/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "CpuOperationUtils.h"
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#include "OperationResolver.h"
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#include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
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#include "tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h"
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#include "Tracing.h"
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namespace android {
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namespace nn {
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namespace concatenation {
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constexpr char kOperationName[] = "CONCATENATION";
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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namespace {
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template <typename T>
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bool concatenation(const std::vector<const T*>& inputDataPtrs,
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const std::vector<Shape>& inputShapes, int32_t axis, T* outputData,
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const Shape& outputShape) {
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NNTRACE_TRANS("concatenation");
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int num_inputs = inputShapes.size();
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std::vector<tflite::Dims<4>*> inputDimsPtr(num_inputs);
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std::vector<tflite::Dims<4> > inputDims(num_inputs);
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for (int i = 0; i < num_inputs; i++) {
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inputDims[i] = convertShapeToDims(inputShapes[i]);
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inputDimsPtr[i] = &inputDims[i];
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}
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NNTRACE_COMP_SWITCH("optimized_ops::Concatenation");
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tflite::optimized_ops::Concatenation<tflite::FusedActivationFunctionType::kNone, T>(
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getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(),
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inputDimsPtr.data(), num_inputs, outputData, convertShapeToDims(outputShape));
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return true;
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}
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template <>
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bool concatenation<uint8_t>(const std::vector<const uint8_t*>& inputDataPtrs,
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const std::vector<Shape>& inputShapes, int32_t axis,
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uint8_t* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("concatenationQuant8");
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int num_inputs = inputShapes.size();
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std::vector<float> inputScales(num_inputs);
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std::vector<int32> inputOffsets(num_inputs);
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std::vector<tflite::Dims<4>*> inputDimsPtr(num_inputs);
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std::vector<tflite::Dims<4> > inputDims(num_inputs);
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for (int i = 0; i < num_inputs; i++) {
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inputScales[i] = inputShapes[i].scale;
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inputOffsets[i] = inputShapes[i].offset;
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inputDims[i] = convertShapeToDims(inputShapes[i]);
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inputDimsPtr[i] = &inputDims[i];
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}
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NNTRACE_COMP_SWITCH("reference_ops::Concatenation");
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tflite::reference_ops::Concatenation(
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getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(),
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inputDimsPtr.data(), inputOffsets.data(), inputScales.data(), num_inputs, outputData,
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convertShapeToDims(outputShape), outputShape.offset, outputShape.scale);
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return true;
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}
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template <typename T>
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inline bool concatenation(IOperationExecutionContext* context) {
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uint32_t inputCount = context->getNumInputs() - 1;
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std::vector<const T*> inputDatas;
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std::vector<Shape> inputShapes;
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for (uint32_t i = 0; i < inputCount; ++i) {
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const T* buffer = context->getInputBuffer<T>(i);
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if (buffer == nullptr) continue;
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inputDatas.push_back(buffer);
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inputShapes.push_back(context->getInputShape(i));
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}
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return concatenation(inputDatas, inputShapes, context->getInputValue<int32_t>(inputCount),
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context->getOutputBuffer<T>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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}
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} // namespace
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bool validate(const IOperationValidationContext* context) {
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uint32_t inputCount = context->getNumInputs();
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NN_RET_CHECK_GE(inputCount, 2);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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const OperandType inputType = context->getInputType(0);
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if (inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_QUANT8_ASYMM) {
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NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_0));
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} else if (inputType == OperandType::TENSOR_FLOAT16) {
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NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
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} else {
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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std::vector<OperandType> inExpectedTypes(inputCount - 1, inputType);
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inExpectedTypes.push_back(OperandType::INT32);
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if (context->getHalVersion() < HalVersion::V1_2 &&
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inputType == OperandType::TENSOR_QUANT8_ASYMM) {
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const Shape& output = context->getOutputShape(kOutputTensor);
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for (uint32_t i = 0; i < inputCount - 1; ++i) {
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const Shape& input = context->getInputShape(i);
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NN_RET_CHECK_EQ(input.scale, output.scale);
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NN_RET_CHECK_EQ(input.offset, output.offset);
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}
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}
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return validateInputTypes(context, inExpectedTypes) &&
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validateOutputTypes(context, {inputType});
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}
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bool prepare(IOperationExecutionContext* context) {
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uint32_t numInputs = context->getNumInputs();
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NN_RET_CHECK_GE(numInputs, 2);
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const Shape& input0 = context->getInputShape(0);
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uint32_t numDimensions = getNumberOfDimensions(input0);
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int32_t axis = context->getInputValue<int32_t>(numInputs - 1);
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NN_RET_CHECK_GE(axis, 0);
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NN_RET_CHECK_LT(axis, numDimensions);
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uint32_t sumAxis = getSizeOfDimension(input0, axis);
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for (uint32_t i = 1; i < numInputs - 1; ++i) {
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const Shape& input = context->getInputShape(i);
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NN_RET_CHECK_EQ(getNumberOfDimensions(input), numDimensions);
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NN_RET_CHECK(input.type == input0.type);
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for (uint32_t d = 0; d < numDimensions; ++d) {
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if (d == axis) {
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sumAxis += getSizeOfDimension(input, axis);
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} else {
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NN_RET_CHECK_EQ(getSizeOfDimension(input0, d), getSizeOfDimension(input, d));
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}
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}
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}
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Shape output = context->getOutputShape(kOutputTensor);
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output.type = input0.type;
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output.dimensions = input0.dimensions;
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output.dimensions[axis] = sumAxis;
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return context->setOutputShape(kOutputTensor, output);
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}
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bool execute(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(0)) {
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case OperandType::TENSOR_FLOAT16:
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return concatenation<_Float16>(context);
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case OperandType::TENSOR_FLOAT32:
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return concatenation<float>(context);
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case OperandType::TENSOR_QUANT8_ASYMM:
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return concatenation<uint8_t>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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}
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} // namespace concatenation
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NN_REGISTER_OPERATION(CONCATENATION, concatenation::kOperationName, concatenation::validate,
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concatenation::prepare, concatenation::execute, .allowZeroSizedInput = true);
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} // namespace nn
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} // namespace android
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