forked from OSSInnovation/mindspore
!14201 Add new GPU kernel Tile
From: @TFbunny Reviewed-by: @tom__chen Signed-off-by:
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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 "backend/kernel_compiler/gpu/arrays/tile_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(Tile, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
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TileGpuKernel, double)
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MS_REG_GPU_KERNEL_ONE(Tile, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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TileGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(Tile, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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TileGpuKernel, half)
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MS_REG_GPU_KERNEL_ONE(Tile, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16), TileGpuKernel,
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int16_t)
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MS_REG_GPU_KERNEL_ONE(Tile, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), TileGpuKernel,
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int)
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MS_REG_GPU_KERNEL_ONE(Tile, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), TileGpuKernel,
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int64_t)
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} // namespace kernel
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} // namespace mindspore
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_TILE_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_TILE_GPU_KERNEL_H_
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#include <vector>
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#include "backend/kernel_compiler/gpu/gpu_kernel.h"
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#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
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#include "backend/kernel_compiler/gpu/cuda_impl/tile_impl.cuh"
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namespace mindspore {
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namespace kernel {
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template <typename T>
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class TileGpuKernel : public GpuKernel {
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public:
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TileGpuKernel() { ResetResource(); }
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~TileGpuKernel() override = default;
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const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
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const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
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const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
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T *input = GetDeviceAddress<T>(inputs, 0);
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size_t *input_shape_ptr = GetDeviceAddress<size_t>(workspace, 0);
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size_t *output_shape_ptr = GetDeviceAddress<size_t>(workspace, 1);
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T *output = GetDeviceAddress<T>(outputs, 0);
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CHECK_CUDA_RET_WITH_EXCEPT(kernel_node_,
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cudaMemcpyAsync(input_shape_ptr, &input_shape_[0], input_shape_.size() * sizeof(size_t),
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cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
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"cudaMemcpyAsync input_shape_ failed");
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CHECK_CUDA_RET_WITH_EXCEPT(
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kernel_node_,
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cudaMemcpyAsync(output_shape_ptr, &output_shape_[0], output_shape_.size() * sizeof(size_t),
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cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
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"cudaMemcpyAsync output_shape_ failed");
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CalTile(output_size_, input_size_, shape_size_, input_shape_ptr, output_shape_ptr, input, output,
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reinterpret_cast<cudaStream_t>(stream_ptr));
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return true;
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}
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bool Init(const CNodePtr &kernel_node) override {
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kernel_node_ = kernel_node;
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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != 1) {
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MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but Tile needs 1 input.";
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return false;
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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if (output_num != 1) {
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MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but Tile has 1 output.";
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return false;
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}
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input_shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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input_size_ = 1;
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for (size_t i = 0; i < input_shape_.size(); i++) {
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input_size_ *= input_shape_[i];
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}
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output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0);
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output_size_ = 1;
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if (output_shape_.size() > TILE_MAX_DIMENSION) {
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MS_LOG(EXCEPTION) << "Output is " << output_shape_.size() << "-D, but Tile supports up to " << TILE_MAX_DIMENSION
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<< "-D.";
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}
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shape_size_ = output_shape_.size();
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for (size_t i = 0; i < output_shape_.size(); i++) {
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output_size_ *= output_shape_[i];
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}
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std::vector<int64_t> multiples = GetAttr<std::vector<int64_t>>(kernel_node, "multiples");
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int64_t filling_value = static_cast<int64_t>(multiples.size()) - static_cast<int64_t>(input_shape_.size());
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// input_shape_.size() == output_shape_.size() == shape_size_
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input_shape_.insert(input_shape_.begin(), LongToSize(filling_value), 1);
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InitSizeLists();
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return true;
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}
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void ResetResource() noexcept override {
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input_size_ = 1;
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output_size_ = 1;
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shape_size_ = 1;
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input_shape_.clear();
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output_shape_.clear();
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input_size_list_.clear();
