diff options
Diffstat (limited to 'libs/tflite/src/ext')
| -rw-r--r-- | libs/tflite/src/ext/kernels/Abs.cpp | 103 | ||||
| -rw-r--r-- | libs/tflite/src/ext/kernels/SquaredDifference.cpp | 112 | ||||
| -rw-r--r-- | libs/tflite/src/ext/kernels/TensorFlowMax.cpp | 405 | ||||
| -rw-r--r-- | libs/tflite/src/ext/kernels/TensorFlowSum.cpp | 400 | ||||
| -rw-r--r-- | libs/tflite/src/ext/kernels/register.cpp | 221 | ||||
| -rw-r--r-- | libs/tflite/src/ext/nnapi_delegate.cpp | 1209 | ||||
| -rw-r--r-- | libs/tflite/src/ext/nnapi_delegate_ex_AddOpsAndParams_lambda.inc | 106 |
7 files changed, 0 insertions, 2556 deletions
diff --git a/libs/tflite/src/ext/kernels/Abs.cpp b/libs/tflite/src/ext/kernels/Abs.cpp deleted file mode 100644 index 7e9c2338d..000000000 --- a/libs/tflite/src/ext/kernels/Abs.cpp +++ /dev/null @@ -1,103 +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/Abs.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <iostream> -#include <cmath> - -namespace nnfw -{ -namespace tflite -{ -namespace custom -{ -namespace Abs -{ - -void *InitAbs(TfLiteContext *context, const char *buffer, size_t length) { return nullptr; } - -void FreeAbs(TfLiteContext *context, void *buffer) {} - -TfLiteStatus PrepareAbs(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); - - const TfLiteTensor *input = ::tflite::GetInput(context, node, 0); - TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); - - TF_LITE_ENSURE_EQ(context, input->type, output->type); - - return context->ResizeTensor(context, output, TfLiteIntArrayCopy(input->dims)); -} - -TfLiteStatus EvalAbs(TfLiteContext *context, TfLiteNode *node) -{ - const TfLiteTensor *input = ::tflite::GetInput(context, node, 0); - TfLiteTensor *output = ::tflite::GetOutput(context, node, 0); - size_t elements = ::tflite::NumElements(input); - switch (input->type) - { - case kTfLiteFloat32: - { - auto *in = input->data.f; - auto *in_end = in + elements; - auto *out = output->data.f; - for (; in < in_end; in++, out++) - *out = std::abs(*in); - return kTfLiteOk; - } - case kTfLiteInt32: - { - auto *in = input->data.i32; - auto *in_end = in + elements; - auto *out = output->data.i32; - for (; in < in_end; in++, out++) - *out = std::abs(*in); - return kTfLiteOk; - } - case kTfLiteInt64: - { - auto *in = input->data.i64; - auto *in_end = in + elements; - auto *out = output->data.i64; - for (; in < in_end; in++, out++) - *out = std::abs(*in); - return kTfLiteOk; - } - case kTfLiteUInt8: - { - auto *in = input->data.uint8; - auto *in_end = in + elements; - auto *out = output->data.uint8; - for (; in < in_end; in++, out++) - *out = std::abs(*in); - return kTfLiteOk; - } - default: - { - context->ReportError(context, "Input type %d is not supported", input->type); - return kTfLiteError; - } - } -} - -} // namespace Abs -} // namespace custom -} // namespace tflite -} // namespace nnfw diff --git a/libs/tflite/src/ext/kernels/SquaredDifference.cpp b/libs/tflite/src/ext/kernels/SquaredDifference.cpp deleted file mode 100644 index 8ac2b1de0..000000000 --- a/libs/tflite/src/ext/kernels/SquaredDifference.cpp +++ /dev/null @@ -1,112 +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/contrib/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace nnfw -{ -namespace tflite -{ -namespace custom -{ -namespace SquaredDifference -{ - -void *InitSquaredDifference(TfLiteContext *context, const char *buffer, size_t length) -{ - return nullptr; -} - -void FreeSquaredDifference(TfLiteContext *context, void *buffer) {} - -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/libs/tflite/src/ext/kernels/TensorFlowMax.cpp b/libs/tflite/src/ext/kernels/TensorFlowMax.cpp deleted file mode 100644 index d72ad242c..000000000 --- a/libs/tflite/src/ext/kernels/TensorFlowMax.cpp +++ /dev/null @@ -1,405 +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/TensorFlowMax.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace nnfw -{ -namespace tflite -{ -namespace custom -{ -namespace TensorFlowMax -{ - -struct TensorFlowMaxOp -{ - TensorFlowMaxOp(TfLiteContext *context, TfLiteNode *node) - { - input = ::tflite::GetInput(context, node, 0); - axis = ::tflite::GetInput(context, node, 1); - output = ::tflite::GetOutput(context, node, 0); - } - const TfLiteTensor *input; - const TfLiteTensor *axis; - TfLiteTensor *output; -}; - -void *InitTensorFlowMax(TfLiteContext *context, const char *buffer, size_t length) -{ - // Creates two temp tensors to store index and axis for internal - // implementation only. - auto *scratch_tensor_index = new int; - context->AddTensors(context, 2, scratch_tensor_index); - return scratch_tensor_index; -} - -void FreeTensorFlowMax(TfLiteContext *context, void *buffer) -{ - delete static_cast<TensorFlowMaxOp *>(buffer); -} - -// Resizes the temp tensor that stores resolved axis. -TfLiteStatus ResizeTempAxis(TfLiteContext *context, TensorFlowMaxOp *op_context, - TfLiteTensor *resolved_axis) -{ - TfLiteIntArray *axis_size = TfLiteIntArrayCreate(1); - axis_size->data[0] = static_cast<int>(::tflite::NumElements(op_context->axis)); - return context->ResizeTensor(context, resolved_axis, axis_size); -} - -// Resizes