forked from mindspore-Ecosystem/mindspore
add gpu oneslike kernel
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qujianwei
parent
bbcefa731d
commit
fb2ac74d9a
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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/oneslike_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(OnesLike, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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OnesLikeGpuKernel, float)
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MS_REG_GPU_KERNEL_ONE(OnesLike, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
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OnesLikeGpuKernel, half)
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MS_REG_GPU_KERNEL_ONE(OnesLike, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
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OnesLikeGpuKernel, int)
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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_ONESLIKE_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_ONESLIKE_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/oneslike_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 OnesLikeGpuKernel : public GpuKernel {
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public:
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OnesLikeGpuKernel() : input_size_(0), output_size_(0) {}
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~OnesLikeGpuKernel() 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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T *input = GetDeviceAddress<T>(inputs, 0);
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T *output = GetDeviceAddress<T>(outputs, 0);
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int size = SizeToInt(input_size_ / sizeof(T));
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CalOnesLike(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) << "Input number is " << input_num << ", but oneslike needs 1 input.";
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return false;
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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if (output_num != 1) {
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MS_LOG(ERROR) << "Output number is " << output_num << ", but oneslike needs 1 output.";
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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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size_t shape_size = input_shape.size();
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input_size_ = 1;
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for (size_t i = 0; i < shape_size; i++) {
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input_size_ *= input_shape[i];
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}
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input_size_ *= sizeof(T);
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output_size_ = input_size_;
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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_);
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output_size_list_.push_back(output_size_);
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return;
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}
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private:
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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 output_size_;
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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_ONESLIKE_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 <cuda_runtime.h>
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#include "oneslike_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 OnesLike(const int size, const T* input, T* output) {
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int one = 1;
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T val = static_cast<T>(one);
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for (int pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
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output[pos] = val;
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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 CalOnesLike(const int size, const T* input, T* output, cudaStream_t cuda_stream) {
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OnesLike<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
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return;
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}
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template void CalOnesLike<float>(const int size, const float* input, float* output, cudaStream_t cuda_stream);
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template void CalOnesLike<half>(const int size, const half* input, half* output, cudaStream_t cuda_stream);
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template void CalOnesLike<int>(const int size, const int* input, int* output, 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_IMPL_ONESLIKE_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ONESLIKE_H_
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template <typename T>
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void CalOnesLike(const int size, const T* input, T* output, cudaStream_t cuda_stream);
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#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ONESLIKE_H_
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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.ops import operations as P
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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class NetOnesLike(nn.Cell):
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def __init__(self):
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super(NetOnesLike, self).__init__()
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self.ones_like = P.OnesLike()
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def construct(self, x):
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return self.ones_like(x)
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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_OnesLike():
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x0_np = np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32)
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x1_np = np.random.uniform(-2, 2, 1).astype(np.float16)
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x2_np = np.zeros([3, 3, 3], dtype=np.int32)
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x0 = Tensor(x0_np)
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x1 = Tensor(x1_np)
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x2 = Tensor(x2_np)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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ones_like = NetOnesLike()
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output0 = ones_like(x0)
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expect0 = np.ones_like(x0_np)
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diff0 = output0.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 output0.shape == expect0.shape
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output1 = ones_like(x1)
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expect1 = np.ones_like(x1_np)
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diff1 = output1.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 output1.shape == expect1.shape
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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ones_like = NetOnesLike()
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output0 = ones_like(x0)
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expect0 = np.ones_like(x0_np)
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diff0 = output0.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 output0.shape == expect0.shape
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output1 = ones_like(x1)
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expect1 = np.ones_like(x1_np)
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diff1 = output1.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 output1.shape == expect1.shape
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output2 = ones_like(x2)
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expect2 = np.ones_like(x2_np)
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diff2 = output2.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 output2.shape == expect2.shape
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