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https://github.com/microsoft/mscclpp.git
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be6a941fba
The PR contains following changes: Python side: - Channel based DSL implementation: decouple channel with chunk. - Users create channel explicitly, only need local_rank, remote_rank and channel_type - Adjust executor json file, add remote_buffer fields, different op can use different channel and remote buffers combination. - Reimplement operation fusion, data dependency check mechanism - Add new op such as semaphore, pipeline - Clean code and enhance document C++ side: - Support new execution file json format - Support semaphore and pipeline operation - code clean, support non-zero copy scenario --------- Co-authored-by: Caio Rocha <caiorocha@microsoft.com> Co-authored-by: Changho Hwang <changhohwang@microsoft.com>
67 lines
2.2 KiB
C++
67 lines
2.2 KiB
C++
// Copyright (c) Microsoft Corporation.
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// Licensed under the MIT license.
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#include <mpi.h>
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#include <filesystem>
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#include <mscclpp/env.hpp>
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#include <mscclpp/npkit/npkit.hpp>
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#include "mp_unit_tests.hpp"
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namespace {
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std::string getExecutablePath() {
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char result[PATH_MAX];
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ssize_t count = readlink("/proc/self/exe", result, PATH_MAX);
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if (count == -1) {
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throw std::runtime_error("Failed to get executable path");
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}
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return std::string(result, count);
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}
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} // namespace
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void ExecutorTest::SetUp() {
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MultiProcessTest::SetUp();
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MSCCLPP_CUDATHROW(cudaSetDevice(rankToLocalRank(gEnv->rank)));
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std::shared_ptr<mscclpp::TcpBootstrap> bootstrap;
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mscclpp::UniqueId id;
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bootstrap = std::make_shared<mscclpp::TcpBootstrap>(gEnv->rank, gEnv->worldSize);
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if (gEnv->rank == 0) id = bootstrap->createUniqueId();
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MPI_Bcast(&id, sizeof(id), MPI_BYTE, 0, MPI_COMM_WORLD);
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bootstrap->initialize(id);
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std::shared_ptr<mscclpp::Communicator> communicator = std::make_shared<mscclpp::Communicator>(bootstrap);
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executor = std::make_shared<mscclpp::Executor>(communicator);
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npkitDumpDir = mscclpp::env()->npkitDumpDir;
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if (npkitDumpDir != "") {
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NpKit::Init(gEnv->rank);
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}
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}
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void ExecutorTest::TearDown() {
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if (npkitDumpDir != "") {
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NpKit::Dump(npkitDumpDir);
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NpKit::Shutdown();
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}
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executor.reset();
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MultiProcessTest::TearDown();
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}
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TEST_F(ExecutorTest, TwoNodesAllreduce) {
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if (gEnv->worldSize != 2 || gEnv->nRanksPerNode != 2) {
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GTEST_SKIP() << "This test requires world size to be 2 and ranks per node to be 2";
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return;
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}
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std::string executablePath = getExecutablePath();
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std::filesystem::path path = executablePath;
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std::filesystem::path executionFilesPath =
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path.parent_path().parent_path().parent_path() / "test/execution-files/allreduce.json";
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mscclpp::ExecutionPlan plan(executionFilesPath.string(), gEnv->rank);
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const int bufferSize = 1024 * 1024;
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std::shared_ptr<char> sendbuff = mscclpp::GpuBuffer(bufferSize).memory();
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mscclpp::CudaStreamWithFlags stream(cudaStreamNonBlocking);
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executor->execute(gEnv->rank, sendbuff.get(), sendbuff.get(), bufferSize, bufferSize, mscclpp::DataType::FLOAT16,
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plan, stream);
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MSCCLPP_CUDATHROW(cudaStreamSynchronize(stream));
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}
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