output array based on the input size and resolved axis. -TfLiteStatus ResizeOutputTensor(TfLiteContext *context, TensorFlowMaxOp *op_context) -{ - size_t num_axis = ::tflite::NumElements(op_context->axis); - TfLiteIntArray *input_dims = op_context->input->dims; - int input_num_dims = ::tflite::NumDimensions(op_context->input); - const int *axis = op_context->axis->data.i32; - - { - // Calculates size of reducing axis. - int num_reduce_axis = num_axis; - for (int i = 0; i < num_axis; ++i) - { - int current = axis[i]; - if (current < 0) - { - current += input_num_dims; - } - TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims); - for (int j = 0; j < i; ++j) - { - int previous = axis[j]; - if (previous < 0) - { - previous += input_num_dims; - } - if (current == previous) - { - --num_reduce_axis; - break; - } - } - } - // Determines output dimensions. - int output_num_dims = ::tflite::NumDimensions(op_context->output); - TF_LITE_ENSURE(context, (input_num_dims == output_num_dims) || - (input_num_dims - num_reduce_axis == output_num_dims)); - - if (input_num_dims == output_num_dims) - { - TfLiteIntArray *output_dims = TfLiteIntArrayCopy(input_dims); - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - int current = axis[axis_idx]; - output_dims->data[current] = 1; - } - return context->ResizeTensor(context, op_context->output, output_dims); - } - else - { - TfLiteIntArray *output_dims = TfLiteIntArrayCreate(output_num_dims); - int num_skip_axis = 0; - for (int idx = 0; idx < input_num_dims; ++idx) - { - bool is_axis = false; - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) - { - ++num_skip_axis; - is_axis = true; - break; - } - } - if (!is_axis) - { - output_dims->data[idx - num_skip_axis] = input_dims->data[idx]; - } - } - return context->ResizeTensor(context, op_context->output, output_dims); - } - } -} - -// Initializes temp tensors to store index and resolved axis. -TfLiteStatus InitializeTemporaries(TfLiteContext *context, TfLiteNode *node, - TensorFlowMaxOp *op_context) -{ - // Creates a temp index to iterate through input data. - int *scratch_tensor_index = reinterpret_cast<int *>(node->user_data); - TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(2); - node->temporaries->data[0] = *scratch_tensor_index; - TfLiteTensor *scratch_tensor = &context->tensors[node->temporaries->data[0]]; - scratch_tensor->type = kTfLiteInt32; - scratch_tensor->allocation_type = kTfLiteArenaRw; - TfLiteIntArray *index_size = TfLiteIntArrayCreate(1); - index_size->data[0] = ::tflite::NumDimensions(op_context->input); - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, index_size)); - - // Creates a temp tensor to store resolved axis given input data. - node->temporaries->data[1] = *scratch_tensor_index + 1; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - resolved_axis->type = kTfLiteInt32; - return kTfLiteOk; -} - -TfLiteStatus PrepareTensorFlowMax(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); - - TensorFlowMaxOp op_context(context, node); - TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); - - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Leaves work to Eval if axis is not constant; else resizes output. - if (!::tflite::IsConstantTensor(op_context.axis)) - { - ::tflite::SetTensorToDynamic(op_context.output); - ::tflite::SetTensorToDynamic(resolved_axis); - return kTfLiteOk; - } - resolved_axis->allocation_type = kTfLiteArenaRw; - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - return ResizeOutputTensor(context, &op_context); -} - -// Gets offset of index if expanded on axis. When expanded, the flattened offset -// will not change, if the output index changes on the given axis. For example, -// if you have a 2D tensor and you are expanding to 3D on axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map from the same flattened -// offset. -inline size_t ExpandedInputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - int out_idx = 0; - for (int in_idx = 0; in_idx < num_dims; ++in_idx) - { - // if we need to expand this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (in_idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[in_idx]) + static_cast<size_t>(index[out_idx]); - out_idx++; - } - else - { - offset = offset * static_cast<size_t>(dims[in_idx]); - } - } - return offset; -} - -// Gets offset of index if reducing on axis. When reducing, the flattened offset -// will not change, if the input index changes on the given axis. For example, -// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened -// offset. -// TODO(kanlig): uses Dims to represent dimensions. -inline size_t ReducedOutputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) - { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[idx]) + static_cast<size_t>(index[idx]); - } - } - return offset; -} - -// Gets next index to iterate through a multidimensional array. -inline bool NextIndex(TfLiteContext *context, const int num_dims, const int *dims, int *current) -{ - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) - { - int current_val = current[idx] + carry; - TF_LITE_ENSURE(context, (dims[idx] >= current_val)); - if (dims[idx] == current_val) - { - current[idx] = 0; - } - else - { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -template <typename T> -inline TfLiteStatus -CustomMax(TfLiteContext *context, T *input_data, const int *input_dims, const int input_num_dims, - T *output_data, const int *output_dims, const int output_num_dims, const int *axis, - const int num_axis_dimensions, bool keep_dims, int *temp_index, int *resolved_axis) -{ - // resolves axis. - int num_resolved_axis = 0; - for (int idx = 0; idx < num_axis_dimensions; ++idx) - { - int current = axis[idx]; - TF_LITE_ENSURE(context, (current < input_num_dims && current + input_num_dims >= 0)); - if (current < 0) - { - current += input_num_dims; - } - bool is_dup = false; - for (int j = 0; j < num_resolved_axis; ++j) - { - if (resolved_axis[j] == current) - { - is_dup = true; - break; - } - } - if (!is_dup) - { - resolved_axis[num_resolved_axis++] = current; - } - } - - TF_LITE_ENSURE(context, (input_num_dims > 0)); - TF_LITE_ENSURE(context, (input_dims != nullptr)); - TF_LITE_ENSURE(context, (temp_index != nullptr)); - - // resets output data. - for (int idx = 0; idx < output_num_dims; ++idx) - { - temp_index[idx] = 0; - } - for (bool has_next = true; has_next; - has_next = NextIndex(context, output_num_dims, output_dims, temp_index)) - { - size_t output_offset = - ReducedOutputOffset(output_num_dims, output_dims, temp_index, 0, nullptr); - size_t input_offset = ExpandedInputOffset(input_num_dims, input_dims, temp_index, - num_resolved_axis, resolved_axis); - output_data[output_offset] = input_data[input_offset]; - } - - // resets temp index. - for (int idx = 0; idx < input_num_dims; ++idx) - { - temp_index[idx] = 0; - } - - // iterates through input_data. - for (bool has_next = true; has_next; - has_next = NextIndex(context, input_num_dims, input_dims, temp_index)) - { - size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr); - size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, - num_resolved_axis, resolved_axis); - if (output_data[output_offset] < input_data[input_offset]) - { - output_data[output_offset] = input_data[input_offset]; - } - } - - return kTfLiteOk; -} - -TfLiteStatus EvalTensorFlowMax(TfLiteContext *context, TfLiteNode *node) -{ - - TensorFlowMaxOp op_context(context, node); - int num_axis = static_cast<int>(::tflite::NumElements(op_context.axis)); - TfLiteTensor *temp_index = &context->tensors[node->temporaries->data[0]]; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Resize the output tensor if the output tensor is dynamic. - if (::tflite::IsDynamicTensor(op_context.output)) - { - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - } - - TfLiteStatus returnStatus = kTfLiteOk; - switch (op_context.input->type) - { - case kTfLiteFloat32: - returnStatus = CustomMax<float>( - context, op_context.input->data.f, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.f, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt32: - returnStatus = CustomMax<int>(context, op_context.input->data.i32, - op_context.input->dims->data, op_context.input->dims->size, - op_context.output->data.i32, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteUInt8: - returnStatus = CustomMax<uint8_t>( - context, op_context.input->data.uint8, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.uint8, - op_context.output->dims->data, op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt64: - returnStatus = CustomMax<int64_t>( - context, op_context.input->data.i64, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.i64, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - default: - returnStatus = kTfLiteError; - } - - return returnStatus; -} - -} // namespace TensorFlowMax -} // namespace custom -} // namespace tflite -} // namespace nnfw diff --git a/libs/tflite/src/ext/kernels/TensorFlowSum.cpp b/libs/tflite/src/ext/kernels/TensorFlowSum.cpp deleted file mode 100644 index cbf97970c..000000000 --- a/libs/tflite/src/ext/kernels/TensorFlowSum.cpp +++ /dev/null @@ -1,400 +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/TensorFlowSum.h" -#include "tensorflow/contrib/lite/kernels/kernel_util.h" - -#include <iostream> - -namespace nnfw -{ -namespace tflite -{ -namespace custom -{ -namespace TensorFlowSum -{ - -struct TensorFlowSumOp -{ - TensorFlowSumOp(TfLiteContext *context, TfLiteNode *node) - { - input = ::tflite::GetInput(context, node, 0); - axis = ::tflite::GetInput(context, node, 1); - output = ::tflite::GetOutput(context, node, 0); - } - const TfLiteTensor *input; - const TfLiteTensor *axis; - TfLiteTensor *output; -}; - -void *InitTensorFlowSum(TfLiteContext *context, const char *buffer, size_t length) -{ - // Creates two temp tensors to store index and axis for internal - // implementation only. - auto *scratch_tensor_index = new int; - context->AddTensors(context, 2, scratch_tensor_index); - return scratch_tensor_index; -} - -void FreeTensorFlowSum(TfLiteContext *context, void *buffer) -{ - delete static_cast<TensorFlowSumOp *>(buffer); -} - -// Resizes the temp tensor that stores resolved axis. -TfLiteStatus ResizeTempAxis(TfLiteContext *context, TensorFlowSumOp *op_context, - TfLiteTensor *resolved_axis) -{ - TfLiteIntArray *axis_size = TfLiteIntArrayCreate(1); - axis_size->data[0] = static_cast<int>(::tflite::NumElements(op_context->axis)); - return context->ResizeTensor(context, resolved_axis, axis_size); -} - -// Resizes output array based on the input size and resolved axis. -TfLiteStatus ResizeOutputTensor(TfLiteContext *context, TensorFlowSumOp *op_context) -{ - size_t num_axis = ::tflite::NumElements(op_context->axis); - TfLiteIntArray *input_dims = op_context->input->dims; - int input_num_dims = ::tflite::NumDimensions(op_context->input); - const int *axis = op_context->axis->data.i32; - - { - // Calculates size of reducing axis. - int num_reduce_axis = num_axis; - for (int i = 0; i < num_axis; ++i) - { - int current = axis[i]; - if (current < 0) - { - current += input_num_dims; - } - TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims); - for (int j = 0; j < i; ++j) - { - int previous = axis[j]; - if (previous < 0) - { - previous += input_num_dims; - } - if (current == previous) - { - --num_reduce_axis; - break; - } - } - } - // Determines output dimensions. - int output_num_dims = ::tflite::NumDimensions(op_context->output); - TF_LITE_ENSURE(context, (input_num_dims == output_num_dims) || - (input_num_dims - num_reduce_axis == output_num_dims)); - - if (input_num_dims == output_num_dims) - { - TfLiteIntArray *output_dims = TfLiteIntArrayCopy(input_dims); - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - int current = axis[axis_idx]; - output_dims->data[current] = 1; - } - return context->ResizeTensor(context, op_context->output, output_dims); - } - else - { - TfLiteIntArray *output_dims = TfLiteIntArrayCreate(output_num_dims); - int num_skip_axis = 0; - for (int idx = 0; idx < input_num_dims; ++idx) - { - bool is_axis = false; - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (axis[axis_idx] == idx || axis[axis_idx] + input_num_dims == idx) - { - ++num_skip_axis; - is_axis = true; - break; - } - } - if (!is_axis) - { - output_dims->data[idx - num_skip_axis] = input_dims->data[idx]; - } - } - return context->ResizeTensor(context, op_context->output, output_dims); - } - } -} - -// Initializes temp tensors to store index and resolved axis. -TfLiteStatus InitializeTemporaries(TfLiteContext *context, TfLiteNode *node, - TensorFlowSumOp *op_context) -{ - // Creates a temp index to iterate through input data. - int *scratch_tensor_index = reinterpret_cast<int *>(node->user_data); - TfLiteIntArrayFree(node->temporaries); - node->temporaries = TfLiteIntArrayCreate(2); - node->temporaries->data[0] = *scratch_tensor_index; - TfLiteTensor *scratch_tensor = &context->tensors[node->temporaries->data[0]]; - scratch_tensor->type = kTfLiteInt32; - scratch_tensor->allocation_type = kTfLiteArenaRw; - TfLiteIntArray *index_size = TfLiteIntArrayCreate(1); - index_size->data[0] = ::tflite::NumDimensions(op_context->input); - TF_LITE_ENSURE_OK(context, context->ResizeTensor(context, scratch_tensor, index_size)); - - // Creates a temp tensor to store resolved axis given input data. - node->temporaries->data[1] = *scratch_tensor_index + 1; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - resolved_axis->type = kTfLiteInt32; - return kTfLiteOk; -} - -TfLiteStatus PrepareTensorFlowSum(TfLiteContext *context, TfLiteNode *node) -{ - TF_LITE_ENSURE_EQ(context, ::tflite::NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, ::tflite::NumOutputs(node), 1); - - TensorFlowSumOp op_context(context, node); - TF_LITE_ENSURE_OK(context, InitializeTemporaries(context, node, &op_context)); - - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Leaves work to Eval if axis is not constant; else resizes output. - if (!::tflite::IsConstantTensor(op_context.axis)) - { - ::tflite::SetTensorToDynamic(op_context.output); - ::tflite::SetTensorToDynamic(resolved_axis); - return kTfLiteOk; - } - resolved_axis->allocation_type = kTfLiteArenaRw; - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - return ResizeOutputTensor(context, &op_context); -} - -// Gets offset of index if expanded on axis. When expanded, the flattened offset -// will not change, if the output index changes on the given axis. For example, -// if you have a 2D tensor and you are expanding to 3D on axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map from the same flattened -// offset. -inline size_t ExpandedInputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - int out_idx = 0; - for (int in_idx = 0; in_idx < num_dims; ++in_idx) - { - // if we need to expand this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (in_idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[in_idx]) + static_cast<size_t>(index[out_idx]); - out_idx++; - } - else - { - offset = offset * static_cast<size_t>(dims[in_idx]); - } - } - return offset; -} - -// Gets offset of index if reducing on axis. When reducing, the flattened offset -// will not change, if the input index changes on the