEIGHT>(input, attr), + CalculateOutput<Axis::WIDTH>(input, attr), CalculateOutput<Axis::DEPTH>(input, attr), + attr.weights.shape.get<Axis::OUTPUT_CHANNELS>() * + attr.weights.shape.get<Axis::INPUT_CHANNELS>()); +} + +BHWC CalculateOutputShape(const BHWC &input, const SliceAttributes &attr) +{ + (void)input; + return BHWC(StridedSize(attr.ends.b - attr.starts.b, attr.strides.b), + StridedSize(attr.ends.h - attr.starts.h, attr.strides.h), + StridedSize(attr.ends.w - attr.starts.w, attr.strides.w), + StridedSize(attr.ends.c - attr.starts.c, attr.strides.c)); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const Slice3DAttributes &attr) +{ + (void)input; + return BHWDC(StridedSize(attr.ends.b - attr.starts.b, attr.strides.b), + StridedSize(attr.ends.h - attr.starts.h, attr.strides.h), + StridedSize(attr.ends.w - attr.starts.w, attr.strides.w), + StridedSize(attr.ends.d - attr.starts.d, attr.strides.d), + StridedSize(attr.ends.c - attr.starts.c, attr.strides.c)); +} + +BHWC CalculateOutputShape(const BHWC &input, const PadAttributes &attr) +{ + return BHWC( + attr.appended.b + attr.prepended.b + input.b, attr.appended.h + attr.prepended.h + input.h, + attr.appended.w + attr.prepended.w + input.w, attr.appended.c + attr.prepended.c + input.c); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const Pad3DAttributes &attr) +{ + return BHWDC( + attr.appended.b + attr.prepended.b + input.b, attr.appended.h + attr.prepended.h + input.h, + attr.appended.w + attr.prepended.w + input.w, attr.appended.d + attr.prepended.d + input.d, + attr.appended.c + attr.prepended.c + input.c); +} + +BHWC CalculateOutputShape(const BHWC &input, const FullyConnectedAttributes &attr) +{ + return BHWC(input.b, 1, 1, attr.weights.shape.o); +} + +BHWC CalculateOutputShape(const BHWC &input, const MeanAttributes &attr) +{ + const int b = attr.dims.find(Axis::BATCH) == attr.dims.end() ? input.b : 1; + const int h = attr.dims.find(Axis::HEIGHT) == attr.dims.end() ? input.h : 1; + const int w = attr.dims.find(Axis::WIDTH) == attr.dims.end() ? input.w : 1; + const int c = attr.dims.find(Axis::CHANNELS) == attr.dims.end() ? input.c : 1; + return BHWC(b, h, w, c); +} + +absl::Status CalculateOutputShape(const std::vector<BHWC> &input, const ConcatAttributes &attr, + BHWC *output_shape) +{ + BHWC new_shape = input[0]; + switch (attr.axis) + { + case Axis::CHANNELS: + for (size_t i = 1; i < input.size(); i++) + { + if (input[i].h != new_shape.h || input[i].w != new_shape.w || input[i].b != new_shape.b) + { + return absl::InvalidArgumentError( + "Height, Width and Batch must be the same when concatenating " + "by channels axis"); + } + new_shape.c += input[i].c; + } + break; + case Axis::HEIGHT: + for (size_t i = 1; i < input.size(); i++) + { + if (input[i].w != new_shape.w || input[i].c != new_shape.c || input[i].b != new_shape.b) + { + return absl::InvalidArgumentError( + "Channels, Width and Batch must be the same when concatenating " + "by height axis"); + } + new_shape.h += input[i].h; + } + break; + case Axis::WIDTH: + for (size_t i = 1; i < input.size(); i++) + { + if (input[i].h != new_shape.h || input[i].c != new_shape.c || input[i].b != new_shape.b) + { + return absl::InvalidArgumentError( + "Height, Channels and Batch must be the same when concatenating " + "by width axis"); + } + new_shape.w += input[i].w; + } + break; + case Axis::BATCH: + for (size_t i = 1; i < input.size(); i++) + { + if (input[i].h != new_shape.h || input[i].c != new_shape.c || input[i].w != new_shape.w) + { + return absl::InvalidArgumentError( + "Width, Height and Channels must be the same when concatenating " + "by batch axis"); + } + new_shape.b += input[i].b; + } + break; + default: + return absl::InvalidArgumentError("Invalid axis"); + break; + } + *output_shape = new_shape; + return absl::OkStatus(); +} + +absl::Status CalculateOutputShape(const std::vector<BHWDC> &input, const ConcatAttributes &attr, + BHWDC *output_shape) +{ + BHWDC new_shape = input[0]; + switch (attr.axis) + { + case Axis::CHANNELS: + for (size_t i = 1; i < input.size(); ++i) + { + if (input[i].h != new_shape.h || input[i].w != new_shape.w || input[i].d != new_shape.d || + input[i].b != new_shape.b) + { + return absl::InvalidArgumentError("Height, Width, Batch and Depth must be the same when " + "concatenating " + "by channels axis"); + } + new_shape.c += input[i].c; + } + break; + case Axis::HEIGHT: + for (size_t i = 1; i < input.size(); ++i) + { + if (input[i].w != new_shape.w || input[i].c != new_shape.c || input[i].d != new_shape.d || + input[i].b != new_shape.b) + { + return absl::InvalidArgumentError( + "Width, Depth, Batch and Channels must be the same when " + "concatenating " + "by height axis"); + } + new_shape.h += input[i].h; + } + break; + case Axis::WIDTH: + for (size_t i = 1; i < input.size(); ++i) + { + if (input[i].h != new_shape.h || input[i].c != new_shape.c || input[i].d != new_shape.d || + input[i].b != new_shape.b) + { + return absl::InvalidArgumentError( + "Height, Depth, Batch and Channels must be the same when " + "concatenating " + "by width axis"); + } + new_shape.w += input[i].w; + } + break; + case Axis::DEPTH: + for (size_t i = 1; i < input.size(); ++i) + { + if (input[i].w != new_shape.w || input[i].h != new_shape.h || input[i].c != new_shape.c || + input[i].b != new_shape.b) + { + return absl::InvalidArgumentError( + "Width, Height, Batch and Channels must be the same when " + "concatenating " + "by depth axis"); + } + new_shape.d += input[i].d; + } + break; + case Axis::BATCH: + for (size_t i = 1; i < input.size(); ++i) + { + if (input[i].w != new_shape.w || input[i].h != new_shape.h || input[i].c != new_shape.c || + input[i].d != new_shape.d) + { + return absl::InvalidArgumentError( + "Width, Height, Depth and Channels must be the same when " + "concatenating " + "by batch axis"); + } + new_shape.b += input[i].b; + } + break; + default: + return absl::InvalidArgumentError("Invalid axis"); + } + *output_shape = new_shape; + return absl::OkStatus(); +} + +Padding2D CalculateSamePadding(const BHWC &input, const Convolution2DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding3D CalculateSamePadding(const BHWDC &input, const Convolution3DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding2D CalculateSamePadding(const BHWC &input, const ConvolutionTransposedAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding3D CalculateSamePadding(const BHWDC &input, const ConvolutionTransposed3DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding2D CalculateSamePadding(const BHWC &input, const DepthwiseConvolution2DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding3D CalculateSamePadding(const BHWDC &input, const DepthwiseConvolution3DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding2D CalculateSamePadding(const BHWC &input, const Pooling2DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding3D CalculateSamePadding(const BHWDC &input, const Pooling3DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding2D CalculateSamePadding(const BHWC &input, const MaxUnpooling2DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +Padding3D CalculateSamePadding(const BHWDC &input, const MaxUnpooling3DAttributes &attr) +{ + return MakeSamePadding(input, attr); +} + +float CalculateResizeScale(int32_t input_size, int32_t output_size, const Resize2DAttributes &attr) +{ + return attr.align_corners && input_size > 1 && output_size > 1 + ? static_cast<float>(input_size - 1) / (output_size - 1) + : static_cast<float>(input_size) / output_size; +} + +float CalculateResizeScale(int32_t input_size, int32_t output_size, const Resize3DAttributes &attr) +{ + return attr.align_corners && input_size > 1 && output_size > 1 + ? static_cast<float>(input_size - 1) / (output_size - 1) + : static_cast<float>(input_size) / output_size; +} + +BHWC CalculateOutputShape(const BHWC &input, const Resize2DAttributes &attr) +{ + return BHWC(input.b, attr.new_shape.h, attr.new_shape.w, input.c); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const Resize3DAttributes &attr) +{ + return BHWDC(input.b, attr.new_shape.h, attr.new_shape.w, attr.new_shape.d, input.c); +} + +BHWC CalculateOutputShape(const BHWC &input, const TransposeAttributes &attr) +{ + return BHWC(input.get(attr.perm.b), input.get(attr.perm.h), input.get(attr.perm.w), + input.get(attr.perm.c)); +} + +BHWDC CalculateOutputShape(const BHWDC &input, const Transpose3DAttributes &attr) +{ + return BHWDC(input.get(attr.perm.b), input.get(attr.perm.h), input.get(attr.perm.w), + input.get(attr.perm.d), input.get(attr.perm.c)); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Operations.h b/runtime/onert/backend/gpu_cl/open_cl/Operations.h new file mode 100644 index 000000000..825eb90a4 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Operations.h @@ -0,0 +1,586 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_OPERATIONS_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_OPERATIONS_H__ + +#include <cstdint> +#include <set> +#include <string> +#include <vector> + +#include "absl/types/variant.h" + +#include "DataType.h" +#include "Shape.h" +#include "Status.h" +#include "InternalTensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class OperationType +{ + UNKNOWN = 0, + // ABS, + ADD, + // BATCH_TO_SPACE, + // BATCH_NORMALIZATION, + // BATCHED_MATMUL, + // CONCAT, + // CONST, + // CONVOLUTION_2D, + // CONVOLUTION_TRANSPOSED, + // COPY, + // COS, + // DEPTHWISE_CONVOLUTION, + // DIV, + // ELU, + // EQUAL, + // EXP, + // FULLY_CONNECTED, + // GREATER, + // GREATER_EQUAL, + // HARD_SWISH, + // LESS, + // LESS_EQUAL, + // LOG, + // LSTM, + // MAXIMUM, + // MAX_UNPOOLING_2D, + // MEAN, + // MEAN_STDDEV_NORMALIZATION, + // MINIMUM, + // MUL, + // NEG, + // NOT_EQUAL, + // PAD, + // POOLING_2D, + // POW, + // PRELU, + // Used to accurately run inference on quantized models. + // QUANTIZE_AND_DEQUANTIZE, + // REDUCE_MAXIMUM, + // REDUCE_MINIMUM, + // REDUCE_PRODUCT, + // REDUCE_SUM, + // RELU, + // RESHAPE, + // RESIZE, + // RSQRT, + // SIGMOID, + // SIN, + // SLICE, + // SOFTMAX, + // SPACE_TO_BATCH, + // SPACE_TO_DEPTH, + // SQRT, + // SQUARE, + // SQUARED_DIFF, + // SUB, + // TANH, + // TRANSPOSE, +}; + +std::string ToString(enum OperationType op); + +OperationType OperationTypeFromString(const std::string &name); + +typedef absl::variant<absl::monostate, InternalTensor<HWC, DataType::FLOAT32>, + InternalTensor<Linear, DataType::FLOAT32>, float> + TensorOrScalar; + +struct Padding2D +{ + Padding2D() = default; + Padding2D(const Padding2D &); + Padding2D &operator=(const Padding2D &value); + bool operator==(const Padding2D &value); + bool operator!=(const Padding2D &value); + Padding2D &operator-(const Padding2D &value); + + // Padding values for every axis (if needed), where 'prepended' defines + // padding for the beginning of each axis and 'appended' represents end part + // of the corresponding axis. + HW prepended = HW(-1, -1); + HW appended = HW(-1, -1); +}; + +struct Padding3D +{ + Padding3D() = default; + Padding3D(const Padding3D &); + Padding3D &operator=(const Padding3D &value); + bool operator==(const Padding3D &value); + bool operator!=(const Padding3D &value); + Padding3D &operator-(const Padding3D &value); + // Padding values for every axis (if needed), where 'prepended' defines + // padding for the beginning of each axis and 'appended' represents end part + // of the corresponding axis. + HWD prepended = HWD(0, 0, 0); + HWD appended = HWD(0, 0, 0); +}; + +struct Crop2D : public Padding2D +{ +}; + +struct SpaceToBatchAttributes +{ + HW block; + Padding2D padding; +}; + +struct BatchToSpaceAttributes +{ + HW block; + Crop2D crop; +}; + +enum class PoolingType +{ + UNDEFINED = 0, + + // average pooling + AVERAGE = 1, + + // max pooling + MAX = 2, +}; + +struct Pooling2DAttributes +{ + PoolingType type = PoolingType::UNDEFINED; + // Strides for every axis. + HW strides = HW(-1, -1); + HW kernel = HW(-1, -1); + Padding2D padding; + // NOTE(akulik): technically the number of outputs from Pooling node indicates + // whether indices are needed or not, but I decided to keep it inside + // attributes to simplify processing. + bool output_indices = false; +}; + +struct Pooling3DAttributes +{ + PoolingType type = PoolingType::UNDEFINED; + // Strides for every axis. + HWD strides = HWD(0, 0, 0); + HWD kernel = HWD(0, 0, 0); + Padding3D padding; + // NOTE(akulik): technically the number of outputs from Pooling node indicates + // whether indices are needed or not, but I decided to keep it inside + // attributes to simplify processing. + bool output_indices = false; +}; + +struct MaxUnpooling2DAttributes +{ + // Strides for every axis. + HW strides = HW(-1, -1); + HW kernel = HW(-1, -1); + Padding2D padding; +}; + +struct MaxUnpooling3DAttributes +{ + // Strides for every axis. + HWD strides = HWD(0, 0, 0); + HWD kernel = HWD(0, 0, 0); + Padding3D padding; +}; + +struct MeanAttributes +{ + // The vector of dimensions to calculate mean along. + std::set<Axis> dims; +}; + +struct ConcatAttributes +{ + // Defines axis by which to concat on. + Axis axis = Axis::UNKNOWN; +}; + +// @return shape of a tensor after MaxUnpooling2D operation is applied to +// the given input. +BHWC CalculateOutputShape(const BHWC &input, const MaxUnpooling2DAttributes &attr); + +// @return shape of a tensor after MaxUnpooling3D operation is applied to +// the given input. +BHWDC CalculateOutputShape(const BHWDC &input, const MaxUnpooling3DAttributes &attr); + +// @return shape of a tensor after Pooling2D operation is applied to the given +// input. +BHWC CalculateOutputShape(const BHWC &input, const Pooling2DAttributes &attr); + +// @return shape of a tensor after Pooling3D operation is applied to the given +// input. +BHWDC CalculateOutputShape(const BHWDC &input, const Pooling3DAttributes &attr); + +// @return shape of a tensor after Concat operation is applied to the given +// input. +absl::Status CalculateOutputShape(const std::vector<BHWC> &input, const ConcatAttributes &attr, + BHWC *output_shape); + +// @return shape of a tensor after Concat operation is applied to the given +// input. +absl::Status CalculateOutputShape(const std::vector<BHWDC> &input, const ConcatAttributes &attr, + BHWDC *output_shape); + +// @return padding for pooling operation to make sure output keep the same shape +// as the given input. +Padding2D CalculateSamePadding(const BHWC &input, const Pooling2DAttributes &attr); + +// @return padding for pooling operation to make sure output keep the same shape +// as the given input. +Padding3D CalculateSamePadding(const BHWDC &input, const Pooling3DAttributes &attr); + +// @return padding for max unpooling operation to make sure output keep the same +// shape as the given input. +Padding2D CalculateSamePadding(const BHWC &input, const MaxUnpooling2DAttributes &attr); + +// @return padding for max unpooling operation to make sure output keep the same +// shape as the given input. +Padding3D CalculateSamePadding(const BHWDC &input, const MaxUnpooling3DAttributes &attr); + +struct Convolution2DAttributes +{ + HW strides = HW(1, 1); // Along each axis. + HW dilations = HW(1, 1); // Along each axis. + Padding2D padding; + + InternalTensor<OHWI, DataType::FLOAT32> weights; + InternalTensor<Linear, DataType::FLOAT32> bias; // optional +}; + +struct Convolution3DAttributes +{ + HWD strides = HWD(0, 0, 0); // Along each axis. + HWD dilations = HWD(0, 0, 0); // Along each axis. + Padding3D padding; + + InternalTensor<OHWDI, DataType::FLOAT32> weights; + InternalTensor<Linear, DataType::FLOAT32> bias; // optional +}; + +// @return shape of a tensor after Convolution2D operation is applied to +// the given input. +BHWC CalculateOutputShape(const BHWC &input, const Convolution2DAttributes &attr); + +// @return shape of a tensor after Convolution3D operation is applied to +// the given input. +BHWDC CalculateOutputShape(const BHWDC &input, const Convolution3DAttributes &attr); + +// @return padding for convolution operation to make sure output keep the same +// shape as the given input. +Padding2D CalculateSamePadding(const BHWC &input, const Convolution2DAttributes &attr); + +// @return padding for convolution operation to make sure output keep the same +// shape as the given input. +Padding3D CalculateSamePadding(const BHWDC &input, const Convolution3DAttributes &attr); + +struct ConvolutionTransposedAttributes +{ + HW stride = HW(1, 1); // Along each axis. + HW adjacent; // TODO(sorokin): No op on Flow. + Padding2D padding; + + InternalTensor<OHWI, DataType::FLOAT32> weights; + InternalTensor<Linear, DataType::FLOAT32> bias; // optional +}; + +struct ConvolutionTransposed3DAttributes +{ + HWD stride = HWD(0, 0, 0); // Along each axis. + Padding3D padding; + + InternalTensor<OHWDI, DataType::FLOAT32> weights; + InternalTensor<Linear, DataType::FLOAT32> bias; // optional +}; + +Padding2D CalculateSamePadding(const BHWC &input, const ConvolutionTransposedAttributes &attr); + +Padding3D CalculateSamePadding(const BHWDC &input, const ConvolutionTransposed3DAttributes &attr); + +// @return shape of a tensor after ConvolutionTransposed operation is applied to +// the given input. +BHWC CalculateOutputShape(const BHWC &input, const ConvolutionTransposedAttributes &attr); + +// @return shape of a tensor after ConvolutionTransposed3D operation is applied +// to +// the given input. +BHWDC CalculateOutputShape(const BHWDC &input, const ConvolutionTransposed3DAttributes &attr); + +struct DepthwiseConvolution2DAttributes : public Convolution2DAttributes +{ +}; +struct DepthwiseConvolution3DAttributes : public Convolution3DAttributes +{ +}; + +// @return shape of a tensor after DepthwiseConvolution2D operation is applied +// to the given input. +BHWC CalculateOutputShape(const BHWC &input, const DepthwiseConvolution2DAttributes &attr); + +// @return shape of a tensor after DepthwiseConvolution3D operation is applied +// to the given input. +BHWDC CalculateOutputShape(const BHWDC &input, const DepthwiseConvolution3DAttributes &attr); + +// @return padding for depthwise convolution operation to make sure output keep +// the same shape as the given input. +Padding2D CalculateSamePadding(const BHWC &input, const DepthwiseConvolution2DAttributes &attr); + +// @return padding for depthwise convolution operation to make sure output keep +// the same shape as the given input. +Padding3D CalculateSamePadding(const BHWDC &input, const DepthwiseConvolution3DAttributes &attr); + +// f(x):= { +// if x < 0 : x -> alpha * x +// if x >= 0 : x -> min(clip, x) +// } +// +// Examples: +// - ReLU: clip = 0, alpha = 0 +// - ReLU6: clip = 6, alpha = 0 +// - Leaky ReLU: clip = 0, alpha = a +struct ReLUAttributes +{ + // clip <= 0 mean it is not set. + float clip = 0; + + float alpha = 0; +}; + +struct PReLUAttributes +{ + // clip <= 0 mean it is not set. + float clip = 0; + + // If alpha is linear, then it is sharded across CHANNELS axis, otherwise + // full shape alpha is required. + absl::variant<InternalTensor<Linear, DataType::FLOAT32>, InternalTensor<HWC, DataType::FLOAT32>> + alpha; +}; + +struct ReduceAttributes +{ + Axis axis = Axis::UNKNOWN; +}; + +struct SoftmaxAttributes +{ + Axis axis = Axis::UNKNOWN; +}; + +enum LstmKernelType +{ + FULL = 0, + BASIC = 1, // Currently, only basic is supported. +}; + +struct LstmAttributes +{ + LstmKernelType kernel_type = LstmKernelType::BASIC; +}; + +enum class SamplingType +{ + UNKNOWN = 0, + NEAREST = 1, + BILINEAR = 2, +}; + +struct Resize2DAttributes +{ + HW new_shape; + + SamplingType type = SamplingType::UNKNOWN; + + // If true, the centers of the 4 corner pixels of the input and output tensors + // are aligned, preserving the values at the corner pixels. Defaults to false. + bool align_corners = false; + + bool half_pixel_centers = false; +}; + +// TODO(b/147771327): rename to Resize3D +struct Resize3DAttributes +{ + HWD new_shape; + + SamplingType type = SamplingType::NEAREST; + + // If true, the centers of the 8 corner pixels of the input and output tensors + // are aligned, preserving the values at the corner pixels. Defaults to false. + bool align_corners = false; + + bool half_pixel_centers = false; +}; + +float CalculateResizeScale(int32_t input_size, int32_t output_size, const Resize2DAttributes &attr); + +float CalculateResizeScale(int32_t input_size, int32_t output_size, const Resize3DAttributes &attr); + +// @return shape of a tensor after scale operation is applied to the given +// input. +BHWC CalculateOutputShape(const BHWC &input, const Resize2DAttributes &attr); + +// @return shape of a tensor after scale operation is applied to the given +// input. +BHWDC CalculateOutputShape(const BHWDC &input, const Resize3DAttributes &attr); + +enum class PaddingContentType +{ + ZEROS = 0, + REFLECT = 1, + EDGE = 2, +}; + +struct PadAttributes +{ + PaddingContentType type = PaddingContentType::ZEROS; + + BHWC prepended; + BHWC appended; +}; + +// @return shape of a tensor after Pad operation is applied to the given input. +BHWC CalculateOutputShape(const BHWC &input, const PadAttributes &attr); + +struct Pad3DAttributes +{ + PaddingContentType type = PaddingContentType::ZEROS; + + BHWDC prepended; + BHWDC appended; +}; + +// @return shape of a tensor after Pad3D operation is applied to the given +// input. +BHWDC CalculateOutputShape(const BHWDC &input, const Pad3DAttributes &attr); + +struct ConstTensorAttributes +{ + InternalTensor<BHWC, DataType::FLOAT32> tensor; +}; + +// Simple slicing without advanced support for shrinking, reverse slicing etc. +struct SliceAttributes +{ + // Specifies start and end dimensions for slicing. + BHWC starts; + BHWC ends; + + // Stride should be >= 1. + BHWC strides; +}; + +// @return shape of a tensor after Slice2D operation is applied to the given +// input. +BHWC CalculateOutputShape(const BHWC &input, const SliceAttributes &attr); + +// Simple slicing without advanced support for shrinking, reverse slicing etc. +struct Slice3DAttributes +{ + // Specifies start and end dimensions for slicing. + BHWDC starts; + BHWDC ends; + + // Stride should be >= 1. + BHWDC strides; +}; + +// @return shape of a tensor after Slice3D operation is applied to the given +// input. +BHWDC CalculateOutputShape(const BHWDC &input, const Slice3DAttributes &attr); + +struct FullyConnectedAttributes +{ + InternalTensor<OHWI, DataType::FLOAT32> weights; + InternalTensor<Linear, DataType::FLOAT32> bias; +}; + +// @return shape of a tensor after FullyConnected operation is applied to +// the given input. +BHWC CalculateOutputShape(const BHWC &input, const FullyConnectedAttributes &attr); + +// @return shape of a tensor after Mean operation is applied to the given input. +BHWC CalculateOutputShape(const BHWC &input, const MeanAttributes &attr); + +struct ElementwiseAttributes +{ + TensorOrScalar param; + // For elementwise operation with 2 inputs op(A, B), runtime_tensor_is_second + // true when runtime tensor is B(on second position). this is important for + // ops that non commutative, for example substract. + bool runtime_tensor_is_second = false; +}; + +struct ReshapeAttributes +{ + BHWC new_shape; +}; + +struct Reshape3DAttributes +{ + BHWDC new_shape; +}; + +struct TransposeAttributes +{ + // A permutation of the dimensions of input tensor + BHWC perm; +}; + +// @return shape of a tensor after Transpose operation is applied to +// the given input. +BHWC CalculateOutputShape(const BHWC &input, const TransposeAttributes &attr); + +struct Transpose3DAttributes +{ + // A permutation of the dimensions of input tensor + BHWDC perm; +}; + +// @return shape of a tensor after Transpose3D operation is applied to +// the given input. +BHWDC CalculateOutputShape(const BHWDC &input, const Transpose3DAttributes &attr); + +struct SpaceToDepthAttributes +{ + int block_size; +}; + +// These help perform a combination of Quantize & Dequantize to adjust float +// values like quantized inference would. +struct QuantizeAndDequantizeAttributes +{ + float min = 0; + float max = 0; + float scale = 0; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_OPERATIONS_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Precision.cc b/runtime/onert/backend/gpu_cl/open_cl/Precision.cc new file mode 100644 index 000000000..bd908bd43 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Precision.cc @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Precision.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +std::string ToString(CalculationsPrecision precision) +{ + switch (precision) + { + case CalculationsPrecision::F32_F16: + return "CalculationsPrecision::F32_F16"; + case CalculationsPrecision::F32: + return "CalculationsPrecision::F32"; + case CalculationsPrecision::F16: + return "CalculationsPrecision::F16"; + } + return " "; +} + +DataType DeduceDataTypeFromPrecision(CalculationsPrecision precision) +{ + if (precision == CalculationsPrecision::F32) + { + return DataType::FLOAT32; + } + else + { + return DataType::FLOAT16; + } + return DataType::UNKNOWN; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Precision.h b/runtime/onert/backend/gpu_cl/open_cl/Precision.h new file mode 100644 index 000000000..cb910c783 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Precision.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_PRECISION_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_PRECISION_H__ + +#include <string> + +#include "DataType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class CalculationsPrecision +{ + F32, + F32_F16, + F16 +}; +// F32 - all data and all math ops in F32 +// F16 - all data and all math ops in F16 +// F32_F16 - as F16, but some operations (Convolution, +// DepthwiseConvolution, FullyConnected, ConvolutionTransposed) +// have accumulator in F32 and usually it calculates 4 mads in F16, sum them, +// than converts this partial sum to F32 and add to accumulator. + +DataType DeduceDataTypeFromPrecision(CalculationsPrecision precision); + +std::string ToString(CalculationsPrecision precision); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_PRECISION_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/ProgramCache.cc b/runtime/onert/backend/gpu_cl/open_cl/ProgramCache.cc new file mode 100644 index 000000000..350d7a1c5 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ProgramCache.cc @@ -0,0 +1,97 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ProgramCache.h" + +#include <cstdint> +#include <string> + +#include "ClProgram.h" +#include "Status.h" +#include "Util.h" +#include "farmhash.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +ProgramCache::ProgramDescriptor::ProgramDescriptor(const std::string &code_text, + const std::string &options, + bool use_fingerprints) + : code(code_text), compiler_options(options), use_fingerprint(use_fingerprints) +{ + const uint64_t code_fingerprint = ::util::Fingerprint64(code); + const uint64_t options_fingerprint = ::util::Fingerprint64(compiler_options); + fingerprint = code_fingerprint + options_fingerprint; +} + +ProgramCache::ProgramDescriptor::ProgramDescriptor(uint64_t fingerprints) + : fingerprint(fingerprints), use_fingerprint(true) +{ +} + +ProgramCache::ProgramCache(ProgramCache &&program_cache) + : use_fingerprints_(program_cache.use_fingerprints_), + programs_(std::move(program_cache.programs_)) +{ +} + +ProgramCache &ProgramCache::operator=(ProgramCache &&program_cache) +{ + if (this != &program_cache) + { + use_fingerprints_ = program_cache.use_fingerprints_; + programs_ = std::move(program_cache.programs_); + } + return *this; +} + +absl::Status ProgramCache::GetOrCreateCLKernel(const std::string &code, + const std::string &function_name, + const std::vector<CompilerOptions> &compiler_options, + const CLContext &context, const CLDevice &device, + CLKernel *result) +{ + const std::string options = CompilerOptionsToString(device, compiler_options); + ProgramDescriptor desc{code, options, use_fingerprints_}; + auto it = programs_.find(desc); + if (it != programs_.end()) + { + return result->CreateFromProgram(it->second, function_name); + } + + CLProgram program; + RETURN_IF_ERROR(CreateCLProgram(code, options, context, device, &program)); + RETURN_IF_ERROR(result->CreateFromProgram(program, function_name)); + programs_.insert(std::make_pair(std::move(desc), std::move(program))); + return absl::OkStatus(); +} + +absl::Status ProgramCache::GetOrCreateCLKernel(const std::string &code, + const std::string &function_name, + const CLContext &context, const CLDevice &device, + CLKernel *result) +{ + return GetOrCreateCLKernel(code, function_name, {}, context, device, result); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/ProgramCache.h b/runtime/onert/backend/gpu_cl/open_cl/ProgramCache.h new file mode 100644 index 000000000..3f5ee0215 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/ProgramCache.h @@ -0,0 +1,112 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_PROGRAM_CACHE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_PROGRAM_CACHE_H__ + +#include <cstdint> +#include <string> +#include <vector> + +#include "absl/container/flat_hash_map.h" +#include "absl/types/span.h" +#include "ClContext.h" +#include "ClDevice.h" +#include "ClKernel.h" +#include "ClProgram.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class ProgramCache +{ +public: + ProgramCache() = default; + + ProgramCache(ProgramCache &&program_cache); + ProgramCache &operator=(ProgramCache &&program_cache); + ProgramCache(const ProgramCache &) = delete; + ProgramCache &operator=(const ProgramCache &) = delete; + + absl::Status GetOrCreateCLKernel(const std::string &code, const std::string &function_name, + const std::vector<CompilerOptions> &compiler_options, + const CLContext &context, const CLDevice &device, + CLKernel *result); + + absl::Status GetOrCreateCLKernel(const std::string &code, const std::string &function_name, + const CLContext &context, const CLDevice &device, + CLKernel *result); + +private: + struct ProgramDescriptor + { + ProgramDescriptor() = default; + ProgramDescriptor(const std::string &code_text, const std::string &options, + bool use_fingerprint); + explicit ProgramDescriptor(uint64_t fingerprint); + + std::string code; + std::string compiler_options; + uint64_t fingerprint; + bool use_fingerprint; + }; + struct ProgramDescriptorHasher + { + std::size_t operator()(const ProgramDescriptor &k) const + { + if (k.use_fingerprint) + { + return std::hash<uint64_t>()(k.fingerprint); + } + else + { + return std::hash<std::string>()(k.code) + std::hash<std::string>()(k.compiler_options); + } + } + }; + struct ProgramDescriptorEqual + { + bool operator()(const ProgramDescriptor &a, const ProgramDescriptor &b) const + { + if (a.use_fingerprint && b.use_fingerprint) + { + return a.fingerprint == b.fingerprint; + } + else + { + return a.compiler_options == b.compiler_options && a.code == b.code; + } + } + }; + + // There is a low probability of a hash collision when cache is deserialized + // because only fingerprints are serialized instead of full source code. + bool use_fingerprints_ = false; + absl::flat_hash_map<ProgramDescriptor, CLProgram, ProgramDescriptorHasher, ProgramDescriptorEqual> + programs_; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_PROGRAM_CACHE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Shape.cc b/runtime/onert/backend/gpu_cl/open_cl/Shape.cc new file mode 100644 index 000000000..5a2374516 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Shape.cc @@ -0,0 +1,141 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Shape.h" + +#include <stdint.h> + +#include <string> +#include <vector> + +#include "absl/strings/str_cat.h" +#include "absl/strings/str_join.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +struct GetAxisByIndexFunc +{ + template <Layout T> Axis operator()() const { return GetAxis<T>(index); } + int32_t index; +}; + +struct GetIndexByAxisFunc +{ + template <Layout T> int operator()() const { return GetAxisIndex<T>(axis); } + Axis axis; +}; + +struct NumAxisFunc +{ + template <Layout T> int operator()() const { return Size<T>(); } +}; + +} // namespace + +std::string ToString(Axis axis) +{ + switch (axis) + { + case Axis::BATCH: + return "batch"; + case Axis::CHANNELS: + return "channels"; + case Axis::INPUT_CHANNELS: + return "input_channels"; + case Axis::OUTPUT_CHANNELS: + return "output_channels"; + case Axis::HEIGHT: + return "height"; + case Axis::WIDTH: + return "width"; + case Axis::VALUE: + return "value"; + case Axis::DEPTH: + return "depth"; + case Axis::UNKNOWN: + return "unknown"; + } + return "undefined"; +} + +std::string ToString(Layout layout) +{ + switch (layout) + { + case Layout::SCALAR: + return "scalar"; + case Layout::LINEAR: + return "linear"; + case Layout::HW: + return "hw"; + case Layout::HWD: + return "hwd"; + case Layout::CHW: + return "chw"; + case Layout::HWC: + return "hwc"; + case Layout::HWDC: + return "hwdc"; + case Layout::OHWI: + return "ohwi"; + case Layout::IHWO: + return "ihwo"; + case Layout::OIHW: + return "oihw"; + case Layout::IOHW: + return "iohw"; + case Layout::BHWC: + return "bhwc"; + case Layout::BHWDC: + return "bhwdc"; + case Layout::OHWDI: + return "ohwi"; + case Layout::UNKNOWN: + return "unknown"; + } + return "undefined"; +} + +Axis GetAxis(Layout layout, int32_t index) +{ + return DispatchByLayout(layout, GetAxisByIndexFunc{index}); +} + +int GetAxisIndex(Layout layout, Axis axis) +{ + return DispatchByLayout(layout, GetIndexByAxisFunc{axis}); +} + +bool HasAxis(Layout layout, Axis axis) { return GetAxisIndex(layout, axis) >= 0; } + +int Size(Layout layout) { return DispatchByLayout(layout, NumAxisFunc()); } + +std::string ToString(const Shape &s) +{ + return absl::StrCat("{", ToString(s.layout), ", {", absl::StrJoin(s.dimensions, ", "), "}}"); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Shape.h b/runtime/onert/backend/gpu_cl/open_cl/Shape.h new file mode 100644 index 000000000..3767e106f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Shape.h @@ -0,0 +1,668 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_SHAPE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_SHAPE_H__ + +#include <stddef.h> +#include <stdint.h> + +#include <array> +#include <functional> +#include <numeric> +#include <string> +#include <utility> +#include <vector> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class Axis +{ + UNKNOWN = 0, + CHANNELS = 1, + INPUT_CHANNELS = 2, + OUTPUT_CHANNELS = 3, + HEIGHT = 4, + WIDTH = 5, + BATCH = 6, + VALUE = 7, + DEPTH = 8, +}; + +std::string ToString(Axis t); + +// Layout represents axis order. +enum class Layout +{ + UNKNOWN = 0, + SCALAR = 1, + LINEAR = 2, + HW = 3, + CHW = 4, + HWC = 5, + OIHW = 6, + OHWI = 7, + IHWO = 8, + IOHW = 9, + BHWC = 10, + HWDC = 11, + BHWDC = 12, + HWD = 13, + OHWDI = 14, +}; + +std::string ToString(Layout l); + +// Returns number of axis for the fixed layout. +template <Layout T> constexpr int Size(); + +// Returns number of axis for the given layout. +int Size(Layout layout); + +// Returns Axis for the given index and fixed layout. +template <Layout T> constexpr Axis GetAxis(int index); + +// Returns axis for the given layout and index. +Axis GetAxis(Layout layout, int32_t index); + +// Returns axis index for the given axis and fixed layout. +template <Layout T> constexpr int GetAxisIndex(Axis axis); + +// Returns axis index for the given layout and axis. +int GetAxisIndex(Layout layout, Axis axis); + +// Checks if fixed layout has given axis +template <Layout T> constexpr bool HasAxis(Axis axis); + +// Checks if given layout has given axis +bool HasAxis(Layout layout, Axis axis); + +// Stores Layout(axis set and order) and value for dimensions. +struct Shape +{ + Shape() : layout(Layout::UNKNOWN), dimensions() {} + + explicit Shape(Layout t) : layout(t), dimensions(Size(t)) {} + + Shape(Layout t, std::vector<int32_t> d) : layout(t), dimensions(std::move(d)) {} + + bool operator==(const Shape &other) const + { + return (layout == other.layout) && (dimensions == other.dimensions); + } + + bool operator!=(const Shape &other) const { return !operator==(other); } + + // All methods below are matching same methods defined in StrongShape to + // make sure generic algorithms work both ways. + + // Returns back a dimension or -1 if it is not found. + template <Axis D> int32_t get() const; + int32_t get(Axis axis) const; + + template <Axis D> bool set(int32_t t); + bool set(Axis axis, int32_t t); + + Axis axis(int index) const { return GetAxis(layout, index); } + + int index(Axis axis) const { return GetAxisIndex(layout, axis); } + + bool has(Axis axis) const { return HasAxis(layout, axis); } + + int64_t DimensionsProduct() const + { + return std::accumulate(dimensions.begin(), dimensions.end(), 1ll, std::multiplies<int64_t>()); + } + + Layout layout = Layout::UNKNOWN; + + std::vector<int32_t> dimensions; +}; + +std::string ToString(const Shape &s); + +// StrongShape provides convenient explicit access to dimensions stored in +// shape, e.g. StrongShape<Layout::HW> s; provides s.h and s.w accessors. +// +// There is a conversion possible both ways between Shape and StrongShape. +// +// OIHW oihw; // specific shape +// Shape l = oihw.ToShape(); +// +// OHWI other; // notice not the same but compatible shape. +// if (!other.Adopt(l)) { +// // error handling +// } +// +// StrongShape supports the following set of operations: +// +// // Returns number of axis in the shape class. +// static constexpr int size(); +// +// // Returns Axis for the given index or Axis::UNKNOWN if index +// // falls outside of the defined range in this shape. +// static constexpr Axis axis(int index); +// +// // Returns index for the given axis or -1 if axis is not defined in this +// // shape. +// static constexpr int index(Axis axis); +// +// // Getters +// int32_t get(int index) const; +// int32_t get(Axis axis) const; +// int32_t get<Axis>() const; +// +// // Setters that return false if set was not successful. +// bool set(int index, int32_t v); +// bool set(Axis axis, int32_t v); +// bool set<Axis>(int32_t v); +// +// // Returns shape's layout. +// static const Layout layout; +// +// // Turns specific shape into generic shape. +// Shape ToShape() const; +// +// // Copies all dimensions from the given shape. +// bool Adopt(const Shape&); +// +template <Layout L> struct StrongShape; + +using Scalar = StrongShape<Layout::SCALAR>; +using Linear = StrongShape<Layout::LINEAR>; +using HW = StrongShape<Layout::HW>; +using HWD = StrongShape<Layout::HWD>; + +// Common tensor shape for CNN models working with images. +using CHW = StrongShape<Layout::CHW>; +using HWC = StrongShape<Layout::HWC>; +using HWDC = StrongShape<Layout::HWDC>; +using BHWC = StrongShape<Layout::BHWC>; +using BHWDC = StrongShape<Layout::BHWDC>; + +// Tensor shape used in convolution_2d weights. +using OIHW = StrongShape<Layout::OIHW>; +using OHWI = StrongShape<Layout::OHWI>; +using IHWO = StrongShape<Layout::IHWO>; +using IOHW = StrongShape<Layout::IOHW>; + +// Tensor shape used in convolution_3d weights. +using OHWDI = StrongShape<Layout::OHWDI>; + +// ----------------------------------------------------------------------------- +// Everything below are internal implementation details. +// ----------------------------------------------------------------------------- + +namespace internal_shape +{ + +template <Axis T> struct AxisTraits; + +#define TFLITE_GPU_AXIS_TRAITS(AxisName, HolderName) \ + template <> struct AxisTraits<Axis::AxisName> \ + { \ + struct Holder \ + { \ + int32_t HolderName; \ + \ + protected: \ + int32_t operator()() const { return HolderName; } \ + void operator()(int32_t v) { HolderName = v; } \ + }; \ + \ + using dimension_holder_type = Holder; \ + } + +TFLITE_GPU_AXIS_TRAITS(CHANNELS, c); +TFLITE_GPU_AXIS_TRAITS(HEIGHT, h); +TFLITE_GPU_AXIS_TRAITS(WIDTH, w); +TFLITE_GPU_AXIS_TRAITS(INPUT_CHANNELS, i); +TFLITE_GPU_AXIS_TRAITS(OUTPUT_CHANNELS, o); +TFLITE_GPU_AXIS_TRAITS(BATCH, b); +TFLITE_GPU_AXIS_TRAITS(VALUE, v); +TFLITE_GPU_AXIS_TRAITS(DEPTH, d); + +#undef TFLITE_GPU_AXIS_TRAITS + +template <int N, Axis... As> struct StrongShapeImpl; + +template <int N> struct StrongShapeImpl<N> +{ + static constexpr int size() { return N; } + + static constexpr Axis axis(int) { return Axis::UNKNOWN; } + + static constexpr int index(Axis) { return -1; } + + static constexpr bool has(Axis) { return false; } + + int32_t get(Axis) const { return -1; } + + int32_t get(int) const { return -1; } + + template <Axis B> int32_t get() const { return -1; } + + bool set(Axis, int32_t) { return false; } + + bool set(int, int32_t) { return false; } + + template <Axis B> bool set(int32_t) { return false; } +}; + +// Used to deduce number of axis, and to be a child of a proper holder to +// provide access to the dimension by name +template <int N, Axis A, Axis... As> +struct StrongShapeImpl<N, A, As...> : public AxisTraits<A>::dimension_holder_type, + public StrongShapeImpl<N + 1, As...> +{ + using dimension_holder_type = typename AxisTraits<A>::dimension_holder_type; + + using rest_type = StrongShapeImpl<N + 1, As...>; + + StrongShapeImpl() : dimension_holder_type{0}, rest_type() {} + + template <typename... Ts> + explicit StrongShapeImpl(int32_t t, Ts... ts) : dimension_holder_type{t}, rest_type(ts...) + { + } + + static constexpr Axis axis(int index) { return index == N ? A : rest_type::axis(index); } + + static constexpr int index(Axis axis) { return axis == A ? N : rest_type::index(axis); } + + static constexpr bool has(Axis axis) { return axis == A ? true : rest_type::has(axis); } + + int32_t get(Axis axis) const + { + return axis == A ? dimension_holder_type::operator()() : rest_type::get(axis); + } + + template <Axis B> int32_t get() const + { + return B == A ? dimension_holder_type::operator()() : rest_type::template get<B>(); + } + + int32_t get(int index) const + { + return index == N ? dimension_holder_type::operator()() : rest_type::get(index); + } + + bool set(Axis axis, int32_t t) + { + if (axis == A) + { + dimension_holder_type::operator()(t); + return true; + } + return rest_type::set(axis, t); + } + + bool set(int index, int32_t t) + { + if (index == N) + { + dimension_holder_type::operator()(t); + return true; + } + return rest_type::set(index, t); + } + + template <Axis B> bool set(int32_t t) + { + if (A == B) + { + dimension_holder_type::operator()(t); + return true; + } + return rest_type::template set<B>(t); + } +}; + +template <Layout T> struct LayoutTraits; + +#define TFLITE_GPU_LAYOUT_TRAITS(LayoutName, ...) \ + template <> struct LayoutTraits<Layout::LayoutName> \ + { \ + using strong_shape_type = StrongShapeImpl<0, __VA_ARGS__>; \ + } + +TFLITE_GPU_LAYOUT_TRAITS(HW, Axis::HEIGHT, Axis::WIDTH); +TFLITE_GPU_LAYOUT_TRAITS(HWD, Axis::HEIGHT, Axis::WIDTH, Axis::DEPTH); +TFLITE_GPU_LAYOUT_TRAITS(OHWI, Axis::OUTPUT_CHANNELS, Axis::HEIGHT, Axis::WIDTH, + Axis::INPUT_CHANNELS); +TFLITE_GPU_LAYOUT_TRAITS(OIHW, Axis::OUTPUT_CHANNELS, Axis::INPUT_CHANNELS, Axis::HEIGHT, + Axis::WIDTH); +TFLITE_GPU_LAYOUT_TRAITS(IOHW, Axis::INPUT_CHANNELS, Axis::OUTPUT_CHANNELS, Axis::HEIGHT, + Axis::WIDTH); +TFLITE_GPU_LAYOUT_TRAITS(IHWO, Axis::INPUT_CHANNELS, Axis::HEIGHT, Axis::WIDTH, + Axis::OUTPUT_CHANNELS); +TFLITE_GPU_LAYOUT_TRAITS(CHW, Axis::CHANNELS, Axis::HEIGHT, Axis::WIDTH); +TFLITE_GPU_LAYOUT_TRAITS(HWC, Axis::HEIGHT, Axis::WIDTH, Axis::CHANNELS); +TFLITE_GPU_LAYOUT_TRAITS(HWDC, Axis::HEIGHT, Axis::WIDTH, Axis::DEPTH, Axis::CHANNELS); +TFLITE_GPU_LAYOUT_TRAITS(LINEAR, Axis::VALUE); +TFLITE_GPU_LAYOUT_TRAITS(SCALAR, Axis::VALUE); +TFLITE_GPU_LAYOUT_TRAITS(BHWC, Axis::BATCH, Axis::HEIGHT, Axis::WIDTH, Axis::CHANNELS); +TFLITE_GPU_LAYOUT_TRAITS(BHWDC, Axis::BATCH, Axis::HEIGHT, Axis::WIDTH, Axis::DEPTH, + Axis::CHANNELS); +TFLITE_GPU_LAYOUT_TRAITS(OHWDI, Axis::OUTPUT_CHANNELS, Axis::HEIGHT, Axis::WIDTH, Axis::DEPTH, + Axis::INPUT_CHANNELS); + +#undef TFLITE_GPU_LAYOUT_TRAITS + +template <> struct LayoutTraits<Layout::UNKNOWN> +{ + using strong_shape_type = StrongShapeImpl<0>; +}; + +template <Axis A> struct DimensionGetterFixedAxisFunc +{ + template <Layout T> int32_t operator()() const + { + constexpr int i = GetAxisIndex<T>(A); + return i >= 0 && i < l->dimensions.size() ? l->dimensions[i] : -1; + } + const Shape *l; +}; + +struct DimensionGetterFunc +{ + template <Layout T> int32_t operator()() const + { + uint32_t i = GetAxisIndex<T>(axis); + return i < l->dimensions.size() ? l->dimensions[i] : -1; + } + Axis axis; + const Shape *l; +}; + +template <Axis A> struct DimensionSetterFixedAxisFunc +{ + template <Layout T> bool operator()() const + { + constexpr uint32_t i = GetAxisIndex<T>(A); + if (i < l->dimensions.size()) + { + l->dimensions[i] = v; + return true; + } + return false; + } + Shape *l; + int32_t v; +}; + +struct DimensionSetterFunc +{ + template <Layout T> bool operator()() const + { + uint32_t i = GetAxisIndex<T>(axis); + if (i < l->dimensions.size()) + { + l->dimensions[i] = v; + return true; + } + return false; + } + Axis axis; + Shape *l; + int32_t v; +}; + +template <Layout L> struct ToShapeFunc +{ + template <Layout T> bool operator()() const + { + for (int i = 0; i < StrongShape<L>::size(); ++i) + { + int index = GetAxisIndex<T>(StrongShape<L>::axis(i)); + if (index < 0) + return false; + shape->set(i, l.dimensions[index]); + } + return true; + } + + StrongShape<L> *shape; + const Shape &l; +}; + +} // namespace internal_shape + +// template <Axis... As> +template <Layout L> struct StrongShape : public internal_shape::LayoutTraits<L>::strong_shape_type +{ + using strong_shape_type = typename internal_shape::LayoutTraits<L>::strong_shape_type; + StrongShape() = default; + + template <typename... Ts> explicit StrongShape(Ts... t) : strong_shape_type(t...) {} + + constexpr static Layout layout = L; + + bool operator==(const StrongShape<L> &shape) const + { + // TODO(akulik): implement better alternative. + return this->ToShape() == shape.ToShape(); + } + + bool operator!=(const StrongShape<L> &shape) const + { + // TODO(akulik): implement better alternative. + return this->ToShape() != shape.ToShape(); + } + bool empty() const { return DimensionsProduct() == 0; } + + // Turns StrongShape into generic shape. + Shape ToShape() const + { + std::vector<int32_t> dimensions(StrongShape::size()); + for (int i = 0; i < StrongShape::size(); ++i) + { + dimensions[i] = StrongShape::get(i); + } + return Shape(L, std::move(dimensions)); + } + + // @return all dimensions multiplied + int64_t DimensionsProduct() const + { + int64_t product = 1; + for (int i = 0; i < StrongShape::size(); ++i) + { + product *= StrongShape::get(i); + } + return product; + } + + // Translates given coordinates of the layout into a linear index assuming + // dimensions are sorted in tensor access order e.g. if you access + // foobar[i][j][k] order of coordinates should be i,j,k. + int64_t LinearIndex(const std::array<int32_t, StrongShape::size()> &coordinates) const + { + int64_t index = coordinates[0]; + for (int i = 1; i < StrongShape::size(); ++i) + { + index = index * StrongShape::get(i) + coordinates[i]; + } + return index; + } + + // Copies all dimensions from the given generic shape into specific shape. + // It requires shape to have all axis defined in the given + // StrongShape. For example: + // - If this shape is OHWI but given shape is OIHW, Adopt will copy all + // dimensions and return true. + // - If this shape is OIHW but input shape is HW, Adopt will copy H and W + // dimensions and return true, but if this shape is HW and given shape + // OIHW, then Adopt will return false because not all axis are present in + // the input shape. + // + // @return false if generic shape is not compatible. + bool Adopt(const Shape &shape) + { + return DispatchByLayout(shape.layout, internal_shape::ToShapeFunc<L>{this, shape}); + } + + // For all axis defined in a given shape copies values to this shape. + // Therefore, it is possible to copy dimensions from CHW to BCHW, but not + // the other way around. + // + // BCHW bchw; + // CHW chw; + // bchw.CopyAllGivenAxis(chw); --> true + // chw.CopyAllGivenAxis(bchw); --> false + // + // @return false if axis in source shape is not defined here, thus value + // was not copied. + template <Layout B> bool CopyAllGivenAxis(const StrongShape<B> &source) + { + for (int i = 0; i < source.size(); ++i) + { + if (!StrongShape::set(source.axis(i), source.get(i))) + { + return false; + } + } + return true; + } + + // For all axis defined in this shape copies values from the given shape. + // + // BCHW bchw; + // CHW chw; + // bchw.CopyAllDefinedAxis(chw); --> false + // chw.CopyAllDefinedAxis(bchw); --> true + // + // @return false if given shape does not have axis defined here, + // therefore a value was not copied. + template <Layout B> bool CopyAllDefinedAxis(const StrongShape<B> &source) + { + for (int i = 0; i < StrongShape::size(); ++i) + { + int source_index = source.index(StrongShape::axis(i)); + if (source_index < 0) + { + return false; + } + StrongShape::set(i, source.get(source_index)); // always true + } + return true; + } + + // Copies values only for matching axis. + template <Layout B> void CopyMatchingAxis(const StrongShape<B> &source) + { + for (int i = 0; i < StrongShape::size(); ++i) + { + StrongShape::set(source.axis(i), source.get(i)); + } + } + + // AbslHash function for using in flat hash containers. + template <typename H> friend H AbslHashValue(H hash_state, const StrongShape &strong_shape) + { + for (size_t i = 0; i < strong_shape.size(); ++i) + { + hash_state = H::combine(std::move(hash_state), strong_shape.get(i)); + } + return hash_state; + } +}; + +template <Layout T> inline std::string ToString(const StrongShape<T> &s) +{ + return ToString(s.ToShape()); +} + +template <Layout L> constexpr Layout StrongShape<L>::layout; + +template <class F> +auto DispatchByLayout(Layout type, F f) -> decltype(f.template operator()<Layout::UNKNOWN>()) +{ + switch (type) + { + case Layout::HW: + return f.template operator()<Layout::HW>(); + case Layout::HWD: + return f.template operator()<Layout::HWD>(); + case Layout::HWC: + return f.template operator()<Layout::HWC>(); + case Layout::HWDC: + return f.template operator()<Layout::HWDC>(); + case Layout::CHW: + return f.template operator()<Layout::CHW>(); + case Layout::OIHW: + return f.template operator()<Layout::OIHW>(); + case Layout::IOHW: + return f.template operator()<Layout::IOHW>(); + case Layout::OHWI: + return f.template operator()<Layout::OHWI>(); + case Layout::IHWO: + return f.template operator()<Layout::IHWO>(); + case Layout::LINEAR: + return f.template operator()<Layout::LINEAR>(); + case Layout::SCALAR: + return f.template operator()<Layout::SCALAR>(); + case Layout::BHWC: + return f.template operator()<Layout::BHWC>(); + case Layout::BHWDC: + return f.template operator()<Layout::BHWDC>(); + case Layout::OHWDI: + return f.template operator()<Layout::OHWDI>(); + case Layout::UNKNOWN: + return f.template operator()<Layout::UNKNOWN>(); + } + return f.template operator()<Layout::UNKNOWN>(); +} + +template <Layout T> constexpr int Size() { return StrongShape<T>::size(); } + +template <Layout T> constexpr Axis GetAxis(int index) { return StrongShape<T>::axis(index); } + +template <Layout T> constexpr int GetAxisIndex(Axis axis) { return StrongShape<T>::index(axis); } + +template <Layout T> constexpr bool HasAxis(Axis axis) { return StrongShape<T>::has(axis); } + +template <Axis D> inline int32_t Shape::get() const +{ + return DispatchByLayout(layout, internal_shape::DimensionGetterFixedAxisFunc<D>{this}); +} + +inline int32_t Shape::get(Axis axis) const +{ + return DispatchByLayout(layout, internal_shape::DimensionGetterFunc{axis, this}); +} + +template <Axis D> inline bool Shape::set(int32_t t) +{ + return DispatchByLayout(layout, internal_shape::DimensionSetterFixedAxisFunc<D>{this, t}); +} + +inline bool Shape::set(Axis axis, int32_t t) +{ + return DispatchByLayout(layout, internal_shape::DimensionSetterFunc{axis, this, t}); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_SHAPE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Spi.h b/runtime/onert/backend/gpu_cl/open_cl/Spi.h new file mode 100644 index 000000000..c1d65b67e --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Spi.h @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPEN_CL_SPI_H__ +#define __ONERT_BACKEND_GPU_CL_OPEN_CL_SPI_H__ + +#include <cstdint> + +#include "Api.h" +#include "AccessType.h" +#include "Status.h" + +// Contains only service provider-related interfaces. Users should not use them +// directly. + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// Converts a tensor object into another one. +class TensorObjectConverter +{ +public: + virtual ~TensorObjectConverter() = default; + + virtual absl::Status Convert(const TensorObject &input, const TensorObject &output) = 0; +}; + +class TensorObjectConverterBuilder +{ +public: + virtual ~TensorObjectConverterBuilder() = default; + + virtual bool IsSupported(const TensorObjectDef &input, const TensorObjectDef &output) const = 0; + + virtual absl::Status MakeConverter(const TensorObjectDef &input, const TensorObjectDef &output, + std::unique_ptr<TensorObjectConverter> *converter) = 0; +}; + +// Connects tensor definition provided by a user (external) with tensor +// definition used by the inference engine (internal). +struct TensorTieDef +{ + uint32_t id; + AccessType access_type; + TensorObjectDef internal_def; + TensorObjectDef external_def; +}; + +// Connects external tensor object to internal tensor object and provides +// functionality to copy data to/from external object to internal. +class TensorTie +{ +public: + explicit TensorTie(const TensorTieDef &def) : def_(def) {} + + virtual ~TensorTie() = default; + + virtual absl::Status SetExternalObject(TensorObject obj) = 0; + + virtual TensorObject GetExternalObject() = 0; + + virtual absl::Status CopyToExternalObject() = 0; + + virtual absl::Status CopyFromExternalObject() = 0; + + const TensorTieDef &def() const { return def_; } + +private: + const TensorTieDef def_; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPEN_CL_SPI_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Status.h b/runtime/onert/backend/gpu_cl/open_cl/Status.h new file mode 100644 index 000000000..6295a7e77 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Status.h @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_STATUS_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_STATUS_H__ + +#include "absl/status/status.h" // IWYU pragma: export +#define RETURN_IF_ERROR(s) \ + { \ + auto c = (s); \ + if (!c.ok()) \ + return c; \ + } // IWYU pragma: export + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_STATUS_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/StorageTypeUtil.cc b/runtime/onert/backend/gpu_cl/open_cl/StorageTypeUtil.cc new file mode 100644 index 000000000..eada697ac --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/StorageTypeUtil.cc @@ -0,0 +1,149 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "StorageTypeUtil.h" + +#include "TensorType.h" +#include "DataType.h" +#include "Shape.h" +#include "Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +bool CanCreateTensorWithShape(const DeviceInfo &device_info, const BHWDC &shape, + const TensorDescriptor &descriptor) +{ + const int slices = DivideRoundUp(shape.c, 4); + switch (descriptor.storage_type) + { + case TensorStorageType::BUFFER: + { + const uint64_t flt4_size = 4 * (descriptor.data_type == DataType::FLOAT32 ? 4 : 2); + const uint64_t buffer_size = shape.b * shape.w * shape.h * shape.d * slices * flt4_size; + return buffer_size <= device_info.buffer_max_size; + } + case TensorStorageType::IMAGE_BUFFER: + return (uint64_t)shape.b * shape.w * shape.h * shape.d * slices <= + device_info.image_buffer_max_size; + case TensorStorageType::TEXTURE_3D: + if (device_info.cl_version < OpenCLVersion::CL_1_2 && slices == 1) + { + // clCreateImage3D (that used in CL 1.0/1.1) can not create image with + // depth = 1 by specification; + return false; + } + return (uint64_t)shape.w * shape.b <= device_info.image3d_max_width && + (uint64_t)shape.h <= device_info.image3d_max_height && + (uint64_t)slices * shape.d <= device_info.image3d_max_depth; + case TensorStorageType::TEXTURE_ARRAY: + // Bug on some Adreno. b/131099086 + if (slices == 1 && !device_info.SupportsOneLayerTextureArray()) + { + return false; + } + return (uint64_t)shape.w * shape.b <= device_info.image2d_max_width && + (uint64_t)shape.h <= device_info.image2d_max_height && + (uint64_t)slices * shape.d <= device_info.image_array_max_layers; + case TensorStorageType::TEXTURE_2D: + return (uint64_t)shape.w * shape.b * shape.d <= device_info.image2d_max_width && + (uint64_t)shape.h * slices <= device_info.image2d_max_height; + case TensorStorageType::SINGLE_TEXTURE_2D: + return (uint64_t)shape.c <= 4 && + device_info.SupportsFloatImage2D(descriptor.data_type, shape.c) && + (uint64_t)shape.w * shape.b * shape.d <= device_info.image2d_max_width && + (uint64_t)shape.h <= device_info.image2d_max_height; + default: + return false; + } +} + +bool CanCreateTensorWithShape(const DeviceInfo &device_info, const BHWC &shape, + const TensorDescriptor &descriptor) +{ + const BHWDC shape5D(shape.b, shape.h, shape.w, 1, shape.c); + return CanCreateTensorWithShape(device_info, shape5D, descriptor); +} + +TensorStorageType SelectBestStorageType(const DeviceInfo &device_info, const BHWC &shape, + const TensorStorageType &desired, const DataType &data_type, + const Layout &layout) +{ + if (CanCreateTensorWithShape(device_info, shape, TensorDescriptor{data_type, desired, layout})) + { + return desired; + } + auto GetBestTypeAfterTextureArray = [&]() { + if (device_info.SupportsImageBuffer() && + CanCreateTensorWithShape( + device_info, shape, TensorDescriptor{data_type, TensorStorageType::IMAGE_BUFFER, layout})) + { + return TensorStorageType::IMAGE_BUFFER; + } + else + { + return TensorStorageType::BUFFER; + } + }; + auto GetBestTypeAfterTexture2D = [&]() { + if (device_info.SupportsTextureArray() && + CanCreateTensorWithShape( + device_info, shape, + TensorDescriptor{data_type, TensorStorageType::TEXTURE_ARRAY, layout})) + { + return TensorStorageType::TEXTURE_ARRAY; + } + else + { + return GetBestTypeAfterTextureArray(); + } + }; + auto GetBestTypeAfterTexture3D = [&]() { + if (CanCreateTensorWithShape( + device_info, shape, TensorDescriptor{data_type, TensorStorageType::TEXTURE_2D, layout})) + { + return TensorStorageType::TEXTURE_2D; + } + else + { + return GetBestTypeAfterTexture2D(); + } + }; + switch (desired) + { + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::SINGLE_TEXTURE_2D: + return GetBestTypeAfterTexture2D(); + case TensorStorageType::TEXTURE_ARRAY: + return GetBestTypeAfterTextureArray(); + case TensorStorageType::TEXTURE_3D: + return GetBestTypeAfterTexture3D(); + case TensorStorageType::IMAGE_BUFFER: + case TensorStorageType::BUFFER: + return TensorStorageType::BUFFER; + default: + return TensorStorageType::BUFFER; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/StorageTypeUtil.h b/runtime/onert/backend/gpu_cl/open_cl/StorageTypeUtil.h new file mode 100644 index 000000000..a84c3865f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/StorageTypeUtil.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_STORAGE_TYPE_UTIL_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_STORAGE_TYPE_UTIL_H__ + +#include "DeviceInfo.h" +#include "TensorType.h" +#include "DataType.h" +#include "Shape.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +bool CanCreateTensorWithShape(const DeviceInfo &device_info, const BHWDC &shape, + const TensorDescriptor &descriptor); + +bool CanCreateTensorWithShape(const DeviceInfo &device_info, const BHWC &shape, + const TensorDescriptor &descriptor); + +TensorStorageType SelectBestStorageType(const DeviceInfo &device_info, const BHWC &shape, + const TensorStorageType &desired, const DataType &data_type, + const Layout &layout); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_STORAGE_TYPE_UTIL_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Tensor.cc b/runtime/onert/backend/gpu_cl/open_cl/Tensor.cc new file mode 100644 index 000000000..983e0d29d --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Tensor.cc @@ -0,0 +1,690 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Tensor.h" + +#include <cstring> +#include <vector> + +#include "absl/strings/str_cat.h" + +#include "Buffer.h" +#include "ClImageFormat.h" +#include "ClMemory.h" +#include "GpuObject.h" +#include "TensorType.h" +#include "InternalTensor.h" +#include "DataType.