wehub-resource-sync
137 lines
5.6 KiB
Plaintext
137 lines
5.6 KiB
Plaintext
// Copyright (c) 2024 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/impl/margin_cross_entropy.cu.h"
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namespace phi {
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template <typename T, typename IndexT>
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__global__ void CalculateGrad(T* logits_grad,
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const T* loss_grad,
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const T* logits,
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const IndexT* label,
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const float margin1,
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const float margin2,
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const float scale,
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const int rank,
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const int64_t N,
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const int64_t D,
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const int* class_interval_ptr) {
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using MT = typename MPTypeTrait<T>::Type;
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int start_index = class_interval_ptr[rank];
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CUDA_KERNEL_LOOP(i, N * D) {
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auto row = i / D;
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auto col = i % D;
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if ((col + start_index) == label[row]) {
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logits_grad[i] = (logits_grad[i] - static_cast<T>(1.0)) * loss_grad[row];
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if (fabs(margin1 - 1.0) > 1e-8 || fabs(margin2) > 1e-8) {
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MT dout = static_cast<MT>(logits_grad[i]);
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MT one = static_cast<MT>(1.0f);
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MT x = static_cast<MT>(logits[i]);
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MT m1 = static_cast<MT>(margin1);
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MT m2 = static_cast<MT>(margin2);
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MT d = m1 * sin(m1 * acos(x) + m2) / sqrt(one - x * x);
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logits_grad[i] = static_cast<T>(dout * d);
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}
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} else {
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logits_grad[i] *= loss_grad[row];
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}
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if (fabs(scale - 1.0) > 1e-8) {
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logits_grad[i] *= static_cast<T>(scale);
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}
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}
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}
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template <typename T, typename Context>
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void MarginCrossEntropyGradKernel(const Context& dev_ctx,
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const DenseTensor& logits,
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const DenseTensor& label,
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const DenseTensor& softmax,
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const DenseTensor& loss_grad,
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bool return_softmax,
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int ring_id,
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int rank,
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int nranks,
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float margin1,
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float margin2,
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float margin3,
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float scale,
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DenseTensor* logits_grad) {
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const auto softmax_dims = softmax.dims();
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const int axis = softmax_dims.size() - 1;
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const int64_t N = funcs::SizeToAxis(axis, softmax_dims);
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const int64_t D = funcs::SizeFromAxis(axis, softmax_dims);
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if (return_softmax) {
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Copy<Context>(dev_ctx, softmax, dev_ctx.GetPlace(), false, logits_grad);
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} else {
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logits_grad->ShareDataWith(softmax);
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}
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int64_t blocks = NumBlocks(N * D);
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int threads = kNumCUDAThreads;
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const auto& label_type = label.dtype();
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DenseTensor class_interval;
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GetClassInterval<T, Context>(dev_ctx.stream(),
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dev_ctx.GetPlace(),
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dev_ctx,
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ring_id,
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rank,
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nranks,
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D,
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&class_interval);
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if (label_type == DataType::INT32) {
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typedef int32_t LabelT;
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CalculateGrad<T, LabelT>
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<<<blocks, threads, 0, dev_ctx.stream()>>>(logits_grad->data<T>(),
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loss_grad.data<T>(),
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logits.data<T>(),
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label.data<LabelT>(),
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margin1,
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margin2,
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scale,
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rank,
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N,
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D,
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class_interval.data<int>());
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} else if (label_type == DataType::INT64) {
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typedef int64_t LabelT;
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CalculateGrad<T, LabelT>
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<<<blocks, threads, 0, dev_ctx.stream()>>>(logits_grad->data<T>(),
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loss_grad.data<T>(),
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logits.data<T>(),
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label.data<LabelT>(),
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margin1,
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margin2,
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scale,
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rank,
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N,
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D,
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class_interval.data<int>());
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(margin_cross_entropy_grad,
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GPU,
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ALL_LAYOUT,
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phi::MarginCrossEntropyGradKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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