| /* |
| * Copyright (C) 2017 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| #define LOG_TAG "Operations" |
| |
| #include <algorithm> |
| #include <iterator> |
| #include <vector> |
| |
| #include "OperationResolver.h" |
| #include "OperationsUtils.h" |
| #include "Tracing.h" |
| #include "nnapi/Validation.h" |
| |
| #ifdef NN_INCLUDE_CPU_IMPLEMENTATION |
| #include <tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h> |
| #include <tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h> |
| #include <tensorflow/lite/kernels/internal/reference/reference_ops.h> |
| #include <tensorflow/lite/kernels/internal/types.h> |
| |
| #include "CpuOperationUtils.h" |
| #endif // NN_INCLUDE_CPU_IMPLEMENTATION |
| |
| namespace android { |
| namespace nn { |
| namespace concatenation { |
| |
| constexpr char kOperationName[] = "CONCATENATION"; |
| |
| constexpr uint32_t kNumOutputs = 1; |
| constexpr uint32_t kOutputTensor = 0; |
| |
| #ifdef NN_INCLUDE_CPU_IMPLEMENTATION |
| namespace { |
| |
| template <typename T> |
| bool concatenation(const std::vector<const T*>& inputDataPtrs, |
| const std::vector<Shape>& inputShapes, int32_t axis, T* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("concatenation"); |
| int num_inputs = inputShapes.size(); |
| std::vector<tflite::Dims<4>*> inputDimsPtr(num_inputs); |
| std::vector<tflite::Dims<4>> inputDims(num_inputs); |
| for (int i = 0; i < num_inputs; i++) { |
| inputDims[i] = convertShapeToDims(inputShapes[i]); |
| inputDimsPtr[i] = &inputDims[i]; |
| } |
| NNTRACE_COMP_SWITCH("optimized_ops::Concatenation"); |
| tflite::optimized_ops::Concatenation<tflite::FusedActivationFunctionType::kNone, T>( |
| getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(), |
| inputDimsPtr.data(), num_inputs, outputData, convertShapeToDims(outputShape)); |
| |
| return true; |
| } |
| |
| template <> |
| bool concatenation<uint8_t>(const std::vector<const uint8_t*>& inputDataPtrs, |
| const std::vector<Shape>& inputShapes, int32_t axis, |
| uint8_t* outputData, const Shape& outputShape) { |
| NNTRACE_TRANS("concatenationQuant8"); |
| int num_inputs = inputShapes.size(); |
| std::vector<float> inputScales(num_inputs); |
| std::vector<int32> inputOffsets(num_inputs); |
| std::vector<tflite::Dims<4>*> inputDimsPtr(num_inputs); |
| std::vector<tflite::Dims<4>> inputDims(num_inputs); |
| for (int i = 0; i < num_inputs; i++) { |
| inputScales[i] = inputShapes[i].scale; |
| inputOffsets[i] = inputShapes[i].offset; |
| inputDims[i] = convertShapeToDims(inputShapes[i]); |
| inputDimsPtr[i] = &inputDims[i]; |
| } |
| |
| NNTRACE_COMP_SWITCH("reference_ops::Concatenation"); |
| tflite::reference_ops::Concatenation( |
| getNumberOfDimensions(outputShape) - axis - 1, inputDataPtrs.data(), |
| inputDimsPtr.data(), inputOffsets.data(), inputScales.data(), num_inputs, outputData, |
| convertShapeToDims(outputShape), outputShape.offset, outputShape.scale); |
| |
| return true; |
| } |
| |
| template <typename T> |
| inline bool concatenation(IOperationExecutionContext* context) { |
| uint32_t inputCount = context->getNumInputs() - 1; |
| std::vector<const T*> inputDatas; |
| std::vector<Shape> inputShapes; |
| for (uint32_t i = 0; i < inputCount; ++i) { |
| const T* buffer = context->getInputBuffer<T>(i); |
| if (buffer == nullptr) continue; |
| inputDatas.push_back(buffer); |
| inputShapes.push_back(context->getInputShape(i)); |
| } |
| return concatenation(inputDatas, inputShapes, context->getInputValue<int32_t>(inputCount), |
| context->getOutputBuffer<T>(kOutputTensor), |
| context->getOutputShape(kOutputTensor)); |
| } |
| |
| template <> |
| inline bool concatenation<int8_t>(IOperationExecutionContext* context) { |
| uint32_t inputCount = context->getNumInputs() - 1; |
| std::vector<std::vector<uint8_t>> inputs_uint8(inputCount); |
| for (int i = 0; i < inputCount; ++i) { |
| const auto currentSize = getNumberOfElements(context->getInputShape(i)); |
| inputs_uint8[i].resize(currentSize); |
| if (currentSize != 0) { |
| convertInt8ToUInt8(context->getInputBuffer<int8_t>(i), &inputs_uint8[i]); |
| } |
| } |
| std::vector<const uint8_t*> inputDatas; |
| std::vector<Shape> inputShapes; |
| for (uint32_t i = 0; i < inputCount; ++i) { |
| inputDatas.push_back(inputs_uint8[i].data()); |
| inputShapes.push_back(context->getInputShape(i)); |
| inputShapes[i].offset += 128; |