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +absl::Status AllocateTensorMemory(const CLContext &context, const BHWDC &shape, + const TensorDescriptor &descriptor, const void *data_ptr, + CLMemory *result) +{ + const int slices = DivideRoundUp(shape.c, 4); + cl_mem_flags mem_flags = CL_MEM_READ_WRITE; + if (data_ptr) + { + mem_flags |= CL_MEM_COPY_HOST_PTR; + } + switch (descriptor.storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + { + const size_t data_size = + shape.b * shape.w * shape.h * shape.d * slices * 4 * SizeOf(descriptor.data_type); + cl_int error_code; + cl_mem memory = clCreateBuffer(context.context(), mem_flags, data_size, + const_cast<void *>(data_ptr), &error_code); + if (!memory) + { + return absl::UnknownError(absl::StrCat( + "Failed to allocate device memory (clCreateBuffer): ", CLErrorCodeToString(error_code))); + } + *result = CLMemory(memory, true); + return absl::OkStatus(); + } + case TensorStorageType::TEXTURE_2D: + { + cl_image_desc desc; + desc.image_type = CL_MEM_OBJECT_IMAGE2D; + desc.image_width = shape.w * shape.b * shape.d; + desc.image_height = shape.h * slices; + desc.image_depth = 0; + desc.image_row_pitch = 0; + desc.image_slice_pitch = 0; + desc.num_mip_levels = 0; + desc.num_samples = 0; + desc.buffer = nullptr; + + cl_image_format format; + format.image_channel_order = CL_RGBA; + format.image_channel_data_type = ToImageChannelType(descriptor.data_type); + + cl_int error_code; + cl_mem memory = CreateImage2DLegacy(context.context(), mem_flags, &format, &desc, + const_cast<void *>(data_ptr), &error_code); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to create 2D texture (clCreateImage): ", + CLErrorCodeToString(error_code))); + } + + *result = CLMemory(memory, true); + return absl::OkStatus(); + } + case TensorStorageType::TEXTURE_3D: + { + cl_image_desc desc; + desc.image_type = CL_MEM_OBJECT_IMAGE3D; + desc.image_width = shape.w * shape.b; + desc.image_height = shape.h; + desc.image_depth = slices * shape.d; + desc.image_row_pitch = 0; + desc.image_slice_pitch = 0; + desc.num_mip_levels = 0; + desc.num_samples = 0; + desc.buffer = nullptr; + + cl_image_format format; + format.image_channel_order = CL_RGBA; + format.image_channel_data_type = ToImageChannelType(descriptor.data_type); + + cl_int error_code; + cl_mem memory = CreateImage3DLegacy(context.context(), mem_flags, &format, &desc, + const_cast<void *>(data_ptr), &error_code); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to create 3D texture (clCreateImage): ", + CLErrorCodeToString(error_code))); + } + + *result = CLMemory(memory, true); + return absl::OkStatus(); + } + case TensorStorageType::TEXTURE_ARRAY: + { + cl_image_desc desc; + desc.image_type = CL_MEM_OBJECT_IMAGE2D_ARRAY; + desc.image_width = shape.w * shape.b; + desc.image_height = shape.h; + desc.image_depth = 0; + desc.image_array_size = slices * shape.d; + desc.image_row_pitch = 0; + desc.image_slice_pitch = 0; + desc.num_mip_levels = 0; + desc.num_samples = 0; + desc.buffer = nullptr; + + cl_image_format format; + format.image_channel_order = CL_RGBA; + format.image_channel_data_type = ToImageChannelType(descriptor.data_type); + + cl_int error_code; + cl_mem memory = clCreateImage(context.context(), mem_flags, &format, &desc, + const_cast<void *>(data_ptr), &error_code); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat( + "Failed to create 2D texture array (clCreateImage): ", CLErrorCodeToString(error_code))); + } + + *result = CLMemory(memory, true); + return absl::OkStatus(); + } + + case TensorStorageType::SINGLE_TEXTURE_2D: + { + if (slices != 1) + { + return absl::InvalidArgumentError(absl::StrCat( + "SINGLE_TEXTURE_2D support only channels in range [1-4], but ", shape.c, "was provided")); + } + cl_image_desc desc; + desc.image_type = CL_MEM_OBJECT_IMAGE2D; + desc.image_width = shape.w * shape.b * shape.d; + desc.image_height = shape.h; + desc.image_depth = 0; + desc.image_row_pitch = 0; + desc.image_slice_pitch = 0; + desc.num_mip_levels = 0; + desc.num_samples = 0; + desc.buffer = nullptr; + + cl_image_format format; + if (context.IsFloatTexture2DSupported(shape.c, descriptor.data_type)) + { + format.image_channel_order = ToChannelOrder(shape.c); + format.image_channel_data_type = ToImageChannelType(descriptor.data_type); + } + else + { + return absl::InvalidArgumentError( + absl::StrCat("This device doesn't support ", shape.c, "-channel textures.")); + } + + cl_int error_code; + cl_mem memory = CreateImage2DLegacy(context.context(), mem_flags, &format, &desc, + const_cast<void *>(data_ptr), &error_code); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat( + "Failed to create single 2D texture (clCreateImage): ", CLErrorCodeToString(error_code))); + } + + *result = CLMemory(memory, true); + return absl::OkStatus(); + } + + default: + return absl::InternalError("Unsupported tensor storage type"); + } +} + +absl::Status CreateImageBufferFromBuffer(const CLContext &context, cl_mem memory, + DataType data_type, int width, cl_mem *result) +{ + cl_image_format format; + cl_image_desc desc; + std::memset(&desc, 0, sizeof(desc)); + desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER; + desc.image_width = width; + desc.mem_object = memory; + + format.image_channel_data_type = ToImageChannelType(data_type); + format.image_channel_order = CL_RGBA; + + cl_int error_code; + *result = + clCreateImage(context.context(), CL_MEM_READ_WRITE, &format, &desc, nullptr, &error_code); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to create Image from Buffer (clCreateImage): ", + CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +absl::Status CreateTensor(const CLContext &context, const BHWDC &shape, + const TensorDescriptor &descriptor, cl_mem memory, Tensor *result) +{ + const bool memory_owner = memory == nullptr; + if (memory_owner) + { + CLMemory mem; + RETURN_IF_ERROR(AllocateTensorMemory(context, shape, descriptor, nullptr, &mem)); + memory = mem.Release(); + } + if (descriptor.storage_type == TensorStorageType::IMAGE_BUFFER) + { + cl_mem image_memory; + RETURN_IF_ERROR(CreateImageBufferFromBuffer( + context, memory, descriptor.data_type, + shape.b * shape.w * shape.h * shape.d * DivideRoundUp(shape.c, 4), &image_memory)); + *result = Tensor(memory, memory_owner, image_memory, shape, descriptor); + } + else + { + *result = Tensor(memory, memory_owner, shape, descriptor); + } + return absl::OkStatus(); +} + +absl::Status CreateTensorShared(const CLContext &context, const BHWDC &shape, + const TensorDescriptor &descriptor, cl_mem memory, Tensor *result) +{ + const bool memory_owner = false; + if (descriptor.storage_type == TensorStorageType::IMAGE_BUFFER) + { + cl_mem image_memory; + RETURN_IF_ERROR(CreateImageBufferFromBuffer( + context, memory, descriptor.data_type, + shape.b * shape.w * shape.h * shape.d * DivideRoundUp(shape.c, 4), &image_memory)); + *result = Tensor(memory, memory_owner, image_memory, shape, descriptor); + } + else + { + *result = Tensor(memory, memory_owner, shape, descriptor); + } + return absl::OkStatus(); +} + +} // namespace + +absl::Status TensorDescriptor::CreateGPUObject(CLContext *context, GPUObjectPtr *result) const +{ + Tensor gpu_tensor; + RETURN_IF_ERROR(gpu_tensor.CreateFromDescriptor(*this, context)); + *result = absl::make_unique<Tensor>(std::move(gpu_tensor)); + return absl::OkStatus(); +} + +Tensor::Tensor(cl_mem memory, bool memory_owner, const BHWC &shape, + const TensorDescriptor &descriptor) + : memory_(memory), image_buffer_memory_(nullptr), memory_owner_(memory_owner), + shape_(shape.b, shape.h, shape.w, 1, shape.c), descriptor_(descriptor) +{ +} + +Tensor::Tensor(cl_mem memory, bool memory_owner, const BHWDC &shape, + const TensorDescriptor &descriptor) + : memory_(memory), image_buffer_memory_(nullptr), memory_owner_(memory_owner), shape_(shape), + descriptor_(descriptor) +{ +} + +Tensor::Tensor(cl_mem memory, bool memory_owner, cl_mem image_buffer_memory, const BHWC &shape, + const TensorDescriptor &descriptor) + : memory_(memory), image_buffer_memory_(image_buffer_memory), memory_owner_(memory_owner), + shape_(shape.b, shape.h, shape.w, 1, shape.c), descriptor_(descriptor) +{ +} + +Tensor::Tensor(cl_mem memory, bool memory_owner, cl_mem image_buffer_memory, const BHWDC &shape, + const TensorDescriptor &descriptor) + : memory_(memory), image_buffer_memory_(image_buffer_memory), memory_owner_(memory_owner), + shape_(shape), descriptor_(descriptor) +{ +} + +Tensor::Tensor(Tensor &&tensor) + : memory_(tensor.memory_), image_buffer_memory_(tensor.image_buffer_memory_), + memory_owner_(tensor.memory_owner_), shape_(tensor.shape_), descriptor_(tensor.descriptor_) +{ + tensor.memory_ = nullptr; + tensor.image_buffer_memory_ = nullptr; +} + +Tensor &Tensor::operator=(Tensor &&tensor) +{ + if (this != &tensor) + { + Release(); + std::swap(memory_, tensor.memory_); + std::swap(image_buffer_memory_, tensor.image_buffer_memory_); + std::swap(memory_owner_, tensor.memory_owner_); + std::swap(shape_, tensor.shape_); + std::swap(descriptor_, tensor.descriptor_); + } + return *this; +} + +void Tensor::Release() +{ + // image_buffer_memory_ always owned by object + if (image_buffer_memory_) + { + clReleaseMemObject(image_buffer_memory_); + image_buffer_memory_ = nullptr; + } + if (memory_owner_ && memory_) + { + clReleaseMemObject(memory_); + memory_ = nullptr; + } +} + +absl::Status Tensor::GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const +{ + const auto *buffer_desc = dynamic_cast<const BufferDescriptor *>(obj_ptr); + if (buffer_desc) + { + if (descriptor_.storage_type != TensorStorageType::BUFFER) + { + return absl::InvalidArgumentError("Tensor can be used with BufferDescriptor only wtih " + "TensorStorageType::BUFFER."); + } + resources->buffers.push_back({"buffer", memory_}); + return absl::OkStatus(); + } + const auto *tensor_desc = dynamic_cast<const TensorDescriptor *>(obj_ptr); + if (!tensor_desc) + { + return absl::InvalidArgumentError("Expected TensorDescriptor on input."); + } + if (descriptor_.HasAxis(Axis::WIDTH)) + { + resources->ints.push_back({"width", Width()}); + resources->ints.push_back({"width_div2", Width() / 2}); + resources->ints.push_back({"width_div4", Width() / 4}); + resources->ints.push_back({"width_batched", Width() * Batch()}); + resources->ints.push_back({"width_batched_div2", Width() * Batch() / 2}); + resources->ints.push_back({"width_batched_div4", Width() * Batch() / 4}); + } + if (descriptor_.HasAxis(Axis::HEIGHT)) + { + resources->ints.push_back({"height", Height()}); + } + if (descriptor_.HasAxis(Axis::CHANNELS)) + { + resources->ints.push_back({"slices", Slices()}); + resources->ints.push_back({"channels", Channels()}); + } + if (descriptor_.HasAxis(Axis::BATCH)) + { + resources->ints.push_back({"batch", Batch()}); + } + if (descriptor_.HasAxis(Axis::DEPTH)) + { + resources->ints.push_back({"depth", Depth()}); + } + + if (descriptor_.storage_type == TensorStorageType::BUFFER) + { + resources->buffers.push_back({"buffer", memory_}); + } + else if (descriptor_.storage_type == TensorStorageType::TEXTURE_2D || + descriptor_.storage_type == TensorStorageType::SINGLE_TEXTURE_2D) + { + resources->images2d.push_back({"image2d", memory_}); + } + else if (descriptor_.storage_type == TensorStorageType::TEXTURE_ARRAY) + { + resources->image2d_arrays.push_back({"image2d_array", memory_}); + } + else if (descriptor_.storage_type == TensorStorageType::TEXTURE_3D) + { + resources->images3d.push_back({"image3d", memory_}); + } + else if (descriptor_.storage_type == TensorStorageType::IMAGE_BUFFER) + { + if (obj_ptr->GetAccess() == AccessType::READ) + { + resources->image_buffers.push_back({"image_buffer", image_buffer_memory_}); + } + else + { + resources->buffers.push_back({"buffer", memory_}); + } + } + + return absl::OkStatus(); +} + +int3 Tensor::GetFullTensorRegion() const +{ + switch (descriptor_.storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + case TensorStorageType::IMAGE_BUFFER: + return {shape_.w * shape_.b, shape_.h, shape_.d * Slices()}; + case TensorStorageType::TEXTURE_2D: + return {shape_.w * shape_.b * shape_.d, shape_.h * Slices(), 1}; + case TensorStorageType::SINGLE_TEXTURE_2D: + return {shape_.w * shape_.b * shape_.d, shape_.h, 1}; + case TensorStorageType::UNKNOWN: + return {-1, -1, -1}; + } + return {-1, -1, -1}; +} + +absl::Status Tensor::IsValid(const BHWC &shape) const +{ + if (shape.b != shape_.b) + { + return absl::InvalidArgumentError("Shape batch does not match tensor batch"); + } + if (shape.w != shape_.w) + { + return absl::InvalidArgumentError("Shape width does not match tensor width"); + } + if (shape.h != shape_.h) + { + return absl::InvalidArgumentError("Shape height does not match tensor height"); + } + if (shape.c != shape_.c) + { + return absl::InvalidArgumentError("Shape channels does not match tensor channels"); + } + return absl::OkStatus(); +} + +absl::Status Tensor::IsValid(const BHWDC &shape) const +{ + if (shape.b != shape_.b) + { + return absl::InvalidArgumentError("Shape batch does not match tensor batch"); + } + if (shape.w != shape_.w) + { + return absl::InvalidArgumentError("Shape width does not match tensor width"); + } + if (shape.h != shape_.h) + { + return absl::InvalidArgumentError("Shape height does not match tensor height"); + } + if (shape.d != shape_.d) + { + return absl::InvalidArgumentError("Shape depth does not match tensor depth"); + } + if (shape.c != shape_.c) + { + return absl::InvalidArgumentError("Shape channels does not match tensor channels"); + } + return absl::OkStatus(); +} + +int Tensor::GetAlignedChannels() const +{ + return descriptor_.storage_type == TensorStorageType::SINGLE_TEXTURE_2D ? shape_.c + : AlignByN(shape_.c, 4); +} + +uint64_t Tensor::GetMemorySizeInBytes() const +{ + const uint64_t flt_size = static_cast<uint64_t>(SizeOf(descriptor_.data_type)); + const uint64_t flt4_size = 4 * flt_size; + switch (descriptor_.storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::TEXTURE_3D: + return flt4_size * shape_.b * shape_.w * shape_.h * shape_.d * Slices(); + case TensorStorageType::SINGLE_TEXTURE_2D: + return flt_size * shape_.w * shape_.h * shape_.c * shape_.b * shape_.d; + default: + return 0; + } +} + +cl_mem Tensor::GetMemoryPtr() const +{ + return descriptor_.storage_type == TensorStorageType::IMAGE_BUFFER ? image_buffer_memory_ + : memory_; +} + +cl_mem Tensor::GetMemoryPtrForWriting() const { return memory_; } + +absl::Status Tensor::WriteDataBHWDC(absl::Span<const float> in, CLCommandQueue *queue) +{ + void *data_ptr = nullptr; + const int aligned_channels = GetAlignedChannels(); + const int elements_count = shape_.b * shape_.w * shape_.h * shape_.d * aligned_channels; + + const size_t data_size = elements_count * SizeOf(descriptor_.data_type); + std::vector<float> data_f; + data_f.resize(elements_count); + data_ptr = data_f.data(); + DataFromBHWDC(in, shape_, descriptor_, absl::MakeSpan(data_f.data(), data_f.size())); + + switch (descriptor_.storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + RETURN_IF_ERROR(queue->EnqueueWriteBuffer(memory_, data_size, data_ptr)); + break; + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::TEXTURE_3D: + case TensorStorageType::SINGLE_TEXTURE_2D: + RETURN_IF_ERROR(queue->EnqueueWriteImage(memory_, GetFullTensorRegion(), data_ptr)); + break; + default: + return absl::InternalError("Unsupported tensor storage type"); + } + + return absl::OkStatus(); +} + +absl::Status Tensor::WriteData(CLCommandQueue *queue, const TensorFloat32 &src) +{ + RETURN_IF_ERROR(IsValid(src.shape)); + return WriteDataBHWDC(absl::MakeConstSpan(src.data), queue); +} + +absl::Status Tensor::WriteData(CLCommandQueue *queue, + const InternalTensor<Linear, DataType::FLOAT32> &src) +{ + return WriteDataBHWDC(absl::MakeConstSpan(src.data), queue); +} + +absl::Status Tensor::WriteData(CLCommandQueue *queue, + const InternalTensor<HWC, DataType::FLOAT32> &src) +{ + return WriteDataBHWDC(absl::MakeConstSpan(src.data), queue); +} + +absl::Status Tensor::WriteData(CLCommandQueue *queue, const Tensor5DFloat32 &src) +{ + RETURN_IF_ERROR(IsValid(src.shape)); + return WriteDataBHWDC(absl::MakeConstSpan(src.data), queue); +} + +absl::Status Tensor::ReadDataBHWDC(absl::Span<float> out, CLCommandQueue *queue) const +{ + void *data_ptr = nullptr; + const int aligned_channels = GetAlignedChannels(); + const int elements_count = shape_.b * shape_.w * shape_.h * shape_.d * aligned_channels; + const size_t data_size = elements_count * SizeOf(descriptor_.data_type); + + std::vector<float> data_f; + data_f.resize(elements_count); + data_ptr = data_f.data(); + switch (descriptor_.storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + RETURN_IF_ERROR(queue->EnqueueReadBuffer(memory_, data_size, data_ptr)); + break; + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::TEXTURE_3D: + case TensorStorageType::SINGLE_TEXTURE_2D: + RETURN_IF_ERROR(queue->EnqueueReadImage(memory_, GetFullTensorRegion(), data_ptr)); + break; + default: + return absl::InternalError("Unsupported tensor storage type"); + } + + if (descriptor_.data_type == DataType::FLOAT32) + { + DataToBHWDC(absl::MakeConstSpan(data_f.data(), data_f.size()), shape_, descriptor_, out); + } + + return absl::OkStatus(); +} + +absl::Status Tensor::ReadData(CLCommandQueue *queue, TensorFloat32 *dst) const +{ + RETURN_IF_ERROR(IsValid(dst->shape)); + return ReadDataBHWDC(absl::MakeSpan(dst->data), queue); +} + +absl::Status Tensor::ReadData(CLCommandQueue *queue, Tensor5DFloat32 *dst) const +{ + RETURN_IF_ERROR(IsValid(dst->shape)); + return ReadDataBHWDC(absl::MakeSpan(dst->data), queue); +} + +absl::Status Tensor::CreateFromDescriptor(const TensorDescriptor &desc, CLContext *context) +{ + shape_ = desc.shape; + descriptor_.data_type = desc.data_type; + descriptor_.storage_type = desc.storage_type; + descriptor_.layout = desc.layout; + memory_owner_ = true; + CLMemory memory; + uint8_t *data_ptr = desc.data.empty() ? nullptr : const_cast<unsigned char *>(desc.data.data()); + RETURN_IF_ERROR(AllocateTensorMemory(*context, shape_, descriptor_, data_ptr, &memory)); + memory_ = memory.Release(); + if (desc.storage_type == TensorStorageType::IMAGE_BUFFER) + { + RETURN_IF_ERROR(CreateImageBufferFromBuffer(*context, memory_, desc.data_type, + shape_.b * shape_.w * shape_.h * shape_.d * + DivideRoundUp(shape_.c, 4), + &image_buffer_memory_)); + } + return absl::OkStatus(); +} + +absl::Status CreateTensor(const CLContext &context, const BHWC &shape, + const TensorDescriptor &descriptor, Tensor *result) +{ + const BHWDC shape5D(shape.b, shape.h, shape.w, 1, shape.c); + return CreateTensor(context, shape5D, descriptor, nullptr, result); +} + +absl::Status CreateTensor(const CLContext &context, const BHWDC &shape, + const TensorDescriptor &descriptor, Tensor *result) +{ + return CreateTensor(context, shape, descriptor, nullptr, result); +} + +absl::Status CreateSharedTensor(const CLContext &context, cl_mem memory, const BHWC &shape, + const TensorDescriptor &descriptor, Tensor *result) +{ + const BHWDC shape5D(shape.b, shape.h, shape.w, 1, shape.c); + return CreateTensorShared(context, shape5D, descriptor, memory, result); +} + +absl::Status CreateSharedTensor(const CLContext &context, cl_mem memory, const BHWDC &shape, + const TensorDescriptor &descriptor, Tensor *result) +{ + return CreateTensorShared(context, shape, descriptor, memory, result); +} + +absl::Status AllocateTensorMemory(const CLContext &context, const BHWC &shape, + const TensorDescriptor &descriptor, CLMemory *result) +{ + const BHWDC shape5D(shape.b, shape.h, shape.w, 1, shape.c); + return AllocateTensorMemory(context, shape5D, descriptor, nullptr, result); +} + +absl::Status AllocateTensorMemory(const CLContext &context, const BHWDC &shape, + const TensorDescriptor &descriptor, CLMemory *result) +{ + return AllocateTensorMemory(context, shape, descriptor, nullptr, result); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Tensor.h b/runtime/onert/backend/gpu_cl/open_cl/Tensor.h new file mode 100644 index 000000000..b1930a423 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Tensor.h @@ -0,0 +1,142 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_H__ + +#include <cstdint> +#include <memory> + +#include "absl/types/span.h" +#include "ClCommandQueue.h" +#include "OpenclWrapper.h" +#include "ClContext.h" +#include "ClDevice.h" +#include "ClMemory.h" +#include "GpuObject.h" +#include "TensorType.h" +#include "Util.h" +#include "DataType.h" +#include "Shape.h" +#include "Status.h" +#include "InternalTensor.h" +#include "Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class Tensor : public GPUObject +{ +public: + Tensor() : memory_(nullptr), image_buffer_memory_(nullptr), memory_owner_(true) {} + Tensor(cl_mem memory, bool memory_owner, const BHWC &shape, const TensorDescriptor &descriptor); + Tensor(cl_mem memory, bool memory_owner, const BHWDC &shape, const TensorDescriptor &descriptor); + Tensor(cl_mem memory, bool memory_owner, cl_mem image_buffer_memory, const BHWC &shape, + const TensorDescriptor &descriptor); + Tensor(cl_mem memory, bool memory_owner, cl_mem image_buffer_memory, const BHWDC &shape, + const TensorDescriptor &descriptor); + + // Move only + Tensor(Tensor &&tensor); + Tensor &operator=(Tensor &&tensor); + Tensor(const Tensor &) = delete; + Tensor &operator=(const Tensor &) = delete; + + virtual ~Tensor() { Release(); } + + absl::Status GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const override; + + int Width() const { return shape_.w; } + int Height() const { return shape_.h; } + int Depth() const { return shape_.d; } + int Channels() const { return shape_.c; } + int Slices() const { return DivideRoundUp(shape_.c, 4); } + int Batch() const { return shape_.b; } + TensorDescriptor GetDescriptor() const { return descriptor_; } + DataType GetDataType() const { return descriptor_.data_type; } + TensorStorageType GetStorageType() const { return descriptor_.storage_type; } + + // for profiling and memory statistics + uint64_t GetMemorySizeInBytes() const; + + cl_mem GetMemoryPtr() const; + + // This function returns buffer memory ptr for IMAGE_BUFFER instead of image + // memory ptr. + cl_mem GetMemoryPtrForWriting() const; + + absl::Status WriteData(CLCommandQueue *queue, const TensorFloat32 &src); + absl::Status WriteData(CLCommandQueue *queue, + const InternalTensor<Linear, DataType::FLOAT32> &src); + absl::Status WriteData(CLCommandQueue *queue, const InternalTensor<HWC, DataType::FLOAT32> &src); + + absl::Status WriteData(CLCommandQueue *queue, const Tensor5DFloat32 &src); + absl::Status ReadData(CLCommandQueue *queue, TensorFloat32 *dst) const; + absl::Status ReadData(CLCommandQueue *queue, Tensor5DFloat32 *dst) const; + + absl::Status CreateFromDescriptor(const TensorDescriptor &desc, CLContext *context); + +private: + absl::Status IsValid(const BHWC &shape) const; + absl::Status IsValid(const BHWDC &shape) const; + + int GetChannelsAlignment() const; + int GetAlignedChannels() const; + + absl::Status WriteDataBHWDC(absl::Span<const float> in, CLCommandQueue *queue); + absl::Status ReadDataBHWDC(absl::Span<float> out, CLCommandQueue *queue) const; + + int3 GetFullTensorRegion() const; + void Release(); + + cl_mem memory_; + cl_mem image_buffer_memory_; // for TensorStorageType::IMAGE_BUFFER only + bool memory_owner_; + BHWDC shape_; + TensorDescriptor descriptor_; +}; + +using TensorPtr = std::shared_ptr<Tensor>; + +absl::Status AllocateTensorMemory(const CLContext &context, const BHWC &shape, + const TensorDescriptor &descriptor, CLMemory *result); + +absl::Status AllocateTensorMemory(const CLContext &context, const BHWDC &shape, + const TensorDescriptor &descriptor, CLMemory *result); + +absl::Status CreateTensor(const CLContext &context, const BHWC &shape, + const TensorDescriptor &descriptor, Tensor *result); + +absl::Status CreateTensor(const CLContext &context, const BHWDC &shape, + const TensorDescriptor &descriptor, Tensor *result); + +absl::Status CreateSharedTensor(const CLContext &context, cl_mem memory, const BHWC &shape, + const TensorDescriptor &descriptor, Tensor *result); + +absl::Status CreateSharedTensor(const CLContext &context, cl_mem memory, const BHWDC &shape, + const TensorDescriptor &descriptor, Tensor *result); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/TensorType.cc b/runtime/onert/backend/gpu_cl/open_cl/TensorType.cc new file mode 100644 index 000000000..7ede38795 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/TensorType.cc @@ -0,0 +1,1116 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TensorType.h" + +#include "absl/strings/str_cat.h" +#include "absl/strings/substitute.h" +#include "Shape.h" +#include "DataType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::string GetWriteImageFromDataType(DataType data_type) +{ + if (data_type == DataType::FLOAT32) + { + return "write_imagef"; + } + else if (data_type == DataType::FLOAT16) + { + return "write_imageh"; + } + else + { + throw std::runtime_error("Not supported data type"); + } +} + +} // namespace + +std::string TextureAddressModeToString(TextureAddressMode address_mode) +{ + switch (address_mode) + { + case TextureAddressMode::DONT_CARE: + return "smp_none"; + case TextureAddressMode::ZERO: + return "smp_zero"; + } + return ""; +} + +std::string ToString(TensorStorageType type) +{ + switch (type) + { + case TensorStorageType::UNKNOWN: + return "TensorStorageType::UNKNOWN"; + case TensorStorageType::BUFFER: + return "TensorStorageType::BUFFER"; + case TensorStorageType::TEXTURE_ARRAY: + return "TensorStorageType::TEXTURE_ARRAY"; + case TensorStorageType::TEXTURE_2D: + return "TensorStorageType::TEXTURE_2D"; + case TensorStorageType::TEXTURE_3D: + return "TensorStorageType::TEXTURE_3D"; + case TensorStorageType::SINGLE_TEXTURE_2D: + return "TensorStorageType::SINGLE_TEXTURE_2D"; + case TensorStorageType::IMAGE_BUFFER: + return "TensorStorageType::IMAGE_BUFFER"; + } + return ""; +} + +TensorDescriptor::TensorDescriptor(TensorDescriptor &&desc) + : GPUObjectDescriptor(std::move(desc)), data_type(desc.data_type), + storage_type(desc.storage_type), layout(desc.layout), shape(desc.shape), + data(std::move(desc.data)) +{ +} +TensorDescriptor &TensorDescriptor::operator=(TensorDescriptor &&desc) +{ + if (this != &desc) + { + std::swap(data_type, desc.data_type); + std::swap(storage_type, desc.storage_type); + std::swap(layout, desc.layout); + std::swap(shape, desc.shape); + data = std::move(desc.data); + GPUObjectDescriptor::operator=(std::move(desc)); + } + return *this; +} + +GPUResources TensorDescriptor::GetGPUResources() const +{ + GPUResources resources; + if (HasAxis(Axis::WIDTH)) + { + resources.ints.push_back("width"); + resources.ints.push_back("width_div2"); + resources.ints.push_back("width_div4"); + resources.ints.push_back("width_batched"); + resources.ints.push_back("width_batched_div2"); + resources.ints.push_back("width_batched_div4"); + } + if (HasAxis(Axis::HEIGHT)) + { + resources.ints.push_back("height"); + } + if (HasAxis(Axis::CHANNELS)) + { + resources.ints.push_back("slices"); + resources.ints.push_back("channels"); + } + if (HasAxis(Axis::BATCH)) + { + resources.ints.push_back("batch"); + } + if (HasAxis(Axis::DEPTH)) + { + resources.ints.push_back("depth"); + } + if (storage_type == TensorStorageType::BUFFER) + { + GPUBufferDescriptor desc; + desc.data_type = data_type; + desc.access_type = access_type_; + desc.element_size = 4; + auto it1 = state_vars_.find("ElementsX2"); + if (it1 != state_vars_.end() && it1->second == "true") + { + desc.element_size = 8; + } + auto it2 = state_vars_.find("ElementsX4"); + if (it2 != state_vars_.end() && it2->second == "true") + { + desc.element_size = 16; + } + resources.buffers.push_back({"buffer", desc}); + } + else if (storage_type == TensorStorageType::SINGLE_TEXTURE_2D || + storage_type == TensorStorageType::TEXTURE_2D) + { + GPUImage2DDescriptor desc; + desc.data_type = data_type; + desc.access_type = access_type_; + resources.images2d.push_back({"image2d", desc}); + } + else if (storage_type == TensorStorageType::TEXTURE_ARRAY) + { + GPUImage2DArrayDescriptor desc; + desc.data_type = data_type; + desc.access_type = access_type_; + resources.image2d_arrays.push_back({"image2d_array", desc}); + } + else if (storage_type == TensorStorageType::TEXTURE_3D) + { + GPUImage3DDescriptor desc; + desc.data_type = data_type; + desc.access_type = access_type_; + resources.images3d.push_back({"image3d", desc}); + } + else if (storage_type == TensorStorageType::IMAGE_BUFFER) + { + if (access_type_ == AccessType::READ) + { + GPUImageBufferDescriptor desc; + desc.data_type = data_type; + desc.access_type = access_type_; + resources.image_buffers.push_back({"image_buffer", desc}); + } + else + { + GPUBufferDescriptor desc; + desc.data_type = data_type; + desc.access_type = access_type_; + desc.element_size = 4; + resources.buffers.push_back({"buffer", desc}); + } + } + return resources; +} + +absl::Status TensorDescriptor::PerformSelector(const std::string &selector, + const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const +{ + if (selector == "Width") + { + *result = GetWidth(); + return absl::OkStatus(); + } + else if (selector == "Height") + { + *result = "height"; + return absl::OkStatus(); + } + else if (selector == "Slices") + { + *result = "slices"; + return absl::OkStatus(); + } + else if (selector == "SliceStride") + { + *result = GetSliceStride(); + return absl::OkStatus(); + } + else if (selector == "Channels") + { + *result = "channels"; + return absl::OkStatus(); + } + else if (selector == "Batch") + { + if (HasAxis(Axis::BATCH)) + { + *result = "batch"; + } + else + { + *result = "1"; + } + return absl::OkStatus(); + } + else if (selector == "Depth") + { + *result = "depth"; + return absl::OkStatus(); + } + else if (selector == "SetBatchRef") + { + if (args.size() != 1) + { + return absl::InvalidArgumentError("Unsupported arguments in SetBatchRef selector"); + } + state_vars_["batch_id"] = args[0]; + *result = ""; + return absl::OkStatus(); + } + else if (selector == "Read") + { + return PerformReadSelector(args, template_args, result); + } + else if (selector == "Write") + { + return PerformWriteSelector(args, result); + } + else if (selector == "WriteLinear") + { + return PerformWriteLinearSelector(args, result); + } + else if (selector == "GetAddress") + { + return PerformGetAddressSelector(args, result); + } + else if (selector == "GetPtrWithSliceOffset") + { + return PerformGetPtrWithSliceOffsetSelector(args, result); + } + else if (selector == "GetWHOffset") + { + return PerformGetWHOffsetSelector(args, result); + } + else if (selector == "GetHandle") + { + return PerformGetHandleSelector(args, result); + } + else + { + return absl::NotFoundError( + absl::StrCat("TensorDescriptor don't have selector with name - ", selector)); + } +} + +absl::Status TensorDescriptor::PerformReadSelector(const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const +{ + DataType read_as_type = data_type; + if (!template_args.empty()) + { + if (template_args.size() != 1) + { + return absl::NotFoundError("Unrecognized Read selector template arguments."); + } + else + { + RETURN_IF_ERROR(GetDataTypeFromTemplateArgs(template_args[0], &read_as_type)); + } + } + if (args.size() == 1) + { // function overload for 1D linear types. + if (storage_type == TensorStorageType::BUFFER || + storage_type == TensorStorageType::IMAGE_BUFFER) + { + *result = Read(read_as_type, args[0]); + return absl::OkStatus(); + } + else + { + return absl::InvalidArgumentError( + "Read selector with single argument can be used only with linear " + "storage types(BUFFER or IMAGE_BUFFER)"); + } + } + std::string xc; + std::string yc; + std::string zc; + std::string sc; + std::string bc; + bool parsed = ParseCoordsFromArgs(args, 0, &xc, &yc, &zc, &sc, &bc); + if (args.size() < 2 || !parsed) + { + return absl::NotFoundError("Unrecognized Read selector"); + } + + *result = Read(read_as_type, GetGlobalAddressNoDeclaration(xc, yc, zc, sc, bc)); + return absl::OkStatus(); +} + +absl::Status TensorDescriptor::GetLinkingContextFromWriteSelector( + const std::vector<std::string> &args, std::string *value_name, std::string *x_coord, + std::string *y_coord, std::string *s_coord) const +{ + std::string xc; + std::string yc; + std::string zc; + std::string sc; + std::string bc; + bool parsed = ParseCoordsFromArgs(args, 1, &xc, &yc, &zc, &sc, &bc); + if (args.size() < 2 || !parsed) + { + return absl::NotFoundError("Unrecognized Write selector"); + } + *value_name = args[0]; + if (HasAxis(Axis::BATCH) && !IsBatchedWidth()) + { + *x_coord = absl::StrCat("((", xc, ") * batch + (", bc, "))"); + } + else + { + *x_coord = absl::StrCat("(", xc, ")"); + } + *y_coord = absl::StrCat("(", yc, ")"); + *s_coord = absl::StrCat("(", sc, ")"); + return absl::OkStatus(); +} + +absl::Status TensorDescriptor::PerformWriteSelector(const std::vector<std::string> &args, + std::string *result) const +{ + std::string xc; + std::string yc; + std::string zc; + std::string sc; + std::string bc; + bool parsed = ParseCoordsFromArgs(args, 1, &xc, &yc, &zc, &sc, &bc); + if (args.size() < 2 || !parsed) + { + return absl::NotFoundError("Unrecognized Write selector"); + } + *result = Write(args[0], GetGlobalAddressNoDeclaration(xc, yc, zc, sc, bc)); + return absl::OkStatus(); +} + +absl::Status TensorDescriptor::PerformWriteLinearSelector(const std::vector<std::string> &args, + std::string *result) const +{ + if (storage_type != TensorStorageType::BUFFER && storage_type != TensorStorageType::IMAGE_BUFFER) + { + return absl::InvalidArgumentError("WriteLinear selector can be used only with linear " + "storages(BUFFER/IMAGE_BUFFER)"); + } + if (args.size() != 2) + { + return absl::NotFoundError("Unrecognized WriteLinear selector"); + } + *result = Write(args[0], "(" + args[1] + ")"); + return absl::OkStatus(); +} + +std::string TensorDescriptor::Read(DataType read_as_type, const std::string &global_address) const +{ + const std::string read_as = read_as_type == DataType::FLOAT16 ? "read_imageh" : "read_imagef"; + std::string image_type; + if (storage_type == TensorStorageType::TEXTURE_2D || + storage_type == TensorStorageType::SINGLE_TEXTURE_2D) + { + image_type = "image2d"; + } + else if (storage_type == TensorStorageType::TEXTURE_3D) + { + image_type = "image3d"; + } + else if (storage_type == TensorStorageType::TEXTURE_ARRAY) + { + image_type = "image2d_array"; + } + switch (storage_type) + { + case TensorStorageType::BUFFER: + if (read_as_type == data_type) + { + return absl::StrCat("buffer[", global_address, "]"); + } + else + { + const std::string conversion = + read_as_type == DataType::FLOAT16 ? "convert_half4" : "convert_float4"; + return absl::StrCat(conversion, "(buffer[", global_address, "])"); + } + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::TEXTURE_3D: + case TensorStorageType::SINGLE_TEXTURE_2D: + case TensorStorageType::TEXTURE_ARRAY: + return absl::StrCat(read_as, "(", image_type, + ", " + TextureAddressModeToString(ModeFromState()) + ", ", global_address, + ")"); + case TensorStorageType::IMAGE_BUFFER: + return absl::StrCat(read_as, "(image_buffer, ", global_address, ")"); + case TensorStorageType::UNKNOWN: + return ""; + } + return ""; +} + +std::string TensorDescriptor::Write(const std::string &var_name, + const std::string &global_address) const +{ + std::string image_type; + if (storage_type == TensorStorageType::TEXTURE_2D || + storage_type == TensorStorageType::SINGLE_TEXTURE_2D) + { + image_type = "image2d"; + } + else if (storage_type == TensorStorageType::TEXTURE_3D) + { + image_type = "image3d"; + } + else if (storage_type == TensorStorageType::TEXTURE_ARRAY) + { + image_type = "image2d_array"; + } + switch (storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + return absl::StrCat("buffer[", global_address, "] = ", var_name, ";\n"); + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::TEXTURE_3D: + case TensorStorageType::SINGLE_TEXTURE_2D: + case TensorStorageType::TEXTURE_ARRAY: + return absl::StrCat(GetWriteImageFromDataType(data_type), "(", image_type, ", ", + global_address, ", ", var_name, ");\n"); + case TensorStorageType::UNKNOWN: + return ""; + } + return ""; +} + +absl::Status TensorDescriptor::PerformGetAddressSelector(const std::vector<std::string> &args, + std::string *result) const +{ + std::string xc; + std::string yc; + std::string zc; + std::string sc; + std::string bc; + bool parsed = ParseCoordsFromArgs(args, 1, &xc, &yc, &zc, &sc, &bc); + if (args.size() < 3 || !parsed) + { + return absl::NotFoundError("Unrecognized GetAddress selector"); + } + + *result = DeclareAddress(args[0], GetGlobalAddressNoDeclaration(xc, yc, zc, sc, bc)); + return absl::OkStatus(); +} + +absl::Status +TensorDescriptor::PerformGetPtrWithSliceOffsetSelector(const std::vector<std::string> &args, + std::string *result) const +{ + if (storage_type != TensorStorageType::BUFFER) + { + return absl::InvalidArgumentError( + "GetPtrWithSliceOffset selector can be used only with BUFFER"); + } + if (args.size() != 1) + { + return absl::NotFoundError( + absl::StrCat("GetPtrWithSliceOffset require one argument(slice coordinate), but ", + args.size(), " was passed")); + } + *result = absl::StrCat("buffer + ", args[0], " * ", GetSliceStride()); + return absl::OkStatus(); +} + +absl::Status TensorDescriptor::PerformGetWHOffsetSelector(const std::vector<std::string> &args, + std::string *result) const +{ + if (storage_type != TensorStorageType::BUFFER && storage_type != TensorStorageType::IMAGE_BUFFER) + { + return absl::InvalidArgumentError( + "GetWHOffset selector can be used only with BUFFER/IMAGE_BUFFER"); + } + if (args.size() != 2) + { + return absl::NotFoundError(absl::StrCat( + "GetWHOffset require two arguments(X and Y coordinates), but ", args.size(), " was passed")); + } + if (HasAxis(Axis::BATCH) && !IsBatchedWidth()) + { + auto it = state_vars_.find("batch_id"); + std::string batch_id; + if (it == state_vars_.end()) + { + return absl::NotFoundError( + "Not found batch_id. Should be setted up by SetBatchRef(). method"); + } + else + { + batch_id = it->second; + } + *result = absl::StrCat("((", args[1], ") * ", GetWidth(), " + (", args[0], ")) * batch + (", + batch_id, ")"); + } + else + { + *result = absl::StrCat("(", args[1], ") * ", GetWidth(), " + (", args[0], ")"); + } + return absl::OkStatus(); +} + +absl::Status TensorDescriptor::PerformGetHandleSelector(const std::vector<std::string> &args, + std::string *result) const +{ + if (!args.empty()) + { + return absl::NotFoundError( + absl::StrCat("GetHandle does not require arguments, but ", args.size(), " was passed")); + } + switch (storage_type) + { + case TensorStorageType::BUFFER: + *result = "buffer"; + return absl::OkStatus(); + case TensorStorageType::IMAGE_BUFFER: + if (access_type_ == AccessType::READ) + { + *result = "image_buffer"; + } + else + { + *result = "buffer"; + } + return absl::OkStatus(); + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::SINGLE_TEXTURE_2D: + *result = "image2d"; + return absl::OkStatus(); + case TensorStorageType::TEXTURE_ARRAY: + *result = "image2d_array"; + return absl::OkStatus(); + case TensorStorageType::TEXTURE_3D: + *result = "image3d"; + return absl::OkStatus(); + case TensorStorageType::UNKNOWN: + return absl::UnavailableError("Unknown type"); + } + return absl::UnavailableError("Unknown type"); +} + +std::string TensorDescriptor::DeclareAddress(const std::string &var_name, + const std::string &address) const +{ + return absl::StrCat(StorageTypeToAddressType(), " ", var_name, " = ", address, ";"); +} + +std::string TensorDescriptor::StorageTypeToAddressType() const +{ + switch (storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + return "int"; + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::SINGLE_TEXTURE_2D: + return "int2"; + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + return "int4"; + case TensorStorageType::UNKNOWN: + return ""; + } + return ""; +} + +std::string TensorDescriptor::GetGlobalAddressNoDeclarationWHS(const std::string &x, + const std::string &y, + const std::string &s) const +{ + switch (storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + { + return absl::Substitute("((($2) * height + ($1)) * $3 + ($0))", x, y, s, GetWidth()); + } + case TensorStorageType::TEXTURE_2D: + return absl::Substitute("(int2)(($0), ($1) * slices + ($2))", x, y, s); + case TensorStorageType::SINGLE_TEXTURE_2D: + return absl::StrCat("(int2)(", x, ", ", y, ")"); + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + return absl::StrCat("(int4)(", x, ", ", y, ", ", s, ", 0)"); + case TensorStorageType::UNKNOWN: + return "error"; + } + return "error"; +} + +std::string TensorDescriptor::GetGlobalAddressNoDeclarationWHSB(const std::string &x, + const std::string &y, + const std::string &s, + const std::string &b) const +{ + switch (storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + return absl::Substitute("(((($3) * height + $2) * width + ($1)) * batch + ($0))", b, x, y, s); + case TensorStorageType::TEXTURE_2D: + return absl::Substitute("(int2)(($0) * batch + ($1), ($2) * slices + ($3))", x, b, y, s); + case TensorStorageType::SINGLE_TEXTURE_2D: + return absl::Substitute("(int2)(($0) * batch + ($1), ($2))", x, b, y); + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + return absl::Substitute("(int4)(($0) * batch + ($1), ($2), ($3), 0)", x, b, y, s); + default: + throw std::runtime_error("Unknown storage type"); + } +} + +std::string TensorDescriptor::GetGlobalAddressNoDeclarationWHDS(const std::string &x, + const std::string &y, + const std::string &z, + const std::string &s) const +{ + switch (storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + { + return absl::Substitute("(((($3) * slices + ($2)) * height + ($1)) * $4 + ($0))", x, y, s, z, + GetWidth()); + } + case TensorStorageType::TEXTURE_2D: + return absl::Substitute("(int2)(($0) * depth + ($1), ($2) * slices + ($3))", x, z, y, s); + case TensorStorageType::SINGLE_TEXTURE_2D: + return absl::Substitute("(int2)(($0) * depth + ($1), ($2))", x, z, y); + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + return absl::Substitute("(int4)(($0), ($1), ($2) * slices + ($3), 0)", x, y, z, s); + case TensorStorageType::UNKNOWN: + return "error"; + } + return "error"; +} + +std::string TensorDescriptor::GetGlobalAddressNoDeclarationWHDSB(const std::string &x, + const std::string &y, + const std::string &z, + const std::string &s, + const std::string &b) const +{ + switch (storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + return absl::Substitute("((((($4) * slices + ($3)) * height + $2) * width + ($1)) * batch + " + "($0))", + b, x, y, s, z); + case TensorStorageType::TEXTURE_2D: + return absl::Substitute("(int2)((($0) * batch + ($1)) * depth + ($2), ($3) * slices + ($4))", + x, b, z, y, s); + case TensorStorageType::SINGLE_TEXTURE_2D: + return absl::Substitute("(int2)((($0) * batch + ($1)) * depth + ($2), ($3))", x, b, z, y); + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + return absl::Substitute("(int4)(($0) * batch + ($1), ($2), ($3) * slices + ($4), 0)", x, b, y, + z, s); + default: + throw std::runtime_error("Unknown storage type"); + } +} + +std::string TensorDescriptor::GetGlobalAddressNoDeclaration(const std::string &xc, + const std::string &yc, + const std::string &zc, + const std::string &sc, + const std::string &bc) const +{ + if (layout == Layout::HWC || (IsBatchedWidth() && layout == Layout::BHWC)) + { + return GetGlobalAddressNoDeclarationWHS(xc, yc, sc); + } + else if (layout == Layout::BHWC) + { + return GetGlobalAddressNoDeclarationWHSB(xc, yc, sc, bc); + } + else if (layout == Layout::HWDC || (IsBatchedWidth() && layout == Layout::BHWDC)) + { + return GetGlobalAddressNoDeclarationWHDS(xc, yc, zc, sc); + } + else if (layout == Layout::BHWDC) + { + return GetGlobalAddressNoDeclarationWHDSB(xc, yc, zc, sc, bc); + } + else + { + throw std::runtime_error("Unsupported layout"); + } +} + +absl::Status TensorDescriptor::GetDataTypeFromTemplateArgs(const std::string &template_arg, + DataType *result) const +{ + std::string read_type = template_arg; + if (read_type == "FLT" || read_type == "ACCUM_FLT") + { + auto it = state_vars_.find(read_type); + if (it == state_vars_.end()) + { + return absl::UnavailableError( + absl::StrCat("Read selector template argument ", read_type, " uninitialized.")); + } + else + { + read_type = it->second; + } + } + + if (read_type == "half") + { + *result = DataType::FLOAT16; + } + else if (read_type == "float") + { + *result = DataType::FLOAT32; + } + else + { + return absl::NotFoundError( + absl::StrCat("Unrecognized Read selector template argument - ", read_type)); + } + return absl::OkStatus(); +} + +bool TensorDescriptor::HasAxis(Axis axis) const +{ + if (axis == Axis::WIDTH || axis == Axis::HEIGHT || axis == Axis::CHANNELS) + { + return true; + } + if (axis == Axis::BATCH && (layout == Layout::BHWC || layout == Layout::BHWDC)) + { + return true; + } + if (axis == Axis::DEPTH && (layout == Layout::HWDC || layout == Layout::BHWDC)) + { + return true; + } + return false; +} + +void TensorDescriptor::SetTextureAddressMode(TextureAddressMode mode) +{ + if (mode == TextureAddressMode::ZERO) + { + state_vars_["TextureMode"] = "ZERO"; + } + else + { + state_vars_["TextureMode"] = "DONT_CARE"; + } +} + +bool TensorDescriptor::ParseCoordsFromArgs(const std::vector<std::string> &args, int offset, + std::string *xc, std::string *yc, std::string *zc, + std::string *sc, std::string *bc) const +{ + if (HasAxis(Axis::WIDTH)) + { + if ((size_t)offset >= args.size()) + return false; + *xc = args[offset++]; + } + if (HasAxis(Axis::HEIGHT)) + { + if ((size_t)offset >= args.size()) + return false; + *yc = args[offset++]; + } + if (HasAxis(Axis::DEPTH)) + { + if ((size_t)offset >= args.size()) + return false; + *zc = args[offset++]; + } + if (HasAxis(Axis::CHANNELS)) + { + if ((size_t)offset >= args.size()) + { + auto it = state_vars_.find("slice_id"); + if (it == state_vars_.end()) + { + return false; + } + else + { + *sc = it->second; + } + } + else + { + *sc = args[offset++]; + } + } + if (HasAxis(Axis::BATCH) && !IsBatchedWidth()) + { + if ((size_t)offset >= args.size()) + { + auto it = state_vars_.find("batch_id"); + if (it == state_vars_.end()) + { + return false; + } + else + { + *bc = it->second; + } + } + else + { + *bc = args[offset++]; + } + } + return true; +} + +bool TensorDescriptor::IsBatchedWidth() const +{ + auto it = state_vars_.find("BatchedWidth"); + return it != state_vars_.end() && it->second == "true"; +} + +std::string TensorDescriptor::GetWidth() const +{ + std::string div; + auto it1 = state_vars_.find("ElementsX2"); + if (it1 != state_vars_.end() && it1->second == "true") + { + div = "_div2"; + } + auto it2 = state_vars_.find("ElementsX4"); + if (it2 != state_vars_.end() && it2->second == "true") + { + div = "_div4"; + } + auto it = state_vars_.find("BatchedWidth"); + if (it != state_vars_.end() && it->second == "true") + { + return "width_batched" + div; + } + else + { + return "width" + div; + } +} + +std::string TensorDescriptor::GetSliceStride() const +{ + if (IsBatchedWidth()) + { + return GetWidth() + " * height"; + } + else + { + if (HasAxis(Axis::BATCH)) + { + return GetWidth() + " * height * batch"; + } + else + { + return GetWidth() + " * height"; + } + } +} + +TextureAddressMode TensorDescriptor::ModeFromState() const +{ + auto it = state_vars_.find("TextureMode"); + if (it != state_vars_.end()) + { + if (it->second == "ZERO") + { + return TextureAddressMode::ZERO; + } + else + { + return TextureAddressMode::DONT_CARE; + } + } + else + { + return TextureAddressMode::DONT_CARE; + } +} + +void TensorDescriptor::UploadData(const InternalTensor<HWC, DataType::FLOAT32> &src) +{ + shape = BHWDC(1, src.shape.h, src.shape.w, 1, src.shape.c); + UploadData(absl::MakeConstSpan(src.data)); +} + +void TensorDescriptor::UploadData(const InternalTensor<Linear, DataType::FLOAT32> &src) +{ + shape = BHWDC(1, 1, 1, 1, src.shape.v); + UploadData(absl::MakeConstSpan(src.data)); +} + +void TensorDescriptor::UploadData(absl::Span<const float> src) +{ + int aligned_channels = + storage_type == TensorStorageType::SINGLE_TEXTURE_2D ? shape.c : AlignByN(shape.c, 4); + int elements_count = shape.b * shape.w * shape.h * shape.d * aligned_channels; + data.resize(elements_count * SizeOf(data_type)); + if (data_type == DataType::FLOAT32) + { + float *gpu_data = reinterpret_cast<float *>(data.data()); + DataFromBHWDC(src, shape, *this, absl::MakeSpan(gpu_data, elements_count)); + } +} + +bool TensorDescriptor::SupportsZeroClamp(const Axis &axis) const +{ + switch (storage_type) + { + case TensorStorageType::UNKNOWN: + return false; + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + return false; + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::SINGLE_TEXTURE_2D: + return axis == Axis::WIDTH || axis == Axis::HEIGHT; + case TensorStorageType::TEXTURE_3D: + return axis == Axis::WIDTH || axis == Axis::HEIGHT || axis == Axis::DEPTH; + } + return false; +} + +bool TensorDescriptor::CanReadOutOfBorder(const Axis &) const +{ + switch (storage_type) + { + case TensorStorageType::UNKNOWN: + return false; + case TensorStorageType::BUFFER: + return false; + case TensorStorageType::IMAGE_BUFFER: + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::TEXTURE_3D: + case TensorStorageType::SINGLE_TEXTURE_2D: + case TensorStorageType::TEXTURE_ARRAY: + return true; + } + return false; +} + +bool TensorDescriptor::IsLinear() const +{ + return storage_type == TensorStorageType::BUFFER || + storage_type == TensorStorageType::IMAGE_BUFFER; +} + +bool TensorDescriptor::ReturnsZeroForNegOneRead() const +{ + return storage_type == TensorStorageType::IMAGE_BUFFER; +} + +namespace +{ +int GetLinearIndex(const TensorDescriptor &desc, const BHWDC &shape, int b, int x, int y, int d, + int s, int sub_c) +{ + const int slices = DivideRoundUp(shape.c, 4); + switch (desc.storage_type) + { + case TensorStorageType::BUFFER: + case TensorStorageType::IMAGE_BUFFER: + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + return ((((d * slices + s) * shape.h + y) * shape.w + x) * shape.b + b) * 4 + + sub_c; // DSHWBC4 + case TensorStorageType::TEXTURE_2D: + return ((((y * slices + s) * shape.w + x) * shape.b + b) * shape.d + d) * 4 + + sub_c; // HSWBDC4 + case TensorStorageType::SINGLE_TEXTURE_2D: + return (((y * shape.w + x) * shape.b + b) * shape.d + d) * shape.c + sub_c; // HWBDC + default: + return -1; + } + return -1; +} + +int GetChannelsAlignment(const TensorDescriptor &desc, const BHWDC &shape) +{ + return desc.storage_type == TensorStorageType::SINGLE_TEXTURE_2D ? shape.c : 4; +} +} // namespace + +template <typename T> +void DataFromBHWDC(absl::Span<const float> src, const BHWDC &shape, const TensorDescriptor &desc, + absl::Span<T> dst) +{ + const int channels_alignment = GetChannelsAlignment(desc, shape); + const int slices = DivideRoundUp(shape.c, 4); + for (int b = 0; b < shape.b; ++b) + { + for (int s = 0; s < slices; ++s) + { + for (int y = 0; y < shape.h; ++y) + { + for (int x = 0; x < shape.w; ++x) + { + for (int d = 0; d < shape.d; ++d) + { + for (int c = 0; c < channels_alignment; ++c) + { + float value; + if (s * 4 + c < shape.c) + { + const int cpu_index = shape.LinearIndex({b, y, x, d, s * 4 + c}); + value = src[cpu_index]; + } + else + { + value = 0.0f; + } + int gpu_index = GetLinearIndex(desc, shape, b, x, y, d, s, c); + dst[gpu_index] = value; + } + } + } + } + } + } +} + +template void DataFromBHWDC<float>(absl::Span<const float> src, const BHWDC &shape, + const TensorDescriptor &desc, absl::Span<float> dst); + +template <typename T> +void DataToBHWDC(absl::Span<const T> src, const BHWDC &shape, const TensorDescriptor &desc, + absl::Span<float> dst) +{ + const int channels_alignment = GetChannelsAlignment(desc, shape); + const int slices = DivideRoundUp(shape.c, 4); + for (int b = 0; b < shape.b; ++b) + { + for (int s = 0; s < slices; ++s) + { + for (int y = 0; y < shape.h; ++y) + { + for (int x = 0; x < shape.w; ++x) + { + for (int d = 0; d < shape.d; ++d) + { + for (int c = 0; c < channels_alignment; ++c) + { + if (s * 4 + c >= shape.c) + { + continue; + } + int cpu_index = shape.LinearIndex({b, y, x, d, s * 4 + c}); + int gpu_index = GetLinearIndex(desc, shape, b, x, y, d, s, c); + dst[cpu_index] = src[gpu_index]; + } + } + } + } + } + } +} + +template void DataToBHWDC<float>(absl::Span<const float> src, const BHWDC &shape, + const TensorDescriptor &desc, absl::Span<float> dst); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/TensorType.h b/runtime/onert/backend/gpu_cl/open_cl/TensorType.h new file mode 100644 index 000000000..45523783f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/TensorType.h @@ -0,0 +1,188 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_TYPE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_TYPE_H__ + +#include <cstddef> +#include <string> + +#include "absl/types/span.h" +#include "GpuObject.h" +#include "DataType.h" +#include "InternalTensor.h" +#include "Shape.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class TextureAddressMode +{ + DONT_CARE, // translated to CLK_ADDRESS_NONE + ZERO, // translated to CLK_ADDRESS_CLAMP +}; + +std::string TextureAddressModeToString(TextureAddressMode address_mode); + +enum class TensorStorageType +{ + UNKNOWN, + BUFFER, + IMAGE_BUFFER, + TEXTURE_2D, + TEXTURE_3D, + TEXTURE_ARRAY, + SINGLE_TEXTURE_2D +}; + +struct TensorDescriptor : public GPUObjectDescriptor +{ + TensorDescriptor() = default; + TensorDescriptor(DataType dt, TensorStorageType st, Layout l) + : data_type(dt), storage_type(st), layout(l) + { + } + + TensorDescriptor(const TensorDescriptor &) = default; + TensorDescriptor &operator=(const TensorDescriptor &) = default; + TensorDescriptor(TensorDescriptor &&desc); + TensorDescriptor &operator=(TensorDescriptor &&desc); + + bool operator==(const TensorDescriptor &d) const + { + return data_type == d.data_type && storage_type == d.storage_type && layout == d.layout; + } + + bool operator!=(const TensorDescriptor &d) const { return !(*this == d); } + + absl::Status PerformSelector(const std::string &selector, const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const override; + + GPUResources GetGPUResources() const override; + + absl::Status CreateGPUObject(CLContext *context, GPUObjectPtr *result) const override; + void Release() override { data.clear(); } + + bool HasAxis(Axis axis) const; + void SetTextureAddressMode(TextureAddressMode mode); + + absl::Status GetLinkingContextFromWriteSelector(const std::vector<std::string> &args, + std::string *value_name, std::string *x_coord, + std::string *y_coord, std::string *s_coord) const; + + void UploadData(const InternalTensor<HWC, DataType::FLOAT32> &src); + void UploadData(const InternalTensor<Linear, DataType::FLOAT32> &src); + + bool SupportsZeroClamp(const Axis &axis) const; + bool CanReadOutOfBorder(const Axis &axis) const; + bool IsLinear() const; + + // applicable only for types that: IsLinear -> true. + // In this case for address we have 1d component - addr (int) + // If for addr == -1 this linear storage type returns FLT4(0.0), this function + // returns true, otherwise false + bool ReturnsZeroForNegOneRead() const; + + DataType data_type = DataType::UNKNOWN; + TensorStorageType storage_type = TensorStorageType::UNKNOWN; + // This field describes logical layout, actual(physical) GPU layout can be + // totally different. + Layout layout = Layout::UNKNOWN; // Supported layouts is HWC, BHWC, HWDC, BHWDC + + // optional + BHWDC shape; + std::vector<uint8_t> data; + +private: + absl::Status PerformReadSelector(const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const; + + absl::Status PerformGetAddressSelector(const std::vector<std::string> &args, + std::string *result) const; + + absl::Status PerformGetPtrWithSliceOffsetSelector(const std::vector<std::string> &args, + std::string *result) const; + + absl::Status PerformGetWHOffsetSelector(const std::vector<std::string> &args, + std::string *result) const; + + absl::Status PerformGetHandleSelector(const std::vector<std::string> &args, + std::string *result) const; + + std::string DeclareAddress(const std::string &var_name, const std::string &address) const; + + std::string StorageTypeToAddressType() const; + + absl::Status PerformWriteSelector(const std::vector<std::string> &args, + std::string *result) const; + + absl::Status PerformWriteLinearSelector(const std::vector<std::string> &args, + std::string *result) const; + + std::string Read(DataType read_as_type, const std::string &global_address) const; + std::string Write(const std::string &var_name, const std::string &global_address) const; + + bool IsBatchedWidth() const; + + std::string GetWidth() const; + std::string GetSliceStride() const; + + TextureAddressMode ModeFromState() const; + + absl::Status GetDataTypeFromTemplateArgs(const std::string &template_arg, DataType *result) const; + + std::string GetGlobalAddressNoDeclarationWHS(const std::string &x, const std::string &y, + const std::string &s) const; + std::string GetGlobalAddressNoDeclarationWHSB(const std::string &x, const std::string &y, + const std::string &s, const std::string &b) const; + std::string GetGlobalAddressNoDeclarationWHDS(const std::string &x, const std::string &y, + const std::string &z, const std::string &s) const; + std::string GetGlobalAddressNoDeclarationWHDSB(const std::string &x, const std::string &y, + const std::string &z, const std::string &s, + const std::string &b) const; + std::string GetGlobalAddressNoDeclaration(const std::string &xc, const std::string &yc, + const std::string &zc, const std::string &sc, + const std::string &bc) const; + + bool ParseCoordsFromArgs(const std::vector<std::string> &args, int offset, std::string *xc, + std::string *yc, std::string *zc, std::string *sc, + std::string *bc) const; + + void UploadData(absl::Span<const float> src); +}; + +template <typename T> +void DataFromBHWDC(absl::Span<const float> src, const BHWDC &shape, const TensorDescriptor &desc, + absl::Span<T> dst); + +template <typename T> +void DataToBHWDC(absl::Span<const T> src, const BHWDC &shape, const TensorDescriptor &desc, + absl::Span<float> dst); + +std::string ToString(TensorStorageType type); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_TYPE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/TensorTypeUtil.cc b/runtime/onert/backend/gpu_cl/open_cl/TensorTypeUtil.cc new file mode 100644 index 000000000..b1f8309e4 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/TensorTypeUtil.cc @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TensorTypeUtil.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +ObjectType ToObjectType(TensorStorageType type) +{ + switch (type) + { + case TensorStorageType::IMAGE_BUFFER: + case TensorStorageType::BUFFER: + return ObjectType::OPENCL_BUFFER; + case TensorStorageType::SINGLE_TEXTURE_2D: + case TensorStorageType::TEXTURE_2D: + case TensorStorageType::TEXTURE_ARRAY: + case TensorStorageType::TEXTURE_3D: + return ObjectType::OPENCL_TEXTURE; + default: + return ObjectType::UNKNOWN; + } +} + +DataLayout ToDataLayout(TensorStorageType type) +{ + switch (type) + { + case TensorStorageType::BUFFER: + return DataLayout::DHWC4; + case TensorStorageType::IMAGE_BUFFER: + return DataLayout::DHWC4; + case TensorStorageType::SINGLE_TEXTURE_2D: + return DataLayout::BHWC; + case TensorStorageType::TEXTURE_2D: + return DataLayout::HDWC4; + case TensorStorageType::TEXTURE_ARRAY: + return DataLayout::DHWC4; + case TensorStorageType::TEXTURE_3D: + return DataLayout::DHWC4; + default: + return DataLayout::UNKNOWN; + } +} + +TensorStorageType ToTensorStorageType(ObjectType object_type, DataLayout data_layout) +{ + switch (object_type) + { + case ObjectType::OPENCL_BUFFER: + return TensorStorageType::BUFFER; + case ObjectType::OPENCL_TEXTURE: + switch (data_layout) + { + case DataLayout::BHWC: + return TensorStorageType::SINGLE_TEXTURE_2D; + case DataLayout::DHWC4: + return TensorStorageType::TEXTURE_ARRAY; + case DataLayout::HDWC4: + return TensorStorageType::TEXTURE_2D; + default: + return TensorStorageType::UNKNOWN; + } + default: + return TensorStorageType::UNKNOWN; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/TensorTypeUtil.h b/runtime/onert/backend/gpu_cl/open_cl/TensorTypeUtil.h new file mode 100644 index 000000000..f56fc3d83 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/TensorTypeUtil.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_TYPE_UTIL_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_TYPE_UTIL_H__ + +#include "Api.h" +#include "TensorType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +ObjectType ToObjectType(TensorStorageType type); + +DataLayout ToDataLayout(TensorStorageType type); + +TensorStorageType ToTensorStorageType(ObjectType object_type, DataLayout data_layout); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_TENSOR_TYPE_UTIL_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Texture2d.cc b/runtime/onert/backend/gpu_cl/open_cl/Texture2d.cc new file mode 100644 index 000000000..ae25e85d0 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Texture2d.cc @@ -0,0 +1,237 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Texture2d.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +// Creates new 4-channel 2D texture with cl_channel_type elements +absl::Status CreateTexture2D(int width, int height, DataType type, void *data, CLContext *context, + Texture2D *result) +{ + cl_mem texture; + cl_channel_type channel_type = DataTypeToChannelType(type); + RETURN_IF_ERROR( + CreateRGBAImage2D(context->context(), width, height, channel_type, data, &texture)); + *result = Texture2D(texture, width, height, channel_type); + + return absl::OkStatus(); +} +} // namespace + +Texture2DDescriptor::Texture2DDescriptor(Texture2DDescriptor &&desc) + : GPUObjectDescriptor(std::move(desc)), element_type(desc.element_type), + normalized(desc.normalized), normalized_type(desc.normalized_type), size(desc.size), + data(std::move(desc.data)) +{ +} + +Texture2DDescriptor &Texture2DDescriptor::operator=(Texture2DDescriptor &&desc) +{ + if (this != &desc) + { + std::swap(element_type, desc.element_type); + std::swap(normalized, desc.normalized); + std::swap(normalized_type, desc.normalized_type); + std::swap(size, desc.size); + data = std::move(desc.data); + GPUObjectDescriptor::operator=(std::move(desc)); + } + return *this; +} + +void Texture2DDescriptor::Release() { data.clear(); } + +GPUResources Texture2DDescriptor::GetGPUResources() const +{ + GPUResources resources; + GPUImage2DDescriptor desc; + desc.data_type = element_type; + desc.access_type = access_type_; + resources.images2d.push_back({"tex2d", desc}); + return resources; +} + +absl::Status Texture2DDescriptor::PerformSelector(const std::string &selector, + const std::vector<std::string> &args, + const std::vector<std::string> &, + std::string *result) const +{ + if (selector == "Read") + { + return PerformReadSelector(args, result); + } + else + { + return absl::NotFoundError( + absl::StrCat("Texture2DDescriptor don't have selector with name - ", selector)); + } +} + +absl::Status Texture2DDescriptor::PerformReadSelector(const std::vector<std::string> &args, + std::string *result) const +{ + if (args.size() != 2) + { + return absl::NotFoundError(absl::StrCat("Texture2DDescriptor Read require two arguments, but ", + args.size(), " was passed")); + } + std::string read; + switch (element_type) + { + case DataType::FLOAT32: + read = "read_imagef"; + break; + case DataType::FLOAT16: + read = "read_imageh"; + break; + case DataType::INT8: + case DataType::INT16: + case DataType::INT32: + if (normalized) + { + read = normalized_type == DataType::FLOAT16 ? "read_imageh" : "read_imagef"; + } + else + { + read = "read_imagei"; + } + break; + case DataType::UINT8: + case DataType::UINT16: + case DataType::UINT32: + if (normalized) + { + read = normalized_type == DataType::FLOAT16 ? "read_imageh" : "read_imagef"; + } + else + { + read = "read_imageui"; + } + break; + default: + read = "unknown_type"; + break; + } + *result = absl::StrCat(read, "(tex2d, smp_none, (int2)(", args[0], ", " + args[1] + "))"); + return absl::OkStatus(); +} + +absl::Status Texture2DDescriptor::CreateGPUObject(CLContext *context, GPUObjectPtr *result) const +{ + Texture2D gpu_texture; + RETURN_IF_ERROR(gpu_texture.CreateFromTexture2DDescriptor(*this, context)); + *result = absl::make_unique<Texture2D>(std::move(gpu_texture)); + return absl::OkStatus(); +} + +Texture2D::Texture2D(cl_mem texture, int width, int height, cl_channel_type type) + : texture_(texture), width_(width), height_(height), channel_type_(type) +{ +} + +Texture2D::Texture2D(Texture2D &&texture) + : texture_(texture.texture_), width_(texture.width_), height_(texture.height_), + channel_type_(texture.channel_type_) +{ + texture.texture_ = nullptr; + texture.width_ = 0; + texture.height_ = 0; +} + +Texture2D &Texture2D::operator=(Texture2D &&texture) +{ + if (this != &texture) + { + Release(); + std::swap(channel_type_, texture.channel_type_); + std::swap(width_, texture.width_); + std::swap(height_, texture.height_); + std::swap(texture_, texture.texture_); + } + return *this; +} + +void Texture2D::Release() +{ + if (texture_) + { + clReleaseMemObject(texture_); + texture_ = nullptr; + width_ = 0; + height_ = 0; + } +} + +absl::Status Texture2D::GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const +{ + const auto *texture_desc = dynamic_cast<const Texture2DDescriptor *>(obj_ptr); + if (!texture_desc) + { + return absl::InvalidArgumentError("Expected Texture2DDescriptor on input."); + } + + resources->images2d.push_back({"tex2d", texture_}); + return absl::OkStatus(); +} + +absl::Status Texture2D::CreateFromTexture2DDescriptor(const Texture2DDescriptor &desc, + CLContext *context) +{ + width_ = desc.size.x; + height_ = desc.size.y; + channel_type_ = DataTypeToChannelType(desc.element_type, desc.normalized); + uint8_t *data_ptr = desc.data.empty() ? nullptr : const_cast<unsigned char *>(desc.data.data()); + return CreateRGBAImage2D(context->context(), desc.size.x, desc.size.y, channel_type_, data_ptr, + &texture_); +} + +// Creates new 4-channel 2D texture with f32 elements +absl::Status CreateTexture2DRGBA32F(int width, int height, CLContext *context, Texture2D *result) +{ + return CreateTexture2D(width, height, DataType::FLOAT32, nullptr, context, result); +} + +// Creates new 4-channel 2D texture with f16 elements +absl::Status CreateTexture2DRGBA16F(int width, int height, CLContext *context, Texture2D *result) +{ + return CreateTexture2D(width, height, DataType::FLOAT16, nullptr, context, result); +} + +absl::Status CreateTexture2DRGBA(DataType type, int width, int height, CLContext *context, + Texture2D *result) +{ + return CreateTexture2D(width, height, type, nullptr, context, result); +} + +absl::Status CreateTexture2DRGBA(DataType type, int width, int height, void *data, + CLContext *context, Texture2D *result) +{ + return CreateTexture2D(width, height, type, data, context, result); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Texture2d.h b/runtime/onert/backend/gpu_cl/open_cl/Texture2d.h new file mode 100644 index 000000000..264507079 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Texture2d.h @@ -0,0 +1,160 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_TEXTURE2D_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_TEXTURE2D_H__ + +#include "absl/strings/str_cat.h" +#include "absl/types/span.h" +#include "ClCommandQueue.h" +#include "ClContext.h" +#include "GpuObject.h" +#include "OpenclWrapper.h" +#include "TensorType.h" +#include "Util.h" +#include "DataType.h" +#include "Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +struct Texture2DDescriptor : public GPUObjectDescriptor +{ + DataType element_type; + bool normalized = false; // used with INT data types, if normalized, we read + // in kernel float data. + DataType normalized_type; // can be FLOAT32 or FLOAT16, using with normalized + // = true + + // optional + int2 size = int2(0, 0); + std::vector<uint8_t> data; + + Texture2DDescriptor() = default; + Texture2DDescriptor(const Texture2DDescriptor &) = default; + Texture2DDescriptor &operator=(const Texture2DDescriptor &) = default; + Texture2DDescriptor(Texture2DDescriptor &&desc); + Texture2DDescriptor &operator=(Texture2DDescriptor &&desc); + + absl::Status PerformSelector(const std::string &selector, const std::vector<std::string> &args, + const std::vector<std::string> &template_args, + std::string *result) const override; + + GPUResources GetGPUResources() const override; + absl::Status PerformReadSelector(const std::vector<std::string> &args, std::string *result) const; + + absl::Status CreateGPUObject(CLContext *context, GPUObjectPtr *result) const override; + void Release() override; +}; + +// Texture2D represent formatted GPU data storage. +// Texture2D is moveable but not copyable. +class Texture2D : public GPUObject +{ +public: + Texture2D() {} // just for using Texture2D as a class members + Texture2D(cl_mem texture, int width, int height, cl_channel_type type); + + // Move only + Texture2D(Texture2D &&texture); + Texture2D &operator=(Texture2D &&texture); + Texture2D(const Texture2D &) = delete; + Texture2D &operator=(const Texture2D &) = delete; + + virtual ~Texture2D() { Release(); } + + cl_mem GetMemoryPtr() const { return texture_; } + + // Writes data to a texture. Data should point to a region that + // has exact width * height * sizeof(pixel) bytes. + template <typename T> absl::Status WriteData(CLCommandQueue *queue, const absl::Span<T> data); + + // Reads data from Texture2D into CPU memory. + template <typename T> absl::Status ReadData(CLCommandQueue *queue, std::vector<T> *result) const; + + absl::Status GetGPUResources(const GPUObjectDescriptor *obj_ptr, + GPUResourcesWithValue *resources) const override; + + absl::Status CreateFromTexture2DDescriptor(const Texture2DDescriptor &desc, CLContext *context); + +private: + void Release(); + + cl_mem texture_ = nullptr; + int width_; + int height_; + cl_channel_type channel_type_; +}; + +using Texture2DPtr = std::shared_ptr<Texture2D>; + +// Creates new 4-channel 2D texture with f32 elements +absl::Status CreateTexture2DRGBA32F(int width, int height, CLContext *context, Texture2D *result); + +// Creates new 4-channel 2D texture with f16 elements +absl::Status CreateTexture2DRGBA16F(int width, int height, CLContext *context, Texture2D *result); + +absl::Status CreateTexture2DRGBA(DataType type, int width, int height, CLContext *context, + Texture2D *result); + +absl::Status CreateTexture2DRGBA(DataType type, int width, int height, void *data, + CLContext *context, Texture2D *result); + +template <typename T> +absl::Status Texture2D::WriteData(CLCommandQueue *queue, const absl::Span<T> data) +{ + const int element_size = ChannelTypeToSizeInBytes(channel_type_); + if (sizeof(T) % element_size != 0) + { + return absl::InvalidArgumentError( + "Template type T has not suitable element type for created texture."); + } + if (4 * width_ * height_ * element_size != data.size() * sizeof(T)) + { + return absl::InvalidArgumentError( + "absl::Span<T> data size is different from texture allocated size."); + } + + RETURN_IF_ERROR(queue->EnqueueWriteImage(texture_, int3(width_, height_, 1), data.data())); + + return absl::OkStatus(); +} + +template <typename T> +absl::Status Texture2D::ReadData(CLCommandQueue *queue, std::vector<T> *result) const +{ + const int element_size = ChannelTypeToSizeInBytes(channel_type_); + if (sizeof(T) != element_size) + { + return absl::InvalidArgumentError("Pixel format is different."); + } + + const int elements_count = width_ * height_ * 4; + result->resize(elements_count); + + return queue->EnqueueReadImage(texture_, int3(width_, height_, 1), result->data()); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_TEXTURE2D_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Types.h b/runtime/onert/backend/gpu_cl/open_cl/Types.h new file mode 100644 index 000000000..f3cf33450 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Types.h @@ -0,0 +1,183 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_TYPES_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_TYPES_H__ + +#include <array> +#include <cstddef> +#include <cstdint> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// TODO(akulik): make these types Google-style compliant. + +template <typename T> struct alignas(sizeof(T)) Vec4 +{ + union { + struct + { + T x, y, z, w; + }; + std::array<T, 4> data_; + }; + + Vec4() : Vec4(T(0.0f)) {} + + template <typename S> Vec4(S x_, S y_, S z_, S w_) : x(x_), y(y_), z(z_), w(w_) {} + explicit Vec4(T v) : x(v), y(v), z(v), w(v) {} + + template <typename S> explicit Vec4(S v) : x(v), y(v), z(v), w(v) {} + + Vec4(const Vec4 &f) : x(f.x), y(f.y), z(f.z), w(f.w) {} + + template <typename S> Vec4(const Vec4<S> &f) : x(f.x), y(f.y), z(f.z), w(f.w) {} + + Vec4 &operator=(const Vec4 &other) + { + x = other.x; + y = other.y; + z = other.z; + w = other.w; + return *this; + } + + static constexpr int size() { return 4; } + + T &operator[](size_t n) { return data_[n]; } + T operator[](size_t n) const { return data_[n]; } + + bool operator==(const Vec4 &value) const + { + return data_[0] == value[0] && data_[1] == value[1] && data_[2] == value[2] && + data_[3] == value[3]; + } + bool operator!=(const Vec4 &value) const { return !(this->operator==(value)); } +}; + +template <typename T> struct alignas(sizeof(T)) Vec3 +{ + union { + struct + { + T x, y, z; + }; + std::array<T, 3> data_; + }; + + Vec3() : Vec3(T(0.0f)) {} + + template <typename S> constexpr Vec3(S x_, S y_, S z_) : x(x_), y(y_), z(z_) {} + explicit Vec3(T v) : x(v), y(v), z(v) {} + + template <typename S> explicit Vec3(S v) : x(v), y(v), z(v) {} + + Vec3(const Vec3 &f) : x(f.x), y(f.y), z(f.z) {} + + template <typename S> Vec3(const Vec3<S> &f) : x(f.x), y(f.y), z(f.z) {} + + Vec3 &operator=(const Vec3 &other) + { + x = other.x; + y = other.y; + z = other.z; + return *this; + } + + static constexpr int size() { return 3; } + + T &operator[](size_t n) { return data_[n]; } + T operator[](size_t n) const { return data_[n]; } + bool operator==(const Vec3 &value) const + { + return data_[0] == value[0] && data_[1] == value[1] && data_[2] == value[2]; + } + bool operator!=(const Vec3 &value) const { return !(this->operator==(value)); } +}; + +template <typename T> struct alignas(sizeof(T)) Vec2 +{ + union { + struct + { + T x, y; + }; + std::array<T, 2> data_; + }; + + Vec2() : Vec2(T(0.0f)) {} + + template <typename S> Vec2(S x_, S y_) : x(x_), y(y_) {} + explicit Vec2(T v) : x(v), y(v) {} + + template <typename S> explicit Vec2(S v) : x(v), y(v) {} + + Vec2(const Vec2 &f) : x(f.x), y(f.y) {} + + template <typename S> Vec2(const Vec2<S> &f) : x(f.x), y(f.y) {} + + Vec2 &operator=(const Vec2 &other) + { + x = other.x; + y = other.y; + return *this; + } + + bool operator==(const Vec2 &value) const { return data_[0] == value[0] && data_[1] == value[1]; } + + bool operator!=(const Vec2 &value) const { return !