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output_size_list_.clear();
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workspace_size_list_.clear();
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}
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protected:
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void InitSizeLists() override {
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input_size_list_.push_back(input_size_ * sizeof(T));
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workspace_size_list_.push_back(input_shape_.size() * sizeof(size_t));
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workspace_size_list_.push_back(output_shape_.size() * sizeof(size_t));
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output_size_list_.push_back(output_size_ * sizeof(T));
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}
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private:
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size_t input_size_;
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size_t output_size_;
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size_t shape_size_;
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std::vector<size_t> input_shape_;
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std::vector<size_t> output_shape_;
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std::vector<size_t> input_size_list_;
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std::vector<size_t> output_size_list_;
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std::vector<size_t> workspace_size_list_;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_TILE_GPU_KERNEL_H_
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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 "backend/kernel_compiler/gpu/cuda_impl/tile_impl.cuh"
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template <typename T>
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__global__ void Tile(const size_t output_size, const size_t input_size, const size_t shape_size,
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const size_t *input_shape, const size_t *output_shape, const T *input, T *output) {
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// for example 4-D: pos = pos_array[0] * output_shape[1] * output_shape[2] * output_shape[3] +
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// pos_array[1] * output_shape[2] * output_shape[3] +
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// pos_array[2] * output_shape[3] +
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// pos_array[3]
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size_t pos_array[TILE_MAX_DIMENSION];
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < output_size; pos += blockDim.x * gridDim.x) {
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size_t tmp_pos = pos;
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size_t pos_size = output_size / output_shape[0];
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pos_array[0] = tmp_pos / pos_size;
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for (size_t i = 1; i < shape_size; i++) {
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tmp_pos -= pos_array[i - 1] * pos_size;
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pos_size = pos_size / output_shape[i];
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pos_array[i] = tmp_pos / pos_size;
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}
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for (size_t i = 0; i < shape_size; i++) {
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pos_array[i] = pos_array[i] % input_shape[i];
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}
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pos_size = input_size;
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size_t input_pos = 0;
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for (size_t i = 0; i < shape_size; i++) {
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pos_size /= input_shape[i];
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input_pos += (pos_array[i] * pos_size);
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}
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output[pos] = input[input_pos];
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}
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}
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template <typename T>
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void CalTile(const size_t output_size, const size_t input_size, const size_t shape_size, const size_t *input_shape,
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const size_t *output_shape, const T *input, T *output, cudaStream_t cuda_stream) {
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Tile<<<GET_BLOCKS(output_size), GET_THREADS, 0, cuda_stream>>>(output_size, input_size, shape_size, input_shape,
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output_shape, input, output);
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return;
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}
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template void CalTile<double>(const size_t output_size, const size_t input_size, const size_t shape_size,
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const size_t *input_shape, const size_t *output_shape, const double *input,
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double *output, cudaStream_t cuda_stream);
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template void CalTile<float>(const size_t output_size, const size_t input_size, const size_t shape_size,
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const size_t *input_shape, const size_t *output_shape, const float *input, float *output,
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cudaStream_t cuda_stream);
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template void CalTile<half>(const size_t output_size, const size_t input_size, const size_t shape_size,
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const size_t *input_shape, const size_t *output_shape, const half *input, half *output,
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cudaStream_t cuda_stream);
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template void CalTile<int16_t>(const size_t output_size, const size_t input_size, const size_t shape_size,
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const size_t *input_shape, const size_t *output_shape, const int16_t *input,
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int16_t *output, cudaStream_t cuda_stream);
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template void CalTile<int>(const size_t output_size, const size_t input_size, const size_t shape_size,
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const size_t *input_shape, const size_t *output_shape, const int *input, int *output,
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cudaStream_t cuda_stream);
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template void CalTile<int64_t>(const size_t output_size, const size_t input_size, const size_t shape_size,
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const size_t *input_shape, const size_t *output_shape, const int64_t *input,
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int64_t *output, cudaStream_t cuda_stream);
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_TILE_IMPL_CUH_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_TILE_IMPL_CUH_
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#define TILE_MAX_DIMENSION 100
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#include "runtime/device/gpu/cuda_common.h"
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template <typename T>
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void CalTile(const size_t output_size, const size_t input_size, const size_t shape_size, const size_t *input_shape,
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const size_t *output_shape, const T *input, T *output, cudaStream_t cuda_stream);
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_TILE_IMPL_CUH_
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