chore: import upstream snapshot with attribution
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wehub-resource-sync
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// 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_kernel.h"
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#include <algorithm>
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#include <set>
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#include <vector>
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#include "paddle/common/hostdevice.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/cpu/graph_send_ue_recv_funcs.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/impl/graph_message_passing_impl.h"
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namespace phi {
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template <typename T, typename IndexT, typename ComputeFunctor>
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void GraphSendUERecvSumCpuKernel(const BroadCastInfo& bcast,
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const T* x_data,
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const T* y_data,
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const IndexT* src_indices,
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const IndexT* dst_indices,
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T* output,
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int64_t index_size,
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ComputeFunctor cfunctor) {
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for
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#endif
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for (int64_t i = 0; i < index_size; i++) {
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IndexT src = src_indices[i];
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IndexT dst = dst_indices[i];
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T* out_off = output + dst * bcast.out_len;
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const T* x_off = x_data + src * bcast.l_len;
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const T* y_off = y_data + i * bcast.r_len;
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for (int64_t j = 0; j < bcast.out_len; j++) {
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int64_t x_add = bcast.use_bcast ? bcast.l_offset[j] : j;
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int64_t y_add = bcast.use_bcast ? bcast.r_offset[j] : j;
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T val = cfunctor(x_off[x_add], y_off[y_add]);
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if (val != 0) {
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#ifdef PADDLE_WITH_MKLML
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#pragma omp atomic
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#endif
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out_off[j] += val;
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}
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}
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}
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}
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template <typename T,
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typename IndexT,
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typename ComputeFunctor,
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typename CmpFunctor>
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void GraphSendUERecvMinMaxCpuKernel(const BroadCastInfo& bcast,
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const T* x_data,
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const T* y_data,
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const IndexT* src_indices,
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const IndexT* dst_indices,
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T* output,
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int64_t index_size,
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ComputeFunctor cfunctor,
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CmpFunctor pfunctor) {
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std::set<IndexT> existed_dst;
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#ifdef PADDLE_WITH_MKLML
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#pragma omp parallel for
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#endif
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for (int64_t i = 0; i < index_size; i++) {
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IndexT src = src_indices[i];
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IndexT dst = dst_indices[i];
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T* out_off = output + dst * bcast.out_len;
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const T* x_off = x_data + src * bcast.l_len;
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const T* y_off = y_data + i * bcast.r_len;
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bool in_set = existed_dst.find(dst) != existed_dst.end();
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for (int64_t j = 0; j < bcast.out_len; j++) {
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int64_t x_add = bcast.use_bcast ? bcast.l_offset[j] : j;
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int64_t y_add = bcast.use_bcast ? bcast.r_offset[j] : j;
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T val = cfunctor(x_off[x_add], y_off[y_add]);
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#ifdef PADDLE_WITH_MKLML
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#pragma omp critical
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#endif
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if (!in_set) {
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out_off[j] = val;
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} else {
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out_off[j] = pfunctor(out_off[j], val);
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}
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}
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#ifdef PADDLE_WITH_MKLML
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#pragma omp critical
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#endif
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if (!in_set) {
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existed_dst.emplace(dst);
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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 GraphSendUERecvOpKernelLaunchHelper(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& src_index,
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const DenseTensor& dst_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 out_size,
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DenseTensor* out,
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DenseTensor* dst_count = nullptr) {
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// TODO(large-tensor): downstream functors may still use int; guard until
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// upgraded.
