wehub-resource-sync
115 lines
5.1 KiB
Plaintext
115 lines
5.1 KiB
Plaintext
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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#include "paddle/phi/kernels/nll_loss_grad_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/gpu/nll_loss.h"
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namespace phi {
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template <typename T, typename Context>
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void NllLossGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& labels,
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const optional<DenseTensor>& weight,
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const DenseTensor& total_weight,
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const DenseTensor& dout,
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int64_t ignore_index,
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const std::string& reduction,
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DenseTensor* dx) {
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auto dx_data = dev_ctx.template Alloc<T>(dx);
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auto dout_data = dout.data<T>();
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auto label_data = labels.data<int64_t>();
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auto weight_data = weight.get_ptr() ? weight.get_ptr()->data<T>() : nullptr;
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auto total_weight_data = total_weight.data<T>();
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#ifdef PADDLE_WITH_HIP
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hipMemset(dx_data, 0, dx->numel() * sizeof(T));
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#else
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cudaMemset(dx_data, 0, dx->numel() * sizeof(T));
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#endif
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int64_t size_average = (int64_t)(reduction == "mean");
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auto x_dims = x.dims();
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auto batch_size = x_dims[0];
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auto n_classes = x_dims[1];
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if (x_dims.size() == 2) {
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int blocks = NumBlocks(batch_size);
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int threads = kNumCUDAThreads;
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if (reduction == "none") {
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GPUNLLLossBackward1D_no_reduce<T>
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<<<blocks, threads, 0, dev_ctx.stream()>>>(dx_data,
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label_data,
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weight_data,
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dout_data,
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batch_size,
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n_classes,
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ignore_index);
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} else {
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GPUNLLLossBackward1D_with_reduce<T>
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<<<1, NTHREADS, 0, dev_ctx.stream()>>>(dx_data,
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total_weight_data,
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label_data,
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weight_data,
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dout_data,
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batch_size,
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n_classes,
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size_average,
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ignore_index);
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}
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} else if (x_dims.size() == 4) {
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const auto in_dim2 = x_dims[2];
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const auto in_dim3 = x_dims[3];
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const auto map_size = in_dim2 * in_dim3;
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const auto out_numel = batch_size * in_dim2 * in_dim3;
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int blocks = NumBlocks(out_numel);
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int threads = kNumCUDAThreads;
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if (reduction == "none") {
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GPUNLLLossBackward2D_no_reduce<T>
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<<<blocks, threads, 0, dev_ctx.stream()>>>(dx_data,
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label_data,
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weight_data,
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dout_data,
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batch_size,
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n_classes,
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in_dim2,
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in_dim3,
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ignore_index);
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} else {
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int blocks_per_sample = NumBlocks(map_size) / 128;
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blocks_per_sample = (blocks_per_sample == 0) ? 1 : blocks_per_sample;
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int total_blocks = blocks_per_sample * batch_size;
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GPUNLLLossBackward2D_with_reduce<T>
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<<<total_blocks, threads, 0, dev_ctx.stream()>>>(dx_data,
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total_weight_data,
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label_data,
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weight_data,
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dout_data,
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batch_size,
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n_classes,
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map_size,
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blocks_per_sample,
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size_average,
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ignore_index);
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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nll_loss_grad, GPU, ALL_LAYOUT, phi::NllLossGradKernel, float, double) {}
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