!10752 Add relugrad implementation gpu
From: @yuan_shen_zhou Reviewed-by: @liangchenghui,@wuxuejian Signed-off-by: @liangchenghuipull/10752/MERGE
mindspore-ci-bot
4 years ago
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/**
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* Copyright 2020 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/relu_grad_impl.cuh"
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#include "runtime/device/gpu/cuda_common.h"
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template <typename T>
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__global__ void CalReLUGradKernel(int size, T *dy, T *y, T *dx) {
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for (int pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
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dx[pos] = y[pos] > static_cast<T>(0) ? dy[pos] : static_cast<T>(0);
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}
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}
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template <typename T>
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void CalReLUGrad(int size, T *dy, T *y, T *dx, cudaStream_t cuda_stream) {
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CalReLUGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, dy, y, dx);
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return;
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}
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template void CalReLUGrad(int size, double *dy, double *y, double *dx, cudaStream_t cuda_stream);
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template void CalReLUGrad(int size, float *dy, float *y, float *dx, cudaStream_t cuda_stream);
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template void CalReLUGrad(int size, half *dy, half *y, half *dx, cudaStream_t cuda_stream);
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template void CalReLUGrad(int size, int8_t *dy, int8_t *y, int8_t *dx, cudaStream_t cuda_stream);
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template void CalReLUGrad(int size, int16_t *dy, int16_t *y, int16_t *dx, cudaStream_t cuda_stream);
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template void CalReLUGrad(int size, int32_t *dy, int32_t *y, int32_t *dx, cudaStream_t cuda_stream);
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template void CalReLUGrad(int size, int64_t *dy, int64_t *y, int64_t *dx, cudaStream_t cuda_stream);
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/**
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* Copyright 2020 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_KERNEL_GPU_CUDA_IMP_RELU_GRAD_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_RELU_GRAD_H_
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#include "runtime/device/gpu/cuda_common.h"
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template <typename T>
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void CalReLUGrad(int input_size, T *dy, T *y, T *dx, cudaStream_t cuda_stream);
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#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_RELU_GRAD_H_
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/**
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* Copyright 2020 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/nn/relu_grad_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(
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ReluGrad,
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KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
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ReluGradGpuFwdKernel, double)
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MS_REG_GPU_KERNEL_ONE(
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ReluGrad,
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KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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ReluGradGpuFwdKernel, float)
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MS_REG_GPU_KERNEL_ONE(
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ReluGrad,
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KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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ReluGradGpuFwdKernel, half)
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MS_REG_GPU_KERNEL_ONE(
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ReluGrad, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
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ReluGradGpuFwdKernel, int64_t)
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MS_REG_GPU_KERNEL_ONE(
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ReluGrad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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ReluGradGpuFwdKernel, int32_t)
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MS_REG_GPU_KERNEL_ONE(
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ReluGrad, KernelAttr().AddInputAttr(kNumberTypeInt16).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
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ReluGradGpuFwdKernel, int16_t)
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MS_REG_GPU_KERNEL_ONE(
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ReluGrad, KernelAttr().AddInputAttr(kNumberTypeInt8).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
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ReluGradGpuFwdKernel, int8_t)
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} // namespace kernel
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} // namespace mindspore
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/**
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* Copyright 2020 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_NN_RELU_GRAD_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_RELU_GRAD_KERNEL_H_
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#include <vector>
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#include <map>
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#include <string>
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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/kernel_constants.h"
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#include "backend/kernel_compiler/gpu/cuda_impl/relu_grad_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 ReluGradGpuFwdKernel : public GpuKernel {
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public:
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ReluGradGpuFwdKernel() { ResetResource(); }
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~ReluGradGpuFwdKernel() 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> &,
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const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
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if (is_null_input_) {
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return true;
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}
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T *dy = GetDeviceAddress<T>(inputs, 0);
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T *y = GetDeviceAddress<T>(inputs, 1);
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T *dx = GetDeviceAddress<T>(outputs, 0);
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const int size = input_size_ / sizeof(T);
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CalReLUGrad(size, dy, y, dx, 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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InitResource();
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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != 2) {
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MS_LOG(ERROR) << "Argument number is " << input_num << ", but ReluGradGpuKernel needs 2.";
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return false;
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}
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auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
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is_null_input_ = CHECK_NULL_INPUT(input_shape);
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if (is_null_input_) {
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MS_LOG(WARNING) << "ActivationGradGpuKernel input is null.";
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}
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size_t size = 1;
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for (size_t i = 0; i < input_shape.size(); i++) {
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size *= input_shape[i];
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}
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input_size_ = size * sizeof(T);
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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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is_null_input_ = false;
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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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input_size_ = 0;
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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_);
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output_size_list_.push_back(input_size_);
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input_size_list_.push_back(input_size_);
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}
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private:
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bool is_null_input_;
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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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size_t input_size_;
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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_NN_RELU_GRAD_KERNEL_H_
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