forked from OSSInnovation/mindspore
!3259 add gpu cumsum op and reducemin
Merge pull request !3259 from baihuawei/automl
This commit is contained in:
Gitee
commit
1e88d64b20
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@ -30,5 +30,9 @@ MS_REG_GPU_KERNEL_ONE(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeFloat32).A
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ArrayReduceGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(ReduceSum, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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ArrayReduceGpuKernel, half)
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MS_REG_GPU_KERNEL_ONE(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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ArrayReduceGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(ReduceMin, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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ArrayReduceGpuKernel, half)
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} // namespace kernel
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} // namespace mindspore
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@ -29,6 +29,7 @@ const std::map<std::string, cudnnReduceTensorOp_t> kReduceTypeMap = {
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{"ReduceMax", CUDNN_REDUCE_TENSOR_MAX},
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{"ReduceMean", CUDNN_REDUCE_TENSOR_AVG},
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{"ReduceSum", CUDNN_REDUCE_TENSOR_ADD},
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{"ReduceMin", CUDNN_REDUCE_TENSOR_MIN},
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};
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template <typename T>
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class ArrayReduceGpuKernel : public GpuKernel {
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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 "cumsum_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 CumSumKernel(T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride,
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size_t stride2) {
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size_t num = dim0 * dim2;
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size_t i, k, offset;
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for (size_t write_index = blockIdx.x * blockDim.x + threadIdx.x; write_index < num;
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write_index += blockDim.x * gridDim.x) {
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i = write_index / dim2 % dim0;
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k = write_index % dim2;
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offset = i * stride + k;
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for (size_t j = 0; j < dim1; ++j) {
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size_t read_index = j * stride2 + offset;
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if (j == 0) {
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output[read_index] = input[read_index];
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} else {
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size_t read_index2 = (j - 1) * stride2 + offset;
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output[read_index] = output[read_index2] + input[read_index];
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}
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}
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}
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}
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template <typename T>
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void CumSum(T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride, size_t stride2,
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cudaStream_t stream) {
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int size = dim0 * dim2;
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CumSumKernel<<<GET_BLOCKS(size), GET_THREADS, 0, stream>>>(input, output, dim0, dim1, dim2, stride, stride2);
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return;
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}
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template void CumSum<float>(float *input, float *output, size_t dim0, size_t dim1, size_t dim2, size_t stride,
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size_t stride2, cudaStream_t stream);
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@ -0,0 +1,22 @@
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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_CUMSUM_IMPL_CUH_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUMSUM_IMPL_CUH_
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template <typename T>
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void CumSum(T *input, T *output, size_t dim0, size_t dim1, size_t dim2, size_t stride, size_t stride2,
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cudaStream_t stream);
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUMSUM_IMPL_CUH_
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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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#include "backend/kernel_compiler/gpu/math/cumsum_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(CumSum, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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CumSumGpuKernel, float)
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,102 @@
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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_CUMSUM_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUMSUM_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/cumsum_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 CumSumGpuKernel : public GpuKernel {
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public:
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CumSumGpuKernel() : axis_(0), input_size_0_(0), stride_(0), stride2_(0) {}
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~CumSumGpuKernel() = 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_addr = GetDeviceAddress<T>(inputs, 0);
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T *output_addr = GetDeviceAddress<T>(outputs, 0);
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CumSum(input_addr, output_addr, dims_[0], dims_[1], dims_[2], stride_, stride2_,
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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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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != 1) {
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MS_LOG(EXCEPTION) << "Argument number is " << input_num << ", but CumSumGpuKernel needs 1.";
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return false;
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}
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input_size_0_ = sizeof(T);
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shape_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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axis_ = GetAttr<int>(kernel_node, "axis");
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int input_dim_length = SizeToInt(shape_.size());
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if (axis_ >= input_dim_length) {
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MS_LOG(EXCEPTION) << "Axis out of bounds.";
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}
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while (axis_ < 0) {
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axis_ += input_dim_length;
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}
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for (size_t i = 0; i < shape_.size(); i++) {
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input_size_0_ *= shape_[i];
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}
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Reshape();
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InitSizeLists();
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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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input_size_list_.push_back(input_size_0_);
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output_size_list_.push_back(input_size_0_);
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}
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private:
