mirror of
https://github.com/saymrwulf/pytorch.git
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Stop ignoring mypy errors in torch/testing/_internal/common_utils.py (#144483)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144483 Approved by: https://github.com/Skylion007
This commit is contained in:
Aaron Orenstein
PyTorch MergeBot
parent
ea3395e4f2
commit
8ad37ed710
3 changed files with 61 additions and 59 deletions
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@ -276,7 +276,7 @@ class GraphInfoProvider:
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vmin=min(self.get_knapsack_memory_input()),
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vmax=max(self.get_knapsack_memory_input()),
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)
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cmap = cm.viridis
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cmap = cm.viridis # type: ignore[attr-defined]
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# Assign colors based on memory
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node_colors = [
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@ -72,21 +72,21 @@ def is_metal_capture_enabled() -> bool:
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"""Checks if `metal_capture` context manager is usable
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To enable metal capture, set MTL_CAPTURE_ENABLED envvar
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"""
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return torch._C._mps_isCaptureEnabled()
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return torch._C._mps_isCaptureEnabled() # type: ignore[attr-defined]
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def is_capturing_metal() -> bool:
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"""Cheks if metal capture is in progress"""
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return torch._C._mps_isCapturing()
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return torch._C._mps_isCapturing() # type: ignore[attr-defined]
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@contextlib.contextmanager
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def metal_capture(fname: str):
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"""Conext manager that enables capturing of Metal calls into gputrace"""
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try:
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torch._C._mps_startCapture(fname)
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torch._C._mps_startCapture(fname) # type: ignore[attr-defined]
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yield
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# Drain all the work that were enqueued during the context call
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torch.mps.synchronize()
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finally:
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torch._C._mps_stopCapture()
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torch._C._mps_stopCapture() # type: ignore[attr-defined]
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@ -1,4 +1,4 @@
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# mypy: ignore-errors
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# mypy: allow-untyped-defs
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r"""Importing this file must **not** initialize CUDA context. test_distributed
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relies on this assumption to properly run. This means that when this is imported
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@ -156,6 +156,7 @@ class TestEnvironment:
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implied_by_fn=lambda: False,
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):
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enabled = default
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env_var_val = None
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if env_var is not None:
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env_var_val = os.getenv(env_var)
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enabled = enabled_fn(env_var_val, default)
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@ -351,17 +352,15 @@ def get_tracked_input() -> Optional[TrackedInput]:
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test_fn = extract_test_fn()
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if test_fn is None:
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return None
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if not hasattr(test_fn, "tracked_input"):
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return None
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return test_fn.tracked_input
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return getattr(test_fn, "tracked_input", None)
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def clear_tracked_input():
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def clear_tracked_input() -> None:
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test_fn = extract_test_fn()
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if test_fn is None:
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return
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if not hasattr(test_fn, "tracked_input"):
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return None
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test_fn.tracked_input = None
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return
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test_fn.tracked_input = None # type: ignore[attr-defined]
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# Wraps an iterator and tracks the most recent value the iterator produces
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# for debugging purposes. Tracked values are stored on the test function.
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@ -428,7 +427,7 @@ class TrackedInputIter:
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return
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if not hasattr(self.test_fn, "tracked_input"):
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return
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self.test_fn.tracked_input = tracked_input
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self.test_fn.tracked_input = tracked_input # type: ignore[attr-defined]
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class _TestParametrizer:
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"""
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@ -690,7 +689,7 @@ class parametrize(_TestParametrizer):
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for idx, values in enumerate(self.arg_values):
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maybe_name = None
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decorators = []
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decorators: List[Any] = []
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if isinstance(values, subtest):
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sub = values
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values = sub.arg_values
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@ -705,7 +704,7 @@ class parametrize(_TestParametrizer):
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else:
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gen_test = test
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values = list(values) if len(self.arg_names) > 1 else [values]
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values = list(values) if len(self.arg_names) > 1 else [values] # type: ignore[call-overload]
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if len(values) != len(self.arg_names):
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raise RuntimeError(f'Expected # values == # arg names, but got: {len(values)} '
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f'values and {len(self.arg_names)} names for test "{test.__name__}"')
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@ -863,6 +862,8 @@ def cppProfilingFlagsToProfilingMode():
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@contextmanager
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def enable_profiling_mode_for_profiling_tests():
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old_prof_exec_state = False
