wehub-resource-sync
132 lines
5.3 KiB
C++
132 lines
5.3 KiB
C++
// 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_uv_kernel.h"
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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 GraphSendUVCpuKernel(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 + i * 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 + dst * 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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out_off[j] = val;
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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 GraphSendUVOpKernelLaunchHelper(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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DenseTensor* out) {
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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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PADDLE_ENFORCE_GT(
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index_size,
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0,
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errors::InvalidArgument("The first dimension of src_index or dst_index "
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"should be greater than 0, but received %d.",
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index_size));
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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 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 (message_op == "ADD") {
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GraphAddFunctor<T> add_functor;
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GraphSendUVCpuKernel<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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GraphSendUVCpuKernel<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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}
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template <typename T, typename Context>
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void SendUVKernel(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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DenseTensor* out) {
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auto index_type = src_index.dtype();
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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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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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return;
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}
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if (index_type == DataType::INT32) {
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GraphSendUVOpKernelLaunchHelper<Context, T, int32_t>(
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dev_ctx, x, y, src_index, dst_index, message_op, out);
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} else if (index_type == DataType::INT64) {
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GraphSendUVOpKernelLaunchHelper<Context, T, int64_t>(
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dev_ctx, x, y, src_index, dst_index, message_op, out);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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send_uv, CPU, ALL_LAYOUT, phi::SendUVKernel, float, double, int, int64_t) {}
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