/* * Copyright (C) 2018 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 "Tile.h" #include "Tracing.h" namespace android { namespace nn { namespace tile { namespace { template <typename T> void CopyMultipleTimes(const T* in_data, int32_t in_size, int32_t multiplier, T* out_data) { for (int i = 0; i < multiplier; ++i) { const T* in_end = in_data + in_size; T* new_out_data = std::copy(in_data, in_end, out_data); in_data = out_data; out_data = new_out_data; } } template <typename T, typename M> std::pair<int, int> TileOneDimension(const Shape& input_shape, const T* in_data, const M* multipliers, T* out_data, int dimension) { const int dimension_size = input_shape.dimensions[dimension]; if (dimension == input_shape.dimensions.size() - 1) { CopyMultipleTimes(in_data, dimension_size, multipliers[dimension], out_data); return std::make_pair(dimension_size, dimension_size * static_cast<int>(multipliers[dimension])); } int total_stride_size = 0, total_tiled_stride_size = 0; const T* copy_from_data = in_data; T* copy_to_data = out_data; for (int i = 0; i < dimension_size; ++i) { int stride_size = 0, tiled_stride_size = 0; std::tie(stride_size, tiled_stride_size) = TileOneDimension( input_shape, copy_from_data, multipliers, copy_to_data, dimension + 1); copy_from_data += stride_size; copy_to_data += tiled_stride_size; total_stride_size += stride_size; total_tiled_stride_size += tiled_stride_size; } CopyMultipleTimes(out_data, total_tiled_stride_size, multipliers[dimension] - 1, out_data + total_tiled_stride_size); return std::make_pair(total_stride_size, total_tiled_stride_size * multipliers[dimension]); } template <typename T> void tileImpl(const T* inputData, const Shape& inputShape, const int32_t* multiples, T* outputData, const Shape& outputShape) { TileOneDimension(inputShape, inputData, multiples, outputData, 0); } } // namespace bool prepare(const Shape& input, const int32_t* multiples, const Shape& multiplesShape, Shape* output) { output->type = input.type; output->offset = input.offset; output->scale = input.scale; output->dimensions.assign(input.dimensions.begin(), input.dimensions.end()); for (size_t i = 0; i < output->dimensions.size(); ++i) { output->dimensions[i] *= multiples[i]; } return true; } bool eval(const uint8_t* inputData, const Shape& inputShape, const int32_t* multiples, uint8_t* outputData, const Shape& outputShape) { NNTRACE_TRANS("tile::eval"); #define ANDROID_NN_IMPL_TILE(operandType, dataType) \ case operandType: { \ NNTRACE_COMP_SWITCH("tileImpl::" #dataType); \ tileImpl(reinterpret_cast<const dataType*>(inputData), inputShape, multiples, \ reinterpret_cast<dataType*>(outputData), outputShape); \ return true; \ } switch (inputShape.type) { ANDROID_NN_IMPL_TILE(OperandType::TENSOR_FLOAT16, _Float16); ANDROID_NN_IMPL_TILE(OperandType::TENSOR_FLOAT32, float); ANDROID_NN_IMPL_TILE(OperandType::TENSOR_INT32, int32_t); ANDROID_NN_IMPL_TILE(OperandType::TENSOR_QUANT8_ASYMM, uint8_t); default: LOG(ERROR) << "Unsupported data type"; return false; } #undef ANDROID_NN_IMPL_TILE } } // namespace tile } // namespace nn } // namespace android