zhaoting
5 years ago
parent
45ad430af2
commit
5c0962acfa
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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/arrays/split_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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Split, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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SplitGpuFwdKernel, float)
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MS_REG_GPU_KERNEL_ONE(Split,
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KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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SplitGpuFwdKernel, int)
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MS_REG_GPU_KERNEL_ONE(
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Split, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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SplitGpuFwdKernel, half)
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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_KERNEL_GPU_SPLIT_GPU_KERNEL_H
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#define MINDSPORE_CCSRC_KERNEL_GPU_SPLIT_GPU_KERNEL_H
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#include <vector>
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#include <memory>
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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/split_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 SplitGpuFwdKernel : public GpuKernel {
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public:
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SplitGpuFwdKernel()
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: axis_(0),
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output_num_(1),
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input_size_(1),
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axis_step_(1),
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all_size_before_axis_(1),
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all_size_axis_(1),
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outputs_host_(nullptr) {}
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~SplitGpuFwdKernel() 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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T **outputs_device = GetDeviceAddress<T *>(workspace, 0);
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for (size_t i = 0; i < outputs.size(); i++) {
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outputs_host_[i] = GetDeviceAddress<T>(outputs, i);
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}
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CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpyAsync(outputs_device, outputs_host_.get(), sizeof(T *) * output_num_,
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cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
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"Split opt cudaMemcpyAsync outputs failed");
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SplitKernel(input_size_, axis_step_, all_size_before_axis_, all_size_axis_, input, outputs_device,
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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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axis_ = GetAttr<int>(kernel_node, "axis");
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if (axis_ < 0) {
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auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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axis_ += SizeToInt(input_shape.size());
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}
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output_num_ = GetAttr<int>(kernel_node, "output_num");
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if (!CheckParam(kernel_node)) {
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return false;
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}
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auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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input_size_ = 1;
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all_size_before_axis_ = 1;
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all_size_axis_ = 1;
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for (int i = 0; i < SizeToInt(input_shape.size()); i++) {
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input_size_ *= input_shape[i];
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if (i > axis_) {
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all_size_before_axis_ *= input_shape[i];
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all_size_axis_ *= input_shape[i];
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}
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if (i == axis_) {
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all_size_before_axis_ *= input_shape[i];
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}
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}
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input_size_list_.push_back(IntToSize(input_size_ * sizeof(T)));
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axis_step_ = input_shape[axis_] / output_num_;
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for (int i = 0; i < output_num_; i++) {
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size_t output_size = 1;
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auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, i);
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for (size_t j = 0; j < output_shape.size(); j++) {
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output_size *= output_shape[j];
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}
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output_size_list_.push_back(output_size * sizeof(T));
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}
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workspace_size_list_.push_back(sizeof(T *) * output_num_);
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InitSizeLists();
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outputs_host_ = std::make_unique<T *[]>(output_num_);
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return true;
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}
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protected:
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void InitSizeLists() override {}
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private:
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bool CheckParam(const CNodePtr &kernel_node) {
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auto input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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int dims = SizeToInt(input_shape.size());
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int output_num = SizeToInt(AnfAlgo::GetOutputTensorNum(kernel_node));
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if (input_num != 1) {
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MS_LOG(ERROR) << "Input number is " << input_num << ", but Split needs 1 input.";
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return false;
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}
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if (dims == 0) {
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MS_LOG(ERROR) << "Input dims is " << dims << ", scalar is not supported.";
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return false;
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}
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if (axis_ < -dims || axis_ >= dims) {
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MS_LOG(ERROR) << "Attr axis " << axis_ << " must be in " << -dims << "~" << dims;
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return false;
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}
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if (output_num_ > SizeToInt(input_shape[axis_])) {
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MS_LOG(ERROR) << "Attr output_num " << output_num_ << "must less than" << input_shape[axis_];
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return false;
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}
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if (input_shape[axis_] % output_num_ != 0) {
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MS_LOG(ERROR) << "Attr output_num " << output_num_ << "must be divided by" << input_shape[axis_];
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return false;
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}
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if (output_num_ != output_num) {
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MS_LOG(ERROR) << "Output num is " << output_num << ", but need " << output_num_;
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return false;
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}
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return true;
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}
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int axis_;
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int output_num_;
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int input_size_;
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int axis_step_;
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int all_size_before_axis_;
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int all_size_axis_;
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std::unique_ptr<T *[]> outputs_host_;
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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_KERNEL_GPU_SPLIT_GPU_KERNEL_H
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@ -0,0 +1,50 @@
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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 <stdio.h>
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#include <stdint.h>
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#include <cuda_runtime.h>
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#include "backend/kernel_compiler/gpu/cuda_impl/split_impl.cuh"
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template <typename T>
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__global__ void Split(const int size, const int axis_step, const int all_size_before_axis,
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const int all_size_axis, const T* input, T** outputs) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
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int num = pos % all_size_before_axis / all_size_axis;
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int block = num / axis_step;
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int block_pos = pos / all_size_before_axis * axis_step * all_size_axis +
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num % axis_step * all_size_axis + pos % all_size_axis;
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outputs[block][block_pos] = input[pos];
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}
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return;
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}
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template <typename T>
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void SplitKernel(const int size, const int axis_step, const int all_size_before_axis,
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const int all_size_axis, const T* input, T** outputs, cudaStream_t cuda_stream) {
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Split<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, axis_step, all_size_before_axis,
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all_size_axis, input, outputs);
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return;
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}
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template void SplitKernel(const int size, const int axis_step, const int all_size_before_axis,
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const int all_size_axis, const float* input, float** outputs,
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cudaStream_t cuda_stream);
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template void SplitKernel(const int size, const int axis_step, const int all_size_before_axis,
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const int all_size_axis, const int* input, int** outputs,
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cudaStream_t cuda_stream);
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template void SplitKernel(const int size, const int axis_step, const int all_size_before_axis,
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const int all_size_axis, const half* input, half** outputs,
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cudaStream_t cuda_stream);
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@ -0,0 +1,24 @@
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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_IMPL_SPLIT_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPLIT_H_
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#include "runtime/device/gpu/cuda_common.h"
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template <typename T>
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void SplitKernel(const int size, const int axis_step, const int all_size_before_axis,
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const int all_size_axis, const T* input, T** outputs, cudaStream_t cuda_stream);
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#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPLIT_H_
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@ -0,0 +1,58 @@
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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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import numpy as np
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import pytest
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import mindspore.context as context
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from mindspore import Tensor
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, axis=0, out_nums=1):
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super(Net, self).__init__()
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self.split = P.Split(axis, out_nums)
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def construct(self, x):
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return self.split(x)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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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_split():
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x = np.array([[[1, -1, 1], [2, -2, 2]],
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[[3, -3, 3], [4, -4, 4]],
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[[5, -5, 5], [6, -6, 6]]]).astype(np.float32)
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split_op = Net(0, 3)
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outputs = split_op(Tensor(x))
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for i, out in enumerate(outputs):
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assert (out.asnumpy() == x[i]).all()
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def test_split_4d():
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x_np = np.random.randn(2, 6, 4, 4).astype(np.float32)
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y = np.split(x_np, 3, axis=1)
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split_op = Net(1, 3)
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outputs = split_op(Tensor(x_np))
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for i, out in enumerate(outputs):
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assert (out.asnumpy() == y[i]).all()
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