wehub-resource-sync
630 lines
25 KiB
Plaintext
630 lines
25 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/send_ue_recv_grad_kernel.h"
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#include "paddle/common/enforce.h"
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#include "paddle/common/hostdevice.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/empty_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/elementwise_functor.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/gpu/graph_send_recv_funcs.h"
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#include "paddle/phi/kernels/gpu/graph_send_ue_recv_funcs.h"
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#include "paddle/phi/kernels/impl/graph_message_passing_impl.h"
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#include "paddle/phi/kernels/reduce_sum_kernel.h"
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namespace phi {
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template <typename Context, typename T, typename IndexT>
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void CalculateXEGradForMinMax(const Context& dev_ctx,
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const T* out_grad,
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const T* x_data,
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const T* e_data,
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const DDim& x_dims,
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const DDim& e_dims,
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const IndexT* s_index,
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const IndexT* d_index,
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const std::string& message_op,
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const std::string& reduce_op,
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int64_t index_size,
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T* x_grad,
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T* e_grad,
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const DenseTensor* out = nullptr) {
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const T* out_data = out->data<T>();
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const auto& bcast_info = CalcBCastInfo(x_dims, e_dims);
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thrust::device_vector<int64_t> l_bcastoff, r_bcastoff;
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if (bcast_info.use_bcast) {
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CopyBCastOff(bcast_info, &l_bcastoff, &r_bcastoff);
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}
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int64_t out_len = bcast_info.out_len;
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const int ntx = FindNumThreads(out_len, dev_ctx.GetMaxThreadsPerBlock());
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const int nty = dev_ctx.GetMaxThreadsPerBlock() / ntx;
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const int64_t nbx_64 = (out_len + ntx - 1) / ntx;
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PADDLE_ENFORCE_LE_INT_MAX(nbx_64, "grid.x");
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const int nbx = static_cast<int>(nbx_64);
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const int64_t nby_64 = (index_size + nty - 1) / nty;
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const int nby = FindNumBlocks('y', nby_64);
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const dim3 grid(nbx, nby);
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const dim3 block(ntx, nty);
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if (message_op == "ADD") {
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ManipulateMinMaxGradCUDAKernelForAdd<T, IndexT>
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<<<grid, block, 0, dev_ctx.stream()>>>(
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x_data,
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e_data,
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out_data,
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out_grad,
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d_index,
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s_index,
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thrust::raw_pointer_cast(l_bcastoff.data()),
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thrust::raw_pointer_cast(r_bcastoff.data()),
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x_grad,
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e_grad,
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index_size,
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bcast_info.l_len,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast);
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} else if (message_op == "MUL") {
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ManipulateMinMaxGradCUDAKernelForMul<T, IndexT>
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<<<grid, block, 0, dev_ctx.stream()>>>(
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x_data,
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e_data,
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out_data,
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out_grad,
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d_index,
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s_index,
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thrust::raw_pointer_cast(l_bcastoff.data()),
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thrust::raw_pointer_cast(r_bcastoff.data()),
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x_grad,
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e_grad,
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index_size,
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bcast_info.l_len,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast);
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}
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}
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template <typename Context, typename T, typename IndexT>
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void CalculateXGrad(const Context& dev_ctx,
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const T* out_grad,
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const T* x_data,
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const T* e_data,
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const DDim& out_grad_dims,
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const DDim& x_dims,
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const DDim& e_dims,
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const IndexT* s_index,
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const IndexT* d_index,
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const std::string& message_op,
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const std::string& reduce_op,
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int64_t index_size,
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int64_t slice_size,
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T* x_grad,
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const DenseTensor& out_grad_tensor,
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const DenseTensor* dst_count = nullptr,
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const DenseTensor* out = nullptr) {
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int block = 1024;
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int64_t n = slice_size * index_size;
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int max_grid_dimx = dev_ctx.GetCUDAMaxGridDimSize()[0];
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int64_t grid_tmp = (n + block - 1) / block;
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int64_t grid = grid_tmp < max_grid_dimx ? grid_tmp : max_grid_dimx;
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std::vector<int64_t> reduce_idx;
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bool reduce = ReduceGrad(out_grad_dims, x_dims, reduce_idx);
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if (reduce_op == "SUM") {
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if (message_op == "ADD") {
