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https://github.com/zebrajr/pytorch.git
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1149b9bbb5
Summary: Implementation of polling async net executor. Notes: - New net executor async_polling - schedules CPU and GPU ops asynchronously, uses single polling thread - Events: update to Caffe2 events to support async CPU events, adding new methods: Query() - non-blocking checking of event states: INITIALIZED -> RECORDED -> SUCCESS/FAILED ErrorMessage() - when operation runs asynchronously and fails calling this on event will give error message - Tasks: using existing DAGNet's algorithm to compute CPU and GPU chains, a separate task for each chain - Polling: using single thread to query state of events - for CPU tasks atomically queries task state, for GPU task - uses cudaEventQuery; using Event - Scheduling of CPU ops: using global thread pools - Scheduling of GPU ops: using GPU thread pool per GPU device Reviewed By: dzhulgakov Differential Revision: D5985110 fbshipit-source-id: a9de7fcbb71d046a3aa1b573072b89a65dfeee8c
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from caffe2.python import workspace
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from caffe2.python.test.executor_test_util import (
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build_conv_model,
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build_resnet50_dataparallel_model,
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run_resnet50_epoch,
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ExecutorTestBase,
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executor_test_settings,
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executor_test_model_names)
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from hypothesis import given
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import hypothesis.strategies as st
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import unittest
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EXECUTORS = ["async_polling", "dag", "async_dag"]
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ITERATIONS = 2
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class ExecutorCPUConvNetTest(ExecutorTestBase):
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@given(executor=st.sampled_from(EXECUTORS),
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model_name=st.sampled_from(executor_test_model_names()),
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batch_size=st.sampled_from([8]),
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num_workers=st.sampled_from([8]))
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@executor_test_settings
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def test_executor(self, executor, model_name, batch_size, num_workers):
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model = build_conv_model(model_name, batch_size)
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model.Proto().num_workers = num_workers
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def run_model():
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iterations = ITERATIONS
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if model_name == "MLP":
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iterations = 1 # avoid numeric instability with MLP gradients
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workspace.RunNet(model.net, iterations)
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self.compare_executors(
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model,
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ref_executor="simple",
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test_executor=executor,
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model_run_func=run_model,
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)
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@unittest.skipIf(not workspace.has_gpu_support, "no gpu")
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class ExecutorGPUResNetTest(ExecutorTestBase):
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@given(executor=st.sampled_from(EXECUTORS),
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num_workers=st.sampled_from([8]))
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@executor_test_settings
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def test_executor(self, executor, num_workers):
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model = build_resnet50_dataparallel_model(
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num_gpus=workspace.NumCudaDevices(), batch_size=8, epoch_size=8)
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model.Proto().num_workers = num_workers
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def run_model():
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run_resnet50_epoch(model, batch_size=8, epoch_size=8)
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self.compare_executors(
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model,
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ref_executor="simple",
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test_executor=executor,
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model_run_func=run_model,
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)
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if __name__ == '__main__':
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unittest.main()
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