(this->operator==(value)); } + + static constexpr int size() { return 2; } + + T &operator[](size_t n) { return data_[n]; } + T operator[](size_t n) const { return data_[n]; } +}; + +using float2 = Vec2<float>; +using byte2 = Vec2<int8_t>; +using ubyte2 = Vec2<uint8_t>; +using short2 = Vec2<int16_t>; +using ushort2 = Vec2<uint16_t>; +using int2 = Vec2<int32_t>; +using uint2 = Vec2<uint32_t>; + +using float3 = Vec3<float>; +using byte3 = Vec3<int8_t>; +using ubyte3 = Vec3<uint8_t>; +using short3 = Vec3<int16_t>; +using ushort3 = Vec3<uint16_t>; +using int3 = Vec3<int32_t>; +using uint3 = Vec3<uint32_t>; + +using float4 = Vec4<float>; +using byte4 = Vec4<int8_t>; +using ubyte4 = Vec4<uint8_t>; +using short4 = Vec4<int16_t>; +using ushort4 = Vec4<uint16_t>; +using int4 = Vec4<int32_t>; +using uint4 = Vec4<uint32_t>; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_TYPES_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/Util.cc b/runtime/onert/backend/gpu_cl/open_cl/Util.cc new file mode 100644 index 000000000..9f5a8388b --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Util.cc @@ -0,0 +1,264 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Util.h" + +#include "absl/strings/str_cat.h" +#include "absl/strings/substitute.h" +#include "Status.h" +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +std::string CLErrorCodeToString(cl_int error_code) +{ + switch (error_code) + { + case CL_SUCCESS: + return "Success"; + case CL_DEVICE_NOT_FOUND: + return "Device not found"; + case CL_DEVICE_NOT_AVAILABLE: + return "Device not available"; + case CL_COMPILER_NOT_AVAILABLE: + return "Compiler not available"; + case CL_MEM_OBJECT_ALLOCATION_FAILURE: + return "Memory object allocation failure"; + case CL_OUT_OF_RESOURCES: + return "Out of resources"; + case CL_OUT_OF_HOST_MEMORY: + return "Out of host memory"; + case CL_PROFILING_INFO_NOT_AVAILABLE: + return "Profiling information not available"; + case CL_MEM_COPY_OVERLAP: + return "Memory copy overlap"; + case CL_IMAGE_FORMAT_MISMATCH: + return "Image format mismatch"; + case CL_IMAGE_FORMAT_NOT_SUPPORTED: + return "Image format not supported"; + case CL_BUILD_PROGRAM_FAILURE: + return "Build program failure"; + case CL_MAP_FAILURE: + return "Mapping failure"; + case CL_MISALIGNED_SUB_BUFFER_OFFSET: + return "Misaligned sub-buffer offset"; + case CL_EXEC_STATUS_ERROR_FOR_EVENTS_IN_WAIT_LIST: + return "Execution status error for events in wait list"; + case CL_COMPILE_PROGRAM_FAILURE: + return "Compile program failure"; + case CL_LINKER_NOT_AVAILABLE: + return "Linker not available"; + case CL_LINK_PROGRAM_FAILURE: + return "Link program failure"; + case CL_DEVICE_PARTITION_FAILED: + return "Device partition failed"; + case CL_KERNEL_ARG_INFO_NOT_AVAILABLE: + return "Kernel argument information not available"; + + case CL_INVALID_VALUE: + return "Invalid value"; + case CL_INVALID_DEVICE_TYPE: + return "Invalid device type"; + case CL_INVALID_PLATFORM: + return "Invalid platform"; + case CL_INVALID_DEVICE: + return "Invalid device"; + case CL_INVALID_CONTEXT: + return "Invalid context"; + case CL_INVALID_QUEUE_PROPERTIES: + return "Invalid queue properties"; + case CL_INVALID_COMMAND_QUEUE: + return "Invalid command queue"; + case CL_INVALID_HOST_PTR: + return "Invalid host pointer"; + case CL_INVALID_MEM_OBJECT: + return "Invalid memory object"; + case CL_INVALID_IMAGE_FORMAT_DESCRIPTOR: + return "Invalid image format descriptor"; + case CL_INVALID_IMAGE_SIZE: + return "Invalid image size"; + case CL_INVALID_SAMPLER: + return "Invalid sampler"; + case CL_INVALID_BINARY: + return "Invalid binary"; + case CL_INVALID_BUILD_OPTIONS: + return "Invalid build options"; + case CL_INVALID_PROGRAM: + return "Invalid program"; + case CL_INVALID_PROGRAM_EXECUTABLE: + return "Invalid program executable"; + case CL_INVALID_KERNEL_NAME: + return "Invalid kernel name"; + case CL_INVALID_KERNEL_DEFINITION: + return "Invalid kernel definition"; + case CL_INVALID_KERNEL: + return "Invalid kernel"; + case CL_INVALID_ARG_INDEX: + return "Invalid argument index"; + case CL_INVALID_ARG_VALUE: + return "Invalid argument value"; + case CL_INVALID_ARG_SIZE: + return "Invalid argument size"; + case CL_INVALID_KERNEL_ARGS: + return "Invalid kernel arguments"; + case CL_INVALID_WORK_DIMENSION: + return "Invalid work dimension"; + case CL_INVALID_WORK_GROUP_SIZE: + return "Invalid work group size"; + case CL_INVALID_WORK_ITEM_SIZE: + return "Invalid work item size"; + case CL_INVALID_GLOBAL_OFFSET: + return "Invalid global offset"; + case CL_INVALID_EVENT_WAIT_LIST: + return "Invalid event wait list"; + case CL_INVALID_EVENT: + return "Invalid event"; + case CL_INVALID_OPERATION: + return "Invalid operation"; + case CL_INVALID_GL_OBJECT: + return "Invalid GL object"; + case CL_INVALID_BUFFER_SIZE: + return "Invalid buffer size"; + case CL_INVALID_MIP_LEVEL: + return "Invalid mip-level"; + case CL_INVALID_GLOBAL_WORK_SIZE: + return "Invalid global work size"; + case CL_INVALID_PROPERTY: + return "Invalid property"; + case CL_INVALID_IMAGE_DESCRIPTOR: + return "Invalid image descriptor"; + case CL_INVALID_COMPILER_OPTIONS: + return "Invalid compiler options"; + case CL_INVALID_LINKER_OPTIONS: + return "Invalid linker options"; + case CL_INVALID_DEVICE_PARTITION_COUNT: + return "Invalid device partition count"; + case CL_INVALID_PIPE_SIZE: + return "Invalid pipe size"; + case CL_INVALID_DEVICE_QUEUE: + return "Invalid device queue"; + case CL_INVALID_GL_SHAREGROUP_REFERENCE_KHR: + return "Invalid GL sharegroup reference KHR"; + + default: + return "Unknown OpenCL"; + } +} + +int ChannelTypeToSizeInBytes(cl_channel_type type) +{ + switch (type) + { + case CL_FLOAT: + return 4; + default: + return 0; + } +} + +absl::Status CreateCLBuffer(cl_context context, int size_in_bytes, bool read_only, void *data, + cl_mem *result) +{ + cl_mem_flags flags = read_only ? CL_MEM_READ_ONLY : CL_MEM_READ_WRITE; + if (data) + { + flags |= CL_MEM_COPY_HOST_PTR; + } + cl_int error_code; + *result = clCreateBuffer(context, flags, size_in_bytes, data, &error_code); + if (!*result) + { + return absl::UnknownError(absl::StrCat("Failed to allocate device memory (clCreateBuffer): ", + CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +cl_channel_type DataTypeToChannelType(DataType type, bool normalized) +{ + switch (type) + { + case DataType::FLOAT32: + return CL_FLOAT; + case DataType::INT8: + return normalized ? CL_SNORM_INT8 : CL_SIGNED_INT8; + case DataType::UINT8: + return normalized ? CL_UNORM_INT8 : CL_UNSIGNED_INT8; + case DataType::INT16: + return normalized ? CL_SNORM_INT16 : CL_SIGNED_INT16; + case DataType::UINT16: + return normalized ? CL_UNORM_INT16 : CL_UNSIGNED_INT16; + case DataType::INT32: + return CL_SIGNED_INT32; + case DataType::UINT32: + return CL_UNSIGNED_INT32; + default: + return CL_FLOAT; + } +} + +absl::Status CreateRGBAImage2D(cl_context context, int width, int height, + cl_channel_type channel_type, void *data, cl_mem *result) +{ + cl_image_desc desc; + desc.image_type = CL_MEM_OBJECT_IMAGE2D; + desc.image_width = width; + desc.image_height = height; + desc.image_depth = 0; + desc.image_row_pitch = 0; + desc.image_slice_pitch = 0; + desc.num_mip_levels = 0; + desc.num_samples = 0; + desc.buffer = nullptr; + + cl_image_format format; + format.image_channel_order = CL_RGBA; + format.image_channel_data_type = channel_type; + + cl_mem_flags flags = CL_MEM_READ_WRITE; + if (data) + { + flags |= CL_MEM_COPY_HOST_PTR; + } + + cl_int error_code; + *result = CreateImage2DLegacy(context, flags, &format, &desc, data, &error_code); + if (error_code != CL_SUCCESS) + { + return absl::UnknownError(absl::StrCat("Failed to create 2D texture (clCreateImage): ", + CLErrorCodeToString(error_code))); + } + return absl::OkStatus(); +} + +std::string GetXStrideCorrected(const std::string &src_x, const std::string &batch_size, + const std::string &stride_x, const std::string &padding_x) +{ + // TODO(sorokin) check perf and optimize with floor() if needed + // int p0 = src_x / batch_size;\n"; + // int b0 = src_x % batch_size;\n"; + // return p0 * stride_x * batch_size + b0 + padding_x;\n"; + return absl::Substitute("((($0) / $1) * $2 * $1 + (($0) % $1) + $3)", src_x, batch_size, stride_x, + padding_x); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/Util.h b/runtime/onert/backend/gpu_cl/open_cl/Util.h new file mode 100644 index 000000000..996c564f4 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/Util.h @@ -0,0 +1,278 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_UTIL_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_UTIL_H__ + +#include <string> + +#include "absl/types/span.h" +#include "OpenclWrapper.h" +#include "DataType.h" +#include "InternalTensor.h" +#include "Status.h" +#include "Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +// Calculates correct X coordinate when stride != 1 and batch != 1 for layouts +// with B after W (for example HWBC4) and WB stored in one axis of GPU +// resources. +std::string GetXStrideCorrected(const std::string &src_x, const std::string &batch_size, + const std::string &stride_x, const std::string &padding_x); + +// @param n must be non negative +// @param divisor must be greater than zero +template <typename T, typename N> T DivideRoundUp(T n, N divisor) +{ + const T div = static_cast<T>(divisor); + const T q = n / div; + return n % div == 0 ? q : q + 1; +} + +template <> inline uint3 DivideRoundUp(uint3 n, uint3 divisor) +{ + return uint3(DivideRoundUp(n.x, divisor.x), DivideRoundUp(n.y, divisor.y), + DivideRoundUp(n.z, divisor.z)); +} + +// @param number or its components must be greater than zero +// @param n must be greater than zero +template <typename T, typename N> T AlignByN(T number, N n) { return DivideRoundUp(number, n) * n; } + +std::string CLErrorCodeToString(cl_int error_code); + +int ChannelTypeToSizeInBytes(cl_channel_type type); + +template <DataType S, typename T> +void CopyLinearFLT4(const InternalTensor<Linear, S> &src, absl::Span<T> dst) +{ + const int dst_depth = dst.size(); + for (int d = 0; d < dst_depth; ++d) + { + T val; + for (int i = 0; i < 4; ++i) + { + const int dst_ch = d * 4 + i; + val[i] = dst_ch >= src.shape.v ? 0.0f : src.data[dst_ch]; + } + dst[d] = val; + } +} + +absl::Status CreateCLBuffer(cl_context context, int size_in_bytes, bool read_only, void *data, + cl_mem *result); + +cl_channel_type DataTypeToChannelType(DataType type, bool normalized = false); +absl::Status CreateRGBAImage2D(cl_context context, int width, int height, + cl_channel_type channel_type, void *data, cl_mem *result); + +template <DataType S, typename T> +void RearrangeWeightsToOHWIOGroupI4O4(const InternalTensor<OHWI, S> &weights, int out_group_size, + absl::Span<T> dst) +{ + const int dst_slices = DivideRoundUp(weights.shape.o, 4); + const int src_slices = DivideRoundUp(weights.shape.i, 4); + const int dst_groups = DivideRoundUp(dst_slices, out_group_size); + + int counter = 0; + for (int d = 0; d < dst_groups; ++d) + { + for (int y = 0; y < weights.shape.h; ++y) + { + for (int x = 0; x < weights.shape.w; ++x) + { + for (int s = 0; s < src_slices; ++s) + { + for (int d_group = 0; d_group < out_group_size; ++d_group) + { + for (int j = 0; j < 4; ++j) + { + T filter; + for (int i = 0; i < 4; ++i) + { + const int s_ch = s * 4 + j; + const int d_ch = (d * out_group_size + d_group) * 4 + i; + if (s_ch < weights.shape.i && d_ch < weights.shape.o) + { + const int f_index = weights.shape.LinearIndex({d_ch, y, x, s_ch}); + filter[i] = weights.data[f_index]; + } + else + { + filter[i] = 0.0f; + } + } + dst[counter++] = filter; + } + } + } + } + } + } +} + +template <DataType S, typename T> +void RearrangeWeightsToODHWIOGroupI4O4(const InternalTensor<OHWDI, S> &weights, int out_group_size, + absl::Span<T> dst) +{ + const int dst_slices = DivideRoundUp(weights.shape.o, 4); + const int src_slices = DivideRoundUp(weights.shape.i, 4); + const int dst_groups = DivideRoundUp(dst_slices, out_group_size); + + int counter = 0; + for (int d = 0; d < dst_groups; ++d) + { + for (int z = 0; z < weights.shape.d; ++z) + { + for (int y = 0; y < weights.shape.h; ++y) + { + for (int x = 0; x < weights.shape.w; ++x) + { + for (int s = 0; s < src_slices; ++s) + { + for (int d_group = 0; d_group < out_group_size; ++d_group) + { + for (int j = 0; j < 4; ++j) + { + T filter; + for (int i = 0; i < 4; ++i) + { + const int s_ch = s * 4 + j; + const int d_ch = (d * out_group_size + d_group) * 4 + i; + if (s_ch < weights.shape.i && d_ch < weights.shape.o) + { + const int f_index = weights.shape.LinearIndex({d_ch, y, x, z, s_ch}); + filter[i] = weights.data[f_index]; + } + else + { + filter[i] = 0.0f; + } + } + dst[counter++] = filter; + } + } + } + } + } + } + } +} + +template <DataType S, typename T> +void RearrangeWeightsToI4HWIOOGroupO4(const InternalTensor<OHWI, S> &weights, int out_group_size, + absl::Span<T> dst) +{ + const int dst_slices = DivideRoundUp(weights.shape.o, 4); + const int src_slices = DivideRoundUp(weights.shape.i, 4); + const int dst_groups = DivideRoundUp(dst_slices, out_group_size); + + int counter = 0; + for (int j = 0; j < 4; ++j) + { + for (int y = 0; y < weights.shape.h; ++y) + { + for (int x = 0; x < weights.shape.w; ++x) + { + for (int s = 0; s < src_slices; ++s) + { + for (int d = 0; d < dst_groups; ++d) + { + for (int d_group = 0; d_group < out_group_size; ++d_group) + { + T filter; + for (int i = 0; i < 4; ++i) + { + const int s_ch = s * 4 + j; + const int d_ch = (d * out_group_size + d_group) * 4 + i; + if (s_ch < weights.shape.i && d_ch < weights.shape.o) + { + const int f_index = weights.shape.LinearIndex({d_ch, y, x, s_ch}); + filter[i] = weights.data[f_index]; + } + else + { + filter[i] = 0.0f; + } + } + dst[counter++] = filter; + } + } + } + } + } + } +} + +template <DataType S, typename T> +void RearrangeWeightsToI4DHWIOOGroupO4(const InternalTensor<OHWDI, S> &weights, int out_group_size, + absl::Span<T> dst) +{ + const int dst_slices = DivideRoundUp(weights.shape.o, 4); + const int src_slices = DivideRoundUp(weights.shape.i, 4); + const int dst_groups = DivideRoundUp(dst_slices, out_group_size); + + int counter = 0; + for (int j = 0; j < 4; ++j) + { + for (int z = 0; z < weights.shape.d; ++z) + { + for (int y = 0; y < weights.shape.h; ++y) + { + for (int x = 0; x < weights.shape.w; ++x) + { + for (int s = 0; s < src_slices; ++s) + { + for (int d = 0; d < dst_groups; ++d) + { + for (int d_group = 0; d_group < out_group_size; ++d_group) + { + T filter; + for (int i = 0; i < 4; ++i) + { + const int s_ch = s * 4 + j; + const int d_ch = (d * out_group_size + d_group) * 4 + i; + if (s_ch < weights.shape.i && d_ch < weights.shape.o) + { + const int f_index = weights.shape.LinearIndex({d_ch, y, x, z, s_ch}); + filter[i] = weights.data[f_index]; + } + else + { + filter[i] = 0.0f; + } + } + dst[counter++] = filter; + } + } + } + } + } + } + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_UTIL_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/WinogradUtil.cc b/runtime/onert/backend/gpu_cl/open_cl/WinogradUtil.cc new file mode 100644 index 000000000..5f1103ad9 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/WinogradUtil.cc @@ -0,0 +1,178 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "open_cl/WinogradUtil.h" + +#include <cmath> +#include <vector> + +#include "open_cl/DataType.h" +#include "open_cl/Shape.h" +#include "open_cl/Tensor.h" + +namespace onert +{ +namespace backend +{ +namespace +{ +// Matrices for Winograd trasformations were computed with the method described +// here https://openreview.net/pdf?id=H1ZaRZVKg +std::vector<float> GetTransposedMatrixForWinograd(int width, int height) +{ + const float kDelta = std::sqrt(2.0f) / 2.0f; + std::vector<float> px(width); + + px[0] = 0.0f; + const int points_count = (width - 1) / 2; + for (int i = 0; i < points_count; ++i) + { + px[i * 2 + 1] = kDelta * (i + 1.0f); + px[i * 2 + 2] = -kDelta * (i + 1.0f); + } + px[width - 1] = 1.0f; + + std::vector<float> py(width, 1.0f); + py[width - 1] = 0.0f; + + std::vector<float> result(height * width); + for (int y = 0; y < width; ++y) + { + for (int x = 0; x < height; ++x) + { + result[x * width + y] = std::pow(px[y], 1.0f * x) * std::pow(py[y], (height - 1.0f) - x); + } + } + return result; +} + +std::vector<float> GetInversedMatrixForWinograd(int rank) +{ + auto matrix = GetTransposedMatrixForWinograd(rank, rank); + std::vector<float> inverted(rank * rank, 0.0f); + for (int i = 0; i < rank; ++i) + { + inverted[i * rank + i] = 1.0f; + } + + for (int i = 1; i < rank - 1; ++i) + { + float inv_t = 1.0f / matrix[i * rank + i]; + for (int x = i; x < rank; ++x) + { + matrix[i * rank + x] *= inv_t; + } + for (int x = 0; x < rank; ++x) + { + inverted[i * rank + x] *= inv_t; + } + + for (int y = 0; y < rank; ++y) + { + if (y == i) + continue; + float t = matrix[y * rank + i]; + for (int x = i; x < rank; ++x) + { + matrix[y * rank + x] -= t * matrix[i * rank + x]; + } + for (int x = 0; x < rank; ++x) + { + inverted[y * rank + x] -= t * inverted[i * rank + x]; + } + } + } + + return inverted; +} + +std::vector<float> Multiply(const std::vector<float> &a_mat, const std::vector<float> &b_mat, int m, + int n, int k) +{ + std::vector<float> result(m * k); + for (int y = 0; y < m; ++y) + { + for (int x = 0; x < k; ++x) + { + float sum = 0.0f; + for (int i = 0; i < n; ++i) + { + sum += a_mat[y * n + i] * b_mat[i * k + x]; + } + result[y * k + x] = sum; + } + } + return result; +} +} // namespace + +std::vector<float> AtMatrixForWinograd4x4To6x6() { return GetTransposedMatrixForWinograd(6, 4); } + +std::vector<float> BtMatrixForWinograd4x4To6x6() { return GetInversedMatrixForWinograd(6); } + +void RearrangeWeightsToWinograd4x4To6x6Weights( + const gpu_cl::InternalTensor<gpu_cl::OHWI, gpu_cl::DataType::FLOAT32> &src_weights, + gpu_cl::InternalTensor<gpu_cl::OHWI, gpu_cl::DataType::FLOAT32> *dst_weights) +{ + gpu_cl::OHWI dst_shape; + dst_shape.o = src_weights.shape.o; + dst_shape.h = 6; + dst_shape.w = 6; + dst_shape.i = src_weights.shape.i; + dst_weights->shape = dst_shape; + dst_weights->data.resize(dst_shape.DimensionsProduct()); + + auto gt_mat = GetTransposedMatrixForWinograd(6, 3); + std::vector<float> g_mat(gt_mat.size()); + for (int y = 0; y < 3; ++y) + { + for (int x = 0; x < 6; ++x) + { + g_mat[x * 3 + y] = gt_mat[y * 6 + x]; + } + } + + for (int d = 0; d < src_weights.shape.o; ++d) + { + for (int s = 0; s < src_weights.shape.i; ++s) + { + std::vector<float> in_vals(9); + for (int y = 0; y < 3; ++y) + { + for (int x = 0; x < 3; ++x) + { + const int f_index = src_weights.shape.LinearIndex({d, y, x, s}); + in_vals[y * 3 + x] = src_weights.data[f_index]; + } + } + + auto temp_vals = Multiply(g_mat, in_vals, 6, 3, 3); + auto out_vals = Multiply(temp_vals, gt_mat, 6, 3, 6); + for (int y = 0; y < 6; ++y) + { + for (int x = 0; x < 6; ++x) + { + const int f_index = dst_shape.LinearIndex({d, y, x, s}); + dst_weights->data[f_index] = out_vals[y * 6 + x]; + } + } + } + } +} + +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/WinogradUtil.h b/runtime/onert/backend/gpu_cl/open_cl/WinogradUtil.h new file mode 100644 index 000000000..32e21760d --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/WinogradUtil.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_WINOGRAD_UTIL_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_WINOGRAD_UTIL_H__ + +#include <vector> + +#include "open_cl/DataType.h" +#include "open_cl/Shape.h" +#include "open_cl/InternalTensor.h" + +namespace onert +{ +namespace backend +{ + +// Matrices for Winograd trasformations received with method described here +// https://openreview.net/pdf?id=H1ZaRZVKg + +// returns A transposed matrix(6 * 4) as array (24 values) for Winograd4x4To6x6 +std::vector<float> AtMatrixForWinograd4x4To6x6(); + +// returns B transposed matrix(6 * 6) as array (36 values) for Winograd4x4To6x6 +std::vector<float> BtMatrixForWinograd4x4To6x6(); + +void RearrangeWeightsToWinograd4x4To6x6Weights( + const gpu_cl::InternalTensor<gpu_cl::OHWI, gpu_cl::DataType::FLOAT32> &src_weights, + gpu_cl::InternalTensor<gpu_cl::OHWI, gpu_cl::DataType::FLOAT32> *dst_weights); + +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_WINOGRAD_UTIL_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/WorkgroupSelection.cc b/runtime/onert/backend/gpu_cl/open_cl/WorkgroupSelection.cc new file mode 100644 index 000000000..847c2a2aa --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/WorkgroupSelection.cc @@ -0,0 +1,258 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "WorkgroupSelection.h" + +#include <math.h> + +#include <set> +#include <vector> + +#include "Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +namespace +{ + +template <typename T> +void AddCornerCases(const T &grid, int max_work_group_total_size, const T &max_work_group_sizes, + WorkGroupSizeAlignment x_alignment, WorkGroupSizeAlignment y_alignment, + WorkGroupSizeAlignment z_alignment, std::vector<T> *work_groups) +{ + for (int x = 1; x <= 4; ++x) + { + for (int y = 1; y <= 4; ++y) + { + for (int z = 1; z <= 4; ++z) + { + u_int32_t wg_x = DivideRoundUp(grid.x, x); + u_int32_t wg_y = DivideRoundUp(grid.y, y); + u_int32_t wg_z = DivideRoundUp(grid.z, z); + if (wg_x > static_cast<u_int32_t>(max_work_group_sizes.x) || + wg_y > static_cast<u_int32_t>(max_work_group_sizes.y) || + wg_z > static_cast<u_int32_t>(max_work_group_sizes.z) || + wg_x * wg_y * wg_z > static_cast<u_int32_t>(max_work_group_total_size)) + { + continue; + } + if (x_alignment == WorkGroupSizeAlignment::PRECISE && grid.x % wg_x != 0) + { + continue; + } + if (y_alignment == WorkGroupSizeAlignment::PRECISE && grid.y % wg_y != 0) + { + continue; + } + if (z_alignment == WorkGroupSizeAlignment::PRECISE && grid.z % wg_z != 0) + { + continue; + } + work_groups->push_back({wg_x, wg_y, wg_z}); + } + } + } + + // this will add at least {1, 1, 1} always. + for (u_int32_t x = 1; x <= 4; ++x) + { + for (u_int32_t y = 1; y <= 4; ++y) + { + for (u_int32_t z = 1; z <= 4; ++z) + { + if (x > static_cast<u_int32_t>(max_work_group_sizes.x) || + y > static_cast<u_int32_t>(max_work_group_sizes.y) || + z > static_cast<u_int32_t>(max_work_group_sizes.z) || + x * y * z > static_cast<u_int32_t>(max_work_group_total_size)) + { + continue; + } + if (x_alignment == WorkGroupSizeAlignment::PRECISE && grid.x % x != 0) + { + continue; + } + if (y_alignment == WorkGroupSizeAlignment::PRECISE && grid.y % y != 0) + { + continue; + } + if (z_alignment == WorkGroupSizeAlignment::PRECISE && grid.z % z != 0) + { + continue; + } + work_groups->push_back({x, y, z}); + } + } + } +} + +std::vector<int> GetDivisors(int number) +{ + const int max_divisor = static_cast<int>(sqrt(number)); + std::vector<int> divisors; + // we don't know the number of dividers, so it is just heuristic. + divisors.reserve(max_divisor / 3 + 1); + for (int i = 1; i <= max_divisor; ++i) + { + const int d = number / i; + if (i * d == number) + { + divisors.push_back(i); + if (d != i) + { + divisors.push_back(d); + } + } + } + return divisors; +} + +std::vector<int> GetDivisorsForRange(int number, int range) +{ + const int last_number = number + range; + const int max_divisor = static_cast<int>(sqrt(last_number)); + std::set<int> divisors; + for (int i = 1; i <= max_divisor; ++i) + { + const int reminder = number % i; + // iterate through numbers that divisible by i in our range; + const int first_number = number + (i - reminder) % i; + if (first_number <= last_number) + { + divisors.insert(i); + } + for (int j = first_number; j <= last_number; j += i) + { + const int d = j / i; + if (d != i) + { + divisors.insert(d); + } + } + } + return std::vector<int>(divisors.begin(), divisors.end()); +} + +} // namespace + +std::vector<int> GetPossibleSizes(int number, WorkGroupSizeAlignment z_alignment) +{ + if (z_alignment == WorkGroupSizeAlignment::PRECISE) + { + // we will use for potential sizes, sizes that cover grid precisely + // work group size * k (k is integer) == grid_size + return GetDivisors(number); + } + else + { + // when we chose work group size we can use work group size that + // work group size * k (k is integer) != grid_size (slightly bigger) + // so in this heuristic we trying to find potential size, that satisfies + // to this : work group size * k (k is integer) <= grid_size + 5 + // and this : work group size * k (k is integer) >= grid_size + return GetDivisorsForRange(number, 5); + } +} + +template <typename T> +std::vector<T> +GenerateWorkGroupSizes(const T &grid, int min_work_group_total_size, int max_work_group_total_size, + const T &max_work_group_sizes, WorkGroupSizeAlignment x_alignment, + WorkGroupSizeAlignment y_alignment, WorkGroupSizeAlignment z_alignment) +{ + std::vector<T> work_groups; + work_groups.reserve(64); + + std::vector<int> sizes_x = GetPossibleSizes(grid.x, x_alignment); + std::vector<int> sizes_y = GetPossibleSizes(grid.y, y_alignment); + std::vector<int> sizes_z = GetPossibleSizes(grid.z, z_alignment); + + for (auto x : sizes_x) + { + if (static_cast<int>(x) > static_cast<int>(max_work_group_sizes.x)) + continue; + for (auto y : sizes_y) + { + if (static_cast<int>(y) > static_cast<int>(max_work_group_sizes.y)) + continue; + for (auto z : sizes_z) + { + if (static_cast<int>(z) > static_cast<int>(max_work_group_sizes.z)) + continue; + const int work_group_size = x * y * z; + if (work_group_size < min_work_group_total_size || + work_group_size > max_work_group_total_size) + continue; + work_groups.push_back({x, y, z}); + } + } + } + + return work_groups; +} + +// Specializations of GenerateWorkGroupSizes for int3 and uint3 + +template std::vector<int3> GenerateWorkGroupSizes(const int3 &grid, int min_work_group_total_size, + int max_work_group_total_size, + const int3 &max_work_group_sizes, + WorkGroupSizeAlignment x_alignment, + WorkGroupSizeAlignment y_alignment, + WorkGroupSizeAlignment z_alignment); + +template std::vector<uint3> GenerateWorkGroupSizes(const uint3 &grid, int min_work_group_total_size, + int max_work_group_total_size, + const uint3 &max_work_group_sizes, + WorkGroupSizeAlignment x_alignment, + WorkGroupSizeAlignment y_alignment, + WorkGroupSizeAlignment z_alignment); + +template <typename T> +void GenerateWorkGroupSizesAlignedToGrid(const T &grid, const T &max_work_group_size, + const int max_work_group_invocations, + std::vector<T> *work_groups) +{ + auto alignment = WorkGroupSizeAlignment::PRECISE; + *work_groups = + GenerateWorkGroupSizes<T>(grid, /*min_work_group_total_size = */ 32, max_work_group_invocations, + max_work_group_size, alignment, alignment, alignment); + // If the grid parameter too small, method below cannot generate workgroups. + if (work_groups->empty()) + { + AddCornerCases(grid, max_work_group_invocations, max_work_group_size, alignment, alignment, + alignment, work_groups); + } +} + +// Specializations of GenerateWorkGroupSizesAlignedToGrid for int3 and uint3 + +template void GenerateWorkGroupSizesAlignedToGrid(const int3 &grid, const int3 &max_work_group_size, + const int max_work_group_invocations, + std::vector<int3> *work_groups); + +template void GenerateWorkGroupSizesAlignedToGrid(const uint3 &grid, + const uint3 &max_work_group_size, + const int max_work_group_invocations, + std::vector<uint3> *work_groups); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/WorkgroupSelection.h b/runtime/onert/backend/gpu_cl/open_cl/WorkgroupSelection.h new file mode 100644 index 000000000..b0702ac7c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/WorkgroupSelection.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_WORK_GROUP_SELECTION_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_WORK_GROUP_SELECTION_H__ + +#include <vector> + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// PRECISE assume that WorkGroupSize * k = GridSize; +// NO_ALIGNMENT no restrictions; +// We need PRECISE when we don't have check in kernel for boundaries +// If we have the check, we can use PRECISE or NO_ALIGNMENT as well. +enum class WorkGroupSizeAlignment +{ + PRECISE, + NO_ALIGNMENT +}; + +std::vector<int> GetPossibleSizes(int number, WorkGroupSizeAlignment z_alignment); + +// Specializations exist for int3 and uint3 in the .cc file + +template <typename T> +std::vector<T> +GenerateWorkGroupSizes(const T &grid, int min_work_group_total_size, int max_work_group_total_size, + const T &max_work_group_sizes, WorkGroupSizeAlignment x_alignment, + WorkGroupSizeAlignment y_alignment, WorkGroupSizeAlignment z_alignment); + +template <typename T> +void GenerateWorkGroupSizesAlignedToGrid(const T &grid, const T &max_work_group_size, + const int max_work_group_invocations, + std::vector<T> *work_groups); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_WORK_GROUP_SELECTION_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Add.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Add.cc new file mode 100644 index 000000000..09100fe1f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Add.cc @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Add.h" + +#include <cstring> +#include <string> + +#include "absl/strings/str_cat.h" +#include "Util.h" +#include "open_cl/Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +GPUOperation CreateAdd(const OperationDef &definition, const std::vector<int> &channels, + int dst_channels) +{ + GPUOperation add(definition); + int dst_depth = DivideRoundUp(dst_channels, 4); + int src0_depth = DivideRoundUp(channels[0], 4); + add.elementwise_ = true; + add.linkable_ = dst_depth == src0_depth; + if (src0_depth < dst_depth) + { + add.check_src_channels_size_ = true; + } + for (uint32_t i = 1; i < definition.src_tensors.size(); ++i) + { + const std::string tensor_name = absl::StrCat("src_data_", i); + auto src_desc = definition.src_tensors[i]; + if (definition.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + add.AddSrcTensor(tensor_name, src_desc); + add.code_ += "if (S_COORD < args." + tensor_name + ".Slices()) {\n"; + add.code_ += " in_out_value += args." + tensor_name + ".Read(X_COORD, Y_COORD, S_COORD);\n"; + add.code_ += "}\n"; + } + return add; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Add.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Add.h new file mode 100644 index 000000000..2335a901c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Add.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_ADD_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_ADD_H__ + +#include <string> +#include <vector> + +#include "GpuOperation.h" +#include "open_cl/Operations.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// Add operation supports not equal tensors on input (for possibility to +// remove Padding operation with zeroes in channels dimension) +GPUOperation CreateAdd(const OperationDef &definition, const std::vector<int> &channels, + int dst_channels); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_ADD_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvBuffer1x1.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvBuffer1x1.cc new file mode 100644 index 000000000..1b9014fdf --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvBuffer1x1.cc @@ -0,0 +1,480 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "open_cl/kernels/ConvBuffer1x1.h" + +#include <array> +#include <string> +#include <utility> + +#include "open_cl/ClDevice.h" +#include "open_cl/kernels/Util.h" +#include "open_cl/kernels/WorkGroupPicking.h" +#include "open_cl/Precision.h" +#include "open_cl/TensorType.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +// element_size must be 1, 2 or 4 +// 1 - is FLT4 +// 2 - is FLT8 +// 4 - is FLT16 +// This function generates code for arithmetic part of convolution +std::string GetComputationPart(const int3 &block_size, int element_size, + CalculationsPrecision precision) +{ + const std::string hexes[16] = {"0", "1", "2", "3", "4", "5", "6", "7", + "8", "9", "a", "b", "c", "d", "e", "f"}; + std::string c; + for (int z = 0; z < block_size.z; ++z) + { + const std::string z_s = std::to_string(z); + c += " FLT16 W" + z_s + " = weights_cache[" + z_s + "];\n"; + for (int y = 0; y < block_size.y; ++y) + { + for (int x = 0; x < block_size.x; ++x) + { + std::string s_index = std::to_string(y * block_size.x + x); + for (int e = 0; e < element_size; ++e) + { + std::string r_index = z_s + std::to_string(y) + std::to_string(x * element_size + e); + const std::string f0 = "W" + z_s + ".s0123"; + const std::string f1 = "W" + z_s + ".s4567"; + const std::string f2 = "W" + z_s + ".s89ab"; + const std::string f3 = "W" + z_s + ".scdef"; + switch (precision) + { + case CalculationsPrecision::F32: + case CalculationsPrecision::F16: + c += " r" + r_index + " += " + f0 + " * s" + s_index + ".s" + hexes[e * 4 + 0] + + ";\n"; + c += " r" + r_index + " += " + f1 + " * s" + s_index + ".s" + hexes[e * 4 + 1] + + ";\n"; + c += " r" + r_index + " += " + f2 + " * s" + s_index + ".s" + hexes[e * 4 + 2] + + ";\n"; + c += " r" + r_index + " += " + f3 + " * s" + s_index + ".s" + hexes[e * 4 + 3] + + ";\n"; + break; + case CalculationsPrecision::F32_F16: + c += " r" + r_index + " += convert_float4(" + f0 + " * s" + s_index + ".s" + + hexes[e * 4 + 0] + " + " + f1 + " * s" + s_index + ".s" + hexes[e * 4 + 1] + + " + " + f2 + " * s" + s_index + ".s" + hexes[e * 4 + 2] + " + " + f3 + " * s" + + s_index + ".s" + hexes[e * 4 + 3] + ");\n"; + break; + } + } + } + } + } + return c; +} + +ConvBuffer1x1::ConvParams GetBestParams(const DeviceInfo &device_info, + const OperationDef &definition, const BHWC &shape, int, + int dst_depth) +{ + ConvBuffer1x1::ConvParams conv_params; + conv_params.element_size = 4; + conv_params.block_size = int3(1, 1, 1); + if (!device_info.IsMali()) + { + return conv_params; + } + bool can_use_flt8 = + (shape.w * shape.b) % 2 == 0 && definition.precision != CalculationsPrecision::F32; + bool is_midgard = device_info.IsMali() && device_info.mali_info.IsMidgard(); + if (is_midgard) + { + if (can_use_flt8) + { + conv_params.element_size = 8; + } + if (definition.precision == CalculationsPrecision::F16 || !can_use_flt8) + { + conv_params.block_size.x = 2; + } + return conv_params; + } + + int task_size = shape.w * shape.b * shape.h * dst_depth; + int block_size = GetRecommendedBlockSizeForConv(device_info, definition.precision, task_size); + + if (!can_use_flt8 && block_size > 4) + { + block_size = 4; + } + + if (can_use_flt8 && block_size >= 2) + { + conv_params.element_size = 8; + block_size /= 2; + } + if (block_size == 4) + { + conv_params.block_size.x = 2; + if (definition.precision == CalculationsPrecision::F32 && dst_depth < 32) + { + conv_params.block_size.y = 2; + } + else + { + conv_params.block_size.z = 2; + } + } + else if (block_size == 2) + { + if (dst_depth >= 32) + { + conv_params.block_size.z = 2; + } + else + { + conv_params.block_size.x = 2; + } + } + + return conv_params; +} + +ConvBuffer1x1::ConvParams GetBestParams(const DeviceInfo &device_info, + const OperationDef &definition, int, int) +{ + ConvBuffer1x1::ConvParams conv_params; + conv_params.element_size = 4; + conv_params.block_size = int3(1, 1, 1); + if (device_info.IsMali() && definition.precision == CalculationsPrecision::F16 && + device_info.compute_units_count <= 4) + { + conv_params.block_size.x *= 2; + } + return conv_params; +} + +} // namespace + +ConvBuffer1x1::ConvBuffer1x1(const OperationDef &definition, const ConvParams &conv_params) + : GPUOperation(definition), conv_params_(conv_params) +{ + code_ = GenerateConvBuffer1x1(definition_, conv_params_, &args_); + work_group_size_ = int3(2, 4, 1); +} + +ConvBuffer1x1::ConvBuffer1x1(ConvBuffer1x1 &&operation) + : GPUOperation(std::move(operation)), conv_params_(std::move(operation.conv_params_)) +{ +} + +ConvBuffer1x1 &ConvBuffer1x1::operator=(ConvBuffer1x1 &&operation) +{ + if (this != &operation) + { + std::swap(conv_params_, operation.conv_params_); + GPUOperation::operator=(std::move(operation)); + } + return *this; +} + +std::string ConvBuffer1x1::GenerateConvBuffer1x1(const OperationDef &op_def, + const ConvBuffer1x1::ConvParams &conv_params, + Arguments *) +{ + auto src_desc = op_def.src_tensors[0]; + if (op_def.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + if (conv_params_.element_size == 8) + { + src_desc.SetStateVar("ElementsX2", "true"); + } + else if (conv_params_.element_size == 16) + { + src_desc.SetStateVar("ElementsX4", "true"); + } + AddSrcTensor("src_tensor", src_desc); + if (op_def.src_tensors.size() == 2) + { + // dynamic weights + BufferDescriptor desc; + desc.element_type = op_def.src_tensors[1].data_type; + desc.element_size = 16; + desc.memory_type = MemoryType::GLOBAL; + AddSrcBuffer("weights", desc); + } + + auto dst_desc = op_def.dst_tensors[0]; + if (op_def.IsBatchSupported()) + { + dst_desc.SetStateVar("BatchedWidth", "true"); + } + AddDstTensor("dst_tensor", dst_desc); + + std::string c = GetCommonDefines(op_def.precision); + switch (op_def.precision) + { + case CalculationsPrecision::F32: + c += "#define FLT8 float8\n"; + c += "#define FLT16 float16\n"; + break; + case CalculationsPrecision::F32_F16: + case CalculationsPrecision::F16: + c += "#define FLT8 half8\n"; + c += "#define FLT16 half16\n"; + break; + } + + const int3 block_size = conv_params.block_size; + const int element_size = conv_params.element_size / 4; + + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int X = get_global_id(0) * " + std::to_string(block_size.x * element_size) + ";\n"; + c += " int X_SRC = get_global_id(0) * " + std::to_string(block_size.x) + ";\n"; + c += " int Y = get_global_id(1) * " + std::to_string(block_size.y) + ";\n"; + c += " int Z = get_global_id(2) * " + std::to_string(block_size.z) + ";\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() || " + "Z >= args.dst_tensor.Slices()) return;\n"; + if (conv_params.different_weights_for_height) + { + c += " __global FLT16* weights_cache = args.weights.GetPtr() + (Z * " + "args.src_tensor.Height() + " + "Y * " + + std::to_string(block_size.z) + + ") * " + "args.src_tensor.Slices();\n"; + } + else + { + c += " __global FLT16* weights_cache = args.weights.GetPtr() + Z * " + "args.src_tensor.Slices();\n"; + } + for (int z = 0; z < block_size.z; ++z) + { + const std::string z_s = std::to_string(z); + c += " ACCUM_FLT4 bias_val_" + z_s + " = TO_ACCUM_TYPE(args.biases.Read(Z + " + z_s + "));\n"; + for (int y = 0; y < block_size.y; ++y) + { + for (int x = 0; x < block_size.x * element_size; ++x) + { + c += " ACCUM_FLT4 r" + z_s + std::to_string(y) + std::to_string(x) + " = bias_val_" + z_s + + ";\n"; + } + } + } + for (int x = 0; x < block_size.x; ++x) + { + std::string x_s = std::to_string(x); + c += " int xc" + x_s + " = min(X_SRC + " + std::to_string(x) + + ", args.src_tensor.Width() - 1);\n"; + } + for (int y = 0; y < block_size.y; ++y) + { + std::string y_s = std::to_string(y); + c += " int yc" + y_s + " = min(Y + " + y_s + ", args.src_tensor.Height() - 1);\n"; + } + for (int y = 0; y < block_size.y; ++y) + { + std::string y_s = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + std::string x_s = std::to_string(x); + std::string i_s = std::to_string(y * block_size.x + x); + c += " int src_addr_" + i_s + " = (yc" + y_s + ") * args.src_tensor.Width() + (xc" + x_s + + ");\n"; + } + } + c += " for (int s = 0; s < args.src_tensor.Slices(); ++s) {\n"; + for (int y = 0; y < block_size.y; ++y) + { + std::string y_s = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + std::string x_s = std::to_string(x); + std::string i_s = std::to_string(y * block_size.x + x); + c += " FLT" + std::to_string(element_size * 4) + " s" + i_s + + " = args.src_tensor.Read(src_addr_" + i_s + ");\n"; + } + } + c += GetComputationPart(block_size, element_size, op_def.precision); + for (int i = 0; i < block_size.x * block_size.y; ++i) + { + std::string i_s = std::to_string(i); + c += " src_addr_" + i_s + " += args.src_tensor.SliceStride();\n"; + } + c += " weights_cache += " + std::to_string(block_size.z) + ";\n"; + c += " }\n"; // SRC_SLICES + + for (int z = 0; z < block_size.z; ++z) + { + const std::string z_s = std::to_string(z); + if (z != 0) + { + c += " if (Z + " + z_s + " >= args.dst_tensor.Slices()) return;\n"; + } + for (int y = 0; y < block_size.y; ++y) + { + const std::string y_s = std::to_string(y); + for (int x = 0; x < block_size.x * element_size; ++x) + { + const std::string x_s = std::to_string(x); + c += " if (X + " + x_s + " < args.dst_tensor.Width() && Y + " + y_s + + " < args.dst_tensor.Height()) {\n"; + c += " FLT4 res = TO_FLT4(r" + z_s + y_s + x_s + ");\n"; + c += " args.dst_tensor.Write(res, X + " + x_s + ", Y + " + y_s + ", Z + " + z_s + ");\n"; + c += " }\n"; + } + } + } + c += "}\n"; + return c; +} + +int3 ConvBuffer1x1::GetGridSize() const +{ + const int dst_width_elements = + DivideRoundUp(dst_[0]->Width() * dst_[0]->Batch(), (conv_params_.element_size / 4)); + const int grid_x = DivideRoundUp(dst_width_elements, conv_params_.block_size.x); + const int grid_y = DivideRoundUp(dst_[0]->Height(), conv_params_.block_size.y); + const int grid_z = DivideRoundUp(dst_[0]->Slices(), conv_params_.block_size.z); + return int3(grid_x, grid_y, grid_z); +} + +void ConvBuffer1x1::GetPossibleKernelWorkGroups(TuningType tuning_type, + const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const +{ + GetPossibleWorkGroupsConv(tuning_type, device_info, kernel_info, grid_size_, work_groups); +} + +bool IsConvBuffer1x1Supported(const OperationDef &definition, const Convolution2DAttributes &attr) +{ + auto src_storage_type = definition.src_tensors[0].storage_type; + return src_storage_type == TensorStorageType::BUFFER && attr.weights.shape.w == 1 && + attr.weights.shape.h == 1 && attr.dilations.w == 1 && attr.dilations.h == 1 && + attr.strides.w == 1 && attr.strides.h == 1 && attr.padding.prepended.w == 0 && + attr.padding.prepended.h == 0 && attr.padding.appended.w == 0 && + attr.padding.appended.h == 0; +} + +bool IsConvBuffer1x1Supported(const OperationDef &definition, const BHWC &weights_shape, + const Convolution2DAttributes &attr) +{ + auto src_storage_type = definition.src_tensors[0].storage_type; + return src_storage_type == TensorStorageType::BUFFER && weights_shape.w == 1 && + weights_shape.h == 1 && attr.dilations.w == 1 && attr.dilations.h == 1 && + attr.strides.w == 1 && attr.strides.h == 1 && attr.padding.prepended.w == 0 && + attr.padding.prepended.h == 0 && attr.padding.appended.w == 0 && + attr.padding.appended.h == 0; +} + +ConvBuffer1x1 CreateConvBuffer1x1(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC *shape) +{ + const int dst_depth = DivideRoundUp(attr.weights.shape.o, 4); + const int src_depth = DivideRoundUp(attr.weights.shape.i, 4); + ConvBuffer1x1::ConvParams conv_params; + if (shape) + { + conv_params = GetBestParams(device_info, definition, *shape, src_depth, dst_depth); + } + else + { + conv_params = GetBestParams(device_info, definition, src_depth, dst_depth); + } + ConvBuffer1x1 result(definition, conv_params); + result.UploadData(attr.weights, attr.bias); + return result; +} + +ConvBuffer1x1 CreateConvBuffer1x1(const DeviceInfo &device_info, const OperationDef &definition, + const FullyConnectedAttributes &attr, const BHWC *shape) +{ + const int dst_depth = DivideRoundUp(attr.weights.shape.o, 4); + const int src_depth = DivideRoundUp(attr.weights.shape.i, 4); + ConvBuffer1x1::ConvParams conv_params; + if (shape) + { + conv_params = GetBestParams(device_info, definition, *shape, src_depth, dst_depth); + } + else + { + conv_params = GetBestParams(device_info, definition, src_depth, dst_depth); + } + conv_params.block_size.x *= conv_params.block_size.y; + conv_params.block_size.y = 1; + ConvBuffer1x1 result(definition, conv_params); + result.UploadData(attr.weights, attr.bias); + return result; +} + +ConvBuffer1x1 CreateConvBuffer1x1Wino4x4To6x6(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *shape) +{ + const int dst_depth = DivideRoundUp(attr.weights.shape.o, 4); + const int src_depth = DivideRoundUp(attr.weights.shape.i, 4); + ConvBuffer1x1::ConvParams conv_params; + if (shape) + { + conv_params = GetBestParams(device_info, definition, *shape, src_depth, dst_depth); + } + else + { + conv_params = GetBestParams(device_info, definition, src_depth, dst_depth); + } + conv_params.block_size.x *= conv_params.block_size.y; + conv_params.block_size.y = 1; + conv_params.different_weights_for_height = true; + ConvBuffer1x1 result(definition, conv_params); + result.UploadDataForWinograd4x4To6x6(attr.weights); + return result; +} + +ConvBuffer1x1 CreateConvBuffer1x1DynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC &weights_shape, const BHWC *dst_shape) +{ + const int dst_depth = DivideRoundUp(weights_shape.b, 4); + const int src_depth = DivideRoundUp(weights_shape.c, 4); + ConvBuffer1x1::ConvParams conv_params; + if (dst_shape) + { + conv_params = GetBestParams(device_info, definition, *dst_shape, src_depth, dst_depth); + } + else + { + conv_params = GetBestParams(device_info, definition, src_depth, dst_depth); + } + ConvBuffer1x1 result(definition, conv_params); + result.UploadBiases(attr.bias); + return result; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvBuffer1x1.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvBuffer1x1.h new file mode 100644 index 000000000..0abd6051f --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvBuffer1x1.h @@ -0,0 +1,205 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_BUFFER_1X1_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_BUFFER_1X1_H__ + +#include "open_cl/Buffer.h" +#include "open_cl/ClKernel.h" +#include "open_cl/kernels/ConvCommon.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/kernels/Util.h" +#include "open_cl/LinearStorage.h" +#include "open_cl/Precision.h" +#include "open_cl/InternalTensor.h" +#include "open_cl/Util.h" +#include "open_cl/DataType.h" +#include "open_cl/Operations.h" +#include "open_cl/Shape.h" +#include "open_cl/Status.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" +#include "open_cl/WinogradUtil.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class ConvBuffer1x1 : public GPUOperation +{ +public: + ConvBuffer1x1() = default; + + // Move only + ConvBuffer1x1(ConvBuffer1x1 &&operation); + ConvBuffer1x1 &operator=(ConvBuffer1x1 &&operation); + ConvBuffer1x1(const ConvBuffer1x1 &) = delete; + ConvBuffer1x1 &operator=(const ConvBuffer1x1 &) = delete; + + void GetPossibleKernelWorkGroups(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const override; + int3 GetGridSize() const override; + + ConvWeightsDescription GetConvWeightsDescription() const + { + ConvWeightsDescription desc; + desc.layout = ConvWeightsLayout::kOHWIOGroupI4O4; + desc.output_group_size = conv_params_.block_size.z; + return desc; + } + + struct ConvParams + { + int3 block_size = int3(1, 1, 1); + int element_size = 4; // can be 4, 8 or 16 + + // By default in 2d convolution we have the same weights for WH dims, but in + // some cases we need separate weights for H dimension and convolution + // kernel requires very small modifications to support it. + bool different_weights_for_height = false; + }; + +private: + ConvBuffer1x1(const OperationDef &definition, const ConvParams &conv_params); + friend ConvBuffer1x1 CreateConvBuffer1x1(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC *shape); + friend ConvBuffer1x1 CreateConvBuffer1x1(const DeviceInfo &device_info, + const OperationDef &definition, + const FullyConnectedAttributes &attr, const BHWC *shape); + friend ConvBuffer1x1 CreateConvBuffer1x1Wino4x4To6x6(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *shape); + friend ConvBuffer1x1 CreateConvBuffer1x1DynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC &weights_shape, + const BHWC *dst_shape); + + template <DataType T> + void UploadData(const InternalTensor<OHWI, T> &weights, const InternalTensor<Linear, T> &biases); + template <DataType T> void UploadDataForWinograd4x4To6x6(const InternalTensor<OHWI, T> &weights); + + template <DataType T> void UploadWeights(const InternalTensor<OHWI, T> &weights); + + template <DataType T> void UploadBiases(const InternalTensor<Linear, T> &biases); + + std::string GenerateConvBuffer1x1(const OperationDef &op_def, + const ConvBuffer1x1::ConvParams &conv_params, Arguments *args); + + ConvParams conv_params_; +}; + +template <DataType T> +void ConvBuffer1x1::UploadData(const InternalTensor<OHWI, T> &weights, + const InternalTensor<Linear, T> &biases) +{ + UploadWeights(weights); + UploadBiases(biases); +} + +template <DataType T> +void ConvBuffer1x1::UploadDataForWinograd4x4To6x6(const InternalTensor<OHWI, T> &weights) +{ + InternalTensor<OHWI, T> wino_weights; + RearrangeWeightsToWinograd4x4To6x6Weights(weights, &wino_weights); + UploadWeights(wino_weights); + InternalTensor<Linear, DataType::FLOAT32> bias; + bias.shape = Linear(weights.shape.o); + bias.data.resize(weights.shape.o, 0.0f); + UploadBiases(bias); +} + +template <DataType T> void ConvBuffer1x1::UploadWeights(const InternalTensor<OHWI, T> &weights) +{ + const int dst_depth = DivideRoundUp(weights.shape.o, 4); + const int src_depth = DivideRoundUp(weights.shape.i, 4); + + const bool f32_weights = definition_.precision == CalculationsPrecision::F32; + const int float4_size = sizeof(float4); + // TODO + // f32_weights ? sizeof(float4) : sizeof(half4); + + const int dst_depth_aligned = AlignByN(dst_depth, conv_params_.block_size.z); + const int elements_count = weights.shape.h * weights.shape.w * src_depth * dst_depth_aligned * 4; + + BufferDescriptor desc; + desc.element_type = f32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.element_size = 16; + desc.memory_type = MemoryType::GLOBAL; + desc.size = float4_size * elements_count; + desc.data.resize(desc.size); + + if (f32_weights) + { + float4 *ptr = reinterpret_cast<float4 *>(desc.data.data()); + RearrangeWeightsToOHWIOGroupI4O4(weights, conv_params_.block_size.z, + absl::MakeSpan(ptr, elements_count)); + } + // else + // { + // half4 *ptr = reinterpret_cast<half4 *>(desc.data.data()); + // RearrangeWeightsToOHWIOGroupI4O4(weights, conv_params_.block_size.z, + // absl::MakeSpan(ptr, elements_count)); + // } + + args_.AddObject("weights", absl::make_unique<BufferDescriptor>(std::move(desc))); +} + +template <DataType T> void ConvBuffer1x1::UploadBiases(const InternalTensor<Linear, T> &biases) +{ + TensorLinearDescriptor desc; + desc.storage_type = LinearStorageType::BUFFER; + desc.element_type = definition_.GetDataType(); + int depth = AlignByN(biases.shape.v, 4 * conv_params_.block_size.z) / 4; + desc.UploadLinearData(biases, depth); + args_.AddObject("biases", absl::make_unique<TensorLinearDescriptor>(std::move(desc))); +} + +bool IsConvBuffer1x1Supported(const OperationDef &definition, const Convolution2DAttributes &attr); + +bool IsConvBuffer1x1Supported(const OperationDef &definition, const BHWC &weights_shape, + const Convolution2DAttributes &attr); + +ConvBuffer1x1 CreateConvBuffer1x1(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC *shape = nullptr); + +ConvBuffer1x1 CreateConvBuffer1x1(const DeviceInfo &device_info, const OperationDef &definition, + const FullyConnectedAttributes &attr, + const BHWC *shape = nullptr); + +ConvBuffer1x1 CreateConvBuffer1x1DynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC &weights_shape, + const BHWC *dst_shape = nullptr); + +ConvBuffer1x1 CreateConvBuffer1x1Wino4x4To6x6(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *shape = nullptr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_BUFFER_1X1_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvCommon.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvCommon.h new file mode 100644 index 000000000..4700381dc --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvCommon.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_COMMON_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_COMMON_H__ + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class ConvWeightsLayout +{ + kUnknown, + kOHWIOGroupI4O4, +}; + +struct ConvWeightsDescription +{ + ConvWeightsLayout layout; + int output_group_size; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_COMMON_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvConstants.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvConstants.cc new file mode 100644 index 000000000..0a51bab5c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvConstants.cc @@ -0,0 +1,282 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "open_cl/kernels/ConvConstants.h" + +#include <string> +#include <utility> + +#include "open_cl/kernels/Util.h" +#include "open_cl/kernels/WorkGroupPicking.h" +#include "open_cl/Precision.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ +// Adreno can provide up to ~3-4KB of constant memory, but in some cases even +// 3KB can have very bad performance. +int GetAdrenoOptimalMaxConstantSize(int gpu_version) +{ + if (gpu_version < 600) + { + return 256 * 10; // 2.5KB + } + else + { + return 256 * 14; // 3.5KB + } +} + +int GetOptimalMaxConstantSize(const DeviceInfo &info) +{ + if (!info.IsAdreno()) + { + // In general we do not expect that this kernel will be used with non Adreno + // so as it tuned for __constant memory that have big profit on Adreno + return 1024; // 1KB + } + else + { + return GetAdrenoOptimalMaxConstantSize(info.adreno_info.gpu_version); + } +} + +std::string GenerateConvolutionConstantCode(const OperationDef &op_def, const OHWI &weights_shape, + bool stride_correction, GPUOperation *op) +{ + auto src_desc = op_def.src_tensors[0]; + src_desc.SetTextureAddressMode(TextureAddressMode::ZERO); + if (op_def.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddSrcTensor("src_tensor", src_desc); + + auto dst_desc = op_def.dst_tensors[0]; + if (op_def.IsBatchSupported()) + { + dst_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddDstTensor("dst_tensor", dst_desc); + + std::string c = GetCommonDefines(op_def.precision); + + const int out_z = DivideRoundUp(weights_shape.o, 4); + const std::string kOutZ = std::to_string(out_z); + const int src_depth = DivideRoundUp(weights_shape.i, 4); + + const auto src_tensor_type = op_def.src_tensors[0].storage_type; + const bool manual_clamp = src_tensor_type == TensorStorageType::BUFFER || + src_tensor_type == TensorStorageType::IMAGE_BUFFER; + + switch (op_def.precision) + { + case CalculationsPrecision::F32: + case CalculationsPrecision::F16: + c += "#define CONV4(R, SRC, F, i) \\\n"; + c += " R += SRC.x * F[i + 0]; \\\n"; + c += " R += SRC.y * F[i + 1]; \\\n"; + c += " R += SRC.z * F[i + 2]; \\\n"; + c += " R += SRC.w * F[i + 3]; \n"; + + c += "#define CONV3(R, SRC, F, i) \\\n"; + c += " R += SRC.x * F[i + 0]; \\\n"; + c += " R += SRC.y * F[i + 1]; \\\n"; + c += " R += SRC.z * F[i + 2]; \n"; + + c += "#define CONV2(R, SRC, F, i) \\\n"; + c += " R += SRC.x * F[i + 0]; \\\n"; + c += " R += SRC.y * F[i + 1]; \n"; + + c += "#define CONV1(R, SRC, F, i) \\\n"; + c += " R += SRC * F[i + 0]; \n"; + break; + case CalculationsPrecision::F32_F16: + c += "#define CONV4(R, SRC, F, i) \\\n"; + c += " R += convert_float4(SRC.x * F[i + 0] + SRC.y * F[i + 1]"; + c += " + SRC.z * F[i + 2] + SRC.w * F[i + 3]);\n"; + + c += "#define CONV3(R, SRC, F, i) \\\n"; + c += " R += convert_float4(SRC.x * F[i + 0] + SRC.y * F[i + 1]"; + c += " + SRC.z * F[i + 2]);\n"; + + c += "#define CONV2(R, SRC, F, i) \\\n"; + c += " R += convert_float4(SRC.x * F[i + 0] + SRC.y * F[i + 1]);\n"; + + c += "#define CONV1(R, SRC, F, i) \\\n"; + c += " R += convert_float4(SRC * F[i + 0]);\n"; + break; + } + + const std::string postfixes[] = {".x", ".xy", ".xyz", ""}; + + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int X = get_global_id(0);\n"; + c += " int Y = get_global_id(1);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height()) " + "return;\n"; + if (stride_correction) + { + c += " int start_x = " + + GetXStrideCorrectedV2("X", "args.src_tensor.Batch()", "args.stride_x", "args.padding_x") + + ";\n"; + } + else + { + if (op_def.IsBatchSupported()) + { + c += " int start_x = X * args.stride_x + args.padding_x * " + "args.src_tensor.Batch();\n"; + } + else + { + c += " int start_x = X * args.stride_x + args.padding_x;\n"; + } + } + c += " int start_y = Y * args.stride_y + args.padding_y;\n"; + c += " ACCUM_FLT4 r[" + kOutZ + "];\n"; + c += " for (int i = 0; i < " + kOutZ + "; ++i) {\n"; + c += " r[i] = (ACCUM_FLT4)(0.0f, 0.0f, 0.0f, 0.0f);\n"; + c += " }\n"; + int filters_counter = 0; + for (int s = 0; s < src_depth; ++s) + { + const int ch_count = std::min(4, weights_shape.i - s * 4); + const std::string s_conv = "CONV" + std::to_string(ch_count); + const std::string s_count = ch_count == 1 ? "" : std::to_string(ch_count); + const std::string s_type = absl::StrCat("FLT", s_count); + const std::string s_postfix = postfixes[ch_count - 1]; + const std::string dilation_x = + op_def.IsBatchSupported() ? "args.dilation_x * args.src_tensor.Batch()" : "args.dilation_x"; + for (int ky = 0; ky < weights_shape.h; ++ky) + { + std::string s_y = absl::StrCat("(start_y + ", ky, " * args.dilation_y)"); + if (manual_clamp) + { + c += " {\n"; + c += " bool y_out = " + s_y + " < 0 || " + s_y + " >= args.src_tensor.Height();\n"; + } + for (int kx = 0; kx < weights_shape.w; ++kx) + { + c += " {\n"; + std::string s_x = absl::StrCat("(start_x + ", kx, " * " + dilation_x + ")"); + if (manual_clamp) + { + c += " bool x_out = " + s_x + "< 0 || " + s_x + ">= args.src_tensor.Width();\n"; + c += " " + s_type + " src = x_out || y_out ?"; + c += "(" + s_type + ")(0.0) : args.src_tensor.Read(" + s_x + ", " + s_y + ", " + + std::to_string(s) + ")" + s_postfix + ";\n"; + } + else + { + c += " " + s_type + " src = args.src_tensor.Read(" + s_x + ", " + s_y + ", " + + std::to_string(s) + ")" + s_postfix + ";\n"; + } + for (int d = 0; d < out_z; ++d) + { + c += " " + s_conv + "(r[" + std::to_string(d) + "], src, args.weigths.GetPtr(),"; + c += " " + std::to_string(filters_counter) + ");\n"; + filters_counter += ch_count; + } + c += " }\n"; + } + if (manual_clamp) + { + c += " }\n"; + } + } + } + for (int i = 0; i < out_z; ++i) + { + std::string s_i = std::to_string(i); + c += " {\n"; + c += " FLT4 res = TO_FLT4(r[" + s_i + "]) + args.biases.Read(" + s_i + ");\n"; + c += " args.dst_tensor.Write(res, X, Y, " + s_i + ");\n"; + c += " }\n"; + } + c += "}\n"; + return c; +} + +} // namespace + +bool IsConvConstantsSupported(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr) +{ + if (device_info.IsAMD() && definition.precision != CalculationsPrecision::F32 && + definition.src_tensors[0].storage_type != TensorStorageType::BUFFER) + { + // BUG, some AMD gpus crashe without it + return false; + } + + const auto &w_shape = attr.weights.shape; + const int dst_channels = AlignByN(w_shape.o, 4); + const int filters_count = w_shape.i * dst_channels * w_shape.h * w_shape.w; + const int float_size = sizeof(float); + // TODO F32 and F16 + // definition.precision == CalculationsPrecision::F32 ? sizeof(float) : sizeof(half); + const int filters_buffer_size = filters_count * float_size; + const int kConstantMaxSize = GetOptimalMaxConstantSize(device_info); + const int flt4_registers = DivideRoundUp(w_shape.o, 4); + return filters_buffer_size <= kConstantMaxSize && flt4_registers <= 8; +} + +GPUOperation CreateConvConstants(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr) +{ + GPUOperation op(definition); + UploadWeightsForConvConstants(attr.weights, definition.precision, &op); + op.args_.AddInt("stride_x", attr.strides.w); + op.args_.AddInt("stride_y", attr.strides.h); + op.args_.AddInt("padding_x", -attr.padding.prepended.w); + op.args_.AddInt("padding_y", -attr.padding.prepended.h); + op.args_.AddInt("dilation_x", attr.dilations.w); + op.args_.AddInt("dilation_y", attr.dilations.h); + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_ZIs1; + + const bool stride_correction = definition.IsBatchSupported() && attr.strides.w != 1; + op.code_ = + GenerateConvolutionConstantCode(definition, attr.weights.shape, stride_correction, &op); + if (definition.precision == CalculationsPrecision::F16 && device_info.IsAdreno3xx()) + { + op.compiler_options_.push_back(CompilerOptions::ADRENO_FULL_SIMD_LINE); + } + if (definition.precision != CalculationsPrecision::F32 && device_info.IsPowerVR()) + { + // BUG, some PowerVRs (GE8320) produce incorrect result without it + op.compiler_options_.push_back(CompilerOptions::CL_OPT_DISABLE); + } + + TensorLinearDescriptor desc; + desc.storage_type = LinearStorageType::BUFFER; + desc.element_type = definition.GetDataType(); + desc.memory_type = MemoryType::CONSTANT; + desc.UploadLinearData(attr.bias); + op.args_.AddObject("biases", absl::make_unique<TensorLinearDescriptor>(std::move(desc))); + return op; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvConstants.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvConstants.h new file mode 100644 index 000000000..be6670c53 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvConstants.h @@ -0,0 +1,137 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_CONSTANTS_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_CONSTANTS_H__ + +#include "open_cl/Buffer.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/LinearStorage.h" +#include "open_cl/Tensor.h" +#include "open_cl/Util.h" +#include "open_cl/DataType.h" +#include "open_cl/Operations.h" +#include "open_cl/Shape.h" +#include "open_cl/Status.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <DataType S, typename T> +void RearrangeWeightsForConvConstants(const InternalTensor<OHWI, S> &weights, absl::Span<T> dst) +{ + const int dst_depth = DivideRoundUp(weights.shape.o, 4); + const int src_depth = DivideRoundUp(weights.shape.i, 4); + const int kernel_x = weights.shape.w; + const int kernel_y = weights.shape.h; + + int counter = 0; + for (int s = 0; s < src_depth; ++s) + { + for (int y = 0; y < kernel_y; ++y) + { + for (int x = 0; x < kernel_x; ++x) + { + for (int d = 0; d < dst_depth; ++d) + { + const int channels_count = std::min(4, weights.shape.i - s * 4); + T filters[4]; + for (int i = 0; i < 4; ++i) + { + for (int j = 0; j < channels_count; ++j) + { + const int s_ch = s * 4 + j; + const int d_ch = d * 4 + i; + if (s_ch < weights.shape.i && d_ch < weights.shape.o) + { + const int f_index = weights.shape.LinearIndex({d_ch, y, x, s_ch}); + filters[i][j] = weights.data[f_index]; + } + else + { + filters[i][j] = 0.0f; + } + } + } + T filters_new[4]; + for (int i = 0; i < 4; ++i) + { + for (int j = 0; j < 4; ++j) + { + filters_new[i][j] = filters[j][i]; + } + } + for (int i = 0; i < channels_count; ++i) + { + dst[counter++] = filters_new[i]; + } + } + } + } + } +} + +template <DataType T> +void UploadWeightsForConvConstants(const InternalTensor<OHWI, T> &weights, + CalculationsPrecision precision, GPUOperation *op) +{ + const int dst_depth = DivideRoundUp(weights.shape.o, 4); + const int kernel_x = weights.shape.w; + const int kernel_y = weights.shape.h; + + const bool f32_weights = precision == CalculationsPrecision::F32; + const int float_size = f32_weights ? 4 : 2; + const int float_count = weights.shape.i * dst_depth * 4 * kernel_x * kernel_y; + + BufferDescriptor desc; + desc.element_type = f32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.element_size = 4; + desc.memory_type = MemoryType::CONSTANT; + desc.size = float_size * float_count; + desc.data.resize(desc.size); + + if (f32_weights) + { + float4 *ptr = reinterpret_cast<float4 *>(desc.data.data()); + RearrangeWeightsForConvConstants(weights, absl::MakeSpan(ptr, float_count / 4)); + } + // else + // { + // half4 *ptr = reinterpret_cast<half4 *>(desc.data.data()); + // RearrangeWeightsForConvConstants(weights, absl::MakeSpan(ptr, float_count / 4)); + // } + + op->args_.AddObject("weigths", absl::make_unique<BufferDescriptor>(std::move(desc))); +} + +bool IsConvConstantsSupported(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr); + +GPUOperation CreateConvConstants(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_CONSTANTS_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvPowervr.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvPowervr.cc new file mode 100644 index 000000000..5cb0c2719 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvPowervr.cc @@ -0,0 +1,1653 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "open_cl/kernels/ConvPowervr.h" + +#include <algorithm> +#include <string> +#include <utility> + +#include "absl/strings/substitute.h" +#include "open_cl/kernels/Util.h" +#include "open_cl/kernels/WorkGroupPicking.h" +#include "open_cl/Precision.h" +#include "open_cl/TensorType.h" +#include "open_cl/DataType.h" +#include "open_cl/Shape.