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const int64_t& index_size = src_index.dims()[0];
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// NOLINT
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auto out_dims = out->dims();
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int64_t memset_size = 1;
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std::vector<int64_t> dims_ = vectorize(out_dims);
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if (out_size <= 0) {
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dims_[0] = x.dims()[0];
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} else {
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dims_[0] = out_size;
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}
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out->Resize(dims_);
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for (auto dim : dims_) {
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memset_size *= dim;
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}
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dev_ctx.template Alloc<T>(out);
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T* out_data = out->data<T>();
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const size_t& memset_bytes = memset_size * sizeof(T);
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memset(out_data, 0, memset_bytes);
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if (index_size == 0) return;
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const auto& bcast_info = CalcBCastInfo(x.dims(), y.dims());
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const T* x_data = x.data<T>();
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const T* y_data = y.data<T>();
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const IndexT* s_index = src_index.data<IndexT>();
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const IndexT* d_index = dst_index.data<IndexT>();
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if (reduce_op == "SUM" || reduce_op == "MEAN") {
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if (message_op == "ADD") {
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GraphAddFunctor<T> add_functor;
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GraphSendUERecvSumCpuKernel<T, IndexT, GraphAddFunctor<T>>(bcast_info,
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x_data,
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y_data,
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s_index,
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d_index,
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out_data,
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index_size,
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add_functor);
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} else if (message_op == "MUL") {
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GraphMulFunctor<T> mul_functor;
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GraphSendUERecvSumCpuKernel<T, IndexT, GraphMulFunctor<T>>(bcast_info,
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x_data,
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y_data,
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s_index,
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d_index,
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out_data,
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index_size,
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mul_functor);
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}
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if (reduce_op == "MEAN") {
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int64_t input_size = out_size <= 0 ? x.dims()[0] : out_size;
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dst_count->Resize({input_size});
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int* dst_count_data = dev_ctx.template Alloc<int>(dst_count);
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memset(dst_count_data, 0, input_size * sizeof(int));
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for (int i = 0; i < index_size; i++) {
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IndexT dst_idx = d_index[i];
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dst_count_data[dst_idx] += 1;
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}
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for (int i = 0; i < input_size; i++) {
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if (dst_count_data[i] == 0) continue;
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auto out_slice = out->Slice(i, i + 1);
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auto eigen_out = EigenVector<T>::Flatten(out_slice);
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eigen_out = eigen_out / static_cast<T>(dst_count_data[i]);
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}
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}
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} else if (reduce_op == "MIN") {
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GraphMinFunctor<T> min_functor;
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if (message_op == "ADD") {
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GraphAddFunctor<T> add_functor;
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GraphSendUERecvMinMaxCpuKernel<T,
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IndexT,
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GraphAddFunctor<T>,
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GraphMinFunctor<T>>(bcast_info,
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x_data,
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y_data,
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s_index,
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d_index,
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out_data,
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index_size,
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add_functor,
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min_functor);
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} else if (message_op == "MUL") {
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GraphMulFunctor<T> mul_functor;
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GraphSendUERecvMinMaxCpuKernel<T,
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IndexT,
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GraphMulFunctor<T>,
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GraphMinFunctor<T>>(bcast_info,
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x_data,
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y_data,
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s_index,
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d_index,
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out_data,
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index_size,
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mul_functor,
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min_functor);
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}
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} else if (reduce_op == "MAX") {
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GraphMaxFunctor<T> max_functor;
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if (message_op == "ADD") {
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GraphAddFunctor<T> add_functor;
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GraphSendUERecvMinMaxCpuKernel<T,
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IndexT,
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GraphAddFunctor<T>,
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GraphMaxFunctor<T>>(bcast_info,
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x_data,
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y_data,
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s_index,
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d_index,
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out_data,
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index_size,
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add_functor,
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max_functor);
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} else if (message_op == "MUL") {
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GraphMulFunctor<T> mul_functor;
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GraphSendUERecvMinMaxCpuKernel<T,
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IndexT,
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GraphMulFunctor<T>,
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GraphMaxFunctor<T>>(bcast_info,
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x_data,
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y_data,
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s_index,
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d_index,
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out_data,
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index_size,
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mul_functor,
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max_functor);
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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 SendUERecvKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& src_index,
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const DenseTensor& dst_index,
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const std::string& message_op,
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const std::string& reduce_op,
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const IntArray& out_size,
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DenseTensor* out,
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DenseTensor* dst_count) {
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auto index_type = src_index.dtype();
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auto& out_size_data = out_size.GetData();
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if (x.numel() == 0 || y.numel() == 0 || src_index.numel() == 0 ||
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dst_index.numel() == 0) {
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std::vector<int64_t> dims_ = vectorize(out->dims());
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if (out_size_data[0] <= 0) {
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dims_[0] = x.dims()[0];
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} else {
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dims_[0] = out_size_data[0];
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}
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if (reduce_op == "MEAN") {
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int64_t input_size =
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out_size_data[0] <= 0 ? x.dims()[0] : out_size_data[0];
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dst_count->Resize({input_size});
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}
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out->Resize(dims_);
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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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Full<int, Context>(dev_ctx, dst_count->dims(), 0, dst_count);
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return;
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}
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if (index_type == DataType::INT32) {
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GraphSendUERecvOpKernelLaunchHelper<Context, T, int32_t>(dev_ctx,
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x,
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y,
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src_index,
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dst_index,
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message_op,
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reduce_op,
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out_size_data[0],
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out,
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dst_count);
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} else if (index_type == DataType::INT64) {
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GraphSendUERecvOpKernelLaunchHelper<Context, T, int64_t>(dev_ctx,
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x,
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y,
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src_index,
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dst_index,
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message_op,
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reduce_op,
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out_size_data[0],
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out,
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dst_count);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(send_ue_recv,
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CPU,
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ALL_LAYOUT,
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phi::SendUERecvKernel,
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float,
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double,
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int,
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int64_t) {
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kernel->OutputAt(1).SetDataType(phi::DataType::INT32);
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}
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