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GraphSendRecvSumCUDAFunctor<T, IndexT> functor;
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if (!reduce) {
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GraphSendRecvCUDAKernel<T,
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IndexT,
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GraphSendRecvSumCUDAFunctor<T, IndexT>>
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<<<grid, block, 0, dev_ctx.stream()>>>(out_grad,
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d_index,
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s_index,
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x_grad,
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index_size,
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slice_size,
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functor);
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} else {
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const auto& bcast_info = CalcBCastInfo(out_grad_dims, e_dims);
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DenseTensor x_grad_v2 = EmptyLike<T, Context>(dev_ctx, out_grad_tensor);
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funcs::SetConstant<Context, T>()(dev_ctx, &x_grad_v2, T(0));
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T* x_grad_v2_data = x_grad_v2.data<T>();
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GraphSendRecvCUDAKernel<T,
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IndexT,
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GraphSendRecvSumCUDAFunctor<T, IndexT>>
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<<<grid, block, 0, dev_ctx.stream()>>>(out_grad,
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d_index,
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s_index,
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x_grad_v2_data,
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index_size,
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bcast_info.out_len,
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functor);
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// Run reduce_sum
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DenseTensor x_grad_out = Sum<T, Context>(dev_ctx,
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x_grad_v2,
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IntArray(reduce_idx),
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CppTypeToDataType<T>::Type(),
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true);
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#ifdef PADDLE_WITH_HIP
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hipMemcpy(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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hipMemcpyDeviceToDevice);
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#else
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cudaMemcpyAsync(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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cudaMemcpyDeviceToDevice,
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dev_ctx.stream());
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#endif
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}
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} else if (message_op == "MUL") {
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const auto& bcast_info = CalcBCastInfo(out_grad_dims, e_dims);
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thrust::device_vector<int64_t> l_bcastoff, r_bcastoff;
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if (bcast_info.use_bcast) {
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CopyBCastOff(bcast_info, &l_bcastoff, &r_bcastoff);
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}
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int64_t out_len = bcast_info.out_len;
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const int ntx = FindNumThreads(out_len, dev_ctx.GetMaxThreadsPerBlock());
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const int nty = dev_ctx.GetMaxThreadsPerBlock() / ntx;
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const int64_t nbx_64 = (out_len + ntx - 1) / ntx;
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PADDLE_ENFORCE_LE_INT_MAX(nbx_64, "grid.x");
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const int nbx = static_cast<int>(nbx_64);
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const int64_t nby_64 = (index_size + nty - 1) / nty;
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const int nby = FindNumBlocks('y', nby_64);
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const dim3 grid_(nbx, nby);
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const dim3 block_(ntx, nty);
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funcs::MultiplyFunctor<T> mul_functor;
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GraphSendUERecvSumCUDAFunctor<T> sum_functor;
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if (!reduce) {
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GraphSendUERecvCUDAKernel<T,
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IndexT,
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GraphSendUERecvSumCUDAFunctor<T>,
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funcs::MultiplyFunctor<T>>
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<<<grid_, block_, 0, dev_ctx.stream()>>>(
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out_grad,
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e_data,
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d_index,
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s_index,
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thrust::raw_pointer_cast(l_bcastoff.data()),
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thrust::raw_pointer_cast(r_bcastoff.data()),
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x_grad,
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index_size,
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bcast_info.l_len,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast,
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mul_functor,
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sum_functor);
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} else {
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DenseTensor x_grad_v2 = EmptyLike<T, Context>(dev_ctx, out_grad_tensor);
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funcs::SetConstant<Context, T>()(dev_ctx, &x_grad_v2, T(0));
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T* x_grad_v2_data = x_grad_v2.data<T>();
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GraphSendUERecvCUDAKernel<T,
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IndexT,
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GraphSendUERecvSumCUDAFunctor<T>,
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funcs::MultiplyFunctor<T>>
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<<<grid_, block_, 0, dev_ctx.stream()>>>(
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out_grad,
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e_data,
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d_index,
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s_index,
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thrust::raw_pointer_cast(l_bcastoff.data()),
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thrust::raw_pointer_cast(r_bcastoff.data()),
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x_grad_v2_data,
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index_size,
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bcast_info.l_len,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast,
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mul_functor,
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sum_functor);
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DenseTensor x_grad_out = Sum<T, Context>(dev_ctx,
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x_grad_v2,
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IntArray(reduce_idx),
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CppTypeToDataType<T>::Type(),
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true);
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#ifdef PADDLE_WITH_HIP