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ +std::string GenerateUploadByThreads(const std::string &local_ptr_name, + const std::string &global_ptr_name, + const std::string &global_offset_name, + const std::string &lid_name, int total_work_items, + int elements_to_upload) +{ + std::string c; + std::string offset = global_offset_name.empty() ? "" : global_offset_name + " + "; + const int groups = elements_to_upload / total_work_items; + const int reminder = elements_to_upload % total_work_items; + for (int i = 0; i < groups; ++i) + { + c += " " + local_ptr_name + "[" + lid_name + " + " + std::to_string(total_work_items * i) + + "] = " + global_ptr_name + "[" + offset + lid_name + " + " + + std::to_string(total_work_items * i) + "];\n"; + } + if (reminder != 0) + { + c += " if (" + lid_name + " < " + std::to_string(reminder) + ") {\n"; + c += " " + local_ptr_name + "[" + lid_name + " + " + + std::to_string(total_work_items * groups) + "] = " + global_ptr_name + "[" + offset + + lid_name + " + " + std::to_string(total_work_items * groups) + "];\n"; + c += " }\n"; + } + return c; +} + +std::string GenerateAsyncUpload(const std::string &local_ptr_name, + const std::string &global_ptr_name, + const std::string &global_offset_name, int elements_to_upload) +{ + std::string c; + std::string offset = global_offset_name.empty() ? "" : " + " + global_offset_name; + c += " async_work_group_copy(" + local_ptr_name + ", " + global_ptr_name + offset + ", " + + std::to_string(elements_to_upload) + ", 0);\n"; + return c; +} + +std::string GenerateBlockCoords(const int4 &block_size, const int3 &work_group_launch_order, + bool linear_spatial, bool need_depth) +{ + std::string c; + int3 launch_remap; + launch_remap[work_group_launch_order.x] = 0; + launch_remap[work_group_launch_order.y] = 1; + launch_remap[work_group_launch_order.z] = 2; + if (linear_spatial) + { + if (work_group_launch_order[0] == 0) + { + c += " int linear_spatial = get_global_id(0);\n"; + } + else + { + c += " int linear_spatial = get_group_id(" + std::to_string(launch_remap[0]) + + ") * get_local_size(0) + get_local_id(0);\n"; + } + if (need_depth) + { + c += " int DST_X = (linear_spatial % args.task_size_x) * " + std::to_string(block_size.x) + + ";\n"; + c += " linear_spatial = linear_spatial / args.task_size_x;\n"; + c += " int DST_Y = (linear_spatial % args.task_size_y) * " + std::to_string(block_size.y) + + ";\n"; + c += " int DST_Z = (linear_spatial / args.task_size_y) * " + std::to_string(block_size.z) + + ";\n"; + } + else + { + c += " int DST_Y = (linear_spatial / args.task_size_x) * " + std::to_string(block_size.y) + + ";\n"; + c += " int DST_X = (linear_spatial % args.task_size_x) * " + std::to_string(block_size.x) + + ";\n"; + } + if (work_group_launch_order[1] == 1) + { + c += " int DST_S = get_global_id(1) * " + std::to_string(block_size.w) + ";\n"; + } + else + { + c += " int DST_S = (get_group_id(" + std::to_string(launch_remap[1]) + + ") * get_local_size(1) + get_local_id(1)) * " + std::to_string(block_size.w) + ";\n"; + } + } + else + { + if (work_group_launch_order[0] == 0) + { + c += " int DST_X = get_global_id(0) * " + std::to_string(block_size.x) + ";\n"; + } + else + { + c += " int DST_X = (get_group_id(" + std::to_string(launch_remap[0]) + + ") * get_local_size(0) + get_local_id(0)) * " + std::to_string(block_size.x) + ";\n"; + } + std::string global_id_1; + if (work_group_launch_order[1] == 1) + { + global_id_1 = "get_global_id(1)"; + } + else + { + global_id_1 = "(get_group_id(" + std::to_string(launch_remap[1]) + + ") * get_local_size(1) + get_local_id(1))"; + } + if (need_depth) + { + c += " int linear_id_1 = " + global_id_1 + ";\n"; + c += + " int DST_Z = (linear_id_1 / args.task_size_y) * " + std::to_string(block_size.z) + ";\n"; + c += + " int DST_Y = (linear_id_1 % args.task_size_y) * " + std::to_string(block_size.y) + ";\n"; + } + else + { + c += " int DST_Y = " + global_id_1 + " * " + std::to_string(block_size.y) + ";\n"; + } + if (work_group_launch_order[2] == 2) + { + c += " int DST_S = get_global_id(2) * " + std::to_string(block_size.w) + ";\n"; + } + else + { + c += " int DST_S = (get_group_id(" + std::to_string(launch_remap[2]) + + ") * get_local_size(2) + get_local_id(2)) * " + std::to_string(block_size.w) + ";\n"; + } + } + + return c; +} +} // namespace + +ConvPowerVR::ConvPowerVR(const OperationDef &definition, const Convolution2DAttributes &attr, + const DeviceInfo &device_info, const BHWC *dst_shape) + : GPUOperation(definition), stride_(attr.strides.w, attr.strides.h, 1, 1), + padding_(-attr.padding.prepended.w, -attr.padding.prepended.h, 0, 0), + kernel_size_(attr.weights.shape.w, attr.weights.shape.h, 1, 1), + dilation_(attr.dilations.w, attr.dilations.h, 1, 1), + conv_params_(GuessBestParams(device_info, definition, attr, dst_shape)) +{ +} + +ConvPowerVR::ConvPowerVR(const OperationDef &definition, const Convolution2DAttributes &attr, + const BHWC &weights_shape, const DeviceInfo &device_info, + const BHWC *dst_shape) + : GPUOperation(definition), stride_(attr.strides.w, attr.strides.h, 1, 1), + padding_(-attr.padding.prepended.w, -attr.padding.prepended.h, 0, 0), + kernel_size_(weights_shape.w, weights_shape.h, 1, 1), + dilation_(attr.dilations.w, attr.dilations.h, 1, 1), + conv_params_(GuessBestParams(device_info, definition, attr, weights_shape, dst_shape)) +{ +} + +ConvPowerVR::ConvPowerVR(const OperationDef &definition, const FullyConnectedAttributes &attr, + const DeviceInfo &device_info, const BHWC *dst_shape) + : GPUOperation(definition), stride_(1, 1, 1, 1), padding_(0, 0, 0, 0), kernel_size_(1, 1, 1, 1), + dilation_(1, 1, 1, 1), conv_params_(GuessBestParams(device_info, definition, attr, dst_shape)) +{ +} + +ConvPowerVR::ConvPowerVR(const OperationDef &definition) + : GPUOperation(definition), stride_(1, 1, 1, 1), padding_(0, 0, 0, 0), kernel_size_(1, 1, 1, 1), + dilation_(1, 1, 1, 1) +{ +} + +ConvPowerVR::ConvPowerVR(ConvPowerVR &&operation) + : GPUOperation(std::move(operation)), stride_(operation.stride_), padding_(operation.padding_), + kernel_size_(operation.kernel_size_), dilation_(operation.dilation_), + conv_params_(operation.conv_params_) +{ +} + +ConvPowerVR::ConvPowerVR(const OperationDef &definition, const Convolution3DAttributes &attr, + const DeviceInfo &device_info, const BHWDC *dst_shape) + : GPUOperation(definition), stride_(attr.strides.w, attr.strides.h, attr.strides.d, 1), + padding_(-attr.padding.prepended.w, -attr.padding.prepended.h, -attr.padding.prepended.d, 0), + kernel_size_(attr.weights.shape.w, attr.weights.shape.h, attr.weights.shape.d, 1), + dilation_(attr.dilations.w, attr.dilations.h, attr.dilations.d, 1), + conv_params_(GuessBestParams(device_info, definition, attr, dst_shape)) +{ +} + +ConvPowerVR &ConvPowerVR::operator=(ConvPowerVR &&operation) +{ + if (this != &operation) + { + std::swap(stride_, operation.stride_); + std::swap(padding_, operation.padding_); + std::swap(kernel_size_, operation.kernel_size_); + std::swap(dilation_, operation.dilation_); + std::swap(conv_params_, operation.conv_params_); + GPUOperation::operator=(std::move(operation)); + } + return *this; +} + +void ConvPowerVR::GenerateCode(const DeviceInfo &device_info) +{ + if (conv_params_.linear_spatial) + { + grid_dimension_ = 2; + } + const bool stride_correction = definition_.IsBatchSupported() && stride_.x != 1; + code_ = GenerateConv(device_info, definition_, stride_correction, conv_params_); + if (definition_.precision == CalculationsPrecision::F16 && device_info.IsPowerVR()) + { + compiler_options_.push_back(CompilerOptions::POWERVR_FP16); + } + if (conv_params_.IsPrivateMemBroadcast() && device_info.IsCL20OrHigher()) + { + compiler_options_.push_back(CompilerOptions::CL_2_0); + } + bool kernel_is_trivial = conv_params_.x_kernel_is_1 && conv_params_.y_kernel_is_1; + if (definition_.src_tensors[0].HasAxis(Axis::DEPTH)) + { + kernel_is_trivial = kernel_is_trivial & conv_params_.z_kernel_is_1; + } + if (device_info.IsAdreno3xx() && definition_.precision == CalculationsPrecision::F16 && + kernel_is_trivial) + { + compiler_options_.push_back(CompilerOptions::ADRENO_FULL_SIMD_LINE); + } +} + +absl::Status ConvPowerVR::BindArguments(ArgumentsBinder *args) +{ + if (!conv_params_.x_kernel_is_1) + { + RETURN_IF_ERROR(args->SetInt("stride_x", stride_.x)); + RETURN_IF_ERROR(args->SetInt("padding_x", padding_.x * src_[0]->Batch())); + RETURN_IF_ERROR(args->SetInt("kernel_size_x", kernel_size_.x)); + RETURN_IF_ERROR(args->SetInt("dilation_x", dilation_.x * src_[0]->Batch())); + } + if (!conv_params_.y_kernel_is_1) + { + RETURN_IF_ERROR(args->SetInt("stride_y", stride_.y)); + RETURN_IF_ERROR(args->SetInt("padding_y", padding_.y)); + RETURN_IF_ERROR(args->SetInt("kernel_size_y", kernel_size_.y)); + RETURN_IF_ERROR(args->SetInt("dilation_y", dilation_.y)); + } + if (definition_.src_tensors[0].HasAxis(Axis::DEPTH) && !conv_params_.z_kernel_is_1) + { + RETURN_IF_ERROR(args->SetInt("stride_z", stride_.z)); + RETURN_IF_ERROR(args->SetInt("padding_z", padding_.z)); + RETURN_IF_ERROR(args->SetInt("kernel_size_z", kernel_size_.z)); + RETURN_IF_ERROR(args->SetInt("dilation_z", dilation_.z)); + } + if (conv_params_.linear_spatial) + { + const int grid_x = + DivideRoundUp(dst_[0]->Width() * dst_[0]->Batch(), conv_params_.block_size.x); + RETURN_IF_ERROR(args->SetInt("task_size_x", grid_x)); + } + if (definition_.src_tensors[0].HasAxis(Axis::DEPTH)) + { + const int task_size_y = DivideRoundUp(dst_[0]->Height(), conv_params_.block_size.y); + RETURN_IF_ERROR(args->SetInt("task_size_y", task_size_y)); + } + return absl::OkStatus(); +} + +int3 ConvPowerVR::GetGridSize() const +{ + const int task_size_x = + DivideRoundUp(dst_[0]->Width() * dst_[0]->Batch(), conv_params_.block_size.x); + const int task_size_y = DivideRoundUp(dst_[0]->Height(), conv_params_.block_size.y); + const int task_size_z = DivideRoundUp(dst_[0]->Depth(), conv_params_.block_size.z); + const int task_size_s = DivideRoundUp(dst_[0]->Slices(), conv_params_.block_size.w); + int3 wg; + + if (conv_params_.linear_spatial) + { + int grid_x = task_size_x * task_size_y; + if (definition_.src_tensors[0].HasAxis(Axis::DEPTH)) + { + grid_x *= task_size_z; + } + return int3(grid_x, task_size_s, 1); + } + else + { + int grid_y = task_size_y; + if (definition_.src_tensors[0].HasAxis(Axis::DEPTH)) + { + grid_y *= task_size_z; + } + return int3(task_size_x, grid_y, task_size_s); + } +} + +void ConvPowerVR::GetPossibleKernelWorkGroups(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const +{ + if (conv_params_.weights_upload_type == WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP || + conv_params_.weights_upload_type == WeightsUploadType::LOCAL_MEM_BY_THREADS || + conv_params_.fixed_work_group_size) + { + work_groups->push_back(work_group_size_); + return; + } + GetPossibleWorkGroupsConv(tuning_type, device_info, kernel_info, grid_size_, work_groups); +} + +std::string ConvPowerVR::GenerateConv(const DeviceInfo &device_info, const OperationDef &op_def, + bool stride_correction, const ConvParams &conv_params) +{ + auto src_desc = op_def.src_tensors[0]; + src_desc.SetTextureAddressMode(TextureAddressMode::ZERO); + if (op_def.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + AddSrcTensor("src_tensor", src_desc); + if (op_def.src_tensors.size() == 2) + { + // dynamic weights + BufferDescriptor desc; + desc.element_type = op_def.src_tensors[1].data_type; + desc.element_size = 4; + desc.memory_type = + conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::CONSTANT_MEM + ? MemoryType::CONSTANT + : MemoryType::GLOBAL; + + AddSrcBuffer("weights", desc); + } + + const auto &src_def = op_def.src_tensors[0]; + + auto generate_id = [&](const std::string &x, const std::string &y, const std::string &z) { + std::string id; + if (src_def.HasAxis(Axis::WIDTH)) + { + id += "_w" + x; + } + if (src_def.HasAxis(Axis::HEIGHT)) + { + id += "_h" + y; + } + if (src_def.HasAxis(Axis::DEPTH)) + { + id += "_d" + z; + } + return id; + }; + + auto generate_id_full = [&](const std::string &x, const std::string &y, const std::string &z, + const std::string &s) { return generate_id(x, y, z) + "_s" + s; }; + + auto generate_check = [&](const std::string &x, const std::string &y, const std::string &z) { + std::string check; + const std::vector<Axis> axes{Axis::WIDTH, Axis::HEIGHT, Axis::DEPTH}; + const std::vector<std::string> names{"in_x", "in_y", "in_z"}; + const std::vector<bool> is_1{conv_params_.x_kernel_is_1, conv_params_.y_kernel_is_1, + conv_params_.z_kernel_is_1}; + const std::vector<std::string> coords{x, y, z}; + for (size_t i = 0; i < axes.size(); ++i) + { + const auto &axis = axes[i]; + if (src_def.HasAxis(axis) && !src_def.SupportsZeroClamp(axis) && !is_1[i]) + { + if (!check.empty()) + { + check += " && "; + } + check += names[i] + coords[i]; + } + } + return check; + }; + + auto dst_desc = op_def.dst_tensors[0]; + if (op_def.IsBatchSupported()) + { + dst_desc.SetStateVar("BatchedWidth", "true"); + } + AddDstTensor("dst_tensor", dst_desc); + + if (!conv_params_.x_kernel_is_1) + { + args_.AddInt("stride_x"); + args_.AddInt("padding_x"); + args_.AddInt("kernel_size_x"); + args_.AddInt("dilation_x"); + } + if (!conv_params_.y_kernel_is_1) + { + args_.AddInt("stride_y"); + args_.AddInt("padding_y"); + args_.AddInt("kernel_size_y"); + args_.AddInt("dilation_y"); + } + if (src_def.HasAxis(Axis::DEPTH) && !conv_params_.z_kernel_is_1) + { + args_.AddInt("stride_z"); + args_.AddInt("padding_z"); + args_.AddInt("kernel_size_z"); + args_.AddInt("dilation_z"); + } + if (conv_params_.linear_spatial) + { + args_.AddInt("task_size_x"); + } + if (src_def.HasAxis(Axis::DEPTH)) + { + args_.AddInt("task_size_y"); + } + + const bool need_local_mem = + conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::LOCAL_MEM_BY_THREADS || + conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP; + + const int local_mem_size = conv_params.block_size.w * 4 * conv_params.src_depth_loop_size; + + const bool use_simd_broadcast = conv_params.IsPrivateMemBroadcast(); + const int simd_size = conv_params.simd_size; + + const bool late_oob_check = need_local_mem || use_simd_broadcast; + + const std::string weights_space = + conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::CONSTANT_MEM ? "__constant" + : "__global"; + + const std::string weights_data_type = + conv_params.weights_data_type == DataType::FLOAT32 ? "float4" : "half4"; + + const std::string weights_global_ptr = weights_space + " " + weights_data_type + "*"; + + std::string c = GetCommonDefines(op_def.precision); + if (use_simd_broadcast) + { + if (device_info.cl_version == OpenCLVersion::CL_2_0) + { + c += "#pragma OPENCL EXTENSION cl_khr_subgroups : enable\n"; + } + else if (device_info.SupportsExtension("cl_intel_subgroups")) + { + c += "#pragma OPENCL EXTENSION cl_intel_subgroups : enable\n"; + } + } + const int4 block_size = conv_params.block_size; + if (conv_params.fixed_work_group_size) + { + c += "__attribute__((reqd_work_group_size(" + std::to_string(work_group_size_.x) + ", " + + std::to_string(work_group_size_.y) + ", " + std::to_string(work_group_size_.z) + ")))\n"; + } + if (use_simd_broadcast && device_info.IsIntel()) + { + c += "__attribute__((intel_reqd_sub_group_size(" + std::to_string(simd_size) + ")))\n"; + } + std::string dst_oob_check; + if (src_def.HasAxis(Axis::DEPTH)) + { + if (conv_params.linear_spatial) + { + dst_oob_check = "DST_Z >= args.dst_tensor.Depth() || DST_S >= " + "args.dst_tensor.Slices()"; + } + else + { + dst_oob_check = "DST_X >= args.dst_tensor.Width() || DST_Z >= " + "args.dst_tensor.Depth() || DST_S >= args.dst_tensor.Slices()"; + } + } + else + { + if (conv_params.linear_spatial) + { + dst_oob_check = "DST_Y >= args.dst_tensor.Height() || DST_S >= " + "args.dst_tensor.Slices()"; + } + else + { + dst_oob_check = "DST_X >= args.dst_tensor.Width() || DST_Y >= " + "args.dst_tensor.Height() || DST_S >= args.dst_tensor.Slices()"; + } + } + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += GenerateBlockCoords(conv_params.block_size, work_group_launch_order_, + conv_params.linear_spatial, src_def.HasAxis(Axis::DEPTH)); + if (!late_oob_check) + { + c += " if (" + dst_oob_check + ") {\n"; + c += " return;\n"; + c += " }\n"; + } + if (conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::LOCAL_MEM_BY_THREADS) + { + if (conv_params.linear_spatial) + { + c += " int lid = get_local_id(0);\n"; + } + else + { + c += " int lid = get_local_id(1) * " + std::to_string(work_group_size_.x) + + " + get_local_id(0);\n"; + } + } + if (use_simd_broadcast) + { + c += " int simd_id = get_sub_group_local_id();\n"; + } + for (int s = 0; s < block_size.w; ++s) + { + const std::string sind = std::to_string(s); + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + c += " ACCUM_FLT4 r" + generate_id_full(xind, yind, zind, sind) + + " = (ACCUM_FLT4)(0.0f, 0.0f, 0.0f, 0.0f);\n"; + } + } + } + } + if (!conv_params_.x_kernel_is_1) + { + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + const std::string xc = "(DST_X + " + xind + ")"; + if (stride_correction) + { + c += " int xc" + xind + " = " + + GetXStrideCorrected(xc, "args.src_tensor.Batch()", "args.stride_x", "args.padding_x") + + ";\n"; + } + else + { + c += " int xc" + xind + " = " + xc + " * args.stride_x + args.padding_x;\n"; + } + } + } + else + { + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + c += " int xc" + xind + " = DST_X + " + xind + ";\n"; + if (!src_def.CanReadOutOfBorder(Axis::WIDTH)) + { + c += " xc" + xind + " = clamp(xc" + xind + ", 0, args.src_tensor.Width() - 1);\n"; + } + } + } + if (!conv_params_.y_kernel_is_1) + { + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + const std::string yc = "(DST_Y + " + yind + ")"; + c += " int yc" + yind + " = " + yc + " * args.stride_y + args.padding_y;\n"; + } + } + else + { + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + c += " int yc" + yind + " = DST_Y + " + yind + ";\n"; + if (!src_def.CanReadOutOfBorder(Axis::HEIGHT)) + { + c += " yc" + yind + " = clamp(yc" + yind + ", 0, args.src_tensor.Height() - 1);\n"; + } + } + } + if (src_def.HasAxis(Axis::DEPTH)) + { + if (!conv_params_.z_kernel_is_1) + { + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + const std::string zc = "(DST_Z + " + zind + ")"; + c += " int zc" + zind + " = " + zc + " * args.stride_z + args.padding_z;\n"; + } + } + else + { + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + c += " int zc" + zind + " = DST_Z + " + zind + ";\n"; + if (!src_def.CanReadOutOfBorder(Axis::DEPTH)) + { + c += " zc" + zind + " = clamp(zc" + zind + ", 0, args.src_tensor.Depth() - 1);\n"; + } + } + } + } + bool trivial_kernel_size = conv_params_.x_kernel_is_1 && conv_params_.y_kernel_is_1; + if (src_def.HasAxis(Axis::DEPTH)) + { + trivial_kernel_size = trivial_kernel_size && conv_params_.z_kernel_is_1; + } + if (need_local_mem) + { + c += " __local " + weights_data_type + " weights_cache[" + std::to_string(local_mem_size) + + "];\n"; + } + else if (conv_params.AreWeightsBuffer()) + { + c += " " + weights_global_ptr + " weights_cache;\n"; + } + else if (!trivial_kernel_size) + { + c += " int filter_offset = 0;\n"; + } + if (conv_params.AreWeightsBuffer()) + { + if (conv_params.different_weights_for_height) + { + c += " " + weights_global_ptr + + " filters_loc = args.weights.GetPtr() + (DST_S * " + "args.src_tensor.Height() + DST_Y * " + + std::to_string(block_size.w) + ") * 4 * args.src_tensor.Slices();\n"; + } + else + { + std::string kernel_spatial_offset = ""; + if (!conv_params_.x_kernel_is_1) + { + kernel_spatial_offset += " * args.kernel_size_x"; + } + if (!conv_params_.y_kernel_is_1) + { + kernel_spatial_offset += " * args.kernel_size_y"; + } + if (src_def.HasAxis(Axis::DEPTH) && !conv_params_.z_kernel_is_1) + { + kernel_spatial_offset += " * args.kernel_size_z"; + } + c += " " + weights_global_ptr + + " filters_loc = args.weights.GetPtr() + DST_S * 4 * " + "args.src_tensor.Slices()" + + kernel_spatial_offset + ";\n"; + } + } + if (src_def.HasAxis(Axis::DEPTH) && !conv_params_.z_kernel_is_1) + { + c += " for (int kz = 0; kz < args.kernel_size_z; ++kz) {\n"; + for (int z = 0; z < block_size.z; ++z) + { + const std::string zck = "zck" + std::to_string(z); + c += " int zck" + std::to_string(z) + " = kz * args.dilation_z + zc" + std::to_string(z) + + ";\n"; + if (!src_def.SupportsZeroClamp(Axis::DEPTH)) + { + c += " bool in_z" + std::to_string(z) + " = " + zck + " >= 0 && " + zck + + " < args.src_tensor.Depth();\n"; + if (!src_def.CanReadOutOfBorder(Axis::DEPTH)) + { + c += " " + zck + " = clamp(" + zck + ", 0, args.src_tensor.Depth() - 1);\n"; + } + } + } + } + if (!conv_params_.y_kernel_is_1) + { + c += " for (int ky = 0; ky < args.kernel_size_y; ++ky) {\n"; + for (int y = 0; y < block_size.y; ++y) + { + const std::string yck = "yck" + std::to_string(y); + c += " int " + yck + " = ky * args.dilation_y + yc" + std::to_string(y) + ";\n"; + if (!src_def.SupportsZeroClamp(Axis::HEIGHT)) + { + c += " bool in_y" + std::to_string(y) + " = " + yck + " >= 0 && " + yck + + " < args.src_tensor.Height();\n"; + if (!src_def.CanReadOutOfBorder(Axis::HEIGHT)) + { + c += " " + yck + " = clamp(" + yck + ", 0, args.src_tensor.Height() - 1);\n"; + } + } + } + } + if (!conv_params_.x_kernel_is_1) + { + c += " for (int kx = 0; kx < args.kernel_size_x; ++kx) {\n"; + for (int x = 0; x < block_size.x; ++x) + { + const std::string xck = "xck" + std::to_string(x); + c += " int xck" + std::to_string(x) + " = kx * args.dilation_x + xc" + std::to_string(x) + + ";\n"; + if (!src_def.SupportsZeroClamp(Axis::WIDTH)) + { + c += " bool in_x" + std::to_string(x) + " = " + xck + " >= 0 && " + xck + + " < args.src_tensor.Width();\n"; + if (!src_def.CanReadOutOfBorder(Axis::WIDTH)) + { + c += " " + xck + " = clamp(" + xck + ", 0, args.src_tensor.Width() - 1);\n"; + } + } + } + } + const bool need_multiple_slice_strides = + src_def.ReturnsZeroForNegOneRead() && !trivial_kernel_size; + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + std::string xc = conv_params.x_kernel_is_1 ? "xc" + xind : "xck" + xind; + std::string yc = conv_params.y_kernel_is_1 ? "yc" + yind : "yck" + yind; + const std::string id = generate_id(xind, yind, zind); + std::string coords = "" + xc + ", " + yc; + if (src_def.HasAxis(Axis::DEPTH)) + { + std::string zc = conv_params.z_kernel_is_1 ? "zc" + zind : "zck" + zind; + coords += ", " + zc; + } + if (src_def.IsLinear()) + { + c += " args.src_tensor.GetAddress(addr" + id + ", " + coords + ", 0);\n"; + if (need_multiple_slice_strides) + { + const std::string check = generate_check(xind, yind, zind); + c += " addr" + id + " = select(-1, addr" + id + ", (" + check + "));\n"; + c += + " int ds" + id + " = select(0, args.src_tensor.SliceStride(), (" + check + "));\n"; + } + } + } + } + } + if (src_def.IsLinear() && !need_multiple_slice_strides) + { + c += " int ds = args.src_tensor.SliceStride();\n"; + } + + auto declare_src = [&]() { + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + const std::string id = generate_id(xind, yind, zind); + c += " " + weights_data_type + " src" + id + ";\n"; + } + } + } + }; + const bool conditional_read = device_info.IsMali(); + auto read_src = [&]() { + const std::string cl_type = ToCLDataType(conv_params.weights_data_type); + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + std::string id = generate_id(xind, yind, zind); + const std::string check = generate_check(xind, yind, zind); + std::string address; + if (src_def.IsLinear()) + { + address = "addr" + id; + } + else + { + std::string xc = conv_params.x_kernel_is_1 ? "xc" + xind : "xck" + xind; + std::string yc = conv_params.y_kernel_is_1 ? "yc" + yind : "yck" + yind; + address = "" + xc + ", " + yc; + if (src_def.HasAxis(Axis::DEPTH)) + { + std::string zc = conv_params.z_kernel_is_1 ? "zc" + zind : "zck" + zind; + address += ", " + zc; + } + address += ", s"; + } + if (src_def.ReturnsZeroForNegOneRead()) + { + c += " src" + id + " = args.src_tensor.Read<" + cl_type + ">(" + address + ");\n"; + const std::string ds = trivial_kernel_size ? "ds" : "ds" + id; + c += " " + address + " += " + ds + ";\n"; + } + else + { + if (!check.empty()) + { + if (conditional_read) + { + c += " src" + id + " = " + check + " ? args.src_tensor.Read<" + cl_type + ">(" + + address + ") : (FLT4)(0.0f);\n"; + } + else + { + c += " src" + id + " = args.src_tensor.Read<" + cl_type + ">(" + address + + ") * (FLT)(" + check + ");\n"; + } + } + else + { + c += " src" + id + " = args.src_tensor.Read<" + cl_type + ">(" + address + ");\n"; + } + if (src_def.IsLinear()) + { + c += " " + address + " += ds;\n"; + } + } + } + } + } + }; + const bool weights_type_as_accum_type = !(op_def.precision == CalculationsPrecision::F32_F16 && + conv_params.weights_data_type == DataType::FLOAT16); + auto conv_core = [&](int shared_offset) { + const std::string channels[] = {"x", "y", "z", "w"}; + for (int s = 0; s < block_size.w; ++s) + { + const std::string sind = std::to_string(s); + if (weights_type_as_accum_type) + { + for (int ch = 0; ch < 4; ++ch) + { + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + std::string R = "r" + generate_id_full(xind, yind, zind, sind); + std::string S = "src" + generate_id(xind, yind, zind); + if (use_simd_broadcast) + { + int simd_id = (s * 4 + ch + shared_offset) / simd_size; + int thread_id = (s * 4 + ch + shared_offset) % simd_size; + std::string w_val_x = "sub_group_broadcast(simd_w" + std::to_string(simd_id) + + ".x, " + std::to_string(thread_id) + "u)"; + std::string w_val_y = "sub_group_broadcast(simd_w" + std::to_string(simd_id) + + ".y, " + std::to_string(thread_id) + "u)"; + std::string w_val_z = "sub_group_broadcast(simd_w" + std::to_string(simd_id) + + ".z, " + std::to_string(thread_id) + "u)"; + std::string w_val_w = "sub_group_broadcast(simd_w" + std::to_string(simd_id) + + ".w, " + std::to_string(thread_id) + "u)"; + c += " " + R + ".x += " + w_val_x + " * " + S + "." + channels[ch] + ";\n"; + c += " " + R + ".y += " + w_val_y + " * " + S + "." + channels[ch] + ";\n"; + c += " " + R + ".z += " + w_val_z + " * " + S + "." + channels[ch] + ";\n"; + c += " " + R + ".w += " + w_val_w + " * " + S + "." + channels[ch] + ";\n"; + } + else + { + const std::string weight_id = std::to_string(s * 4 + ch + shared_offset); + std::string w_val; + if (conv_params.AreWeightsBuffer()) + { + w_val = "weights_cache[" + weight_id + "]"; + } + else + { + w_val = "f" + weight_id; + } + c += " " + R + " += " + w_val + " * " + S + "." + channels[ch] + ";\n"; + } + } + } + } + } + } + else + { // F32_F16 precision and weights type is float16 + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + std::string R = "r" + generate_id_full(xind, yind, zind, sind); + std::string S = "src" + generate_id(xind, yind, zind); + std::vector<std::string> F(4); + for (int i = 0; i < 4; ++i) + { + std::string weight_id = std::to_string(s * 4 + i + shared_offset); + if (conv_params.AreWeightsBuffer()) + { + F[i] = "weights_cache[" + weight_id + "]"; + } + else + { + F[i] = "f" + weight_id; + } + } + c += " " + R + " += convert_float4(" + S + ".x * " + F[0] + " + " + S + ".y * " + + F[1] + " + " + S + ".z * " + F[2] + " + " + S + ".w * " + F[3] + ");\n"; + } + } + } + } + } + }; + + c += " int s = 0;\n"; + c += " do {\n"; + declare_src(); + const int total_work_items = work_group_size_.x * work_group_size_.y * work_group_size_.z; + if (conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP) + { + c += GenerateAsyncUpload("weights_cache", "filters_loc", + /*global_offset_name*/ "", local_mem_size); + } + else if (conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::LOCAL_MEM_BY_THREADS) + { + c += " barrier(CLK_LOCAL_MEM_FENCE);\n"; + c += + GenerateUploadByThreads("weights_cache", "filters_loc", + /*global_offset_name*/ "", "lid", total_work_items, local_mem_size); + } + else if (use_simd_broadcast) + { + int parts = local_mem_size / simd_size; + int reminder = local_mem_size % simd_size; + for (int i = 0; i < parts; ++i) + { + c += " FLT4 simd_w" + std::to_string(i) + " = filters_loc[simd_id + " + + std::to_string(i * simd_size) + "];\n"; + } + if (reminder) + { + c += " FLT4 simd_w" + std::to_string(parts) + ";\n"; + c += " if (simd_id < " + std::to_string(reminder) + ") {\n"; + c += " simd_w" + std::to_string(parts) + " = filters_loc[simd_id + " + + std::to_string(parts * simd_size) + "];\n"; + c += " }\n"; + } + } + else if (conv_params.AreWeightsBuffer()) + { // GLOBAL_MEM/CONSTANT_MEM + c += " weights_cache = filters_loc;\n"; + } + else + { // TEXTURES_MEM + for (int dst_s = 0; dst_s < block_size.w; ++dst_s) + { + std::string f_y = trivial_kernel_size ? "s" : "filter_offset"; + if (conv_params.different_weights_for_height) + { + f_y = "DST_Y * args.src_tensor.Slices() + s"; + } + c += absl::Substitute( + R"( FLT4 f$2 = args.weights0.Read(DST_S + $0, $1); + FLT4 f$3 = args.weights1.Read(DST_S + $0, $1); + FLT4 f$4 = args.weights2.Read(DST_S + $0, $1); + FLT4 f$5 = args.weights3.Read(DST_S + $0, $1); +)", + dst_s, f_y, dst_s * 4 + 0, dst_s * 4 + 1, dst_s * 4 + 2, dst_s * 4 + 3); + } + if (!trivial_kernel_size) + { + c += " filter_offset++;\n"; + } + } + read_src(); + c += " s += 1;\n"; + if (conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::LOCAL_MEM_BY_THREADS) + { + c += " barrier(CLK_LOCAL_MEM_FENCE);\n"; + } + conv_core(0); + for (int i = 1; i < conv_params.src_depth_loop_size; ++i) + { + read_src(); + conv_core(i * block_size.w * 4); + c += " s += 1;\n"; + } + if (conv_params.AreWeightsBuffer()) + { + c += " filters_loc += " + std::to_string(local_mem_size) + ";\n"; + } + c += " } while (s < args.src_tensor.Slices());\n"; + if (!conv_params.x_kernel_is_1) + { + c += " };\n"; + } + if (!conv_params.y_kernel_is_1) + { + c += " };\n"; + } + if (src_def.HasAxis(Axis::DEPTH) && !conv_params_.z_kernel_is_1) + { + c += " };\n"; + } + if (conv_params.AreWeightsBuffer()) + { + if (conv_params.weights_upload_type == ConvPowerVR::WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP) + { + c += GenerateAsyncUpload("weights_cache", "args.biases.GetPtr()", "DST_S", block_size.w); + } + else if (conv_params.weights_upload_type == + ConvPowerVR::WeightsUploadType::LOCAL_MEM_BY_THREADS) + { + c += " barrier(CLK_LOCAL_MEM_FENCE);\n"; + c += GenerateUploadByThreads("weights_cache", "args.biases.GetPtr()", "DST_S", "lid", + total_work_items, block_size.w); + c += " barrier(CLK_LOCAL_MEM_FENCE);\n"; + } + else + { + c += " weights_cache = args.biases.GetPtr() + DST_S;\n"; + } + } + if (late_oob_check) + { + c += " if (" + dst_oob_check + ") {\n"; + c += " return;\n"; + c += " }\n"; + } + + auto generate_dst_check = [&](int x, int y, int z) { + std::string check; + const std::vector<Axis> axes{Axis::WIDTH, Axis::HEIGHT, Axis::DEPTH}; + const std::vector<std::string> names{"Width()", "Height()", "Depth()"}; + std::vector<std::string> coords(3); + coords[0] = "DST_X + " + std::to_string(x); + coords[1] = "DST_Y + " + std::to_string(y); + coords[2] = "DST_Z + " + std::to_string(z); + const std::vector<int> ids{x, y, z}; + for (size_t i = 0; i < axes.size(); ++i) + { + const auto &axis = axes[i]; + if (src_def.HasAxis(axis) && ids[i] != 0) + { + if (!check.empty()) + { + check += " && "; + } + check += coords[i] + " < args.dst_tensor." + names[i]; + } + } + return check; + }; + + for (int s = 0; s < block_size.w; ++s) + { + const std::string sind = std::to_string(s); + c += " if (DST_S + " + sind + " >= args.dst_tensor.Slices()) return;\n"; + c += " {\n"; + if (conv_params.AreWeightsBuffer()) + { + c += " FLT4 bias_val = TO_FLT4(weights_cache[" + sind + "]);\n"; + } + else + { + c += " FLT4 bias_val = args.biases.Read(DST_S + " + sind + ");\n"; + } + for (int z = 0; z < block_size.z; ++z) + { + const std::string zind = std::to_string(z); + for (int y = 0; y < block_size.y; ++y) + { + const std::string yind = std::to_string(y); + for (int x = 0; x < block_size.x; ++x) + { + const std::string xind = std::to_string(x); + const std::string id = generate_id_full(xind, yind, zind, sind); + const std::string check = generate_dst_check(x, y, z); + std::string coords = "DST_X + " + xind + ", DST_Y + " + yind; + if (src_def.HasAxis(Axis::DEPTH)) + { + coords += ", DST_Z + " + zind; + } + coords += ", DST_S + " + sind; + if (!check.empty()) + { + c += " if (" + check + ") {\n"; + } + else + { + c += " {\n"; + } + c += " FLT4 res = TO_FLT4(r" + id + ") + bias_val;\n"; + c += " args.dst_tensor.Write(res, " + coords + ");\n"; + c += " }\n"; + } + } + } + c += " }\n"; + } + c += "}\n"; + return c; +} + +ConvPowerVR::ConvParams +ConvPowerVR::GuessBestParams(const DeviceInfo &device_info, const OperationDef &definition, + int src_depth, int dst_depth, bool x_kernel_is_1, bool y_kernel_is_1, + bool different_weights_for_height, const BHWC *dst_shape) +{ + ConvParams conv_params; + conv_params.linear_spatial = false; + conv_params.weights_data_type = DeduceDataTypeFromPrecision(definition.precision); + conv_params.x_kernel_is_1 = x_kernel_is_1; + conv_params.y_kernel_is_1 = y_kernel_is_1; + conv_params.different_weights_for_height = different_weights_for_height; + if (device_info.IsNvidia()) + { + if (different_weights_for_height) + { + work_group_size_ = int3(32, 1, 1); + work_group_launch_order_ = int3(2, 0, 1); + conv_params.fixed_work_group_size = true; + } + else + { + conv_params.linear_spatial = true; + work_group_size_ = int3(32, 1, 1); + work_group_launch_order_ = int3(1, 0, 2); + conv_params.fixed_work_group_size = true; + } + conv_params.block_size = int4(2, 1, 1, 4); + conv_params.src_depth_loop_size = 1; + conv_params.weights_upload_type = WeightsUploadType::LOCAL_MEM_BY_THREADS; + if (dst_depth % 4 == 0 || dst_depth >= 8) + { + conv_params.block_size.w = 4; + } + else if (dst_depth % 2 == 0 || dst_depth >= 4) + { + conv_params.block_size.w = 2; + } + else + { + conv_params.block_size.w = dst_depth; + } + if (dst_shape) + { + int task_size = dst_shape->w * dst_shape->b * dst_shape->h * dst_depth; + float task_size_per_cu = static_cast<float>(task_size) / device_info.compute_units_count; + int block_size = + conv_params.block_size.x * conv_params.block_size.y * conv_params.block_size.w; + float threads_per_cu = task_size_per_cu / block_size; + float warps_per_cu = threads_per_cu / 32 /*warp_size*/; + if (warps_per_cu < 8.0f) + { + conv_params.block_size.x = 1; + } + if (warps_per_cu < 4.0f && conv_params.block_size.w >= 4) + { + conv_params.block_size.w /= 2; + } + if (warps_per_cu < 2.0f && conv_params.block_size.w >= 2) + { + conv_params.block_size.w /= 2; + } + } + if (src_depth % 2 == 0) + { + conv_params.src_depth_loop_size = 2; + } + if (src_depth % 4 == 0 && conv_params.block_size.w <= 2) + { + conv_params.src_depth_loop_size = 4; + } + } + else if (device_info.IsPowerVR()) + { + if (different_weights_for_height) + { + work_group_size_ = int3(32, 1, 1); + work_group_launch_order_ = int3(2, 0, 1); + conv_params.fixed_work_group_size = true; + } + else + { + conv_params.linear_spatial = true; + work_group_size_ = int3(32, 1, 1); + work_group_launch_order_ = int3(1, 0, 2); + conv_params.fixed_work_group_size = true; + } + conv_params.weights_data_type = + definition.precision == CalculationsPrecision::F16 ? DataType::FLOAT16 : DataType::FLOAT32; + conv_params.block_size = int4(1, 1, 1, 4); + conv_params.src_depth_loop_size = 1; + conv_params.weights_upload_type = WeightsUploadType::LOCAL_MEM_ASYNC_SUBGROUP; + if (dst_depth % 8 == 0 || dst_depth >= 32) + { + conv_params.block_size.w = 8; + } + else if (dst_depth % 4 == 0 || dst_depth >= 8) + { + conv_params.block_size.w = 4; + } + else if (dst_depth % 2 == 0 || dst_depth >= 4) + { + conv_params.block_size.w = 2; + } + else + { + conv_params.block_size.w = dst_depth; + } + if (definition.precision == CalculationsPrecision::F16) + { + conv_params.block_size.w = std::min(4, conv_params.block_size.w); + if (src_depth % 2 == 0) + { + conv_params.src_depth_loop_size = 2; + } + if (src_depth % 4 == 0 && conv_params.block_size.w <= 2) + { + conv_params.src_depth_loop_size = 4; + } + if (conv_params.block_size.w == 1) + { + if (src_depth % 2 == 0) + { + conv_params.src_depth_loop_size = 2; + } + if (src_depth % 4 == 0) + { + conv_params.src_depth_loop_size = 4; + } + if (src_depth <= 8) + { + conv_params.src_depth_loop_size = src_depth; + } + } + conv_params.block_size.x = 2; + } + } + else if (device_info.IsAMD()) + { + if (different_weights_for_height) + { + work_group_size_ = int3(32, 1, 1); + work_group_launch_order_ = int3(2, 0, 1); + conv_params.fixed_work_group_size = true; + } + else + { + work_group_size_ = int3(8, 4, 1); + work_group_launch_order_ = int3(2, 0, 1); + conv_params.fixed_work_group_size = true; + } + + conv_params.block_size = int4(2, 1, 1, 1); + if (x_kernel_is_1 && y_kernel_is_1) + { + conv_params.block_size.y = 2; + } + conv_params.src_depth_loop_size = 1; + conv_params.weights_upload_type = WeightsUploadType::CONSTANT_MEM; + if (dst_depth % 8 == 0 || dst_depth >= 32) + { + conv_params.block_size.w = 8; + } + else if (dst_depth % 4 == 0 || dst_depth >= 8) + { + conv_params.block_size.w = 4; + } + else if (dst_depth % 2 == 0 || dst_depth >= 4) + { + conv_params.block_size.w = 2; + } + else + { + conv_params.block_size.w = 1; + } + if (src_depth % 2 == 0 && src_depth >= 16) + { + conv_params.src_depth_loop_size = 2; + } + } + else if (device_info.IsMali()) + { + int block_size = 2; + if (dst_shape) + { + int task_size = dst_shape->w * dst_shape->b * dst_shape->h * dst_depth; + block_size = GetRecommendedBlockSizeForConv(device_info, definition.precision, task_size); + } + if (!x_kernel_is_1 || !y_kernel_is_1) + { + block_size = std::min(block_size, 4); + } + if (block_size == 8) + { + if (dst_depth == 1 || dst_depth == 3) + { + conv_params.block_size = int4(2, 2, 1, 1); + } + else + { + conv_params.block_size = int4(2, 2, 1, 2); + } + } + else if (block_size == 4) + { + if (dst_depth == 1 || dst_depth == 3) + { + conv_params.block_size = int4(2, 2, 1, 1); + } + else + { + conv_params.block_size = int4(2, 1, 1, 2); + } + } + else if (block_size == 2) + { + conv_params.block_size = int4(2, 1, 1, 1); + } + else + { + conv_params.block_size = int4(1, 1, 1, 1); + } + conv_params.src_depth_loop_size = 1; + MaliInfo mali_info = device_info.mali_info; + if (src_depth % 2 == 0 && block_size <= 2 && !mali_info.IsMidgard()) + { + conv_params.src_depth_loop_size = 2; + } + if (src_depth % 4 == 0 && block_size == 1 && !mali_info.IsMidgard() && + definition.precision == CalculationsPrecision::F16) + { + conv_params.src_depth_loop_size = 4; + } + work_group_size_ = int3(4, 4, 1); + work_group_launch_order_ = int3(0, 1, 2); + conv_params.fixed_work_group_size = false; + conv_params.weights_upload_type = WeightsUploadType::GLOBAL_MEM; + } + else if (device_info.IsAdreno()) + { + conv_params.block_size = int4(2, 2, 1, 2); + if (device_info.IsAdreno3xx()) + { + if (definition.precision == CalculationsPrecision::F16) + { + conv_params.block_size = int4(2, 2, 1, 2); + } + else if (definition.precision == CalculationsPrecision::F32_F16) + { + conv_params.block_size = int4(2, 1, 1, 2); + } + else + { // F32 + conv_params.block_size = int4(2, 2, 1, 1); + } + } + work_group_size_ = int3(8, 2, 1); + work_group_launch_order_ = int3(0, 1, 2); + conv_params.fixed_work_group_size = false; + conv_params.src_depth_loop_size = 1; + if (definition.src_tensors.size() == 2) + { + // dynamic weights supported only with buffers. + conv_params.weights_upload_type = WeightsUploadType::GLOBAL_MEM; + } + else + { + conv_params.weights_upload_type = WeightsUploadType::TEXTURES_MEM_X4; + } + } + else if (device_info.IsIntel()) + { + if (different_weights_for_height) + { + work_group_size_ = int3(16, 1, 1); + work_group_launch_order_ = int3(0, 1, 2); + conv_params.fixed_work_group_size = true; + } + else + { + conv_params.linear_spatial = true; + work_group_size_ = int3(16, 1, 1); + work_group_launch_order_ = int3(0, 1, 2); + conv_params.fixed_work_group_size = true; + } + conv_params.block_size = int4(1, 1, 1, 4); + conv_params.src_depth_loop_size = 1; + int sub_group_size = 16; + const bool supports_subgroups = device_info.SupportsExtension("cl_khr_subgroups") || + device_info.SupportsExtension("cl_intel_subgroups"); + if (definition.precision != CalculationsPrecision::F32_F16 && supports_subgroups && + device_info.SupportsExtension("cl_intel_required_subgroup_size") && + device_info.SupportsSubGroupWithSize(sub_group_size)) + { + conv_params.weights_upload_type = WeightsUploadType::PRIVATE_MEM_SIMD_BROADCAST; + conv_params.simd_size = sub_group_size; + } + else + { + conv_params.weights_upload_type = WeightsUploadType::LOCAL_MEM_BY_THREADS; + } + if (dst_depth % 4 == 0 || dst_depth >= 8) + { + conv_params.block_size.w = 4; + } + else if (dst_depth % 2 == 0 || dst_depth >= 4) + { + conv_params.block_size.w = 2; + } + else + { + conv_params.block_size.w = dst_depth; + } + if (src_depth % 2 == 0) + { + conv_params.src_depth_loop_size = 2; + } + if (src_depth % 4 == 0 && conv_params.block_size.w <= 2) + { + conv_params.src_depth_loop_size = 4; + } + } + else + { + conv_params.block_size = int4(1, 1, 1, 4); + work_group_size_ = int3(8, 2, 1); + work_group_launch_order_ = int3(0, 1, 2); + conv_params.fixed_work_group_size = false; + conv_params.src_depth_loop_size = 1; + conv_params.weights_upload_type = WeightsUploadType::GLOBAL_MEM; + if (dst_depth % 4 == 0 || dst_depth >= 8) + { + conv_params.block_size.w = 4; + } + else if (dst_depth % 2 == 0 || dst_depth >= 4) + { + conv_params.block_size.w = 2; + } + else + { + conv_params.block_size.w = dst_depth; + } + if (src_depth % 2 == 0) + { + conv_params.src_depth_loop_size = 2; + } + if (src_depth % 4 == 0 && conv_params.block_size.w <= 2) + { + conv_params.src_depth_loop_size = 4; + } + } + + return conv_params; +} + +ConvPowerVR::ConvParams ConvPowerVR::GuessBestParams(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *dst_shape) +{ + const int dst_depth = DivideRoundUp(attr.weights.shape.o, 4); + const int src_depth = DivideRoundUp(attr.weights.shape.i, 4); + const bool x_kernel_is_1 = attr.weights.shape.w == 1 && attr.strides.w == 1 && + attr.dilations.w == 1 && attr.padding.prepended.w == 0 && + attr.padding.appended.w == 0; + const bool y_kernel_is_1 = attr.weights.shape.h == 1 && attr.strides.h == 1 && + attr.dilations.h == 1 && attr.padding.prepended.h == 0 && + attr.padding.appended.h == 0; + return GuessBestParams(device_info, definition, src_depth, dst_depth, x_kernel_is_1, + y_kernel_is_1, false, dst_shape); +} + +ConvPowerVR::ConvParams ConvPowerVR::GuessBestParams(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution3DAttributes &attr, + const BHWDC *dst_shape) +{ + const int dst_depth = DivideRoundUp(attr.weights.shape.o, 4); + const int src_depth = DivideRoundUp(attr.weights.shape.i, 4); + const bool x_kernel_is_1 = attr.weights.shape.w == 1 && attr.strides.w == 1 && + attr.dilations.w == 1 && attr.padding.prepended.w == 0 && + attr.padding.appended.w == 0; + const bool y_kernel_is_1 = attr.weights.shape.h == 1 && attr.strides.h == 1 && + attr.dilations.h == 1 && attr.padding.prepended.h == 0 && + attr.padding.appended.h == 0; + const bool z_kernel_is_1 = attr.weights.shape.d == 1 && attr.strides.d == 1 && + attr.dilations.d == 1 && attr.padding.prepended.d == 0 && + attr.padding.appended.d == 0; + + ConvPowerVR::ConvParams result; + BHWC shape; + if (dst_shape) + { + shape.b = dst_shape->b; + shape.h = dst_shape->h * dst_shape->d; + shape.w = dst_shape->w; + shape.c = dst_shape->c; + result = GuessBestParams(device_info, definition, src_depth, dst_depth, x_kernel_is_1, + y_kernel_is_1, false, &shape); + } + else + { + result = GuessBestParams(device_info, definition, src_depth, dst_depth, x_kernel_is_1, + y_kernel_is_1, false, nullptr); + } + result.z_kernel_is_1 = z_kernel_is_1; + return result; +} + +ConvPowerVR::ConvParams ConvPowerVR::GuessBestParams(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC &weights_shape, + const BHWC *dst_shape) +{ + const int dst_depth = DivideRoundUp(weights_shape.b, 4); + const int src_depth = DivideRoundUp(weights_shape.c, 4); + const bool x_kernel_is_1 = weights_shape.w == 1 && attr.strides.w == 1 && attr.dilations.w == 1 && + attr.padding.prepended.w == 0 && attr.padding.appended.w == 0; + const bool y_kernel_is_1 = weights_shape.h == 1 && attr.strides.h == 1 && attr.dilations.h == 1 && + attr.padding.prepended.h == 0 && attr.padding.appended.h == 0; + return GuessBestParams(device_info, definition, src_depth, dst_depth, x_kernel_is_1, + y_kernel_is_1, false, dst_shape); +} + +ConvPowerVR::ConvParams ConvPowerVR::GuessBestParams(const DeviceInfo &device_info, + const OperationDef &definition, + const FullyConnectedAttributes &attr, + const BHWC *dst_shape) +{ + const int dst_depth = DivideRoundUp(attr.weights.shape.o, 4); + const int src_depth = DivideRoundUp(attr.weights.shape.i, 4); + ConvPowerVR::ConvParams params = + GuessBestParams(device_info, definition, src_depth, dst_depth, true, true, false, dst_shape); + work_group_size_.x *= work_group_size_.y; + work_group_size_.y = 1; + params.block_size.x *= params.block_size.y; + params.block_size.y = 1; + return params; +} + +ConvPowerVR::ConvParams ConvPowerVR::GuessBestParamsWinograd(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *dst_shape) +{ + const int dst_depth = DivideRoundUp(attr.weights.shape.o, 4); + const int src_depth = DivideRoundUp(attr.weights.shape.i, 4); + ConvPowerVR::ConvParams params = + GuessBestParams(device_info, definition, src_depth, dst_depth, true, true, true, dst_shape); + params.block_size.x *= params.block_size.y; + params.block_size.y = 1; + return params; +} + +ConvPowerVR CreateConvPowerVR(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC *dst_shape) +{ + ConvPowerVR result(definition, attr, device_info, dst_shape); + result.GenerateCode(device_info); + result.UploadData(attr.weights, attr.bias); + return result; +} + +ConvPowerVR CreateConvPowerVR(const DeviceInfo &device_info, const OperationDef &definition, + const FullyConnectedAttributes &attr, const BHWC *dst_shape) +{ + ConvPowerVR result(definition, attr, device_info, dst_shape); + result.GenerateCode(device_info); + result.UploadData(attr.weights, attr.bias); + return result; +} + +ConvPowerVR CreateConvPowerVRDynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC &weights_shape, const BHWC *dst_shape) +{ + ConvPowerVR result(definition, attr, weights_shape, device_info, dst_shape); + result.GenerateCode(device_info); + result.UploadBias(attr.bias); + return result; +} + +ConvPowerVR CreateConvPowerVRWino4x4To6x6(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *dst_shape) +{ + ConvPowerVR result(definition); + result.conv_params_ = result.GuessBestParamsWinograd(device_info, definition, attr, dst_shape); + result.GenerateCode(device_info); + result.UploadDataForWinograd4x4To6x6(attr.weights); + return result; +} + +ConvPowerVR CreateConvPowerVR3D(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution3DAttributes &attr, const BHWDC *dst_shape) +{ + ConvPowerVR result(definition, attr, device_info, dst_shape); + result.GenerateCode(device_info); + result.UploadWeights(attr.weights); + result.UploadBias(attr.bias); + return result; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvPowervr.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvPowervr.h new file mode 100644 index 000000000..f83f05730 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvPowervr.h @@ -0,0 +1,413 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_POWERVR_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_POWERVR_H__ + +#include <cstring> +#include <vector> + +#include "open_cl/Buffer.h" +#include "open_cl/ClDevice.h" +#include "open_cl/kernels/ConvCommon.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/kernels/Util.h" +#include "open_cl/LinearStorage.h" +#include "open_cl/Tensor.h" +#include "open_cl/Texture2d.h" +#include "open_cl/Util.h" +#include "open_cl/DataType.h" +#include "open_cl/Operations.h" +#include "open_cl/Shape.h" +#include "open_cl/Status.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" +#include "open_cl/WinogradUtil.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class ConvPowerVR : public GPUOperation +{ +public: + ConvPowerVR() = default; + void GetPossibleKernelWorkGroups(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const override; + absl::Status BindArguments(ArgumentsBinder *args) override; + int3 GetGridSize() const override; + + ConvWeightsDescription GetConvWeightsDescription() const + { + ConvWeightsDescription desc; + desc.layout = ConvWeightsLayout::kOHWIOGroupI4O4; + desc.output_group_size = conv_params_.block_size.w; + return desc; + } + + // Move only + ConvPowerVR(ConvPowerVR &&operation); + ConvPowerVR &operator=(ConvPowerVR &&operation); + ConvPowerVR(const ConvPowerVR &) = delete; + ConvPowerVR &operator=(const ConvPowerVR &) = delete; + +private: + enum class WeightsUploadType + { + LOCAL_MEM_ASYNC_SUBGROUP, // we use it for PowerVR with workgroup size = 32 + LOCAL_MEM_BY_THREADS, + GLOBAL_MEM, + CONSTANT_MEM, + PRIVATE_MEM_SIMD_BROADCAST, + TEXTURES_MEM_X4, // 4 textures for weights + }; + + struct ConvParams + { + // Usually we use this combinations for CalculationPrecision: + // F32: all F32 + // F16: all F16 + // F32_F16: all besides accumulator is F16, including weights + // But for PowerVR we can achieve better performance in F32_F16 with F32 + // weights, so for PowerVR in this kernel we have F32 weights for + // F32_F16 precision mode + DataType weights_data_type; // used for weights and biases + int4 block_size; // WHDS + bool fixed_work_group_size; + bool linear_spatial; // spatial dimensions are Width/Height/Depth + bool different_weights_for_height; + int src_depth_loop_size; + WeightsUploadType weights_upload_type; + bool x_kernel_is_1; + bool y_kernel_is_1; + bool z_kernel_is_1; + + // used only with PRIVATE_MEM_SIMD_BROADCAST + int simd_size = 1; + + bool AreWeightsBuffer() const + { + return weights_upload_type != WeightsUploadType::TEXTURES_MEM_X4; + } + + bool IsPrivateMemBroadcast() const + { + return weights_upload_type == WeightsUploadType::PRIVATE_MEM_SIMD_BROADCAST; + } + }; + + ConvPowerVR(const OperationDef &definition, const Convolution2DAttributes &attr, + const DeviceInfo &device_info, const BHWC *dst_shape = nullptr); + ConvPowerVR(const OperationDef &definition, const Convolution2DAttributes &attr, + const BHWC &weights_shape, const DeviceInfo &device_info, + const BHWC *dst_shape = nullptr); + ConvPowerVR(const OperationDef &definition, const FullyConnectedAttributes &attr, + const DeviceInfo &device_info, const BHWC *dst_shape = nullptr); + explicit ConvPowerVR(const OperationDef &definition); + ConvPowerVR(const OperationDef &definition, const Convolution3DAttributes &attr, + const DeviceInfo &device_info, const BHWDC *dst_shape = nullptr); + + void GenerateCode(const DeviceInfo &device_info); + + template <DataType T> + void UploadData(const InternalTensor<OHWI, T> &weights, const InternalTensor<Linear, T> &biases); + template <DataType T> void UploadDataForWinograd4x4To6x6(const InternalTensor<OHWI, T> &weights); + + template <DataType T> void UploadWeights(const InternalTensor<OHWI, T> &weights); + + template <DataType T> void UploadWeights(const InternalTensor<OHWDI, T> &weights); + + template <DataType T> void UploadBias(const InternalTensor<Linear, T> &bias); + + friend ConvPowerVR CreateConvPowerVR(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC *dst_shape); + + friend ConvPowerVR CreateConvPowerVR(const DeviceInfo &device_info, + const OperationDef &definition, + const FullyConnectedAttributes &attr, const BHWC *dst_shape); + + friend ConvPowerVR CreateConvPowerVRDynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC &weights_shape, + const BHWC *dst_shape); + + friend ConvPowerVR CreateConvPowerVRWino4x4To6x6(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *dst_shape); + + friend ConvPowerVR CreateConvPowerVR3D(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution3DAttributes &attr, + const BHWDC *dst_shape); + + ConvParams GuessBestParams(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC *dst_shape = nullptr); + ConvParams GuessBestParams(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC &weights_shape, + const BHWC *dst_shape = nullptr); + ConvParams GuessBestParams(const DeviceInfo &device_info, const OperationDef &definition, + const FullyConnectedAttributes &attr, const BHWC *dst_shape = nullptr); + ConvParams GuessBestParamsWinograd(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *dst_shape = nullptr); + ConvParams GuessBestParams(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution3DAttributes &attr, const BHWDC *dst_shape = nullptr); + ConvParams GuessBestParams(const DeviceInfo &device_info, const OperationDef &definition, + int src_depth, int dst_depth, bool x_kernel_is_1, bool y_kernel_is_1, + bool different_weights_for_height, const BHWC *dst_shape = nullptr); + + std::string GenerateConv(const DeviceInfo &device_info, const OperationDef &op_def, + bool stride_correction, const ConvParams &conv_params); + + int4 stride_; + int4 padding_; + int4 kernel_size_; + int4 dilation_; + ConvParams conv_params_; +}; + +template <DataType T> +void ConvPowerVR::UploadData(const InternalTensor<OHWI, T> &weights, + const InternalTensor<Linear, T> &biases) +{ + UploadWeights(weights); + UploadBias(biases); +} + +template <DataType T> +void ConvPowerVR::UploadDataForWinograd4x4To6x6(const InternalTensor<OHWI, T> &weights) +{ + InternalTensor<OHWI, T> wino_weights; + RearrangeWeightsToWinograd4x4To6x6Weights(weights, &wino_weights); + UploadWeights(wino_weights); + InternalTensor<Linear, DataType::FLOAT32> biases; + biases.shape = Linear(weights.shape.o); + biases.data.resize(weights.shape.o, 0.0f); + UploadBias(biases); +} + +template <DataType T> void ConvPowerVR::UploadBias(const InternalTensor<Linear, T> &bias) +{ + BufferDescriptor desc; + desc.element_type = conv_params_.weights_data_type; + desc.element_size = 4; + desc.memory_type = + conv_params_.weights_upload_type == ConvPowerVR::WeightsUploadType::CONSTANT_MEM + ? MemoryType::CONSTANT + : MemoryType::GLOBAL; + const int float_size = sizeof(float); + // TODO + // conv_params_.weights_data_type == DataType::FLOAT32 ? sizeof(float) : sizeof(half); + int aligned_channels = AlignByN(bias.shape.v, 4 * conv_params_.block_size.w); + desc.size = float_size * aligned_channels; + desc.data.resize(desc.size); + if (conv_params_.weights_data_type == DataType::FLOAT32) + { + float *gpu_data = reinterpret_cast<float *>(desc.data.data()); + for (int i = 0; i < aligned_channels; ++i) + { + gpu_data[i] = i < bias.shape.v ? bias.data[i] : 0.0f; + } + } + // else + // { + // half *gpu_data = reinterpret_cast<half *>(desc.data.data()); + // for (int i = 0; i < aligned_channels; ++i) + // { + // gpu_data[i] = i < bias.shape.v ? bias.data[i] : 0.0f; + // } + // } + args_.AddObject("biases", absl::make_unique<BufferDescriptor>(std::move(desc))); +} + +template <DataType T> void ConvPowerVR::UploadWeights(const InternalTensor<OHWI, T> &weights) +{ + const int dst_slices = AlignByN(DivideRoundUp(weights.shape.o, 4), conv_params_.block_size.w); + const int src_slices = DivideRoundUp(weights.shape.i, 4); + + const bool f32_weights = conv_params_.weights_data_type == DataType::FLOAT32; + const int float4_size = sizeof(float4); + // TODO + // f32_weights ? sizeof(float4) : sizeof(half4); + + const int elements_count = weights.shape.h * weights.shape.w * src_slices * dst_slices * 4; + + std::vector<uint8_t> data(float4_size * elements_count); + + if (f32_weights) + { + float4 *ptr = reinterpret_cast<float4 *>(data.data()); + if (conv_params_.AreWeightsBuffer()) + { + RearrangeWeightsToOHWIOGroupI4O4(weights, conv_params_.block_size.w, + absl::MakeSpan(ptr, elements_count)); + } + else + { + RearrangeWeightsToI4HWIOOGroupO4(weights, conv_params_.block_size.w, + absl::MakeSpan(ptr, elements_count)); + } + } + // else + // { + // half4 *ptr = reinterpret_cast<half4 *>(data.data()); + // if (conv_params_.AreWeightsBuffer()) + // { + // RearrangeWeightsToOHWIOGroupI4O4(weights, conv_params_.block_size.w, + // absl::MakeSpan(ptr, elements_count)); + // } + // else + // { + // RearrangeWeightsToI4HWIOOGroupO4(weights, conv_params_.block_size.w, + // absl::MakeSpan(ptr, elements_count)); + // } + // } + if (conv_params_.AreWeightsBuffer()) + { + BufferDescriptor desc; + desc.element_type = conv_params_.weights_data_type; + desc.element_size = 4; + desc.memory_type = + conv_params_.weights_upload_type == ConvPowerVR::WeightsUploadType::CONSTANT_MEM + ? MemoryType::CONSTANT + : MemoryType::GLOBAL; + desc.size = float4_size * elements_count; + desc.data = std::move(data); + args_.AddObject("weights", absl::make_unique<BufferDescriptor>(std::move(desc))); + } + else + { + const int texture_width = dst_slices; + const int texture_height = src_slices * weights.shape.h * weights.shape.w; + const int sub_size = float4_size * texture_width * texture_height; + for (int i = 0; i < 4; ++i) + { + Texture2DDescriptor desc; + desc.element_type = conv_params_.weights_data_type; + desc.size = int2(texture_width, texture_height); + desc.data.resize(sub_size); + std::memcpy(desc.data.data(), data.data() + sub_size * i, sub_size); + const std::string name = "weights" + std::to_string(i); + args_.AddObject(name, absl::make_unique<Texture2DDescriptor>(std::move(desc))); + } + } +} + +template <DataType T> void ConvPowerVR::UploadWeights(const InternalTensor<OHWDI, T> &weights) +{ + const int block_size = conv_params_.block_size.w; + const int dst_slices = AlignByN(DivideRoundUp(weights.shape.o, 4), block_size); + const int src_slices = DivideRoundUp(weights.shape.i, 4); + + const int elements_count = + weights.shape.d * weights.shape.h * weights.shape.w * src_slices * dst_slices * 4; + const bool f32_weights = definition_.precision == CalculationsPrecision::F32; + + const int float4_size = f32_weights ? 16 : 8; + + std::vector<uint8_t> data(float4_size * elements_count); + + if (f32_weights) + { + float4 *ptr = reinterpret_cast<float4 *>(data.data()); + if (conv_params_.AreWeightsBuffer()) + { + RearrangeWeightsToODHWIOGroupI4O4(weights, conv_params_.block_size.w, + absl::MakeSpan(ptr, elements_count)); + } + else + { + RearrangeWeightsToI4DHWIOOGroupO4(weights, conv_params_.block_size.w, + absl::MakeSpan(ptr, elements_count)); + } + } + // else + // { + // half4 *ptr = reinterpret_cast<half4 *>(data.data()); + // if (conv_params_.AreWeightsBuffer()) + // { + // RearrangeWeightsToODHWIOGroupI4O4(weights, conv_params_.block_size.w, + // absl::MakeSpan(ptr, elements_count)); + // } + // else + // { + // RearrangeWeightsToI4DHWIOOGroupO4(weights, conv_params_.block_size.w, + // absl::MakeSpan(ptr, elements_count)); + // } + // } + + if (conv_params_.AreWeightsBuffer()) + { + BufferDescriptor desc; + desc.element_type = f32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.element_size = 4; + desc.size = float4_size * elements_count; + desc.data = std::move(data); + args_.AddObject("weights", absl::make_unique<BufferDescriptor>(std::move(desc))); + } + else + { + const int texture_width = dst_slices; + const int texture_height = src_slices * weights.shape.d * weights.shape.h * weights.shape.w; + int sub_size = float4_size * texture_width * texture_height; + for (int i = 0; i < 4; ++i) + { + Texture2DDescriptor desc; + desc.element_type = f32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.size = int2(texture_width, texture_height); + desc.data.resize(sub_size); + memcpy(desc.data.data(), data.data() + sub_size * i, sub_size); + const std::string name = "weights" + std::to_string(i); + args_.AddObject(name, absl::make_unique<Texture2DDescriptor>(std::move(desc))); + } + } +} + +ConvPowerVR CreateConvPowerVR(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution2DAttributes &attr, const BHWC *dst_shape = nullptr); + +ConvPowerVR CreateConvPowerVR(const DeviceInfo &device_info, const OperationDef &definition, + const FullyConnectedAttributes &attr, + const BHWC *dst_shape = nullptr); + +ConvPowerVR CreateConvPowerVRDynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC &weights_shape, + const BHWC *dst_shape = nullptr); + +ConvPowerVR CreateConvPowerVRWino4x4To6x6(const DeviceInfo &device_info, + const OperationDef &definition, + const Convolution2DAttributes &attr, + const BHWC *dst_shape = nullptr); + +ConvPowerVR CreateConvPowerVR3D(const DeviceInfo &device_info, const OperationDef &definition, + const Convolution3DAttributes &attr, + const BHWDC *dst_shape = nullptr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_POWERVR_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvWeightsConverter.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvWeightsConverter.cc new file mode 100644 index 000000000..95172bd05 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvWeightsConverter.cc @@ -0,0 +1,143 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "open_cl/kernels/ConvWeightsConverter.h" + +#include <string> + +#include "open_cl/kernels/Util.h" +#include "open_cl/kernels/WorkGroupPicking.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +ConverterToConvWeights::ConverterToConvWeights(const OperationDef &definition, + const ConvWeightsDescription &conv_weights_desc) + : GPUOperation(definition), conv_weights_desc_(conv_weights_desc) +{ + code_ = GetConverterToConvWeightsCode(definition_, conv_weights_desc_); +} + +ConverterToConvWeights::ConverterToConvWeights(ConverterToConvWeights &&operation) + : GPUOperation(std::move(operation)), conv_weights_desc_(operation.conv_weights_desc_) +{ +} + +ConverterToConvWeights &ConverterToConvWeights::operator=(ConverterToConvWeights &&operation) +{ + if (this != &operation) + { + conv_weights_desc_ = operation.conv_weights_desc_; + GPUOperation::operator=(std::move(operation)); + } + return *this; +} + +std::string ConverterToConvWeights::GetConverterToConvWeightsCode( + const OperationDef &op_def, const ConvWeightsDescription &conv_weights_desc) +{ + AddSrcTensor("src_tensor", op_def.src_tensors[0]); + AddDstTensor("dst_tensor", op_def.dst_tensors[0]); + args_.AddFloat("mask_x"); + args_.AddFloat("mask_y"); + args_.AddFloat("mask_z"); + args_.AddFloat("mask_w"); + + std::string c = GetCommonDefines(op_def.precision); + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int GROUP_SIZE = " + std::to_string(conv_weights_desc.output_group_size) + ";\n"; + c += " int O = get_global_id(0) * 4;\n"; + c += " int I = get_global_id(1);\n"; + c += " int Z = get_global_id(2);\n"; + c += " int W = Z % args.src_tensor.Width();\n"; + c += " int H = Z / args.src_tensor.Width();\n"; + c += " if (O >= args.src_tensor.Batch() || I >= args.src_tensor.Slices() || " + "H >= args.src_tensor.Height()) return;\n"; + c += " FLT4 v0 = args.src_tensor.Read(W, H, I, O + 0);\n"; + c += " FLT4 v1 = (FLT4)(0.0f, 0.0f, 0.0f, 0.0f);\n"; + c += " FLT4 v2 = (FLT4)(0.0f, 0.0f, 0.0f, 0.0f);\n"; + c += " FLT4 v3 = (FLT4)(0.0f, 0.0f, 0.0f, 0.0f);\n"; + c += " if (O + 1 < args.src_tensor.Batch()) {\n"; + c += " v1 = args.src_tensor.Read(W, H, I, O + 1);\n"; + c += " }\n"; + c += " if (O + 2 < args.src_tensor.Batch()) {\n"; + c += " v2 = args.src_tensor.Read(W, H, I, O + 2);\n"; + c += " }\n"; + c += " if (O + 3 < args.src_tensor.Batch()) {\n"; + c += " v3 = args.src_tensor.Read(W, H, I, O + 3);\n"; + c += " }\n"; + c += " if (I == args.src_tensor.Slices() - 1) {\n"; + c += " FLT4 mask = (FLT4)(args.mask_x, args.mask_y, args.mask_z, " + "args.mask_w);\n"; + c += " v0 *= mask;\n"; + c += " v1 *= mask;\n"; + c += " v2 *= mask;\n"; + c += " v3 *= mask;\n"; + c += " }\n"; + c += " FLT4 r0 = (FLT4)(v0.x, v1.x, v2.x, v3.x);\n"; + c += " FLT4 r1 = (FLT4)(v0.y, v1.y, v2.y, v3.y);\n"; + c += " FLT4 r2 = (FLT4)(v0.z, v1.z, v2.z, v3.z);\n"; + c += " FLT4 r3 = (FLT4)(v0.w, v1.w, v2.w, v3.w);\n"; + c += " int d_index = O / (GROUP_SIZE * 4);\n"; + c += " int k_index = (O % (GROUP_SIZE * 4)) / 4;\n"; + c += " int dst_offset = (((d_index * args.src_tensor.Height() + H) * " + "args.src_tensor.Width() + W) * " + "args.src_tensor.Slices() + I) * GROUP_SIZE + " + "k_index;\n"; + c += " int address0 = dst_offset * 4 + 0;\n"; + c += " int address1 = dst_offset * 4 + 1;\n"; + c += " int address2 = dst_offset * 4 + 2;\n"; + c += " int address3 = dst_offset * 4 + 3;\n"; + c += " args.dst_tensor.WriteLinear(r0, dst_offset * 4 + 0)\n;"; + c += " args.dst_tensor.WriteLinear(r1, dst_offset * 4 + 1)\n;"; + c += " args.dst_tensor.WriteLinear(r2, dst_offset * 4 + 2)\n;"; + c += " args.dst_tensor.WriteLinear(r3, dst_offset * 4 + 3)\n;"; + c += "}\n"; + return c; +} + +absl::Status ConverterToConvWeights::BindArguments(ArgumentsBinder *args) +{ + float4 mask = GetMaskForLastPlane(src_[0]->Channels()); + RETURN_IF_ERROR(args->SetFloat("mask_x", mask.x)); + RETURN_IF_ERROR(args->SetFloat("mask_y", mask.y)); + RETURN_IF_ERROR(args->SetFloat("mask_z", mask.z)); + return args->SetFloat("mask_w", mask.w); +} + +int3 ConverterToConvWeights::GetGridSize() const +{ + const int grid_x = + DivideRoundUp(AlignByN(src_[0]->Batch(), 4 * conv_weights_desc_.output_group_size), 4); + const int grid_y = src_[0]->Slices(); + const int grid_z = src_[0]->Width() * src_[0]->Height(); + return int3(grid_x, grid_y, grid_z); +} + +ConverterToConvWeights CreateConverterToConvWeights(const OperationDef &definition, + const ConvWeightsDescription &conv_weights_desc) +{ + return ConverterToConvWeights(definition, conv_weights_desc); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvWeightsConverter.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvWeightsConverter.h new file mode 100644 index 000000000..bb68977eb --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/ConvWeightsConverter.h @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_WEIGHTS_CONVERTER_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_WEIGHTS_CONVERTER_H__ + +#include "open_cl/ClCommandQueue.h" +#include "open_cl/ClKernel.h" +#include "open_cl/kernels/ConvCommon.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/Status.h" +#include "open_cl/Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class ConverterToConvWeights : public GPUOperation +{ +public: + ConverterToConvWeights(const OperationDef &definition, + const ConvWeightsDescription &conv_weights_desc); + absl::Status BindArguments(ArgumentsBinder *args) override; + int3 GetGridSize() const override; + + // Move only + ConverterToConvWeights(ConverterToConvWeights &&operation); + ConverterToConvWeights &operator=(ConverterToConvWeights &&operation); + ConverterToConvWeights(const ConverterToConvWeights &) = delete; + ConverterToConvWeights &operator=(const ConverterToConvWeights &) = delete; + +private: + std::string GetConverterToConvWeightsCode(const OperationDef &op_def, + const ConvWeightsDescription &conv_weights_desc); + + ConvWeightsDescription conv_weights_desc_; +}; + +// We expect src BHWC tensor and we assume that B is O, H = H, W = W, C is I +// as dst we expect Tensor with storage type BUFFER and +// dst.b * dst.h * dst.w * dst.c = AlignByN(src.b, 4) * src.h * src.w +// AlignByN(src.c, 4) +ConverterToConvWeights +CreateConverterToConvWeights(const OperationDef &definition, + const ConvWeightsDescription &conv_weights_desc); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONV_WEIGHTS_CONVERTER_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Converter.