given axis. For example, -// if you have a 3D tensor and you are reducing to 2D by eliminating axis 0, -// then index (0, 1, 2) and index (1, 1, 2) will map to the same flattened -// offset. -// TODO(kanlig): uses Dims to represent dimensions. -inline size_t ReducedOutputOffset(const int num_dims, const int *dims, const int *index, - const int num_axis, const int *axis) -{ - size_t offset = 0; - for (int idx = 0; idx < num_dims; ++idx) - { - // if we need to skip this axis - bool is_axis = false; - if (axis != nullptr) - { - for (int axis_idx = 0; axis_idx < num_axis; ++axis_idx) - { - if (idx == axis[axis_idx]) - { - is_axis = true; - break; - } - } - } - if (!is_axis) - { - offset = offset * static_cast<size_t>(dims[idx]) + static_cast<size_t>(index[idx]); - } - } - return offset; -} - -// Gets next index to iterate through a multidimensional array. -inline bool NextIndex(TfLiteContext *context, const int num_dims, const int *dims, int *current) -{ - int carry = 1; - for (int idx = num_dims - 1; idx >= 0; --idx) - { - int current_val = current[idx] + carry; - TF_LITE_ENSURE(context, (dims[idx] >= current_val)); - if (dims[idx] == current_val) - { - current[idx] = 0; - } - else - { - current[idx] = current_val; - carry = 0; - break; - } - } - return (carry == 0); -} - -template <typename T> -inline TfLiteStatus -CustomSum(TfLiteContext *context, T *input_data, const int *input_dims, const int input_num_dims, - T *output_data, const int *output_dims, const int output_num_dims, const int *axis, - const int num_axis_dimensions, bool keep_dims, int *temp_index, int *resolved_axis) -{ - // resolves axis. - int num_resolved_axis = 0; - for (int idx = 0; idx < num_axis_dimensions; ++idx) - { - int current = axis[idx]; - TF_LITE_ENSURE(context, (current < input_num_dims && current + input_num_dims >= 0)); - if (current < 0) - { - current += input_num_dims; - } - bool is_dup = false; - for (int j = 0; j < num_resolved_axis; ++j) - { - if (resolved_axis[j] == current) - { - is_dup = true; - break; - } - } - if (!is_dup) - { - resolved_axis[num_resolved_axis++] = current; - } - } - - TF_LITE_ENSURE(context, (input_num_dims > 0)); - TF_LITE_ENSURE(context, (input_dims != nullptr)); - TF_LITE_ENSURE(context, (temp_index != nullptr)); - - // resets output data. - for (int idx = 0; idx < output_num_dims; ++idx) - { - temp_index[idx] = 0; - } - for (bool has_next = true; has_next; - has_next = NextIndex(context, output_num_dims, output_dims, temp_index)) - { - size_t output_offset = - ReducedOutputOffset(output_num_dims, output_dims, temp_index, 0, nullptr); - output_data[output_offset] = 0; - } - - // resets temp index. - for (int idx = 0; idx < input_num_dims; ++idx) - { - temp_index[idx] = 0; - } - - // iterates through input_data. - for (bool has_next = true; has_next; - has_next = NextIndex(context, input_num_dims, input_dims, temp_index)) - { - size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr); - size_t output_offset = ReducedOutputOffset(input_num_dims, input_dims, temp_index, - num_resolved_axis, resolved_axis); - output_data[output_offset] += input_data[input_offset]; - } - - return kTfLiteOk; -} - -TfLiteStatus EvalTensorFlowSum(TfLiteContext *context, TfLiteNode *node) -{ - - TensorFlowSumOp op_context(context, node); - int num_axis = static_cast<int>(::tflite::NumElements(op_context.axis)); - TfLiteTensor *temp_index = &context->tensors[node->temporaries->data[0]]; - TfLiteTensor *resolved_axis = &context->tensors[node->temporaries->data[1]]; - // Resize the output tensor if the output tensor is dynamic. - if (::tflite::IsDynamicTensor(op_context.output)) - { - TF_LITE_ENSURE_OK(context, ResizeTempAxis(context, &op_context, resolved_axis)); - TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); - } - - TfLiteStatus returnStatus = kTfLiteOk; - switch (op_context.input->type) - { - case kTfLiteFloat32: - returnStatus = CustomSum<float>( - context, op_context.input->data.f, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.f, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt32: - returnStatus = CustomSum<int>(context, op_context.input->data.i32, - op_context.input->dims->data, op_context.input->dims->size, - op_context.output->data.i32, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteUInt8: - returnStatus = CustomSum<uint8_t>( - context, op_context.input->data.uint8, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.uint8, - op_context.output->dims->data, op_context.output->dims->size, op_context.axis->data.i32, - num_axis, false, temp_index->data.i32, resolved_axis->data.i32); - break; - case kTfLiteInt64: - returnStatus = CustomSum<int64_t>( - context, op_context.input->data.i64, op_context.input->dims->data, - op_context.input->dims->size, op_context.output->data.i64, op_context.output->dims->data, - op_context.output->dims->size, op_context.axis->data.i32, num_axis, false, - temp_index->data.i32, resolved_axis->data.i32); - break; - default: - returnStatus = kTfLiteError; - } - - return returnStatus; -} - -} // namespace TensorFlowSum -} // namespace custom -} // namespace