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old_prof_mode_state = False
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if GRAPH_EXECUTOR == ProfilingMode.PROFILING:
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old_prof_exec_state = torch._C._jit_set_profiling_executor(True)
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old_prof_mode_state = torch._C._get_graph_executor_optimize(True)
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@ -1175,7 +1176,7 @@ def sanitize_pytest_xml(xml_file: str):
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def get_pytest_test_cases(argv: List[str]) -> List[str]:
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class TestCollectorPlugin:
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def __init__(self) -> None:
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self.tests = []
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self.tests: List[Any] = []
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def pytest_collection_finish(self, session):
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for item in session.items:
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@ -1286,6 +1287,7 @@ def run_tests(argv=UNITTEST_ARGS):
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assert not failed, "Some test shards have failed"
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elif USE_PYTEST:
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pytest_args = argv + ["--use-main-module"]
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test_report_path = ""
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if TEST_SAVE_XML:
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test_report_path = get_report_path(pytest=True)
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print(f'Test results will be stored in {test_report_path}')
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@ -1411,7 +1413,7 @@ else:
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def is_privateuse1_backend_available():
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privateuse1_backend_name = torch._C._get_privateuse1_backend_name()
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privateuse1_backend_module = getattr(torch, privateuse1_backend_name, None)
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return hasattr(privateuse1_backend_module, "is_available") and privateuse1_backend_module.is_available()
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return (is_available := getattr(privateuse1_backend_module, "is_available", None)) and is_available()
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IS_FILESYSTEM_UTF8_ENCODING = sys.getfilesystemencoding() == 'utf-8'
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@ -1628,8 +1630,8 @@ def skipIfTorchDynamo(msg="test doesn't currently work with dynamo"):
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assert isinstance(fn, type)
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if TEST_WITH_TORCHDYNAMO:
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fn.__unittest_skip__ = True
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fn.__unittest_skip_why__ = msg
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fn.__unittest_skip__ = True # type: ignore[attr-defined]
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fn.__unittest_skip_why__ = msg # type: ignore[attr-defined]
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return fn
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@ -1649,8 +1651,8 @@ def skipIfTorchInductor(msg="test doesn't currently work with torchinductor",
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assert isinstance(fn, type)
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if condition:
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fn.__unittest_skip__ = True
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fn.__unittest_skip_why__ = msg
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fn.__unittest_skip__ = True # type: ignore[attr-defined]
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fn.__unittest_skip_why__ = msg # type: ignore[attr-defined]
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return fn
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@ -1723,8 +1725,8 @@ def skipIfLegacyJitExecutor(msg="test doesn't currently work with legacy JIT exe
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assert isinstance(fn, type)
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if GRAPH_EXECUTOR == ProfilingMode.LEGACY:
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fn.__unittest_skip__ = True
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fn.__unittest_skip_why__ = msg
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fn.__unittest_skip__ = True # type: ignore[attr-defined]
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fn.__unittest_skip_why__ = msg # type: ignore[attr-defined]
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return fn
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@ -1832,8 +1834,8 @@ def skipIfNNModuleInlined(
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assert isinstance(fn, type)
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if condition:
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fn.__unittest_skip__ = True
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fn.__unittest_skip_why__ = msg
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fn.__unittest_skip__ = True # type: ignore[attr-defined]
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fn.__unittest_skip_why__ = msg # type: ignore[attr-defined]
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return fn
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@ -2013,17 +2015,17 @@ class DeterministicGuard:
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def __enter__(self):
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self.deterministic_restore = torch.are_deterministic_algorithms_enabled()
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self.warn_only_restore = torch.is_deterministic_algorithms_warn_only_enabled()
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self.fill_uninitialized_memory_restore = torch.utils.deterministic.fill_uninitialized_memory
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self.fill_uninitialized_memory_restore = torch.utils.deterministic.fill_uninitialized_memory # type: ignore[attr-defined]
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torch.use_deterministic_algorithms(
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self.deterministic,
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warn_only=self.warn_only)
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torch.utils.deterministic.fill_uninitialized_memory = self.fill_uninitialized_memory
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torch.utils.deterministic.fill_uninitialized_memory = self.fill_uninitialized_memory # type: ignore[attr-defined]
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def __exit__(self, exception_type, exception_value, traceback):
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torch.use_deterministic_algorithms(
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self.deterministic_restore,
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warn_only=self.warn_only_restore)
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torch.utils.deterministic.fill_uninitialized_memory = self.fill_uninitialized_memory_restore
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torch.utils.deterministic.fill_uninitialized_memory = self.fill_uninitialized_memory_restore # type: ignore[attr-defined]
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class AlwaysWarnTypedStorageRemoval:
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def __init__(self, always_warn):
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@ -2176,7 +2178,7 @@ def _test_function(fn, device):
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def skipIfNoXNNPACK(fn):
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@wraps(fn)
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def wrapper(*args, **kwargs):
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if not torch.backends.xnnpack.enabled:
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if not torch.backends.xnnpack.enabled: # type: ignore[attr-defined]
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raise unittest.SkipTest('XNNPACK must be enabled for these tests. Please build with USE_XNNPACK=1.')
