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https://github.com/saymrwulf/pytorch.git
synced 2026-05-14 20:57:59 +00:00
Remove a number of fixed skips (#103162)
Also adds `PYTORCH_TEST_WITH_AOT_EAGER` to distinguish errors coming from aot_autograd and not inductor (not tested in ci, but useful for local debugging) Pull Request resolved: https://github.com/pytorch/pytorch/pull/103162 Approved by: https://github.com/desertfire
This commit is contained in:
Elias Ellison
PyTorch MergeBot
parent
3c896a5adb
commit
40d70ba7ed
3 changed files with 7 additions and 29 deletions
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@ -13,7 +13,7 @@ from torch.testing._internal.common_device_type import (
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from torch.testing._internal.common_modules import module_db, modules, TrainEvalMode
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from torch.testing._internal.common_utils import (
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TestCase, run_tests, freeze_rng_state, mock_wrapper, get_tensors_from, gradcheck,
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gradgradcheck, skipIfTorchInductor)
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gradgradcheck)
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from unittest.mock import patch, call
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@ -324,7 +324,6 @@ class TestModule(TestCase):
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self._traverse_obj(obj, inner_zero_grad)
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@modules(module_db)
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@skipIfTorchInductor("to be fixed")
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def test_non_contiguous_tensors(self, device, dtype, module_info, training):
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# Check modules work with non-contiguous tensors
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@ -488,7 +487,6 @@ class TestModule(TestCase):
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@toleranceOverride({torch.float32: tol(5e-2, 0),
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torch.float64: tol(4e-4, 0)})
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@modules(module_db)
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@skipIfTorchInductor("to be fixed")
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def test_cpu_gpu_parity(self, device, dtype, module_info, training):
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# TODO: RNN / GRU / LSTM don't support backwards on eval mode for cuDNN; skip this in a
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# nicer way for eval mode only.
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@ -580,7 +578,6 @@ class TestModule(TestCase):
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@with_tf32_off
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@modules(module_db)
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@skipIfTorchInductor("to be fixed")
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def test_memory_format(self, device, dtype, module_info, training):
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is_sm86or80 = device.startswith("cuda") and (torch.cuda.get_device_capability(0) == (8, 6)
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or torch.cuda.get_device_capability(0) == (8, 0))
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@ -32,7 +32,7 @@ from torch.testing import make_tensor
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from torch.testing._internal.common_utils import (
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TEST_WITH_TORCHINDUCTOR, TestCase, TEST_WITH_ROCM, run_tests, IS_JETSON,
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IS_WINDOWS, IS_FILESYSTEM_UTF8_ENCODING, NO_MULTIPROCESSING_SPAWN,
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IS_SANDCASTLE, IS_FBCODE, IS_REMOTE_GPU, load_tests, skipIfTorchInductor, slowTest,
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IS_SANDCASTLE, IS_FBCODE, IS_REMOTE_GPU, skipIfTorchInductor, load_tests, slowTest,
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TEST_WITH_CROSSREF, skipIfTorchDynamo,
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skipCUDAMemoryLeakCheckIf, BytesIOContext,
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skipIfRocm, skipIfNoSciPy, TemporaryFileName, TemporaryDirectoryName,
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@ -519,7 +519,6 @@ class TestTorchDeviceType(TestCase):
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# collected tests of ops that used scalar_check in Declarations.cwrap for
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# correctness
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@skipIfTorchInductor("segfaults")
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def test_scalar_check(self, device):
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zero_d = torch.randn((), device=device)
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one_d = torch.randn((1,), device=device)
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@ -981,7 +980,6 @@ class TestTorchDeviceType(TestCase):
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torch.set_default_tensor_type(default_type)
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# TODO: this test should be in test_nn.py
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@skipIfTorchInductor("Please convert all Tensors to FakeTensors")
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def test_conv_transposed_backward_agnostic_to_memory_format(self, device):
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in_channels = 64
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out_channels = 128
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@ -3260,7 +3258,6 @@ else:
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# FIXME: move to test indexing
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@onlyCPU
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@skipIfTorchInductor("FIXME")
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def test_errors_index_copy(self, device):
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# We do not test the GPU as the CUDA_ASSERT would break the CUDA context
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idx_dim = 8
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@ -3643,7 +3640,6 @@ else:
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@dtypes(*floating_and_complex_types())
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@dtypesIfCPU(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
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@dtypesIfCUDA(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
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@skipIfTorchInductor("FIXME")
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def test_scatter_reduce_non_unique_index(self, device, dtype):
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height = 2
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width = 2
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@ -4401,7 +4397,6 @@ else:
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# FIXME: move to test distributions
