zhouyuanshen
4 years ago
parent
09ee838320
commit
2e2c01d6f0
@ -1,37 +0,0 @@
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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, 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, 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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@ -1,23 +0,0 @@
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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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@ -0,0 +1,38 @@
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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_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
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ReLUGpuFwdKernel, double)
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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ReLUGpuFwdKernel, float)
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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ReLUGpuFwdKernel, half)
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
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ReLUGpuFwdKernel, int64_t)
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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ReLUGpuFwdKernel, int32_t)
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
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ReLUGpuFwdKernel, int16_t)
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8), ReLUGpuFwdKernel,
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int8_t)
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MS_REG_GPU_KERNEL_ONE(ReLU, KernelAttr().AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
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ReLUGpuFwdKernel, uint8_t)
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,98 @@
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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_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_RELU_GPU_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/cuda_impl/relu_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 ReLUGpuFwdKernel : public GpuKernel {
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public:
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ReLUGpuFwdKernel() { ResetResource(); }
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~ReLUGpuFwdKernel() override {}
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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 *input = GetDeviceAddress<T>(inputs, 0);
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T *output = GetDeviceAddress<T>(outputs, 0);
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const int size = input_size_ / sizeof(T);
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CalReLU(size, input, output, 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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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != 1) {
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MS_LOG(ERROR) << "Argument number is " << input_num << ", but ReLUGpuFwdKernel needs 1.";
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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) << "ReLUGpuFwdKernel 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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workspace_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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workspace_size_list_.push_back(workspace_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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size_t workspace_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_GPU_KERNEL_H_
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@ -1,84 +0,0 @@
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# Copyright 2019 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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops.operations import _grad_ops as G
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class NetReluGrad(nn.Cell):
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def __init__(self):
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super(NetReluGrad, self).__init__()
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self.rekuGrad = G.ReluGrad()
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def construct(self, x, dy):
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return self.rekuGrad(dy, x)
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def relu_grad_base(dtype):
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x = Tensor(np.array([[[[-1, 1, 1],
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[1, -1, 1],
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[1, 1, -1]]]]).astype(dtype))
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dy = Tensor(np.array([[[[1, 0, 1],
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[0, 1, 0],
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[1, 1, 1]]]]).astype(dtype))
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expect = np.array([[[[0, 0, 1,], [0, 0, 0,], [1, 1, 0.]]]]).astype(np.dtype)
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error = np.ones(shape=[3, 3]) * 1.0e-6
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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relu_grad = NetReluGrad()
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output = relu_grad(x, dy)
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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assert output.asnumpy().dtype == dtype
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu_grad_float16():
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relu_grad_base(np.float16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu_grad_float32():
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relu_grad_base(np.float32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu_grad_int8():
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relu_grad_base(np.int8)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu_grad_int32():
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relu_grad_base(np.int32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_relu_grad_int64():
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relu_grad_base(np.int64)
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