| /* |
| * Copyright (C) 2020 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 <tensorflow/lite/kernels/internal/optimized/optimized_ops.h> |
| |
| #include <algorithm> |
| #include <vector> |
| |
| #include "CpuOperationUtils.h" |
| #include "OperationResolver.h" |
| #include "Tracing.h" |
| |
| namespace android { |
| namespace nn { |
| namespace local_response_norm { |
| |
| constexpr char kOperationName[] = "LOCAL_RESPONSE_NORMALIZATION"; |
| |
| constexpr uint32_t kNumInputs = 6; |
| constexpr uint32_t kInputTensor = 0; |
| constexpr uint32_t kRadiusScalar = 1; |
| constexpr uint32_t kBiasScalar = 2; |
| constexpr uint32_t kAlphaScalar = 3; |
| constexpr uint32_t kBetaScalar = 4; |
| constexpr uint32_t kAxisScalar = 5; |
| |
| constexpr uint32_t kNumOutputs = 1; |
| constexpr uint32_t kOutputTensor = 0; |
| |
| namespace { |
| |
| inline bool localResponseNormFloat32Impl(const float* inputData, const Shape& inputShape, |
| int32_t radius, float bias, float alpha, float beta, |
| int32_t axis, float* outputData, |
| const Shape& outputShape) { |
| NNTRACE_TRANS("localResponseNormFloat32"); |
| const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis); |
| const uint32_t axisSize = getSizeOfDimension(inputShape, axis); |
| const uint32_t innerSize = |
| getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape)); |
| for (uint32_t outer = 0; outer < outerSize; ++outer) { |
| const float* inputBase = inputData + outer * axisSize * innerSize; |
| float* outputBase = outputData + outer * axisSize * innerSize; |
| for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBase, ++outputBase) { |
| for (int32_t i = 0; i < axisSize; i++) { |
| const int32_t dBegin = std::max(0, i - radius); |
| // Add 1 on dEnd to comply with optimized_ops in TFLite |
| const int32_t dEnd = std::min(static_cast<int32_t>(axisSize), i + radius + 1); |
| float sum = 0.0f; |
| for (int32_t d = dBegin; d < dEnd; d++) { |
| float val = inputBase[d * innerSize]; |
| sum += val * val; |
| } |
| float multiplier = std::pow(bias + alpha * sum, -beta); |
| outputBase[i * innerSize] = inputBase[i * innerSize] * multiplier; |
| } |
| } |
| } |
| return true; |
| } |
| |
| template <typename T> |
| bool localResponseNorm(const T* inputData, const Shape& inputShape, int32_t radius, T bias, T alpha, |
| T beta, int32_t axis, T* outputData, const Shape& outputShape); |
| |
| template <> |
| bool localResponseNorm<float>(const float* inputData, const Shape& inputShape, int32_t radius, |
| float bias, float alpha, float beta, int32_t axis, float* outputData, |
| const Shape& outputShape) { |
| int32_t ndim = getNumberOfDimensions(inputShape); |
| NN_CHECK(handleNegativeAxis(inputShape, &axis)); |
| // TFLite optimized implementation only supports computation along the last axis |
| if (axis == ndim - 1) { |
| NNTRACE_COMP("optimized_ops::LocalResponseNormalization::float"); |
| tflite::LocalResponseNormalizationParams param = { |
| .range = radius, .bias = bias, .alpha = alpha, .beta = beta}; |
| tflite::optimized_ops::LocalResponseNormalization( |
| param, convertShapeToTflshape(inputShape), inputData, |
| convertShapeToTflshape(outputShape), outputData); |
| return true; |
| } else { |
| return localResponseNormFloat32Impl(inputData, inputShape, radius, bias, alpha, beta, axis, |
| outputData, outputShape); |
| } |
| } |
| |
| template <> |
| bool localResponseNorm<_Float16>(const _Float16* inputData, const Shape& inputShape, int32_t radius, |
| _Float16 bias, _Float16 alpha, _Float16 beta, int32_t axis, |
| _Float16* outputData, const Shape& outputShape) { |
| NNTRACE_TRANS("localResponseNormFloat16"); |
| std::vector<float> inputDataFloat32(getNumberOfElements(inputShape)); |
| convertFloat16ToFloat32(inputData, &inputDataFloat32); |
| std::vector<float> outputDataFloat32(getNumberOfElements(outputShape)); |
