mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
748bac8757
Applies some more harmless pyupgrades. This one gets rid of deprecated aliases in unit_tests and more upgrades yield for loops into yield from generators which are more performance and propagates more information / exceptions from original generator. This is the modern recommended way of forwarding generators. Pull Request resolved: https://github.com/pytorch/pytorch/pull/94309 Approved by: https://github.com/albanD
91 lines
4 KiB
Python
91 lines
4 KiB
Python
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from caffe2.python import core, workspace
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from caffe2.python.test_util import TestCase
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import unittest
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class TestAtomicOps(TestCase):
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@unittest.skip("Test is flaky: https://github.com/pytorch/pytorch/issues/28179")
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def test_atomic_ops(self):
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"""
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Test that both countdown and checksum are update atomically by having
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cowntdown count from 20k to 0 from parallel the workers and updating
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the checksum to the value fetched. If operations are trully atomic,
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each value from 1 to 20k should be fetched exactly once from the
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countdown, and fed exactly once to the checksum, such that at the end
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checksum must contain the exact value of sum[i=0..20000](i).
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"""
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init_net = core.Net('init')
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mutex_countdown = init_net.CreateMutex([])
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mutex_checksum = init_net.CreateMutex([])
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countdown = init_net.ConstantFill([], shape=[], value=20000,
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dtype=core.DataType.INT32)
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checksum = init_net.ConstantFill(
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[], shape=[], value=0, dtype=core.DataType.INT32)
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minus_one = init_net.ConstantFill(
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[], shape=[], value=-1, dtype=core.DataType.INT32)
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steps = []
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for i in range(0, 100):
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net = core.Net('net:%d' % i)
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_, fetched_count = net.AtomicFetchAdd(
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[mutex_countdown, countdown, minus_one],
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[countdown, 'fetched_count:%d' % i])
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net.AtomicFetchAdd(
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[mutex_checksum, checksum, fetched_count],
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[checksum, 'not_used'])
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steps.append(
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core.execution_step('worker:%d' % i, net, num_iter=200))
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super_step = core.execution_step(
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'parent', steps, concurrent_substeps=True)
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plan = core.Plan('plan')
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plan.AddStep(core.execution_step('init', init_net))
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plan.AddStep(super_step)
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workspace.RunPlan(plan)
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# checksum = sum[i=1..20000](i) = 20000 * 20001 / 2 = 200010000
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self.assertEqual(workspace.FetchBlob(checksum), 200010000)
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@unittest.skip("Test is flaky: https://github.com/pytorch/pytorch/issues/28179")
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def test_atomic64_ops(self):
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"""
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Test that both countdown and checksum are update atomically by having
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cowntdown count from 20k to 0 from parallel the workers and updating
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the checksum to the value fetched. If operations are trully atomic,
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each value from 1 to 20k should be fetched exactly once from the
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countdown, and fed exactly once to the checksum, such that at the end
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checksum must contain the exact value of sum[i=0..20000](i).
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"""
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init_net = core.Net('init')
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mutex_countdown = init_net.CreateMutex([])
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mutex_checksum = init_net.CreateMutex([])
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countdown = init_net.ConstantFill([], shape=[], value=20000,
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dtype=core.DataType.INT64)
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checksum = init_net.ConstantFill(
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[], shape=[], value=0, dtype=core.DataType.INT64)
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minus_one = init_net.ConstantFill(
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[], shape=[], value=-1, dtype=core.DataType.INT64)
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steps = []
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for i in range(0, 100):
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net = core.Net('net:%d' % i)
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_, fetched_count = net.AtomicFetchAdd64(
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[mutex_countdown, countdown, minus_one],
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[countdown, 'fetched_count:%d' % i])
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net.AtomicFetchAdd64(
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[mutex_checksum, checksum, fetched_count],
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[checksum, 'not_used'])
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steps.append(
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core.execution_step('worker:%d' % i, net, num_iter=200))
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super_step = core.execution_step(
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'parent', steps, concurrent_substeps=True)
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plan = core.Plan('plan')
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plan.AddStep(core.execution_step('init', init_net))
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plan.AddStep(super_step)
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workspace.RunPlan(plan)
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# checksum = sum[i=1..20000](i) = 20000 * 20001 / 2 = 200010000
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self.assertEqual(workspace.FetchBlob(checksum), 200010000)
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if __name__ == "__main__":
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unittest.main()
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