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@onlyCUDA
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@skipIfTorchInductor("out_wrapper does not check devices correctly")
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def test_multinomial_device_constrain(self, device):
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x = torch.empty(3, device="cpu")
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y = torch.empty(3, device=device)
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@ -4574,7 +4569,6 @@ else:
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y = ndhwc.permute(0, 1, 4, 3, 2).permute(0, 1, 4, 3, 2)
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self.assertTrue(y.is_contiguous(memory_format=torch.channels_last_3d))
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@skipIfTorchInductor("To be supported")
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def test_memory_format_propagation_rules(self, device):
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contiguous = torch.rand(10, 3, 5, 5, device=device)
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@ -4618,7 +4612,6 @@ else:
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self.assertEqual(ambiguous.stride(), result.stride())
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@skipIfMps
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@skipIfTorchInductor("To be supported")
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def test_memory_format_empty_like(self, device):
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def test_helper(x, memory_format):
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xc = x.contiguous(memory_format=memory_format)
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@ -4661,7 +4654,6 @@ else:
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x.is_contiguous(memory_format=torch.channels_last_3d), x_rep.is_contiguous(memory_format=torch.channels_last_3d))
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# FIXME: make this a elementwise unary and elementwise binary OpInfo test
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@skipIfTorchInductor("To be supported")
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def test_memory_format_operators(self, device):
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def _chunk_op(x, y):
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x1, x2 = x.chunk(2, dim=1)
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@ -4874,7 +4866,6 @@ else:
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@onlyCUDA
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@unittest.skipIf(PYTORCH_CUDA_MEMCHECK, "is_pinned uses failure to detect pointer property")
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@skipIfTorchInductor("pin_memory isn't yet supported in TorchInductor")
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def test_pin_memory_from_constructor(self, device):
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def _get_like(t, **kwargs):
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return [
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@ -5096,7 +5087,6 @@ else:
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x = x.permute(permutation)
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self.assertEqual(x.stride(), transformation_fn(x, memory_format=torch.preserve_format).stride())
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@skipIfTorchInductor("To be supported")
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def test_memory_format_to(self, device):
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def get_generator(memory_format, shape):
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def input_generator_fn(device):
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@ -5114,7 +5104,6 @@ else:
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self._test_memory_format_transformations(
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device, get_generator(mf, shape), transformation_fn, mf, default_is_preserve=True)
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@skipIfTorchInductor("To be supported")
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def test_memory_format_type(self, device):
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def get_generator(memory_format, shape):
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def input_generator_fn(device):
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@ -5132,7 +5121,6 @@ else:
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self._test_memory_format_transformations(
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device, get_generator(mf, shape), transformation_fn, mf, default_is_preserve=True)
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@skipIfTorchInductor("To be supported")
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def test_memory_format_clone(self, device):
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def get_generator(memory_format, shape):
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def input_generator_fn(device):
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@ -5176,7 +5164,6 @@ else:
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self._test_memory_format_transformations(
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device, get_generator(mf, shape), transformation_fn, mf, compare_data=False, default_is_preserve=True)
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@skipIfTorchInductor("To be supported")
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def test_memory_format_type_shortcuts(self, device):
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def get_generator(memory_format, shape, dtype):
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def input_generator_fn(device):
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@ -5210,7 +5197,6 @@ else:
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device, get_generator(mf, shape, torch.float64), get_fn('float'), mf, default_is_preserve=True)
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@onlyCUDA
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@skipIfTorchInductor("To be supported")
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def test_memory_format_cpu_and_cuda_ops(self, device):
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def get_generator(memory_format, shape):
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def input_generator_fn(device):
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@ -5411,7 +5397,6 @@ else:
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# FIXME: get PyTorch/XLA to run test_testing
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# This test should ideally be in test_testing.py,
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# but since pytorch/xla runs tests from test_torch.py, we have it here.
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@skipIfTorchInductor("random_.from needs to be renamed")
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def test_assertRaisesRegex_ignore_msg_non_native_device(self, device):
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# Verify that self.assertRaisesRegex only checks the Error and ignores
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# message for non-native devices.
