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105 lines
3.8 KiB
105 lines
3.8 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/fluid/operators/collective/recv_v2_op.h"
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#if defined(PADDLE_WITH_NCCL)
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#include "paddle/fluid/platform/collective_helper.h"
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#include "paddle/fluid/platform/nccl_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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template <typename T>
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class RecvOpV2CUDAKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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#if defined(PADDLE_WITH_NCCL) && NCCL_VERSION_CODE >= 2703
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int rid = ctx.Attr<int>("ring_id");
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PADDLE_ENFORCE_GE(
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rid, 0,
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platform::errors::InvalidArgument(
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"The ring_id (%d) for recv_v2 op must be non-negative.", rid));
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int peer = ctx.Attr<int>("peer");
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PADDLE_ENFORCE_GE(
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peer, 0,
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platform::errors::InvalidArgument(
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"The peer (%d) for recv_v2 op must be non-negative.", peer));
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auto out = ctx.Output<framework::LoDTensor>("Out");
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auto out_dims = out->dims();
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int data_type = ctx.Attr<int>("dtype");
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framework::proto::VarType::Type type =
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framework::proto::VarType::Type(data_type);
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cudaStream_t stream = nullptr;
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auto place = ctx.GetPlace();
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auto comm = platform::NCCLCommContext::Instance().Get(rid, place);
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if (ctx.Attr<bool>("use_calc_stream")) {
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auto dev_ctx = platform::DeviceContextPool::Instance().Get(place);
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stream = static_cast<platform::CUDADeviceContext *>(dev_ctx)->stream();
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} else {
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stream = comm->stream();
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}
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PADDLE_ENFORCE_LT(
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peer, comm->nranks(),
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platform::errors::InvalidArgument("The value of peer (%d) you set must "
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"be less than comm->nranks (%d).",
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peer, comm->nranks()));
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ncclDataType_t dtype = platform::ToNCCLDataType(type);
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// Recv the number of elements to receive first
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int numel = 0;
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int *numel_ptr = nullptr;
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PADDLE_ENFORCE_CUDA_SUCCESS(cudaMalloc(&numel_ptr, sizeof(int)));
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PADDLE_ENFORCE_CUDA_SUCCESS(
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platform::dynload::ncclRecv(static_cast<void *>(numel_ptr), 1, ncclInt,
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peer, comm->comm(), stream));
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PADDLE_ENFORCE_CUDA_SUCCESS(
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cudaMemcpy(&numel, numel_ptr, sizeof(int), cudaMemcpyDeviceToHost));
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int rest_numel = 1;
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for (int i = 1; i < out_dims.size(); ++i) {
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rest_numel = rest_numel * out_dims[i];
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}
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out_dims[0] = numel / rest_numel;
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out->mutable_data<T>(out_dims, place);
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PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::ncclRecv(
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out->data<T>(), numel, dtype, peer, comm->comm(), stream));
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VLOG(3) << "rank " << comm->rank() << " recv "
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<< framework::product(out->dims()) << " from " << peer;
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#else
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PADDLE_THROW(platform::errors::Unavailable(
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"PaddlePaddle should be compiled with NCCL and "
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"NCCL version >= 2.7.3 is needed."));
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#endif
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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REGISTER_OP_CUDA_KERNEL(recv_v2, ops::RecvOpV2CUDAKernel<float>,
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ops::RecvOpV2CUDAKernel<double>,
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ops::RecvOpV2CUDAKernel<int>,
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ops::RecvOpV2CUDAKernel<int64_t>,
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ops::RecvOpV2CUDAKernel<plat::float16>);
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