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else:
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fn(*args, **kwargs)
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@ -2280,7 +2282,7 @@ def to_gpu(obj, type_map=None):
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res.requires_grad = obj.requires_grad
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return res
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elif torch.is_storage(obj):
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return obj.new().resize_(obj.size()).copy_(obj)
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return obj.new().resize_(obj.size()).copy_(obj) # type: ignore[attr-defined, union-attr]
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elif isinstance(obj, list):
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return [to_gpu(o, type_map) for o in obj]
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elif isinstance(obj, tuple):
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@ -2482,13 +2484,13 @@ class CudaMemoryLeakCheck:
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if not discrepancy_detected:
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continue
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if caching_allocator_discrepancy and not driver_discrepancy:
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if caching_allocator_discrepancy and not driver_discrepancy: # type: ignore[possibly-undefined]
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# Just raises a warning if the leak is not validated by the
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# driver API
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# NOTE: this may be a problem with how the caching allocator collects its
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# statistics or a leak too small to trigger the allocation of an
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# additional block of memory by the CUDA driver
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msg = ("CUDA caching allocator reports a memory leak not "
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msg = ("CUDA caching allocator reports a memory leak not " # type: ignore[possibly-undefined]
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f"verified by the driver API in {self.name}! "
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f"Caching allocator allocated memory was {self.caching_allocator_befores[i]} "
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f"and is now reported as {caching_allocator_mem_allocated} "
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@ -2498,7 +2500,7 @@ class CudaMemoryLeakCheck:
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elif caching_allocator_discrepancy and driver_discrepancy:
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# A caching allocator discrepancy validated by the driver API is a
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# failure (except on ROCm, see below)
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msg = (f"CUDA driver API confirmed a leak in {self.name}! "
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msg = (f"CUDA driver API confirmed a leak in {self.name}! " # type: ignore[possibly-undefined]
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f"Caching allocator allocated memory was {self.caching_allocator_befores[i]} "
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f"and is now reported as {caching_allocator_mem_allocated} "
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f"on device {i}. "
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@ -3022,7 +3024,7 @@ class TestCase(expecttest.TestCase):
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lambda repro_parts=repro_parts: print_repro_on_failure(repro_parts))
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except Exception as e:
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# Don't fail entirely if we can't get the test filename
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log.info("could not print repro string", extra=str(e))
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log.info("could not print repro string", extra=str(e)) # type: ignore[arg-type]
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def assertLeaksNoCudaTensors(self, name=None):
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name = self.id() if name is None else name
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@ -3133,7 +3135,7 @@ class TestCase(expecttest.TestCase):
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using_unittest = isinstance(result, unittest.TestResult)
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super_run = super().run
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test_cls = super_run.__self__
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test_cls = super_run.__self__ # type: ignore[attr-defined]
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# Are we compiling?