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@ -5586,7 +5571,6 @@ class TestDevicePrecision(TestCase):
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# FIXME: moved to indexing test suite
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@deviceCountAtLeast(1)
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@skipIfTorchInductor("FIXME")
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def test_advancedindex_mixed_cpu_devices(self, devices) -> None:
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def test(x: torch.Tensor, ia: torch.Tensor, ib: torch.Tensor) -> None:
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# test getitem
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@ -5620,7 +5604,6 @@ class TestDevicePrecision(TestCase):
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test(x, ia, ib)
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@deviceCountAtLeast(1)
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@skipIfTorchInductor("FIXME")
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def test_advancedindex_mixed_devices_error(self, devices) -> None:
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def test(x: torch.Tensor, ia: torch.Tensor, ib: torch.Tensor) -> None:
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# test getitem
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@ -7423,7 +7406,6 @@ tensor([[[1.+1.j, 1.+1.j, 1.+1.j, ..., 1.+1.j, 1.+1.j, 1.+1.j],
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self.assertRaises(RuntimeError, lambda: x.new(z.storage()))
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@unittest.skipIf(PYTORCH_CUDA_MEMCHECK, "is_pinned uses failure to detect pointer property")
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@skipIfTorchInductor("pin_memory isn't yet supported in TorchInductor")
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def test_pin_memory(self):
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x = torch.randn(3, 5)
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self.assertFalse(x.is_pinned())
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@ -7643,7 +7625,6 @@ tensor([[[1.+1.j, 1.+1.j, 1.+1.j, ..., 1.+1.j, 1.+1.j, 1.+1.j],
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weight), torch.tensor(bias), 1, epsilon, True)
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torch.testing.assert_close(expected_norm, actual_norm)
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@skipIfTorchInductor("To be supported")
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def test_memory_format(self):
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def test_helper(x, memory_format):
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y = x.contiguous(memory_format=memory_format)
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@ -7834,7 +7815,6 @@ tensor([[[1.+1.j, 1.+1.j, 1.+1.j, ..., 1.+1.j, 1.+1.j, 1.+1.j],
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self.assertEqual(z.size(), (2 * 10 ** 8, 3, 4 * 10 ** 8))
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self.assertRaises(RuntimeError, lambda: z[0][0][0].item())
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@skipIfTorchInductor("https://github.com/pytorch/pytorch/issues/97414")
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def test_upsample_nearest2d_meta(self):
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# TODO: the out tests cannot be triggered by test_nn.py because
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# we don't actually do out= arguments for nn functions, so there
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@ -8180,7 +8160,6 @@ tensor([[[1.+1.j, 1.+1.j, 1.+1.j, ..., 1.+1.j, 1.+1.j, 1.+1.j],
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self.assertEqual(y[:, 40], range(4000, 4100))
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# FIXME: Port to a more appropriate test suite
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@skipIfTorchInductor("FIXME")
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def test_copy_broadcast(self):
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torch.zeros(5, 6).copy_(torch.zeros(6))
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self.assertRaises(RuntimeError, lambda: torch.zeros(5, 6).copy_(torch.zeros(30)))
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@ -8194,7 +8173,6 @@ tensor([[[1.+1.j, 1.+1.j, 1.+1.j, ..., 1.+1.j, 1.+1.j, 1.+1.j],
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# storage to a single storage would cause RuntimeError to be thrown
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self.assertRaises(RuntimeError, lambda: torch.zeros(1, 6).expand(5, 6).copy_(torch.zeros(5, 6)))
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@skipIfTorchInductor("FIXME")
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def test_copy_float16(self):
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# Check that fbgemm code no longer reads memory out of bounds, see
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# copy_impl and fbgemm::Float16ToFloat_ref.
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@ -8378,7 +8356,6 @@ tensor([[[1.+1.j, 1.+1.j, 1.+1.j, ..., 1.+1.j, 1.+1.j, 1.+1.j],
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s0 = t0.as_subclass(BadSubTensor)
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# FIXME: Port to a test suite that better fits slicing
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@skipIfTorchInductor("FIXME")
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def test_slice(self):
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empty = torch.empty(0, 4)
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x = torch.arange(0., 16).view(4, 4)
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@ -1034,7 +1034,9 @@ class CrossRefMode(torch.overrides.TorchFunctionMode):
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# Run PyTorch tests with TorchDynamo
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TEST_WITH_TORCHINDUCTOR = os.getenv('PYTORCH_TEST_WITH_INDUCTOR') == '1'
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TEST_WITH_TORCHDYNAMO = os.getenv('PYTORCH_TEST_WITH_DYNAMO') == '1' or TEST_WITH_TORCHINDUCTOR
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# AOT_EAGER not tested in ci, useful for debugging
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TEST_WITH_AOT_EAGER = os.getenv('PYTORCH_TEST_WITH_AOT_EAGER') == '1'
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TEST_WITH_TORCHDYNAMO = os.getenv('PYTORCH_TEST_WITH_DYNAMO') == '1' or TEST_WITH_TORCHINDUCTOR or TEST_WITH_AOT_EAGER
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if TEST_WITH_TORCHDYNAMO:
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import torch._dynamo
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@ -2263,6 +2265,8 @@ class TestCase(expecttest.TestCase):
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super_run = super().run
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if TEST_WITH_TORCHINDUCTOR:
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super_run = torch._dynamo.optimize("inductor")(super_run)
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elif TEST_WITH_AOT_EAGER:
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super_run = torch._dynamo.optimize("aot_eager")(super_run)
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elif TEST_WITH_TORCHDYNAMO:
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# TorchDynamo optimize annotation
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super_run = torch._dynamo.optimize("eager")(super_run)
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