/
graph_send_ue_recv_kernel.cc
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/
graph_send_ue_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_ue_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_ue_recv_funcs.h"
#include "paddle/phi/kernels/impl/graph_messaage_passing_impl.h"
namespace phi {
template <typename T, typename IndexT, typename ComputeFunctor>
void GraphSendUERecvSumCpuKernel(const BroadCastInfo& bcast,
const T* x_data,
const T* y_data,
const IndexT* src_indices,
const IndexT* dst_indices,
T* output,
int64_t index_size,
ComputeFunctor cfunctor) {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
for (int64_t i = 0; i < index_size; i++) {
IndexT src = src_indices[i];
IndexT dst = dst_indices[i];
T* out_off = output + dst * bcast.out_len;
const T* x_off = x_data + src * bcast.l_len;
const T* y_off = y_data + i * bcast.r_len;
for (int64_t j = 0; j < bcast.out_len; j++) {
int64_t x_add = bcast.use_bcast ? bcast.l_offset[j] : j;
int64_t y_add = bcast.use_bcast ? bcast.r_offset[j] : j;
T val = cfunctor(x_off[x_add], y_off[y_add]);
if (val != 0) {
#ifdef PADDLE_WITH_MKLML
#pragma omp atomic
#endif
out_off[j] += val;
}
}
}
}
template <typename T,
typename IndexT,
typename ComputeFunctor,
typename CmpFunctor>
void GraphSendUERecvMinMaxCpuKernel(const BroadCastInfo& bcast,
const T* x_data,
const T* y_data,
const IndexT* src_indices,
const IndexT* dst_indices,
T* output,
int64_t index_size,
ComputeFunctor cfunctor,
CmpFunctor pfunctor) {
std::set<IndexT> existed_dst;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
for (int64_t i = 0; i < index_size; i++) {
IndexT src = src_indices[i];
IndexT dst = dst_indices[i];
T* out_off = output + dst * bcast.out_len;
const T* x_off = x_data + src * bcast.l_len;
const T* y_off = y_data + i * bcast.r_len;
bool in_set = existed_dst.find(dst) != existed_dst.end();
for (int64_t j = 0; j < bcast.out_len; j++) {
int64_t x_add = bcast.use_bcast ? bcast.l_offset[j] : j;
int64_t y_add = bcast.use_bcast ? bcast.r_offset[j] : j;
T val = cfunctor(x_off[x_add], y_off[y_add]);
#ifdef PADDLE_WITH_MKLML
#pragma omp critical
#endif
if (!in_set) {
out_off[j] = val;
} else {
out_off[j] = pfunctor(out_off[j], val);
}
}
#ifdef PADDLE_WITH_MKLML
#pragma omp critical
#endif
if (!in_set) {
existed_dst.emplace(dst);
}
}
}
template <typename Context, typename T, typename IndexT>
void GraphSendUERecvOpKernelLaunchHelper(const Context& ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& src_index,
const DenseTensor& dst_index,
const std::string& compute_type,
const std::string& pool_type,
int64_t out_size,
DenseTensor* out,
DenseTensor* dst_count = nullptr) {
const int& index_size = src_index.dims()[0];
auto out_dims = out->dims();
int64_t memset_size = 1;
std::vector<int64_t> dims_ = phi::vectorize(out_dims);
if (out_size <= 0) {
dims_[0] = x.dims()[0];
} else {
dims_[0] = out_size;
}
out->Resize(phi::make_ddim(dims_));
for (size_t i = 0; i < dims_.size(); i++) {
memset_size *= dims_[i];
}
ctx.template Alloc<T>(out);
T* out_data = out->data<T>();
const size_t& memset_bytes = memset_size * sizeof(T);
memset(out_data, 0, memset_bytes);
