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Diffstat (limited to 'compiler/luci-interpreter/src/kernels/Fill.test.cpp')
| -rw-r--r-- | compiler/luci-interpreter/src/kernels/Fill.test.cpp | 169 |
1 files changed, 169 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/kernels/Fill.test.cpp b/compiler/luci-interpreter/src/kernels/Fill.test.cpp new file mode 100644 index 000000000..cf56df507 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Fill.test.cpp @@ -0,0 +1,169 @@ +/* + * Copyright (c) 2022 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES 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/Fill.h" +#include "kernels/TestUtils.h" +#include "luci_interpreter/TestMemoryManager.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +class FillTest : public ::testing::Test +{ +protected: + void SetUp() override { _memory_manager = std::make_unique<TestMemoryManager>(); } + + std::unique_ptr<IMemoryManager> _memory_manager; +}; + +template <typename T, DataType DT> void runFillIntKernel(IMemoryManager *memory_manager) +{ + Shape dims_shape{2}; + + std::vector<int32_t> dims_data = {2, 3}; + std::vector<T> value_data = {5}; + + Tensor dims = makeInputTensor<loco::DataType::S32>(dims_shape, dims_data, memory_manager); + Tensor value = makeInputTensor<DT>(/*scalar*/ {}, value_data, memory_manager); + + Tensor output_tensor = makeOutputTensor(DT); + + Fill kernel(&dims, &value, &output_tensor); + + kernel.configure(); + memory_manager->allocate_memory(output_tensor); + kernel.execute(); + + std::vector<T> ref_output_data{5, 5, 5, 5, 5, 5}; + EXPECT_THAT(extractTensorData<T>(output_tensor), ref_output_data); + + std::vector<int32_t> ref_output_shape{2, 3}; + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +template <DataType DT> void runFillQuantIntKernel(IMemoryManager *memory_manager) +{ + Shape dims_shape{2}; + + std::vector<int32_t> dims_data = {2, 3}; + std::vector<float> value_data = {5}; + + int32_t zero_point = 0; + + if (DT == loco::DataType::S8) + zero_point = 1; + + Tensor dims = makeInputTensor<loco::DataType::S32>(dims_shape, dims_data, memory_manager); + Tensor value = makeInputTensor<DT>(/*scalar*/ {}, /*scale*/ 0.25, /*zero_point*/ zero_point, + value_data, memory_manager); + + Tensor output_tensor = makeOutputTensor(DT, /*scale*/ 0.25, /*zero_point*/ zero_point); + + Fill kernel(&dims, &value, &output_tensor); + + kernel.configure(); + memory_manager->allocate_memory(output_tensor); + kernel.execute(); + + std::vector<float> ref_output_data{5, 5, 5, 5, 5, 5}; + EXPECT_THAT(dequantizeTensorData(output_tensor), FloatArrayNear(ref_output_data)); + + std::vector<int32_t> ref_output_shape{2, 3}; + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST_F(FillTest, FillInt) +{ + // Run for int32_t input + runFillIntKernel<int32_t, loco::DataType::S32>(_memory_manager.get()); + // Run for int64_t input + runFillIntKernel<int64_t, loco::DataType::S64>(_memory_manager.get()); + // Run for int8_t input + runFillQuantIntKernel<loco::DataType::S8>(_memory_manager.get()); + // Run for int16_t input + runFillQuantIntKernel<loco::DataType::S16>(_memory_manager.get()); + + SUCCEED(); +} + +TEST_F(FillTest, FillFloat) +{ + Shape dims_shape{3}; + + std::vector<int64_t> dims_data = {2, 2, 2}; + std::vector<float> value_data = {5}; + + Tensor dims = makeInputTensor<loco::DataType::S64>(dims_shape, dims_data, _memory_manager.get()); + Tensor value = + makeInputTensor<loco::DataType::FLOAT32>(/*scalar*/ {}, value_data, _memory_manager.get()); + + Tensor output_tensor = makeOutputTensor(loco::DataType::FLOAT32); + + Fill kernel(&dims, &value, &output_tensor); + + kernel.configure(); + _memory_manager->allocate_memory(output_tensor); + kernel.execute(); + + std::vector<float> ref_output_data{5, 5, 5, 5, 5, 5, 5, 5}; + + std::vector<int32_t> ref_output_shape{2, 2, 2}; + EXPECT_THAT(extractTensorData<float>(output_tensor), ref_output_data); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST_F(FillTest, Invalid_Input_Shape_NEG) +{ + Shape dims_shape{1, 3}; + + std::vector<int32_t> dims_data = {2, 2, 2}; + std::vector<float> value_data = {5}; + + Tensor dims = makeInputTensor<loco::DataType::S32>(dims_shape, dims_data, _memory_manager.get()); + Tensor value = + makeInputTensor<loco::DataType::FLOAT32>(/*scalar*/ {}, value_data, _memory_manager.get()); + + Tensor output_tensor = makeOutputTensor(loco::DataType::FLOAT32); + + Fill kernel(&dims, &value, &output_tensor); + EXPECT_ANY_THROW(kernel.configure()); +} + +TEST_F(FillTest, Invalid_Value_Shape_NEG) +{ + Shape dims_shape{3}; + + std::vector<int32_t> dims_data = {2, 2, 2}; + std::vector<float> value_data = {5}; + + Tensor dims = makeInputTensor<loco::DataType::S32>(dims_shape, dims_data, _memory_manager.get()); + Tensor value = makeInputTensor<loco::DataType::FLOAT32>({1}, value_data, _memory_manager.get()); + + Tensor output_tensor = makeOutputTensor(loco::DataType::FLOAT32); + + Fill kernel(&dims, &value, &output_tensor); + EXPECT_ANY_THROW(kernel.configure()); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter |