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d3839b624b
For ROCm/HIP, each stream is lazily initialized rather than creating all streams when the first stream is requested. HIP streams are not as lightweight as CUDA streams; the pooling strategy can affect performance. Pull Request resolved: https://github.com/pytorch/pytorch/pull/119996 Approved by: https://github.com/ezyang
127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
# Owner(s): ["module: cuda"]
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import sys
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import unittest
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import unittest.mock
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import torch
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import torch.utils._cuda_trace as cuda_trace
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from torch.testing._internal.common_utils import TestCase, run_tests, NoTest, TEST_CUDA
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# NOTE: Each test needs to be run in a brand new process, to reset the registered hooks
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# and make sure the CUDA streams are initialized for each test that uses them.
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if not TEST_CUDA:
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print("CUDA not available, skipping tests", file=sys.stderr)
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TestCase = NoTest # noqa: F811
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@torch.testing._internal.common_utils.markDynamoStrictTest
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class TestCudaTrace(TestCase):
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def setUp(self):
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torch._C._activate_cuda_trace()
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self.mock = unittest.mock.MagicMock()
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def test_event_creation_callback(self):
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cuda_trace.register_callback_for_cuda_event_creation(self.mock)
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event = torch.cuda.Event()
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event.record()
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self.mock.assert_called_once_with(event._as_parameter_.value)
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def test_event_deletion_callback(self):
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cuda_trace.register_callback_for_cuda_event_deletion(self.mock)
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event = torch.cuda.Event()
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event.record()
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event_id = event._as_parameter_.value
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del event
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self.mock.assert_called_once_with(event_id)
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def test_event_record_callback(self):
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cuda_trace.register_callback_for_cuda_event_record(self.mock)
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event = torch.cuda.Event()
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event.record()
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self.mock.assert_called_once_with(
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event._as_parameter_.value, torch.cuda.default_stream().cuda_stream
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)
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def test_event_wait_callback(self):
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cuda_trace.register_callback_for_cuda_event_wait(self.mock)
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event = torch.cuda.Event()
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event.record()
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event.wait()
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self.mock.assert_called_once_with(
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event._as_parameter_.value, torch.cuda.default_stream().cuda_stream
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)
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def test_memory_allocation_callback(self):
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cuda_trace.register_callback_for_cuda_memory_allocation(self.mock)
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tensor = torch.empty(10, 4, device="cuda")
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self.mock.assert_called_once_with(tensor.data_ptr())
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def test_memory_deallocation_callback(self):
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cuda_trace.register_callback_for_cuda_memory_deallocation(self.mock)
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tensor = torch.empty(3, 8, device="cuda")
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data_ptr = tensor.data_ptr()
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del tensor
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self.mock.assert_called_once_with(data_ptr)
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def test_stream_creation_callback(self):
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cuda_trace.register_callback_for_cuda_stream_creation(self.mock)
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# see Note [HIP Lazy Streams]
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if torch.version.hip:
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user_stream = torch.cuda.Stream()
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with torch.cuda.stream(user_stream):
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tensor = torch.ones(5, device="cuda")
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else:
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torch.cuda.Stream()
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self.mock.assert_called()
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def test_device_synchronization_callback(self):
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cuda_trace.register_callback_for_cuda_device_synchronization(self.mock)
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torch.cuda.synchronize()
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self.mock.assert_called()
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def test_stream_synchronization_callback(self):
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cuda_trace.register_callback_for_cuda_stream_synchronization(self.mock)
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stream = torch.cuda.Stream()
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stream.synchronize()
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self.mock.assert_called_once_with(stream.cuda_stream)
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def test_event_synchronization_callback(self):
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cuda_trace.register_callback_for_cuda_event_synchronization(self.mock)
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event = torch.cuda.Event()
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event.record()
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event.synchronize()
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self.mock.assert_called_once_with(event._as_parameter_.value)
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def test_memcpy_synchronization(self):
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cuda_trace.register_callback_for_cuda_stream_synchronization(self.mock)
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tensor = torch.rand(5, device="cuda")
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tensor.nonzero()
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self.mock.assert_called_once_with(torch.cuda.default_stream().cuda_stream)
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def test_all_trace_callbacks_called(self):
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other = unittest.mock.MagicMock()
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cuda_trace.register_callback_for_cuda_memory_allocation(self.mock)
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cuda_trace.register_callback_for_cuda_memory_allocation(other)
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tensor = torch.empty(10, 4, device="cuda")
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self.mock.assert_called_once_with(tensor.data_ptr())
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other.assert_called_once_with(tensor.data_ptr())
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if __name__ == "__main__":
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run_tests()
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