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void Reshape() {
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dims_[0] = 1;
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dims_[1] = shape_[IntToSize(axis_)];
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dims_[2] = 1;
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for (size_t i = 0; i < IntToSize(axis_); i++) {
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dims_[0] *= shape_[i];
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}
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for (size_t i = IntToSize(axis_) + 1; i < shape_.size(); i++) {
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dims_[2] *= shape_[i];
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}
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stride_ = dims_[1] * dims_[2];
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stride2_ = dims_[2];
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return;
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}
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int axis_;
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size_t input_size_0_;
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size_t stride_;
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size_t stride2_;
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size_t dims_[3] = {};
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std::vector<size_t> 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_CUMSUM_GPU_KERNEL_H_
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@ -0,0 +1,132 @@
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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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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.ops import operations as P
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x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis0 = 3
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x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis1 = 3
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x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
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axis2 = 2
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x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
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axis3 = 2
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x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis4 = 1
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x5 = np.random.rand(2, 3).astype(np.float32)
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axis5 = 1
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x6 = np.random.rand(1, 1, 1, 1).astype(np.float32)
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axis6 = 0
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context.set_context(device_target='GPU')
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class CumSum(nn.Cell):
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def __init__(self):
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super(CumSum, self).__init__()
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self.x0 = Tensor(x0)
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self.axis0 = axis0
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self.x1 = Tensor(x1)
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self.axis1 = axis1
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self.x2 = Tensor(x2)
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self.axis2 = axis2
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self.x3 = Tensor(x3)
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self.axis3 = axis3
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self.x4 = Tensor(x4)
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self.axis4 = axis4
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self.x5 = Tensor(x5)
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self.axis5 = axis5
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self.x6 = Tensor(x6)
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self.axis6 = axis6
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@ms_function
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def construct(self):
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return (P.CumSum()(self.x0, self.axis0),
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P.CumSum()(self.x1, self.axis1),
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P.CumSum()(self.x2, self.axis2),
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P.CumSum()(self.x3, self.axis3),
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P.CumSum()(self.x4, self.axis4),
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P.CumSum()(self.x5, self.axis5),
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P.CumSum()(self.x6, self.axis6))
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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_CumSum():
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cumsum = CumSum()
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output = cumsum()
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expect0 = np.cumsum(x0, axis=axis0)
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diff0 = abs(output[0].asnumpy() - expect0)
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error0 = np.ones(shape=expect0.shape) * 1.0e-5
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assert np.all(diff0 < error0)
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assert output[0].shape == expect0.shape
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expect1 = np.cumsum(x1, axis=axis1)
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diff1 = abs(output[1].asnumpy() - expect1)
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error1 = np.ones(shape=expect1.shape) * 1.0e-5
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assert np.all(diff1 < error1)
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assert output[1].shape == expect1.shape
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expect2 = np.cumsum(x2, axis=axis2)
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diff2 = abs(output[2].asnumpy() - expect2)
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error2 = np.ones(shape=expect2.shape) * 1.0e-5
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assert np.all(diff2 < error2)
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assert output[2].shape == expect2.shape
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expect3 = np.cumsum(x3, axis=axis3)
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diff3 = abs(output[3].asnumpy() - expect3)
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error3 = np.ones(shape=expect3.shape) * 1.0e-5
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assert np.all(diff3 < error3)
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assert output[3].shape == expect3.shape
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expect4 = np.cumsum(x4, axis=axis4)
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diff4 = abs(output[4].asnumpy() - expect4)
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error4 = np.ones(shape=expect4.shape) * 1.0e-5
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assert np.all(diff4 < error4)
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assert output[4].shape == expect4.shape
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expect5 = np.cumsum(x5, axis=axis5)
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diff5 = abs(output[5].asnumpy() - expect5)
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error5 = np.ones(shape=expect5.shape) * 1.0e-5
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assert np.all(diff5 < error5)
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assert output[5].shape == expect5.shape
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expect6 = np.cumsum(x6, axis=axis6)
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diff6 = abs(output[6].asnumpy() - expect6)
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error6 = np.ones(shape=expect6.shape) * 1.0e-5
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assert np.all(diff6 < error6)
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assert output[6].shape == expect6.shape
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|
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@ -0,0 +1,177 @@
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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");