tflite -} // namespace nnfw diff --git a/libs/tflite/src/ext/kernels/register.cpp b/libs/tflite/src/ext/kernels/register.cpp deleted file mode 100644 index b822bd616..000000000 --- a/libs/tflite/src/ext/kernels/register.cpp +++ /dev/null @@ -1,221 +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) -// 'externals/tensorflow/tensorflow/contrib/lite/kernels/register.cc' -#include "tflite/ext/kernels/register.h" -#include "tflite/ext/kernels/CustomOps.h" - -namespace tflite { -namespace ops { -namespace builtin { - -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_SKIP_GRAM(); -TfLiteRegistration *Register_SPACE_TO_DEPTH(); -TfLiteRegistration *Register_GATHER(); -TfLiteRegistration *Register_TRANSPOSE(); -TfLiteRegistration *Register_MEAN(); -TfLiteRegistration *Register_SPLIT(); -TfLiteRegistration *Register_SQUEEZE(); -TfLiteRegistration *Register_STRIDED_SLICE(); -TfLiteRegistration *Register_EXP(); -TfLiteRegistration *Register_TOPK_V2(); -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_GREATER(); -TfLiteRegistration *Register_GREATER_EQUAL(); -TfLiteRegistration *Register_LESS(); -TfLiteRegistration *Register_LESS_EQUAL(); -TfLiteRegistration *Register_FLOOR(); -TfLiteRegistration *Register_NEG(); -TfLiteRegistration *Register_SELECT(); -TfLiteRegistration *Register_SLICE(); -TfLiteRegistration *Register_SIN(); -TfLiteRegistration *Register_TRANSPOSE_CONV(); -TfLiteRegistration *Register_SPARSE_TO_DENSE(); -#ifndef OBS_BUILD -TfLiteRegistration *Register_SUM(); -TfLiteRegistration *Register_REDUCE_MAX(); -TfLiteRegistration *Register_REDUCE_MIN(); -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(); -#endif // OBS_BUILD - -} // namespace builtin -} // namespace ops -} // namespace tflite - -namespace nnfw { -namespace tflite { - -BuiltinOpResolver::BuiltinOpResolver() -{ - // Using namespace directive to minimize diff with upstream tensorflow - using namespace ::tflite::ops::builtin; - using namespace ::tflite; - - 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()); - 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()); - 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()); - 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_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_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_SOFTMAX, Register_LOG_SOFTMAX()); - AddBuiltin(BuiltinOperator_CAST, Register_CAST()); - AddBuiltin(BuiltinOperator_DEQUANTIZE, Register_DEQUANTIZE()); - AddBuiltin(BuiltinOperator_PRELU, Register_PRELU()); - AddBuiltin(BuiltinOperator_MAXIMUM, Register_MAXIMUM()); - AddBuiltin(BuiltinOperator_MINIMUM, Register_MINIMUM()); - AddBuiltin(BuiltinOperator_ARG_MAX, Register_ARG_MAX()); - 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()); -#ifndef OBS_BUILD - AddBuiltin(BuiltinOperator_SUM, Register_SUM()); - AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX()); - AddBuiltin(BuiltinOperator_REDUCE_MIN, Register_REDUCE_MIN()); - AddBuiltin(BuiltinOperator_TRANSPOSE_CONV, Register_TRANSPOSE_CONV()); - 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()); -#endif // OBS_BUILD - - AddCustom("TensorFlowMax", nnfw::tflite::custom::Register_TensorFlowMax()); - AddCustom("SquaredDifference", nnfw::tflite::custom::Register_SquaredDifference()); - AddCustom("TensorFlowSum", nnfw::tflite::custom::Register_TensorFlowSum()); - AddCustom("Abs", nnfw::tflite::custom::Register_Abs()); -} - -} // namespace tflite -} // namespace nnfw diff --git a/libs/tflite/src/ext/nnapi_delegate.cpp b/libs/tflite/src/ext/nnapi_delegate.cpp deleted file mode 100644 index 25858a7b4..000000000 --- a/libs/tflite/src/ext/nnapi_delegate.cpp +++ /dev/null @@ -1,1209 +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 v1.12) -// 'externals/tensorflow/tensorflow/contrib/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> -#ifdef OBS_BUILD -#include "tensorflow/contrib/lite/builtin_op_data.h" -#include "tensorflow/contrib/lite/error_reporter.h" -#else -#include "tensorflow/contrib/lite/c/builtin_op_data.h" -#include "tensorflow/contrib/lite/core/api/error_reporter.h" -#endif -#include "tensorflow/contrib/lite/model.h" -#include "NeuralNetworksShim.h" -#include "NeuralNetworksExShim.h" - -#ifdef __ANDROID__ -#include <android/log.h> -#include <sys/system_properties.h> -#endif - -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; - } - } - return atoi(sdkVersion); - } - FATAL("No %s prop", sdkProp); -#endif // __ANDROID__ - return 0; -} - -int32_t GetAndroidSdkVersionCached() { - static int32_t androidSdkVersion = GetAndroidSdkVersion(); - return androidSdkVersion; -} - -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 interpreter to the NN API model. -TfLiteStatus addTensorOperands(::tflite::Interpreter* interpreter, - ANeuralNetworksModel* nn_model, - uint32_t* no_of_operands_added, - std::vector<int64_t>* nnapi_ids) { - uint32_t next_id = 0; - for (size_t i = 0; i < interpreter->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 = interpreter->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: - nn_type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; - scale = tensor->params.scale; - // FIXME The next line is a workaround because currently zero scale is - // passed down from TF - // Lite. Note that the latest NeuralNetworks.h (see - // https://android.googlesource.com/platform/frameworks/ml/+/master/nn/runtime/include/NeuralNetworks.h) - // requires scale to be greater than zero. Remove this workaround - // when the scale - // value is correctly passed. - 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_QUANT8_ASYMM; - scale = 1.0f; - zeroPoint = 0; - 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 (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::Interpreter* interpreter, 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 < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->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) { - ANeuralNetworksOperandType 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) { - ANeuralNetworksOperandType 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) { - ANeuralNetworksOperandType operand_type{ - .type = ANEURALNETWORKS_TENSOR_INT32, - .dimensionCount = 1, - .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 = - [interpreter, &nn_model, &next_id, &augmented_inputs, - &model_state_inputs, &model_state_outputs](int tensor_id) { - const TfLiteTensor* tensor = interpreter->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 = [interpreter, &node, &nn_model, - &next_id, &augmented_outputs]() { - if (node.temporaries->size == 0) return; - int scratch_buffer_index = node.temporaries->data[0]; - const TfLiteTensor* tensor = interpreter->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) { -#ifdef OBS_BUILD - auto builtin = reinterpret_cast<TfLiteMeanParams*>(data); -#else - auto builtin = reinterpret_cast<TfLiteReducerParams*>(data); -#endif - 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++) { - if (augmented_inputs[idx] == 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" - - ANeuralNetworksOperationType nn_op_type; - - // 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) && - (interpreter->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) && - (interpreter->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 (interpreter->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_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: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_CAST_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_TOPK_V2: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TOPK_V2_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_GATHER: - add_gather_ex_params(node.builtin_data); - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_GATHER_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_SPLIT: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SPLIT_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_NEG: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_NEG_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_EXP: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_EXP_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_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: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_PRELU_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_ARG_MAX: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_ARGMAX_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; -#ifndef OBS_BUILD - 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: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_SQRT_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_RSQRT: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_RSQRT_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_EQUAL: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_EQUAL_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_NOT_EQUAL: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_NOT_EQUAL_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_SUM: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_REDUCE_SUM_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_REDUCE_MAX: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TENSORFLOW_MAX_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_REDUCE_MIN: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_REDUCE_MIN_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_LOGICAL_AND: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_LOGICAL_AND_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_LOGICAL_OR: - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_LOGICAL_OR_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; -#endif - 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_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: -#ifndef OBS_BUILD - case tflite::BuiltinOperator_ARG_MIN: -#endif - 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: -#ifndef OBS_BUILD - 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: -#endif - 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("TensorFlowMax") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_TENSORFLOW_MAX_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("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("TensorFlowSum") == 