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https://github.com/saymrwulf/pytorch.git
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ed242cbec5
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42682 ghstack-source-id: 109834351 Test Plan: CI Reviewed By: malfet Differential Revision: D22978717 fbshipit-source-id: 18b7cbdb532e78ff9259e82f0f92ad279124419d
59 lines
1.5 KiB
C++
59 lines
1.5 KiB
C++
#include <gtest/gtest.h>
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#include "e2e_test_base.h"
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#include <c10d/ProcessGroupGloo.hpp>
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#include <torch/csrc/distributed/rpc/request_callback_no_python.h>
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#include <torch/csrc/distributed/rpc/tensorpipe_agent.h>
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#include <torch/torch.h>
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namespace torch {
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namespace distributed {
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namespace rpc {
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using namespace torch::distributed::autograd;
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#ifdef USE_TENSORPIPE
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class TestE2ETensorPipe : public TestE2EBase {
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protected:
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void buildRpcAgent() override {
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c10d::ProcessGroupGloo::Options options;
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options.devices.push_back(
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::c10d::ProcessGroupGloo::createDeviceForHostname(serverAddress));
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float rpcTimeout = 30;
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// Initialize server rpc agent.
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auto pg =
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std::make_shared<c10d::ProcessGroupGloo>(store, 0, numWorkers, options);
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TensorPipeRpcBackendOptions opts(
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/*numWorkerThreads=*/std::max(16U, std::thread::hardware_concurrency()),
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/*transports=*/nullopt,
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/*channels=*/nullopt,
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/*rpc_timeout=*/rpcTimeout,
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/*init_method=*/"unused");
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rpcAgent = std::make_shared<TensorPipeAgent>(
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store,
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"worker",
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0,
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numWorkers,
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pg,
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opts,
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std::make_unique<RequestCallbackNoPython>());
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}
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};
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// End to end training loop test in C++ so that we can run LSAN on this test to
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// catch memory leaks. Enabling LSAN with python multiprocessing has been
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// challenging and we don't have a good solution yet.
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TEST_F(TestE2ETensorPipe, TestTrainingLoop) {
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runTrainingLoop();
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}
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#endif
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} // namespace rpc
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} // namespace distributed
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} // namespace torch
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