forked from mindspore-Ecosystem/mindspore
add SigmoidCrossEntropyWithLogitsGrad op
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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 "kernel/gpu/cuda_impl/sigmoid_cross_entropy_with_logits_grad_impl.cuh"
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template <typename T, typename S>
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__global__ void SigmoidCrossEntropyWithLogitsGradKernel(const size_t size, const T *logits, const S *labels,
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T *outputs) {
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
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if (logits[i] >= 0) {
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outputs[i] = 1. / (1. + exp(-logits[i])) - labels[i];
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} else {
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const T exp_val = exp(logits[i]);
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outputs[i] = exp_val / (1. + exp_val) - labels[i];
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}
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}
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}
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template <typename T, typename S>
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void SigmoidCrossEntropyWithLogitsGrad(const size_t size, const T *logits, const S *labels, T *outputs,
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cudaStream_t cuda_stream) {
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SigmoidCrossEntropyWithLogitsGradKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, logits, labels,
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outputs);
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}
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template void SigmoidCrossEntropyWithLogitsGrad<float, float>(const size_t size, const float *logits,
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const float *labels, float *outputs,
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cudaStream_t cuda_stream);
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_IMPL_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_IMPL_H_
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#include "device/gpu/cuda_common.h"
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template <typename T, typename S>
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void SigmoidCrossEntropyWithLogitsGrad(const size_t size, const T *logits, const S *labels, T *outputs,
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cudaStream_t cuda_stream);
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#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_IMPL_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 "kernel/gpu/nn/sigmoid_cross_entropy_with_logits_grad_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_TWO(SigmoidCrossEntropyWithLogitsGrad,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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SigmoidCrossEntropyWithLogitsGradGpuKernel, float, float)
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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_NN_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_KERNEL_GPU_NN_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_GPU_KERNEL_H_
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#include <vector>
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#include "kernel/gpu/gpu_kernel.h"
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#include "kernel/gpu/gpu_kernel_factory.h"
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#include "kernel/gpu/cuda_impl/sigmoid_cross_entropy_with_logits_grad_impl.cuh"
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namespace mindspore {
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namespace kernel {
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template <typename T, typename S>
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class SigmoidCrossEntropyWithLogitsGradGpuKernel : public GpuKernel {
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public:
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SigmoidCrossEntropyWithLogitsGradGpuKernel() : logits_size_(0), labels_size_(0), outputs_size_(0) {}
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~SigmoidCrossEntropyWithLogitsGradGpuKernel() 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 *logits_addr = GetDeviceAddress<T>(inputs, 0);
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S *labels_addr = GetDeviceAddress<S>(inputs, 1);
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T *outputs_addr = GetDeviceAddress<T>(outputs, 0);
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SigmoidCrossEntropyWithLogitsGrad(inputs[0]->size / sizeof(T), logits_addr, labels_addr, outputs_addr,
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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 != 3) {
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MS_LOG(ERROR) << "Input number is " << input_num << ", but SigmoidCrossEntropyWithLogitsGrad needs 3 inputs.";
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return false;
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}
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logits_size_ = sizeof(T);
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labels_size_ = sizeof(S);
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outputs_size_ = sizeof(T);
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auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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for (size_t i = 0; i < logits_shape.size(); i++) {
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logits_size_ *= logits_shape[i];
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}
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auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
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for (size_t i = 0; i < labels_shape.size(); i++) {
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labels_size_ *= labels_shape[i];
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}
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auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
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for (size_t i = 0; i < output_shape.size(); i++) {
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outputs_size_ *= output_shape[i];
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}
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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(logits_size_);
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input_size_list_.push_back(labels_size_);
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output_size_list_.push_back(outputs_size_);
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}
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private:
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size_t logits_size_;
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size_t labels_size_;
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size_t outputs_size_;
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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_NN_SIGMOID_CROSS_ENTROPY_WITH_LOGITS_GRAD_GPU_KERNEL_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.operations import _grad_ops as G
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class NetSigmoidCrossEntropyWithLogits(nn.Cell):
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def __init__(self):
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super(NetSigmoidCrossEntropyWithLogits, self).__init__()
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self.sigmoid_cross_entropy_with_logits_grad = G.SigmoidCrossEntropyWithLogitsGrad()
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def construct(self, logits, labels, dout):
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return self.sigmoid_cross_entropy_with_logits_grad(logits, labels, dout)
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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_sigmoid_cross_entropy_with_logits():
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logits = Tensor(np.array([[1, 1, 2],
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[1, 2, 1],
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[2, 1, 1]]).astype(np.float32))
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labels = Tensor(np.array([[0, 0, 1],
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[0, 1, 0],
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[1, 0, 0]]).astype(np.float32))
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dout = Tensor(np.ones(shape=[3, 3]).astype(np.float32))
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expect = np.array([[0.731059, 0.731059, -0.119203],
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[0.731059, -0.119203, 0.731059],
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[-0.119203, 0.731059, 0.731059]]).astype(np.float32)
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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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sigmoid_cross_entropy_with_logits = NetSigmoidCrossEntropyWithLogits()
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output = sigmoid_cross_entropy_with_logits(logits, labels, dout)
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diff = output.asnumpy() - expect
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assert np.all(abs(diff) < error)
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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sigmoid_cross_entropy_with_logits = NetSigmoidCrossEntropyWithLogits()
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output = sigmoid_cross_entropy_with_logits(logits, labels, dout)
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diff = output.asnumpy() - expect
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assert np.all(abs(diff) < error)
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