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compiled = TEST_WITH_TORCHDYNAMO or TEST_WITH_AOT_EAGER or TEST_WITH_TORCHINDUCTOR
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@ -3244,9 +3246,9 @@ class TestCase(expecttest.TestCase):
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# Create dummy TestInfo to record results correctly
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from xmlrunner.result import _TestInfo # type: ignore[import]
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case = _TestInfo(result, case)
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case.output = _TestInfo.ERROR
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case.elapsed_time = 0.0
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case.test_description = "TestSuiteEarlyFailure"
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case.output = _TestInfo.ERROR # type: ignore[attr-defined]
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case.elapsed_time = 0.0 # type: ignore[attr-defined]
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case.test_description = "TestSuiteEarlyFailure" # type: ignore[attr-defined]
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# This shouldn't really happen, but if does add fake failure
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# For more details see https://github.com/pytorch/pytorch/issues/71973
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result.failures.append((case, "TestSuite execution was aborted early"))
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@ -3680,7 +3682,7 @@ class TestCase(expecttest.TestCase):
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return get_sparse_data_with_block(pattern, blocksize)
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# batch data is created recursively:
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batch_data = {}
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batch_data = {} # type: ignore[var-annotated]
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for i, item in enumerate(pattern):
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for layout, d in get_batch_sparse_data(item, blocksize).items():
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target = batch_data.get(layout)
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@ -3842,30 +3844,30 @@ class TestCase(expecttest.TestCase):
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for blocksize in blocksizes:
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for densesize in densesizes:
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if layout == torch.strided:
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indices = ()
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indices = () # type: ignore[assignment]
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values = torch.empty((basesize + densesize), device=device, dtype=dtype)
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elif layout == torch.sparse_coo:
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indices = (torch.empty(len(basesize), 0, device=device, dtype=index_dtype),)
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indices = (torch.empty(len(basesize), 0, device=device, dtype=index_dtype),) # type: ignore[assignment]
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values = torch.empty((0, *densesize), device=device, dtype=dtype)
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elif layout == torch.sparse_csr:
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crow_indices = torch.tensor([0] * (basesize[0] + 1), device=device, dtype=index_dtype)
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col_indices = torch.empty(0, device=device, dtype=index_dtype)
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indices = (crow_indices, col_indices)
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indices = (crow_indices, col_indices) # type: ignore[assignment]
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values = torch.empty((0, *densesize), device=device, dtype=dtype)
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elif layout == torch.sparse_csc:
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ccol_indices = torch.tensor([0] * (basesize[1] + 1), device=device, dtype=index_dtype)
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row_indices = torch.empty(0, device=device, dtype=index_dtype)
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indices = (ccol_indices, row_indices)
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indices = (ccol_indices, row_indices) # type: ignore[assignment]
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values = torch.empty((0, *densesize), device=device, dtype=dtype)
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elif layout == torch.sparse_bsr:
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crow_indices = torch.tensor([0] * (basesize[0] // blocksize[0] + 1), device=device, dtype=index_dtype)
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col_indices = torch.empty(0, device=device, dtype=index_dtype)
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indices = (crow_indices, col_indices)
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indices = (crow_indices, col_indices) # type: ignore[assignment]
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values = torch.empty((0, *blocksize, *densesize), device=device, dtype=dtype)
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elif layout == torch.sparse_bsc:
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ccol_indices = torch.tensor([0] * (basesize[1] // blocksize[1] + 1), device=device, dtype=index_dtype)
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row_indices = torch.empty(0, device=device, dtype=index_dtype)
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indices = (ccol_indices, row_indices)
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indices = (ccol_indices, row_indices) # type: ignore[assignment]
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values = torch.empty((0, *blocksize, *densesize), device=device, dtype=dtype)
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else:
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assert 0 # unreachable
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@ -4029,9 +4031,9 @@ class TestCase(expecttest.TestCase):
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if error_metas:
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# See [ErrorMeta Cycles]
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error_metas = [error_metas]
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error_metas = [error_metas] # type: ignore[list-item]
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# TODO: compose all metas into one AssertionError
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raise error_metas.pop()[0].to_error(
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raise error_metas.pop()[0].to_error( # type: ignore[index]
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# This emulates unittest.TestCase's behavior if a custom message passed and
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# TestCase.longMessage (https://docs.python.org/3/library/unittest.html#unittest.TestCase.longMessage)
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# is True (default)
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@ -4062,7 +4064,7 @@ class TestCase(expecttest.TestCase):
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context: Optional[AssertRaisesContextIgnoreNotImplementedError] = \
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AssertRaisesContextIgnoreNotImplementedError(expected_exception, self) # type: ignore[call-arg]
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try:
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return context.handle('assertRaises', args, kwargs) # type: ignore[union-attr]
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return context.handle('assertRaises', args, kwargs) # type: ignore[union-attr, arg-type]
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finally:
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# see https://bugs.python.org/issue23890
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context = None
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@ -4087,7 +4089,7 @@ class TestCase(expecttest.TestCase):
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if self._ignore_not_implemented_error:
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context = AssertRaisesContextIgnoreNotImplementedError( # type: ignore[call-arg]
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expected_exception, self, expected_regex)
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return context.handle('assertRaisesRegex', args, kwargs) # type: ignore[attr-defined]
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return context.handle('assertRaisesRegex', args, kwargs) # type: ignore[attr-defined, arg-type]
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else:
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return super().assertRaisesRegex(expected_exception, expected_regex, *args, **kwargs)
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@ -4185,8 +4187,8 @@ class TestCase(expecttest.TestCase):
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# test/common_utils.py, but it matters in onnx-pytorch
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module_id = self.__class__.__module__
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munged_id = remove_prefix(self.id(), module_id + ".")