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Converter.cc new file mode 100644 index 000000000..cc2bc41d4 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Converter.cc @@ -0,0 +1,592 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Converter.h" + +#include <algorithm> +#include <array> +#include <string> + +#include "open_cl/Arguments.h" +#include "open_cl/ClCommandQueue.h" +#include "open_cl/ClErrors.h" +#include "open_cl/kernels/Util.h" +#include "open_cl/Precision.h" +#include "open_cl/InternalTensor.h" +#include "open_cl/TensorType.h" +#include "open_cl/TensorTypeUtil.h" +#include "open_cl/Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +class OpenClConverterImpl : public TensorObjectConverter +{ +public: + virtual absl::Status Init(const TensorObjectDef &input_def, const TensorObjectDef &output_def, + Environment *environment) = 0; + +protected: + absl::Status DispatchKernel(cl_mem buffer_mem, Tensor *tensor) + { + kernel_.ResetBindingCounter(); + RETURN_IF_ERROR(kernel_.SetMemoryAuto(buffer_mem)); + RETURN_IF_ERROR(args_.SetObjectRef("tensor", tensor)); + RETURN_IF_ERROR(args_.Bind(kernel_.kernel(), kernel_.GetBindingCounter())); + const int3 grid = int3(tensor->Width() * tensor->Batch(), tensor->Height(), tensor->Slices()); + const int3 work_group_size = {16, 8, 1}; + const int3 work_groups_count = GetWorkGroupsCount(grid, work_group_size); + return queue_->Dispatch(kernel_, work_groups_count, work_group_size); + } + + Arguments args_; + BHWC shape_; + CLKernel kernel_; + TensorDescriptor tensor_descriptor_; + CLCommandQueue *queue_ = nullptr; + const CLContext *context_ = nullptr; +}; + +bool IsSupportedDataType(DataType type) +{ + return type == DataType::FLOAT16 || type == DataType::FLOAT32; +} + +bool IsBHWCOpenCLBuffer(const ObjectDef &def) +{ + return IsSupportedDataType(def.data_type) && def.object_type == ObjectType::OPENCL_BUFFER && + def.data_layout == DataLayout::BHWC; +} + +bool IsOpenCLTensor(const ObjectDef &def) +{ + const bool is_buffer_tensor = + def.object_type == ObjectType::OPENCL_BUFFER && def.data_layout == DataLayout::DHWC4; + const bool is_image2d_tensor = + def.object_type == ObjectType::OPENCL_TEXTURE && def.data_layout == DataLayout::HDWC4; + const bool is_image2d_array_tensor = + def.object_type == ObjectType::OPENCL_TEXTURE && def.data_layout == DataLayout::DHWC4; + const bool is_single_image_tensor = + def.object_type == ObjectType::OPENCL_TEXTURE && def.data_layout == DataLayout::BHWC; + return IsSupportedDataType(def.data_type) && (is_buffer_tensor || is_image2d_tensor || + is_image2d_array_tensor || is_single_image_tensor); +} + +absl::Status GetOpenCLMemory(const TensorObject &obj, cl_mem *memory) +{ + auto texture = absl::get_if<OpenClTexture>(&obj); + auto buffer = absl::get_if<OpenClBuffer>(&obj); + if (texture && texture->memobj) + { + *memory = texture->memobj; + } + else if (buffer && buffer->memobj) + { + *memory = buffer->memobj; + } + else + { + return absl::InvalidArgumentError("Missing OpenCL object."); + } + return absl::OkStatus(); +} + +// Implements conversion from OpenCL tensor to another OpenCL tensor. +class TensorToTensorConverter : public OpenClConverterImpl +{ +public: + static bool IsSupported(const ObjectDef &input, const ObjectDef &output) + { + return IsOpenCLTensor(input) && IsOpenCLTensor(output); + } + + absl::Status Init(const TensorObjectDef &input_def, const TensorObjectDef &output_def, + Environment *environment) final + { + src_tensor_descriptor_.layout = Layout::BHWC; + src_tensor_descriptor_.storage_type = + ToTensorStorageType(input_def.object_def.object_type, input_def.object_def.data_layout); + src_tensor_descriptor_.data_type = input_def.object_def.data_type; + args_.AddObjectRef("src_tensor", AccessType::READ, + absl::make_unique<TensorDescriptor>(src_tensor_descriptor_)); + + dst_tensor_descriptor_.layout = Layout::BHWC; + dst_tensor_descriptor_.storage_type = + ToTensorStorageType(output_def.object_def.object_type, output_def.object_def.data_layout); + dst_tensor_descriptor_.data_type = output_def.object_def.data_type; + args_.AddObjectRef("dst_tensor", AccessType::WRITE, + absl::make_unique<TensorDescriptor>(dst_tensor_descriptor_)); + + const bool need_fp16_support = input_def.object_def.data_type == DataType::FLOAT16 || + output_def.object_def.data_type == DataType::FLOAT16; + const std::string out_data_type = ToCLDataType(output_def.object_def.data_type); + std::string shader_src; + if (need_fp16_support) + { + shader_src += "#pragma OPENCL EXTENSION cl_khr_fp16 : enable\n"; + } + shader_src += + R"(__kernel void tensor_to_tensor($0) { + int linear_id = get_global_id(0); + int x = linear_id / args.dst_tensor.Batch(); + int b = linear_id % args.dst_tensor.Batch(); + int y = get_global_id(1); + int d = get_global_id(2); + if (x >= args.dst_tensor.Width() || y >= args.dst_tensor.Height() || d >= args.dst_tensor.Slices()) return; +)"; + shader_src += + " " + out_data_type + "4 input = args.src_tensor.Read<" + out_data_type + ">(x, y, d, b);\n"; + shader_src += " args.dst_tensor.Write(input, x, y, d, b);\n}"; + queue_ = environment->queue(); + context_ = &environment->context(); + shape_ = BHWC(input_def.dimensions.b, input_def.dimensions.h, input_def.dimensions.w, + input_def.dimensions.c); + RETURN_IF_ERROR(args_.TransformToCLCode(environment->device().info_, {}, &shader_src)); + return environment->program_cache()->GetOrCreateCLKernel( + shader_src, "tensor_to_tensor", environment->context(), environment->device(), &kernel_); + } + + absl::Status Convert(const TensorObject &input_obj, const TensorObject &output_obj) override + { + cl_mem in_memory = nullptr; + RETURN_IF_ERROR(GetOpenCLMemory(input_obj, &in_memory)); + cl_mem out_memory = nullptr; + RETURN_IF_ERROR(GetOpenCLMemory(output_obj, &out_memory)); + + Tensor src_tensor; + RETURN_IF_ERROR( + CreateSharedTensor(*context_, in_memory, shape_, src_tensor_descriptor_, &src_tensor)); + Tensor dst_tensor; + RETURN_IF_ERROR( + CreateSharedTensor(*context_, out_memory, shape_, dst_tensor_descriptor_, &dst_tensor)); + + RETURN_IF_ERROR(args_.SetObjectRef("src_tensor", &src_tensor)); + RETURN_IF_ERROR(args_.SetObjectRef("dst_tensor", &dst_tensor)); + + RETURN_IF_ERROR(args_.Bind(kernel_.kernel())); + const int3 grid = + int3(dst_tensor.Width() * dst_tensor.Batch(), dst_tensor.Height(), dst_tensor.Slices()); + const int3 work_group_size = {16, 8, 1}; + const int3 work_groups_count = GetWorkGroupsCount(grid, work_group_size); + return queue_->Dispatch(kernel_, work_groups_count, work_group_size); + } + +private: + TensorDescriptor src_tensor_descriptor_; + TensorDescriptor dst_tensor_descriptor_; +}; + +// Implements conversion from OpenCL-specific tensor layout to BHWC OpenCL +// buffer. +class TensorToBHWCBufferConverter : public OpenClConverterImpl +{ +public: + static bool IsSupported(const ObjectDef &input, const ObjectDef &output) + { + return IsOpenCLTensor(input) && IsBHWCOpenCLBuffer(output); + } + + absl::Status Init(const TensorObjectDef &input_def, const TensorObjectDef &output_def, + Environment *environment) final + { + TensorStorageType src_tensor_type = + ToTensorStorageType(input_def.object_def.object_type, input_def.object_def.data_layout); + tensor_descriptor_.layout = Layout::BHWC; + tensor_descriptor_.storage_type = src_tensor_type; + tensor_descriptor_.data_type = input_def.object_def.data_type; + args_.AddObjectRef("tensor", AccessType::READ, + absl::make_unique<TensorDescriptor>(tensor_descriptor_)); + + const bool need_fp16_support = input_def.object_def.data_type == DataType::FLOAT16 || + output_def.object_def.data_type == DataType::FLOAT16; + std::string shader_src; + if (need_fp16_support) + { + shader_src += "#pragma OPENCL EXTENSION cl_khr_fp16 : enable\n"; + } + const std::string out_data_type = ToCLDataType(output_def.object_def.data_type); + shader_src += "__kernel void tensor_to_bhwc("; + shader_src += "__global " + out_data_type + "* dst, $0) {\n"; + shader_src += R"( int linear_id = get_global_id(0); + int x = linear_id / args.tensor.Batch(); + int b = linear_id % args.tensor.Batch(); + int y = get_global_id(1); + int d = get_global_id(2); + if (x >= args.tensor.Width() || y >= args.tensor.Height() || d >= args.tensor.Slices()) return; +)"; + shader_src += + " " + out_data_type + "4 input = args.tensor.Read<" + out_data_type + ">(x, y, d, b);\n"; + shader_src += R"( int c = d * 4; + int index = ((b * args.tensor.Height() + y) * args.tensor.Width() + x) * args.tensor.Channels() + c; + + dst[index] = input.x; + if (c + 1 < args.tensor.Channels()) { + dst[index + 1] = input.y; + } + if (c + 2 < args.tensor.Channels()) { + dst[index + 2] = input.z; + } + if (c + 3 < args.tensor.Channels()) { + dst[index + 3] = input.w; + } +})"; + queue_ = environment->queue(); + context_ = &environment->context(); + shape_ = BHWC(input_def.dimensions.b, input_def.dimensions.h, input_def.dimensions.w, + input_def.dimensions.c); + RETURN_IF_ERROR(args_.TransformToCLCode(environment->device().info_, {}, &shader_src)); + return environment->program_cache()->GetOrCreateCLKernel( + shader_src, "tensor_to_bhwc", environment->context(), environment->device(), &kernel_); + } + + absl::Status Convert(const TensorObject &input_obj, const TensorObject &output_obj) override + { + auto output = absl::get_if<OpenClBuffer>(&output_obj); + if (!output || !output->memobj) + { + return absl::InvalidArgumentError("Missing output in tensor_to_bhwc converter"); + } + + cl_mem in_memory = nullptr; + RETURN_IF_ERROR(GetOpenCLMemory(input_obj, &in_memory)); + Tensor tensor; + RETURN_IF_ERROR(CreateSharedTensor(*context_, in_memory, shape_, tensor_descriptor_, &tensor)); + return DispatchKernel(output->memobj, &tensor); + } +}; + +// Implements conversion from BHWC OpenCL buffer to OpenCL-specific tensor +// layout. +class BHWCBufferToTensorConverter : public OpenClConverterImpl +{ +public: + static bool IsSupported(const ObjectDef &input, const ObjectDef &output) + { + return IsBHWCOpenCLBuffer(input) && IsOpenCLTensor(output); + } + + std::pair<std::string, std::string> GetFromBhwcKernel(const TensorObjectDef &input_def, + const TensorObjectDef &) const + { + return std::make_pair("__global " + ToCLDataType(input_def.object_def.data_type) + "* src", + R"(int c = d * 4; + int index = ((b * args.tensor.Height() + y) * args.tensor.Width() + x) * args.tensor.Channels() + c; + result.x = src[index]; + result.y = c + 1 < args.tensor.Channels() ? src[index + 1] : 1; + result.z = c + 2 < args.tensor.Channels() ? src[index + 2] : 2; + result.w = c + 3 < args.tensor.Channels() ? src[index + 3] : 3; +)"); + } + + absl::Status Init(const TensorObjectDef &input_def, const TensorObjectDef &output_def, + Environment *environment) final + { + auto params_kernel = GetFromBhwcKernel(input_def, output_def); + + TensorStorageType dst_tensor_type = + ToTensorStorageType(output_def.object_def.object_type, output_def.object_def.data_layout); + tensor_descriptor_.layout = Layout::BHWC; + tensor_descriptor_.storage_type = dst_tensor_type; + tensor_descriptor_.data_type = output_def.object_def.data_type; + args_.AddObjectRef("tensor", AccessType::WRITE, + absl::make_unique<TensorDescriptor>(tensor_descriptor_)); + + const bool need_fp16_support = input_def.object_def.data_type == DataType::FLOAT16 || + output_def.object_def.data_type == DataType::FLOAT16; + std::string shader_src; + if (need_fp16_support) + { + shader_src += "#pragma OPENCL EXTENSION cl_khr_fp16 : enable\n"; + } + const std::string in_data_type = ToCLDataType(input_def.object_def.data_type); + const std::string out_data_type = ToCLDataType(output_def.object_def.data_type); + shader_src += "__kernel void bhwc_to_tensor("; + shader_src += "__global " + in_data_type + "* src, $0) {\n"; + + shader_src += R"( int linear_id = get_global_id(0); + int x = linear_id / args.tensor.Batch(); + int b = linear_id % args.tensor.Batch(); + int y = get_global_id(1); + int d = get_global_id(2); + + if (x >= args.tensor.Width() || y >= args.tensor.Height() || d >= args.tensor.Slices()) return; +)"; + shader_src += " " + out_data_type + "4 result;\n"; + shader_src += R"( int c = d * 4; + int index = ((b * args.tensor.Height() + y) * args.tensor.Width() + x) * args.tensor.Channels() + c; + result.x = src[index]; + result.y = c + 1 < args.tensor.Channels() ? src[index + 1] : 1; + result.z = c + 2 < args.tensor.Channels() ? src[index + 2] : 2; + result.w = c + 3 < args.tensor.Channels() ? src[index + 3] : 3; +)"; + shader_src += " args.tensor.Write(result, x, y, d, b);\n}"; + queue_ = environment->queue(); + context_ = &environment->context(); + shape_ = BHWC(output_def.dimensions.b, output_def.dimensions.h, output_def.dimensions.w, + output_def.dimensions.c); + RETURN_IF_ERROR(args_.TransformToCLCode(environment->device().info_, {}, &shader_src)); + return environment->program_cache()->GetOrCreateCLKernel( + shader_src, "bhwc_to_tensor", environment->context(), environment->device(), &kernel_); + } + + absl::Status Convert(const TensorObject &input_obj, const TensorObject &output_obj) override + { + auto input = absl::get_if<OpenClBuffer>(&input_obj); + if (!input || !input->memobj) + { + return absl::InvalidArgumentError("Missing input in bhwc_to_tensor converter"); + } + cl_mem out_memory = nullptr; + RETURN_IF_ERROR(GetOpenCLMemory(output_obj, &out_memory)); + Tensor tensor; + RETURN_IF_ERROR(CreateSharedTensor(*context_, out_memory, shape_, tensor_descriptor_, &tensor)); + return DispatchKernel(input->memobj, &tensor); + } +}; + +std::array<size_t, 3> CalculateTextureRegion(const TensorObjectDef &def) +{ + const auto &dims = def.dimensions; + std::array<size_t, 3> region = {0, 0, 1}; + switch (ToTensorStorageType(def.object_def.object_type, def.object_def.data_layout)) + { + case TensorStorageType::SINGLE_TEXTURE_2D: + region[0] = static_cast<size_t>(dims.w * dims.b); + region[1] = static_cast<size_t>(dims.h); + break; + case TensorStorageType::TEXTURE_2D: + region[0] = static_cast<size_t>(dims.w * dims.b); + region[1] = static_cast<size_t>(dims.h * dims.d()); + break; + case TensorStorageType::TEXTURE_ARRAY: + region[0] = static_cast<size_t>(dims.w * dims.b); + region[1] = static_cast<size_t>(dims.h); + region[2] = static_cast<size_t>(dims.d()); + break; + default: + break; + } + return region; +} + +bool IsOpenClTextureOrBuffer(ObjectType type) +{ + return type == ObjectType::OPENCL_BUFFER || type == ObjectType::OPENCL_TEXTURE; +} + +// Copies data from one object of the same type and layout to another object. +class TrivialCopier : public OpenClConverterImpl +{ +public: + static bool IsSupported(const ObjectDef &input, const ObjectDef &output) + { + return IsOpenClTextureOrBuffer(input.object_type) && input.data_type == output.data_type && + input.object_type == output.object_type && input.data_layout == output.data_layout; + } + + absl::Status Init(const TensorObjectDef &input_def, const TensorObjectDef &output_def, + Environment *environment) final + { + shape_ = BHWC(input_def.dimensions.b, input_def.dimensions.h, input_def.dimensions.w, + input_def.dimensions.c); + data_type_ = input_def.object_def.data_type; + queue_ = environment->queue(); + region_ = CalculateTextureRegion(output_def); + return absl::OkStatus(); + } + + absl::Status Convert(const TensorObject &input_obj, const TensorObject &output_obj) override + { + auto texture_input = absl::get_if<OpenClTexture>(&input_obj); + auto texture_output = absl::get_if<OpenClTexture>(&output_obj); + if (texture_input && texture_output) + { + return Copy(*texture_input, *texture_output); + } + auto buffer_input = absl::get_if<OpenClBuffer>(&input_obj); + auto buffer_output = absl::get_if<OpenClBuffer>(&output_obj); + if (buffer_input && buffer_output) + { + return Copy(*buffer_input, *buffer_output); + } + return absl::InternalError("Unexpected object"); + } + + absl::Status Copy(const OpenClBuffer &input, const OpenClBuffer &output) + { + if (input.memobj == output.memobj) + { + return absl::OkStatus(); + } + return GetOpenCLError(clEnqueueCopyBuffer(queue_->queue(), input.memobj, output.memobj, 0, 0, + SizeOf(data_type_) * shape_.w * shape_.h * + AlignByN(shape_.c, 4) * shape_.b, + 0, nullptr, nullptr)); + } + + absl::Status Copy(const OpenClTexture &input, const OpenClTexture &output) + { + if (input.memobj == output.memobj) + { + return absl::OkStatus(); + } + size_t origin[3] = {0, 0, 0}; + return GetOpenCLError(clEnqueueCopyImage(queue_->queue(), input.memobj, output.memobj, origin, + origin, region_.data(), 0, nullptr, nullptr)); + } + +private: + DataType data_type_ = DataType::UNKNOWN; + std::array<size_t, 3> region_; +}; + +// Copies data from/to CPU into a tensor. +class CpuCopier : public OpenClConverterImpl +{ +public: + static bool IsSupported(const ObjectDef &input, const ObjectDef &output) + { + return input.data_type == output.data_type && input.data_layout == output.data_layout && + ((input.object_type == ObjectType::CPU_MEMORY && + IsOpenClTextureOrBuffer(output.object_type)) || + (output.object_type == ObjectType::CPU_MEMORY && + IsOpenClTextureOrBuffer(input.object_type))); + } + + absl::Status Init(const TensorObjectDef &input_def, const TensorObjectDef &output_def, + Environment *environment) final + { + + region_ = CalculateTextureRegion( + input_def.object_def.object_type == ObjectType::CPU_MEMORY ? output_def : input_def); + queue_ = environment->queue(); + return absl::OkStatus(); + } + + absl::Status Convert(const TensorObject &input_obj, const TensorObject &output_obj) override + { + auto cpu_input = absl::get_if<CpuMemory>(&input_obj); + auto cpu_output = absl::get_if<CpuMemory>(&output_obj); + + if (cpu_input) + { + auto texture_output = absl::get_if<OpenClTexture>(&output_obj); + if (texture_output) + { + return queue_->EnqueueWriteImage(texture_output->memobj, + int3(region_[0], region_[1], region_[2]), cpu_input->data); + } + auto buffer_output = absl::get_if<OpenClBuffer>(&output_obj); + if (buffer_output) + { + return queue_->EnqueueWriteBuffer(buffer_output->memobj, cpu_input->size_bytes, + cpu_input->data); + } + } + else if (cpu_output) + { + auto texture_input = absl::get_if<OpenClTexture>(&input_obj); + if (texture_input) + { + return queue_->EnqueueReadImage(texture_input->memobj, + int3(region_[0], region_[1], region_[2]), cpu_output->data); + } + auto buffer_input = absl::get_if<OpenClBuffer>(&input_obj); + if (buffer_input) + { + return queue_->EnqueueReadBuffer(buffer_input->memobj, cpu_output->size_bytes, + cpu_output->data); + } + } + return absl::InternalError("Unexpected object"); + } + +private: + std::array<size_t, 3> region_; +}; + +class OpenClTensorConverterBuilder : public TensorObjectConverterBuilder +{ +public: + explicit OpenClTensorConverterBuilder(Environment *environment) : environment_(environment) {} + + bool IsSupported(const TensorObjectDef &input, const TensorObjectDef &output) const final + { + const auto &input_def = input.object_def; + const auto &output_def = output.object_def; + return input.dimensions == output.dimensions && + (TrivialCopier::IsSupported(input_def, output_def) || + TensorToTensorConverter::IsSupported(input_def, output_def) || + CpuCopier::IsSupported(input_def, output_def) || + TensorToBHWCBufferConverter::IsSupported(input_def, output_def) || + BHWCBufferToTensorConverter::IsSupported(input_def, output_def)); + } + + absl::Status MakeConverter(const TensorObjectDef &input, const TensorObjectDef &output, + std::unique_ptr<TensorObjectConverter> *converter) final + { + std::unique_ptr<OpenClConverterImpl> impl; + const auto &input_def = input.object_def; + const auto &output_def = output.object_def; + if (TrivialCopier::IsSupported(input_def, output_def)) + { + impl = absl::make_unique<TrivialCopier>(); + } + else if (TensorToTensorConverter::IsSupported(input_def, output_def)) + { + impl = absl::make_unique<TensorToTensorConverter>(); + } + else if (CpuCopier::IsSupported(input_def, output_def)) + { + impl = absl::make_unique<CpuCopier>(); + } + else if (TensorToBHWCBufferConverter::IsSupported(input_def, output_def)) + { + impl = absl::make_unique<TensorToBHWCBufferConverter>(); + } + else if (BHWCBufferToTensorConverter::IsSupported(input_def, output_def)) + { + impl = absl::make_unique<BHWCBufferToTensorConverter>(); + } + else + { + return absl::UnimplementedError("Unsupported conversion"); + } + RETURN_IF_ERROR(impl->Init(input, output, environment_)); + *converter = std::move(impl); + return absl::OkStatus(); + } + + Environment *environment_; +}; + +} // namespace + +std::unique_ptr<TensorObjectConverterBuilder> NewConverterBuilder(Environment *environment) +{ + return absl::make_unique<OpenClTensorConverterBuilder>(environment); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Converter.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Converter.h new file mode 100644 index 000000000..d69ec85bb --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Converter.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONVERTER_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONVERTER_H__ + +#include <memory> + +#include "open_cl/Environment.h" +#include "open_cl/Spi.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +// Supports conversions from BHWC to internal OpenCL tensor representation and +// back. Also supports F16/F32. +std::unique_ptr<TensorObjectConverterBuilder> NewConverterBuilder(Environment *environment); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_CONVERTER_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv.cc new file mode 100644 index 000000000..e409fef47 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv.cc @@ -0,0 +1,382 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DepthwiseConv.h" + +#include <string> +#include <utility> +#include <vector> + +#include "open_cl/ClDevice.h" +#include "open_cl/kernels/Util.h" +#include "open_cl/kernels/WorkGroupPicking.h" +#include "open_cl/LinearStorage.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +bool IsSpecializedCase(int channel_multiplier) +{ + return channel_multiplier == 1 || channel_multiplier == 2 || channel_multiplier == 4; +} + +std::string GetSrcValue(int channel_multiplier, const std::string coords) +{ + std::string c; + if (channel_multiplier == 1) + { + c += " FLT4 src_final = args.src_tensor.Read(" + coords + ", S);\n"; + } + else if (channel_multiplier == 2) + { + c += " int s_layer = S / 2;\n"; + c += " FLT4 src = args.src_tensor.Read(" + coords + ", s_layer);\n"; + c += " FLT2 t0 = S % 2 == 0 ? src.xy : src.zw;\n"; + c += " FLT4 src_final = (FLT4)(t0.x, t0.x, t0.y, t0.y);\n"; + } + else if (channel_multiplier == 4) + { + c += " int s_layer = S / 4;\n"; + c += " FLT4 src = args.src_tensor.Read(" + coords + ", s_layer);\n"; + c += " FLT t0 = src.x;\n"; + c += " int reminder = S % 4;\n"; + c += " if (reminder == 1) t0 = src.y;\n"; + c += " if (reminder == 2) t0 = src.z;\n"; + c += " if (reminder == 3) t0 = src.w;\n"; + c += " FLT4 src_final = (FLT4)(t0, t0, t0, t0);\n"; + } + else + { + c += " int s_layer = S / args.ch_multiplier;\n"; + c += " FLT4 src = args.src_tensor.Read(" + coords + ", s_layer);\n"; + c += " int s_offset = (S % args.ch_multiplier) * 4;\n"; + c += " FLT4 src_final;\n"; + c += " FLT temp_arr[4] = {src.x, src.y, src.z, src.w};\n"; + c += " src_final.x = temp_arr[(s_offset + 0) / args.ch_multiplier];\n"; + c += " src_final.y = temp_arr[(s_offset + 1) / args.ch_multiplier];\n"; + c += " src_final.z = temp_arr[(s_offset + 2) / args.ch_multiplier];\n"; + c += " src_final.w = temp_arr[(s_offset + 3) / args.ch_multiplier];\n"; + } + + return c; +} + +std::string GenerateDepthwiseConvolutionCode(const OperationDef &op_def, bool stride_correction, + int channel_multiplier, bool weights_are_buffer, + bool dynamic_weights, GPUOperation *op) +{ + auto src_desc = op_def.src_tensors[0]; + src_desc.SetTextureAddressMode(TextureAddressMode::ZERO); + if (op_def.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddSrcTensor("src_tensor", src_desc); + if (dynamic_weights) + { + op->AddSrcTensor("weights", op_def.src_tensors[1]); + } + + auto dst_desc = op_def.dst_tensors[0]; + if (op_def.IsBatchSupported()) + { + dst_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddDstTensor("dst_tensor", dst_desc); + + const auto src_tensor_type = op_def.src_tensors[0].storage_type; + + std::string c = GetCommonDefines(op_def.precision); + + const bool manual_clamp = src_tensor_type == TensorStorageType::BUFFER || + src_tensor_type == TensorStorageType::IMAGE_BUFFER; + + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int X = get_global_id(0);\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " int linear_id_1 = get_global_id(1);\n"; + c += " int Y = linear_id_1 / args.dst_tensor.Depth();\n"; + c += " int Z = linear_id_1 % args.dst_tensor.Depth();\n"; + } + else + { + c += " int Y = get_global_id(1);\n"; + } + c += " int S = get_global_id(2);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() || " + "S >= args.dst_tensor.Slices()) { \n"; + c += " return; \n"; + c += " } \n"; + c += " ACCUM_FLT4 r = (ACCUM_FLT4)(0.0f, 0.0f, 0.0f, 0.0f);\n"; + if (stride_correction) + { + c += " int x_offseted = " + + GetXStrideCorrectedV2("X", "args.src_tensor.Batch()", "args.stride_x", "args.padding_x") + + ";\n"; + } + else + { + if (op_def.IsBatchSupported()) + { + c += " int x_offseted = X * args.stride_x + args.padding_x * " + "args.src_tensor.Batch();\n"; + } + else + { + c += " int x_offseted = X * args.stride_x + args.padding_x;\n"; + } + } + c += " int y_offseted = Y * args.stride_y + args.padding_y;\n"; + if (!dynamic_weights) + { + std::string weights_offset = "args.kernel_size_x * args.kernel_size_y"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " int z_offseted = Z * args.stride_z + args.padding_z;\n"; + weights_offset += " * args.kernel_size_z"; + } + if (weights_are_buffer) + { + c += " int fx_c = S * " + weights_offset + ";\n"; + } + else + { + c += " int fx_c = 0;\n"; + } + } + std::string kernel_size_x = dynamic_weights ? "args.weights.Width()" : "args.kernel_size_x"; + std::string kernel_size_y = dynamic_weights ? "args.weights.Height()" : "args.kernel_size_y"; + std::string kernel_size_z = dynamic_weights ? "args.weights.Depth()" : "args.kernel_size_z"; + + std::string flat_coords = "x_c, y_c"; + if (manual_clamp) + { + std::string check = "!outside_x && !outside_y"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + check += " && !outside_z"; + flat_coords += ", z_c"; + c += " for (int kz = 0; kz < " + kernel_size_z + "; ++kz) {\n"; + c += " int z_c = z_offseted + kz * args.dilation_z;\n"; + c += " bool outside_z = z_c < 0 || z_c >= args.src_tensor.Depth();\n"; + } + c += " for (int ky = 0; ky < " + kernel_size_y + "; ++ky) {\n"; + c += " int y_c = y_offseted + ky * args.dilation_y;\n"; + c += " bool outside_y = y_c < 0 || y_c >= args.src_tensor.Height();\n"; + c += " for (int kx = 0; kx < " + kernel_size_x + "; ++kx) {\n"; + const std::string dilation_x = + op_def.IsBatchSupported() ? "args.dilation_x * args.src_tensor.Batch()" : "args.dilation_x"; + c += " int x_c = x_offseted + kx * " + dilation_x + ";\n"; + c += " bool outside_x = x_c < 0 || x_c >= args.src_tensor.Width();\n"; + c += " if (" + check + ") {\n"; + if (dynamic_weights) + { + c += " FLT4 f = args.weights.Read(kx, ky, S);\n"; + } + else + { + if (weights_are_buffer) + { + c += " FLT4 f = args.weights.Read(fx_c);\n"; + } + else + { + c += " FLT4 f = args.weights.Read(fx_c, S);\n"; + } + } + c += GetSrcValue(channel_multiplier, flat_coords); + c += " r += TO_ACCUM_TYPE(src_final * f);\n"; + c += " };\n"; + if (!dynamic_weights) + { + c += " fx_c++;\n"; + } + c += " }\n"; + c += " }\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " }\n"; + } + } + else + { // Texture types with ZERO clamping + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + flat_coords += ", z_c"; + c += " for (int kz = 0; kz < " + kernel_size_z + "; ++kz) {\n"; + c += " int z_c = z_offseted + kz * args.dilation_z;\n"; + if (src_tensor_type != TensorStorageType::TEXTURE_3D) + { // Only TEXTURE_3D supports clamping + // in DEPTH dimension + c += " if (z_c < 0 || z_c >= args.src_tensor.Depth()) {\n"; + c += " fx_c += args.kernel_size_y * args.kernel_size_x;\n"; + c += " continue;\n"; + c += " }\n"; + } + } + c += " for (int ky = 0; ky < " + kernel_size_y + "; ++ky) {\n"; + c += " int y_c = y_offseted + ky * args.dilation_y;\n"; + c += " for (int kx = 0; kx < " + kernel_size_x + "; ++kx) {\n"; + const std::string dilation_x = + op_def.IsBatchSupported() ? "args.dilation_x * args.src_tensor.Batch()" : "args.dilation_x"; + c += " int x_c = x_offseted + kx * " + dilation_x + ";\n"; + c += GetSrcValue(channel_multiplier, flat_coords); + if (dynamic_weights) + { + c += " FLT4 f = args.weights.Read(kx, ky, S);\n"; + } + else + { + if (weights_are_buffer) + { + c += " FLT4 f = args.weights.Read(fx_c);\n"; + } + else + { + c += " FLT4 f = args.weights.Read(fx_c, S);\n"; + } + c += " fx_c++;\n"; + } + c += " r += TO_ACCUM_TYPE(src_final * f);\n"; + c += " }\n"; + c += " }\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " }\n"; + } + } + c += " FLT4 res0 = TO_FLT4(r) + args.biases.Read(S);\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " args.dst_tensor.Write(res0, X, Y, Z, S);\n"; + } + else + { + c += " args.dst_tensor.Write(res0, X, Y, S);\n"; + } + c += "}\n"; + + return c; +} +} // namespace + +GPUOperation CreateDepthwiseConvolution2D(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution2DAttributes &attr) +{ + bool weights_are_buffer = device_info.IsMali(); + GPUOperation op(definition); + op.args_.AddInt("kernel_size_x", attr.weights.shape.w); + op.args_.AddInt("stride_x", attr.strides.w); + op.args_.AddInt("padding_x", -attr.padding.prepended.w); + op.args_.AddInt("dilation_x", attr.dilations.w); + op.args_.AddInt("kernel_size_y", attr.weights.shape.h); + op.args_.AddInt("stride_y", attr.strides.h); + op.args_.AddInt("padding_y", -attr.padding.prepended.h); + op.args_.AddInt("dilation_y", attr.dilations.h); + if (!IsSpecializedCase(attr.weights.shape.o)) + { + op.args_.AddInt("ch_multiplier", attr.weights.shape.o); + } + const bool stride_correction = definition.IsBatchSupported() && attr.strides.w != 1; + op.code_ = GenerateDepthwiseConvolutionCode(definition, stride_correction, attr.weights.shape.o, + weights_are_buffer, false, &op); + UploadWeightsForDWConv2D(attr.weights, weights_are_buffer, definition.precision, &op); + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_SToZ; + + TensorLinearDescriptor desc; + desc.storage_type = + weights_are_buffer ? LinearStorageType::BUFFER : LinearStorageType::TEXTURE_2D; + desc.element_type = definition.GetDataType(); + desc.UploadLinearData(attr.bias); + op.args_.AddObject("biases", absl::make_unique<TensorLinearDescriptor>(std::move(desc))); + return op; +} + +GPUOperation +CreateDepthwiseConvolution2DDynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution2DAttributes &attr) +{ + GPUOperation op(definition); + op.args_.AddInt("stride_x", attr.strides.w); + op.args_.AddInt("padding_x", -attr.padding.prepended.w); + op.args_.AddInt("dilation_x", attr.dilations.w); + op.args_.AddInt("stride_y", attr.strides.h); + op.args_.AddInt("padding_y", -attr.padding.prepended.h); + op.args_.AddInt("dilation_y", attr.dilations.h); + const bool stride_correction = definition.IsBatchSupported() && attr.strides.w != 1; + op.code_ = GenerateDepthwiseConvolutionCode(definition, stride_correction, 1, false, true, &op); + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_SToZ; + + TensorLinearDescriptor desc; + desc.storage_type = + device_info.IsMali() ? LinearStorageType::BUFFER : LinearStorageType::TEXTURE_2D; + desc.element_type = definition.GetDataType(); + desc.UploadLinearData(attr.bias); + op.args_.AddObject("biases", absl::make_unique<TensorLinearDescriptor>(std::move(desc))); + return op; +} + +GPUOperation CreateDepthwiseConvolution3D(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution3DAttributes &attr) +{ + bool weights_are_buffer = device_info.IsMali(); + GPUOperation op(definition); + op.args_.AddInt("kernel_size_x", attr.weights.shape.w); + op.args_.AddInt("stride_x", attr.strides.w); + op.args_.AddInt("padding_x", -attr.padding.prepended.w); + op.args_.AddInt("dilation_x", attr.dilations.w); + op.args_.AddInt("kernel_size_y", attr.weights.shape.h); + op.args_.AddInt("stride_y", attr.strides.h); + op.args_.AddInt("padding_y", -attr.padding.prepended.h); + op.args_.AddInt("dilation_y", attr.dilations.h); + op.args_.AddInt("kernel_size_z", attr.weights.shape.d); + op.args_.AddInt("stride_z", attr.strides.d); + op.args_.AddInt("padding_z", -attr.padding.prepended.d); + op.args_.AddInt("dilation_z", attr.dilations.d); + if (!IsSpecializedCase(attr.weights.shape.o)) + { + op.args_.AddInt("ch_multiplier", attr.weights.shape.o); + } + const bool stride_correction = definition.IsBatchSupported() && attr.strides.w != 1; + op.code_ = GenerateDepthwiseConvolutionCode(definition, stride_correction, attr.weights.shape.o, + weights_are_buffer, false, &op); + UploadWeightsForDWConv3D(attr.weights, weights_are_buffer, definition.precision, &op); + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_SToZ; + + TensorLinearDescriptor desc; + desc.storage_type = + weights_are_buffer ? LinearStorageType::BUFFER : LinearStorageType::TEXTURE_2D; + desc.element_type = definition.GetDataType(); + desc.UploadLinearData(attr.bias); + op.args_.AddObject("biases", absl::make_unique<TensorLinearDescriptor>(std::move(desc))); + return op; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv.h new file mode 100644 index 000000000..cbadd9fde --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv.h @@ -0,0 +1,233 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_DEPTHWISE_CONV_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_DEPTHWISE_CONV_H__ + +#include <vector> + +#include "open_cl/Buffer.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/LinearStorage.h" +#include "open_cl/Tensor.h" +#include "open_cl/Texture2d.h" +#include "open_cl/Util.h" +#include "open_cl/DataType.h" +#include "open_cl/Operations.h" +#include "open_cl/Shape.h" +#include "open_cl/Status.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +template <DataType S, typename T> +void RearrangeWeightsForDWConv2D(const InternalTensor<OHWI, S> &weights, absl::Span<T> dst) +{ + const int dst_channels = weights.shape.i * weights.shape.o; + const int dst_depth = DivideRoundUp(dst_channels, 4); + const int kernel_x = weights.shape.w; + const int kernel_y = weights.shape.h; + + int counter = 0; + for (int d = 0; d < dst_depth; ++d) + { + for (int y = 0; y < kernel_y; ++y) + { + for (int x = 0; x < kernel_x; ++x) + { + T filter_val; + for (int i = 0; i < 4; ++i) + { + const int d_ch = d * 4 + i; + if (d_ch < dst_channels) + { + const int f_index = + weights.shape.LinearIndex({d_ch % weights.shape.o, y, x, d_ch / weights.shape.o}); + filter_val[i] = weights.data[f_index]; + } + else + { + filter_val[i] = 0.0f; + } + } + dst[counter++] = filter_val; + } + } + } +} + +template <DataType T> +void UploadWeightsForDWConv2D(const InternalTensor<OHWI, T> &weights, bool weights_are_buffer, + CalculationsPrecision precision, GPUOperation *op) +{ + const int dst_channels = weights.shape.i * weights.shape.o; + const int dst_slices = DivideRoundUp(dst_channels, 4); + const int kernel_x = weights.shape.w; + const int kernel_y = weights.shape.h; + + const int elements_count = kernel_x * kernel_y * dst_slices; + + const bool fp32_weights = precision == CalculationsPrecision::F32; + const int float4_size = fp32_weights ? 16 : 8; + + std::vector<uint8_t> data(float4_size * elements_count); + + if (fp32_weights) + { + float4 *ptr = reinterpret_cast<float4 *>(data.data()); + RearrangeWeightsForDWConv2D(weights, absl::MakeSpan(ptr, elements_count)); + } + // TODO + // It doesn't support F16 yet. I will try to add it later. + // + // else { + // half4* ptr = reinterpret_cast<half4*>(data.data()); + // RearrangeWeightsForDWConv2D(weights, absl::MakeSpan(ptr, elements_count)); + // } + + if (weights_are_buffer) + { + BufferDescriptor desc; + desc.element_type = fp32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.element_size = 4; + desc.size = float4_size * elements_count; + desc.data = std::move(data); + op->args_.AddObject("weights", absl::make_unique<BufferDescriptor>(desc)); + } + else + { + Texture2DDescriptor desc; + desc.element_type = fp32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.size = int2(kernel_x * kernel_y, dst_slices); + desc.data = std::move(data); + op->args_.AddObject("weights", absl::make_unique<Texture2DDescriptor>(desc)); + } +} + +template <DataType S, typename T> +void RearrangeWeightsForDWConv3D(const InternalTensor<OHWDI, S> &weights, absl::Span<T> dst) +{ + const int dst_channels = weights.shape.i * weights.shape.o; + const int dst_slices = DivideRoundUp(dst_channels, 4); + const int kernel_x = weights.shape.w; + const int kernel_y = weights.shape.h; + const int kernel_z = weights.shape.d; + + int counter = 0; + for (int d = 0; d < dst_slices; ++d) + { + for (int z = 0; z < kernel_z; ++z) + { + for (int y = 0; y < kernel_y; ++y) + { + for (int x = 0; x < kernel_x; ++x) + { + T filter_val; + for (int i = 0; i < 4; ++i) + { + const int d_ch = d * 4 + i; + if (d_ch < dst_channels) + { + const int f_index = weights.shape.LinearIndex( + {d_ch % weights.shape.o, y, x, z, d_ch / weights.shape.o}); + filter_val[i] = weights.data[f_index]; + } + else + { + filter_val[i] = 0.0f; + } + } + dst[counter++] = filter_val; + } + } + } + } +} + +template <DataType T> +void UploadWeightsForDWConv3D(const InternalTensor<OHWDI, T> &weights, bool weights_are_buffer, + CalculationsPrecision precision, GPUOperation *op) +{ + const int dst_channels = weights.shape.i * weights.shape.o; + const int dst_slices = DivideRoundUp(dst_channels, 4); + const int kernel_x = weights.shape.w; + const int kernel_y = weights.shape.h; + const int kernel_z = weights.shape.d; + + const int elements_count = kernel_x * kernel_y * kernel_z * dst_slices; + + const bool fp32_weights = precision == CalculationsPrecision::F32; + const int float4_size = fp32_weights ? 16 : 8; + + std::vector<uint8_t> data(float4_size * elements_count); + + if (fp32_weights) + { + float4 *ptr = reinterpret_cast<float4 *>(data.data()); + RearrangeWeightsForDWConv3D(weights, absl::MakeSpan(ptr, elements_count)); + } + // TODO + // It doesn't support F16 yet. I will try to add it later. + // + // else { + // half4* ptr = reinterpret_cast<half4*>(data.data()); + // RearrangeWeightsForDWConv3D(weights, absl::MakeSpan(ptr, elements_count)); + // } + + if (weights_are_buffer) + { + BufferDescriptor desc; + desc.element_type = fp32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.element_size = 4; + desc.size = float4_size * elements_count; + desc.data = std::move(data); + op->args_.AddObject("weights", absl::make_unique<BufferDescriptor>(std::move(desc))); + } + else + { + Texture2DDescriptor desc; + desc.element_type = fp32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.size = int2(kernel_x * kernel_y * kernel_z, dst_slices); + desc.data = std::move(data); + op->args_.AddObject("weights", absl::make_unique<Texture2DDescriptor>(std::move(desc))); + } +} + +GPUOperation CreateDepthwiseConvolution2D(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution2DAttributes &attr); + +GPUOperation +CreateDepthwiseConvolution2DDynamicWeights(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution2DAttributes &attr); + +GPUOperation CreateDepthwiseConvolution3D(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution3DAttributes &attr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_DEPTHWISE_CONV_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv3x3.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv3x3.cc new file mode 100644 index 000000000..89a14f14d --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv3x3.cc @@ -0,0 +1,358 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DepthwiseConv3x3.h" + +#include <string> +#include <utility> + +#include "open_cl/kernels/Util.h" +#include "open_cl/kernels/WorkGroupPicking.h" +#include "open_cl/Precision.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +DepthwiseConv3x3::DepthwiseConv3x3(const OperationDef &definition, bool weights_are_buffer, + bool local_mem_uploads, const DeviceInfo &device_info) + : GPUOperation(definition), local_mem_uploads_(local_mem_uploads) +{ + work_group_size_ = int3(8, 4, 1); + code_ = GenerateDepthwiseConvCode(definition_, weights_are_buffer, local_mem_uploads_); + + if (definition_.precision == CalculationsPrecision::F16 && device_info.IsPowerVR()) + { + compiler_options_.push_back(CompilerOptions::POWERVR_FP16); + } +} + +DepthwiseConv3x3::DepthwiseConv3x3(DepthwiseConv3x3 &&operation) + : GPUOperation(std::move(operation)), local_mem_uploads_(operation.local_mem_uploads_) +{ +} + +DepthwiseConv3x3 &DepthwiseConv3x3::operator=(DepthwiseConv3x3 &&operation) +{ + if (this != &operation) + { + std::swap(local_mem_uploads_, operation.local_mem_uploads_); + GPUOperation::operator=(std::move(operation)); + } + return *this; +} + +std::string DepthwiseConv3x3::GenerateDepthwiseConvCode(const OperationDef &op_def, + bool weights_are_buffer, + bool local_mem_uploads) +{ + auto src_desc = op_def.src_tensors[0]; + src_desc.SetTextureAddressMode(TextureAddressMode::ZERO); + AddSrcTensor("src_tensor", src_desc); + AddDstTensor("dst_tensor", op_def.dst_tensors[0]); + + const auto src_tensor_type = op_def.src_tensors[0].storage_type; + + const bool manual_clamp = src_tensor_type == TensorStorageType::BUFFER || + src_tensor_type == TensorStorageType::IMAGE_BUFFER; + + std::string c = GetCommonDefines(op_def.precision); + if (local_mem_uploads) + { + c += "__attribute__((reqd_work_group_size(8, 4, 1)))\n"; + } + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::BATCH)) + { + c += " int linear_id = get_global_id(0);\n"; + c += " int X = (linear_id / args.dst_tensor.Batch()) * 2;\n"; + c += " int B = linear_id % args.dst_tensor.Batch();\n"; + c += " args.dst_tensor.SetBatchRef(B);\n"; + c += " args.src_tensor.SetBatchRef(B);\n"; + } + else + { + c += " int X = get_global_id(0) * 2;\n"; + } + c += " int Y = get_global_id(1) * 2;\n"; + c += " int S = get_global_id(2);\n"; + c += " ACCUM_FLT4 r0 = (ACCUM_FLT4)(0.0f);\n"; + c += " ACCUM_FLT4 r1 = (ACCUM_FLT4)(0.0f);\n"; + c += " ACCUM_FLT4 r2 = (ACCUM_FLT4)(0.0f);\n"; + c += " ACCUM_FLT4 r3 = (ACCUM_FLT4)(0.0f);\n"; + if (!local_mem_uploads) + { + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() " + "|| S >= args.dst_tensor.Slices()) { \n"; + c += " return; \n"; + c += " } \n"; + } + if (local_mem_uploads) + { + c += " __local FLT4 f[10];\n"; + c += " event_t e = async_work_group_copy(f, args.weights.GetPtr() + S * " + "10, 10, 0);\n"; + c += " wait_group_events(1, &e);\n"; + } + else if (weights_are_buffer) + { + c += " __global FLT4* f = args.weights.GetPtr() + S * 10;\n"; + } + c += " FLT4 s0;\n"; + c += " FLT4 s1;\n"; + c += " FLT4 s2;\n"; + c += " FLT4 s3;\n"; + std::string W[9] = {"f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8"}; + std::string bias = "bias"; + std::string xc[4] = {"X - 1", "X", "X + 1", "X + 2"}; + std::string yc[4] = {"Y - 1", "Y", "Y + 1", "Y + 2"}; + if (!weights_are_buffer) + { + c += " FLT4 f0 = args.weights.Read(0, S);\n"; + c += " FLT4 f1 = args.weights.Read(1, S);\n"; + c += " FLT4 f2 = args.weights.Read(2, S);\n"; + c += " FLT4 f3 = args.weights.Read(3, S);\n"; + c += " FLT4 f4 = args.weights.Read(4, S);\n"; + c += " FLT4 f5 = args.weights.Read(5, S);\n"; + c += " FLT4 f6 = args.weights.Read(6, S);\n"; + c += " FLT4 f7 = args.weights.Read(7, S);\n"; + c += " FLT4 f8 = args.weights.Read(8, S);\n"; + } + if (manual_clamp) + { + c += " int x0 = X - 1;\n"; + c += " int x1 = X;\n"; + c += " int x2 = X + 1;\n"; + c += " int x3 = X + 2;\n"; + c += " int y0 = Y - 1;\n"; + c += " int y1 = Y;\n"; + c += " int y2 = Y + 1;\n"; + c += " int y3 = Y + 2;\n"; + c += " bool x0_in = x0 >= 0 && x0 < args.dst_tensor.Width();\n"; + c += " bool x1_in = x1 >= 0 && x1 < args.dst_tensor.Width();\n"; + c += " bool x2_in = x2 >= 0 && x2 < args.dst_tensor.Width();\n"; + c += " bool x3_in = x3 >= 0 && x3 < args.dst_tensor.Width();\n"; + c += " bool y0_in = y0 >= 0 && y0 < args.dst_tensor.Height();\n"; + c += " bool y1_in = y1 >= 0 && y1 < args.dst_tensor.Height();\n"; + c += " bool y2_in = y2 >= 0 && y2 < args.dst_tensor.Height();\n"; + c += " bool y3_in = y3 >= 0 && y3 < args.dst_tensor.Height();\n"; + c += " x0 = clamp(x0, 0, args.dst_tensor.Width() - 1);\n"; + c += " x1 = clamp(x1, 0, args.dst_tensor.Width() - 1);\n"; + c += " x2 = clamp(x2, 0, args.dst_tensor.Width() - 1);\n"; + c += " x3 = clamp(x3, 0, args.dst_tensor.Width() - 1);\n"; + c += " y0 = clamp(y0, 0, args.dst_tensor.Height() - 1);\n"; + c += " y1 = clamp(y1, 0, args.dst_tensor.Height() - 1);\n"; + c += " y2 = clamp(y2, 0, args.dst_tensor.Height() - 1);\n"; + c += " y3 = clamp(y3, 0, args.dst_tensor.Height() - 1);\n"; + if (src_tensor_type == TensorStorageType::BUFFER) + { + c += " __global FLT4* src_loc = " + "args.src_tensor.GetPtrWithSliceOffset(S);\n"; + } + xc[0] = "x0"; + xc[1] = "x1"; + xc[2] = "x2"; + xc[3] = "x3"; + yc[0] = "y0"; + yc[1] = "y1"; + yc[2] = "y2"; + yc[3] = "y3"; + } + if (local_mem_uploads || weights_are_buffer) + { + W[0] = "f[0]"; + W[1] = "f[1]"; + W[2] = "f[2]"; + W[3] = "f[3]"; + W[4] = "f[4]"; + W[5] = "f[5]"; + W[6] = "f[6]"; + W[7] = "f[7]"; + W[8] = "f[8]"; + bias = "f[9]"; + } + auto read_4x_line = [&](int y) { + if (src_tensor_type == TensorStorageType::BUFFER) + { + const std::string y_in = "y" + std::to_string(y) + "_in"; + c += " s0 = src_loc[args.src_tensor.GetWHOffset(" + xc[0] + ", " + yc[y] + + ")] * (FLT)(x0_in && " + y_in + ");\n"; + c += " s1 = src_loc[args.src_tensor.GetWHOffset(" + xc[1] + ", " + yc[y] + + ")] * (FLT)(x1_in && " + y_in + ");\n"; + c += " s2 = src_loc[args.src_tensor.GetWHOffset(" + xc[2] + ", " + yc[y] + + ")] * (FLT)(x2_in && " + y_in + ");\n"; + c += " s3 = src_loc[args.src_tensor.GetWHOffset(" + xc[3] + ", " + yc[y] + + ")] * (FLT)(x3_in && " + y_in + ");\n"; + } + else if (src_tensor_type == TensorStorageType::IMAGE_BUFFER) + { + const std::string y_in = "y" + std::to_string(y) + "_in"; + c += " s0 = args.src_tensor.Read(" + xc[0] + ", " + yc[y] + ", S) * (FLT)(x0_in && " + + y_in + ");\n"; + c += " s1 = args.src_tensor.Read(" + xc[1] + ", " + yc[y] + ", S) * (FLT)(x1_in && " + + y_in + ");\n"; + c += " s2 = args.src_tensor.Read(" + xc[2] + ", " + yc[y] + ", S) * (FLT)(x2_in && " + + y_in + ");\n"; + c += " s3 = args.src_tensor.Read(" + xc[3] + ", " + yc[y] + ", S) * (FLT)(x3_in && " + + y_in + ");\n"; + } + else + { + c += " s0 = args.src_tensor.Read(" + xc[0] + ", " + yc[y] + ", S);\n"; + c += " s1 = args.src_tensor.Read(" + xc[1] + ", " + yc[y] + ", S);\n"; + c += " s2 = args.src_tensor.Read(" + xc[2] + ", " + yc[y] + ", S);\n"; + c += " s3 = args.src_tensor.Read(" + xc[3] + ", " + yc[y] + ", S);\n"; + } + }; + c += " {\n"; + read_4x_line(0); + c += " r0 += TO_ACCUM_TYPE(" + W[0] + " * s0);\n"; + c += " r0 += TO_ACCUM_TYPE(" + W[1] + " * s1);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[0] + " * s1);\n"; + c += " r0 += TO_ACCUM_TYPE(" + W[2] + " * s2);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[1] + " * s2);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[2] + " * s3);\n"; + c += " }\n"; + c += " {\n"; + read_4x_line(1); + c += " r0 += TO_ACCUM_TYPE(" + W[3] + " * s0);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[0] + " * s0);\n"; + c += " r0 += TO_ACCUM_TYPE(" + W[4] + " * s1);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[3] + " * s1);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[1] + " * s1);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[0] + " * s1);\n"; + c += " r0 += TO_ACCUM_TYPE(" + W[5] + " * s2);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[4] + " * s2);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[2] + " * s2);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[1] + " * s2);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[5] + " * s3);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[2] + " * s3);\n"; + c += " }\n"; + c += " {\n"; + read_4x_line(2); + c += " r0 += TO_ACCUM_TYPE(" + W[6] + " * s0);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[3] + " * s0);\n"; + c += " r0 += TO_ACCUM_TYPE(" + W[7] + " * s1);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[6] + " * s1);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[4] + " * s1);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[3] + " * s1);\n"; + c += " r0 += TO_ACCUM_TYPE(" + W[8] + " * s2);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[7] + " * s2);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[5] + " * s2);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[4] + " * s2);\n"; + c += " r1 += TO_ACCUM_TYPE(" + W[8] + " * s3);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[5] + " * s3);\n"; + c += " }\n"; + c += " {\n"; + read_4x_line(3); + c += " r2 += TO_ACCUM_TYPE(" + W[6] + " * s0);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[7] + " * s1);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[6] + " * s1);\n"; + c += " r2 += TO_ACCUM_TYPE(" + W[8] + " * s2);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[7] + " * s2);\n"; + c += " r3 += TO_ACCUM_TYPE(" + W[8] + " * s3);\n"; + c += " }\n"; + if (!weights_are_buffer) + { + c += " FLT4 bias = args.weights.Read(9, S);\n"; + } + c += " r0 += TO_ACCUM_TYPE(" + bias + ");\n"; + c += " r1 += TO_ACCUM_TYPE(" + bias + ");\n"; + c += " r2 += TO_ACCUM_TYPE(" + bias + ");\n"; + c += " r3 += TO_ACCUM_TYPE(" + bias + ");\n"; + if (local_mem_uploads) + { + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() " + "|| S >= args.dst_tensor.Slices()) { \n"; + c += " return; \n"; + c += " } \n"; + } + c += " if(X + 0 < args.dst_tensor.Width() && Y + 0 < " + "args.dst_tensor.Height()) {\n"; + c += " FLT4 result = TO_FLT4(r0);\n"; + c += " args.dst_tensor.Write(result, X + 0, Y + 0, S)\n"; + c += " }\n"; + c += " if(X + 1 < args.dst_tensor.Width() && Y + 0 < " + "args.dst_tensor.Height()) {\n"; + c += " FLT4 result = TO_FLT4(r1);\n"; + c += " args.dst_tensor.Write(result, X + 1, Y + 0, S)\n"; + c += " }\n"; + c += " if(X + 0 < args.dst_tensor.Width() && Y + 1 < " + "args.dst_tensor.Height()) {\n"; + c += " FLT4 result = TO_FLT4(r2);\n"; + c += " args.dst_tensor.Write(result, X + 0, Y + 1, S)\n"; + c += " }\n"; + c += " if(X + 1 < args.dst_tensor.Width() && Y + 1 < " + "args.dst_tensor.Height()) {\n"; + c += " FLT4 result = TO_FLT4(r3);\n"; + c += " args.dst_tensor.Write(result, X + 1, Y + 1, S)\n"; + c += " }\n"; + c += "}\n"; + + return c; +} + +int3 DepthwiseConv3x3::GetGridSize() const +{ + const int grid_x = DivideRoundUp(dst_[0]->Width(), 2) * dst_[0]->Batch(); + const int grid_y = DivideRoundUp(dst_[0]->Height(), 2); + const int grid_z = dst_[0]->Slices(); + return int3(grid_x, grid_y, grid_z); +} + +void DepthwiseConv3x3::GetPossibleKernelWorkGroups(TuningType tuning_type, + const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const +{ + if (local_mem_uploads_) + { + work_groups->push_back(work_group_size_); + } + else + { + GetPossibleWorkGroups(tuning_type, device_info, kernel_info, grid_size_, work_groups); + } +} + +bool IsDepthwiseConv3x3Supported(const DepthwiseConvolution2DAttributes &attr) +{ + return attr.weights.shape.o == 1 && attr.dilations.w == 1 && attr.dilations.h == 1 && + attr.weights.shape.w == 3 && attr.weights.shape.h == 3 && attr.strides.w == 1 && + attr.strides.h == 1 && attr.padding.prepended.w == 1 && attr.padding.prepended.h == 1 && + attr.padding.appended.w == 1 && attr.padding.appended.h == 1; +} + +DepthwiseConv3x3 CreateDepthwiseConv3x3(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution2DAttributes &attr) +{ + bool weights_are_buffer = device_info.IsPowerVR() || device_info.IsMali(); + bool local_mem_uploads = weights_are_buffer && device_info.IsPowerVR(); + DepthwiseConv3x3 result(definition, weights_are_buffer, local_mem_uploads, device_info); + result.UploadWeightsAndBiases(attr.weights, attr.bias, weights_are_buffer); + return result; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv3x3.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv3x3.h new file mode 100644 index 000000000..8c571105a --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/DepthwiseConv3x3.h @@ -0,0 +1,177 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_DEPTHWISE_CONV_3X3_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_DEPTHWISE_CONV_3X3_H__ + +#include <memory> +#include <vector> + +#include "open_cl/Buffer.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/Tensor.h" +#include "open_cl/Texture2d.h" +#include "open_cl/Util.h" +#include "open_cl/DataType.h" +#include "open_cl/Operations.h" +#include "open_cl/Shape.h" +#include "open_cl/Status.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class DepthwiseConv3x3 : public GPUOperation +{ +public: + DepthwiseConv3x3() = default; + void GetPossibleKernelWorkGroups(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const override; + int3 GetGridSize() const override; + + // Move only + DepthwiseConv3x3(DepthwiseConv3x3 &&operation); + DepthwiseConv3x3 &operator=(DepthwiseConv3x3 &&operation); + DepthwiseConv3x3(const DepthwiseConv3x3 &) = delete; + DepthwiseConv3x3 &operator=(const DepthwiseConv3x3 &) = delete; + +private: + explicit DepthwiseConv3x3(const OperationDef &definition, bool weights_are_buffer, + bool local_mem_uploads, const DeviceInfo &device_info); + template <DataType T> + void UploadWeightsAndBiases(const InternalTensor<OHWI, T> &weights, + const InternalTensor<Linear, T> &biases, bool weights_are_buffer); + + friend DepthwiseConv3x3 CreateDepthwiseConv3x3(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution2DAttributes &attr); + + template <DataType S, typename T> + void RearrangeWeightsAndBiasesData(const InternalTensor<OHWI, S> &weights, + const InternalTensor<Linear, S> &biases, absl::Span<T> dst); + + std::string GenerateDepthwiseConvCode(const OperationDef &op_def, bool weights_are_buffer, + bool local_mem_uploads); + + bool local_mem_uploads_; +}; + +template <DataType T> +void DepthwiseConv3x3::UploadWeightsAndBiases(const InternalTensor<OHWI, T> &weights, + const InternalTensor<Linear, T> &biases, + bool weights_are_buffer) +{ + const int src_depth = DivideRoundUp(weights.shape.i, 4); + int texture_width = 10; // 3x3 kernel + 1 bias + int texture_height = src_depth; + const int elements_count = texture_width * texture_height; + const bool fp32_weights = definition_.precision == CalculationsPrecision::F32; + const int float4_size = fp32_weights ? 16 : 8; + + std::vector<uint8_t> data(float4_size * elements_count); + if (fp32_weights) + { + float4 *ptr = reinterpret_cast<float4 *>(data.data()); + RearrangeWeightsAndBiasesData(weights, biases, absl::MakeSpan(ptr, elements_count)); + } + // TODO + // It doesn't support F16 yet. I will try to add it later. + // + // else { + // half4* ptr = reinterpret_cast<half4*>(data.data()); + // RearrangeWeightsAndBiasesData(weights, biases, + // absl::MakeSpan(ptr, elements_count)); + // } + + if (weights_are_buffer) + { + BufferDescriptor desc; + desc.element_type = fp32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.element_size = 4; + desc.size = float4_size * elements_count; + desc.data = std::move(data); + args_.AddObject("weights", absl::make_unique<BufferDescriptor>(std::move(desc))); + } + else + { + Texture2DDescriptor desc; + desc.element_type = fp32_weights ? DataType::FLOAT32 : DataType::FLOAT16; + desc.size = int2(texture_width, texture_height); + desc.data = std::move(data); + args_.AddObject("weights", absl::make_unique<Texture2DDescriptor>(std::move(desc))); + } +} + +template <DataType S, typename T> +void DepthwiseConv3x3::RearrangeWeightsAndBiasesData(const InternalTensor<OHWI, S> &weights, + const InternalTensor<Linear, S> &biases, + absl::Span<T> dst) +{ + const int src_depth = DivideRoundUp(weights.shape.i, 4); + + int counter = 0; + for (int s = 0; s < src_depth; ++s) + { + for (int y = 0; y < 3; ++y) + { + for (int x = 0; x < 3; ++x) + { + T filter_val; + for (int i = 0; i < 4; ++i) + { + const int s_ch = s * 4 + i; + if (s_ch < weights.shape.i) + { + const int f_index = weights.shape.LinearIndex({0, y, x, s_ch}); + filter_val[i] = weights.data[f_index]; + } + else + { + filter_val[i] = 0.0f; + } + } + dst[counter++] = filter_val; + } + } + + T bias_val; + for (int i = 0; i < 4; ++i) + { + const int dst_ch = s * 4 + i; + bias_val[i] = dst_ch >= biases.shape.v ? 0.0f : biases.data[dst_ch]; + } + dst[counter++] = bias_val; + } +} + +bool IsDepthwiseConv3x3Supported(const DepthwiseConvolution2DAttributes &attr); + +DepthwiseConv3x3 CreateDepthwiseConv3x3(const DeviceInfo &device_info, + const OperationDef &definition, + const DepthwiseConvolution2DAttributes &attr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_DEPTHWISE_CONV_3X3_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/GpuOperation.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/GpuOperation.cc new file mode 100644 index 000000000..8839d9687 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/GpuOperation.cc @@ -0,0 +1,385 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GpuOperation.h" + +#include "Util.h" +#include "WorkGroupPicking.h" +#include "open_cl/AccessType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::string GetElementWiseCode(const OperationDef &op_def, bool check_src_slices) +{ + std::string c = GetCommonDefines(op_def.precision); + + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int X = get_global_id(0);\n"; + c += " int Y = get_global_id(1);\n"; + c += " int Z = get_global_id(2);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() || " + "Z >= args.dst_tensor.Slices()) return; \n"; + if (check_src_slices) + { + c += " FLT4 src = (FLT4)(0.0f);\n"; + c += " if (Z < args.src_tensor.Slices()) {\n"; + c += " src = args.src_tensor.Read(X, Y, Z);\n"; + c += " }\n"; + } + else + { + c += " FLT4 src = args.src_tensor.Read(X, Y, Z);\n"; + } + c += " args.dst_tensor.Write(src, X, Y, Z);\n"; + c += "} \n"; + return c; +} + +int3 GetWorkGroupsCount(int grid_dimension, const int3 &grid_size, const int3 &work_group_size, + const int3 &work_group_launch_order) +{ + int3 work_groups_count; + if (grid_dimension == 1) + { + work_groups_count.x = DivideRoundUp(grid_size.x, work_group_size.x); + work_groups_count.y = 1; + work_groups_count.z = 1; + } + else if (grid_dimension == 2) + { + int3 wgs; + wgs.x = DivideRoundUp(grid_size.x, work_group_size.x); + wgs.y = DivideRoundUp(grid_size.y, work_group_size.y); + work_groups_count.x = wgs[work_group_launch_order[0]]; + work_groups_count.y = wgs[work_group_launch_order[1]]; + work_groups_count.z = 1; + } + else + { // grid_dimension == 3 + int3 wgs; + wgs.x = DivideRoundUp(grid_size.x, work_group_size.x); + wgs.y = DivideRoundUp(grid_size.y, work_group_size.y); + wgs.z = DivideRoundUp(grid_size.z, work_group_size.z); + work_groups_count.x = wgs[work_group_launch_order[0]]; + work_groups_count.y = wgs[work_group_launch_order[1]]; + work_groups_count.z = wgs[work_group_launch_order[2]]; + } + return work_groups_count; +} + +} // namespace + +DataType OperationDef::GetDataType() const { return DeduceDataTypeFromPrecision(precision); } + +DataType OperationDef::GetPrimaryDataType() const { return src_tensors[0].data_type; } +TensorStorageType OperationDef::GetPrimaryStorageType() const +{ + return src_tensors[0].storage_type; +} + +bool OperationDef::IsBatchSupported() const +{ + for (const auto &src : src_tensors) + { + if (HasAxis(src.layout, Axis::BATCH)) + { + return true; + } + } + for (const auto &dst : dst_tensors) + { + if (HasAxis(dst.layout, Axis::BATCH)) + { + return true; + } + } + return false; +} + +GPUOperation::GPUOperation(const OperationDef &definition) : definition_(definition) {} + +void GPUOperation::SetSrc(Tensor *ptr, int index) +{ + if (index >= (int)src_.size()) + { + src_.resize(index + 1, nullptr); + } + src_[index] = ptr; +} + +void GPUOperation::SetDst(Tensor *ptr, int index) +{ + if (index >= (int)dst_.size()) + { + dst_.resize(index + 1, nullptr); + } + dst_[index] = ptr; +} + +GPUOperation::GPUOperation(GPUOperation &&operation) + : args_(std::move(operation.args_)), code_(std::move(operation.code_)), + work_group_size_(operation.work_group_size_), + compiler_options_(std::move(operation.compiler_options_)), + tensor_to_grid_(operation.tensor_to_grid_), elementwise_(operation.elementwise_), + linkable_(operation.linkable_), check_src_channels_size_(operation.check_src_channels_size_), + definition_(std::move(operation.definition_)), src_(std::move(operation.src_)), + dst_(std::move(operation.dst_)), kernel_(std::move(operation.kernel_)), + grid_dimension_(operation.grid_dimension_), + work_group_launch_order_(operation.work_group_launch_order_), grid_size_(operation.grid_size_), + src_tensors_names_(std::move(operation.src_tensors_names_)), + dst_tensors_names_(std::move(operation.dst_tensors_names_)), + work_groups_count_(operation.work_groups_count_), linkable_count_(operation.linkable_count_), + elementwise_code_(std::move(operation.elementwise_code_)) +{ +} + +GPUOperation &GPUOperation::operator=(GPUOperation &&operation) +{ + if (this != &operation) + { + args_ = std::move(operation.args_); + code_ = std::move(operation.code_); + std::swap(work_group_size_, operation.work_group_size_); + compiler_options_ = std::move(operation.compiler_options_); + tensor_to_grid_ = operation.tensor_to_grid_; + elementwise_ = operation.elementwise_; + linkable_ = operation.linkable_; + check_src_channels_size_ = operation.check_src_channels_size_; + definition_ = std::move(operation.definition_); + src_ = std::move(operation.src_); + dst_ = std::move(operation.dst_); + kernel_ = std::move(operation.kernel_); + std::swap(grid_dimension_, operation.grid_dimension_); + std::swap(work_group_launch_order_, operation.work_group_launch_order_); + std::swap(grid_size_, operation.grid_size_); + src_tensors_names_ = std::move(operation.src_tensors_names_); + dst_tensors_names_ = std::move(operation.dst_tensors_names_); + std::swap(work_groups_count_, operation.work_groups_count_); + std::swap(linkable_count_, operation.linkable_count_); + elementwise_code_ = std::move(operation.elementwise_code_); + } + return *this; +} + +absl::Status GPUOperation::AddOperation(GPUOperation *operation) +{ + linkable_count_ += 1; + std::string code = operation->code_; + std::string unique_postfix = absl::StrCat("_link", linkable_count_); + operation->args_.RenameArgs(unique_postfix, &code); + elementwise_code_ += "{\n" + code + "\n}\n"; + RETURN_IF_ERROR(args_.Merge(std::move(operation->args_), unique_postfix)); + for (size_t i = 0; i < operation->src_tensors_names_.size(); ++i) + { + definition_.src_tensors.push_back(operation->definition_.src_tensors[i + 1]); + src_tensors_names_.push_back(operation->src_tensors_names_[i] + unique_postfix); + } + for (size_t i = 0; i < operation->dst_tensors_names_.size(); ++i) + { + dst_tensors_names_.push_back(operation->dst_tensors_names_[i] + unique_postfix); + } + return absl::OkStatus(); +} + +void GPUOperation::AddSrcTensor(const std::string &tensor_name, const TensorDescriptor &desc) +{ + src_tensors_names_.push_back(tensor_name); + auto desc_new = std::make_unique<TensorDescriptor>(desc); + args_.AddObjectRef(tensor_name, AccessType::READ, std::move(desc_new)); +} + +void GPUOperation::AddSrcBuffer(const std::string &buffer_name, const BufferDescriptor &desc) +{ + src_tensors_names_.push_back(buffer_name); + auto desc_new = std::make_unique<BufferDescriptor>(desc); + args_.AddObjectRef(buffer_name, AccessType::READ, std::move(desc_new)); +} + +void GPUOperation::AddDstTensor(const std::string &tensor_name, const TensorDescriptor &desc) +{ + dst_tensors_names_.push_back(tensor_name); + auto desc_new = std::make_unique<TensorDescriptor>(desc); + args_.AddObjectRef(tensor_name, AccessType::WRITE, std::move(desc_new)); +} + +absl::Status GPUOperation::UpdateParams() +{ + for (size_t i = 0; i < src_tensors_names_.size(); ++i) + { + RETURN_IF_ERROR(args_.SetObjectRef(src_tensors_names_[i], src_[i])); + } + for (size_t i = 0; i < dst_tensors_names_.size(); ++i) + { + RETURN_IF_ERROR(args_.SetObjectRef(dst_tensors_names_[i], dst_[i])); + } + RETURN_IF_ERROR(BindArguments(&args_)); + grid_size_ = GetGridSize(); + work_groups_count_ = + GetWorkGroupsCount(grid_dimension_, grid_size_, work_group_size_, work_group_launch_order_); + return absl::OkStatus(); +} + +absl::Status GPUOperation::AssembleCode(const DeviceInfo &device_info, CLContext *context) +{ + if (elementwise_) + { + auto src_desc = absl::make_unique<TensorDescriptor>(definition_.src_tensors[0]); + if (definition_.IsBatchSupported()) + { + src_desc->SetStateVar("BatchedWidth", "true"); + } + src_tensors_names_.insert(src_tensors_names_.begin(), "src_tensor"); + args_.AddObjectRef("src_tensor", AccessType::READ, std::move(src_desc)); + + auto dst_desc = absl::make_unique<TensorDescriptor>(definition_.dst_tensors[0]); + if (definition_.IsBatchSupported()) + { + dst_desc->SetStateVar("BatchedWidth", "true"); + } + dst_tensors_names_.insert(dst_tensors_names_.begin(), "dst_tensor"); + args_.AddObjectRef("dst_tensor", AccessType::WRITE, std::move(dst_desc)); + + elementwise_code_ = "{\n" + code_ + "\n}\n" + elementwise_code_; + code_ = GetElementWiseCode(definition_, check_src_channels_size_); + RETURN_IF_ERROR(args_.AllocateObjects(context)); + RETURN_IF_ERROR( + args_.TransformToCLCode(device_info, {{dst_tensors_names_[0], elementwise_code_}}, &code_)); + } + else + { + RETURN_IF_ERROR(args_.AllocateObjects(context)); + RETURN_IF_ERROR( + args_.TransformToCLCode(device_info, {{dst_tensors_names_[0], elementwise_code_}}, &code_)); + } + return absl::OkStatus(); +} + +absl::Status GPUOperation::Compile(const CreationContext &creation_context) +{ + RETURN_IF_ERROR(AssembleCode(creation_context.GetDeviceInfo(), creation_context.context)); + RETURN_IF_ERROR(creation_context.cache->GetOrCreateCLKernel( + code_, "main_function", compiler_options_, *creation_context.context, *creation_context.device, + &kernel_)); + return PostCompileCheck(creation_context.device->info_, kernel_.info_); +} + +absl::Status GPUOperation::CompileDeserialized(const CreationContext &creation_context) +{ + return creation_context.cache->GetOrCreateCLKernel(code_, "main_function", compiler_options_, + *creation_context.context, + *creation_context.device, &kernel_); +} + +void GPUOperation::GetPossibleKernelWorkGroups(TuningType tuning_type, + const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const +{ + GetPossibleWorkGroups(tuning_type, device_info, kernel_info, grid_size_, work_groups); +} + +absl::Status GPUOperation::Tune(const TuningParameters ¶ms) +{ + std::vector<int3> possible_work_groups; + GetPossibleKernelWorkGroups(params.tuning_type, *params.info, kernel_.info_, + &possible_work_groups); + if (possible_work_groups.empty()) + { + return absl::NotFoundError("Can not found work_group size to launch kernel"); + } + if (possible_work_groups.size() == 1) + { + work_group_size_ = possible_work_groups[0]; + work_groups_count_ = + GetWorkGroupsCount(grid_dimension_, grid_size_, work_group_size_, work_group_launch_order_); + return absl::OkStatus(); + } + else + { + std::vector<int3> work_groups_count(possible_work_groups.size()); + for (size_t i = 0; i < work_groups_count.size(); ++i) + { + work_groups_count[i] = GetWorkGroupsCount(grid_dimension_, grid_size_, + possible_work_groups[i], work_group_launch_order_); + } + RETURN_IF_ERROR(args_.Bind(kernel_.kernel())); + int best_work_group_index; + RETURN_IF_ERROR(params.queue->GetBestWorkGroupIndex( + kernel_, *params.info, work_groups_count, possible_work_groups, &best_work_group_index)); + work_group_size_ = possible_work_groups[best_work_group_index]; + work_groups_count_ = + GetWorkGroupsCount(grid_dimension_, grid_size_, work_group_size_, work_group_launch_order_); + return absl::OkStatus(); + } +} + +int3 GPUOperation::GetGridSize() const +{ + if (elementwise_ || tensor_to_grid_ == TensorToGrid::kWBToX_HDToY_SToZ) + { + const int grid_x = dst_[0]->Width() * dst_[0]->Batch(); + const int grid_y = dst_[0]->Height() * dst_[0]->Depth(); + const int grid_z = dst_[0]->Slices(); + return int3(grid_x, grid_y, grid_z); + } + if (tensor_to_grid_ == TensorToGrid::kWBToX_HDToY_ZIs1) + { + const int grid_x = dst_[0]->Width() * dst_[0]->Batch(); + const int grid_y = dst_[0]->Height() * dst_[0]->Depth(); + const int grid_z = 1; + return int3(grid_x, grid_y, grid_z); + } + if (tensor_to_grid_ == TensorToGrid::kWBToX_HToY_DToZ) + { + const int grid_x = dst_[0]->Width() * dst_[0]->Batch(); + const int grid_y = dst_[0]->Height(); + const int grid_z = dst_[0]->Depth(); + return int3(grid_x, grid_y, grid_z); + } + if (tensor_to_grid_ == TensorToGrid::kBToX_YIs1_ZIs1) + { + const int grid_x = dst_[0]->Batch(); + const int grid_y = 1; + const int grid_z = 1; + return int3(grid_x, grid_y, grid_z); + } + return grid_size_; +} + +void GPUOperation::AddUniquePostfix(const std::string &unique_postfix) +{ + for (uint32_t i = 0; i < src_tensors_names_.size(); ++i) + { + src_tensors_names_[i] += unique_postfix; + } + for (uint32_t i = 0; i < dst_tensors_names_.size(); ++i) + { + dst_tensors_names_[i] += unique_postfix; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/GpuOperation.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/GpuOperation.h new file mode 100644 index 000000000..4f531c629 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/GpuOperation.h @@ -0,0 +1,203 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_GPU_OPERATION_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_GPU_OPERATION_H__ + +#include <string> +#include <vector> + +#include "TuningParameters.h" + +#include "open_cl/Arguments.h" +#include "open_cl/Buffer.h" +#include "open_cl/ClCommandQueue.h" +#include "open_cl/ClContext.h" +#include "open_cl/ClDevice.h" +#include "open_cl/ClKernel.h" +#include "open_cl/ClProgram.h" +#include "open_cl/DataType.h" +#include "open_cl/DeviceInfo.h" +#include "open_cl/Precision.h" +#include "open_cl/ProgramCache.h" +#include "open_cl/Tensor.h" +#include "open_cl/TensorType.h" +#include "open_cl/Types.