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hipMemcpy(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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hipMemcpyDeviceToDevice);
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#else
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cudaMemcpyAsync(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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cudaMemcpyDeviceToDevice,
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dev_ctx.stream());
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#endif
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}
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}
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} else if (reduce_op == "MEAN") {
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const int* s_count = dst_count->data<int>();
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if (message_op == "ADD") {
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if (!reduce) {
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ManipulateMeanGradCUDAKernel<T, IndexT>
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<<<grid, block, 0, dev_ctx.stream()>>>(out_grad,
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d_index,
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s_index,
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x_grad,
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index_size,
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slice_size,
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s_count);
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} else {
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const auto& bcast_info = CalcBCastInfo(out_grad_dims, e_dims);
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DenseTensor x_grad_v2 = EmptyLike<T, Context>(dev_ctx, out_grad_tensor);
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funcs::SetConstant<Context, T>()(dev_ctx, &x_grad_v2, T(0));
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T* x_grad_v2_data = x_grad_v2.data<T>();
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ManipulateMeanGradCUDAKernel<T, IndexT>
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<<<grid, block, 0, dev_ctx.stream()>>>(out_grad,
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d_index,
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s_index,
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x_grad_v2_data,
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index_size,
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bcast_info.out_len,
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s_count);
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// Run reduce_sum
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DenseTensor x_grad_out = Sum<T, Context>(dev_ctx,
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x_grad_v2,
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IntArray(reduce_idx),
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CppTypeToDataType<T>::Type(),
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true);
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#ifdef PADDLE_WITH_HIP
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hipMemcpy(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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hipMemcpyDeviceToDevice);
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#else
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cudaMemcpyAsync(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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cudaMemcpyDeviceToDevice,
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dev_ctx.stream());
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#endif
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}
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} else if (message_op == "MUL") {
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const auto& bcast_info = CalcBCastInfo(out_grad_dims, e_dims);
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thrust::device_vector<int64_t> l_bcastoff, r_bcastoff;
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if (bcast_info.use_bcast) {
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CopyBCastOff(bcast_info, &l_bcastoff, &r_bcastoff);
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}
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int64_t out_len = bcast_info.out_len;
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const int ntx = FindNumThreads(out_len, dev_ctx.GetMaxThreadsPerBlock());
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const int nty = dev_ctx.GetMaxThreadsPerBlock() / ntx;
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const int64_t nbx_64 = (out_len + ntx - 1) / ntx;
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PADDLE_ENFORCE_LE_INT_MAX(nbx_64, "grid.x");
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const int nbx = static_cast<int>(nbx_64);
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const int64_t nby_64 = (index_size + nty - 1) / nty;
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const int nby = FindNumBlocks('y', nby_64);
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const dim3 grid_(nbx, nby);
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const dim3 block_(ntx, nty);
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if (!reduce) {
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ManipulateMeanGradCUDAKernelForMulX<T, IndexT>
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<<<grid_, block_, 0, dev_ctx.stream()>>>(
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out_grad,
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e_data,
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d_index,
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s_index,
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s_count,
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thrust::raw_pointer_cast(l_bcastoff.data()),
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thrust::raw_pointer_cast(r_bcastoff.data()),
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x_grad,
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index_size,
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bcast_info.l_len,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast);
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} else {
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DenseTensor x_grad_v2 = EmptyLike<T, Context>(dev_ctx, out_grad_tensor);
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funcs::SetConstant<Context, T>()(dev_ctx, &x_grad_v2, T(0));
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T* x_grad_v2_data = x_grad_v2.data<T>();
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ManipulateMeanGradCUDAKernelForMulX<T, IndexT>
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<<<grid_, block_, 0, dev_ctx.stream()>>>(
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out_grad,
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e_data,
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d_index,
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s_index,
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s_count,
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thrust::raw_pointer_cast(l_bcastoff.data()),
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thrust::raw_pointer_cast(r_bcastoff.data()),
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x_grad_v2_data,
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index_size,
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bcast_info.l_len,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast);
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// Run reduce_sum
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DenseTensor x_grad_out = Sum<T, Context>(dev_ctx,
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x_grad_v2,
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IntArray(reduce_idx),
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CppTypeToDataType<T>::Type(),
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true);
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// TODO(daisiming): Whether use x_grad instead.