| } |
| |
| std::vector<uint8_t> output_uint8(getNumberOfElements(context->getOutputShape(kOutputTensor))); |
| Shape outputShape(context->getOutputShape(kOutputTensor)); |
| outputShape.offset += 128; |
| NN_RET_CHECK(concatenation(inputDatas, inputShapes, context->getInputValue<int32_t>(inputCount), |
| output_uint8.data(), outputShape)); |
| |
| convertUInt8ToInt8(output_uint8, context->getOutputBuffer<int8_t>(kOutputTensor)); |
| |
| return true; |
| } |
| |
| } // namespace |
| #endif // NN_INCLUDE_CPU_IMPLEMENTATION |
| |
| Result<Version> validate(const IOperationValidationContext* context) { |
| uint32_t inputCount = context->getNumInputs(); |
| NN_RET_CHECK_GE(inputCount, 2); |
| NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); |
| const OperandType inputType = context->getInputType(0); |
| auto minSupportedVersion = Version::ANDROID_OC_MR1; |
| if (inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| minSupportedVersion = Version::ANDROID_OC_MR1; |
| } else if (inputType == OperandType::TENSOR_FLOAT16) { |
| minSupportedVersion = Version::ANDROID_Q; |
| } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) { |
| minSupportedVersion = Version::ANDROID_R; |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName; |
| } |
| std::vector<OperandType> inExpectedTypes(inputCount - 1, inputType); |
| inExpectedTypes.push_back(OperandType::INT32); |
| if (inputType == OperandType::TENSOR_QUANT8_ASYMM) { |
| const Shape& output = context->getOutputShape(kOutputTensor); |
| for (uint32_t i = 0; i < inputCount - 1; ++i) { |
| const Shape& input = context->getInputShape(i); |
| if (input.scale != output.scale || input.offset != output.offset) { |
| minSupportedVersion = combineVersions(minSupportedVersion, Version::ANDROID_Q); |
| } |
| } |
| } |
| for (uint32_t i = 0; i < inputCount - 1; ++i) { |
| const uint32_t inputRank = getNumberOfDimensions(context->getInputShape(i)); |
| if (inputRank != 0) { |
| NN_RET_CHECK_LE(inputRank, 4); |
| } |
| } |
| NN_RET_CHECK(validateInputTypes(context, inExpectedTypes)); |
| NN_RET_CHECK(validateOutputTypes(context, {inputType})); |
| return minSupportedVersion; |
| } |
| |
| #ifdef NN_INCLUDE_CPU_IMPLEMENTATION |
| bool prepare(IOperationExecutionContext* context) { |
| uint32_t numInputs = context->getNumInputs(); |
| NN_RET_CHECK_GE(numInputs, 2); |
| const Shape& input0 = context->getInputShape(0); |
| uint32_t numDimensions = getNumberOfDimensions(input0); |
| int32_t axis = context->getInputValue<int32_t>(numInputs - 1); |
| NN_RET_CHECK_GE(axis, 0); |
| NN_RET_CHECK_LT(axis, numDimensions); |
| NN_RET_CHECK_LE(numDimensions, 4); |
| |
| uint32_t sumAxis = getSizeOfDimension(input0, axis); |
| for (uint32_t i = 1; i < numInputs - 1; ++i) { |
| const Shape& input = context->getInputShape(i); |
| NN_RET_CHECK_EQ(getNumberOfDimensions(input), numDimensions); |
| NN_RET_CHECK(input.type == input0.type); |
| for (uint32_t d = 0; d < numDimensions; ++d) { |
| if (d == axis) { |
| sumAxis += getSizeOfDimension(input, axis); |
| } else { |
| NN_RET_CHECK_EQ(getSizeOfDimension(input0, d), getSizeOfDimension(input, d)); |
| } |
| } |
| } |
| |
| Shape output = context->getOutputShape(kOutputTensor); |
| output.type = input0.type; |
| output.dimensions = input0.dimensions; |
| output.dimensions[axis] = sumAxis; |
| return context->setOutputShape(kOutputTensor, output); |
| } |
| |
| bool execute(IOperationExecutionContext* context) { |
| // Bypass execution in the case of zero-sized input. |
| if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true; |
| switch (context->getInputType(0)) { |
| case OperandType::TENSOR_FLOAT16: |
| return concatenation<_Float16>(context); |
| case OperandType::TENSOR_FLOAT32: |
| return concatenation<float>(context); |
| case OperandType::TENSOR_QUANT8_ASYMM: |
| return concatenation<uint8_t>(context); |
| case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: |
| return concatenation<int8_t>(context); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName; |
| } |
| } |
| #endif // NN_INCLUDE_CPU_IMPLEMENTATION |
| |
| } // namespace concatenation |
| |
| NN_REGISTER_OPERATION(CONCATENATION, concatenation::kOperationName, concatenation::validate, |
| concatenation::prepare, concatenation::execute, .allowZeroSizedInput = true); |
| |
| } // namespace nn |
| } // namespace android |