0) { - CHECK_NN(ANeuralNetworksModel_addOperationEx( - nn_model, ANEURALNETWORKS_REDUCE_SUM_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; - } -#ifdef OBS_BUILD - default: - logError("Op code %d is currently not delegated to NNAPI", builtin); - return kTfLiteError; - break; -#endif - } - - //if (nnapi_version == 11 && GetAndroidSdkVersionCached() < 28) { - // FATAL("Op %d needs NNAPI1.1", builtin); - //} - - // 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::Interpreter* interpreter) { - 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(interpreter->tensors_size(), - kOperandNotNeeded); - auto set_ids_to_not_set = [&tensor_id_to_nnapi_id](const int* buf, - size_t 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 < interpreter->nodes_size(); i++) { - const auto* node_and_registration = interpreter->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(interpreter->inputs().data(), - interpreter->inputs().size()); - set_ids_to_not_set(interpreter->outputs().data(), - interpreter->outputs().size()); - - uint32_t next_id = 0; - RETURN_ERROR_IF_TFLITE_FAILED(addTensorOperands( - interpreter, nn_model_, &next_id, &tensor_id_to_nnapi_id)); - RETURN_ERROR_IF_TFLITE_FAILED( - AddOpsAndParams(interpreter, nn_model_, next_id, &model_states_inputs_, - &model_states_outputs_, tensor_id_to_nnapi_id)); - - std::vector<uint32_t> augmented_inputs; - MapAndAddTensorIds(interpreter->inputs().data(), - interpreter->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(interpreter->outputs().data(), - interpreter->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_, interpreter->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; -} - -#include <unordered_map> - -TfLiteStatus NNAPIDelegate::Invoke(::tflite::Interpreter* interpreter) { - if (!nn_model_) { - model_status_ = BuildGraph(interpreter); - 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 < interpreter->inputs().size(); i++) - { - int input = interpreter->inputs()[i]; - TfLiteTensor* tensor = interpreter->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 < interpreter->outputs().size(); i++) - { - int output = interpreter->outputs()[i]; - TfLiteTensor* tensor = interpreter->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 < interpreter->inputs().size(); i++) { - int input = interpreter->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 = interpreter->tensor(input); - 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 < interpreter->outputs().size(); i++) { - int output = interpreter->outputs()[i]; - TfLiteTensor* tensor = interpreter->tensor(output); - - 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 = interpreter->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 + interpreter->inputs().size(), nullptr, tensor->data.raw, - tensor->bytes)); - // Tell NNAPI where to output the state_out. - CHECK_NN(ANeuralNetworksExecution_setOutput( - execution, i + interpreter->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 < interpreter->inputs().size(); i++) { - int input = interpreter->inputs()[i]; - TfLiteTensor* tensor = interpreter->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 < interpreter->outputs().size(); i++) { - int output = interpreter->outputs()[i]; - TfLiteTensor* tensor = interpreter->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 = interpreter->tensor(interpreter->outputs()[0]); - if (float* data = - interpreter->typed_tensor<float>(interpreter->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/libs/tflite/src/ext/nnapi_delegate_ex_AddOpsAndParams_lambda.inc b/libs/tflite/src/ext/nnapi_delegate_ex_AddOpsAndParams_lambda.inc deleted file mode 100644 index a91e4de60..000000000 --- a/libs/tflite/src/ext/nnapi_delegate_ex_AddOpsAndParams_lambda.inc +++ /dev/null @@ -1,106 +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_resize_bilinear_params = [&add_scalar_int32, &interpreter, &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 = interpreter->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 check_l2normalization_params = [interpreter, &node](void* data) { - auto builtin = reinterpret_cast<TfLiteL2NormParams*>(data); - if (builtin->activation != kTfLiteActNone) { - FATAL("NNAPI does not support L2Normalization with fused activations"); - } - if ((node.inputs->size > 0) && - (interpreter->tensor(node.inputs->data[0])->dims->size != 4)) { - FATAL("NNAPI only supports input rank 4 for L2Normalization"); - } - }; - - 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_ex_params = [&add_scalar_int32](void* data) { - auto builtin = reinterpret_cast<TfLiteGatherParams*>(data); - add_scalar_int32(builtin->axis); - if (builtin->axis != 0) { - FATAL("GATHER does not support axis>0 in NNAPI"); - } - }; - -#ifndef OBS_BUILD - 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); - }; -#endif - - auto check_batch_to_space_params = [interpreter, &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 = interpreter->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(); - } - } - }; |