wehub-resource-sync
136 lines
5.8 KiB
Plaintext
136 lines
5.8 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_kernel.h"
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#include "paddle/common/flags.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/common/amp_type_traits.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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COMMON_DECLARE_bool(use_accuracy_compatible_kernel);
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namespace phi {
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template <typename T, typename Context>
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void NllLossRawKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const DenseTensor& label,
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const optional<DenseTensor>& weight,
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int64_t ignore_index,
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const std::string& reduction,
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DenseTensor* out,
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DenseTensor* total_weight) {
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auto* x = &input;
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auto x_data = x->data<T>();
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auto out_data = dev_ctx.template Alloc<T>(out);
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auto total_weight_data = dev_ctx.template Alloc<T>(total_weight);
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auto label_data = label.data<int64_t>();
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auto weight_data = weight.get_ptr() ? weight.get_ptr()->data<T>() : nullptr;
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#ifdef PADDLE_WITH_HIP
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hipMemset(total_weight_data, 0, sizeof(T));
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#else
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cudaMemset(total_weight_data, 0, sizeof(T));
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#endif
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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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int size_average = static_cast<int>(reduction == "mean");
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using AccT = typename MPTypeTrait<T>::Type;
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if (x_dims.size() == 2) {
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int64_t blocks = NumBlocks(batch_size);
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int threads = kNumCUDAThreads;
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if (reduction == "none") {
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GPUNLLLossForward1D_no_reduce<T>
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<<<blocks, threads, 0, dev_ctx.stream()>>>(out_data,
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x_data,
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label_data,
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weight_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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if (FLAGS_use_accuracy_compatible_kernel) {
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int nthreads = nll_loss_threads(batch_size);
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GPUNLLLossForward1D_with_reduce_compatible<T, AccT>
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<<<1, nthreads, nthreads * sizeof(AccT) * 2, dev_ctx.stream()>>>(
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out_data,
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total_weight_data,
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x_data,
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label_data,
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weight_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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} else {
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GPUNLLLossForward1D_with_reduce<T, AccT>
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<<<1, NTHREADS, 0, dev_ctx.stream()>>>(out_data,
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total_weight_data,
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x_data,
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label_data,
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weight_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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}
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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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int64_t blocks = NumBlocks(out_numel);
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int threads = kNumCUDAThreads;
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if (reduction == "none") {
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GPUNLLLossForward2D_no_reduce<T>
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<<<blocks, threads, 0, dev_ctx.stream()>>>(out_data,
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x_data,
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label_data,
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weight_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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int64_t total_blocks = blocks_per_sample * batch_size;
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GPUNLLLossForward2D_with_reduce<T, AccT>
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<<<total_blocks, threads, 0, dev_ctx.stream()>>>(out_data,
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total_weight_data,
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x_data,
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label_data,
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weight_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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ignore_index);
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if (size_average) {
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GPUNLLLossForward2D_size_average<T>
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<<<1, 1, 0, dev_ctx.stream()>>>(out_data, total_weight_data);
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}
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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, GPU, ALL_LAYOUT, phi::NllLossRawKernel, float, double) {}
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