chore: import upstream snapshot with attribution
This commit is contained in:
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_u_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_recv_funcs.h"
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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename IndexT, typename Functor>
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void GraphSendRecvCpuLoop(const int& input_size,
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const int& index_size,
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const IndexT* s_index,
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const IndexT* d_index,
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const DenseTensor& src,
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DenseTensor* dst,
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const std::string& reduce_op,
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int* dst_count = nullptr) {
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Functor functor;
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if (reduce_op == "SUM") {
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for (int i = 0; i < index_size; ++i) {
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const IndexT& src_idx = s_index[i];
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const IndexT& dst_idx = d_index[i];
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ElementwiseInnerOperation<T, IndexT, Functor>(
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src, dst, src_idx, dst_idx, false, functor);
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}
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} else if (reduce_op == "MEAN") {
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for (int i = 0; i < index_size; ++i) {
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const IndexT& src_idx = s_index[i];
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const IndexT& dst_idx = d_index[i];
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ElementwiseInnerOperation<T, IndexT, Functor>(
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src, dst, src_idx, dst_idx, false, functor);
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}
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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 + 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 + i) == 0) continue;
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auto dst_slice = dst->Slice(i, i + 1);
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auto eigen_dst = EigenVector<T>::Flatten(dst_slice);
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eigen_dst = eigen_dst / static_cast<T>(*(dst_count + i));
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}
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} else if (reduce_op == "MIN" || reduce_op == "MAX") {
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std::set<IndexT> existed_dst;
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for (int i = 0; i < index_size; ++i) {
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const IndexT& src_idx = s_index[i];
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const IndexT& dst_idx = d_index[i];
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bool in_set = existed_dst.find(dst_idx) != existed_dst.end();
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if (!in_set) {
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ElementwiseInnerOperation<T, IndexT, Functor>(
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src, dst, src_idx, dst_idx, true, functor);
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existed_dst.emplace(dst_idx);
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} else {
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ElementwiseInnerOperation<T, IndexT, Functor>(
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src, dst, src_idx, dst_idx, false, functor);
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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 GraphSendRecvOpKernelLaunchHelper(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& src_index,
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const DenseTensor& dst_index,
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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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const auto& src_dims = x.dims();
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int64_t memset_size = 1;
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if (out_size <= 0) {
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out->Resize(src_dims);
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for (int i = 0; i < src_dims.size(); ++i) {
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memset_size *= src_dims[i];
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}
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} else {
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// Set out dim following out_size.
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std::vector<int64_t> dims_ = vectorize(src_dims);
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if (!dims_.empty()) {
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dims_[0] = out_size;
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}
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out->Resize(dims_);
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memset_size = out_size;
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for (int i = 1; i < src_dims.size(); ++i) {
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memset_size *= src_dims[i];
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}
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}
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dev_ctx.template Alloc<T>(out);
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T* p_output = out->data<T>();
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const size_t& memset_bytes = memset_size * sizeof(T);
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memset(p_output, 0, memset_bytes);
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if (index_size == 0) return;
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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") {
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GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvSumFunctor<T>>(
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src_dims[0], index_size, s_index, d_index, x, out, reduce_op);
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} else if (reduce_op == "MIN") {
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GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvMinFunctor<T>>(
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src_dims[0], index_size, s_index, d_index, x, out, reduce_op);
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} else if (reduce_op == "MAX") {
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GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvMaxFunctor<T>>(
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src_dims[0], index_size, s_index, d_index, x, out, reduce_op);
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} else if (reduce_op == "MEAN") {
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int64_t input_size = out_size <= 0 ? src_dims[0] : out_size;
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dst_count->Resize({input_size});
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dev_ctx.template Alloc<int>(dst_count);
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int* p_dst_count = dst_count->data<int>();
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memset(p_dst_count, 0, input_size * sizeof(int));
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GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvSumFunctor<T>>(input_size,
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index_size,
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s_index,
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d_index,
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x,
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out,
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reduce_op,
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p_dst_count);
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}
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}
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template <typename T, typename Context>
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void SendURecvKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& src_index,
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const DenseTensor& dst_index,
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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 || src_index.numel() == 0 || dst_index.numel() == 0) {
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if (out_size_data[0] <= 0) {
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out->Resize(x.dims());
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} else {
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out->Resize(out_size_data);
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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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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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Full<int32_t, 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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GraphSendRecvOpKernelLaunchHelper<Context, T, int32_t>(dev_ctx,
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x,
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src_index,
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dst_index,
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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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GraphSendRecvOpKernelLaunchHelper<Context, T, int64_t>(dev_ctx,
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x,
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src_index,
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dst_index,
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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_u_recv,
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CPU,
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ALL_LAYOUT,
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phi::SendURecvKernel,
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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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