forked from mindspore-Ecosystem/mindspore
added nll_loss_grad for gpu
This commit is contained in:
markuskunej
parent
56f6288fa7
commit
2fece8a7c2
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@ -126,6 +126,13 @@ __global__ void LossInitKernel(T *loss) {
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loss[0] = static_cast<T>(0.);
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}
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template <typename T>
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__global__ void InitZero(T *array, int size) {
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += blockDim.x * gridDim.x) {
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array[i] = static_cast<T>(0.);
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}
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}
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template <typename T>
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__global__ void KLDivLossKernel(const int input_size, const int reduction, const T *input_x, const T *input_y, T *loss,
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T *tmp_loss) {
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@ -332,6 +339,50 @@ void NLLLoss(const int n, const int c, const int reduction, const T *input, cons
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CopyEqual<<<1, 1, 0, stream>>>(tmp_target_weight, total_weight, 1);
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}
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template <typename T, typename S>
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__global__ void NLLLossGradKernel(const int n, const int c, const int reduction, const T *input, const int32_t *target,
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const S *weight, const S *total_weight, const T *dloss, T *dinput) {
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int input_idx;
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int target_class;
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S tmp_quot;
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if (reduction == 0) {
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
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target_class = static_cast<int>(target[i]);
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input_idx = (i * c) + target_class;
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MultiplyDevice(-weight[target_class], dloss[i], dinput + input_idx);
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}
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} else if (reduction == 1) {
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
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target_class = static_cast<int>(target[i]);
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input_idx = (i * c) + target_class;
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tmp_quot = (-weight[target_class]) / *total_weight;
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MultiplyDevice(tmp_quot, dloss[0], dinput + input_idx);
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}
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} else if (reduction == 2) {
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for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
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target_class = static_cast<int>(target[i]);
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input_idx = (i * c) + target_class;
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MultiplyDevice(-weight[target_class], dloss[0], dinput + input_idx);
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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 NLLLossGrad(const int n, const int c, const int reduction, const T *input, const int32_t *target, const S *weight,
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const S *total_weight, const T *dloss, T *dinput, cudaStream_t stream) {
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int input_size = n * c;
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InitZero<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(dinput, input_size);
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NLLLossGradKernel<<<GET_BLOCKS(n), GET_THREADS, 0, stream>>>(n, c, reduction, input, target, weight, total_weight,
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dloss, dinput);
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}
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template void KLDivLoss<float>(const int &input_size, const int &reduction, const float *input_x, const float *input_y,
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float *loss, float *tmp_loss, cudaStream_t stream);
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@ -354,6 +405,14 @@ template void NLLLoss<float, half>(const int n, const int c, const int reduction
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const int32_t *target, const half *weight, float *loss, half *total_weight,
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float *tmp_loss, half *tmp_target_weight, cudaStream_t stream);
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template void NLLLossGrad<float, float>(const int n, const int c, const int reduction, const float *input,
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const int32_t *target, const float *weight, const float *total_weight,
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const float *dloss, float *dinput, cudaStream_t stream);
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template void NLLLossGrad<float, half>(const int n, const int c, const int reduction, const float *input,
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const int32_t *target, const half *weight, const half *total_weight,
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const float *dloss, float *dinput, cudaStream_t stream);
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template void KLDivLoss<half>(const int &input_size, const int &reduction, const half *input_x, const half *input_y,
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half *loss, half *tmp_loss, cudaStream_t stream);
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@ -375,3 +434,11 @@ template void NLLLoss<half, half>(const int n, const int c, const int reduction,
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template void NLLLoss<half, float>(const int n, const int c, const int reduction, const half *input,
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const int32_t *target, const float *weight, half *loss, float *total_weight,
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half *tmp_loss, float *tmp_target_weight, cudaStream_t stream);
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template void NLLLossGrad<half, half>(const int n, const int c, const int reduction, const half *input,
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const int32_t *target, const half *weight, const half *total_weight,
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const half *dloss, half *dinput, cudaStream_t stream);
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template void NLLLossGrad<half, float>(const int n, const int c, const int reduction, const half *input,
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const int32_t *target, const float *weight, const float *total_weight,
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const half *dloss, half *dinput, cudaStream_t stream);
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@ -32,4 +32,8 @@ void KLDivLossGrad(const int &input_size, const int &reduction, const T *input_x