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#ifdef PADDLE_WITH_HIP
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hipMemcpy(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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hipMemcpyDeviceToDevice);
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#else
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cudaMemcpyAsync(x_grad,
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x_grad_out.data<T>(),
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x_grad_out.numel() * sizeof(T),
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cudaMemcpyDeviceToDevice,
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dev_ctx.stream());
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#endif
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}
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}
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}
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}
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template <typename Context, typename T, typename IndexT>
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void CalculateEGrad(const Context& dev_ctx,
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const T* out_grad,
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const T* x_data,
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const T* e_data,
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const DDim& x_dims,
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const DDim& e_dims,
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const IndexT* s_index,
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const IndexT* d_index,
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const std::string& message_op,
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const std::string& reduce_op,
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int64_t index_size,
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T* e_grad,
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const DenseTensor* dst_count = nullptr) {
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const auto& bcast_info = CalcBCastInfo(x_dims, e_dims);
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thrust::device_vector<int64_t> l_bcastoff, r_bcastoff;
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if (bcast_info.use_bcast) {
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CopyBCastOff(bcast_info, &l_bcastoff, &r_bcastoff);
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}
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int64_t out_len = bcast_info.out_len;
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const int ntx = FindNumThreads(out_len, dev_ctx.GetMaxThreadsPerBlock());
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const int nty = dev_ctx.GetMaxThreadsPerBlock() / ntx;
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const int64_t nbx_64 = (out_len + ntx - 1) / ntx;
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PADDLE_ENFORCE_LE_INT_MAX(nbx_64, "grid.x");
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const int nbx = static_cast<int>(nbx_64);
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const int64_t nby_64 = (index_size + nty - 1) / nty;
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const int nby = FindNumBlocks('y', nby_64);
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const dim3 grid(nbx, nby);
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const dim3 block(ntx, nty);
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if (reduce_op == "SUM") {
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if (message_op == "ADD") {
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ManipulateSumGradCUDAKernelForAddE<T, IndexT>
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<<<grid, block, 0, dev_ctx.stream()>>>(
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out_grad,
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d_index,
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thrust::raw_pointer_cast(r_bcastoff.data()),
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e_grad,
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index_size,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast);
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} else if (message_op == "MUL") {
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ManipulateSumGradCUDAKernelForMulE<T, IndexT>
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<<<grid, block, 0, dev_ctx.stream()>>>(
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x_data,
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out_grad,
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s_index,
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d_index,
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thrust::raw_pointer_cast(l_bcastoff.data()),
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thrust::raw_pointer_cast(r_bcastoff.data()),
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e_grad,
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index_size,
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bcast_info.l_len,
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bcast_info.r_len,
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out_len,
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bcast_info.use_bcast);
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}
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} else if (reduce_op == "MEAN") {
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const int* s_count = dst_count->data<int>();
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if (message_op == "ADD") {
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ManipulateMeanGradCUDAKernelForAddE<T, IndexT>
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<<<grid, block, 0, dev_ctx.stream()>>>(
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out_grad,
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d_index,
|
|
s_count,
|
|
thrust::raw_pointer_cast(r_bcastoff.data()),
|
|
e_grad,
|
|
index_size,
|
|
bcast_info.r_len,
|
|
out_len,
|
|
bcast_info.use_bcast);
|
|
} else if (message_op == "MUL") {
|
|
ManipulateMeanGradCUDAKernelForMulE<T, IndexT>
|
|
<<<grid, block, 0, dev_ctx.stream()>>>(
|
|
x_data,
|
|
out_grad,
|
|
s_index,
|
|
d_index,
|
|
s_count,
|
|
thrust::raw_pointer_cast(l_bcastoff.data()),
|
|
thrust::raw_pointer_cast(r_bcastoff.data()),
|
|
e_grad,
|
|
index_size,
|
|
bcast_info.l_len,
|
|
bcast_info.r_len,
|
|
out_len,
|
|
bcast_info.use_bcast);
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename Context, typename T, typename IndexT>
|
|
void GraphSendUERecvGradOpCUDAKernelLaunchHelper(
|
|
const Context& dev_ctx,
|
|
const DenseTensor& out_grad,
|
|
const DenseTensor& x,
|
|
const DenseTensor& e,
|
|
const DenseTensor& src_index,
|
|
const DenseTensor& dst_index,
|
|
const std::string& message_op,
|
|
const std::string& reduce_op,
|
|
DenseTensor* x_grad,
|
|
DenseTensor* e_grad,
|
|
const DenseTensor* dst_count = nullptr,
|
|
const DenseTensor* out = nullptr) {
|
|
// TODO(large-tensor): downstream functors may still use int; guard until
|
|
// upgraded.
