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Fix ModuleInfo skip logic (#80471)
Fixes #80247 This PR: * Refactors the skip logic as done for OpInfo in #62713, fixing the logic error * For tests that were wrongly skipped before and now fail: * Fix `TestModule.test_cpu_gpu_parity` to support Lazy modules - this was affecting `LazyConv*` * Adds `@expectedFailure` decorators and a follow-up message to address `Conv*` failures on `TestModule.test_memory_format` Pull Request resolved: https://github.com/pytorch/pytorch/pull/80471 Approved by: https://github.com/mruberry
This commit is contained in:
Joel Benjamin Schlosser
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PyTorch MergeBot
PyTorch MergeBot
parent
8fab682e47
commit
d3dba3c42a
@ -533,6 +533,12 @@ class TestModule(TestCase):
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gpu_module = module_cls(*args, **kwargs).to(dtype).to(device)
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gpu_module.train(training)
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# === Lazy modules need to see an input to initialize params ===
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if issubclass(module_cls, torch.nn.modules.lazy.LazyModuleMixin):
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with torch.no_grad():
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cpu_module(*cpu_forward_args, **cpu_forward_kwargs)
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gpu_module(*gpu_forward_args, **gpu_forward_kwargs)
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for cpu_p, gpu_p in zip(cpu_module.parameters(), gpu_module.parameters()):
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gpu_p.data.copy_(cpu_p)
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@ -10,7 +10,7 @@ from torch.testing import make_tensor
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from torch.testing._internal.common_cuda import TEST_CUDNN
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from torch.testing._internal.common_dtype import floating_types
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from torch.testing._internal.common_device_type import (
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_TestParametrizer, _update_param_kwargs, skipIf, toleranceOverride, tol,
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_TestParametrizer, _update_param_kwargs, toleranceOverride, tol,
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skipCUDAIfCudnnVersionLessThan, skipCUDAIfRocm, precisionOverride, skipMeta)
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from torch.testing._internal.common_methods_invocations import DecorateInfo
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from torch.testing._internal.common_nn import nllloss_reference, get_reduction
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@ -104,25 +104,13 @@ class modules(_TestParametrizer):
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_update_param_kwargs(param_kwargs, 'training', training)
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try:
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active_decorators = [set_single_threaded_if_parallel_tbb]
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if module_info.should_skip(generic_cls.__name__, test.__name__, device_cls.device_type, dtype):
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active_decorators.append(skipIf(True, "Skipped!"))
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if module_info.decorators is not None:
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for decorator in module_info.decorators:
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# Can't use isinstance as it would cause a circular import
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if decorator.__class__.__name__ == 'DecorateInfo':
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if decorator.is_active(generic_cls.__name__, test.__name__,
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device_cls.device_type, dtype):
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active_decorators += decorator.decorators
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else:
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active_decorators.append(decorator)
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@wraps(test)
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def test_wrapper(*args, **kwargs):
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return test(*args, **kwargs)
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for decorator in active_decorators:
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for decorator in module_info.get_decorators(generic_cls.__name__, test.__name__,
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device_cls.device_type, dtype):
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test_wrapper = decorator(test_wrapper)
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yield (test_wrapper, test_name, param_kwargs)
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@ -187,16 +175,22 @@ class ModuleInfo(object):
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):
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self.module_cls = module_cls
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self.module_inputs_func = module_inputs_func
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self.skips = skips
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self.decorators = decorators
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self.decorators = (*(decorators if decorators else []), *(skips if skips else []))
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self.dtypes = dtypes
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self.supports_gradgrad = supports_gradgrad
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self.gradcheck_nondet_tol = gradcheck_nondet_tol
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self.module_memformat_affects_out = module_memformat_affects_out
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self.train_and_eval_differ = train_and_eval_differ
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def should_skip(self, cls_name, test_name, device_type, dtype):
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return any(si.is_active(cls_name, test_name, device_type, dtype) for si in self.skips)
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def get_decorators(self, test_class, test_name, device, dtype):
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result = [set_single_threaded_if_parallel_tbb]
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for decorator in self.decorators:
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if isinstance(decorator, DecorateInfo):
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if decorator.is_active(test_class, test_name, device, dtype):
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result.extend(decorator.decorators)
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else:
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result.append(decorator)
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return result
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@property
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def name(self):
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@ -1094,6 +1088,10 @@ module_db: List[ModuleInfo] = [
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# Failure on ROCM for float32 issue #70125
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DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]),
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DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format",
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device_type='cuda', dtypes=[torch.float64]),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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@ -1108,6 +1106,9 @@ module_db: List[ModuleInfo] = [
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# Failure on ROCM for float32 issue #70125
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DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]),
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DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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@ -1136,6 +1137,11 @@ module_db: List[ModuleInfo] = [
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# Failure on ROCM for float32 issue #70125
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DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]),
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DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cpu'),
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda',
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dtypes=[torch.float64]),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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@ -1150,6 +1156,9 @@ module_db: List[ModuleInfo] = [
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# Failure on ROCM for float32 issue #70125
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DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]),
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DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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@ -1196,6 +1205,10 @@ module_db: List[ModuleInfo] = [
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# Lazy modules don't currently play well with ModuleInfo tests on the meta device.
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# See https://github.com/pytorch/pytorch/issues/70505 for more info.
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DecorateInfo(skipMeta),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format",
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device_type='cuda', dtypes=[torch.float64]),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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@ -1213,6 +1226,9 @@ module_db: List[ModuleInfo] = [
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# See https://github.com/pytorch/pytorch/issues/70505 for more info.
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DecorateInfo(skipMeta),
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DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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@ -1247,6 +1263,11 @@ module_db: List[ModuleInfo] = [
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# See https://github.com/pytorch/pytorch/issues/70505 for more info.
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DecorateInfo(skipMeta),
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DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cpu'),
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda',
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dtypes=[torch.float64]),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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@ -1264,6 +1285,9 @@ module_db: List[ModuleInfo] = [
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# See https://github.com/pytorch/pytorch/issues/70505 for more info.
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DecorateInfo(skipMeta),
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DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
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# This was wrongly being skipped before and needs investigation.
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# See https://github.com/pytorch/pytorch/issues/80247
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DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format"),
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),
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decorators=(
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DecorateInfo(precisionOverride({torch.float32: 1e-04}), 'TestModule', 'test_memory_format'),
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