/
graph_send_recv_kernel.cu
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/
graph_send_recv_kernel.cu
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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 <thrust/device_vector.h>
#include <thrust/fill.h>
#include <algorithm>
#include <vector>
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/hostdevice.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/graph_send_recv_funcs.h"
namespace phi {
template <typename Context, typename T, typename IndexT>
void GraphSendRecvOpCUDAKernelLaunchHelper(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) {
out->Resize(src_dims);
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(out->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);
if (pool_type == "SUM" || pool_type == "MEAN") {
#ifdef PADDLE_WITH_HIP
hipMemset(p_output, 0, memset_bytes);
#else
cudaMemset(p_output, 0, memset_bytes);
#endif
} else if (pool_type == "MAX") {
thrust::device_ptr<T> p_output_ptr(p_output);
thrust::fill(thrust::device,
p_output_ptr,
p_output_ptr + memset_size,
std::numeric_limits<T>::lowest());
} else if (pool_type == "MIN") {
thrust::device_ptr<T> p_output_ptr(p_output);
thrust::fill(thrust::device,
p_output_ptr,
p_output_ptr + memset_size,
std::numeric_limits<T>::max());
}
if (index_size == 0) return;
int64_t slice_size = 1;
for (int i = 1; i < src_dims.size(); ++i) {
slice_size *= src_dims[i];
}
const T* p_src = x.data<T>();
const IndexT* s_index = src_index.data<IndexT>();
const IndexT* d_index = dst_index.data<IndexT>();
#ifdef PADDLE_WITH_HIP
int block = 256;
#else
int block = 1024;
#endif
int64_t n = slice_size * index_size;
int64_t max_grid_dimx = ctx.GetCUDAMaxGridDimSize()[0];
int64_t grid_tmp = (n + block - 1) / block;
int64_t grid = grid_tmp < max_grid_dimx ? grid_tmp : max_grid_dimx;
int64_t input_size = out_size <= 0 ? src_dims[0] : out_size;
if (pool_type == "SUM") {
GraphSendRecvSumCUDAFunctor<T, IndexT> functor;
GraphSendRecvCUDAKernel<T, IndexT, GraphSendRecvSumCUDAFunctor<T, IndexT>>
<<<grid, block, 0, ctx.stream()>>>(
p_src, s_index, d_index, p_output, index_size, slice_size, functor);
} else if (pool_type == "MAX") {
GraphSendRecvMaxCUDAFunctor<T, IndexT> functor;
GraphSendRecvCUDAKernel<T, IndexT, GraphSendRecvMaxCUDAFunctor<T, IndexT>>
<<<grid, block, 0, ctx.stream()>>>(
p_src, s_index, d_index, p_output, index_size, slice_size, functor);
int64_t grid_max_tmp = (input_size * slice_size + block - 1) / block;
int64_t grid_max =
grid_max_tmp < max_grid_dimx ? grid_max_tmp : max_grid_dimx;
InputResetMaxCUDAKernel<T><<<grid_max, block, 0, ctx.stream()>>>(
p_output, input_size, slice_size);
} else if (pool_type == "MIN") {
GraphSendRecvMinCUDAFunctor<T, IndexT> functor;
GraphSendRecvCUDAKernel<T, IndexT, GraphSendRecvMinCUDAFunctor<T, IndexT>>
<<<grid, block, 0, ctx.stream()>>>(
p_src, s_index, d_index, p_output, index_size, slice_size, functor);
int64_t grid_min_tmp = (input_size * slice_size + block - 1) / block;
int64_t grid_min =
grid_min_tmp < max_grid_dimx ? grid_min_tmp : max_grid_dimx;
InputResetMinCUDAKernel<T><<<grid_min, block, 0, ctx.stream()>>>(
p_output, input_size, slice_size);
} else if (pool_type == "MEAN") {
GraphSendRecvSumCUDAFunctor<T, IndexT> functor;
GraphSendRecvCUDAKernel<T, IndexT, GraphSendRecvSumCUDAFunctor<T, IndexT>>
<<<grid, block, 0, ctx.stream()>>>(
p_src, s_index, d_index, p_output, index_size, slice_size, functor);
dst_count->Resize({input_size});
ctx.template Alloc<int32_t>(dst_count);
int* p_dst_count = dst_count->data<int>();
#ifdef PADDLE_WITH_HIP
hipMemset(p_dst_count, 0, input_size * sizeof(int));
#else
cudaMemset(p_dst_count, 0, input_size * sizeof(int));
#endif
int64_t grid_count = (index_size + block - 1) / block;
ComputeCountCUDAKernel<T, IndexT><<<grid_count, block, 0, ctx.stream()>>>(
p_dst_count, d_index, index_size);
int64_t grid_mean_tmp = (input_size * slice_size + block - 1) / block;
int64_t grid_mean =
grid_mean_tmp < max_grid_dimx ? grid_mean_tmp : max_grid_dimx;
ManipulateMeanCUDAKernel<T><<<grid_mean, block, 0, ctx.stream()>>>(
p_output, p_dst_count, input_size, slice_size);
}
}
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) {
GraphSendRecvOpCUDAKernelLaunchHelper<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) {
GraphSendRecvOpCUDAKernelLaunchHelper<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,
GPU,
ALL_LAYOUT,
phi::GraphSendRecvKernel,
float,
double,
int,
int64_t) {}