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// kCustom: default value +// GPUOperation::GetGridSize must be overloaded +// kWBToX_HDToY_SToZ: +// grid_x = dst_[0]->Width() * dst_[0]->Batch(); +// grid_y = dst_[0]->Height() * dst_[0]->Depth(); +// grid_z = dst_[0]->Slices(); +// kWBToX_HDToY_ZIs1: +// grid_x = dst_[0]->Width() * dst_[0]->Batch(); +// grid_y = dst_[0]->Height() * dst_[0]->Depth(); +// grid_z = 1; +// kWBToX_HToY_DToZ: +// grid_x = dst_[0]->Width() * dst_[0]->Batch(); +// grid_y = dst_[0]->Height(); +// grid_z = dst_[0]->Depth(); +// kBToX_YIs1_ZIs1: +// grid_x = dst_[0]->Batch(); +// grid_y = 1; +// grid_z = 1; +enum class TensorToGrid +{ + kCustom, + kWBToX_HDToY_SToZ, + kWBToX_HDToY_ZIs1, + kWBToX_HToY_DToZ, + kBToX_YIs1_ZIs1 +}; + +struct CreationContext +{ + const CLDevice *device; + CLContext *context; + CLCommandQueue *queue; + ProgramCache *cache; + + const DeviceInfo &GetDeviceInfo() const { return device->info_; } +}; + +struct OperationDef +{ + CalculationsPrecision precision; + std::vector<TensorDescriptor> src_tensors; + std::vector<TensorDescriptor> dst_tensors; + + // returns FLOAT32 for F32 precision and FLOAT16 for F16 precision + DataType GetDataType() const; + // Primary means the first src tensor, because first tensor usually defines + // the structure of kernel, all other resources(biases) types and etc. + DataType GetPrimaryDataType() const; + TensorStorageType GetPrimaryStorageType() const; + bool IsBatchSupported() const; +}; + +// GPUOperation represents some implementation of neural network operation on +// GPU. GPUOperation can contain another GPU operations with flag elementwise_. +// When GPUOperation contains another GPU ops, this GPUoperation replaces +// some sequence of operations Op + op0 + op1 + ... +// Because of this abilities of GPUOperation, usage scenario is next: +// Create instance of GPUOperation. +// Create all instances of GPUOperations that we will(probably) attach +// to GPUOperation. Attach all GPUOperations to GPUOperation. Call +// GPUOperation.Compile(). Don't call GPUOperations.Compile() if it +// attached, it useless(and may be error) +class GPUOperation +{ +public: + GPUOperation() = default; + explicit GPUOperation(const OperationDef &definition); + virtual ~GPUOperation() = default; + // Move only + GPUOperation(GPUOperation &&operation); + GPUOperation &operator=(GPUOperation &&operation); + GPUOperation(const GPUOperation &) = delete; + GPUOperation &operator=(const GPUOperation &) = delete; + + absl::Status AddOperation(GPUOperation *operation); + + void SetSrc(Tensor *ptr, int index = 0); + void SetDst(Tensor *ptr, int index = 0); + + // should be called after changes of inputs/outputs. + absl::Status UpdateParams(); + + absl::Status AddToQueue(CLCommandQueue *queue) + { + RETURN_IF_ERROR(args_.Bind(kernel_.kernel())); + return queue->Dispatch(kernel_, work_groups_count_, work_group_size_); + } + + virtual void GetPossibleKernelWorkGroups(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, + std::vector<int3> *work_groups) const; + + absl::Status Tune(const TuningParameters ¶ms); + + absl::Status AssembleCode(const DeviceInfo &device_info, CLContext *context); + + absl::Status Compile(const CreationContext &creation_context); + + absl::Status CompileDeserialized(const CreationContext &creation_context); + + virtual absl::Status PostCompileCheck(const DeviceInfo &, const KernelInfo &) + { + return absl::OkStatus(); + } + + const OperationDef &GetDefinition() const { return definition_; } + + void AddSrcTensor(const std::string &tensor_name, const TensorDescriptor &desc); + void AddSrcBuffer(const std::string &buffer_name, const BufferDescriptor &desc); + void AddDstTensor(const std::string &tensor_name, const TensorDescriptor &desc); + + bool IsLinkable() const { return elementwise_ && linkable_; } + + // for linking + void AddUniquePostfix(const std::string &unique_postfix); + + Arguments args_; + std::string code_; + int3 work_group_size_ = int3(8, 4, 1); + std::vector<CompilerOptions> compiler_options_; + // not applicable to elementwise + TensorToGrid tensor_to_grid_ = TensorToGrid::kCustom; + + bool elementwise_ = false; + // applicable only with elementwise_ = true; + bool linkable_ = true; // by default every elementwise is linkable + // applicable only with elementwise_ = true; + bool check_src_channels_size_ = false; + +protected: + virtual absl::Status BindArguments(ArgumentsBinder *) { return absl::OkStatus(); } + virtual int3 GetGridSize() const; + + // Defines operation calculation precision and format of src/dst tensors. + OperationDef definition_; + std::vector<Tensor *> src_; + std::vector<Tensor *> dst_; + CLKernel kernel_; + int grid_dimension_ = 3; // can be 1, 2 or 3 + int3 work_group_launch_order_ = int3(0, 1, 2); + int3 grid_size_ = int3(0, 0, 0); + std::vector<std::string> src_tensors_names_; + std::vector<std::string> dst_tensors_names_; + +private: + int3 work_groups_count_ = int3(0, 0, 0); + int linkable_count_ = 0; + std::string elementwise_code_; // temporary, used during op construction +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_GPU_OPERATION_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Pooling.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Pooling.cc new file mode 100644 index 000000000..ceeab2f39 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Pooling.cc @@ -0,0 +1,400 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Pooling.h" + +#include <string> + +#include "Util.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::string GetAveragePoolingKernelCode(const OperationDef &op_def, bool stride_correction, + GPUOperation *op) +{ + auto src_desc = op_def.src_tensors[0]; + + src_desc.SetTextureAddressMode(TextureAddressMode::ZERO); + + if (op_def.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddSrcTensor("src_tensor", src_desc); + auto dst_desc = op_def.dst_tensors[0]; + if (op_def.IsBatchSupported()) + { + dst_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddDstTensor("dst_tensor", dst_desc); + + std::map<Axis, std::string> axis_to_src_coord = { + {Axis::WIDTH, "x_c"}, {Axis::HEIGHT, "y_c"}, {Axis::DEPTH, "d_c"}, + {Axis::CHANNELS, "Z"}, {Axis::BATCH, "B"}, + }; + + std::map<Axis, std::string> axis_to_dst_coord = { + {Axis::WIDTH, "X"}, {Axis::HEIGHT, "Y"}, {Axis::DEPTH, "D"}, + {Axis::CHANNELS, "Z"}, {Axis::BATCH, "B"}, + }; + + std::vector<std::string> src_coords; + std::vector<std::string> dst_coords; + for (auto axis : {Axis::WIDTH, Axis::HEIGHT, Axis::DEPTH, Axis::CHANNELS}) + { + if (op_def.dst_tensors[0].HasAxis(axis)) + { + dst_coords.push_back(axis_to_dst_coord[axis]); + } + if (op_def.src_tensors[0].HasAxis(axis)) + { + src_coords.push_back(axis_to_src_coord[axis]); + } + } + std::string src_coord = src_coords[0]; + for (size_t i = 1; i < src_coords.size(); ++i) + { + src_coord += ", " + src_coords[i]; + } + std::string dst_coord = dst_coords[0]; + for (size_t i = 1; i < dst_coords.size(); ++i) + { + dst_coord += ", " + dst_coords[i]; + } + + const bool manual_clamp = op_def.src_tensors[0].storage_type == TensorStorageType::BUFFER || + op_def.src_tensors[0].storage_type == TensorStorageType::IMAGE_BUFFER; + + std::string c = GetCommonDefines(op_def.precision); + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int X = get_global_id(0);\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " int linear_id_1 = get_global_id(1);\n"; + c += " int Y = linear_id_1 / args.dst_tensor.Depth();\n"; + c += " int D = linear_id_1 % args.dst_tensor.Depth();\n"; + } + else + { + c += " int Y = get_global_id(1);\n"; + } + c += " int Z = get_global_id(2);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() || " + "Z >= args.dst_tensor.Slices()) { \n"; + c += " return; \n"; + c += " } \n"; + c += " float4 r = (float4)(0.0f);\n"; + c += " float window_size = 0.0;\n"; + if (stride_correction) + { + c += " int xs = " + + GetXStrideCorrectedV2("X", "args.src_tensor.Batch()", "args.stride_x", "args.padding_x") + + ";\n"; + } + else + { + if (op_def.IsBatchSupported()) + { + c += " int xs = X * args.stride_x + args.padding_x * " + "args.src_tensor.Batch();\n"; + } + else + { + c += " int xs = X * args.stride_x + args.padding_x;\n"; + } + } + c += " int ys = Y * args.stride_y + args.padding_y;\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " int ds = D * args.stride_z + args.padding_z;\n"; + c += " for (int kz = 0; kz < args.kernel_size_z; ++kz) {\n"; + c += " int d_c = ds + kz;\n"; + c += " if (d_c < 0 || d_c >= args.src_tensor.Depth()) continue;\n"; + } + c += " for (int ky = 0; ky < args.kernel_size_y; ++ky) {\n"; + c += " int y_c = ys + ky;\n"; + c += " bool outside_y = y_c < 0 || y_c >= args.src_tensor.Height();\n"; + c += " for (int kx = 0; kx < args.kernel_size_x; ++kx) {\n"; + if (op_def.IsBatchSupported()) + { + c += " int x_c = xs + kx * args.src_tensor.Batch();\n"; + } + else + { + c += " int x_c = xs + kx;\n"; + } + c += " bool outside = outside_y || x_c < 0 || x_c >= " + "args.src_tensor.Width();\n"; + if (manual_clamp) + { + c += " r += !outside ? args.src_tensor.Read<float>(" + src_coord + + ") : " + "(float4)(0.0f);\n"; + } + else + { + c += " r += args.src_tensor.Read<float>(" + src_coord + ");\n"; + } + c += " window_size += !outside ? 1.0 : 0.0;\n"; + c += " }\n"; + c += " }\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " } // Depth\n"; + } + // If window_size==0, window covered nothing. This situation is a sign of + // incorrectly constructed operation. NaNs are expected as output. + c += " FLT4 result = TO_FLT4(r / window_size);\n"; + c += " args.dst_tensor.Write(result, " + dst_coord + ");\n"; + c += "}\n"; + + return c; +} + +std::string GetMaxPoolingKernelCode(const OperationDef &op_def, bool stride_correction, + bool output_indices, GPUOperation *op) +{ + auto src_desc = op_def.src_tensors[0]; + if (op_def.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddSrcTensor("src_tensor", src_desc); + auto dst_desc = op_def.dst_tensors[0]; + if (op_def.IsBatchSupported()) + { + dst_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddDstTensor("dst_tensor", dst_desc); + if (output_indices) + { + auto dst_ind_desc = op_def.dst_tensors[1]; + if (op_def.IsBatchSupported()) + { + dst_ind_desc.SetStateVar("BatchedWidth", "true"); + } + op->AddDstTensor("dst_indices", dst_ind_desc); + } + + std::map<Axis, std::string> axis_to_src_coord = { + {Axis::WIDTH, "x_c"}, {Axis::HEIGHT, "y_c"}, {Axis::DEPTH, "d_c"}, + {Axis::CHANNELS, "Z"}, {Axis::BATCH, "B"}, + }; + + std::map<Axis, std::string> axis_to_dst_coord = { + {Axis::WIDTH, "X"}, {Axis::HEIGHT, "Y"}, {Axis::DEPTH, "D"}, + {Axis::CHANNELS, "Z"}, {Axis::BATCH, "B"}, + }; + + std::vector<std::string> src_coords; + std::vector<std::string> dst_coords; + for (auto axis : {Axis::WIDTH, Axis::HEIGHT, Axis::DEPTH, Axis::CHANNELS}) + { + if (op_def.dst_tensors[0].HasAxis(axis)) + { + dst_coords.push_back(axis_to_dst_coord[axis]); + } + if (op_def.src_tensors[0].HasAxis(axis)) + { + src_coords.push_back(axis_to_src_coord[axis]); + } + } + std::string src_coord = src_coords[0]; + for (size_t i = 1; i < src_coords.size(); ++i) + { + src_coord += ", " + src_coords[i]; + } + std::string dst_coord = dst_coords[0]; + for (size_t i = 1; i < dst_coords.size(); ++i) + { + dst_coord += ", " + dst_coords[i]; + } + + std::string c = GetCommonDefines(op_def.precision); + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int X = get_global_id(0);\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " int linear_id_1 = get_global_id(1);\n"; + c += " int Y = linear_id_1 / args.dst_tensor.Depth();\n"; + c += " int D = linear_id_1 % args.dst_tensor.Depth();\n"; + } + else + { + c += " int Y = get_global_id(1);\n"; + } + c += " int Z = get_global_id(2);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() || " + "Z >= args.dst_tensor.Slices()) { \n"; + c += " return; \n"; + c += " } \n"; + c += " FLT4 maximum = (FLT4)(-10000.0f);\n"; + if (output_indices) + { + c += " FLT4 indexes = (FLT4)(0.0f);\n"; + } + if (stride_correction) + { + c += " int xs = " + + GetXStrideCorrectedV2("X", "args.src_tensor.Batch()", "args.stride_x", "args.padding_x") + + ";\n"; + } + else + { + if (op_def.IsBatchSupported()) + { + c += " int xs = X * args.stride_x + args.padding_x * " + "args.src_tensor.Batch();\n"; + } + else + { + c += " int xs = X * args.stride_x + args.padding_x;\n"; + } + } + c += " int ys = Y * args.stride_y + args.padding_y;\n"; + c += " for (int ky = 0; ky < args.kernel_size_y; ++ky) {\n"; + c += " int y_c = ys + ky;\n"; + c += " if (y_c < 0 || y_c >= args.src_tensor.Height()) continue;\n"; + c += " for (int kx = 0; kx < args.kernel_size_x; ++kx) {\n"; + if (op_def.IsBatchSupported()) + { + c += " int x_c = xs + kx * args.src_tensor.Batch();\n"; + } + else + { + c += " int x_c = xs + kx;\n"; + } + c += " if (x_c < 0 || x_c >= args.src_tensor.Width()) continue;\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " int ds = D * args.stride_z + args.padding_z;\n"; + c += " for (int kz = 0; kz < args.kernel_size_z; ++kz) {\n"; + c += " int d_c = ds + kz;\n"; + c += " if (d_c < 0 || d_c >= args.src_tensor.Depth()) continue;\n"; + } + c += " FLT4 src = args.src_tensor.Read(" + src_coord + ");\n"; + if (output_indices) + { + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " FLT index_counter = (FLT)((ky * args.kernel_size_x + kx) * " + "args.kernel_size_z + kz) + (FLT)(0.1f);\n"; + } + else + { + c += " FLT index_counter = (FLT)(ky * args.kernel_size_x + kx) + " + "(FLT)(0.1f);\n"; + } + c += " if (src.x > maximum.x) {\n"; + c += " indexes.x = index_counter;\n"; + c += " maximum.x = src.x;\n"; + c += " }\n"; + c += " if (src.y > maximum.y) {\n"; + c += " indexes.y = index_counter;\n"; + c += " maximum.y = src.y;\n"; + c += " }\n"; + c += " if (src.z > maximum.z) {\n"; + c += " indexes.z = index_counter;\n"; + c += " maximum.z = src.z;\n"; + c += " }\n"; + c += " if (src.w > maximum.w) {\n"; + c += " indexes.w = index_counter;\n"; + c += " maximum.w = src.w;\n"; + c += " }\n"; + } + else + { + c += " maximum = max(src, maximum);\n"; + } + if (op_def.dst_tensors[0].HasAxis(Axis::DEPTH)) + { + c += " } // Depth\n"; + } + c += " }\n"; + c += " }\n"; + c += " args.dst_tensor.Write(maximum, " + dst_coord + ");\n"; + if (output_indices) + { + c += " args.dst_indices.Write(indexes, " + dst_coord + ");\n"; + } + c += "}\n"; + + return c; +} +} // namespace + +GPUOperation CreatePooling(const OperationDef &definition, const Pooling2DAttributes &attr) +{ + GPUOperation op(definition); + op.args_.AddInt("kernel_size_x", attr.kernel.w); + op.args_.AddInt("padding_x", -attr.padding.prepended.w); + op.args_.AddInt("stride_x", attr.strides.w); + op.args_.AddInt("kernel_size_y", attr.kernel.h); + op.args_.AddInt("padding_y", -attr.padding.prepended.h); + op.args_.AddInt("stride_y", attr.strides.h); + + const bool stride_correction = definition.IsBatchSupported() && attr.strides.w != 1; + if (attr.type == PoolingType::AVERAGE) + { + op.code_ = GetAveragePoolingKernelCode(definition, stride_correction, &op); + } + else if (attr.type == PoolingType::MAX) + { + op.code_ = GetMaxPoolingKernelCode(definition, stride_correction, attr.output_indices, &op); + } + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_SToZ; + return op; +} + +GPUOperation CreatePooling(const OperationDef &definition, const Pooling3DAttributes &attr) +{ + GPUOperation op(definition); + op.args_.AddInt("kernel_size_x", attr.kernel.w); + op.args_.AddInt("padding_x", -attr.padding.prepended.w); + op.args_.AddInt("stride_x", attr.strides.w); + op.args_.AddInt("kernel_size_y", attr.kernel.h); + op.args_.AddInt("padding_y", -attr.padding.prepended.h); + op.args_.AddInt("stride_y", attr.strides.h); + op.args_.AddInt("kernel_size_z", attr.kernel.d); + op.args_.AddInt("padding_z", -attr.padding.prepended.d); + op.args_.AddInt("stride_z", attr.strides.d); + const bool stride_correction = definition.IsBatchSupported() && attr.strides.w != 1; + if (attr.type == PoolingType::AVERAGE) + { + op.code_ = GetAveragePoolingKernelCode(definition, stride_correction, &op); + } + else if (attr.type == PoolingType::MAX) + { + op.code_ = GetMaxPoolingKernelCode(definition, stride_correction, attr.output_indices, &op); + } + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_SToZ; + return op; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Pooling.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Pooling.h new file mode 100644 index 000000000..166d81591 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Pooling.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_POOLING_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_POOLING_H__ + +#include "GpuOperation.h" + +#include "open_cl/Operations.h" +#include "open_cl/Precision.h" +#include "open_cl/ClKernel.h" +#include "open_cl/Tensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +GPUOperation CreatePooling(const OperationDef &definition, const Pooling2DAttributes &attr); + +GPUOperation CreatePooling(const OperationDef &definition, const Pooling3DAttributes &attr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_ADD_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Relu.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Relu.cc new file mode 100644 index 000000000..37f87e599 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Relu.cc @@ -0,0 +1,80 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Relu.h" + +#include <string> +#include "Util.h" +#include "GpuOperation.h" +#include "absl/strings/str_cat.h" +#include "open_cl/Precision.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +GPUOperation CreateReLU(const OperationDef &definition, const ReLUAttributes &attr) +{ + GPUOperation op(definition); + op.elementwise_ = true; + + std::string min_func; + if (attr.alpha != 0.0f) + { + min_func = "min(in_out_value * args.alpha, (FLT)(0.0f))"; + if (definition.precision == CalculationsPrecision::F32) + { + op.args_.AddFloat("alpha", attr.alpha); + } + else + { +#ifdef FIXME_PORTING_HALF_REQIRED + op.args_.AddHalf("alpha", half(attr.alpha)); +#endif + } + } + else + { + min_func = "(FLT)(0.0f)"; + } + if (attr.clip != 0.0f) + { + if (definition.precision == CalculationsPrecision::F32) + { + op.args_.AddFloat("clip", attr.clip); + } + else + { +#ifdef FIXME_PORTING_HALF_REQIRED + op.args_.AddHalf("clip", half(attr.clip)); +#endif + } + op.code_ = absl::StrCat("in_out_value = clamp(in_out_value, " + min_func + ", args.clip);"); + } + else + { + op.code_ = absl::StrCat("in_out_value = max(in_out_value, ", min_func, ");"); + } + return op; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Relu.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Relu.h new file mode 100644 index 000000000..eb6b1ad1d --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Relu.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_RELU_H__ +#define __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_RELU_H__ + +#include "open_cl/ClKernel.h" +#include "GpuOperation.h" +#include "open_cl/Precision.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" +#include "open_cl/Operations.h" +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +GPUOperation CreateReLU(const OperationDef &definition, const ReLUAttributes &attr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_RELU_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshape.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshape.cc new file mode 100644 index 000000000..cdd3e8364 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshape.cc @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Reshape.h" + +#include <string> + +#include "Util.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ +std::string GetReshapeCode(const OperationDef &op_def) +{ + std::string c = GetCommonDefines(op_def.precision); + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::BATCH)) + { + c += " int linear_id = get_global_id(0);\n"; + c += " int X = linear_id / args.dst_tensor.Batch();\n"; + c += " int B = linear_id % args.dst_tensor.Batch();\n"; + c += " args.dst_tensor.SetBatchRef(B);\n"; + } + else + { + c += " int X = get_global_id(0);\n"; + } + c += " int Y = get_global_id(1);\n"; + c += " int Z = get_global_id(2);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() || " + "Z >= args.dst_tensor.Slices()) { \n"; + c += " return; \n"; + c += " } \n"; + c += " FLT temps[4];\n"; + c += " temps[0] = (FLT)(0.0f);\n"; + c += " temps[1] = (FLT)(0.0f);\n"; + c += " temps[2] = (FLT)(0.0f);\n"; + c += " temps[3] = (FLT)(0.0f);\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::BATCH)) + { + c += " int base = B;\n"; + } + else + { + c += " int base = 0;\n"; + } + c += " base = ((base * args.dst_tensor.Height() + Y) * " + "args.dst_tensor.Width() + X) * args.dst_tensor.Channels() + Z * 4;\n"; + c += " for (int i = 0; i < 4; ++i) {\n"; + c += " int dst_channel = Z * 4 + i;\n"; + c += " if (dst_channel < args.dst_tensor.Channels()) {;\n"; + c += " int p = base + i;\n"; + c += " int src_c = p % args.src_tensor.Channels();\n"; + c += " p = p / args.src_tensor.Channels();\n"; + c += " int src_x = p % args.src_tensor.Width();\n"; + c += " p = p / args.src_tensor.Width();\n"; + c += " int src_y = p % args.src_tensor.Height();\n"; + if (op_def.src_tensors[0].HasAxis(Axis::BATCH)) + { + c += " int src_b = p / args.src_tensor.Height();\n"; + c += " args.src_tensor.SetBatchRef(src_b);\n"; + } + c += " int src_z = src_c / 4;\n"; + c += " int src_sub_ch = src_c % 4;\n"; + c += " FLT4 t = args.src_tensor.Read(src_x, src_y, src_z);\n"; + c += " FLT t_ar[4] = {t.x, t.y, t.z, t.w};\n"; + c += " temps[i] = t_ar[src_sub_ch];\n"; + c += " }\n"; + c += " }\n"; + c += " FLT4 result = (FLT4)(temps[0], temps[1], temps[2], temps[3]);\n"; + c += " args.dst_tensor.Write(result, X, Y, Z);\n"; + c += "}\n"; + return c; +} + +} // namespace + +GPUOperation CreateReshape(const OperationDef &definition) +{ + GPUOperation op(definition); + op.AddSrcTensor("src_tensor", definition.src_tensors[0]); + op.AddDstTensor("dst_tensor", definition.dst_tensors[0]); + op.code_ = GetReshapeCode(definition); + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_SToZ; + return op; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshape.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshape.h new file mode 100644 index 000000000..4f7c5ea38 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshape.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_RESHAPE_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_RESHAPE_H__ + +#include "GpuOperation.h" + +#include "open_cl/Operations.h" +#include "open_cl/Precision.h" +#include "open_cl/ClKernel.h" +#include "open_cl/Tensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +GPUOperation CreateReshape(const OperationDef &definition); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_RESHAPE_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshapex4.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshapex4.cc new file mode 100644 index 000000000..13010e791 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshapex4.cc @@ -0,0 +1,96 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Reshape.h" + +#include <string> + +#include "Util.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::string GetReshapeCode(const OperationDef &op_def) +{ + std::string c = GetCommonDefines(op_def.precision); + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + if (op_def.dst_tensors[0].HasAxis(Axis::BATCH)) + { + c += " int linear_id = get_global_id(0);\n"; + c += " int X = linear_id / args.dst_tensor.Batch();\n"; + c += " int B = linear_id % args.dst_tensor.Batch();\n"; + c += " args.dst_tensor.SetBatchRef(B);\n"; + } + else + { + c += " int X = get_global_id(0);\n"; + } + c += " int Y = get_global_id(1);\n"; + c += " int Z = get_global_id(2);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height() || " + "Z >= args.dst_tensor.Slices()) { \n"; + c += " return; \n"; + c += " } \n"; + if (op_def.dst_tensors[0].HasAxis(Axis::BATCH)) + { + c += " int dst_bhwc4 = B;\n"; + } + else + { + c += " int dst_bhwc4 = 0;\n"; + } + c += " dst_bhwc4 = ((dst_bhwc4 * args.dst_tensor.Height() + Y) * " + "args.dst_tensor.Width() + X) * args.dst_tensor.Slices() + Z;\n"; + c += " int src_z = dst_bhwc4 % args.src_tensor.Slices();\n"; + c += " dst_bhwc4 = dst_bhwc4 / args.src_tensor.Slices();\n"; + c += " int src_x = dst_bhwc4 % args.src_tensor.Width();\n"; + c += " dst_bhwc4 = dst_bhwc4 / args.src_tensor.Width();\n"; + c += " int src_y = dst_bhwc4 % args.src_tensor.Height();\n"; + if (op_def.src_tensors[0].HasAxis(Axis::BATCH)) + { + c += " int src_b = dst_bhwc4 / args.src_tensor.Height();\n"; + c += " args.src_tensor.SetBatchRef(src_b);\n"; + } + c += " FLT4 result = args.src_tensor.Read(src_x, src_y, src_z);\n"; + c += " args.dst_tensor.Write(result, X, Y, Z);\n"; + c += "}\n"; + return c; +} + +} // namespace + +GPUOperation CreateReshapex4(const OperationDef &definition) +{ + GPUOperation op(definition); + op.AddSrcTensor("src_tensor", definition.src_tensors[0]); + op.AddDstTensor("dst_tensor", definition.dst_tensors[0]); + op.code_ = GetReshapeCode(definition); + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_SToZ; + return op; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshapex4.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshapex4.h new file mode 100644 index 000000000..8988e8bd4 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Reshapex4.h @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_RESHAPEX4_H__ +#define __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_RESHAPEX4_H__ + +#include "GpuOperation.h" + +#include "open_cl/Operations.h" +#include "open_cl/Precision.h" +#include "open_cl/ClKernel.h" +#include "open_cl/Tensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// More optimized, but require src_channels % 4 == 0 and dst_channels % 4 == 0 +GPUOperation CreateReshapex4(const OperationDef &definition); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_RESHAPEX4_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax.cc new file mode 100644 index 000000000..4ee164d82 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax.cc @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Softmax.h" + +#include <string> + +#include "Util.h" +#include "WorkGroupPicking.h" +#include "GpuOperation.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +namespace +{ +std::string GetSoftmaxKernelCode(const OperationDef &op_def) +{ + std::string c = GetCommonDefines(op_def.precision); + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + c += " int X = get_global_id(0);\n"; + c += " int Y = get_global_id(1);\n"; + c += " if (X >= args.dst_tensor.Width() || Y >= args.dst_tensor.Height()) " + "return; \n"; + c += " float sum = 0.0f;\n"; + c += " for (int d = 0; d < args.dst_tensor.Slices(); ++d) {\n"; + c += " float4 t = args.src_tensor.Read<float>(X, Y, d);\n"; + c += " sum += exp(t.x);\n"; + c += " if (d * 4 + 1 < args.dst_tensor.Channels()) sum += exp(t.y);\n"; + c += " if (d * 4 + 2 < args.dst_tensor.Channels()) sum += exp(t.z);\n"; + c += " if (d * 4 + 3 < args.dst_tensor.Channels()) sum += exp(t.w);\n"; + c += " }\n"; + c += " for (int d = 0; d < args.dst_tensor.Slices(); ++d) {\n"; + c += " float4 t = args.src_tensor.Read<float>(X, Y, d);\n"; + c += " t = exp(t) / sum;\n"; + c += " FLT4 result = TO_FLT4(t);\n"; + c += " args.dst_tensor.Write(result, X, Y, d);\n"; + c += " }\n"; + c += "}\n"; + return c; +} +} // namespace + +GPUOperation CreateSoftmax(const OperationDef &definition) +{ + GPUOperation op(definition); + auto src_desc = definition.src_tensors[0]; + if (definition.IsBatchSupported()) + { + src_desc.SetStateVar("BatchedWidth", "true"); + } + op.AddSrcTensor("src_tensor", src_desc); + auto dst_desc = definition.dst_tensors[0]; + if (definition.IsBatchSupported()) + { + dst_desc.SetStateVar("BatchedWidth", "true"); + } + op.AddDstTensor("dst_tensor", dst_desc); + op.code_ = GetSoftmaxKernelCode(definition); + op.tensor_to_grid_ = TensorToGrid::kWBToX_HDToY_ZIs1; + return op; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax.h new file mode 100644 index 000000000..594bab042 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_SOFTMAX_H__ +#define __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_SOFTMAX_H__ + +#include "open_cl/ClKernel.h" +#include "GpuOperation.h" +#include "open_cl/Precision.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +GPUOperation CreateSoftmax(const OperationDef &definition); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_SOFTMAX_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax1x1.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax1x1.cc new file mode 100644 index 000000000..590952dca --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax1x1.cc @@ -0,0 +1,138 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Softmax1x1.h" + +#include <string> + +#include "Util.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +Softmax1x1::Softmax1x1(const OperationDef &definition) : GPUOperation(definition) +{ + work_group_size_ = int3(32, 1, 1); + code_ = GetSoftmaxKernelCode(definition_); +} + +Softmax1x1::Softmax1x1(Softmax1x1 &&kernel) : GPUOperation(std::move(kernel)) {} + +Softmax1x1 &Softmax1x1::operator=(Softmax1x1 &&kernel) +{ + if (this != &kernel) + { + GPUOperation::operator=(std::move(kernel)); + } + return *this; +} + +std::string Softmax1x1::GetSoftmaxKernelCode(const OperationDef &op_def) +{ + AddSrcTensor("src_tensor", op_def.src_tensors[0]); + AddDstTensor("dst_tensor", op_def.dst_tensors[0]); + args_.AddFloat("mask_x"); + args_.AddFloat("mask_y"); + args_.AddFloat("mask_z"); + args_.AddFloat("mask_w"); + args_.AddInt("slices_x32"); + + std::string c = GetCommonDefines(op_def.precision); + c += "__kernel void main_function(\n"; + c += "$0) {\n"; + if (op_def.IsBatchSupported()) + { + c += " int batch_id = get_global_id(1);\n"; + c += " if (batch_id >= args.dst_tensor.Batch()) return;\n"; + c += " args.dst_tensor.SetBatchRef(batch_id);\n"; + c += " args.src_tensor.SetBatchRef(batch_id);\n"; + } + c += " float4 mask = (float4)(args.mask_x, args.mask_y, args.mask_z, " + "args.mask_w);\n"; + c += " int offset = 0;\n"; + c += " float sum = 0.0f;\n"; + c += " int s = 0;\n"; + c += " int tid = get_local_id(0);\n"; + c += " do {\n"; + c += " int z = offset + tid;\n"; + c += " if (z < args.dst_tensor.Slices()) {\n"; + c += " float4 mask_temp = z == args.dst_tensor.Slices() - 1 ? mask : " + "(float4)(1.0f);\n"; + c += " float4 src = args.src_tensor.Read<float>(0, 0, z);\n"; + c += " sum += dot(mask_temp, exp(src));\n"; + c += " offset += 32;\n"; + c += " }\n"; + c += " s++;\n"; + c += " } while (s < args.slices_x32);\n"; + c += "\n"; + c += " __local float4 tmp[8];\n"; + c += " __local float* tmpx1 = (__local float*)tmp;\n"; + c += " tmpx1[tid] = sum;\n"; + c += " barrier(CLK_LOCAL_MEM_FENCE);\n"; + c += " if (tid == 0) {\n"; + c += " sum = dot((float4)(1.0f), tmp[0]);\n"; + c += " sum += dot((float4)(1.0f), tmp[1]);\n"; + c += " sum += dot((float4)(1.0f), tmp[2]);\n"; + c += " sum += dot((float4)(1.0f), tmp[3]);\n"; + c += " sum += dot((float4)(1.0f), tmp[4]);\n"; + c += " sum += dot((float4)(1.0f), tmp[5]);\n"; + c += " sum += dot((float4)(1.0f), tmp[6]);\n"; + c += " sum += dot((float4)(1.0f), tmp[7]);\n"; + c += " tmpx1[0] = 1.0f / sum;\n"; + c += " }\n"; + c += " barrier(CLK_LOCAL_MEM_FENCE);\n"; + c += " sum = tmpx1[0];\n"; + c += "\n"; + c += " offset = 0;\n"; + c += " s = 0;\n"; + c += " do {\n"; + c += " int z = offset + tid;\n"; + c += " if (z < args.dst_tensor.Slices()) {\n"; + c += " FLT4 res = TO_FLT4(exp(args.src_tensor.Read<float>(0, 0, " + "z))*sum);\n"; + c += " args.dst_tensor.Write(res, 0, 0, z);\n"; + c += " offset += 32;\n"; + c += " }\n"; + c += " s++;\n"; + c += " } while (s < args.slices_x32);\n"; + c += "}\n"; + return c; +} + +absl::Status Softmax1x1::BindArguments(ArgumentsBinder *args) +{ + float4 mask = GetMaskForLastPlane(src_[0]->Channels()); + RETURN_IF_ERROR(args->SetFloat("mask_x", mask.x)); + RETURN_IF_ERROR(args->SetFloat("mask_y", mask.y)); + RETURN_IF_ERROR(args->SetFloat("mask_z", mask.z)); + RETURN_IF_ERROR(args->SetFloat("mask_w", mask.w)); + RETURN_IF_ERROR(args->SetInt("slices_x32", DivideRoundUp(src_[0]->Slices(), 32))); + return absl::OkStatus(); +} + +int3 Softmax1x1::GetGridSize() const { return int3(32, dst_[0]->Batch(), 1); } + +Softmax1x1 CreateSoftmax1x1(const OperationDef &definition) { return Softmax1x1(definition); } + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax1x1.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax1x1.h new file mode 100644 index 000000000..da375d457 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Softmax1x1.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_SOFTMAX1X1_H__ +#define __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_SOFTMAX1X1_H__ + +#include "GpuOperation.h" + +#include "open_cl/Precision.h" +#include "open_cl/ClKernel.h" +#include "open_cl/Tensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +class Softmax1x1 : public GPUOperation +{ +public: + Softmax1x1() = default; + explicit Softmax1x1(const OperationDef &definition); + + absl::Status BindArguments(ArgumentsBinder *args) override; + int3 GetGridSize() const override; + + // Move only + Softmax1x1(Softmax1x1 &&kernel); + Softmax1x1 &operator=(Softmax1x1 &&kernel); + Softmax1x1(const Softmax1x1 &) = delete; + Softmax1x1 &operator=(const Softmax1x1 &) = delete; + + friend Softmax1x1 CreateSoftmax1x1(); + +private: + std::string GetSoftmaxKernelCode(const OperationDef &op_def); +}; + +Softmax1x1 CreateSoftmax1x1(const OperationDef &definition); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPEN_CL_KERNELS_SOFTMAX1X1_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/TuningParameters.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/TuningParameters.h new file mode 100644 index 000000000..3d99b4fda --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/TuningParameters.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_KERNELS_TUNING_PARAMETERS_H__ +#define __ONERT_BACKEND_GPU_CL_KERNELS_TUNING_PARAMETERS_H__ + +#include "open_cl/ClCommandQueue.h" +#include "open_cl/DeviceInfo.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +enum class TuningType +{ + EXHAUSTIVE, + FAST +}; + +struct TuningParameters +{ + ProfilingCommandQueue *queue; + const DeviceInfo *info; + TuningType tuning_type = TuningType::EXHAUSTIVE; +}; + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_KERNELS_TUNING_PARAMETERS_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Util.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/Util.cc new file mode 100644 index 000000000..df42c66e8 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Util.cc @@ -0,0 +1,230 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Util.h" + +#include <cfloat> +#include <cmath> +#include <string> +#include <vector> + +#include "absl/strings/str_cat.h" +#include "absl/strings/substitute.h" +#include "open_cl/Precision.h" +#include "open_cl/DataType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +std::string GetCommonDefines(CalculationsPrecision precision) +{ + std::string result; + + switch (precision) + { + case CalculationsPrecision::F32: + result += "#pragma OPENCL EXTENSION cl_khr_3d_image_writes : enable\n"; + result += "#define ACCUM_FLT4 float4\n"; + result += "#define FLT float\n"; + result += "#define FLT2 float2\n"; + result += "#define FLT3 float3\n"; + result += "#define FLT4 float4\n"; + result += "#define TO_FLT4 convert_float4\n"; + result += "#define TO_ACCUM_TYPE convert_float4\n"; + result += "#define TO_ACCUM_FLT convert_float\n"; + break; + case CalculationsPrecision::F16: + result += "#pragma OPENCL EXTENSION cl_khr_3d_image_writes : enable\n"; + result += "#pragma OPENCL EXTENSION cl_khr_fp16 : enable\n"; + result += "#define ACCUM_FLT4 half4\n"; + result += "#define FLT half\n"; + result += "#define FLT2 half2\n"; + result += "#define FLT3 half3\n"; + result += "#define FLT4 half4\n"; + result += "#define TO_FLT4 convert_half4\n"; + result += "#define TO_ACCUM_TYPE convert_half4\n"; + result += "#define TO_ACCUM_FLT convert_half\n"; + break; + case CalculationsPrecision::F32_F16: + result += "#pragma OPENCL EXTENSION cl_khr_3d_image_writes : enable\n"; + result += "#pragma OPENCL EXTENSION cl_khr_fp16 : enable\n"; + result += "#define ACCUM_FLT4 float4\n"; + result += "#define FLT half\n"; + result += "#define FLT2 half2\n"; + result += "#define FLT3 half3\n"; + result += "#define FLT4 half4\n"; + result += "#define TO_FLT4 convert_half4\n"; + result += "#define TO_ACCUM_TYPE convert_float4\n"; + result += "#define TO_ACCUM_FLT convert_float\n"; + break; + } + return result; +} + +std::string GetXStrideCorrectedV2(const std::string &src_x, const std::string &batch_size, + const std::string &stride_x, const std::string &padding_x) +{ + // int p0 = src_x / batch_size;\n"; + // int b0 = src_x % batch_size;\n"; + // return (p0 * stride_x + padding_x) * batch_size + b0;\n"; + return absl::Substitute("(((($0) / $1) * $2 + $3) * $1 + ($0) % $1)", src_x, batch_size, stride_x, + padding_x); +} + +float4 GetMaskForLastPlane(int channels) +{ + float4 mask = float4(0.0f); + const int reminder = channels % 4 == 0 ? 4 : channels % 4; + for (int i = 0; i < reminder; ++i) + { + mask[i] = 1.0f; + } + return mask; +} + +int3 GetFirstSuitableWorkGroup(const std::vector<int3> &wgs, int max_wg_size) +{ + for (const auto &wg : wgs) + { + const int wg_size = wg.x * wg.y * wg.z; + if (wg_size <= max_wg_size) + { + return wg; + } + } + return {1, 1, 1}; +} + +int GetRecommendedBlockSizeForConv(const DeviceInfo &device_info, CalculationsPrecision precision, + int task_size) +{ + const float task_size_per_cu = task_size / static_cast<float>(device_info.compute_units_count); + int block_size = 1; + float threshold_1 = FLT_MAX; + float threshold_2 = FLT_MAX; + float threshold_4 = FLT_MAX; + if (!device_info.IsMali()) + { + return 1; + } + MaliInfo mali_info = device_info.mali_info; + switch (precision) + { + case CalculationsPrecision::F16: + if (mali_info.IsBifrostGen1()) + { + threshold_1 = 256.0f; + threshold_2 = 256.0f * 4.0f; + threshold_4 = 256.0f * 8.0f; + } + else if (mali_info.IsBifrostGen2()) + { + threshold_1 = 256.0f * 2.0f; + threshold_2 = 256.0f * 8.0f; + threshold_4 = 256.0f * 16.0f; + } + else if (mali_info.IsBifrostGen3() || mali_info.IsValhall()) + { + threshold_1 = 256.0f; + threshold_2 = 256.0f * 6.0f; + threshold_4 = 256.0f * 16.0f; + } + else if (mali_info.IsMidgard()) + { + threshold_1 = 256.0f * 4.0f; + threshold_2 = 256.0f * 16.0f; + } + break; + case CalculationsPrecision::F32_F16: + if (mali_info.IsBifrostGen1()) + { + threshold_1 = 256.0f; + threshold_2 = 256.0f * 3.0f; + threshold_4 = 256.0f * 32.0f; + } + else if (mali_info.IsBifrostGen2()) + { + threshold_1 = 256.0f * 2.0f; + threshold_2 = 256.0f * 8.0f; + } + else if (mali_info.IsBifrostGen3() || mali_info.IsValhall()) + { + threshold_1 = 256.0f; + threshold_2 = 256.0f * 8.0f; + } + else if (mali_info.IsMidgard()) + { + threshold_1 = 256.0f * 4.0f; + } + break; + case CalculationsPrecision::F32: + if (mali_info.IsBifrostGen1()) + { + threshold_1 = 256.0f; + threshold_2 = 256.0f * 4.0f; + } + else if (mali_info.IsBifrostGen2()) + { + threshold_1 = 128.0f; + threshold_2 = 256.0f * 4.0f; + } + else if (mali_info.IsBifrostGen3() || mali_info.IsValhall()) + { + threshold_1 = 256.0f; + threshold_2 = 256.0f * 12.0f; + } + else if (mali_info.IsMidgard()) + { + threshold_1 = 256.0f * 16.0f; + } + break; + } + if (task_size_per_cu <= threshold_1) + { + block_size = 1; + } + else if (task_size_per_cu <= threshold_2) + { + block_size = 2; + } + else if (task_size_per_cu <= threshold_4) + { + block_size = 4; + } + else + { + block_size = 8; + } + return block_size; +} + +int3 GetWorkGroupsCount(const int3 &grid_size, const int3 &work_group_size) +{ + int3 work_groups_count; + work_groups_count.x = DivideRoundUp(grid_size.x, work_group_size.x); + work_groups_count.y = DivideRoundUp(grid_size.y, work_group_size.y); + work_groups_count.z = DivideRoundUp(grid_size.z, work_group_size.z); + return work_groups_count; +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/Util.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/Util.h new file mode 100644 index 000000000..8363862c1 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/Util.h @@ -0,0 +1,73 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_UTIL_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_UTIL_H__ + +#include <string> +#include <vector> + +#include "open_cl/DeviceInfo.h" +#include "open_cl/Precision.h" +#include "open_cl/DataType.h" +#include "open_cl/Shape.h" +#include "open_cl/Tensor.h" +#include "open_cl/Types.h" +#include "open_cl/Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +std::string GetCommonDefines(CalculationsPrecision precision); + +// Calculates correct X coordinate when stride != 1 and batch != 1 for layouts +// with B after W (for example HWBC4) and WB stored in one axis of GPU +// resources. +std::string GetXStrideCorrected(const std::string &src_x, const std::string &batch_size, + const std::string &stride_x, const std::string &padding_x); + +// Calculates correct X coordinate when stride != 1 and batch != 1 for layouts +// with B after W (for example HWBC4) and WB stored in one axis of GPU +// resources. +std::string GetXStrideCorrectedV2(const std::string &src_x, const std::string &batch_size, + const std::string &stride_x, const std::string &padding_x); + +// Returns float4 mask for last plane(batch of 4 channels) +// assumes that plane size is 4; +// for example we have 7 channels, in our data structures we align it to 8 +// but 8s-channel will be empty, then last plane (batch of 4 channels) will +// have this mask (1, 1, 1, 0). +float4 GetMaskForLastPlane(int channels); + +// returns first work group from wgs that has size not bigger than max_wg_size +// if no suitable groups among wgs, returns {1, 1, 1} +int3 GetFirstSuitableWorkGroup(const std::vector<int3> &wgs, int max_wg_size); + +// task_size as amount of FLT4 processed elements. +int GetRecommendedBlockSizeForConv(const DeviceInfo &device, CalculationsPrecision precision, + int task_size); + +int3 GetWorkGroupsCount(const int3 &grid_size, const int3 &work_group_size); +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_UTIL_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/WorkGroupPicking.cc b/runtime/onert/backend/gpu_cl/open_cl/kernels/WorkGroupPicking.cc new file mode 100644 index 000000000..214fec271 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/WorkGroupPicking.cc @@ -0,0 +1,348 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "WorkGroupPicking.h" + +#include <algorithm> +#include <limits> +#include <set> +#include <vector> + +#include "open_cl/Util.h" +#include "open_cl/Types.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +namespace +{ + +std::vector<int2> Get2DWorkgroupsEqualTo128() +{ + return {{128, 1}, {64, 2}, {32, 4}, {16, 8}, {8, 16}, {4, 32}, {2, 64}, {1, 128}}; +} + +std::vector<int3> GenerateWorkGroupSizesXYMultipleOf(int multiplier, int3 grid, + const KernelInfo &kernel_info, + const DeviceInfo &device_info, + WorkGroupSizeAlignment z_alignment) +{ + std::vector<int3> work_groups; + work_groups.reserve(32); + + std::vector<int> possible_z_sizes = GetPossibleSizes(grid.z, z_alignment); + + for (int x = 1; x <= kernel_info.max_work_group_size; x *= 2) + { + for (int y = 1; y <= kernel_info.max_work_group_size; y *= 2) + { + int work_group_size_xy = x * y; + if (work_group_size_xy % multiplier != 0 || + work_group_size_xy > kernel_info.max_work_group_size) + { + continue; + } + for (auto z : possible_z_sizes) + { + if (work_group_size_xy * z > kernel_info.max_work_group_size) + { + continue; + } + if (x <= device_info.max_work_group_size_x && y <= device_info.max_work_group_size_y && + z <= device_info.max_work_group_size_z) + { + work_groups.push_back({x, y, z}); + } + } + } + } + return work_groups; +} + +std::vector<int3> GenerateWorkGroupSizesXMultipleOf(int multiplier, int3 grid, + const KernelInfo &kernel_info, + const DeviceInfo &device_info, + WorkGroupSizeAlignment z_alignment) +{ + std::vector<int3> work_groups; + work_groups.reserve(32); + + std::vector<int> possible_z_sizes = GetPossibleSizes(grid.z, z_alignment); + std::vector<int> possible_y_sizes = GetPossibleSizes(grid.y, WorkGroupSizeAlignment::PRECISE); + + for (int x = multiplier; x <= kernel_info.max_work_group_size && x < grid.x + multiplier; + x += multiplier) + { + for (auto y : possible_y_sizes) + { + for (auto z : possible_z_sizes) + { + if (x <= device_info.max_work_group_size_x && y <= device_info.max_work_group_size_y && + z <= device_info.max_work_group_size_z && x * y * z <= kernel_info.max_work_group_size) + { + work_groups.push_back({x, y, z}); + } + } + } + } + return work_groups; +} + +void GetWorkGroupsAlignedToGrid(const DeviceInfo &device_info, const KernelInfo &kernel_info, + const int3 &grid, std::vector<int3> *work_groups) +{ + int3 max_wg_size; + max_wg_size.x = device_info.max_work_group_size_x; + max_wg_size.y = device_info.max_work_group_size_y; + max_wg_size.z = device_info.max_work_group_size_z; + GenerateWorkGroupSizesAlignedToGrid(grid, max_wg_size, kernel_info.max_work_group_size, + work_groups); +} + +int GetPenalty(int grid_size, int group_size) +{ + const int reminder = grid_size % group_size; + return reminder == 0 ? 0 : group_size - reminder; +} + +int GetPenalty(int2 grid_size, int2 group_size) +{ + const int p_x = GetPenalty(grid_size.x, group_size.x); + const int p_y = GetPenalty(grid_size.y, group_size.y); + return p_x * grid_size.y + p_y * grid_size.x + p_x * p_y; +} + +int GetMaxSizeWithMinPenalty(int size, int max_size) +{ + int best_size = 128; + int min_penalty = GetPenalty(size, best_size); + for (int i = 2; i * 128 <= max_size; ++i) + { + if (GetPenalty(size, i * 128) == min_penalty) + { + best_size = i * 128; + } + } + return best_size; +} + +int2 GetMaxSizeWithMinPenalty(int2 size, int max_size) +{ + std::vector<int2> base_groups = Get2DWorkgroupsEqualTo128(); + int min_penalty = std::numeric_limits<int>::max(); + for (const auto &group : base_groups) + { + min_penalty = std::min(GetPenalty(size, group), min_penalty); + } + for (const auto &group : base_groups) + { + for (int y = 1; y * group.y <= max_size; ++y) + { + int new_group_y = y * group.y; + for (int x = 1; x * group.x <= max_size; ++x) + { + int new_group_x = x * group.x; + if (new_group_x * new_group_y > max_size) + { + break; + } + if (GetPenalty(size, int2(new_group_x, new_group_y)) == min_penalty) + { + return int2(new_group_x, new_group_y); + } + } + } + } + return int2(0, 0); +} + +int GetBiggestDividerWithPriority(int number, int max_divider) +{ + if (number % 8 == 0 && 8 <= max_divider) + { + return 8; + } + if (number % 4 == 0 && 4 <= max_divider) + { + return 4; + } + if (number % 2 == 0 && 2 <= max_divider) + { + return 2; + } + for (int i = max_divider; i != 0; i--) + { + if (number % i == 0) + { + return i; + } + } + return 1; +} + +int GetBiggestDivider(int number, int max_divider) +{ + for (int i = max_divider; i != 0; i--) + { + if (number % i == 0) + { + return i; + } + } + return 1; +} + +} // namespace + +int3 GetWorkGroupXY128ConvLinear(const int3 &grid) +{ + int grid_z = GetBiggestDividerWithPriority(grid.z, 4); + if (grid.x <= 128) + { + return int3(128, 1, grid_z); + } + int grid_x = GetMaxSizeWithMinPenalty(grid.x, 512 / grid_z); + return {grid_x, 1, grid_z}; +} + +int3 GetWorkGroupXY128Conv(const int3 &grid) +{ + int grid_z = GetBiggestDividerWithPriority(grid.z, 4); + if (grid.x <= 16 && grid.y <= 8) + { + return int3(16, 8, grid_z); + } + int2 grid_xy = GetMaxSizeWithMinPenalty(int2(grid.x, grid.y), 512 / grid_z); + return int3(grid_xy.x, grid_xy.y, grid_z); +} + +// int3 GetWorkGroupXY128Simple(const int3& grid) { return int3(16, 8, 1); } + +int3 GetWorkGroup(const int3 &grid, int max_size) +{ + int wg_z = GetBiggestDividerWithPriority(grid.z, 8); + int wg_xy_size = max_size / wg_z; + int wg_x = std::min(DivideRoundUp(grid.x, 2), wg_xy_size); + int wg_y = std::min(wg_xy_size / wg_x, grid.y); + return int3(wg_x, wg_y, wg_z); +} + +int3 GetWorkGroupConv(const int3 &grid, int max_size, int max_z_size) +{ + int wg_z = GetBiggestDivider(grid.z, max_z_size); + int wg_xy_size = std::min(256, max_size) / wg_z; + int wg_x = std::min(grid.x, wg_xy_size); + int wg_y = std::min(wg_xy_size / wg_x, grid.y); + if (wg_y == grid.y && grid.y % 2 == 0) + { + wg_y = grid.y / 2; + } + return int3(wg_x, wg_y, wg_z); +} + +void GetPossibleWorkGroupsXYMultipleOf(int multiplier, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + WorkGroupSizeAlignment z_alignment, + std::vector<int3> *work_groups) +{ + *work_groups = + GenerateWorkGroupSizesXYMultipleOf(multiplier, grid, kernel_info, device_info, z_alignment); +} + +void GetPossibleWorkGroupsXMultipleOf(int multiplier, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + WorkGroupSizeAlignment z_alignment, + std::vector<int3> *work_groups) +{ + *work_groups = + GenerateWorkGroupSizesXMultipleOf(multiplier, grid, kernel_info, device_info, z_alignment); +} + +bool XY128RequiresMoreWorkGroupsThenXY128Linear(int width, int height) +{ + int planar_work_groups = DivideRoundUp(width * height, 128); + auto base_work_groups = Get2DWorkgroupsEqualTo128(); + bool have_equal_work_groups = false; + for (auto &work_group : base_work_groups) + { + int x_groups = DivideRoundUp(width, work_group.x); + int y_groups = DivideRoundUp(height, work_group.y); + int xy_groups = x_groups * y_groups; + if (xy_groups == planar_work_groups) + { + have_equal_work_groups = true; + break; + } + } + return !have_equal_work_groups; +} + +void GetPossibleWorkGroups(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + std::vector<int3> *work_groups) +{ + switch (tuning_type) + { + case TuningType::FAST: + work_groups->push_back(GetWorkGroup(grid, kernel_info.max_work_group_size)); + return; + case TuningType::EXHAUSTIVE: + { + GetWorkGroupsAlignedToGrid(device_info, kernel_info, grid, work_groups); + return; + } + default: + work_groups->push_back({8, 4, 1}); + return; + } +} + +void GetPossibleWorkGroupsConv(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + std::vector<int3> *work_groups) +{ + switch (tuning_type) + { + case TuningType::FAST: + { + int max_z_size = 16; + if (device_info.IsAdreno()) + { + max_z_size = device_info.IsAdreno3xx() ? 16 : 64; + } + max_z_size = std::min(max_z_size, device_info.max_work_group_size_z); + work_groups->push_back(GetWorkGroupConv(grid, kernel_info.max_work_group_size, max_z_size)); + return; + } + case TuningType::EXHAUSTIVE: + { + GetWorkGroupsAlignedToGrid(device_info, kernel_info, grid, work_groups); + return; + } + default: + work_groups->push_back({8, 4, 1}); + return; + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/kernels/WorkGroupPicking.h b/runtime/onert/backend/gpu_cl/open_cl/kernels/WorkGroupPicking.h new file mode 100644 index 000000000..c19890de1 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/kernels/WorkGroupPicking.h @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_WROK_GROUP_PICKING_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_WROK_GROUP_PICKING_H__ + +#include <vector> + +#include "TuningParameters.h" + +#include "open_cl/ClKernel.h" +#include "open_cl/DeviceInfo.h" +#include "open_cl/Types.h" +#include "open_cl/WorkgroupSelection.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +// multiplier can be power of two only +void GetPossibleWorkGroupsXYMultipleOf(int multiplier, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + WorkGroupSizeAlignment z_alignment, + std::vector<int3> *work_groups); + +void GetPossibleWorkGroupsXMultipleOf(int multiplier, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + WorkGroupSizeAlignment z_alignment, + std::vector<int3> *work_groups); + +int3 GetWorkGroupXY128ConvLinear(const int3 &grid); + +int3 GetWorkGroupXY128Simple(const int3 &grid); +int3 GetWorkGroupXY128Conv(const int3 &grid); + +bool XY128RequiresMoreWorkGroupsThenXY128Linear(int width, int height); + +void GetPossibleWorkGroups(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + std::vector<int3> *work_groups); + +void GetPossibleWorkGroupsConv(TuningType tuning_type, const DeviceInfo &device_info, + const KernelInfo &kernel_info, const int3 &grid, + std::vector<int3> *work_groups); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_KERNELS_WROK_GROUP_PICKING_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/selectors/ConvolutionSelector.cc b/runtime/onert/backend/gpu_cl/open_cl/selectors/ConvolutionSelector.cc new file mode 100644 index 000000000..eac6f3270 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/selectors/ConvolutionSelector.cc @@ -0,0 +1,249 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "ConvolutionSelector.h" + +#include "absl/memory/memory.h" +#include "open_cl/kernels/ConvBuffer1x1.h" +#include "open_cl/kernels/ConvConstants.h" +#include "open_cl/kernels/ConvPowervr.h" +#include "open_cl/kernels/ConvWeightsConverter.h" +#include "open_cl/kernels/WorkGroupPicking.h" +#include "open_cl/TensorType.h" +#include "open_cl/Util.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::unique_ptr<GPUOperation> SelectConvolutionAdreno(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def, ModelHints) +{ + if (IsConvConstantsSupported(device_info, op_def, attr)) + { + GPUOperation conv = CreateConvConstants(device_info, op_def, attr); + return absl::make_unique<GPUOperation>(std::move(conv)); + } + else + { + ConvPowerVR conv = CreateConvPowerVR(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvPowerVR>(std::move(conv)); + } +} + +std::unique_ptr<GPUOperation> SelectConvolutionWinogradAdreno(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def, + ModelHints) +{ + ConvPowerVR conv = CreateConvPowerVRWino4x4To6x6(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvPowerVR>(std::move(conv)); +} + +std::unique_ptr<GPUOperation> +SelectConvolutionDynamicWeightsAdreno(const Convolution2DAttributes &attr, + const BHWC &weights_shape, const BHWC &dst_shape, + const DeviceInfo &device_info, const OperationDef &op_def, + ModelHints, ConvWeightsDescription *weights_desc) +{ + ConvPowerVR conv = + CreateConvPowerVRDynamicWeights(device_info, op_def, attr, weights_shape, &dst_shape); + *weights_desc = conv.GetConvWeightsDescription(); + return absl::make_unique<ConvPowerVR>(std::move(conv)); +} + +std::unique_ptr<GPUOperation> SelectConvolutionNVidia(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def) +{ + if (IsConvConstantsSupported(device_info, op_def, attr)) + { + GPUOperation conv = CreateConvConstants(device_info, op_def, attr); + return absl::make_unique<GPUOperation>(std::move(conv)); + } + else + { + ConvPowerVR conv = CreateConvPowerVR(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvPowerVR>(std::move(conv)); + } +} + +std::unique_ptr<GPUOperation> SelectConvolutionPowerVR(const Convolution2DAttributes &attr, + const DeviceInfo &device_info, + const OperationDef &op_def) +{ + ConvPowerVR conv = CreateConvPowerVR(device_info, op_def, attr); + return absl::make_unique<ConvPowerVR>(std::move(conv)); +} + +std::unique_ptr<GPUOperation> SelectConvolutionMali(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def) +{ + if (op_def.src_tensors[0].storage_type == TensorStorageType::BUFFER && + IsConvBuffer1x1Supported(op_def, attr)) + { + ConvBuffer1x1 conv = CreateConvBuffer1x1(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvBuffer1x1>(std::move(conv)); + } + else + { + ConvPowerVR conv = CreateConvPowerVR(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvPowerVR>(std::move(conv)); + } +} + +std::unique_ptr<GPUOperation> SelectConvolutionWinogradMali(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def) +{ + if (op_def.src_tensors[0].storage_type == TensorStorageType::BUFFER) + { + ConvBuffer1x1 conv = CreateConvBuffer1x1Wino4x4To6x6(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvBuffer1x1>(std::move(conv)); + } + else + { + ConvPowerVR conv = CreateConvPowerVRWino4x4To6x6(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvPowerVR>(std::move(conv)); + } +} + +std::unique_ptr<GPUOperation> +SelectConvolutionDynamicWeightsMali(const Convolution2DAttributes &attr, const BHWC &weights_shape, + const BHWC &dst_shape, const DeviceInfo &device_info, + const OperationDef &op_def, ModelHints, + ConvWeightsDescription *weights_desc) +{ + if (op_def.src_tensors[0].storage_type == TensorStorageType::BUFFER && + IsConvBuffer1x1Supported(op_def, weights_shape, attr)) + { + ConvBuffer1x1 conv = + CreateConvBuffer1x1DynamicWeights(device_info, op_def, attr, weights_shape, &dst_shape); + *weights_desc = conv.GetConvWeightsDescription(); + return absl::make_unique<ConvBuffer1x1>(std::move(conv)); + } + else + { + ConvPowerVR conv = + CreateConvPowerVRDynamicWeights(device_info, op_def, attr, weights_shape, &dst_shape); + *weights_desc = conv.GetConvWeightsDescription(); + return absl::make_unique<ConvPowerVR>(std::move(conv)); + } +} + +} // namespace + +std::unique_ptr<GPUOperation> SelectConvolution(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def, ModelHints hints) +{ + if (device_info.IsAdreno()) + { + return SelectConvolutionAdreno(attr, dst_shape, device_info, op_def, hints); + } + else if (device_info.IsPowerVR() || device_info.IsAMD() || device_info.IsIntel()) + { + return SelectConvolutionPowerVR(attr, device_info, op_def); + } + else if (device_info.IsNvidia()) + { + return SelectConvolutionNVidia(attr, dst_shape, device_info, op_def); + } + else if (device_info.IsMali()) + { + return SelectConvolutionMali(attr, dst_shape, device_info, op_def); + } + else + { + return SelectConvolutionAdreno(attr, dst_shape, device_info, op_def, hints); + } +} + +std::unique_ptr<GPUOperation> SelectConvolutionForWinograd(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def, + ModelHints hints) +{ + if (device_info.IsAdreno()) + { + return SelectConvolutionWinogradAdreno(attr, dst_shape, device_info, op_def, hints); + } + else if (device_info.IsPowerVR() || device_info.IsAMD() || device_info.IsNvidia() || + device_info.IsIntel()) + { + ConvPowerVR conv = CreateConvPowerVRWino4x4To6x6(device_info, op_def, attr, &dst_shape); + return absl::make_unique<ConvPowerVR>(std::move(conv)); + } + else if (device_info.IsMali()) + { + return SelectConvolutionWinogradMali(attr, dst_shape, device_info, op_def); + } + else + { + return SelectConvolutionWinogradAdreno(attr, dst_shape, device_info, op_def, hints); + } +} + +std::unique_ptr<GPUOperation> +SelectConvolutionWithDynamicWeights(const Convolution2DAttributes &attr, const BHWC &weights_shape, + const BHWC &dst_shape, const DeviceInfo &device_info, + const OperationDef &op_def, ModelHints hints, + ConvWeightsDescription *weights_desc) +{ + if (device_info.IsAdreno()) + { + return SelectConvolutionDynamicWeightsAdreno(attr, weights_shape, dst_shape, device_info, + op_def, hints, weights_desc); + } + else if (device_info.IsMali()) + { + return SelectConvolutionDynamicWeightsMali(attr, weights_shape, dst_shape, device_info, op_def, + hints, weights_desc); + } + else + { + ConvPowerVR conv = + CreateConvPowerVRDynamicWeights(device_info, op_def, attr, weights_shape, &dst_shape); + *weights_desc = conv.GetConvWeightsDescription(); + return absl::make_unique<ConvPowerVR>(std::move(conv)); + } +} + +std::unique_ptr<GPUOperation> +SelectConverterToConvWeights(const ConvWeightsDescription &weights_desc, const OperationDef &op_def, + ModelHints) +{ + ConverterToConvWeights converter = ConverterToConvWeights(op_def, weights_desc); + return absl::make_unique<ConverterToConvWeights>(std::move(converter)); +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/selectors/ConvolutionSelector.h b/runtime/onert/backend/gpu_cl/open_cl/selectors/ConvolutionSelector.h new file mode 100644 index 000000000..d45eea8bd --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/selectors/ConvolutionSelector.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_CONVOLUTION_SELECTOR_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_CONVOLUTION_SELECTOR_H__ + +#include <memory> + +#include "open_cl/kernels/ConvCommon.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/ModelHints.h" +#include "open_cl/Operations.h" +#include "open_cl/Shape.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +std::unique_ptr<GPUOperation> SelectConvolution(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def, ModelHints hints); + +std::unique_ptr<GPUOperation> SelectConvolutionForWinograd(const Convolution2DAttributes &attr, + const BHWC &dst_shape, + const DeviceInfo &device_info, + const OperationDef &op_def, + ModelHints hints); + +std::unique_ptr<GPUOperation> +SelectConvolutionWithDynamicWeights(const Convolution2DAttributes &attr, const BHWC &weights_shape, + const BHWC &dst_shape, const DeviceInfo &device_info, + const OperationDef &op_def, ModelHints hints, + ConvWeightsDescription *weights_desc); + +std::unique_ptr<GPUOperation> +SelectConverterToConvWeights(const ConvWeightsDescription &weights_desc, const OperationDef &op_def, + ModelHints hints); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_CONVOLUTION_SELECTOR_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/selectors/DwConvolutionSelector.cc b/runtime/onert/backend/gpu_cl/open_cl/selectors/DwConvolutionSelector.cc new file mode 100644 index 000000000..f07eef689 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/selectors/DwConvolutionSelector.cc @@ -0,0 +1,107 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DwConvolutionSelector.h" + +#include "absl/memory/memory.h" +#include "open_cl/ClDevice.h" +#include "open_cl/kernels/DepthwiseConv.h" +#include "open_cl/kernels/DepthwiseConv3x3.h" +#include "open_cl/Precision.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace +{ + +std::unique_ptr<GPUOperation> +SelectDWConvolutionAdreno(const DepthwiseConvolution2DAttributes &attr, + const DeviceInfo &device_info, const OperationDef &op_def) +{ + if (IsDepthwiseConv3x3Supported(attr)) + { + return absl::make_unique<DepthwiseConv3x3>(CreateDepthwiseConv3x3(device_info, op_def, attr)); + } + else + { + return absl::make_unique<GPUOperation>(CreateDepthwiseConvolution2D(device_info, op_def, attr)); + } +} + +std::unique_ptr<GPUOperation> +SelectDWConvolutionPowerVR(const DepthwiseConvolution2DAttributes &attr, + const DeviceInfo &device_info, const OperationDef &op_def) +{ + if (IsDepthwiseConv3x3Supported(attr)) + { + return absl::make_unique<DepthwiseConv3x3>(CreateDepthwiseConv3x3(device_info, op_def, attr)); + } + else + { + return absl::make_unique<GPUOperation>(CreateDepthwiseConvolution2D(device_info, op_def, attr)); + } +} + +std::unique_ptr<GPUOperation> SelectDWConvolutionMali(const DepthwiseConvolution2DAttributes &attr, + const DeviceInfo &device_info, + const OperationDef &op_def) +{ + const auto storage_type = op_def.src_tensors[0].storage_type; + bool buffer_type = + storage_type == TensorStorageType::BUFFER || storage_type == TensorStorageType::IMAGE_BUFFER; + const MaliInfo mali_info = device_info.mali_info; + if (IsDepthwiseConv3x3Supported(attr) && !mali_info.IsMidgard() && !buffer_type && + op_def.precision != CalculationsPrecision::F32) + { + return absl::make_unique<DepthwiseConv3x3>(CreateDepthwiseConv3x3(device_info, op_def, attr)); + } + else + { + return absl::make_unique<GPUOperation>(CreateDepthwiseConvolution2D(device_info, op_def, attr)); + } +} +} // namespace + +std::unique_ptr<GPUOperation> SelectDWConvolution(const DepthwiseConvolution2DAttributes &attr, + const DeviceInfo &device_info, + const OperationDef &op_def) +{ + if (device_info.IsAdreno()) + { + return SelectDWConvolutionAdreno(attr, device_info, op_def); + } + else if (device_info.IsPowerVR()) + { + return SelectDWConvolutionPowerVR(attr, device_info, op_def); + } + else if (device_info.IsMali()) + { + return SelectDWConvolutionMali(attr, device_info, op_def); + } + else + { + return SelectDWConvolutionAdreno(attr, device_info, op_def); + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/selectors/DwConvolutionSelector.h b/runtime/onert/backend/gpu_cl/open_cl/selectors/DwConvolutionSelector.h new file mode 100644 index 000000000..2fa40c5c3 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/selectors/DwConvolutionSelector.h @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_DW_CONVOLUTION_SELECTOR_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_DW_CONVOLUTION_SELECTOR_H__ + +#include <memory> + +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/Operations.h" +#include "open_cl/Status.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +std::unique_ptr<GPUOperation> SelectDWConvolution(const DepthwiseConvolution2DAttributes &attr, + const DeviceInfo &device_info, + const OperationDef &op_def); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_DW_CONVOLUTION_SELECTOR_H__ diff --git a/runtime/onert/backend/gpu_cl/open_cl/selectors/SimpleSelectors.cc b/runtime/onert/backend/gpu_cl/open_cl/selectors/SimpleSelectors.cc new file mode 100644 index 000000000..ac514b26c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/selectors/SimpleSelectors.cc @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SimpleSelectors.h" + +#include <memory> +#include <set> + +#include "open_cl/kernels/Add.h" +#include "open_cl/kernels/DepthwiseConv.h" +#include "open_cl/kernels/Pooling.h" +#include "open_cl/kernels/Relu.h" +#include "open_cl/kernels/Reshape.h" +#include "open_cl/kernels/Reshapex4.h" +#include "open_cl/kernels/Softmax.h" +#include "open_cl/kernels/Softmax1x1.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +void SelectAdd(const OperationDef &op_def, const std::vector<int> &channels, int dst_channels, + std::unique_ptr<GPUOperation> *ptr) +{ + GPUOperation operation = CreateAdd(op_def, channels, dst_channels); + *ptr = std::make_unique<GPUOperation>(std::move(operation)); +} + +std::unique_ptr<GPUOperation> +SelectDWConvolutionDynamicWeights(const DepthwiseConvolution2DAttributes &attr, + const DeviceInfo &device_info, const OperationDef &op_def) +{ + return absl::make_unique<GPUOperation>( + CreateDepthwiseConvolution2DDynamicWeights(device_info, op_def, attr)); +} + +std::unique_ptr<GPUOperation> SelectPooling(const Pooling2DAttributes &attr, + const OperationDef &op_def) +{ + GPUOperation operation = CreatePooling(op_def, attr); + return absl::make_unique<GPUOperation>(std::move(operation)); +} + +std::unique_ptr<GPUOperation> SelectReLU(const ReLUAttributes &attr, const OperationDef &op_def) +{ + return absl::make_unique<GPUOperation>(CreateReLU(op_def, attr)); +} + +void SelectReshape(int src_channels, int dst_channels, const OperationDef &op_def, + std::unique_ptr<GPUOperation> *ptr) +{ + if (src_channels % 4 == 0 && dst_channels % 4 == 0) + { + GPUOperation operation = CreateReshapex4(op_def); + *ptr = std::make_unique<GPUOperation>(std::move(operation)); + } + else + { + GPUOperation operation = CreateReshape(op_def); + *ptr = std::make_unique<GPUOperation>(std::move(operation)); + } +} + +void SelectSoftmax(const BHWC &shape, const OperationDef &op_def, + std::unique_ptr<GPUOperation> *ptr) +{ + if (shape.w == 1 && shape.h == 1) + { + Softmax1x1 operation = CreateSoftmax1x1(op_def); + *ptr = absl::make_unique<Softmax1x1>(std::move(operation)); + } + else + { + GPUOperation operation = CreateSoftmax(op_def); + *ptr = absl::make_unique<GPUOperation>(std::move(operation)); + } +} + +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/open_cl/selectors/SimpleSelectors.h b/runtime/onert/backend/gpu_cl/open_cl/selectors/SimpleSelectors.h new file mode 100644 index 000000000..2c5837a1d --- /dev/null +++ b/runtime/onert/backend/gpu_cl/open_cl/selectors/SimpleSelectors.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_SIMPLE_SELECTORS_H__ +#define __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_SIMPLE_SELECTORS_H__ + +#include <memory> + +#include "open_cl/ClDevice.h" +#include "open_cl/kernels/GpuOperation.h" +#include "open_cl/Operations.h" +#include "open_cl/Shape.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ + +void SelectAdd(const OperationDef &op_def, const std::vector<int> &channels, int dst_channels, + std::unique_ptr<GPUOperation> *ptr); + +std::unique_ptr<GPUOperation> +SelectDWConvolutionDynamicWeights(const DepthwiseConvolution2DAttributes &attr, + const DeviceInfo &device_info, const OperationDef &op_def); + +std::unique_ptr<GPUOperation> SelectPooling(const Pooling2DAttributes &attr, + const OperationDef &op_def); + +std::unique_ptr<GPUOperation> SelectReLU(const ReLUAttributes &attr, const OperationDef &op_def); + +void SelectReshape(int src_channels, int dst_channels, const OperationDef &op_def, + std::unique_ptr<GPUOperation> *ptr); + +void SelectSoftmax(const BHWC &shape, const OperationDef &op_def, + std::unique_ptr<GPUOperation> *ptr); + +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPENCL_SELECTORS_SIMPLE_SELECTORS_H__ diff --git a/runtime/onert/backend/gpu_cl/operand/CLTensor.cc b/runtime/onert/backend/gpu_cl/operand/CLTensor.cc new file mode 100644 index 000000000..6dd9bd252 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/operand/CLTensor.cc @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CLTensor.h" + +#include "open_cl/Buffer.h" +#include "open_cl/ClContext.h" +#include "open_cl/Tensor.h" +#include "open_cl/TensorType.