if (index_size == 0) return;
const auto& bcast_info = phi::CalcBCastInfo(x.dims(), y.dims());
const T* x_data = x.data<T>();
const T* y_data = y.data<T>();
const IndexT* s_index = src_index.data<IndexT>();
const IndexT* d_index = dst_index.data<IndexT>();
if (pool_type == "SUM" || pool_type == "MEAN") {
if (compute_type == "ADD") {
GraphAddFunctor<T> add_functor;
GraphSendUERecvSumCpuKernel<T, IndexT, GraphAddFunctor<T>>(bcast_info,
x_data,
y_data,
s_index,
d_index,
out_data,
index_size,
add_functor);
} else if (compute_type == "MUL") {
GraphMulFunctor<T> mul_functor;
GraphSendUERecvSumCpuKernel<T, IndexT, GraphMulFunctor<T>>(bcast_info,
x_data,
y_data,
s_index,
d_index,
out_data,
index_size,
mul_functor);
}
if (pool_type == "MEAN") {
int64_t input_size = out_size <= 0 ? x.dims()[0] : out_size;
dst_count->Resize({input_size});
int* dst_count_data = ctx.template Alloc<int>(dst_count);
memset(dst_count_data, 0, input_size * sizeof(int));
for (int i = 0; i < index_size; i++) {
IndexT dst_idx = d_index[i];
dst_count_data[dst_idx] += 1;
}
for (int i = 0; i < input_size; i++) {
if (dst_count_data[i] == 0) continue;
auto out_slice = out->Slice(i, i + 1);
auto eigen_out = phi::EigenVector<T>::Flatten(out_slice);
eigen_out = eigen_out / static_cast<T>(dst_count_data[i]);
}
}
} else if (pool_type == "MIN") {
GraphMinFunctor<T> min_functor;
if (compute_type == "ADD") {
GraphAddFunctor<T> add_functor;
GraphSendUERecvMinMaxCpuKernel<T,
IndexT,
GraphAddFunctor<T>,
GraphMinFunctor<T>>(bcast_info,
x_data,
y_data,
s_index,
d_index,
out_data,
index_size,
add_functor,
min_functor);
} else if (compute_type == "MUL") {
GraphMulFunctor<T> mul_functor;
GraphSendUERecvMinMaxCpuKernel<T,
IndexT,
GraphMulFunctor<T>,
GraphMinFunctor<T>>(bcast_info,
x_data,
y_data,
s_index,
d_index,
out_data,
index_size,
mul_functor,
min_functor);
}
} else if (pool_type == "MAX") {
GraphMaxFunctor<T> max_functor;
if (compute_type == "ADD") {
GraphAddFunctor<T> add_functor;
GraphSendUERecvMinMaxCpuKernel<T,
IndexT,
GraphAddFunctor<T>,
GraphMaxFunctor<T>>(bcast_info,
x_data,
y_data,
s_index,
d_index,
out_data,
index_size,
add_functor,
max_functor);
} else if (compute_type == "MUL") {
GraphMulFunctor<T> mul_functor;
GraphSendUERecvMinMaxCpuKernel<T,
IndexT,
GraphMulFunctor<T>,
GraphMaxFunctor<T>>(bcast_info,
x_data,
y_data,
s_index,
d_index,
out_data,
index_size,
mul_functor,
max_functor);
}
}
}
template <typename T, typename Context>
void GraphSendUERecvKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& y,
const DenseTensor& src_index,
const DenseTensor& dst_index,
const std::string& compute_type,
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) {
GraphSendUERecvOpKernelLaunchHelper<Context, T, int32_t>(ctx,
x,
y,
src_index,
dst_index,
compute_type,
pool_type,
out_size_data[0],
out,
dst_count);
} else if (index_type == phi::DataType::INT64) {
GraphSendUERecvOpKernelLaunchHelper<Context, T, int64_t>(ctx,
x,
y,
src_index,
dst_index,
compute_type,
pool_type,
out_size_data[0],
out,
dst_count);
}
}
} // namespace phi
PD_REGISTER_KERNEL(graph_send_ue_recv,
CPU,
ALL_LAYOUT,
phi::GraphSendUERecvKernel,
float,
double,
int,
int64_t) {}