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test_file = os.path.realpath(sys.modules[module_id].__file__)
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expected_file = os.path.join(os.path.dirname(test_file),
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test_file = os.path.realpath(sys.modules[module_id].__file__) # type: ignore[type-var]
|
||||
expected_file = os.path.join(os.path.dirname(test_file), # type: ignore[type-var, arg-type]
|
||||
"expect",
|
||||
munged_id)
|
||||
|
||||
|
|
@ -4284,7 +4286,7 @@ class TestCase(expecttest.TestCase):
|
|||
if attrs.get("operator") == operator:
|
||||
break
|
||||
|
||||
self.assertEqual(attrs["operator"], operator)
|
||||
self.assertEqual(attrs["operator"], operator) # type: ignore[possibly-undefined]
|
||||
self.assertEqual(attrs.get("overload_name", ""), overload_name)
|
||||
|
||||
def check_nondeterministic_alert(self, fn, caller_name, should_alert=True):
|
||||
|
|
@ -4702,7 +4704,7 @@ def random_lowrank_matrix(rank, rows, columns, *batch_dims, **kwargs):
|
|||
|
||||
def _generate_indices_prefer_all_rows(rows: int, cols: int, num_indices: int) -> torch.Tensor:
|
||||
"""Generate indices for a row x cols matrix, preferring at least one index per row if possible."""
|
||||
indices = []
|
||||
indices = [] # type: ignore[var-annotated]
|
||||
n_per_row = math.ceil(num_indices / rows)
|
||||
col_indices = list(range(cols))
|
||||
|
||||
|
|
@ -4854,7 +4856,7 @@ def do_test_empty_full(self, dtypes, layout, device):
|
|||
int64_dtype, layout, device, fv + 5, False)
|
||||
|
||||
# FIXME: improve load_tests() documentation here
|
||||
running_script_path = None
|
||||
running_script_path = None # type: ignore[var-annotated]
|
||||
def set_running_script_path():
|
||||
global running_script_path
|
||||
try:
|
||||
|
|
@ -5116,7 +5118,7 @@ def copy_func(f):
|
|||
argdefs=f.__defaults__,
|
||||
closure=f.__closure__)
|
||||
g = functools.update_wrapper(g, f)
|
||||
g.__kwdefaults__ = f.__kwdefaults__
|
||||
g.__kwdefaults__ = f.__kwdefaults__ # type: ignore[attr-defined]
|
||||
return g
|
||||
|
||||
|
||||
|
|
@ -5304,8 +5306,8 @@ class TestGradients(TestCase):
|
|||
if is_iterable_of_tensors(sample.input):
|
||||
all_args = chain(sample.input, sample.args, sample.kwargs.values())
|
||||
else:
|
||||
all_args = tuple(chain((sample.input,), sample.args, sample.kwargs.values()))
|
||||
gradcheck_args = tuple(x for x in all_args if (isinstance(x, torch.Tensor) and x.requires_grad))
|
||||
all_args = tuple(chain((sample.input,), sample.args, sample.kwargs.values())) # type: ignore[assignment]
|
||||
gradcheck_args = tuple(x for x in all_args if (isinstance(x, torch.Tensor) and x.requires_grad)) # type: ignore[union-attr]
|
||||
|
||||
# Verifies sample input tensors should have no grad
|
||||
# This may happen if the same tensor is used in two different SampleInputs
|
||||
|
|
@ -5525,7 +5527,7 @@ def check_leaked_tensors(limit=1, matched_type=torch.Tensor):
|
|||
try:
|
||||
gc.collect()
|
||||
gc.set_debug(gc.DEBUG_SAVEALL)
|
||||
garbage_objs = []
|
||||
garbage_objs = [] # type: ignore[var-annotated]
|
||||
|
||||
# run the user code, after cleaning any existing refcycles, and then check for new ones
|
||||
# also allow usercode to check the garbage objs (e.g. for assertion) after exiting ctxmgr
|
||||
|
|
@ -5539,7 +5541,7 @@ def check_leaked_tensors(limit=1, matched_type=torch.Tensor):
|
|||
f"{num_garbage_objs} tensors were found in the garbage. Did you introduce a reference cycle?"
|
||||
)
|
||||
try:
|
||||
import objgraph
|
||||
import objgraph # type: ignore[import-not-found]
|
||||
warnings.warn(
|
||||
f"Dumping first {limit} objgraphs of leaked {matched_type}s rendered to png"
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in a new issue