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graph_send_recv_kernel.cc
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graph_send_recv_kernel.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/graph_send_recv_kernel.h"
#include <algorithm>
#include <set>
#include <vector>
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/hostdevice.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cpu/graph_send_recv_funcs.h"
namespace phi {
template <typename T, typename IndexT, typename Functor>
void GraphSendRecvCpuLoop(const int& input_size,
const int& index_size,
const IndexT* s_index,
const IndexT* d_index,
const DenseTensor& src,
DenseTensor* dst,
const std::string& pool_type,
int* dst_count = nullptr) {
Functor functor;
if (pool_type == "SUM") {
for (int i = 0; i < index_size; ++i) {
const IndexT& src_idx = s_index[i];
const IndexT& dst_idx = d_index[i];
ElementwiseInnerOperation<T, IndexT, Functor>(
src, dst, src_idx, dst_idx, false, functor);
}
} else if (pool_type == "MEAN") {
for (int i = 0; i < index_size; ++i) {
const IndexT& src_idx = s_index[i];
const IndexT& dst_idx = d_index[i];
ElementwiseInnerOperation<T, IndexT, Functor>(
src, dst, src_idx, dst_idx, false, functor);
}
for (int i = 0; i < index_size; ++i) {
IndexT dst_idx = d_index[i];
*(dst_count + dst_idx) += 1;
}
for (int i = 0; i < input_size; ++i) {
if (*(dst_count + i) == 0) continue;
auto dst_slice = dst->Slice(i, i + 1);
auto eigen_dst = phi::EigenVector<T>::Flatten(dst_slice);
eigen_dst = eigen_dst / static_cast<T>(*(dst_count + i));
}
} else if (pool_type == "MIN" || pool_type == "MAX") {
std::set<IndexT> existed_dst;
for (int i = 0; i < index_size; ++i) {
const IndexT& src_idx = s_index[i];
const IndexT& dst_idx = d_index[i];
bool in_set = existed_dst.find(dst_idx) != existed_dst.end();
if (!in_set) {
ElementwiseInnerOperation<T, IndexT, Functor>(
src, dst, src_idx, dst_idx, true, functor);
existed_dst.emplace(dst_idx);
} else {
ElementwiseInnerOperation<T, IndexT, Functor>(
src, dst, src_idx, dst_idx, false, functor);
}
}
}
}
template <typename Context, typename T, typename IndexT>
void GraphSendRecvOpKernelLaunchHelper(const Context& ctx,
const DenseTensor& x,
const DenseTensor& src_index,
const DenseTensor& dst_index,
const std::string& pool_type,
int64_t out_size,
DenseTensor* out,
DenseTensor* dst_count = nullptr) {
const int& index_size = src_index.dims()[0];
const auto& src_dims = x.dims();
int64_t memset_size = 1;
if (out_size <= 0) {
for (int i = 0; i < src_dims.size(); ++i) {
memset_size *= src_dims[i];
}
} else {
// set out dim following out_size.
std::vector<int64_t> dims_ = phi::vectorize(src_dims);
if (dims_.size() > 0) {
dims_[0] = out_size;
}
out->Resize(phi::make_ddim(dims_));
memset_size = out_size;
for (int i = 1; i < src_dims.size(); ++i) {
memset_size *= src_dims[i];
}
}
ctx.template Alloc<T>(out);
T* p_output = out->data<T>();
const size_t& memset_bytes = memset_size * sizeof(T);
memset(p_output, 0, memset_bytes);
if (index_size == 0) return;
const IndexT* s_index = src_index.data<IndexT>();
const IndexT* d_index = dst_index.data<IndexT>();
if (pool_type == "SUM") {
GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvSumFunctor<T>>(
src_dims[0], index_size, s_index, d_index, x, out, pool_type);
} else if (pool_type == "MIN") {
GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvMinFunctor<T>>(
src_dims[0], index_size, s_index, d_index, x, out, pool_type);
} else if (pool_type == "MAX") {
GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvMaxFunctor<T>>(
src_dims[0], index_size, s_index, d_index, x, out, pool_type);
} else if (pool_type == "MEAN") {
int64_t input_size = out_size <= 0 ? src_dims[0] : out_size;
dst_count->Resize({input_size});
ctx.template Alloc<int>(dst_count);
int* p_dst_count = dst_count->data<int>();
memset(p_dst_count, 0, input_size * sizeof(int));
GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvSumFunctor<T>>(input_size,
index_size,
s_index,
d_index,
x,
out,
pool_type,
p_dst_count);
}
}
template <typename T, typename Context>
void GraphSendRecvKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& src_index,
const DenseTensor& dst_index,
const std::string& pool_type,
const IntArray& out_size,
DenseTensor* out,
DenseTensor* dst_count) {
auto index_type = src_index.dtype();
auto& out_size_data = out_size.GetData();
if (index_type == phi::DataType::INT32) {
GraphSendRecvOpKernelLaunchHelper<Context, T, int32_t>(ctx,
x,
src_index,
dst_index,
pool_type,
out_size_data[0],
out,
dst_count);
} else if (index_type == phi::DataType::INT64) {
GraphSendRecvOpKernelLaunchHelper<Context, T, int64_t>(ctx,
x,
src_index,
dst_index,
pool_type,
out_size_data[0],
out,
dst_count);
}
}
} // namespace phi
PD_REGISTER_KERNEL(graph_send_recv,
CPU,
ALL_LAYOUT,
phi::GraphSendRecvKernel,
float,
double,
int,
int64_t) {}