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https://github.com/zebrajr/pytorch.git
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aa88c2c0b6
Summary: Assign `has_gpu_support = has_cuda_support or has_hip_support` and make according changes in python tests. Pull Request resolved: https://github.com/pytorch/pytorch/pull/16748 Differential Revision: D13983132 Pulled By: bddppq fbshipit-source-id: ca496fd8c6ae3549b736bebd3ace7fa20a6dad7f
104 lines
3.1 KiB
Python
104 lines
3.1 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 core, 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 caffe2.python.test_util import TestCase
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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 = ["parallel", "async_scheduling"]
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ITERATIONS = 1
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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([1]),
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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.NumGpuDevices(), 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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class ExecutorFailingOpTest(TestCase):
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def test_failing_op(self):
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def create_failing_net(throw_exception):
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net = core.Net("failing_net")
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if throw_exception:
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net.ThrowException([], [])
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else:
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net.Fail([], [])
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net.Proto().type = "async_scheduling"
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return net
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workspace.ResetWorkspace()
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net = create_failing_net(throw_exception=True)
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workspace.CreateNet(net)
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with self.assertRaises(RuntimeError):
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workspace.RunNet(net)
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with self.assertRaises(RuntimeError):
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workspace.RunNet(net, allow_fail=True)
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workspace.ResetWorkspace()
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net = create_failing_net(throw_exception=False)
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workspace.CreateNet(net)
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with self.assertRaises(RuntimeError):
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workspace.RunNet(net)
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res = workspace.RunNet(net, allow_fail=True)
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self.assertFalse(res)
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if __name__ == '__main__':
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unittest.main()
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