| |
| localResponseNorm<float>(inputDataFloat32.data(), inputShape, radius, bias, alpha, beta, axis, |
| outputDataFloat32.data(), outputShape); |
| convertFloat32ToFloat16(outputDataFloat32, outputData); |
| |
| return true; |
| } |
| |
| template <typename T> |
| bool executeTyped(IOperationExecutionContext* context) { |
| int32_t axis = context->getNumInputs() == kNumInputs |
| ? context->getInputValue<int32_t>(kAxisScalar) |
| : -1; |
| NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis)); |
| return localResponseNorm<T>( |
| context->getInputBuffer<T>(kInputTensor), context->getInputShape(kInputTensor), |
| context->getInputValue<int32_t>(kRadiusScalar), context->getInputValue<T>(kBiasScalar), |
| context->getInputValue<T>(kAlphaScalar), context->getInputValue<T>(kBetaScalar), axis, |
| context->getOutputBuffer<T>(kOutputTensor), context->getOutputShape(kOutputTensor)); |
| } |
| |
| } // namespace |
| |
| Result<Version> validate(const IOperationValidationContext* context) { |
| NN_RET_CHECK(context->getNumInputs() == kNumInputs || |
| context->getNumInputs() == kNumInputs - 1); |
| NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs); |
| |
| const OperandType inputType = context->getInputType(kInputTensor); |
| std::vector<OperandType> inExpectedTypes; |
| std::vector<OperandType> outExpectedTypes; |
| auto minSupportedVersion = Version::ANDROID_OC_MR1; |
| if (inputType == OperandType::TENSOR_FLOAT32) { |
| minSupportedVersion = Version::ANDROID_OC_MR1; |
| inExpectedTypes = { |
| OperandType::TENSOR_FLOAT32, OperandType::INT32, OperandType::FLOAT32, |
| OperandType::FLOAT32, OperandType::FLOAT32, |
| }; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT32}; |
| } else if (inputType == OperandType::TENSOR_FLOAT16) { |
| minSupportedVersion = Version::ANDROID_Q; |
| inExpectedTypes = { |
| OperandType::TENSOR_FLOAT16, OperandType::INT32, OperandType::FLOAT16, |
| OperandType::FLOAT16, OperandType::FLOAT16, |
| }; |
| outExpectedTypes = {OperandType::TENSOR_FLOAT16}; |
| } else { |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName; |
| } |
| |
| if (context->getNumInputs() == kNumInputs) { |
| inExpectedTypes.push_back(OperandType::INT32); |
| minSupportedVersion = Version::ANDROID_Q; |
| } else if (context->getInputShape(kInputTensor).dimensions.size() != 4) { |
| minSupportedVersion = Version::ANDROID_Q; |
| } |
| |
| const Shape& input = context->getInputShape(kInputTensor); |
| if (hasKnownRank(input)) { |
| NN_RET_CHECK_LE(getNumberOfDimensions(input), 4); |
| } |
| NN_RET_CHECK(validateInputTypes(context, inExpectedTypes)); |
| NN_RET_CHECK(validateOutputTypes(context, {inputType})); |
| return minSupportedVersion; |
| } |
| |
| bool prepare(IOperationExecutionContext* context) { |
| const Shape& input = context->getInputShape(kInputTensor); |
| int32_t numDimensions = getNumberOfDimensions(input); |
| int32_t axis = context->getNumInputs() == kNumInputs |
| ? context->getInputValue<int32_t>(kAxisScalar) |
| : -1; |
| NN_RET_CHECK_LE(numDimensions, 4); |
| NN_RET_CHECK_GE(axis, -numDimensions); |
| NN_RET_CHECK_LT(axis, numDimensions); |
| return context->setOutputShape(kOutputTensor, input); |
| } |
| |
| bool execute(IOperationExecutionContext* context) { |
| switch (context->getInputType(kInputTensor)) { |
| case OperandType::TENSOR_FLOAT32: |
| return executeTyped<float>(context); |
| case OperandType::TENSOR_FLOAT16: |
| return executeTyped<_Float16>(context); |
| default: |
| NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName; |
| } |
| } |
| |
| } // namespace local_response_norm |
| |
| NN_REGISTER_OPERATION(LOCAL_RESPONSE_NORMALIZATION, local_response_norm::kOperationName, |
| local_response_norm::validate, local_response_norm::prepare, |
| local_response_norm::execute); |
| |
| } // namespace nn |
| } // namespace android |