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template <typename T, typename S>
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void NLLLoss(const int n, const int c, const int reduction, const T *input, const int32_t *target, const S *weight,
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T *loss, S *total_weight, T *tmp_loss, S *tmp_target_weight, cudaStream_t stream);
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template <typename T, typename S>
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void NLLLossGrad(const int n, const int c, const int reduction, const T *input, const int32_t *target, const S *weight,
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const S *total_weight, const T *dloss, T *dinput, cudaStream_t stream);
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_LOSS_WITH_REDUCTION_IMPL_CUH
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@ -0,0 +1,58 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "backend/kernel_compiler/gpu/nn/nll_loss_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(NLLLossGrad,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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NLLLossGradGpuKernel, float, float)
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MS_REG_GPU_KERNEL_TWO(NLLLossGrad,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat32),
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NLLLossGradGpuKernel, float, half)
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MS_REG_GPU_KERNEL_TWO(NLLLossGrad,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat16),
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NLLLossGradGpuKernel, half, float)
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MS_REG_GPU_KERNEL_TWO(NLLLossGrad,
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KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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NLLLossGradGpuKernel, half, half)
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,113 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_NLL_LOSS_GRAD_GPU_KERNEL_H
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_NLL_LOSS_GRAD_GPU_KERNEL_H
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#include <vector>
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#include <string>
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#include "backend/kernel_compiler/gpu/gpu_kernel.h"
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#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
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#include "backend/kernel_compiler/gpu/cuda_impl/loss_with_reduction_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 NLLLossGradGpuKernel : public GpuKernel {
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public:
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NLLLossGradGpuKernel() { ResetResource(); }
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~NLLLossGradGpuKernel() 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_device = GetDeviceAddress<T>(inputs, 0);
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T *dloss_device = GetDeviceAddress<T>(inputs, 1);
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int32_t *target_device = GetDeviceAddress<int32_t>(inputs, 2); // nll_loss_grad only supports int32 target
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S *weight_device = GetDeviceAddress<S>(inputs, 3);
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S *total_weight_device = GetDeviceAddress<S>(inputs, 4);
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T *dinput_device = GetDeviceAddress<T>(outputs, 0);
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NLLLossGrad(n_, c_, reduction_, input_device, target_device, weight_device, total_weight_device, dloss_device,
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dinput_device, 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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std::vector<size_t> input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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n_ = static_cast<int>(input_shape[0]);
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c_ = static_cast<int>(input_shape[1]);
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for (size_t i = 0; i < input_shape.size(); i++) {
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input_size_ *= input_shape[i];
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}
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string reduction = GetAttr<string>(kernel_node, "reduction");
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// if reduction is not 'none', tmp_nll is (N,) size
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if (reduction == "none") {
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reduction_ = 0;
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num_dloss_ = n_; // dloss is a vector
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} else if (reduction == "sum") {
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reduction_ = 2;
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} else {
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// reduction = 'mean'
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reduction_ = 1;
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}
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InitSizeLists();
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return true;
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}
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void ResetResource() noexcept override {
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input_size_ = 1;
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n_ = 0;
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c_ = 0;
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reduction_ = 1; // default value
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num_dloss_ = 1; // default size (scalar)
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input_size_list_.clear();
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output_size_list_.clear();
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workspace_size_list_.clear();
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}
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protected:
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void InitSizeLists() override {
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input_size_list_.push_back(input_size_ * sizeof(T)); // input tensor with shape (N, C)
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input_size_list_.push_back(num_dloss_ * sizeof(T)); // dloss tensor (either scalar or size N)
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input_size_list_.push_back(n_ * sizeof(int32_t)); // target tensor with shape (N)
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input_size_list_.push_back(c_ * sizeof(S)); // weight tensor with shape (C)
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input_size_list_.push_back(sizeof(S)); // total_weight scalar
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output_size_list_.push_back(input_size_ * sizeof(T)); // dinput
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}
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private:
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size_t input_size_;
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int reduction_;
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int n_;
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int c_;
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int num_dloss_;
