wehub-resource-sync
124 lines
3.6 KiB
C++
124 lines
3.6 KiB
C++
// Copyright (c) 2019 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/fluid/imperative/nccl_context.h"
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#include <thread> // NOLINT
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#include "gtest/gtest.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/framework/variable.h"
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#include "paddle/phi/core/platform/gen_comm_id_helper.h"
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namespace imperative = paddle::imperative;
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namespace platform = paddle::platform;
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namespace framework = paddle::framework;
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int nrings = 2;
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imperative::ParallelStrategy GetStrategy(int local_rank) {
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std::vector<std::string> eps = {"127.0.0.1:9866", "localhost:9867"};
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imperative::ParallelStrategy strategy;
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strategy.trainer_endpoints_ = eps;
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strategy.current_endpoint_ = eps[local_rank];
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strategy.nranks_ = 2;
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strategy.local_rank_ = local_rank;
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strategy.nrings_ = nrings;
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return strategy;
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}
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#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
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void BcastNCCLId(int local_rank, std::vector<ncclUniqueId>* nccl_ids) {
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auto strategy = GetStrategy(local_rank);
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int server_fd = platform::CreateListenSocket(strategy.current_endpoint_);
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phi::GPUPlace gpu(local_rank);
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imperative::NCCLParallelContext ctx(strategy, gpu);
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ctx.BcastNCCLId(*nccl_ids, 0, server_fd);
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platform::CloseSocket(server_fd);
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}
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TEST(BcastNCCLId, Run) {
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std::vector<ncclUniqueId> nccl_ids;
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nccl_ids.resize(nrings);
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for (int i = 0; i < nrings; ++i) {
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phi::dynload::ncclGetUniqueId(&nccl_ids[i]);
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}
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std::thread t(BcastNCCLId, 0, &nccl_ids);
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std::vector<ncclUniqueId> recv_nccl_ids;
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recv_nccl_ids.resize(nrings);
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for (int i = 0; i < nrings; ++i) {
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phi::dynload::ncclGetUniqueId(&recv_nccl_ids[i]);
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}
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BcastNCCLId(1, &recv_nccl_ids);
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t.join();
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for (int i = 0; i < nrings; ++i) {
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EXPECT_EQ(0,
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std::memcmp(nccl_ids[i].internal,
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recv_nccl_ids[i].internal,
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NCCL_UNIQUE_ID_BYTES));
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}
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}
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void Broadcast(int local_rank, int device_id) {
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int data_size = 4;
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float test_data = 7;
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const auto& place = phi::GPUPlace(device_id);
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phi::GPUContext ctx(place);
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imperative::NCCLParallelContext npc(GetStrategy(local_rank), place);
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// init
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npc.Init();
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framework::Variable* src_dev_var(new framework::Variable());
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auto* src_dev_tensor = src_dev_var->GetMutable<phi::DenseTensor>();
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src_dev_tensor->mutable_data<float>(common::make_ddim({data_size}), place);
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// fill data for rank 0 only
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std::vector<float> src_vec;
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if (local_rank == 0) {
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for (int i = 0; i < data_size; i++) {
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src_vec.push_back(test_data);
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}
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framework::TensorFromVector(src_vec, ctx, src_dev_tensor);
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}
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ctx.Wait();
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npc.Broadcast(src_dev_var, 0);
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std::this_thread::sleep_for(std::chrono::milliseconds(1000));
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// check result
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std::vector<float> dst_vec;
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framework::TensorToVector(*src_dev_tensor, ctx, &dst_vec);
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ctx.Wait();
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for (int i = 0; i < data_size; i++) {
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EXPECT_EQ(dst_vec[i], test_data);
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}
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}
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TEST(Broadcast, Run) {
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if (platform::GetGPUDeviceCount() >= 2) {
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std::thread t0(Broadcast, 0, 0);
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std::thread t1(Broadcast, 1, 1);
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t0.join();
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t1.join();
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}
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}
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#endif
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