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace operand +{ + +CLTensor::CLTensor(size_t rank, ir::Shape shape, std::shared_ptr<Environment> environment) + : ICLTensor{rank, shape, environment}, _tensor(std::make_shared<Tensor>()) +{ +} + +const Tensor *CLTensor::handle() const { return _tensor.get(); } + +Tensor *CLTensor::handle() { return _tensor.get(); } + +void CLTensor::setBuffer(void *host_ptr) { (void)host_ptr; } + +} // namespace operand +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/operand/CLTensor.h b/runtime/onert/backend/gpu_cl/operand/CLTensor.h new file mode 100644 index 000000000..7d2e70a99 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/operand/CLTensor.h @@ -0,0 +1,66 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPERAND_CL_TENSOR_H__ +#define __ONERT_BACKEND_GPU_CL_OPERAND_CL_TENSOR_H__ + +#include "ICLTensor.h" + +#include "open_cl/Buffer.h" +#include "open_cl/ClContext.h" +#include "open_cl/Tensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace operand +{ + +class CLTensor : public ICLTensor +{ +public: + CLTensor() = delete; + +public: + CLTensor(size_t rank, ir::Shape shape, std::shared_ptr<Environment> environment); + +public: + const Tensor *handle() const override; + Tensor *handle() override; + +public: + /** Set given buffer as the buffer of the tensor + * + * @note Ownership of the memory is not transferred to this object. + * Thus management (allocate/free) should be done by the client. + * + * @param[in] host_ptr Storage to be used. + */ + void setBuffer(void *host_ptr); + +private: + std::shared_ptr<Tensor> _tensor; +}; + +} // namespace operand +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPERAND_CL_TENSOR_H__ diff --git a/runtime/onert/backend/gpu_cl/operand/ICLTensor.cc b/runtime/onert/backend/gpu_cl/operand/ICLTensor.cc new file mode 100644 index 000000000..3f070be0c --- /dev/null +++ b/runtime/onert/backend/gpu_cl/operand/ICLTensor.cc @@ -0,0 +1,223 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ICLTensor.h" + +#include "open_cl/Api.h" +#include "open_cl/Spi.h" +#include "open_cl/OpenclWrapper.h" +#include "open_cl/TensorTypeUtil.h" +#include "open_cl/kernels/Converter.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace operand +{ + +void ICLTensor::access(const std::function<void(ITensor &tensor)> &fn) +{ + if (total_size() == 0) + return; + + fn(*this); +} + +void ICLTensor::enqueueWriteBuffer(const void *ptr, bool) +{ + const float *arr = (float *)ptr; + TensorObject input_obj = MakeReadableCpuMemory(absl::MakeSpan(arr, total_size() / 4)); + + TensorObject output_obj; + + if (handle()->GetStorageType() == TensorStorageType::BUFFER) + { + output_obj = OpenClBuffer{handle()->GetMemoryPtr()}; + } + else if (handle()->GetStorageType() == TensorStorageType::IMAGE_BUFFER) + { + output_obj = OpenClBuffer{handle()->GetMemoryPtrForWriting()}; + } + else + { + output_obj = OpenClTexture{handle()->GetMemoryPtr()}; + } + + TensorObjectDef input_def; + input_def.dimensions.b = handle()->Batch(); + input_def.dimensions.h = handle()->Height(); + input_def.dimensions.w = handle()->Width(); + input_def.dimensions.c = handle()->Channels(); + input_def.object_def.data_layout = DataLayout::BHWC; + input_def.object_def.data_type = DataType::FLOAT32; + input_def.object_def.object_type = ObjectType::CPU_MEMORY; + input_def.object_def.user_provided = true; + + TensorObjectDef tmp_def; + tmp_def.dimensions.b = handle()->Batch(); + tmp_def.dimensions.h = handle()->Height(); + tmp_def.dimensions.w = handle()->Width(); + tmp_def.dimensions.c = handle()->Channels(); + tmp_def.object_def.data_layout = DataLayout::BHWC; + tmp_def.object_def.data_type = DataType::FLOAT32; + tmp_def.object_def.object_type = ToObjectType(handle()->GetStorageType()); + tmp_def.object_def.user_provided = true; + + auto dims = tmp_def.dimensions; + const BHWC shape(dims.b, dims.h, dims.w, dims.c); + const TensorDescriptor desc{ + tmp_def.object_def.data_type, + ToTensorStorageType(tmp_def.object_def.object_type, tmp_def.object_def.data_layout), + Layout::BHWC}; + if (!AllocateTensorMemory(_environment->context(), shape, desc, &_cl_memory).ok()) + { + throw std::runtime_error("AllocateTensorMemory error."); + } + TensorObject tmp_obj; + if (tmp_def.object_def.object_type == ObjectType::OPENCL_TEXTURE) + { + tmp_obj = OpenClTexture{_cl_memory.memory()}; + } + else + { + tmp_obj = OpenClBuffer{_cl_memory.memory()}; + } + + TensorObjectDef output_def = input_def; + output_def.dimensions.b = handle()->Batch(); + output_def.dimensions.h = handle()->Height(); + output_def.dimensions.w = handle()->Width(); + output_def.dimensions.c = handle()->Channels(); + output_def.object_def.data_layout = ToDataLayout(handle()->GetStorageType()); + output_def.object_def.data_type = handle()->GetDataType(); + output_def.object_def.object_type = ToObjectType(handle()->GetStorageType()); + + _converter_builder = NewConverterBuilder(_environment.get()); + if (!_converter_builder->MakeConverter(input_def, tmp_def, &_converter_cpu).ok()) + { + throw std::runtime_error("MakeConverter<_converter_cpu> error."); + } + if (!_converter_builder->MakeConverter(tmp_def, output_def, &_converter_bhwc).ok()) + { + throw std::runtime_error("MakeConverter<_converter_bhwc> error."); + } + + if (!_converter_cpu->Convert(input_obj, tmp_obj).ok()) + { + throw std::runtime_error("[w] _converter_cpu Convert error."); + } + if (!_converter_bhwc->Convert(tmp_obj, output_obj).ok()) + { + throw std::runtime_error("[w] _converter_bhwc Convert error."); + } +} + +void ICLTensor::enqueueReadBuffer(void *ptr, bool) +{ + float *arr = (float *)ptr; + TensorObject output_obj = MakeCpuMemory(absl::MakeSpan(arr, total_size() / 4)); + + TensorObject input_obj; + + if (handle()->GetStorageType() == TensorStorageType::BUFFER) + { + input_obj = OpenClBuffer{handle()->GetMemoryPtr()}; + } + else if (handle()->GetStorageType() == TensorStorageType::IMAGE_BUFFER) + { + input_obj = OpenClBuffer{handle()->GetMemoryPtrForWriting()}; + } + else + { + input_obj = OpenClTexture{handle()->GetMemoryPtr()}; + } + + TensorObjectDef input_def; + input_def.dimensions.b = handle()->Batch(); + input_def.dimensions.h = handle()->Height(); + input_def.dimensions.w = handle()->Width(); + input_def.dimensions.c = handle()->Channels(); + input_def.object_def.data_layout = ToDataLayout(handle()->GetStorageType()); + input_def.object_def.data_type = handle()->GetDataType(); + input_def.object_def.object_type = ToObjectType(handle()->GetStorageType()); + input_def.object_def.user_provided = false; + + TensorObjectDef tmp_def; + tmp_def.dimensions.b = handle()->Batch(); + tmp_def.dimensions.h = handle()->Height(); + tmp_def.dimensions.w = handle()->Width(); + tmp_def.dimensions.c = handle()->Channels(); + tmp_def.object_def.data_layout = DataLayout::BHWC; + tmp_def.object_def.data_type = DataType::FLOAT32; + tmp_def.object_def.object_type = ToObjectType(handle()->GetStorageType()); + tmp_def.object_def.user_provided = true; + + auto dims = tmp_def.dimensions; + const BHWC shape(dims.b, dims.h, dims.w, dims.c); + const TensorDescriptor desc{ + tmp_def.object_def.data_type, + ToTensorStorageType(tmp_def.object_def.object_type, tmp_def.object_def.data_layout), + Layout::BHWC}; + if (!AllocateTensorMemory(_environment->context(), shape, desc, &_cl_memory).ok()) + { + throw std::runtime_error("AllocateTensorMemory error."); + } + TensorObject tmp_obj; + if (tmp_def.object_def.object_type == ObjectType::OPENCL_TEXTURE) + { + tmp_obj = OpenClTexture{_cl_memory.memory()}; + } + else + { + tmp_obj = OpenClBuffer{_cl_memory.memory()}; + } + TensorObjectDef output_def = input_def; + output_def.dimensions.b = handle()->Batch(); + output_def.dimensions.h = handle()->Height(); + output_def.dimensions.w = handle()->Width(); + output_def.dimensions.c = handle()->Channels(); + output_def.object_def.data_layout = DataLayout::BHWC; + output_def.object_def.data_type = DataType::FLOAT32; + output_def.object_def.object_type = ObjectType::CPU_MEMORY; + output_def.object_def.user_provided = true; + + _converter_builder = NewConverterBuilder(_environment.get()); + if (!_converter_builder->MakeConverter(input_def, tmp_def, &_converter_bhwc).ok()) + { + throw std::runtime_error("MakeConverter<_converter_bhwc> error."); + } + if (!_converter_builder->MakeConverter(tmp_def, output_def, &_converter_cpu).ok()) + { + throw std::runtime_error("MakeConverter<_converter_cpu> error."); + } + + if (!_converter_bhwc->Convert(input_obj, tmp_obj).ok()) + { + throw std::runtime_error("[r] _converter_bhwc Convert error."); + } + if (!_converter_cpu->Convert(tmp_obj, output_obj).ok()) + { + throw std::runtime_error("[r] _converter_cpu Convert error."); + } +} + +} // namespace operand +} // namespace gpu_cl +} // namespace backend +} // namespace onert diff --git a/runtime/onert/backend/gpu_cl/operand/ICLTensor.h b/runtime/onert/backend/gpu_cl/operand/ICLTensor.h new file mode 100644 index 000000000..28e905d48 --- /dev/null +++ b/runtime/onert/backend/gpu_cl/operand/ICLTensor.h @@ -0,0 +1,106 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ONERT_BACKEND_GPU_CL_OPERAND_I_CL_TENSOR_H__ +#define __ONERT_BACKEND_GPU_CL_OPERAND_I_CL_TENSOR_H__ + +#include <backend/ITensor.h> + +#include "open_cl/Api.h" +#include "open_cl/Spi.h" +#include "open_cl/ClCommandQueue.h" +#include "open_cl/kernels/Converter.h" +#include "open_cl/Tensor.h" + +namespace onert +{ +namespace backend +{ +namespace gpu_cl +{ +namespace operand +{ + +class ICLTensor : public ITensor +{ +public: + ICLTensor() = default; + ICLTensor(const ICLTensor &) = delete; + ICLTensor &operator=(const ICLTensor &) = delete; + ICLTensor(ICLTensor &&) = default; + ICLTensor &operator=(ICLTensor &&) = default; + + ICLTensor(size_t rank, ir::Shape shape, std::shared_ptr<Environment> environment) + : _rank{rank}, _shape{shape}, _environment(environment) + { + } + +public: + uint8_t *buffer() const final { return reinterpret_cast<uint8_t *>(handle()->GetMemoryPtr()); } + size_t total_size() const final { return _shape.num_elements() * sizeof(float); } + size_t calcOffset(const ir::Coordinates &coords) const final + { + // NYI + (void)coords; + return 0; + } + ir::Layout layout() const final { return ir::Layout::NHWC; } + ir::DataType data_type() const final { return ir::DataType::FLOAT32; } + float data_scale() const override + { + throw std::runtime_error("ICLTensor::data_scale() is not supported."); + } + int32_t data_zero_point() const override + { + throw std::runtime_error("ICLTensor::data_zero_point() is not supported."); + } + const std::vector<float> &data_scales() const override + { + throw std::runtime_error("ICLTensor::data_scales() is not supported."); + } + const std::vector<int32_t> &data_zero_points() const override + { + throw std::runtime_error("ICLTensor::data_zero_points() is not supported."); + } + bool is_dynamic() const override { return false; } + ir::Shape getShape() const override { return _shape; } + bool has_padding() const override { return false; } + void access(const std::function<void(ITensor &tensor)> &fn) final; + bool needMemoryMap() const final { return true; } + void enqueueWriteBuffer(const void *ptr, bool blocking = true) final; + void enqueueReadBuffer(void *ptr, bool blocking = true) final; + +public: + virtual const Tensor *handle() const = 0; + virtual Tensor *handle() = 0; + +private: +protected: + size_t _rank; // Actual rank (reflects extended rank) + ir::Shape _shape; + std::shared_ptr<Environment> _environment; + std::unique_ptr<TensorObjectConverterBuilder> _converter_builder; + CLMemory _cl_memory; + std::unique_ptr<TensorObjectConverter> _converter_cpu; + std::unique_ptr<TensorObjectConverter> _converter_bhwc; +}; + +} // namespace operand +} // namespace gpu_cl +} // namespace backend +} // namespace onert + +#endif // __ONERT_BACKEND_GPU_CL_OPERAND_I_CL_TENSOR_H__ diff --git a/runtime/onert/core/include/compiler/Compiler.h b/runtime/onert/core/include/compiler/Compiler.h index c2589f6d5..292de4b12 100644 --- a/runtime/onert/core/include/compiler/Compiler.h +++ b/runtime/onert/core/include/compiler/Compiler.h @@ -45,6 +45,11 @@ struct ManualSchedulerOptions std::unordered_map<ir::OperationIndex, std::string> index_to_backend; }; +struct PartialGraphOptions +{ + std::unordered_map<ir::OperationIndex, ir::SubgraphIndex> index_to_graph; +}; + struct CompilerOptions { // GENERAL OPTIONS @@ -59,6 +64,7 @@ struct CompilerOptions bool he_profiling_mode; //< Whether HEScheduler profiling mode ON/OFF bool disable_compile; //< Run with Interpreter if true, try compilation otherwise bool fp16_enable; //< Whether fp16 mode ON/OFF + PartialGraphOptions partial_graph_options; util::TracingCtx *tracing_ctx; //< Profiling information }; @@ -86,6 +92,15 @@ public: */ std::shared_ptr<exec::ExecutorMap> compile(void); + /** + * @brief Do compilation with the options + * + * @return std::vector<std::shared_ptr<exec::ExecutorMap>> Executors as a result of compilation + * for pipeline + */ + std::vector<std::shared_ptr<exec::ExecutorMap>> compile(const char *package_file_path, + const char *map_file_path); + State state(void) const { return _state; } CompilerOptions &options() { return _options; } @@ -95,6 +110,17 @@ public: */ void enableToFp16(); + /** + * @brief Set backends from string-encoded mappings from operation index to backend type (cpu, + * acl_cl) + */ + void set_backend_from_str(const char *backend_settings); + + /** + * @brief Build the partial graphs to compile with original graph + */ + bool buildPartialGraph(uint32_t num_graphs); + private: void checkProfilerConditions(); std::shared_ptr<ir::Graph> &primary_subgraph() { return _subgraphs->at(ir::SubgraphIndex{0}); } diff --git a/runtime/onert/core/include/compiler/LoweredGraph.h b/runtime/onert/core/include/compiler/LoweredGraph.h index 925de3485..d3ef6d4af 100644 --- a/runtime/onert/core/include/compiler/LoweredGraph.h +++ b/runtime/onert/core/include/compiler/LoweredGraph.h @@ -36,9 +36,13 @@ class LoweredGraph { public: LoweredGraph(const ir::Graph &graph, const compiler::CompilerOptions &options); + LoweredGraph(const ir::Graph &parent_graph, const ir::Graph &graph, + const compiler::CompilerOptions &options); ir::Graph &graph() { return _graph; } const ir::Graph &graph() const { return _graph; } + ir::Graph &parent_graph() { return _parent_graph; } + const ir::Graph &parent_graph() const { return _parent_graph; } const compiler::GraphLowerInfo &lower_info() const { return _lower_info_map; } compiler::GraphLowerInfo &lower_info() { return _lower_info_map; } std::shared_ptr<ir::OperationIndexMap<int64_t>> indexed_ranks() { return _indexed_ranks; } @@ -59,6 +63,7 @@ private: private: ir::Graph _graph; + ir::Graph _parent_graph; std::shared_ptr<ir::OperationIndexMap<int64_t>> _indexed_ranks; compiler::GraphLowerInfo _lower_info_map; ir::OperationIndexMap<bool> _has_dynamic_tensor_map; diff --git a/runtime/onert/core/include/exec/Execution.h b/runtime/onert/core/include/exec/Execution.h index d3c5b6dda..b0a5cced3 100644 --- a/runtime/onert/core/include/exec/Execution.h +++ b/runtime/onert/core/include/exec/Execution.h @@ -26,6 +26,8 @@ #include "IODescription.h" #include <thread> +#include <deque> +#include <semaphore.h> namespace onert { @@ -53,6 +55,7 @@ public: */ const ir::Graph &primary_subgraph() const { return primary_executor()->graph(); } + const ir::Graph &primary_parentgraph() const { return primary_executor()->parent_graph(); } /** * @brief Change input shape * @param[in] index Input index @@ -69,6 +72,7 @@ public: */ void setInput(const ir::IOIndex &index, const void *buffer, size_t length, ir::Layout layout = ir::Layout::NHWC); + /** * @brief Set input data's information, especially to specify unknown dimensions on model * build time. @@ -142,6 +146,102 @@ public: ir::Shape getInputShape(ir::IOIndex ind) const; ir::Shape getOutputShape(ir::IOIndex ind) const; + // + // Experimental API + // + + // accessor + std::vector< + std::tuple<std::shared_ptr<onert::exec::Execution>, onert::ir::IOIndex, onert::ir::IOIndex>> + getNextExes() + { + return next_exes; + } + std::deque<std::pair<IODescription *, uint32_t>> *getAsyncIoDescs() { return &_async_io_descs; } + std::deque<std::vector<void *>> *getAsyncResults() { return &_async_results; } + + /** + * @brief Push IO information between related executions into next_exes + * @param[in] next address of next execution + * @param[in] o_index Output index of current execution (it will be the input of next execution) + * @param[in] i_index Input index of next execution + */ + void pushNextExe(std::shared_ptr<onert::exec::Execution> next, onert::ir::IOIndex o_index, + onert::ir::IOIndex i_index) + { + next_exes.push_back({next, o_index, i_index}); + } + + /** + * @brief Create New IODescription instance for new inputs outputs + * @param[in] index instance count number + */ + void createNewAsyncDesc(uint32_t count = 0); + + /** + * @brief Set async input data's information + * @param[in] index Input index + * @param[in] buffer Input data's buffer pointer + * @param[in] length Input data's length + * @param[in] layout Input data's data format + */ + void executeAsyncInput(const ir::IOIndex &index, const void *buffer, size_t length, + ir::Layout layout = ir::Layout::NHWC); + + /** + * @brief Set async output data's information + * @param[in] index Output index + * @param[in] buffer Output data's buffer pointer + * @param[in] length Output data's length + * @param[in] layout Output data's data format + */ + void executeAsyncOutput(const ir::IOIndex &index, void *buffer, size_t length, + ir::Layout layout = ir::Layout::NHWC); + + /** + * @brief Async execution + * @note It should be called after setting input and output buffer + */ + void AsyncExecute(); + + /** + * @brief Set finish + */ + void setFinish(); + + /** + * @brief Check if input queue is empty + * @return @c true if queue is empty, otherwise @c false + */ + bool isEmptyQueue(); + + /** + * @brief Wait semaphore to prevent race condition + */ + void asyncIoDescSemWait(); + + /** + * @brief Post semaphore to prevent race condition + */ + void asyncIoDescSemPost(); + + /** + * @brief Inference + * @note this function provided to the thread for pipelining + */ + void runInference(); + + /** + * @brief Check if stop_wait is true + * @return @c true if stop_wait is true, otherwise @c false + */ + bool stopWait(void) const; + + /** + * @brief Set stop_wait to terminate consumer thread + */ + void sholudStop(); + private: const std::unique_ptr<IExecutor> &primary_executor() const { @@ -152,8 +252,15 @@ private: private: const std::shared_ptr<ExecutorMap> _executors; IODescription _io_desc; + std::deque<std::pair<IODescription *, uint32_t>> _async_io_descs; + sem_t _async_io_descs_sem; + std::deque<std::vector<void *>> _async_results; + std::vector< + std::tuple<std::shared_ptr<onert::exec::Execution>, onert::ir::IOIndex, onert::ir::IOIndex>> + next_exes; std::unique_ptr<std::thread> _exec_thread; bool finished{false}; + bool stop_wait{false}; }; } // namespace exec diff --git a/runtime/onert/core/include/exec/IExecutor.h b/runtime/onert/core/include/exec/IExecutor.h index 51fc67af4..adc68074f 100644 --- a/runtime/onert/core/include/exec/IExecutor.h +++ b/runtime/onert/core/include/exec/IExecutor.h @@ -69,6 +69,13 @@ struct IExecutor virtual const ir::Graph &graph() = 0; /** + * @brief Returns parent graph object + * + * @return Graph object + */ + virtual const ir::Graph &parent_graph() = 0; + + /** * @brief Set an ordering on operations * @param[in] ranks The table encoding the ordering */ diff --git a/runtime/onert/core/include/exec/IODescription.h b/runtime/onert/core/include/exec/IODescription.h index 43d4015d5..14c5ffc2b 100644 --- a/runtime/onert/core/include/exec/IODescription.h +++ b/runtime/onert/core/include/exec/IODescription.h @@ -19,6 +19,7 @@ #include <vector> #include <unordered_map> +#include <semaphore.h> #include "ir/OperandInfo.h" #include "ir/Index.h" diff --git a/runtime/onert/core/include/ir/Graph.h b/runtime/onert/core/include/ir/Graph.h index 5543d9559..7a7688334 100644 --- a/runtime/onert/core/include/ir/Graph.h +++ b/runtime/onert/core/include/ir/Graph.h @@ -88,6 +88,15 @@ public: void removeOperand(const OperandIndex &ind) { _operands.remove(ind); } void setLayout(Layout layout) { _layout = layout; } void setSubgraphs(const std::shared_ptr<Subgraphs> &subgs) { _subgraphs = subgs; } + void setPartialgraphs(const std::shared_ptr<Subgraphs> &partialgraphs) + { + _partialgraphs = partialgraphs; + } + void + setTensorName(std::shared_ptr<std::unordered_map<ir::OperandIndex, std::string>> &tensor_names) + { + _tensor_names = tensor_names; + } private: bool checkOperandsForOperation(const Operation &operation); @@ -128,6 +137,29 @@ public: const std::shared_ptr<Subgraphs> &subgraphs() const { return _subgraphs; } std::shared_ptr<Subgraphs> &subgraphs() { return _subgraphs; } Layout layout() const { return _layout; } + std::shared_ptr<Subgraphs> &partialgraphs() { return _partialgraphs; } + std::shared_ptr<std::unordered_map<ir::OperandIndex, std::string>> &tensor_names() + { + return _tensor_names; + } + std::unordered_map<std::string, IOIndex>::iterator _name_to_input_begin() + { + return _name_to_input.begin(); + } + std::unordered_map<std::string, IOIndex>::iterator _name_to_input_end() + { + return _name_to_input.end(); + } + std::unordered_map<std::string, IOIndex>::iterator _name_to_output_begin() + { + return _name_to_output.begin(); + } + std::unordered_map<std::string, IOIndex>::iterator _name_to_output_end() + { + return _name_to_output.end(); + } + void input_sort() { _inputs.sort(); } + void output_sort() { _outputs.sort(); } // Topological sort public: @@ -144,6 +176,10 @@ private: std::shared_ptr<Subgraphs> _subgraphs; // TFLite and circle's default layout is NHWC; Layout _layout{Layout::NHWC}; + + // Partial Graphs + std::shared_ptr<ir::Subgraphs> _partialgraphs; + std::shared_ptr<std::unordered_map<ir::OperandIndex, std::string>> _tensor_names; }; } // namespace ir diff --git a/runtime/onert/core/include/ir/OperandIndexSequence.h b/runtime/onert/core/include/ir/OperandIndexSequence.h index 846c3f950..dd390748b 100644 --- a/runtime/onert/core/include/ir/OperandIndexSequence.h +++ b/runtime/onert/core/include/ir/OperandIndexSequence.h @@ -19,6 +19,7 @@ #include <initializer_list> #include <vector> +#include <algorithm> #include "ir/Index.h" @@ -45,6 +46,12 @@ public: void append(const OperandIndex &index) { _vec.emplace_back(index); } void append(const OperandIndexSequence &l) { _vec.insert(_vec.end(), l.begin(), l.end()); } + void sort() + { + std::sort(_vec.begin(), _vec.end(), + [](const auto &lhs, const auto &rhs) { return lhs.value() < rhs.value(); }); + } + public: uint32_t size() const { return static_cast<uint32_t>(_vec.size()); } const OperandIndex &at(IOIndex set_index) const { return _vec.at(set_index.value()); } diff --git a/runtime/onert/core/include/ir/operation/ElementwiseBinary.h b/runtime/onert/core/include/ir/operation/ElementwiseBinary.h index dd07f6058..e265e81ec 100644 --- a/runtime/onert/core/include/ir/operation/ElementwiseBinary.h +++ b/runtime/onert/core/include/ir/operation/ElementwiseBinary.h @@ -37,6 +37,7 @@ public: enum class ElementwiseBinaryType { + FLOOR_DIV, LOGICAL_AND, LOGICAL_OR, MAX, diff --git a/runtime/onert/core/include/util/Config.lst b/runtime/onert/core/include/util/Config.lst index d501345c1..89a9a6ac2 100644 --- a/runtime/onert/core/include/util/Config.lst +++ b/runtime/onert/core/include/util/Config.lst @@ -20,7 +20,7 @@ // Name | Type | Default CONFIG(GRAPH_DOT_DUMP , int , "0") -CONFIG(BACKENDS , std::string , "cpu;acl_cl;acl_neon;ruy;xnnpack;bcq") // FIXME Remove bcq +CONFIG(BACKENDS , std::string , "cpu;acl_cl;acl_neon;ruy;xnnpack;gpu_cl;bcq") // FIXME Remove bcq CONFIG(OP_BACKEND_ALLOPS , std::string , "") CONFIG(OP_BACKEND_MAP , std::string , "") CONFIG(DISABLE_COMPILE , bool , "0") diff --git a/runtime/onert/core/src/compiler/Compiler.cc b/runtime/onert/core/src/compiler/Compiler.cc index 082bdc9d0..93792dd1c 100644 --- a/runtime/onert/core/src/compiler/Compiler.cc +++ b/runtime/onert/core/src/compiler/Compiler.cc @@ -38,7 +38,9 @@ #include "util/ConfigSource.h" #include "util/logging.h" #include "ir/OperationDumper.h" +#include "ir/OperationCloner.h" #include "misc/string_helpers.h" +#include "json/json.h" namespace { @@ -139,6 +141,26 @@ Compiler::Compiler(const std::shared_ptr<ir::Subgraphs> &subgs, util::TracingCtx void Compiler::enableToFp16() { _options.fp16_enable = true; } +void Compiler::set_backend_from_str(const char *backend_settings) +{ + // Backend for all + auto &ms_options = _options.manual_scheduler_options; + auto key_val_list = nnfw::misc::split(backend_settings, ';'); + for (const auto &key_val_str : key_val_list) + { + if (key_val_str.empty()) + { + continue; + } + + auto key_val = nnfw::misc::split(key_val_str, '='); + const auto &key_str = key_val.at(0); + const auto &val = key_val.at(1); + auto key = static_cast<uint32_t>(std::stoi(key_str)); + ms_options.index_to_backend.emplace(ir::OperationIndex{key}, val); + } +} + void Compiler::checkProfilerConditions() { if (!_options.he_scheduler) @@ -148,6 +170,164 @@ void Compiler::checkProfilerConditions() throw std::runtime_error("Profiling mode works only with 'Dataflow' executor"); } +bool Compiler::buildPartialGraph(uint32_t num_graphs) +{ + if (_subgraphs->count() > 1) + return false; + + auto partialgraphs = std::make_shared<ir::Subgraphs>(); + + for (uint32_t idx = 0; idx < num_graphs; idx++) + { + auto partialgraph = std::make_unique<ir::Graph>(); + partialgraphs->push(ir::SubgraphIndex{idx}, std::move(partialgraph)); + } + _subgraphs->primary()->setPartialgraphs(partialgraphs); + + auto partial_graph = primary_subgraph()->partialgraphs(); + + primary_subgraph()->operands().iterate( + [&](const ir::OperandIndex &operand_index, const ir::Operand &operand) { + auto use_operations = operand.getUses(); + + for (auto use_operation : use_operations) + { + auto graph_index = _options.partial_graph_options.index_to_graph.find(use_operation); + if (graph_index == _options.partial_graph_options.index_to_graph.end()) + { + throw std::runtime_error("Invalid Partition Map"); + } + auto partition = partial_graph->at(graph_index->second); + + if (partition->operands().exist(operand_index)) + { + continue; + } + + auto new_operand = std::make_unique<ir::Operand>(operand); + new_operand->clearDefUse(); + auto new_operand_ind = partition->addOperand(operand_index, std::move(new_operand)); + UNUSED_RELEASE(new_operand_ind); + assert(new_operand_ind == operand_index); + } + }); + + primary_subgraph()->operations().iterate( + [&](const ir::OperationIndex &operation_index, const ir::Operation &operation) { + auto graph_index = _options.partial_graph_options.index_to_graph.find(operation_index); + if (graph_index == _options.partial_graph_options.index_to_graph.end()) + { + throw std::runtime_error("Invalid Partition Map"); + } + auto partition = partial_graph->at(graph_index->second); + + auto operand_io = (operation.getInputs() + operation.getOutputs()) | ir::Remove::DUPLICATED | + ir::Remove::UNDEFINED; + for (auto operand_index : operand_io) + { + if (partition->operands().exist(operand_index)) + continue; + + const auto &operand = primary_subgraph()->operands().at(operand_index); + + auto new_operand = std::make_unique<ir::Operand>(operand); + new_operand->clearDefUse(); + + auto new_operand_index = partition->addOperand(operand_index, std::move(new_operand)); + UNUSED_RELEASE(new_operand_index); + assert(new_operand_index == operand_index); + } + + auto new_operation_index = partition->addOperation(operation_index, clone(operation)); + UNUSED_RELEASE(new_operation_index); + assert(new_operation_index == operation_index); + }); + + for (uint32_t idx = 0; idx < partial_graph->count(); idx++) + { + auto partition = partial_graph->at(ir::SubgraphIndex{idx}); + + partition->operands().iterate([&](const ir::OperandIndex &operand_index, + const ir::Operand &operand) { + if (primary_subgraph()->getInputs().contains(operand_index) || + (!operand.getDef().valid() && !operand.isConstant())) + { + partition->addInput(operand_index, primary_subgraph()->tensor_names()->at(operand_index)); + } + if (primary_subgraph()->getOutputs().contains(operand_index) || operand.getUses().size() == 0) + { + partition->addOutput(operand_index, primary_subgraph()->tensor_names()->at(operand_index)); + } + + if (primary_subgraph()->operands().at(operand_index).getUses().size() > 1 && + !primary_subgraph()->operands().at(operand_index).isConstant() && + !partition->getInputs().contains(operand_index)) + { + auto use_operations = primary_subgraph()->operands().at(operand_index).getUses(); + auto iter = use_operations.begin(); + ir::SubgraphIndex graph_index = + _options.partial_graph_options.index_to_graph.find(*iter++)->second; + while (iter != use_operations.end()) + { + if (graph_index != _options.partial_graph_options.index_to_graph.find(*iter)->second && + !partition->getOutputs().contains(operand_index)) + { + partition->addOutput(operand_index, + primary_subgraph()->tensor_names()->at(operand_index)); + } + iter++; + } + } + }); + + partition->verify(); + + bool same = true; + if (partition->getInputs().size() == primary_subgraph()->getInputs().size()) + { + for (auto iter = partition->getInputs().begin(); iter != partition->getInputs().end(); ++iter) + { + if (!primary_subgraph()->getInputs().contains(*iter)) + { + same = false; + break; + } + } + if (same == true) + { + partition->getInputs() = primary_subgraph()->getInputs(); + } + else + { + partition->input_sort(); + } + } + + same = true; + if (partition->getOutputs().size() == primary_subgraph()->getOutputs().size()) + { + for (auto iter = partition->getOutputs().begin(); iter != partition->getOutputs().end(); + ++iter) + { + if (!primary_subgraph()->getOutputs().contains(*iter)) + { + same = false; + break; + } + } + if (same == true) + { + partition->getOutputs() = primary_subgraph()->getOutputs(); + } + else + { + partition->output_sort(); + } + } + } + return true; +} + std::shared_ptr<exec::ExecutorMap> Compiler::compile(void) { // Set control flow backend for control flow operators @@ -300,6 +480,226 @@ std::shared_ptr<exec::ExecutorMap> Compiler::compile(void) return executors; } +std::vector<std::shared_ptr<exec::ExecutorMap>> Compiler::compile(const char *package_file_path, + const char *map_file_path) +{ + std::vector<std::shared_ptr<exec::ExecutorMap>> executors; + auto executor_map = std::make_shared<exec::ExecutorMap>(); + + std::string package_path(package_file_path); + std::string partition_map_file; + + if (map_file_path) + { + partition_map_file = map_file_path; + } + else + { + partition_map_file = package_path + "/partition_map.json"; + } + + std::ifstream pmfs(partition_map_file); + Json::Value root; + pmfs >> root; + const Json::Value &map = root["partition_map"]; + const Json::Value &np = root["num_partitions"]; + + uint32_t num_graphs = 1; + + if (pmfs.is_open()) + { + num_graphs = np.asUInt(); + for (uint32_t i = 0; i < (uint32_t)map.size(); ++i) + { + _options.partial_graph_options.index_to_graph[ir::OperationIndex{i}] = + ir::SubgraphIndex{map[i].asUInt()}; + } + } + else + { + throw std::runtime_error("There is no partition map file"); + } + + if (!buildPartialGraph(num_graphs)) + { + throw std::runtime_error("It doesn't support in case there are subgraphs"); + } + + // Set control flow backend for control flow operators + { + auto &builtin_id = backend::builtin::Config::ID; + _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::If] = builtin_id; + _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::While] = builtin_id; + _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::Permute] = builtin_id; + } + + // FIXME This is a workaround for bcq operations, should remove it + { + _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::BCQFullyConnected] = "bcq"; + _options.manual_scheduler_options.opcode_to_backend[ir::OpCode::BCQGather] = "bcq"; + } + + // It doesn't support tracing in case of partial graph + { + _options.tracing_ctx = nullptr; + } + + { + VERBOSE(Compiler) << std::boolalpha << "==== Compiler Options ====" << std::endl; + VERBOSE(Compiler) << "backend_list : " + << nnfw::misc::join(_options.backend_list.begin(), + _options.backend_list.end(), "/") + << std::endl; + VERBOSE(Compiler) << "trace_filepath : " << _options.trace_filepath << std::endl; + VERBOSE(Compiler) << "graph_dump_level : " << _options.graph_dump_level << std::endl; + VERBOSE(Compiler) << "executor : " << _options.executor << std::endl; + VERBOSE(Compiler) << "manual backend_for_all : " + << _options.manual_scheduler_options.backend_for_all << std::endl; + VERBOSE(Compiler) << "manual_scheduler_options : " + << getOpBackends(_options.manual_scheduler_options.opcode_to_backend) + << std::endl; + VERBOSE(Compiler) << "he_scheduler : " << _options.he_scheduler << std::endl; + VERBOSE(Compiler) << "he_profiling_mode : " << _options.he_profiling_mode << std::endl; + VERBOSE(Compiler) << "disable_compile : " << _options.disable_compile << std::endl; + VERBOSE(Compiler) << "fp16_enable : " << _options.fp16_enable << std::endl + << std::noboolalpha; + } + + _subgraphs->iterate([&](const ir::SubgraphIndex &, ir::Graph &subg) { + // Mandatory passes + auto part = subg.partialgraphs(); + part->iterate([&](const ir::SubgraphIndex &, ir::Graph &partialgraph) { + pass::PassRunner{} + .append(std::make_unique<pass::ConstantOutputPass>(partialgraph)) + .append(std::make_unique<pass::OddOutputPass>(partialgraph)) + .run(); + + // Optimizations + pass::PassRunner{} + .append(std::make_unique<pass::UnusedOperandEliminationPass>(partialgraph)) + .run(); + }); + }); + + /*************************************************** + * Prepare compilation phase + ***************************************************/ + + // Compilable check + // TODO: Support hybrid execution - + // execution between interpreter and compiled executor (including control flow) + if (_options.disable_compile) + { + _subgraphs->iterate([&](const ir::SubgraphIndex &index, ir::Graph &subg) { + executor_map->emplace(index, std::make_unique<interp::InterpExecutor>(subg)); + executors.push_back(executor_map); + }); + _state = State::COMPILED; + return executors; + } + + // Mode check + if (_options.he_profiling_mode) + checkProfilerConditions(); + + /*************************************************** + * Backend independent analysis & optimization phase + ***************************************************/ + auto dump_level = static_cast<dumper::dot::DotDumper::Level>(_options.graph_dump_level); + + // Lower: Assign backend + std::unordered_map<ir::SubgraphIndex, std::unique_ptr<compiler::LoweredGraph>> + lowered_partialgraphs; + _subgraphs->iterate([&](const ir::SubgraphIndex &, ir::Graph &subg) { + auto part = subg.partialgraphs(); + part->iterate([&](const ir::SubgraphIndex &pindex, ir::Graph &partialgraph) { + onert::dumper::dot::DotDumper dot_dumper_part(partialgraph, dump_level); + dot_dumper_part.dump(nnfw::misc::str("before_lower_subg_partialgraph-", pindex.value())); + + // // Lower: Assign backend + lowered_partialgraphs[pindex] = + std::make_unique<compiler::LoweredGraph>(subg, partialgraph, _options); + partialgraph.setSubgraphs(nullptr); + }); + }); + + for (auto &pair : lowered_partialgraphs) + { + + const auto &partialgraph_index = pair.first; + auto &lowered_partialgraph = pair.second; + onert::dumper::dot::DotDumper dot_dumper_lowered_part(lowered_partialgraph.get(), dump_level); + dot_dumper_lowered_part.dump("after_lower_subg_partialgraph-" + + std::to_string(partialgraph_index.value())); + } + + // Partial Graph shape inference + for (auto &pair : lowered_partialgraphs) + { + const auto &partialgraph_index = pair.first; + auto &lowered_partialgraph = pair.second; + StaticShapeInferer partial_inferer(partialgraph_index, lowered_partialgraphs); + auto ordered_ops = lowered_partialgraph->graph().topolSortOperations(); + for (auto op_ind : ordered_ops) + { + const auto &op = lowered_partialgraph->graph().operations().at(op_ind); + bool has_dynamic_tensor = partial_inferer.infer(op); + lowered_partialgraph->setHasDynamicTensor(op_ind, has_dynamic_tensor); + } + partial_inferer.dump(); + } + + // Shape validation + // TODO Move shape independent feature check from ShapeValidator to OperationValidator + // TODO Move ShapeValidator into shape inference + // - Check input tensor shape validation + // - Check parameter value validation which valid value is depend on input tensor shape + // - Output tensor shape validation check is needless because + // static/dynamic shape inferer will make valid output shape + for (auto &pair : lowered_partialgraphs) + { + auto &lowered_partialgraph = pair.second; + compiler::ShapeValidator{lowered_partialgraph->graph()}(); + } + + /************************************************************* + * Backend independent analysis & optimization phase finished + *************************************************************/ + std::map<uint32_t, std::unique_ptr<compiler::LoweredGraph>> ordered; + for (auto &pair : lowered_partialgraphs) + { + // const auto &partialgraph_index = pair.first; + auto &lowered_partialgraph = pair.second; + + ordered.insert(make_pair(pair.first.value(), std::move(lowered_partialgraph))); + } + + for (auto &pair : ordered) + { + executor_map = std::make_shared<exec::ExecutorMap>(); + const auto &partialgraph_index = ir::SubgraphIndex(pair.first); + auto &lowered_partialgraph = pair.second; + auto indexed_ranks = lowered_partialgraph->indexed_ranks(); + ir::OperationDumper dumper("Executor generation of Subgraph " + + std::to_string(partialgraph_index.value())); + lowered_partialgraph->graph().operations().iterate( + [&](const ir::OperationIndex &, const ir::Operation &op) { op.accept(dumper); }); + auto executor = std::unique_ptr<exec::IExecutor>{ + ExecutorFactory::get().create(std::move(lowered_partialgraph), _options, executor_map)}; + executor->setIndexedRanks(indexed_ranks); + executor_map->insert(std::make_pair(ir::SubgraphIndex{0}, std::move(executor))); + executors.push_back(executor_map); + } + + _subgraphs.reset(); + /******************************** + * Code generation phase finished + ********************************/ + _state = State::COMPILED; + + return executors; +} + } // namespace compiler } // namespace onert diff --git a/runtime/onert/core/src/compiler/LoweredGraph.cc b/runtime/onert/core/src/compiler/LoweredGraph.cc index b469b991b..3b84d02de 100644 --- a/runtime/onert/core/src/compiler/LoweredGraph.cc +++ b/runtime/onert/core/src/compiler/LoweredGraph.cc @@ -117,6 +117,85 @@ LoweredGraph::LoweredGraph(const ir::Graph &graph, const CompilerOptions &option } } +LoweredGraph::LoweredGraph(const ir::Graph &parent_graph, const ir::Graph &graph, + const CompilerOptions &options) + : _graph{graph}, _parent_graph{parent_graph} +{ + // set tracing_ctx for copied graph + if (options.tracing_ctx) + { + auto subgraph_index = options.tracing_ctx->getSubgraphIndex(&graph); + options.tracing_ctx->setSubgraphIndex(&_graph, subgraph_index.value()); + } + + // Build backend contexts + auto &backend_manager = BackendManager::get(); + // Create contexts for other backends + for (auto backend_str : options.backend_list) + { + backend_manager.loadBackend(backend_str); + auto backend = backend_manager.get(backend_str); + + // TODO As the default value of backend list contains "cpu", "acl_cl" and "acl_neon", and some + // are not available on x64 or some other platforms. So this may be a workaround for x64 and + // we should change it back(throw if backend is not loaded) later. + if (!backend) + { + VERBOSE(LoweredGraph) << "Cannot load backend - " << backend_str << std::endl; + continue; + } + } + if (backend_manager.num_backends() == 0) + throw std::runtime_error{"No available backends loaded."}; + + // TODO Move "schedule" phase out of here + // Schedule + std::unique_ptr<BackendResolver> backend_resolver; + auto all_backends = backend_manager.getAll(); + if (options.he_scheduler) + { + auto scheduler = HEScheduler(all_backends, options); + backend_resolver = scheduler.schedule(_graph); + _indexed_ranks = scheduler.getIndexedRanks(); + } + else + { + auto scheduler = ManualScheduler(all_backends, options); + backend_resolver = scheduler.schedule(_graph); + } + + makeLowerInfo(*backend_resolver); + VERBOSE(LoweredGraph) << "dump before mandatory passes" << std::endl; + dumper::text::dumpLoweredGraph(*this); + + // Mandatory passes - kind of legalization(?) + pass::PassRunner{} + .append(std::make_unique<pass::ConstantInsertionPass>(*this)) + .append(std::make_unique<pass::ConstantLoweringPass>(*this)) + .append(std::make_unique<pass::PermutationOperationPass>(*this)) + .append(std::make_unique<pass::PermutationInsertionPass>(*this)) + .run(); + + dumpLowerInfo(); + + // Optimization passes (optional) + pass::PassRunner{}.append(std::make_unique<pass::PermutationEliminationPass>(*this)).run(); + + VERBOSE(LoweredGraph) << "Dump after all the passes" << std::endl; + for (auto operand : _graph.getInputs()) + VERBOSE(LoweredGraph) << "Graph Input : " << operand << std::endl; + for (auto operand : _graph.getOutputs()) + VERBOSE(LoweredGraph) << "Graph Output : " << operand << std::endl; + dumper::text::dumpLoweredGraph(*this); + + // Graph verifications + { + assert(ir::verifier::InputOutputChecker().verify(_graph)); + assert(ir::verifier::DAGChecker().verify(_graph)); + assert(ir::verifier::EdgeChecker().verify(_graph)); + } +} + void LoweredGraph::makeLowerInfo(const compiler::BackendResolver &backend_resolver) { _graph.operands().iterate([&](const ir::OperandIndex &index, const ir::Operand &) { diff --git a/runtime/onert/core/src/exec/Execution.cc b/runtime/onert/core/src/exec/Execution.cc index 3d88cf5ff..8eff73bac 100644 --- a/runtime/onert/core/src/exec/Execution.cc +++ b/runtime/onert/core/src/exec/Execution.cc @@ -30,6 +30,7 @@ Execution::Execution(const std::shared_ptr<ExecutorMap> &executors) : _executors const auto &primary_subg = primary_subgraph(); _io_desc.inputs.resize(primary_subg.getInputs().size()); _io_desc.outputs.resize(primary_subg.getOutputs().size()); + sem_init(&_async_io_descs_sem, 0, 1); } void Execution::changeInputShape(const ir::IOIndex &index, const ir::Shape &new_shape) @@ -71,6 +72,82 @@ void Execution::setInput(const ir::IOIndex &index, const void *buffer, size_t le _io_desc.inputs.at(index.value()) = std::make_unique<InputDesc>(info, buffer, length, layout); } +void Execution::createNewAsyncDesc(uint32_t count) +{ + IODescription *_async_io_desc = new IODescription; + _async_io_desc->inputs.resize(primary_subgraph().getInputs().size()); + _async_io_desc->outputs.resize(primary_subgraph().getOutputs().size()); + + _async_io_descs.push_back({_async_io_desc, count}); +} + +void Execution::setFinish() { finished = true; } + +bool Execution::isEmptyQueue() +{ + asyncIoDescSemWait(); + bool ret = _async_io_descs.empty(); + if (!ret) + { + for (uint32_t idx = 0; idx < _async_io_descs.front().first->inputs.size(); idx++) + { + if (_async_io_descs.front().first->inputs.at(idx).get() == nullptr) + { + ret = true; + break; + } + } + } + asyncIoDescSemPost(); + return ret; +} + +void Execution::executeAsyncInput(const ir::IOIndex &index, const void *buffer, size_t length, + ir::Layout layout) +{ + const auto input_index = primary_subgraph().getInputs().at(index); + const auto info = primary_subgraph().operands().at(input_index).info(); + IODescription *_async_io_desc = _async_io_descs.back().first; + + { + auto input_shape_sig = _async_io_desc->dynamic_input_shapes.find(index); + auto size_required = + (input_shape_sig != _async_io_desc->dynamic_input_shapes.end()) + ? input_shape_sig->second.num_elements() * onert::ir::sizeOfDataType(info.typeInfo().type()) + : info.total_size(); + + if (length < size_required) + { + throw std::runtime_error{"Too small length"}; + } + } + void *_buffer = (void *)malloc(length); + if (_buffer == NULL) + { + throw std::runtime_error{"malloc failed"}; + } + memcpy(_buffer, buffer, length); + + _async_io_desc->inputs.at(index.value()) = + std::make_unique<InputDesc>(info, _buffer, length, layout); +} + +void Execution::executeAsyncOutput(const ir::IOIndex &index, void *buffer, size_t length, + ir::Layout layout) +{ + const auto output_index = primary_subgraph().getOutputs().at(index); + const auto info = primary_subgraph().operands().at(output_index).info(); + IODescription *_async_io_desc = _async_io_descs.front().first; + + if (length < info.total_size()) + { + throw std::runtime_error{"Too small length"}; + } + + _async_io_desc->outputs.at(index.value()) = + std::make_unique<OutputDesc>(info, buffer, length, layout); +} + // TODO Remove default parameter void Execution::setInput(const ir::IOIndex &index, const ir::TypeInfo &type, const ir::Shape &shape, const void *buffer, size_t length, ir::Layout layout) @@ -137,6 +214,18 @@ void Execution::execute() VERBOSE(Execution) << "Execution finished" << std::endl; } +void Execution::AsyncExecute() +{ + VERBOSE(Execution) << "Start Async execution" << std::endl; + if (_async_io_descs.empty()) + { + VERBOSE(Execution) << "The input is not ready" << std::endl; + return; + } + + primary_executor()->execute(*_async_io_descs.front().first); +} + void Execution::startExecute() { VERBOSE(Execution) << "Create asynchronous execution thread" << std::endl; @@ -178,5 +267,153 @@ ir::Shape Execution::getOutputShape(ir::IOIndex ind) const return output_desc->info.shape(); } +void Execution::asyncIoDescSemWait() { sem_wait(&_async_io_descs_sem); } + +void Execution::asyncIoDescSemPost() { sem_post(&_async_io_descs_sem); } + +void Execution::runInference() +{ + uint32_t inference_cnt; + uint32_t output_sz = primary_subgraph().getOutputs().size(); + while (true) + { + if (isEmptyQueue()) + { + if (isFinished()) + { + if (!next_exes.empty()) + { + for (uint32_t i = 0; i < next_exes.size(); i++) + { + std::get<0>(next_exes[i])->setFinish(); + } + } + else + { + sholudStop(); + } + break; + } + } + else + { + for (uint32_t i = 0; i < output_sz; i++) + { + auto opidx = primary_subgraph().getOutputs().at(i); + auto shape = primary_subgraph().operands().at(opidx).shape(); + auto dtype = primary_subgraph().operands().at(opidx).typeInfo().type(); + auto rank = shape.rank(); + uint32_t tensor_size = 1; + for (int32_t j = 0; j < rank; j++) + { + tensor_size *= shape.dim(j); + } + if (dtype == onert::ir::DataType::FLOAT32 || dtype == onert::ir::DataType::INT32 || + dtype == onert::ir::DataType::UINT32) + tensor_size *= 4; + else if (dtype == onert::ir::DataType::INT64) + tensor_size *= 8; + void *_buffer = (void *)malloc(tensor_size); + if (_buffer == NULL) + { + throw std::runtime_error{"malloc failed"}; + } + executeAsyncOutput(onert::ir::IOIndex(i), _buffer, tensor_size); + } + AsyncExecute(); + + // set inputs of next execution + auto _io_desc = getAsyncIoDescs()->front().first; + inference_cnt = getAsyncIoDescs()->front().second; + getAsyncIoDescs()->pop_front(); + + for (uint32_t i = 0; i < next_exes.size(); i++) + { + auto next_exe = std::get<0>(next_exes[i]); + auto o_index = std::get<1>(next_exes[i]); + auto i_index = std::get<2>(next_exes[i]); + + next_exe->asyncIoDescSemWait(); + auto next_io_descs = next_exe->getAsyncIoDescs(); + bool exist = false; + for (auto iter = next_io_descs->begin(); iter != next_io_descs->end(); iter++) + { + if (inference_cnt == iter->second) + { + exist = true; + } + } + + if (!exist) + { + next_exe->createNewAsyncDesc(inference_cnt); + } + for (auto iter = next_io_descs->begin(); iter != next_io_descs->end(); iter++) + { + if (inference_cnt == iter->second) + { + const auto input_index = next_exe->primary_subgraph().getInputs().at(i_index.value()); + const auto info = next_exe->primary_subgraph().operands().at(input_index).info(); + + size_t length = _io_desc->outputs[o_index.value()]->size; + void *_buffer = (void *)malloc(length); + if (_buffer == NULL) + { + throw std::runtime_error{"malloc failed"}; + } + memcpy(_buffer, _io_desc->outputs[o_index.value()]->buffer, length); + + iter->first->inputs.at(i_index.value()) = std::make_unique<onert::exec::InputDesc>( + info, _buffer, length, onert::ir::Layout::NHWC); + break; + } + } + next_exe->asyncIoDescSemPost(); + } + + if (next_exes.empty()) + { + std::vector<void *> results; + for (uint32_t i = 0; i < _io_desc->outputs.size(); i++) + { + size_t length = _io_desc->outputs[i]->size; + void *_buffer = (void *)malloc(length); + if (_buffer == NULL) + { + throw std::runtime_error{"malloc failed"}; + } + memcpy(_buffer, _io_desc->outputs[i]->buffer, length); + results.push_back(_buffer); + } + _async_results.push_back(results); + } + + for (uint32_t i = 0; i < _io_desc->inputs.size(); i++) + { + auto p = _io_desc->inputs.at(i).release(); + if (p) + { + free((void *)p->buffer); + delete p; + } + } + for (uint32_t i = 0; i < _io_desc->outputs.size(); i++) + { + auto p = _io_desc->outputs.at(i).release(); + if (p) + { + free(p->buffer); + delete p; + } + } + delete _io_desc; + } + } +} + +bool Execution::stopWait(void) const { return stop_wait; } + +void Execution::sholudStop() { stop_wait = true; } + } // namespace exec } // namespace onert diff --git a/runtime/onert/core/src/exec/ExecutorBase.cc b/runtime/onert/core/src/exec/ExecutorBase.cc index 3a624adef..efc22cfa5 100644 --- a/runtime/onert/core/src/exec/ExecutorBase.cc +++ b/runtime/onert/core/src/exec/ExecutorBase.cc @@ -30,8 +30,8 @@ ExecutorBase::ExecutorBase(std::unique_ptr<compiler::LoweredGraph> &&lowered_gra backend::BackendContexts &&backend_contexts, const compiler::TensorRegistries &tensor_regs, const util::TracingCtx *tracing_ctx) - : _lowered_graph{std::move(lowered_graph)}, - _backend_contexts{std::move(backend_contexts)}, _graph{_lowered_graph->graph()}, _mutex(), + : _lowered_graph{std::move(lowered_graph)}, _backend_contexts{std::move(backend_contexts)}, + _graph{_lowered_graph->graph()}, _parent_graph{_lowered_graph->parent_graph()}, _mutex(), _tracing_ctx(tracing_ctx) { auto build_tensor_list = [&](const auto &ind_seq, auto &tensors) { diff --git a/runtime/onert/core/src/exec/ExecutorBase.h b/runtime/onert/core/src/exec/ExecutorBase.h index 3a124bd5b..c0f609d11 100644 --- a/runtime/onert/core/src/exec/ExecutorBase.h +++ b/runtime/onert/core/src/exec/ExecutorBase.h @@ -58,6 +58,8 @@ public: const ir::Graph &graph() final { return _graph; } + const ir::Graph &parent_graph() final { return _parent_graph; } + void execute(const IODescription &desc) final; void execute(const std::vector<backend::IPortableTensor *> &inputs, @@ -90,6 +92,7 @@ protected: std::unique_ptr<compiler::LoweredGraph> _lowered_graph; backend::BackendContexts _backend_contexts; const ir::Graph &_graph; + const ir::Graph &_parent_graph; std::vector<backend::builtin::IOTensor *> _input_tensors; std::vector<backend::builtin::IOTensor *> _output_tensors; std::mutex _mutex; diff --git a/runtime/onert/core/src/exec/LinearExecutor.cc b/runtime/onert/core/src/exec/LinearExecutor.cc index 4d10c869b..a64dadcb1 100644 --- a/runtime/onert/core/src/exec/LinearExecutor.cc +++ b/runtime/onert/core/src/exec/LinearExecutor.cc @@ -26,30 +26,52 @@ namespace exec void LinearExecutor::executeImpl() { - auto profiling_subg_index = _tracing_ctx->getSubgraphIndex(&_graph); - - _subject.notifySubgraphBegin(profiling_subg_index); - for (auto &&code : _code) + if (_tracing_ctx) { - const auto backend = code.lower_info->backend(); + auto profiling_subg_index = _tracing_ctx->getSubgraphIndex(&_graph); + + _subject.notifySubgraphBegin(profiling_subg_index); + for (auto &&code : _code) + { + const auto backend = code.lower_info->backend(); // TODO : Move ruy profiler into ExecutionObserver #ifdef RUY_PROFILER - ruy::profiler::ScopeLabel label(code.op->name()); + ruy::profiler::ScopeLabel label(code.op->name()); #endif - _subject.notifyJobBegin(this, profiling_subg_index, code.op_ind, backend); + _subject.notifyJobBegin(this, profiling_subg_index, code.op_ind, backend); + + auto &fn_seq = code.fn_seq; + + fn_seq->initRunning(); + + bool handle_dynamic_tensor = + _lowered_graph->getHasDynamicTensor(code.op_ind) || hasDynamicInput(); + fn_seq->enableDynamicShapeInferer(handle_dynamic_tensor); + fn_seq->run(); - auto &fn_seq = code.fn_seq; + _subject.notifyJobEnd(this, profiling_subg_index, code.op_ind, backend); + } + _subject.notifySubgraphEnd(profiling_subg_index); + } + else + { + for (auto &&code : _code) + { +// TODO : Move ruy profiler into ExecutionObserver +#ifdef RUY_PROFILER + ruy::profiler::ScopeLabel label(code.op->name()); +#endif - fn_seq->initRunning(); + auto &fn_seq = code.fn_seq; - bool handle_dynamic_tensor = - _lowered_graph->getHasDynamicTensor(code.op_ind) || hasDynamicInput(); - fn_seq->enableDynamicShapeInferer(handle_dynamic_tensor); - fn_seq->run(); + fn_seq->initRunning(); - _subject.notifyJobEnd(this, profiling_subg_index, code.op_ind, backend); + bool handle_dynamic_tensor = + _lowered_graph->getHasDynamicTensor(code.op_ind) || hasDynamicInput(); + fn_seq->enableDynamicShapeInferer(handle_dynamic_tensor); + fn_seq->run(); + } } - _subject.notifySubgraphEnd(profiling_subg_index); } } // namespace exec diff --git a/runtime/onert/core/src/interp/InterpExecutor.h b/runtime/onert/core/src/interp/InterpExecutor.h index 6e3a02327..df6153d09 100644 --- a/runtime/onert/core/src/interp/InterpExecutor.h +++ b/runtime/onert/core/src/interp/InterpExecutor.h @@ -50,6 +50,11 @@ public: * @return Graph object */ const ir::Graph &graph() final { return _graph; } + + const ir::Graph &parent_graph() final + { + throw new std::runtime_error{"Interpreter does not support this function."}; + } void setIndexedRanks(std::shared_ptr<ir::OperationIndexMap<int64_t>>) override{ // Not implemented }; diff --git a/runtime/onert/core/src/ir/operation/ElementwiseBinary.cc b/runtime/onert/core/src/ir/operation/ElementwiseBinary.cc index 8dc42903c..155b660dc 100644 --- a/runtime/onert/core/src/ir/operation/ElementwiseBinary.cc +++ b/runtime/onert/core/src/ir/operation/ElementwiseBinary.cc @@ -40,6 +40,7 @@ std::string ElementwiseBinary::name() const { using ElementwiseBinaryType = onert::ir::operation::ElementwiseBinary::ElementwiseBinaryType; static const std::unordered_map<ElementwiseBinaryType, std::string> name_map{ + {ElementwiseBinaryType::FLOOR_DIV, std::string{"FloorDiv"}}, {ElementwiseBinaryType::LOGICAL_AND, std::string{"LogicalAnd"}}, {ElementwiseBinaryType::LOGICAL_OR, std::string{"LogicalOr"}}, {ElementwiseBinaryType::MAX, std::string{"Max"}}, diff --git a/runtime/onert/frontend/base_loader/include/base_loader.h b/runtime/onert/frontend/base_loader/include/base_loader.h index c096e705d..c444e7365 100644 --- a/runtime/onert/frontend/base_loader/include/base_loader.h +++ b/runtime/onert/frontend/base_loader/include/base_loader.h @@ -68,7 +68,8 @@ public: * @param graph reference on subgraphs */ explicit BaseLoader(std::unique_ptr<ir::Subgraphs> &subgs) - : _base{nullptr}, _pagesize(getpagesize()), _fd(-1), _subgraphs(subgs), _model{nullptr} + : _base{nullptr}, _pagesize(getpagesize()), _fd(-1), _subgraphs(subgs), _model{nullptr}, + _tensor_names(std::make_shared<std::unordered_map<ir::OperandIndex, std::string>>()) { _use_mmaped_data = util::getConfigBool(util::config::USE_MMAPED_DATA); } @@ -181,7 +182,7 @@ protected: const Model *_model; // Maps Tensor indices to onert Operands. std::vector<ir::OperandIndex> _tensor_to_operand; - std::unordered_map<ir::OperandIndex, std::string> _tensor_names; + std::shared_ptr<std::unordered_map<ir::OperandIndex, std::string>> _tensor_names; // Verifier std::unique_ptr<Verifier> _verifier; // Boolean flag to use MMAPED_DATA @@ -387,7 +388,7 @@ ir::OperandIndex BaseLoader<LoaderDomain>::loadOperand(const Tensor *tensor, ir: subg.setOperandValue(operand_index, std::move(data_obj)); } - _tensor_names.emplace(operand_index, tensor->name()->str()); + _tensor_names->emplace(operand_index, tensor->name()->str()); // Variable if (tensor->is_variable()) @@ -1492,6 +1493,10 @@ void BaseLoader<LoaderDomain>::loadOperation(const Operator *op, ir::Graph &subg case BuiltinOperator::BuiltinOperator_UNPACK: loadUnpack(op, subg); return; + case BuiltinOperator::BuiltinOperator_FLOOR_DIV: + loadElementwiseBinary(op, subg, + ir::operation::ElementwiseBinary::ElementwiseBinaryType::FLOOR_DIV); + return; case BuiltinOperator::BuiltinOperator_MINIMUM: loadElementwiseBinary(op, subg, ir::operation::ElementwiseBinary::ElementwiseBinaryType::MIN); return; diff --git a/runtime/onert/frontend/circle/src/circle_loader.cc b/runtime/onert/frontend/circle/src/circle_loader.cc index 652fbc778..4fb0e71d6 100644 --- a/runtime/onert/frontend/circle/src/circle_loader.cc +++ b/runtime/onert/frontend/circle/src/circle_loader.cc @@ -106,13 +106,13 @@ private: for (const std::int32_t input_ind : *circle_subg->inputs()) { subg->addInput(tensorIdxToOperandIdx(input_ind), - _tensor_names.at(_tensor_to_operand[input_ind])); + _tensor_names->at(_tensor_to_operand[input_ind])); } // Set outputs for (const std::int32_t output_ind : *circle_subg->outputs()) { subg->addOutput(tensorIdxToOperandIdx(output_ind), - _tensor_names.at(_tensor_to_operand[output_ind])); + _tensor_names->at(_tensor_to_operand[output_ind])); } // Create operations for (const auto *op : *circle_subg->operators()) diff --git a/runtime/onert/frontend/tflite/src/tflite_loader.cc b/runtime/onert/frontend/tflite/src/tflite_loader.cc index 8669bbb44..a3038b718 100644 --- a/runtime/onert/frontend/tflite/src/tflite_loader.cc +++ b/runtime/onert/frontend/tflite/src/tflite_loader.cc @@ -93,13 +93,13 @@ private: for (const std::int32_t input_ind : *tflite_subg->inputs()) { subg->addInput(tensorIdxToOperandIdx(input_ind), - _tensor_names.at(_tensor_to_operand[input_ind])); + _tensor_names->at(_tensor_to_operand[input_ind])); } // Set outputs for (const std::int32_t output_ind : *tflite_subg->outputs()) { subg->addOutput(tensorIdxToOperandIdx(output_ind), - _tensor_names.at(_tensor_to_operand[output_ind])); + _tensor_names->at(_tensor_to_operand[output_ind])); } // Create operations for (const auto *op : *tflite_subg->operators()) @@ -107,6 +107,7 @@ private: loadOperation(op, *subg); } + subg->setTensorName(_tensor_names); subg->verify(); return subg; diff --git a/tests/nnapi/src/TestMain.cpp b/tests/nnapi/src/TestMain.cpp index 725d77c1c..907407242 100644 --- a/tests/nnapi/src/TestMain.cpp +++ b/tests/nnapi/src/TestMain.cpp @@ -95,22 +95,28 @@ static int test(bool useCpuOnly, Execution::ComputeMode computeMode, bool allowS return RUN_ALL_TESTS(); } +// FIX for onert: disable argument +#if 0 void checkArgs(int argc, char** argv, int nextArg) { if (nextArg != argc) { std::cerr << "Unexpected argument: " << argv[nextArg] << std::endl; exit(1); } } +#endif int main(int argc, char** argv) { ::testing::InitGoogleTest(&argc, argv); + // FIX for onert: disable argument +#if 0 if ((argc > 1) && std::isdigit(argv[1][0])) { allowedPasses = std::stoull(argv[1]); checkArgs(argc, argv, 2); } else { checkArgs(argc, argv, 1); } +#endif #ifndef NNTEST_ONLY_PUBLIC_API android::nn::initVLogMask(); diff --git a/tests/nnfw_api/CMakeLists.txt b/tests/nnfw_api/CMakeLists.txt index 2e7ef6551..2ea2ce87c 100644 --- a/tests/nnfw_api/CMakeLists.txt +++ b/tests/nnfw_api/CMakeLists.txt @@ -24,11 +24,16 @@ if(Xnnpack_FOUND) target_compile_definitions(${RUNTIME_NNFW_API_TEST} PRIVATE TEST_XNNPACK_BACKEND) endif(Xnnpack_FOUND) +nnas_find_package(Opencl_Headers QUIET) +if(Opencl_Headers_FOUND) + target_compile_definitions(${RUNTIME_NNFW_API_TEST} PRIVATE TEST_GPU_CL_BACKEND) +endif(Opencl_Headers_FOUND) + set(RUNTIME_NNFW_API_TEST_INCLUDE ${CMAKE_CURRENT_SOURCE_DIR}/include ${CMAKE_CURRENT_SOURCE_DIR}/src) target_include_directories(${RUNTIME_NNFW_API_TEST} PRIVATE ${RUNTIME_NNFW_API_TEST_INCLUDE}) -target_link_libraries(${RUNTIME_NNFW_API_TEST} nnfw-dev) +target_link_libraries(${RUNTIME_NNFW_API_TEST} nnfw-dev jsoncpp) target_link_libraries(${RUNTIME_NNFW_API_TEST} gtest gmock) target_link_libraries(${RUNTIME_NNFW_API_TEST} ${LIB_PTHREAD} dl) target_link_libraries(${RUNTIME_NNFW_API_TEST} circle_schema) @@ -56,3 +61,10 @@ install(DIRECTORY ${NNPACKAGE_MODEL_DIR} DESTINATION ${NNPACKAGE_INSTALL_TARGET} # Install nnpackage test model (while) set(NNPACKAGE_MODEL_DIR ${NNAS_PROJECT_SOURCE_DIR}/nnpackage/examples/v1.0.0/while_dynamic) install(DIRECTORY ${NNPACKAGE_MODEL_DIR} DESTINATION ${NNPACKAGE_INSTALL_TARGET}/while_dynamic) + +# Install nnpackage test model (mobilenet) +set(NNPACKAGE_MODEL_DIR ${NNAS_PROJECT_SOURCE_DIR}/runtime/contrib/TFLiteSharp/TFLiteTestApp/res/) +set(NNPACKAGE_INSTALL_TARGET unittest_standalone/nnfw_api_gtest_models) + +install(DIRECTORY ${NNPACKAGE_MODEL_DIR} DESTINATION ${NNPACKAGE_INSTALL_TARGET}/mobilenet_v1_1.0_224) + diff --git a/tests/nnfw_api/src/CircleGen.cc b/tests/nnfw_api/src/CircleGen.cc index e3dc57182..579d68c86 100644 --- a/tests/nnfw_api/src/CircleGen.cc +++ b/tests/nnfw_api/src/CircleGen.cc @@ -233,6 +233,12 @@ uint32_t CircleGen::addOperatorFloor(const OperatorParams ¶ms) 0); } +uint32_t CircleGen::addOperatorFloorDiv(const OperatorParams ¶ms) +{ + return addOperatorWithOptions(params, circle::BuiltinOperator_FLOOR_DIV, + circle::BuiltinOptions_NONE, 0); +} + uint32_t CircleGen::addOperatorL2Normalization(const OperatorParams ¶ms) { auto options = circle::CreateL2NormOptions(_fbb).Union(); @@ -335,6 +341,18 @@ uint32_t CircleGen::addOperatorReduce(const OperatorParams ¶ms, return addOperatorWithOptions(params, reduce_op, circle::BuiltinOptions_ReducerOptions, options); } +uint32_t CircleGen::addOperatorRelu(const OperatorParams ¶ms) +{ + return addOperatorWithOptions(params, circle::BuiltinOperator_RELU, circle::BuiltinOptions_NONE, + 0); +} + +uint32_t CircleGen::addOperatorRelu6(const OperatorParams ¶ms) +{ + return addOperatorWithOptions(params, circle::BuiltinOperator_RELU6, circle::BuiltinOptions_NONE, + 0); +} + uint32_t CircleGen::addOperatorReshape(const OperatorParams ¶ms, const Shape *new_shape) { auto options = circle::CreateReshapeOptionsDirect(_fbb, new_shape).Union(); diff --git a/tests/nnfw_api/src/CircleGen.h b/tests/nnfw_api/src/CircleGen.h index 2b88af7b7..ab7707d5a 100644 --- a/tests/nnfw_api/src/CircleGen.h +++ b/tests/nnfw_api/src/CircleGen.h @@ -164,6 +164,7 @@ public: uint32_t addOperatorExpandDims(const OperatorParams ¶ms); uint32_t addOperatorFill(const OperatorParams ¶ms); uint32_t addOperatorFloor(const OperatorParams ¶ms); + uint32_t addOperatorFloorDiv(const OperatorParams ¶ms); uint32_t addOperatorFullyConnected(const OperatorParams ¶ms, circle::FullyConnectedOptionsWeightsFormat weights_format = circle::FullyConnectedOptionsWeightsFormat_DEFAULT); @@ -188,6 +189,8 @@ public: * @brief Create circle Reshape op * the second param new_shape can be optional just like circle::CreateReshapeOptionsDirect */ + uint32_t addOperatorRelu(const OperatorParams ¶ms); + uint32_t addOperatorRelu6(const OperatorParams ¶ms); uint32_t addOperatorReshape(const OperatorParams ¶ms, const Shape *new_shape = nullptr); uint32_t addOperatorResizeBilinear(const OperatorParams ¶ms, bool align_corners = false, bool half_pixel_centers = false); diff --git a/tests/nnfw_api/src/GenModelTest.h b/tests/nnfw_api/src/GenModelTest.h index 3583ce0ac..eee50d112 100644 --- a/tests/nnfw_api/src/GenModelTest.h +++ b/tests/nnfw_api/src/GenModelTest.h @@ -237,6 +237,12 @@ public: _backends.push_back(backend); } #endif +#ifdef TEST_GPU_CL_BACKEND + if (backend == "gpu_cl") + { + _backends.push_back(backend); + } +#endif } } diff --git a/tests/nnfw_api/src/GenModelTests.cc b/tests/nnfw_api/src/GenModelTests.cc index 035a20502..a17a7e8ba 100644 --- a/tests/nnfw_api/src/GenModelTests.cc +++ b/tests/nnfw_api/src/GenModelTests.cc @@ -244,6 +244,54 @@ TEST_F(GenModelTest, neg_Reshape_with_shape_param_as_const) SUCCEED(); } +TEST_F(GenModelTest, Reshape_with_shape_param_as_const_float) +{ + CircleGen cgen; + auto f32 = circle::TensorType::TensorType_FLOAT32; + int input = cgen.addTensor({{4}, f32}); + + std::vector<int32_t> new_shape_data{2, 2}; // const of value [2, 2] + uint32_t new_shape_buf = cgen.addBuffer(new_shape_data); + int new_shape = cgen.addTensor({{2}, f32, new_shape_buf}); + int out = cgen.addTensor({{2, 2}, f32}); + + // reshape with new_shape param + cgen.addOperatorReshape({{input, new_shape}, {out}}, &new_shape_data); + cgen.setInputsAndOutputs({input}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase(uniformTCD<float>({{1, 2, 3, 4}}, {{1, 2, 3, 4}})); + _context->setBackends({"gpu_cl"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_Reshape_with_shape_param_as_const_float) +{ + // We will ses if Reshape with shape param can generate error during compilation if param is wrong + CircleGen cgen; + auto f32 = circle::TensorType::TensorType_FLOAT32; + + int input = cgen.addTensor({{4}, f32}); + + std::vector<int32_t> wrong_new_shape_data{2, 3}; // not match with input shape + uint32_t new_shape_buf = cgen.addBuffer(wrong_new_shape_data); + int new_shape = cgen.addTensor({{2}, f32, new_shape_buf}); + + int out = cgen.addTensor({{2, 2}, f32}); + + cgen.addOperatorReshape({{input, new_shape}, {out}}, &wrong_new_shape_data); + cgen.setInputsAndOutputs({input}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase(uniformTCD<float>({{1, 2, 3, 4}}, {{1, 2, 3, 4}})); + _context->setBackends({"gpu_cl"}); + + _context->expectFailCompile(); + + SUCCEED(); +} + TEST_F(GenModelTest, Reshape_without_shape_param) { CircleGen cgen; diff --git a/tests/nnfw_api/src/NNPackages.cc b/tests/nnfw_api/src/NNPackages.cc index 11e0c8eeb..af8b79933 100644 --- a/tests/nnfw_api/src/NNPackages.cc +++ b/tests/nnfw_api/src/NNPackages.cc @@ -98,3 +98,8 @@ std::string NNPackages::getModelAbsolutePath(const char *package_name) { return _base_path + "/nnfw_api_gtest_models/" + package_name + "/" + package_name; } + +std::string NNPackages::getModelAbsoluteFilePath(const char *package_name) +{ + return _base_path + "/nnfw_api_gtest_models/" + package_name + "/" + package_name + ".tflite"; +} diff --git a/tests/nnfw_api/src/NNPackages.h b/tests/nnfw_api/src/NNPackages.h index a51b7701d..ab3d527f3 100644 --- a/tests/nnfw_api/src/NNPackages.h +++ b/tests/nnfw_api/src/NNPackages.h @@ -73,6 +73,14 @@ public: std::string getModelAbsolutePath(const char *package_name); /** + * @brief Get the absolute of the model file to find + * + * @param package_name Package name + * @return std::string The absolute path of model file + */ + std::string getModelAbsoluteFilePath(const char *package_name); + + /** * @brief Save the current executable's directory based on argv[0] and CWD * * @param argv0 0th command line argument of the current process diff --git a/tests/nnfw_api/src/ValidationTestPipelineSession.cc b/tests/nnfw_api/src/ValidationTestPipelineSession.cc new file mode 100644 index 000000000..1d92095ed --- /dev/null +++ b/tests/nnfw_api/src/ValidationTestPipelineSession.cc @@ -0,0 +1,541 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "fixtures.h" +#include "common.h" +#include <fstream> +#include <stdio.h> +#include <json/json.h> +#include <thread> + +void build_partition_map() +{ + Json::Value root; + Json::Value graphs(Json::arrayValue); + int num = 31; + + for (int i = 0; i < num; i++) + { + if (i < 7) + graphs.append(Json::Value(0)); + else + graphs.append(Json::Value(1)); + } + + root["partition_map"] = graphs; + root["num_partitions"] = 2; + + Json::StyledWriter sw; + std::string jsonString = sw.write(root); + + FILE *pFile = NULL; + + pFile = fopen("./partition_map.json", "wt"); + fwrite(jsonString.c_str(), jsonString.length(), 