|
||||
# 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.
|
||||
# ============================================================================
|
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|
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import numpy as np
|
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import pytest
|
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|
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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.common.api import ms_function
|
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from mindspore.ops import operations as P
|
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|
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x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis0 = 3
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keep_dims0 = True
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x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis1 = 3
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keep_dims1 = False
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x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
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axis2 = 2
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keep_dims2 = True
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x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
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axis3 = 2
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keep_dims3 = False
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x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis4 = ()
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np_axis4 = None
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keep_dims4 = True
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x5 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis5 = ()
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np_axis5 = None
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keep_dims5 = False
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x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis6 = -2
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keep_dims6 = False
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x7 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis7 = (-2, -1)
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keep_dims7 = True
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x8 = np.random.rand(1, 1, 1, 1).astype(np.float32)
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axis8 = ()
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np_axis8 = None
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keep_dims8 = True
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context.set_context(device_target='GPU')
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||||
|
||||
|
||||
class ReduceMin(nn.Cell):
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||||
def __init__(self):
|
||||
super(ReduceMin, self).__init__()
|
||||
|
||||
self.x0 = Tensor(x0)
|
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self.axis0 = axis0
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self.keep_dims0 = keep_dims0
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|
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self.x1 = Tensor(x1)
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self.axis1 = axis1
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self.keep_dims1 = keep_dims1
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|
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self.x2 = Tensor(x2)
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self.axis2 = axis2
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self.keep_dims2 = keep_dims2
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|
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self.x3 = Tensor(x3)
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self.axis3 = axis3
|
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self.keep_dims3 = keep_dims3
|
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|
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self.x4 = Tensor(x4)
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self.axis4 = axis4
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self.keep_dims4 = keep_dims4
|
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|
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self.x5 = Tensor(x5)
|
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self.axis5 = axis5
|
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self.keep_dims5 = keep_dims5
|
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|
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self.x6 = Tensor(x6)
|
||||
self.axis6 = axis6
|
||||
self.keep_dims6 = keep_dims6
|
||||
|
||||
self.x7 = Tensor(x7)
|
||||
self.axis7 = axis7
|
||||
self.keep_dims7 = keep_dims7
|
||||
|
||||
self.x8 = Tensor(x8)
|
||||
self.axis8 = axis8
|
||||
self.keep_dims8 = keep_dims8
|
||||
|
||||
@ms_function
|
||||
def construct(self):
|
||||
return (P.ReduceMin(self.keep_dims0)(self.x0, self.axis0),
|
||||
P.ReduceMin(self.keep_dims1)(self.x1, self.axis1),
|
||||
P.ReduceMin(self.keep_dims2)(self.x2, self.axis2),
|
||||
P.ReduceMin(self.keep_dims3)(self.x3, self.axis3),
|
||||
P.ReduceMin(self.keep_dims4)(self.x4, self.axis4),
|
||||
P.ReduceMin(self.keep_dims5)(self.x5, self.axis5),
|
||||
P.ReduceMin(self.keep_dims6)(self.x6, self.axis6),
|
||||
P.ReduceMin(self.keep_dims7)(self.x7, self.axis7),
|
||||
P.ReduceMin(self.keep_dims8)(self.x8, self.axis8))
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_ReduceMin():
|
||||
reduce_min = ReduceMin()
|
||||
output = reduce_min()
|
||||
|
||||
expect0 = np.min(x0, axis=axis0, keepdims=keep_dims0)
|
||||
diff0 = abs(output[0].asnumpy() - expect0)
|
||||
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
||||
assert np.all(diff0 < error0)
|
||||
assert output[0].shape == expect0.shape
|
||||
|
||||
expect1 = np.min(x1, axis=axis1, keepdims=keep_dims1)
|
||||
diff1 = abs(output[1].asnumpy() - expect1)
|
||||
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
||||
assert np.all(diff1 < error1)
|
||||
assert output[1].shape == expect1.shape
|
||||
|
||||
expect2 = np.min(x2, axis=axis2, keepdims=keep_dims2)
|
||||
diff2 = abs(output[2].asnumpy() - expect2)
|
||||
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
||||
assert np.all(diff2 < error2)
|
||||
assert output[2].shape == expect2.shape
|
||||
|
||||
expect3 = np.min(x3, axis=axis3, keepdims=keep_dims3)
|
||||
diff3 = abs(output[3].asnumpy() - expect3)
|
||||
error3 = np.ones(shape=expect3.shape) * 1.0e-5
|
||||
assert np.all(diff3 < error3)
|
||||
assert output[3].shape == expect3.shape
|
||||
|
||||
expect4 = np.min(x4, axis=np_axis4, keepdims=keep_dims4)
|
||||
diff4 = abs(output[4].asnumpy() - expect4)
|
||||
error4 = np.ones(shape=expect4.shape) * 1.0e-5
|
||||
assert np.all(diff4 < error4)
|
||||
assert output[4].shape == expect4.shape
|
||||
|
||||
expect5 = np.min(x5, axis=np_axis5, keepdims=keep_dims5)
|
||||
diff5 = abs(output[5].asnumpy() - expect5)
|
||||
error5 = np.ones(shape=expect5.shape) * 1.0e-5
|
||||
assert np.all(diff5 < error5)
|
||||
assert output[5].shape == expect5.shape
|
||||
|
||||
expect6 = np.min(x6, axis=axis6, keepdims=keep_dims6)
|
||||
diff6 = abs(output[6].asnumpy() - expect6)
|
||||
error6 = np.ones(shape=expect6.shape) * 1.0e-5
|
||||
assert np.all(diff6 < error6)
|
||||
assert output[6].shape == expect6.shape
|
||||
|
||||
expect7 = np.min(x7, axis=axis7, keepdims=keep_dims7)
|
||||
diff7 = abs(output[7].asnumpy() - expect7)
|
||||
error7 = np.ones(shape=expect7.shape) * 1.0e-5
|
||||
assert np.all(diff7 < error7)
|
||||
|
||||
expect8 = np.min(x8, axis=np_axis8, keepdims=keep_dims8)
|
||||
diff8 = abs(output[8].asnumpy() - expect8)
|
||||
error8 = np.ones(shape=expect8.shape) * 1.0e-5
|
||||
assert np.all(diff8 < error8)
|
||||
Loading…
Reference in New Issue