wehub-resource-sync
100 lines
3.4 KiB
Plaintext
100 lines
3.4 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 <algorithm>
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/cub.h"
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#include "paddle/phi/kernels/funcs/math.h"
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#include "paddle/phi/kernels/impl/sequence_softmax_kernel_impl.h"
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namespace phi {
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template <typename T, int BlockDim>
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using BlockReduce = cub::BlockReduce<T, BlockDim>;
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template <typename T, int BlockDim>
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using BlockReduceTempStorage = typename BlockReduce<T, BlockDim>::TempStorage;
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template <typename T, int BlockDim>
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__global__ void sequence_softmax_grad_kernel(const T *softmax_grad_data,
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const T *softmax_data,
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const size_t *ref_lod,
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const size_t src_height,
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T *dx_data) {
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__shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
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__shared__ T shared_data;
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for (size_t i = blockIdx.x; i < src_height; i += gridDim.x) {
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size_t start = ref_lod[i];
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size_t span = ref_lod[i + 1] - start;
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T result = 0;
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for (size_t tid = threadIdx.x; tid < span; tid += blockDim.x) {
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size_t idx = start + tid;
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T s_g_d = softmax_grad_data[idx];
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T s_d = softmax_data[idx];
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result += s_g_d * s_d;
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}
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result = BlockReduce<T, BlockDim>(temp_storage).Reduce(result, cub::Sum());
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if (threadIdx.x == 0) {
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shared_data = result;
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}
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__syncthreads();
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for (size_t tid = threadIdx.x; tid < span; tid += blockDim.x) {
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size_t idx = start + tid;
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T s_g_d = softmax_grad_data[idx];
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T s_d = softmax_data[idx];
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dx_data[idx] = (s_g_d - shared_data) * s_d;
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}
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}
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}
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template <typename T>
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struct SequenceSoftmaxGradFunctor<GPUContext, T> {
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void operator()(const GPUContext &dev_ctx,
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const DenseTensor &dout,
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const DenseTensor &out,
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const Vector<size_t> &ref_lod, /*referenced lod*/
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DenseTensor *dx) {
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size_t height = ref_lod.size() - 1;
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const int kThreadsPerBlock = 32;
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int thread_x = kThreadsPerBlock;
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int max_threads = dev_ctx.GetMaxPhysicalThreadCount();
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int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
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dim3 block_size(thread_x);
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dim3 grid_size(max_blocks);
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MixVector<size_t> mixv_ref_lod(&ref_lod);
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sequence_softmax_grad_kernel<T, kThreadsPerBlock>
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<<<grid_size, block_size, 0, dev_ctx.stream()>>>(
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dout.data<T>(),
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out.data<T>(),
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mixv_ref_lod.CUDAData(dev_ctx.GetPlace()),
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height,
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dev_ctx.Alloc<T>(dx));
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}
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};
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} // namespace phi
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PD_REGISTER_KERNEL(sequence_softmax_grad,
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GPU,
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ALL_LAYOUT,
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phi::SequenceSoftmaxGradKernel,
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float,
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double) {}
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