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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| import os | ||
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| import torch | ||
| import torch_xla | ||
| import torch_xla.core.xla_model as xm | ||
| import torch_xla.utils.utils as xu | ||
| import torch_xla.debug.profiler as xp | ||
| import torch_xla.utils.utils as xu | ||
| import torch_xla.distributed.parallel_loader as pl | ||
| import unittest | ||
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| def check_env_flag(name, default=''): | ||
| return os.getenv(name, default).upper() in ['ON', '1', 'YES', 'TRUE', 'Y'] | ||
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| def extract_execution_cause(lines): | ||
| causes = [] | ||
| for i in range(len(lines)): | ||
| if 'Execution Cause' in lines[i].decode(): | ||
| causes.append(lines[i + 1].decode()) | ||
| return causes | ||
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| class PtXLADebugTest(unittest.TestCase): | ||
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| @classmethod | ||
| def setUpClass(cls): | ||
| if not check_env_flag('PT_XLA_DEBUG'): | ||
| assert False, "This test should be run with PT_XLA_DEBUG" | ||
| cls.debug_file_name = os.getenv('PT_XLA_DEBUG_FILE') | ||
| if not cls.debug_file_name: | ||
| assert False, "This test should be run with PT_XLA_DEBUG_FILE" | ||
| open(cls.debug_file_name, 'w').close() | ||
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| def test_user_mark_step(self): | ||
| device = xm.xla_device() | ||
| t1 = torch.randn(2, 2, device=device) | ||
| xm.mark_step() | ||
| with open(self.debug_file_name, 'rb') as f: | ||
| lines = f.readlines() | ||
| causes = extract_execution_cause(lines) | ||
| self.assertEqual(len(causes), 1) | ||
| self.assertIn('user mark_step', causes[0]) | ||
| open(self.debug_file_name, 'w').close() | ||
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| def test_step_trace(self): | ||
| device = xm.xla_device() | ||
| with xp.StepTrace('train_pt_xla_debug'): | ||
| t1 = torch.randn(2, 2, device=device) | ||
| with open(self.debug_file_name, 'rb') as f: | ||
| lines = f.readlines() | ||
| causes = extract_execution_cause(lines) | ||
| self.assertEqual(len(causes), 1) | ||
| self.assertIn('mark_step when exiting a profiler StepTrace region', | ||
| causes[0]) | ||
| open(self.debug_file_name, 'w').close() | ||
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| def test_dynamo(self): | ||
| device = xm.xla_device() | ||
| t1 = torch.randn(2, 2, device=device) | ||
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| def toy_program(t1): | ||
| return t1 + t1 | ||
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| compiled = torch.compile(toy_program, backend="openxla") | ||
| res = compiled(t1) | ||
| with open(self.debug_file_name, 'rb') as f: | ||
| lines = f.readlines() | ||
| causes = extract_execution_cause(lines) | ||
| self.assertEqual(len(causes), 3) | ||
| self.assertIn('mark_step when dynamo processing input graphs', causes[0]) | ||
| self.assertIn('mark_step when dynamo processing input graphs', causes[1]) | ||
| self.assertIn('dynamo compiles FX graph to HLO', causes[2]) | ||
| open(self.debug_file_name, 'w').close() | ||
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| def test_parallel_loader(self): | ||
| device = xm.xla_device() | ||
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| train_dataset_len = 100 | ||
| batch_size = 10 | ||
| train_loader = xu.SampleGenerator( | ||
| data=(torch.zeros(batch_size, 3, 128, | ||
| 128), torch.zeros(batch_size, dtype=torch.int64)), | ||
| sample_count=train_dataset_len // 10) | ||
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| train_device_loader = pl.MpDeviceLoader( | ||
| train_loader, | ||
| device, | ||
| loader_prefetch_size=8, | ||
| device_prefetch_size=4, | ||
| host_to_device_transfer_threads=1) | ||
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| for step, (data, target) in enumerate(train_device_loader): | ||
| pass | ||
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| with open(self.debug_file_name, 'rb') as f: | ||
| lines = f.readlines() | ||
| causes = extract_execution_cause(lines) | ||
| self.assertEqual(len(causes), batch_size + 2) | ||
| for cause in causes: | ||
| self.assertIn('mark_step in parallel loader at step end', cause) | ||
| open(self.debug_file_name, 'w').close() | ||
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| def test_print(self): | ||
| device = xm.xla_device() | ||
| t1 = torch.randn(2, 2, device=device) | ||
| print(t1) | ||
| with open(self.debug_file_name, 'rb') as f: | ||
| lines = f.readlines() | ||
| causes = extract_execution_cause(lines) | ||
| self.assertEqual(len(causes), 1) | ||
| self.assertIn('user code trying to access tensor value', causes[0]) | ||
| open(self.debug_file_name, 'w').close() | ||
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| if __name__ == '__main__': | ||
| test = unittest.main() | ||
| sys.exit(0 if test.result.wasSuccessful() else 1) |
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