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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_NN_NLL_LOSS_GRAD_GPU_KERNEL_H
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@ -19,6 +19,7 @@ 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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from mindspore.ops import operations as P
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@ -31,12 +32,23 @@ class Net(nn.Cell):
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return self.loss(predict, target, weight)
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class NLLLossGradNet(nn.Cell):
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def __init__(self, reduction):
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super(NLLLossGradNet, self).__init__()
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self.grad = G.NLLLossGrad(reduction=reduction)
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def construct(self, x, dout_x, target, weight, total_weight):
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gout = self.grad(x, dout_x, target, weight, total_weight)
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return gout
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def nll_loss_template(nptype_input, nptype_weight, reduction):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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nll_loss_net = Net(reduction)
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predict = Tensor(np.array([[0.53, 0.74, -2.12], [1.29, -0.34, -1.13]]).astype(nptype_input))
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predict = Tensor(
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np.array([[0.53, 0.74, -2.12], [1.29, -0.34, -1.13]]).astype(nptype_input))
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target = Tensor(np.array([0, 1]).astype(np.int32))
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@ -67,7 +79,48 @@ def nll_loss_template(nptype_input, nptype_weight, reduction):
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ertol_weight = 1e-03
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np.testing.assert_allclose(loss_np, expected_loss, ertol_loss)
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np.testing.assert_allclose(total_weight_np, expected_tot_weight, ertol_weight)
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np.testing.assert_allclose(
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total_weight_np, expected_tot_weight, ertol_weight)
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def nll_loss_grad_template(nptype_input, nptype_weight, reduction):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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nll_loss_grad_net = NLLLossGradNet(reduction)
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x = Tensor(
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np.array([[0.53, 0.74, -2.12], [1.29, -0.34, -1.13]]).astype(nptype_input))
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if reduction == "none":
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dloss = Tensor(
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np.array([3.24, -2.13]).astype(nptype_input))
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else:
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dloss = Tensor(np.array(1.23).astype(nptype_input))
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target = Tensor(np.array([0, 1]).astype(np.int32))
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weight = Tensor(np.array([0.45, -0.32, 1.21]).astype(nptype_weight))
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total_weight = Tensor(np.array(0.13).astype(nptype_weight))
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dx = nll_loss_grad_net(x, dloss, target, weight, total_weight)
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dx_np = dx.asnumpy()
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print(dx)
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if reduction == "none":
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dx_expected = np.array([[-1.45799994, 0, 0], [0, -0.681600034, 0]])
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elif reduction == "mean":
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dx_expected = np.array([[-4.25769234, 0, 0], [0, 3.02769232, 0]])
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else:
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dx_expected = np.array([[-0.553499997, 0, 0], [0, 0.393599987, 0]])
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if nptype_input == np.float32 and nptype_weight == np.float32:
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ertol_loss = 1e-06
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else:
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ertol_loss = 1e-02
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np.testing.assert_allclose(dx_np, dx_expected, ertol_loss)
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@pytest.mark.level0
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@ -91,6 +144,7 @@ def test_nll_loss_mean_reduction():
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nll_loss_template(np.float16, np.float32, "mean")
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nll_loss_template(np.float16, np.float16, "mean")
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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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@ -100,3 +154,36 @@ def test_nll_loss_sum_reduction():
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nll_loss_template(np.float32, np.float16, "sum")
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nll_loss_template(np.float16, np.float32, "sum")
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nll_loss_template(np.float16, np.float16, "sum")
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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_nll_loss_grad_mean_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_grad_template(np.float32, np.float32, "mean")
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nll_loss_grad_template(np.float32, np.float16, "mean")
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nll_loss_grad_template(np.float16, np.float32, "mean")
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nll_loss_grad_template(np.float16, np.float16, "mean")
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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_nll_loss_grad_sum_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_grad_template(np.float32, np.float32, "sum")
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nll_loss_grad_template(np.float32, np.float16, "sum")
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nll_loss_grad_template(np.float16, np.float32, "sum")
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nll_loss_grad_template(np.float16, np.float16, "sum")
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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_nll_loss_grad_no_reduction():
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# Four combinations of fp32 and fp16 inputs and weights
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nll_loss_grad_template(np.float32, np.float32, "none")
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nll_loss_grad_template(np.float32, np.float16, "none")
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nll_loss_grad_template(np.float16, np.float32, "none")
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nll_loss_grad_template(np.float16, np.float16, "none")
|
||||
|
|
|
|||
Loading…
Reference in New Issue