1, pFile); + fclose(pFile); +} + +TEST_F(ValidationTestPipelineSession, create_pipeline_001) +{ + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + SUCCEED(); +} + +TEST_F(ValidationTestPipelineSession, pipeline_session_test_model) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_set_available_backends(_session, "cpu")); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); + SUCCEED(); +} + +TEST_F(ValidationTestPipelineSession, prepare_pipeline_001) +{ + std::ifstream readFile("./partition_map.json"); + + if (readFile.good()) + { + remove("./partition_map.json"); + } + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + ASSERT_EQ(nnfw_prepare_pipeline(_session, "./partition_map.json"), NNFW_STATUS_ERROR); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); +} + +TEST_F(ValidationTestPipelineSession, prepare_pipeline_002) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); + + SUCCEED(); +} + +TEST_F(ValidationTestPipelineSession, input_tensorinfo_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_input; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + NNFW_ENSURE_SUCCESS(nnfw_input_tensorinfo(_session, 0, &t_input)); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); + + SUCCEED(); +} + +TEST_F(ValidationTestPipelineSession, output_tensorinfo_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_output; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + NNFW_ENSURE_SUCCESS(nnfw_output_tensorinfo(_session, 0, &t_output)); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); + + SUCCEED(); +} + +TEST_F(ValidationTestPipelineSession, input_size_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + uint32_t input_num = -1; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + NNFW_ENSURE_SUCCESS(nnfw_input_size(_session, &input_num)); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + ASSERT_EQ(input_num, 1); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, output_size_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + uint32_t output_num = -1; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + NNFW_ENSURE_SUCCESS(nnfw_output_size(_session, &output_num)); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + ASSERT_EQ(output_num, 1); + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, set_input_tensorinfo_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_input_original; + nnfw_tensorinfo t_input_after; + nnfw_tensorinfo t_input = {NNFW_TYPE_TENSOR_FLOAT32, 4, {1, 224, 224, 3}}; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + NNFW_ENSURE_SUCCESS(nnfw_input_tensorinfo(_session, 0, &t_input_original)); + NNFW_ENSURE_SUCCESS(nnfw_set_input_tensorinfo(_session, 0, &t_input)); + NNFW_ENSURE_SUCCESS(nnfw_input_tensorinfo(_session, 0, &t_input_after)); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + ASSERT_TRUE(tensorInfoEqual(t_input_original, t_input_after)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, input_output_tensorindex) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + uint32_t input_index = 100; + NNFW_ENSURE_SUCCESS(nnfw_input_tensorindex(_session, "input", &input_index)); + ASSERT_EQ(input_index, 0); + + uint32_t output_index = 100; + NNFW_ENSURE_SUCCESS( + nnfw_output_tensorindex(_session, "MobilenetV1/Predictions/Reshape_1", &output_index)); + ASSERT_EQ(output_index, 0); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_create_pipeline_001) +{ + ASSERT_EQ(nnfw_create_session(nullptr), NNFW_STATUS_UNEXPECTED_NULL); +} + +TEST_F(ValidationTestPipelineSession, neg_pipeline_session_model_load) +{ + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + ASSERT_EQ(nnfw_load_model_from_modelfile( + nullptr, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str()), + NNFW_STATUS_UNEXPECTED_NULL); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); +} + +TEST_F(ValidationTestPipelineSession, neg_prepare_pipeline_001) +{ + ASSERT_EQ(nnfw_prepare_pipeline(nullptr, nullptr), NNFW_STATUS_UNEXPECTED_NULL); +} + +TEST_F(ValidationTestPipelineSession, neg_set_in_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + float input_buf[1 * 224 * 224 * 3]; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_set_input(_session, 0, NNFW_TYPE_TENSOR_FLOAT32, input_buf, sizeof(input_buf)), + NNFW_STATUS_ERROR); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_set_out_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + float output_buf[1 * 1001]; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_set_output(_session, 0, NNFW_TYPE_TENSOR_FLOAT32, output_buf, sizeof(output_buf)), + NNFW_STATUS_ERROR); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_input_tensorinfo_pipeline_001) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_input; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_input_tensorinfo(nullptr, 0, &t_input), NNFW_STATUS_UNEXPECTED_NULL); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_input_tensorinfo_pipeline_002) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_input; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_input_tensorinfo(_session, 1, &t_input), NNFW_STATUS_ERROR); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_output_tensorinfo_pipeline_001) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_output; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_output_tensorinfo(nullptr, 0, &t_output), NNFW_STATUS_UNEXPECTED_NULL); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_output_tensorinfo_pipeline_002) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_output; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_output_tensorinfo(_session, 1, &t_output), NNFW_STATUS_ERROR); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_input_output_size_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + uint32_t input_num = -1; + uint32_t output_num = -1; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_input_size(nullptr, &input_num), NNFW_STATUS_UNEXPECTED_NULL); + ASSERT_EQ(input_num, -1); + ASSERT_EQ(nnfw_output_size(nullptr, &output_num), NNFW_STATUS_UNEXPECTED_NULL); + ASSERT_EQ(output_num, -1); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_set_input_tensorinfo_pipeline) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + nnfw_tensorinfo t_input = {NNFW_TYPE_TENSOR_FLOAT32, 4, {1, 224, 224, 3}}; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + ASSERT_EQ(nnfw_set_input_tensorinfo(nullptr, 0, &t_input), NNFW_STATUS_UNEXPECTED_NULL); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_input_output_tensorindex) +{ + std::vector<void *> dummy1; + std::vector<uint32_t> dummy2; + + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + uint32_t input_index = 100; + ASSERT_EQ(nnfw_input_tensorindex(_session, "input1", &input_index), NNFW_STATUS_ERROR); + ASSERT_EQ(input_index, 100); + + uint32_t output_index = 100; + ASSERT_EQ(nnfw_output_tensorindex(_session, "MobilenetV1/Predictions/Reshape_2", &output_index), + NNFW_STATUS_ERROR); + ASSERT_EQ(output_index, 100); + + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, &dummy1, &dummy2)); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, neg_run_pipeline) +{ + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_set_available_backends(_session, "cpu")); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + auto producer = [this]() { + std::vector<void *> inputs; + std::vector<uint32_t> lengths; + inputs.clear(); + lengths.clear(); + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, (void *)&inputs, (void *)&lengths)); + }; + + auto consumer = [this]() { + std::vector<void *> outputs; + ASSERT_EQ(nnfw_pop_pipeline_output(_session, (void *)&outputs), NNFW_STATUS_ERROR); + }; + + auto producer_thread = std::thread(producer); + auto consumer_thread = std::thread(consumer); + + producer_thread.join(); + consumer_thread.join(); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); +} + +TEST_F(ValidationTestPipelineSession, run_pipeline) +{ + build_partition_map(); + + NNFW_ENSURE_SUCCESS(nnfw_create_session(&_session)); + NNFW_ENSURE_SUCCESS(nnfw_load_model_from_modelfile( + _session, NNPackages::get().getModelAbsoluteFilePath("mobilenet_v1_1.0_224").c_str())); + NNFW_ENSURE_SUCCESS(nnfw_set_available_backends(_session, "cpu")); + NNFW_ENSURE_SUCCESS(nnfw_prepare_pipeline(_session, "./partition_map.json")); + + auto producer = [this]() { + std::vector<void *> inputs; + std::vector<uint32_t> lengths; + inputs.clear(); + lengths.clear(); + NNFW_ENSURE_SUCCESS(nnfw_push_pipeline_input(_session, (void *)&inputs, (void *)&lengths)); + }; + + auto producer_thread = std::thread(producer); + + producer_thread.join(); + NNFW_ENSURE_SUCCESS(nnfw_close_session(_session)); + + remove("./partition_map.json"); + + SUCCEED(); +} diff --git a/tests/nnfw_api/src/fixtures.h b/tests/nnfw_api/src/fixtures.h index e2e793ff3..0e43a2f03 100644 --- a/tests/nnfw_api/src/fixtures.h +++ b/tests/nnfw_api/src/fixtures.h @@ -262,4 +262,10 @@ protected: } }; +class ValidationTestPipelineSession : public ValidationTest +{ +protected: + nnfw_session *_session = nullptr; +}; + #endif // __NNFW_API_TEST_FIXTURES_H__ diff --git a/tests/nnfw_api/src/one_op_tests/Add.cc b/tests/nnfw_api/src/one_op_tests/Add.cc index 9c0108b9e..c21022972 100644 --- a/tests/nnfw_api/src/one_op_tests/Add.cc +++ b/tests/nnfw_api/src/one_op_tests/Add.cc @@ -32,7 +32,7 @@ TEST_F(GenModelTest, OneOp_Add_VarToConst) _context = std::make_unique<GenModelTestContext>(cgen.finish()); _context->addTestCase(uniformTCD<float>({{1, 3, 2, 4}}, {{6, 7, 9, 8}})); _context->addTestCase(uniformTCD<float>({{0, 1, 2, 3}}, {{5, 5, 9, 7}})); - _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); SUCCEED(); } @@ -48,7 +48,7 @@ TEST_F(GenModelTest, OneOp_Add_VarToVar) _context = std::make_unique<GenModelTestContext>(cgen.finish()); _context->addTestCase(uniformTCD<float>({{1, 3, 2, 4}, {5, 4, 7, 4}}, {{6, 7, 9, 8}})); - _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); SUCCEED(); } @@ -111,7 +111,7 @@ TEST_F(GenModelTest, OneOp_Add_VarToVarSame) _context = std::make_unique<GenModelTestContext>(cgen.finish()); _context->addTestCase(uniformTCD<float>({{1, 3, 2, 4}}, {{2, 6, 4, 8}})); - _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); SUCCEED(); } diff --git a/tests/nnfw_api/src/one_op_tests/AveragePool2D.cc b/tests/nnfw_api/src/one_op_tests/AveragePool2D.cc index 1a6ded9c7..2fb1d6898 100644 --- a/tests/nnfw_api/src/one_op_tests/AveragePool2D.cc +++ b/tests/nnfw_api/src/one_op_tests/AveragePool2D.cc @@ -34,7 +34,7 @@ struct AvgPool2DParam float scale; int64_t zero_point; } type = {circle::TensorType::TensorType_FLOAT32, 0.0f, 0}; - std::vector<std::string> backend = {"acl_cl", "acl_neon", "cpu"}; + std::vector<std::string> backend = {"acl_cl", "acl_neon", "cpu", "gpu_cl"}; }; class AveragePool2DVariation : public GenModelTest, @@ -80,14 +80,16 @@ INSTANTIATE_TEST_CASE_P( {1, 2, 2, 1}, {1, 1, 1, 1}, {2, 2, 2, 2}, - {circle::TensorType::TensorType_UINT8, 1.2, 3}}, + {circle::TensorType::TensorType_UINT8, 1.2, 3}, + {"acl_cl", "acl_neon", "cpu"}}, // uint8_t data -large AvgPool2DParam{ uniformTCD<uint8_t>({{std::vector<uint8_t>(18 * 36 * 2, 99)}}, {{99, 99, 99, 99}}), {1, 18, 36, 2}, {1, 1, 2, 2}, {18, 18, 18, 18}, - {circle::TensorType::TensorType_UINT8, 1.2, 3}}, + {circle::TensorType::TensorType_UINT8, 1.2, 3}, + {"acl_cl", "acl_neon", "cpu"}}, // int8_t data // TODO enable acl-cl, acl-neon backend AvgPool2DParam{uniformTCD<int8_t>({{2, -6, 4, -8}}, {{-2}}), @@ -117,7 +119,7 @@ TEST_F(GenModelTest, neg_OneOp_AvgPool2D_3DInput) cgen.setInputsAndOutputs({in}, {out}); _context = std::make_unique<GenModelTestContext>(cgen.finish()); - _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); _context->expectFailCompile(); SUCCEED(); @@ -134,7 +136,7 @@ TEST_F(GenModelTest, neg_OneOp_AvgPool2D_2DInput) cgen.setInputsAndOutputs({in}, {out}); _context = std::make_unique<GenModelTestContext>(cgen.finish()); - _context->setBackends({"acl_cl", "acl_neon", "cpu"}); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); _context->expectFailCompile(); SUCCEED(); diff --git a/tests/nnfw_api/src/one_op_tests/Conv2D.cc b/tests/nnfw_api/src/one_op_tests/Conv2D.cc index 615673892..4f58e3d53 100644 --- a/tests/nnfw_api/src/one_op_tests/Conv2D.cc +++ b/tests/nnfw_api/src/one_op_tests/Conv2D.cc @@ -35,7 +35,7 @@ TEST_F(GenModelTest, OneOp_Conv2D) _context->addTestCase(uniformTCD<float>( {{4, 0, -5, 1, 0, 4, -1, 1, -1, -3, 3, -2, -4, 1, -2, 2, 4, -4, 2, 2, 0, 4, -1, -2, 4}}, {{47, -4, -25, 9, 10, 10, -13, 11, -14, -26, -12, 26, 20, 40, 1, 3, 11, 4}})); - _context->setBackends({"acl_cl", "acl_neon", "cpu", "ruy", "xnnpack"}); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "ruy", "xnnpack", "gpu_cl"}); SUCCEED(); } diff --git a/tests/nnfw_api/src/one_op_tests/DepthwiseConv2D.cc b/tests/nnfw_api/src/one_op_tests/DepthwiseConv2D.cc index 57f448b56..658c44cb9 100644 --- a/tests/nnfw_api/src/one_op_tests/DepthwiseConv2D.cc +++ b/tests/nnfw_api/src/one_op_tests/DepthwiseConv2D.cc @@ -39,6 +39,75 @@ TEST_F(GenModelTest, OneOp_DepthwiseConv2D) SUCCEED(); } +TEST_F(GenModelTest, OneOp_DepthwiseConv2D_No_Multiplier) +{ + CircleGen cgen; + std::vector<float> weight_data{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f}; + uint32_t weight_buf = cgen.addBuffer(weight_data); + std::vector<float> bias_data{0.5f, -0.5f}; + uint32_t bias_buf = cgen.addBuffer(bias_data); + int in = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32}); + int weight = cgen.addTensor({{1, 3, 1, 2}, circle::TensorType::TensorType_FLOAT32, weight_buf}); + int bias = cgen.addTensor({{1, 1, 1, 2}, circle::TensorType::TensorType_FLOAT32, bias_buf}); + int out = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorDepthwiseConv2D({{in, weight, bias}, {out}}, circle::Padding_SAME, 1, 1, 1, + circle::ActivationFunctionType_NONE); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase( + uniformTCD<float>({{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f}}, + {{16.5f, 27.5f, 28.5f, 43.5f, 8.5f, 15.5f, 12.5f, 23.5f}})); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); + SUCCEED(); +} + +TEST_F(GenModelTest, OneOp_DepthwiseConv2D_No_Multiplier_RELU6) +{ + CircleGen cgen; + std::vector<float> weight_data{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f}; + uint32_t weight_buf = cgen.addBuffer(weight_data); + std::vector<float> bias_data{0.5f, -0.5f}; + uint32_t bias_buf = cgen.addBuffer(bias_data); + int in = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32}); + int weight = cgen.addTensor({{1, 3, 1, 2}, circle::TensorType::TensorType_FLOAT32, weight_buf}); + int bias = cgen.addTensor({{1, 1, 1, 2}, circle::TensorType::TensorType_FLOAT32, bias_buf}); + int out = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorDepthwiseConv2D({{in, weight, bias}, {out}}, circle::Padding_SAME, 1, 1, 1, + circle::ActivationFunctionType_RELU6); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase(uniformTCD<float>({{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f}}, + {{6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f, 6.0f}})); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); + SUCCEED(); +} + +TEST_F(GenModelTest, OneOp_DepthwiseConv2D_3x3) +{ + CircleGen cgen; + std::vector<float> weight_data{0.0f, 1.0f, 1.0f, 1.0f, 0.0f, 1.0f, 1.0f, 1.0f, 1.0f, + 1.0f, 1.0f, 1.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, 1.0f}; + uint32_t weight_buf = cgen.addBuffer(weight_data); + std::vector<float> bias_data{0.0f, 0.0f}; + uint32_t bias_buf = cgen.addBuffer(bias_data); + int in = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32}); + int weight = cgen.addTensor({{1, 3, 3, 2}, circle::TensorType::TensorType_FLOAT32, weight_buf}); + int bias = cgen.addTensor({{1, 1, 1, 2}, circle::TensorType::TensorType_FLOAT32, bias_buf}); + int out = cgen.addTensor({{1, 2, 2, 2}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorDepthwiseConv2D({{in, weight, bias}, {out}}, circle::Padding_SAME, 1, 1, 1, + circle::ActivationFunctionType_NONE); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase( + uniformTCD<float>({{0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f}}, + {{6.0f, 16.0f, 8.0f, 16.0f, 10.0f, 16.0f, 12.0f, 16.0f}})); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "gpu_cl"}); + SUCCEED(); +} + TEST_F(GenModelTest, OneOp_DepthwiseConv2D_Dilation) { CircleGen cgen; @@ -84,7 +153,7 @@ TEST_F(GenModelTest, OneOp_DepthwiseConv2D_Dilation_N_Stride) _context->addTestCase(uniformTCD<float>({{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {{4, 0, 3, 0, 0, 0, 2, 0, 1}})); - _context->setBackends({"acl_cl", "acl_neon", "cpu", "xnnpack"}); + _context->setBackends({"acl_cl", "acl_neon", "cpu", "xnnpack", "gpu_cl"}); SUCCEED(); } diff --git a/tests/nnfw_api/src/one_op_tests/FloorDiv.cc b/tests/nnfw_api/src/one_op_tests/FloorDiv.cc new file mode 100644 index 000000000..edbca8504 --- /dev/null +++ b/tests/nnfw_api/src/one_op_tests/FloorDiv.cc @@ -0,0 +1,149 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GenModelTest.h" + +#include <memory> + +TEST_F(GenModelTest, OneOp_FloorDiv_VarToVar_Float) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase( + uniformTCD<float>({{1.0, 2.0, -6.8, 24.2}, {1.0, 2.0, 3.0, 4.0}}, {{1.0, 1.0, -3.0, 6.0}})); + _context->setBackends({"cpu"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, OneOp_FloorDiv_VarToVar_Float_Broadcast) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + int rhs = cgen.addTensor({{1}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase(uniformTCD<float>({{1.0, 2.0, -6.8, 24.2}, {2.0}}, {{0.0, 1.0, -4, 12.0}})); + _context->setBackends({"cpu"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_FloorDiv_VarToVar_InvalidDivisor1) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"cpu"}); + _context->expectFailModelLoad(); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_FloorDiv_Broadcast_InvalidDivisor1) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + int rhs = cgen.addTensor({{1}, circle::TensorType::TensorType_INT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"cpu"}); + _context->expectFailModelLoad(); + + SUCCEED(); +} + +TEST_F(GenModelTest, OneOp_FloorDiv_VarToVar_Int) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase( + uniformTCD<int32_t>({{10, 20, -68, 242}, {1, 2, 3, 4}}, {{10, 10, -23, 60}})); + _context->setBackends({"cpu"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, OneOp_FloorDiv_VarToVar_Int_Broadcast) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + int rhs = cgen.addTensor({{1}, circle::TensorType::TensorType_INT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase(uniformTCD<int32_t>({{10, 20, -67, 242}, {2}}, {{5, 10, -34, 121}})); + _context->setBackends({"cpu"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_FloorDiv_VarToVar_InvalidDivisor2) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + int rhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"cpu"}); + _context->expectFailModelLoad(); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_FloorDiv_Broadcast_InvalidDivisor2) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + int rhs = cgen.addTensor({{1}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{1, 2, 2, 1}, circle::TensorType::TensorType_INT32}); + cgen.addOperatorFloorDiv({{lhs, rhs}, {out}}); + cgen.setInputsAndOutputs({lhs, rhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"cpu"}); + _context->expectFailModelLoad(); + + SUCCEED(); +} diff --git a/tests/nnfw_api/src/one_op_tests/Relu.cc b/tests/nnfw_api/src/one_op_tests/Relu.cc new file mode 100644 index 000000000..28c511270 --- /dev/null +++ b/tests/nnfw_api/src/one_op_tests/Relu.cc @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GenModelTest.h" + +TEST_F(GenModelTest, OneOp_Relu) +{ + CircleGen cgen; + int in = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorRelu({{in}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase( + uniformTCD<float>({{0, 1.0, 3.0, 1.0, -1.0, -2.0f}}, {{0, 1.0, 3.0, 1.0, 0, 0}})); + _context->setBackends({"cpu", "gpu_cl"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_Relu_InvalidType) +{ + CircleGen cgen; + int in = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_UINT8}); + int out = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorRelu({{in}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"cpu", "gpu_cl"}); + _context->expectFailModelLoad(); + + SUCCEED(); +} diff --git a/tests/nnfw_api/src/one_op_tests/Relu6.cc b/tests/nnfw_api/src/one_op_tests/Relu6.cc new file mode 100644 index 000000000..88b8eba83 --- /dev/null +++ b/tests/nnfw_api/src/one_op_tests/Relu6.cc @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GenModelTest.h" + +TEST_F(GenModelTest, OneOp_Relu6) +{ + CircleGen cgen; + int in = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorRelu6({{in}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase( + uniformTCD<float>({{4, 7.0, 3.0, 8.0, -1.0, -2.0f}}, {{4, 6.0, 3.0, 6.0, 0, 0}})); + _context->setBackends({"cpu", "gpu_cl"}); + + SUCCEED(); +} + +TEST_F(GenModelTest, neg_OneOp_Relu6_InvalidType) +{ + CircleGen cgen; + int in = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_UINT8}); + int out = cgen.addTensor({{2, 3}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorRelu6({{in}, {out}}); + cgen.setInputsAndOutputs({in}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->setBackends({"cpu", "gpu_cl"}); + _context->expectFailModelLoad(); + + SUCCEED(); +} diff --git a/tests/nnfw_api/src/one_op_tests/Softmax.cc b/tests/nnfw_api/src/one_op_tests/Softmax.cc index 80fd17b12..95debec33 100644 --- a/tests/nnfw_api/src/one_op_tests/Softmax.cc +++ b/tests/nnfw_api/src/one_op_tests/Softmax.cc @@ -62,6 +62,39 @@ TEST_P(SoftmaxVariation, Test) SUCCEED(); } +TEST_F(GenModelTest, neg_OneOp_Softmax_Invaild_Beta) +{ + CircleGen cgen; + int input = cgen.addTensor({{4, 1, 1, 1}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{4, 1, 1, 1}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorSoftmax({{input}, {out}}, 0.1); + cgen.setInputsAndOutputs({input}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase(uniformTCD<float>({{-1., 0., 1., 1.}}, {{-1., -1., -1., -1.}})); + _context->setBackends({"gpu_cl"}); + _context->expectFailCompile(); + + SUCCEED(); +} + +TEST_F(GenModelTest, OneOp_Softmax) +{ + CircleGen cgen; + int lhs = cgen.addTensor({{1, 1, 1, 4}, circle::TensorType::TensorType_FLOAT32}); + int out = cgen.addTensor({{1, 1, 1, 4}, circle::TensorType::TensorType_FLOAT32}); + cgen.addOperatorSoftmax({{lhs}, {out}}, 1.0); + cgen.setInputsAndOutputs({lhs}, {out}); + + _context = std::make_unique<GenModelTestContext>(cgen.finish()); + _context->addTestCase(uniformTCD<float>( + {{-1., 0., 1., 1.}}, + {{0.054064586758613586, 0.14696279168128967, 0.39948627352714539, 0.39948627352714539}})); + _context->setBackends({"acl_cl", "cpu", "gpu_cl"}); + + SUCCEED(); +} + // Test with different value type INSTANTIATE_TEST_CASE_P( GenModelTest, SoftmaxVariation, diff --git a/tests/scripts/list/nnapi_test.aarch64.list b/tests/scripts/list/nnapi_test.aarch64.list index dd8d3b710..c74e8981d 100644 --- a/tests/scripts/list/nnapi_test.aarch64.list +++ b/tests/scripts/list/nnapi_test.aarch64.list @@ -6,7 +6,6 @@ batch_to_space_nd2 cast concat conv_2d -custom depthwise_conv_2d div embedding_lookup diff --git a/tests/scripts/list/nnapi_test.armv7l.list b/tests/scripts/list/nnapi_test.armv7l.list index dd8d3b710..c74e8981d 100644 --- a/tests/scripts/list/nnapi_test.armv7l.list +++ b/tests/scripts/list/nnapi_test.armv7l.list @@ -6,7 +6,6 @@ batch_to_space_nd2 cast concat conv_2d -custom depthwise_conv_2d div embedding_lookup diff --git a/tests/scripts/models/tflite/custom/squared_difference/config.sh b/tests/scripts/models/tflite/custom/squared_difference/config.sh deleted file mode 100755 index 745a84447..000000000 --- a/tests/scripts/models/tflite/custom/squared_difference/config.sh +++ /dev/null @@ -1 +0,0 @@ -MODELFILE_NAME="custom_squared_diff_test.tflite" diff --git a/tests/tools/nnapi_test/src/nnapi_test.cc b/tests/tools/nnapi_test/src/nnapi_test.cc index 921d0dc42..314f162d1 100644 --- a/tests/tools/nnapi_test/src/nnapi_test.cc +++ b/tests/tools/nnapi_test/src/nnapi_test.cc @@ -14,7 +14,6 @@ * limitations under the License. */ -#include "tflite/ext/kernels/register.h" #include "tensorflow/lite/model.h" #include "tflite/interp/FlatBufferBuilder.h" diff --git a/tests/tools/tflite_comparator/src/tflite_comparator.cc b/tests/tools/tflite_comparator/src/tflite_comparator.cc index 7e5190527..65a40b493 100644 --- a/tests/tools/tflite_comparator/src/tflite_comparator.cc +++ b/tests/tools/tflite_comparator/src/tflite_comparator.cc @@ -25,7 +25,7 @@ #include <tflite/Assert.h> #include <tflite/InterpreterSession.h> -#include <tflite/ext/kernels/register.h> +#include <tflite/interp/FlatBufferBuilder.h> #include <iostream> #include <fstream> @@ -286,14 +286,12 @@ int main(const int argc, char **argv) // Read tflite model StderrReporter error_reporter; auto model = FlatBufferModel::BuildFromFile(tflite_file.c_str(), &error_reporter); - - BuiltinOpResolver resolver; - InterpreterBuilder builder(*model, resolver); + auto builder = FlatBufferBuilder(*model); std::unique_ptr<Interpreter> interpreter; try { - TFLITE_ENSURE(builder(&interpreter)); + interpreter = builder.build(); } catch (const std::exception &e) { diff --git a/tests/tools/tflite_run/src/tflite_run.cc b/tests/tools/tflite_run/src/tflite_run.cc index 14f501258..55d2a1b9b 100644 --- a/tests/tools/tflite_run/src/tflite_run.cc +++ b/tests/tools/tflite_run/src/tflite_run.cc @@ -14,7 +14,7 @@ * limitations under the License. */ -#include "tflite/ext/kernels/register.h" +#include "tensorflow/lite/kernels/register.h" #include "tensorflow/lite/model.h" #include "args.h" @@ -110,7 +110,7 @@ int main(const int argc, char **argv) throw std::runtime_error{"Cannot create model"}; } - BuiltinOpResolver resolver; + tflite::ops::builtin::BuiltinOpResolver resolver; InterpreterBuilder builder(*model, resolver); TFLITE_ENSURE(builder(&interpreter)) interpreter->SetNumThreads(nnfw::misc::EnvVar("THREAD").asInt(1)); diff --git a/tools/model_partition_tool/Graph.py b/tools/model_partition_tool/Graph.py new file mode 100644 index 000000000..96caf3889 --- /dev/null +++ b/tools/model_partition_tool/Graph.py @@ -0,0 +1,457 @@ +#! /usr/bin/python +import graph_analysis +import json +import logging +import runtime_stats +import os +import sys +import numpy as np +from queue import LifoQueue + + +class ModelInfo: + def __init__(self, modelfile, vertex_weights): + self._model_dir = os.path.dirname(modelfile) + self._dag = graph_analysis.generate_dag(modelfile) + self._ops = graph_analysis.get_model_ops(modelfile) + self._tensors = graph_analysis.get_model_tensors(modelfile) + self._vertex_weights = vertex_weights + """Return Directed Acyclic Graph (DAG) + """ + + def get_dag(self): + return self._dag + """Return list of model operations + """ + + def get_ops(self): + return self._ops + """Return list of model tensors + """ + + def get_tensors(self): + return self._tensors + """Return vertex weights representing execution times + of model operations + """ + + def get_vertex_weights(self): + return self._vertex_weights + """Return size (bytes) of tensor connecting operation indexes n1 and n2 + """ + + def get_tensor_size(self, n1, n2): + tensor_id = set(self._ops[n1]['outputs']).intersection( + set(self._ops[n2]['inputs'])) + assert (len(tensor_id) == 1) + idx = tensor_id.pop() + tensor = self._tensors[idx]['shape'] + return np.prod(tensor) * tensor.itemsize + + def get_model_path(self): + return self._model_dir + + +class GraphPartition: + def __init__(self, K): + self._K = K + self._indx = np.zeros(K, dtype=int) + self._session_weights = np.zeros(K, dtype=int) + self._session_ids = [] + logging.basicConfig(level=logging.DEBUG) + self._logger = logging.getLogger("Minmax") + + def set_dbglevel(self, dbglevel): + logging.basicConfig(level=dbglevel) + self._logger = logging.getLogger("Minmax") + self._logger.setLevel(dbglevel) + """Generates a session graph out of the provided dag (Directed Acyclic Graph) + Each dag node is associated with a session id, stored under attribute _session_ids. + """ + + def generate_session_graph(self): + def get_session_ids(i, j): + cnt = 0 + idx_i = -1 + idx_j = -1 + for idx in range(self._K): + if i in self._session_ids[idx]: + idx_i = idx + cnt += 1 + if j in self._session_ids[idx]: + idx_j = idx + cnt += 1 + if cnt == 2: + break + return (idx_i, idx_j) + + dag = self._modelObj.get_dag() + n = dag.shape[0] + self._session_graph = np.zeros((self._K, self._K), dtype=int) + for i in range(n - 1): + for j in range(i + 1, n): + if dag[i][j] == 1: + idx1, idx2 = get_session_ids(i, j) + if idx1 == -1 or idx2 == -1: + self._logger.debug("Something wrong with session ids") + self._logger.debug(self._session_ids) + self._logger.debug(i, j) + sys.exit(-1) + if idx1 != idx2: + self._session_graph[idx1][idx2] = 1 + + for i in range(self._K - 1): + for j in range(i + 1, self._K): + if self._session_graph[i][j] == 1 and self._session_graph[j][i] == 1: + self._logger.error("Session graph has cycles (%d, %d)", i, j) + self._logger.error("Session %d: %s", i, self._session_ids[i]) + self._logger.error("Session %d: %s", j, self._session_ids[j]) + sys.exit(-1) + """Generate an initial partition of the topological ordering T, with the + help of provided vertex weights. This method will update _session_weights, that is, + the cumulative sum of vertex weights within a session/partition + """ + + def initial_partition(self, modelObj, T): + self._modelObj = modelObj + self._logger.debug("Topological order: %s", T) + vwgt = modelObj.get_vertex_weights() + sorted_vwgt = np.array([vwgt[i] for i in T]) + self._logger.debug("sorted weights: %s", sorted_vwgt) + sum1 = 0 + c_sorted_vw = [] + for s in sorted_vwgt: + c_sorted_vw.append(sum1 + s) + sum1 += s + + pivot = np.zeros(self._K - 1) + self._logger.debug("Cumulative sum weights: %s", c_sorted_vw) + for i in range(1, self._K): + pivot[i - 1] = round(i * c_sorted_vw[-1] / self._K) + + for i in range(self._K - 1): + self._indx[i + 1] = np.argmin(abs(c_sorted_vw - pivot[i])) + + sum_weights = [] + for i in range(self._K): + if i == self._K - 1: + self._session_ids.append(np.array(T[self._indx[i]:])) + self._session_weights[i] = np.sum(sorted_vwgt[self._indx[i]:]) + else: + self._session_ids.append(np.array(T[self._indx[i]:self._indx[i + 1]])) + self._session_weights[i] = np.sum( + sorted_vwgt[self._indx[i]:self._indx[i + 1]]) + self.generate_session_graph() + """Print a summary that includes session graph, paritition info comprising node ids and their + cumulative vertex weights + """ + + def summarize(self, T): + self._logger.info( + "Session Graph:\n%s", + np.array2string( + self._session_graph, formatter={'int': lambda x: '{:>3}'.format(x)})) + for i in range(self._K): + self._logger.info("Partition %d : %s, sum weight = %s", i, + self._session_ids[i].tolist(), self._session_weights[i]) + """Move nodes from session1 to session2 until the maximum of the two cumulative session weights are exceeded. + The parameters to this method include the session ids, list of vertex weights (vwgt), and the directed adjacency matrix (dag). + At the end of the move, the session ids per session, and the session weights are updated. As node movement may also affect the session + graph, the session graph is updated as well. + """ + + def move_nodes(self, session1, session2): + dag = self._modelObj.get_dag() + vwgt = self._modelObj.get_vertex_weights() + + def session_edges(s1, s2, dag, forward_direction): + sdict = {} + if forward_direction == True: + for k in s1: + tmp_s = set(np.where(dag[k, :] == 1)[0]).difference(set(s1)) + if len(tmp_s) > 0: + sdict[k] = list(tmp_s) + else: + for k in s2: + tmp_s = set(np.where(dag[k, :] == 1)[0]).intersection(set(s1)) + if len(tmp_s) > 0: + for key in tmp_s: + sdict[key] = k + return sdict + + move_success = False + if self._session_graph[session1][session2] == 1: + forward_direction = True + elif self._session_graph[session2][session1] == 1: + forward_direction = False + else: + self._logger.warning("Cannot move nodes between non-neighboring partitions") + return move_success + + maxval = max(self._session_weights[session1], self._session_weights[session2]) + improvement = True + + marked = {} + while improvement == True: + s1 = self._session_ids[session1] + s2 = self._session_ids[session2] + sdict = session_edges(s1, s2, dag, forward_direction) + + found_node = False + rnd_perm = np.random.permutation(list(sdict)) + cnt = 0 + while found_node == False and cnt < len(sdict): + rnd_key = rnd_perm[cnt] + marked[rnd_key] = True + found_node = True + if forward_direction == True: + for k in range(session2): + if len( + set(np.where(dag[rnd_key, :] == 1)[0]).intersection( + set(self._session_ids[k]))) > 0: + found_node = False + cnt += 1 + break + else: + for k in range(session2 + 1, self._K): + if len( + set(np.where(dag[:, rnd_key] == 1)[0]).intersection( + set(self._session_ids[k]))) > 0: + found_node = False + cnt += 1 + break + if found_node == True: + new_maxval = max(self._session_weights[session1] - vwgt[rnd_key], + self._session_weights[session2] + vwgt[rnd_key]) + if new_maxval < maxval: + self._logger.info("[old maxval] %s --> %s [new maxval], id: %s", + maxval, new_maxval, rnd_key) + self._logger.debug("edges : %s", (sdict[rnd_key])) + if type(sdict[rnd_key]) is list: + rnd_val = np.random.choice(sdict[rnd_key]) + else: + rnd_val = sdict[rnd_key] + if forward_direction == True: + if np.where(s2 == rnd_val)[0].size > 0: + s2 = np.insert(s2, np.where(s2 == rnd_val)[0], rnd_key) + else: + s2 = np.insert(s2, 0, rnd_key) + else: + if np.where(s2 == sdict[rnd_key])[0].size > 0: + s2 = np.insert(s2, + np.where(s2 == sdict[rnd_key])[0] + 1, + rnd_key) + else: + s2 = np.insert(s2, len(s2), rnd_key) + s1 = np.delete(s1, np.where(s1 == rnd_key)) + del self._session_ids[session1] + self._session_ids.insert(session1, s1) + del self._session_ids[session2] + self._session_ids.insert(session2, s2) + self._session_weights[session1] -= vwgt[rnd_key] + self._session_weights[session2] += vwgt[rnd_key] + maxval = new_maxval + self.generate_session_graph() + move_success = True + else: + self._logger.warning("Move rejected, max value is greater") + improvement = False + else: + self._logger.warning( + "Candidate %d cannot be moved, as it violates acyclic constraint", + rnd_key) + improvement = False + return move_success + """Method to get the session with the maximum session weight, or cumulative exection time. This + session is then searched for its neighboring sessions. The neighbors are then ranked in increasing order + of their execution times, so that session moves can be performed in that order. + """ + + def get_bottleneck_info(self): + maxval = 0 + ret_id = -1 + for i in range(self._K): + if maxval < self._session_weights[i]: + maxval = self._session_weights[i] + ret_id = i + neighbor_dict = {} + + for i in range(self._K): + if self._session_graph[ret_id][i] == 1 or self._session_graph[i][ret_id] == 1: + neighbor_dict[i] = self._session_weights[i] + sorted_neighbor_list = sorted(neighbor_dict.items(), key=lambda item: item[1]) + self._logger.info("Bottleneck id --> %d, sorted neighbors --> %s", ret_id, + sorted_neighbor_list) + return ret_id, sorted_neighbor_list + """Get the cost and the partition id associated with the maximum value. + """ + + def get_maxPartitionCost(self): + dag = self._modelObj.get_dag() + maxval = 0 + indx = -1 + for i in range(self._K): + if self._session_weights[i] > maxval: + maxval = self._session_weights[i] + indx = i + + def check_edges(dag, session1, session2): + e_cnt = 0 + memory_overhead = 0 + for s1 in self._session_ids[session1]: + for s2 in self._session_ids[session2]: + if dag[s1][s2] == 1: + e_cnt += 1 + memory_overhead += self._modelObj.get_tensor_size(s1, s2) + elif dag[s2][s1] == 1: + self._logger.error("%d (session %d) connects to %d (session %d)", + s2, session2, s1, session1) + self._logger.error(self._session_graph) + sys.exit(-1) + + assert (e_cnt > 0) + return e_cnt, memory_overhead + + edge_cut = 0 + total_memory_overhead = 0 + for i in range(self._K - 1): + for j in range(i + 1, self._K): + if self._session_graph[i][j] == 1: + e_cnt, memory_overhead = check_edges(dag, i, j) + edge_cut += e_cnt + total_memory_overhead += memory_overhead + return indx, maxval, edge_cut, total_memory_overhead + """Get partition information. + """ + + def get_partitions(self): + return self._session_ids, self._session_weights, self._session_graph + + +class GraphTopology: + def __init__(self, tflite_file, trace_file): + vertex_weights = runtime_stats.get_runtime_per_operation(trace_file) + self._modelObj = ModelInfo(tflite_file, vertex_weights) + self._dag = graph_analysis.generate_dag(tflite_file) + self._T = [] + self._vwgt = np.array(vertex_weights) + logging.basicConfig(level=logging.INFO) + self._Graphlogger = logging.getLogger("Topology") + + def set_dbglevel(self, dbglevel): + logging.basicConfig(level=dbglevel) + self._Graphlogger.setLevel(dbglevel) + """Perform Topological sort using the method outlined in https://arxiv.org/abs/1704.00705 + """ + + def topological_sort(self): + del self._T + degree_matrix = np.copy(self._dag) + n = self._dag.shape[0] + S = [] + T = LifoQueue(maxsize=n) + marked = {} + + while T.qsize() < n: + indegree = np.sum(degree_matrix, axis=0) + candidates, = np.where(indegree == 0) + for i in candidates: + if i not in marked: + S.append(i) + np.random.seed() + random_pos = int(np.random.rand() * len(S)) + random_node = S[random_pos] + marked[random_node] = True + T.put(random_node) + neighbors, = np.where(self._dag[random_node, :] == 1) + for i in neighbors: + degree_matrix[random_node][i] = 0 + del S + S = [] + + self._T = list(T.queue) + """Create a partition instance and perform an initial split over the cumulative sum weights + """ + + def partition_graph(self, K): + self._partition = GraphPartition(K) + self._partition.initial_partition(self._modelObj, self._T) + """Move nodes between sessions id1 and id2 + """ + + def partition_move(self, id1, id2): + return self._partition.move_nodes(id1, id2) + """Summarize partition information + """ + + def partition_summary(self): + self._partition.summarize(self._T) + """Optimize for minmax partition. At each iteration, find the bottlenecked partition, and shuffle nodes out of it + to its neighbor with the smallest weight. If the neighbor session cannot accomodate any more nodes (because the minmax criterion is violated), + then select the next neighbor with the smallest weight. Repeat iterations until no further improvement is possible. + """ + + def partition_minmax(self, oneshot=False): + improvement = True + while improvement == True: + improvement = False + bottleneck_id, neighbor_list = self._partition.get_bottleneck_info() + for neighbor, wgt in neighbor_list: + self._Graphlogger.debug("====Moving from session %d to session %d", + bottleneck_id, neighbor) + ret_success = self.partition_move(bottleneck_id, neighbor) + if ret_success == True: + improvement = True + self._Graphlogger.debug( + "====Successful move from session %d to session %d", + bottleneck_id, neighbor) + break + self._Graphlogger.debug("====Failed move from session %d to session %d", + bottleneck_id, neighbor) + if oneshot == True: + self.partition_summary() + + return self._partition.get_maxPartitionCost() + """Perform MinMax partitioning over multiple runs, and pick the best solution. + """ + + def partition_minmax_multiple(self, K=3, nruns=100): + minval = np.inf + session_ids = [] + session_weights = np.zeros(K, dtype=int) + edge_cut_best = 0 + memory_overhead_best = 0 + for run in range(nruns): + self._Graphlogger.debug("****Starting run %d", run) + self.topological_sort() + self.partition_graph(K) + indx, maxval, edge_cut, memory_overhead = self.partition_minmax() + if maxval < minval: + minval = maxval + edge_cut_best = edge_cut + memory_overhead_best = memory_overhead + session_ids, session_weights, session_graph = self._partition.get_partitions( + ) + self._Graphlogger.debug("****Finished run %d", run) + + self._Graphlogger.info("Done.. printing results") + self._Graphlogger.info("Session ids: ") + for i in range(K): + self._Graphlogger.info("Partition %d : %s, sum weight = %s", i, + session_ids[i].tolist(), session_weights[i]) + self._Graphlogger.info( + "Session Graph:\n%s", + np.array2string( + session_graph, formatter={'int': lambda x: '{:>3}'.format(x)})) + self._Graphlogger.info("Edge cut: %d", edge_cut_best) + self._Graphlogger.info("Memory overhead (bytes): %d", memory_overhead_best) + output_data = {} + partition_map = np.zeros(self._dag.shape[0], dtype=int) + with open("".join([self._modelObj.get_model_path(), "/parition_map.json"]), + "w") as ofile: + for i in range(K): + for op_idx in session_ids[i]: + partition_map[op_idx] = i + output_data['partition_map'] = partition_map.tolist() + output_data['num_partitions'] = K + json.dump(output_data, ofile) diff --git a/tools/model_partition_tool/README.md b/tools/model_partition_tool/README.md new file mode 100644 index 000000000..b0b12793f --- /dev/null +++ b/tools/model_partition_tool/README.md @@ -0,0 +1,138 @@ +# Heuristic Graph Partitioning + +This folder contains the necessary scripts to perform a a heuristic-based graph partitioning for machine learning models. + +The main contents of this folder are as follows: + +- [Python Files](#python-scripts) +- [How to Run Partitioning Algorithm?](#how-to-partition-tflite-model) +- [Example Script](#example-script-to-generate-partition-map) + + +## Python Scripts +The python scripts (**python3**) require an installation of Tensorflow 2.x package to retrieve TFLite model operations. Additionally, please ensure that the python `numpy` package has been installed beforehand. The scripts also import the following modules: `queue`, `json` and `argparse`, all of which should be available by default. If not, please install them either by `pip install <package>` or `sudo apt install python-<package>`. + +`Graph.py` is the main script that processes the model graph topology and implements the partitioning algorithm. Correspondingly, there are two classes within, namely `GraphTopology` and `GraphPartition`. `GraphTopology` has a container `GraphPartition` object within. + +`graph_analysis.py` is a helper module for translating TFLite models to graph data structures. `graph_analysis.py` is imported inside +`Graph.py`. + + +## How To Partition TFLite Model? +To partition a TFLite model, simply `import Graph` at the outset. There are two ways to run the partitioning algorithm. If you prefer quick results without having to inspect the intermediate results, follow the steps below: + +### Quick Run For Final Result +To get the partitioning result quickly, follow the steps below: + +1. Create a `GraphTopology` object as shown below: +``` +In [70]: g = Graph.GraphTopology('inceptionV3.tflite', 'inceptionV3.chrome.json') +``` +**Note**: Here, the argument `inceptionV3.chrome.json` is a single-execution trace of operation execution times, and is obtained using the Chrome Trace profiler. + +2. Run the **MinMax** partitioning algorithm over the topology. Specify the number of partitions (K) and the number of topological orderings (nruns) to evaluate before settling for the best result. +``` +In [71]: g.partition_minmax_multiple(K=4, nruns=10) + +INFO:Topology:Session ids: +INFO:Topology:Partition 0 : [0, 1, 2, 3, 4, 5, 6, 13, 7, 10, 14, 11, 12, 8, 9, 15, 22, 16, 23, 19, 17, 20, 21], sum weight = 292393 +INFO:Topology:Partition 1 : [18, 24, 26, 27, 28, 25, 29, 30, 31, 32, 33, 34, 38, 35, 36, 37, 39, 49, 44, 41, 45, 42, 50, 46, 43, 40, 47, 48, 51, 53, 56, 52], sum weight = 293959 +INFO:Topology:Partition 2 : [61, 57, 58, 54, 55, 62, 59, 60, 63, 73, 74, 65, 64, 68, 66, 69, 67, 70, 71, 72, 75, 76, 80, 77, 78, 79, 81, 82], sum weight = 290835 +INFO:Topology:Partition 3 : [83, 84, 85, 86, 87, 90, 94, 91, 88, 89, 92, 93, 95, 96, 101, 97, 106, 98, 102, 103, 104, 99, 105, 107, 100, 108, 109, 110, 111, 114, 119, 120, 112, 115, 116, 117, 113, 118, 121, 122, 123, 124, 125], sum weight = 293819 +INFO:Topology:Session Graph: +[[ 0 1 0 0] + [ 0 0 1 0] + [ 0 0 0 1] + [ 0 0 0 0]] +INFO:Topology:Edge cut: 12 +INFO:Topology:Memory overhead (bytes): 4366144 + +In [72]: +``` + +### Detailed View +For a detailed breakdown of the runtime steps, execute the function calls shown below: + +1. Create a `GraphTopology` object: +``` +In [70]: g = Graph.GraphTopology('inceptionV3.tflite', 'inceptionV3.chrome.json') +``` + +2. Perform a topological sort +``` +In [73]: g.topological_sort() +``` + + +3. Partition the graph into K sub-graphs, using the topological order obtained above +``` +In [74]: g.partition_graph(K=4) +``` + +4. View the execution time of each partition +``` +In [75]: g.partition_summary() +INFO:Minmax:Session Graph: +[[ 0 1 0 0] + [ 0 0 1 0] + [ 0 0 0 1] + [ 0 0 0 0]] +INFO:Minmax:Partition 0 : [0, 1, 2, 3, 4, 5, 6, 13, 8, 7, 14, 9, 10, 11, 12, 15, 22, 23, 17, 16, 18, 19], sum weight = 276635 +INFO:Minmax:Partition 1 : [20, 21, 24, 26, 28, 27, 29, 25, 30, 31, 32, 33, 38, 35, 36, 34, 37, 39, 40, 41, 44, 45, 46, 42, 43, 49, 50, 47, 48, 51, 52, 61], sum weight = 299334 +INFO:Minmax:Partition 2 : [56, 53, 54, 57, 58, 55, 59, 60, 62, 63, 73, 65, 66, 67, 68, 69, 74, 70, 64, 71, 72, 75, 80, 81, 77, 76, 78, 82, 85], sum weight = 291593 +INFO:Minmax:Partition 3 : [83, 86, 84, 79, 87, 94, 90, 91, 88, 92, 89, 93, 95, 106, 107, 96, 97, 101, 102, 98, 104, 103, 99, 100, 105, 108, 114, 109, 119, 120, 110, 112, 111, 113, 115, 117, 116, 118, 121, 122, 123, 124, 125], sum weight = 303444 + ``` + + 5. Run a *OneShot* version of the partitioning algorithm +``` +In [90]: indx, minmax, edge_cnt, memory_overhead = g.partition_minmax(oneshot=True) +INFO:Minmax:Bottleneck id --> 3, sorted neighbors --> [(2, 291593)] +DEBUG:Topology:====Moving from session 3 to session 2 +INFO:Minmax:[old maxval] 303444 --> 300754 [new maxval], id: 86 +WARNING:Minmax:Candidate 87 cannot be moved, as it violates acyclic constraint +WARNING:Minmax:Move rejected, max value is greater +DEBUG:Topology:====Successful move from session 3 to session 2 +INFO:Minmax:Bottleneck id --> 2, sorted neighbors --> [(3, 294283), (1, 299334)] +DEBUG:Topology:====Moving from session 2 to session 3 +WARNING:Minmax:Move rejected, max value is greater +DEBUG:Topology:====Failed move from session 2 to session 3 +DEBUG:Topology:====Moving from session 2 to session 1 +WARNING:Minmax:Move rejected, max value is greater +DEBUG:Topology:====Failed move from session 2 to session 1 +INFO:Minmax:Session Graph: +[[ 0 1 0 0] + [ 0 0 1 0] + [ 0 0 0 1] + [ 0 0 0 0]] +INFO:Minmax:Partition 0 : [0, 1, 2, 3, 4, 5, 6, 13, 8, 7, 14, 9, 10, 11, 12, 15, 22, 23, 17, 16, 18, 19], sum weight = 276635 +INFO:Minmax:Partition 1 : [20, 21, 24, 26, 28, 27, 29, 25, 30, 31, 32, 33, 38, 35, 36, 34, 37, 39, 40, 41, 44, 45, 46, 42, 43, 49, 50, 47, 48, 51, 52, 61], sum weight = 299334 +INFO:Minmax:Partition 2 : [56, 53, 54, 57, 58, 55, 59, 60, 62, 63, 73, 65, 66, 67, 68, 69, 74, 70, 64, 71, 72, 75, 80, 81, 77, 76, 78, 82, 85, 86], sum weight = 300754 +INFO:Minmax:Partition 3 : [83, 84, 79, 87, 94, 90, 91, 88, 92, 89, 93, 95, 106, 107, 96, 97, 101, 102, 98, 104, 103, 99, 100, 105, 108, 114, 109, 119, 120, 110, 112, 111, 113, 115, 117, 116, 118, 121, 122, 123, 124, 125], sum weight = 294283 +``` + +**Note** Please set debug levels in the script accordingly, for example, `g.set_dbglevel(logging.DEBUG)`. + +## Example Script To Generate Partition Map +An example script `test_parition.py` is added to the folder. Please run `python3 test_partition.py --help` for details. The script parses the TFLite model file and the trace JSON as arguments, and creates a `partition_map.json` at the same location as the TFLite file. An output from running `test_partition.py` is shown below: + +``` +$ python3 test_partition.py /tmp/nnpackage/inception_v3/inception_v3.tflite /tmp/inceptionV3.chrome.json --num_parts=4 + +... +... +INFO:Topology:Partition 0 : [0, 1, 2, 3, 4, 5, 6, 8, 13, 7, 9, 14, 10, 11, 12, 15, 19, 17, 16, 20, 21, 22, 23], sum weight = 292393 +INFO:Topology:Partition 1 : [18, 24, 28, 31, 32, 29, 30, 25, 26, 27, 33, 35, 34, 38, 36, 37, 39, 49, 40, 44, 45, 41, 50, 42, 43, 46, 47, 48, 51, 52, 56, 57], sum weight = 296611 +INFO:Topology:Partition 2 : [53, 61, 54, 58, 59, 60, 62, 55, 63, 68, 65, 73, 66, 64, 69, 67, 74, 70, 71, 72, 75, 80, 76, 81, 85, 82, 83, 77, 86], sum weight = 286608 +INFO:Topology:Partition 3 : [78, 79, 84, 87, 94, 90, 91, 88, 89, 92, 93, 95, 96, 106, 101, 102, 104, 107, 103, 97, 99, 105, 98, 100, 108, 114, 119, 120, 115, 117, 110, 116, 118, 112, 109, 111, 113, 121, 122, 123, 124, 125], sum weight = 295394 +INFO:Topology:Session Graph: +[[ 0 1 0 0] + [ 0 0 1 0] + [ 0 0 0 1] + [ 0 0 0 0]] +INFO:Topology:Edge cut: 12 +INFO:Topology:Memory overhead (bytes): 4403136 + +$ cat /tmp/nnpackage/inception_v3/partition_map.json +{"partition_map": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], "num_partitions": 4} +$ +``` diff --git a/tools/model_partition_tool/graph_analysis.py b/tools/model_partition_tool/graph_analysis.py new file mode 100644 index 000000000..18229b385 --- /dev/null +++ b/tools/model_partition_tool/graph_analysis.py @@ -0,0 +1,104 @@ +#! /usr/bin/python + +import sys +import numpy as np +import tensorflow as tf +"""Return list of operations from TFLite model +""" + + +def get_model_ops(tflite_file): + intr = tf.lite.Interpreter(tflite_file) + intr.allocate_tensors() + ops = intr._get_ops_details() + return ops + + +"""Return list of tensors from TFLite model +""" + + +def get_model_tensors(tflite_file): + intr = tf.lite.Interpreter(tflite_file) + intr.allocate_tensors() + tensors = intr.get_tensor_details() + return tensors + + +"""Generate binary adjacency matrix from a tflite model. The adjacency matrix is symmetric and +undirected. +""" + + +def generate_adj_matrix(tflite_file): + intr = tf.lite.Interpreter(tflite_file) + intr.allocate_tensors() + ops = intr._get_ops_details() + adj_mat = np.zeros((len(ops), len(ops)), dtype=int) + for i in range(len(ops) - 1): + for j in range(i + 1, len(ops)): + if i != j: + if len(set(ops[i]['outputs']).intersection(set(ops[j]['inputs']))) > 0: + adj_mat[i][j] = 1 + adj_mat[j][i] = 1 + if len(set(ops[i]['inputs']).intersection(set(ops[j]['outputs']))) > 0: + adj_mat[i][j] = 1 + adj_mat[j][i] = 1 + return adj_mat + + +"""Generate directed acyclic graph (DAG) from a tflite model. +""" + + +def generate_dag(tflite_file): + intr = tf.lite.Interpreter(tflite_file) + intr.allocate_tensors() + ops = intr._get_ops_details() + adj_mat = np.zeros((len(ops), len(ops)), dtype=int) + for i in range(len(ops) - 1): + for j in range(i + 1, len(ops)): + if i != j: + if len(set(ops[i]['outputs']).intersection(set(ops[j]['inputs']))) > 0: + adj_mat[i][j] = 1 + if len(set(ops[i]['inputs']).intersection(set(ops[j]['outputs']))) > 0: + adj_mat[j][i] = 1 + return adj_mat + + +"""Generate Compressed Sparse Row format (CSR) of a adjacency matrix. Details on CSR are given at +https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format). +""" + + +def get_csr(adj_matrix): + row_ptr = [] + col_ind = [] + assert (adj_matrix.shape[0] == adj_matrix.shape[1]) + n = adj_matrix.shape[0] + cnt = 0 + for i in range(n): + first = True + for j in range(n): + if adj_matrix[i][j] == 1: + col_ind.append(j) + if first == True: + first = False + row_ptr.append(cnt) + cnt += 1 + row_ptr.append(cnt) + return row_ptr, col_ind + + +"""Perform basic spectral clustering given a tflite model. The graph in this case is symmetric, undirected with +unit weight per edge. Therefore, the spectral clustering is performed on a binary (0-1) adjacency matrix derived +from the tflite model. +""" + + +def spectral_cluster(tflite_file): + adj_matrix = generate_adj_matrix(tflite_file) + L = np.diag(np.sum(adj_matrix, axis=0)) - adj_matrix + e_val, e_vec = np.linalg.eig(L) + vecs = e_vec[:, np.argsort(e_val)] + return vecs.T[1] diff --git a/tools/model_partition_tool/runtime_stats.py b/tools/model_partition_tool/runtime_stats.py new file mode 100644 index 000000000..c8d9b83d7 --- /dev/null +++ b/tools/model_partition_tool/runtime_stats.py @@ -0,0 +1,30 @@ +#! /usr/bin/python +import json +from queue import LifoQueue + + +def get_runtime_per_operation(trace_file): + with open(trace_file) as ifile: + data = json.load(ifile) + traceEvents = data['traceEvents'] + time_val = {} + stack = LifoQueue(maxsize=1000) + for t in traceEvents: + if t == {}: + continue + if (t["name"].lower() != "graph" and "permute" not in t["name"].lower()) and \ + ("subg" not in t["name"].lower() and "permute" not in t["name"].lower()): + if t["ph"] == "B": + stack.put((t["name"], int(t["ts"]))) + elif t["ph"] == "E": + opname, st_time = stack.get() + assert (opname == t["name"]) + if "$" in t["name"]: + time_val[int( + t["name"].split(" ")[0].lstrip('$'))] = int(t["ts"]) - st_time + else: + time_val[int( + t["name"].split(" ")[0].lstrip('@'))] = int(t["ts"]) - st_time + + time_idx = [y for x, y in (sorted(time_val.items(), key=lambda item: item[0]))] + return time_idx diff --git a/tools/model_partition_tool/test_partition.py b/tools/model_partition_tool/test_partition.py new file mode 100644 index 000000000..553e54c55 --- /dev/null +++ b/tools/model_partition_tool/test_partition.py @@ -0,0 +1,21 @@ +#! /usr/bin/python + +import argparse +import os +import Graph + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + "test_partition.py", description="Example code to partition models") + parser.add_argument("modelfile", type=str, help="TFLite file with path") + parser.add_argument("tracefile", type=str, help="Chrome trace file with path") + parser.add_argument("--num_parts", type=int, default=2, help="Number of partitions") + parser.add_argument( + "--num_runs", type=int, default=10, help="Number of runs (topological orderings)") + + # Parse arguments + args = parser.parse_args() + + # Partition + g = Graph.GraphTopology(args.modelfile, args.tracefile) + g.partition_minmax_multiple(K=args.num_parts, nruns=args.num_runs) diff --git a/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh b/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh index f4e223a9d..2b26e4068 100755 --- a/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh +++ b/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh @@ -27,7 +27,7 @@ usage() { } if [ $# -eq 0 ]; then - echo "For help, type $progname -h" + >&2 echo "For help, type $progname -h" exit 1 fi @@ -44,21 +44,21 @@ done shift $((OPTIND-1)) if [ $# -ne 1 ]; then - echo "error: wrong argument (no argument or too many arguments)." - echo "For help, type $progname -h" + >&2 echo "error: wrong argument (no argument or too many arguments)." + >&2 echo "For help, type $progname -h" exit 1 fi modelfile=$(basename "$1") if [[ "$modelfile" != *.* ]]; then - echo "error: modelfile does not have extension." - echo "Please provide extension so that $progname can identify what type of model you use." + >&2 echo "error: modelfile does not have extension." + >&2 echo "Please provide extension so that $progname can identify what type of model you use." exit 1 fi if [ ! -e $1 ]; then - echo "error: "$1" does not exist." + >&2 echo "error: "$1" does not exist." exit 1 fi @@ -67,7 +67,7 @@ if [ -z "$name" ]; then fi extension=${modelfile##*.} -echo "Generating nnpackage "$name" in "$outdir"" +echo "$progname: Generating nnpackage "$name" in "$outdir"" mkdir -p "$outdir"/"$name"/metadata if [ -s "$config_src" ]; then diff --git a/tools/nnpackage_tool/tflite2circle/README.md b/tools/nnpackage_tool/tflite2circle/README.md deleted file mode 100644 index 94ef5068c..000000000 --- a/tools/nnpackage_tool/tflite2circle/README.md +++ /dev/null @@ -1,28 +0,0 @@ -# tflite2circle - -`tflite2circle` is a tool to convert tflite into circle. - -## Usage - -``` -Usage: tflite2circle.sh [options] tflite -Convert tflite to circle - -Returns - 0 success - non-zero failure - -Options: - -h show this help - -o set output directory (default=.) - -Environment variables: - flatc path to flatc - (default=./build/externals/FLATBUFFERS/build/flatc) - tflite_schema path to schema.fbs - (default=./externals/TENSORFLOW-1.12/tensorflow/contrib/lite/schema/schema.fbs) - -Examples: - tflite2circle.sh Add_000.tflite => convert Add_000.tflite into Add_000.circle - tflite2circle.sh -o my/circles Add_000 => convert Add_000.tflite into my/circles/Add_000.circle -``` diff --git a/tools/nnpackage_tool/tflite2circle/fuse_instance_norm.js b/tools/nnpackage_tool/tflite2circle/fuse_instance_norm.js deleted file mode 100644 index ef1633d3c..000000000 --- a/tools/nnpackage_tool/tflite2circle/fuse_instance_norm.js +++ /dev/null @@ -1,230 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -'use strict' - -// read json and parse -const fs = require('fs') -let inputfile = "./03_2k.json" -if (process.argv.length == 3) - inputfile = process.argv[2] -let raw = fs.readFileSync(inputfile) -let model = JSON.parse(raw) - -// 0. prepare shortcut variables with object destructuring -const { operators, tensors } = model.subgraphs[0] - -//-------------------------------------------------------------------------- -// 0. construct infra - -// List : opcode index (number) => op name (string) -let opcodeIdxToOpName = [] -for (const opcode of model.operator_codes) { - opcodeIdxToOpName.push(opcode.builtin_code) -} - -// List: tensor index (number) => producing operator's index (number) -// assume there is only one op that produces given output tensor. -let defOp = [] -for (let i = 0; i < operators.length; ++i) { - let op = operators[i] - if (op.outputs.length !== 1) { - console.debug("Assumption failed. Multiple output operator exists.") - process.exit(-1); - } - defOp[op.outputs[0]] = i -} - -// List: tensor index (number) => consuming operator indices (list of number) -// Note that there may be multiple consumer ops for a given tensor index -let useOps = [] -for (let i = 0; i < operators.length; ++i) { - let op = operators[i] - for (let inTensorIdx of op.inputs) { - if (useOps[inTensorIdx]) - useOps[inTensorIdx].push(i) - else - useOps[inTensorIdx] = [ i ] - } -} - -// return operator that defines the given tensor index -function getDefOp(iTensor) { - return defOp[iTensor] === undefined ? undefined : operators[defOp[iTensor]] -} - -function getUseOps(iTensor) { - if (useOps[iTensor] === undefined) - return undefined - let ret = [] - for (let i of useOps[iTensor]) - ret.push(operators[i]) - return ret -} - -function opeq(op, str) { - return op === undefined ? undefined : opcodeIdxToOpName[op.opcode_index] === str -} - -function hasUndefined() { - for (let arg of arguments) - if (arg === undefined) - return true - return false -} - -//-------------------------------------------------------------------------- -// find SquaredDifference as starting point -let squaredDifferenceIdxList = [] -for (let i = 0; i < operators.length; ++i) { - if (opeq(operators[i], "SQUARED_DIFFERENCE")) - squaredDifferenceIdxList.push(i) -} - -let instanceNormList = [ ] -for (let idx of squaredDifferenceIdxList) { - const sqd1 = operators[idx] - const findMean0AndInstanceNormInputTensor = function(sqd1) { - let mean0, iInstanceNormInputTensor - for (let i = 0; i < sqd1.inputs.length; ++i) { - let op = getDefOp(sqd1.inputs[i]) - if (opeq(op, "MEAN")) { - mean0 = op - // let's check one of inputs are instance_norm - // the other input is axis of mean operator. - for (let j = 0; j < mean0.inputs.length; ++j) { - // 1 - i means the other input of squared_difference. - if (mean0.inputs[j] === sqd1.inputs[1 - i]) { - iInstanceNormInputTensor = mean0.inputs[j] - } - if (!hasUndefined(iInstanceNormInputTensor)) break // found instance_norm - } - } - if (!hasUndefined(mean0, iInstanceNormInputTensor)) break - } - return [mean0, iInstanceNormInputTensor] - } - const [mean0, iInstanceNormInputTensor] = findMean0AndInstanceNormInputTensor(sqd1) - if (hasUndefined(mean0, iInstanceNormInputTensor)) continue - - const findConsumer = function(op, expectedOp) { - let ops = getUseOps(op.outputs[0]) - if (ops === undefined || ops.length !== 1 || !opeq(ops[0], expectedOp)) - return undefined - return ops[0] - } - const mean2 = findConsumer(sqd1, "MEAN") - if (hasUndefined(mean2)) continue - - const add3 = findConsumer(mean2, "ADD") - if (hasUndefined(add3)) continue - - const isScalar = function(tsr) { return tsr.shape.length === 0 } - const is1D = function(tsr) { return tsr.shape.length === 1 } - const isFloat32 = function(tsr) { return tsr.type === "FLOAT32" } - const asFloat32 = function(arr) { return new Float32Array(new Uint8Array(arr).buffer)[0]; } - const getFloatScalarValueFromInputsOf = function(op) { - for (let i of op.inputs) { - if (isScalar(tensors[i]) && isFloat32(tensors[i])) { - let buf = model.buffers[tensors[i].buffer] - if (buf.data && buf.data.length === 4) - return asFloat32(buf.data) - } - } - return undefined - } - const epsilon = getFloatScalarValueFromInputsOf(add3) - if (hasUndefined(epsilon)) continue - - const rsqrt4 = findConsumer(add3, "RSQRT") - if (hasUndefined(rsqrt4)) continue - - const mul5 = findConsumer(rsqrt4, "MUL") - if (hasUndefined(mul5)) continue - - const getFloat1DTensorIdxFromInputsOf = function(op) { - for (let i of op.inputs) { - if (is1D(tensors[i]) && isFloat32(tensors[i])) - return i - } - return undefined - } - const iGamma = getFloat1DTensorIdxFromInputsOf(mul5) - if (hasUndefined(iGamma)) continue - - let mul6, mul7 - for (let i of useOps[mul5.outputs[0]]) { - const op = operators[i] - if (opcodeIdxToOpName[op.opcode_index] !== "MUL") - break; - const otherInput = op.inputs[0] === mul5.outputs[0] ? op.inputs[1] : op.inputs[0] - if (otherInput === iInstanceNormInputTensor) - mul6 = op - else if (otherInput === mean0.outputs[0]) - mul7 = op - } - if (hasUndefined(mul6, mul7)) continue - - const sub8 = findConsumer(mul7, "SUB") - if (hasUndefined(sub8)) continue - - const iBeta = getFloat1DTensorIdxFromInputsOf(sub8) - if (hasUndefined(iBeta)) continue - - const add9 = findConsumer(sub8, "ADD") - if (hasUndefined(add9)) continue - - const add9_2 = findConsumer(mul6, "ADD") - if (hasUndefined(add9_2)) continue - - if (add9 !== add9_2) - continue - - const getActivation = function(op) { - return op.builtin_options.fused_activation_function - } - const activation = getActivation(add9) - if (hasUndefined(activation)) continue - - //-------------------------------------------------------------------------- - // convert to instance norm - let instanceNormOpcodeIdx = model.operator_codes.findIndex(o => { return o.builtin_code === "INSTANCE_NORM" }) - opcodeIdxToOpName.indexOf('INSTANCE_NORM') - if (instanceNormOpcodeIdx === -1) { - model.operator_codes.push( { "builtin_code": "INSTANCE_NORM", "version": 1 } ) - instanceNormOpcodeIdx = model.operator_codes.length - 1; - } - // construct instance norm operator - let instanceNorm = { - "opcode_index": instanceNormOpcodeIdx, - "inputs": [ iInstanceNormInputTensor, iGamma, iBeta ], - "outputs": [ add9.outputs[0] ], - "builtin_options": { "epsilon": epsilon, "fused_activation_function": activation }, - "builtin_options_type": "InstanceNormOptions", - "custom_options_format": "FLEXBUFFERS", - "mutating_variable_inputs": [], - } - // add instance norm after removing 0~9 nodes - instanceNormList.push(instanceNorm) -} // end of sqd1 -let adjust = 0 -for (let i = 0; i < squaredDifferenceIdxList.length; ++i) { - let idx = squaredDifferenceIdxList[i] + adjust - operators.splice(idx - 1, 10, instanceNormList[i]) - adjust += -9 -} -let raw_fused = JSON.stringify(model) -fs.writeFileSync(inputfile+".fused", raw_fused); diff --git a/tools/nnpackage_tool/tflite2circle/tflite2circle.sh b/tools/nnpackage_tool/tflite2circle/tflite2circle.sh deleted file mode 100755 index 409c05832..000000000 --- a/tools/nnpackage_tool/tflite2circle/tflite2circle.sh +++ /dev/null @@ -1,79 +0,0 @@ -#!/bin/bash - -set -u - -progname=$(basename "${BASH_SOURCE[0]}") -script_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" -nnfw_root="$( cd "${script_dir%*/*/*/*}" && pwd )" -outdir="." -flatc=${flatc:-"$nnfw_root/build/externals/FLATBUFFERS/build/flatc"} -tflite_schema=${tflite_schema:-"$nnfw_root/externals/TENSORFLOW-1.13.1/tensorflow/lite/schema/schema.fbs"} -circle_schema=${circle_schema:-"$nnfw_root/nnpackage/schema/circle_schema.fbs"} - -if ! [ -x "$flatc" ]; then - echo "Please make sure `flatc` is in path." - exit 2 -fi - -if ! { [ -e "$tflite_schema" ] && [ -e "$circle_schema" ]; }; then - echo "Please make sure that the `*.fbs` paths are set properly." - exit 3 -fi - -usage() { - echo "Usage: $progname [options] tflite" - echo "Convert tflite to circle" - echo "" - echo "Returns" - echo " 0 success" - echo " non-zero failure" - echo "" - echo "Options:" - echo " -h show this help" - echo " -o set output directory (default=$outdir)" - echo "" - echo "Environment variables:" - echo " flatc path to flatc" - echo " (default=./build/externals/FLATBUFFERS/build/flatc)" - echo " tflite_schema path to tflite schema (i.e. schema.fbs)" - echo " (default=./externals/TENSORFLOW-1.12/tensorflow/contrib/lite/schema/schema.fbs)" - echo " circle_schema path to circle schema" - echo " (default=./nnpackage/schema/circle_schema.fbs)" - echo "" - echo "Examples:" - echo " $progname Add_000.tflite => convert Add_000.tflite into Add_000.circle" - echo " $progname -o my/circles Add_000 => convert Add_000.tflite into my/circles/Add_000.circle" - exit 1 -} - -if [ $# -eq 0 ]; then - echo "For help, type $progname -h" - exit 1 -fi - -while getopts "ho:" OPTION; do -case "${OPTION}" in - h) usage;; - o) outdir=$OPTARG;; - ?) exit 1;; -esac -done - -shift $((OPTIND-1)) - -if [ $# -ne 1 ]; then - echo "error: wrong argument (no argument or too many arguments)." - echo "For help, type $progname -h" - exit 1 -fi - -tflite_base=$(basename "$1") -name=${tflite_base%.*} - -# convert - -mkdir -p "${outdir}" -${flatc} -o ${outdir} --strict-json -t ${tflite_schema} -- $1 -${script_dir}/tflitejson2circlejson.py "${outdir}/${name}.json" > "${outdir}/${name}.circle" -${flatc} -o ${outdir} -b ${circle_schema} "${outdir}/${name}.circle" -rm -f ${outdir}/${name}.json diff --git a/tools/nnpackage_tool/tflite2circle/tflitejson2circlejson.py b/tools/nnpackage_tool/tflite2circle/tflitejson2circlejson.py deleted file mode 100755 index 272463fb0..000000000 --- a/tools/nnpackage_tool/tflite2circle/tflitejson2circlejson.py +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/python3 - -# Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -import os -import sys -from collections import OrderedDict - - -def usage(): - script = os.path.basename(os.path.basename(__file__)) - print("Usage: {} path_to_tflite_in_json".format(script)) - sys.exit(-1) - - -if __name__ == '__main__': - if len(sys.argv) != 2: - usage() - - json_path = sys.argv[1] - with open(json_path, "r") as f: - try: - json_dict = json.load(f, object_pairs_hook=OrderedDict) - json_dict["version"] = 0 - print(json.dumps(json_dict, indent=2)) - except KeyError: - print("subgraphs attribute does not exist.") - sys.exit(-2) diff --git a/tools/release_tool/onert_version.sh b/tools/release_tool/onert_version.sh index 55ac033ef..5c875e38b 100755 --- a/tools/release_tool/onert_version.sh +++ b/tools/release_tool/onert_version.sh @@ -27,11 +27,7 @@ show_version() { current_version=${version_line#"Version:"} if [ $nightly -eq 0 ]; then - # Get head commit's date - pushd $nnfw_root > /dev/null - date=$(git log -1 --format=%ad --date=format:%y%m%d) - echo $current_version-nightly-$date - popd > /dev/null + echo $current_version~$(date "+%y%m%d%H") else echo $current_version fi diff --git a/tools/tflitefile_tool/requirements.txt b/tools/tflitefile_tool/requirements.txt index 9b4366ae5..2c75ce04d 100644 --- a/tools/tflitefile_tool/requirements.txt +++ b/tools/tflitefile_tool/requirements.txt @@ -1,2 +1,2 @@ -flatbuffers>=1.12 +flatbuffers==1.12 numpy |