|
|
const int64_t& index_size = dst_index.dims()[0];
|
|
|
|
dev_ctx.template Alloc<T>(x_grad);
|
|
T* x_grad_data = x_grad->data<T>();
|
|
dev_ctx.template Alloc<T>(e_grad);
|
|
T* e_grad_data = e_grad->data<T>();
|
|
const auto& x_dims = x.dims();
|
|
const auto& e_dims = e.dims();
|
|
int64_t memset_size_x = 1, memset_size_e = 1;
|
|
int64_t slice_size = 1;
|
|
for (int i = 0; i < x_dims.size(); i++) {
|
|
memset_size_x *= x_dims[i];
|
|
if (i > 0) slice_size *= x_dims[i];
|
|
}
|
|
for (int i = 0; i < e_dims.size(); i++) {
|
|
memset_size_e *= e_dims[i];
|
|
}
|
|
const size_t& memset_bytes_x = memset_size_x * sizeof(T);
|
|
const size_t& memset_bytes_e = memset_size_e * sizeof(T);
|
|
#ifdef PADDLE_WITH_HIP
|
|
hipMemset(x_grad_data, 0, memset_bytes_x);
|
|
hipMemset(e_grad_data, 0, memset_bytes_e);
|
|
#else
|
|
cudaMemsetAsync(x_grad_data, 0, memset_bytes_x, dev_ctx.stream());
|
|
cudaMemsetAsync(e_grad_data, 0, memset_bytes_e, dev_ctx.stream());
|
|
#endif
|
|
|
|
if (index_size == 0) return;
|
|
|
|
const T* out_grad_data = out_grad.data<T>();
|
|
const T* x_data = x.data<T>();
|
|
const T* e_data = e.data<T>();
|
|
const IndexT* s_index = src_index.data<IndexT>();
|
|
const IndexT* d_index = dst_index.data<IndexT>();
|
|
|
|
if (reduce_op == "SUM" || reduce_op == "MEAN") {
|
|
CalculateXGrad<Context, T, IndexT>(dev_ctx,
|
|
out_grad_data,
|
|
x_data,
|
|
e_data,
|
|
out_grad.dims(),
|
|
x_dims,
|
|
e_dims,
|
|
s_index,
|
|
d_index,
|
|
message_op,
|
|
reduce_op,
|
|
index_size,
|
|
slice_size,
|
|
x_grad_data,
|
|
out_grad,
|
|
dst_count,
|
|
out);
|
|
CalculateEGrad<Context, T, IndexT>(dev_ctx,
|
|
out_grad_data,
|
|
x_data,
|
|
e_data,
|
|
x_dims,
|
|
e_dims,
|
|
s_index,
|
|
d_index,
|
|
message_op,
|
|
reduce_op,
|
|
index_size,
|
|
e_grad_data,
|
|
dst_count);
|
|
} else if (reduce_op == "MIN" || reduce_op == "MAX") {
|
|
CalculateXEGradForMinMax<Context, T, IndexT>(dev_ctx,
|
|
out_grad_data,
|
|
x_data,
|
|
e_data,
|
|
x_dims,
|
|
e_dims,
|
|
s_index,
|
|
d_index,
|
|
message_op,
|
|
reduce_op,
|
|
index_size,
|
|
x_grad_data,
|
|
e_grad_data,
|
|
out);
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void SendUERecvGradKernel(const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const DenseTensor& y,
|
|
const DenseTensor& src_index,
|
|
const DenseTensor& dst_index,
|
|
const optional<DenseTensor>& out,
|
|
const optional<DenseTensor>& dst_count,
|
|
const DenseTensor& out_grad,
|
|
const std::string& message_op,
|
|
const std::string& reduce_op,
|
|
DenseTensor* x_grad,
|
|
DenseTensor* y_grad) {
|
|
auto index_type = src_index.dtype();
|
|
|
|
if (out_grad.numel() == 0 || x.numel() == 0 || y.numel() == 0 ||
|
|
src_index.numel() == 0 || dst_index.numel() == 0) {
|
|
Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
|
|
Full<T, Context>(dev_ctx, y_grad->dims(), 0, y_grad);
|
|
return;
|
|
}
|
|
|
|
if (index_type == DataType::INT32) {
|
|
GraphSendUERecvGradOpCUDAKernelLaunchHelper<Context, T, int32_t>(
|
|
dev_ctx,
|
|
out_grad,
|
|
x,
|
|
y,
|
|
src_index,
|
|
dst_index,
|
|
message_op,
|
|
reduce_op,
|
|
x_grad,
|
|
y_grad,
|
|
dst_count.get_ptr(),
|
|
out.get_ptr());
|
|
} else if (index_type == DataType::INT64) {
|
|
GraphSendUERecvGradOpCUDAKernelLaunchHelper<Context, T, int64_t>(
|
|
dev_ctx,
|
|
out_grad,
|
|
x,
|
|
y,
|
|
src_index,
|
|
dst_index,
|
|
message_op,
|
|
reduce_op,
|
|
x_grad,
|
|
y_grad,
|
|
dst_count.get_ptr(),
|
|
out.get_ptr());
|
|
}
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(send_ue_recv_grad,
|
|
GPU,
|
|
ALL_LAYOUT,
|
|
phi::SendUERecvGradKernel,
|
|
float,
|
|
double,
|
|
int,
|
|
int64_t,
|
|
phi::float16) {}
|