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[1/N]Enable some tests in test_ops.TestCommon on Intel GPU (#159944)
For https://github.com/pytorch/pytorch/issues/114850, we will port aten unit tests to Intel GPU. This PR will work on some test case of test/test_ops.py. We could enable Intel GPU with following methods and try the best to keep the original code styles: 1. Extended XPUTestBase.get_all_devices to support multiple devices 2. Added skipXPU decorator 3. Extended onlyOn to support device list 4. Enabled 'xpu' for some test pathes 5. Added allow_xpu=True for supported test class. 6. Replaced onlyCUDA with onlyOn(['cuda', 'xpu']) for supported tests 7. Use skipIfXpu and skipXPU to disable unsupported test. Pull Request resolved: https://github.com/pytorch/pytorch/pull/159944 Approved by: https://github.com/guangyey, https://github.com/EikanWang, https://github.com/albanD
This commit is contained in:
Deng, Daisy
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
e1e5e040cd
commit
4fd70d4e7b
@ -28,9 +28,11 @@ from torch.testing._internal.common_device_type import (
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onlyCPU,
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onlyCUDA,
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onlyNativeDeviceTypesAnd,
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onlyOn,
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OpDTypes,
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ops,
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skipMeta,
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skipXPU,
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)
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from torch.testing._internal.common_dtype import (
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all_types_and_complex_and,
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@ -221,7 +223,7 @@ class TestCommon(TestCase):
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assert len(filtered_ops) == 0, err_msg
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# Validates that each OpInfo works correctly on different CUDA devices
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@onlyCUDA
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@onlyOn(["cuda", "xpu"])
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@deviceCountAtLeast(2)
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@ops(op_db, allowed_dtypes=(torch.float32, torch.long))
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def test_multiple_devices(self, devices, dtype, op):
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@ -331,6 +333,8 @@ class TestCommon(TestCase):
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# NumPy does computation internally using double precision for many functions
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# resulting in possible equality check failures.
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# skip windows case on CPU due to https://github.com/pytorch/pytorch/issues/129947
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# XPU test will be enabled step by step. Skip the tests temporarily.
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@skipXPU
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@onlyNativeDeviceTypesAnd(["hpu"])
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@suppress_warnings
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@ops(_ref_test_ops, allowed_dtypes=(torch.float64, torch.long, torch.complex128))
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@ -340,7 +344,7 @@ class TestCommon(TestCase):
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and op.formatted_name
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in ("signal_windows_exponential", "signal_windows_bartlett")
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and dtype == torch.float64
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and "cuda" in device
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and ("cuda" in device or "xpu" in device)
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or "cpu" in device
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): # noqa: E121
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raise unittest.SkipTest("XXX: raises tensor-likes are not close.")
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@ -353,7 +357,7 @@ class TestCommon(TestCase):
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)
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# Tests that the cpu and gpu results are consistent
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@onlyCUDA
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@onlyOn(["cuda", "xpu"])
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@suppress_warnings
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@slowTest
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@ops(_ops_and_refs_with_no_numpy_ref, dtypes=OpDTypes.any_common_cpu_cuda_one)
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@ -385,6 +389,7 @@ class TestCommon(TestCase):
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# Tests that experimental Python References can propagate shape, dtype,
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# and device metadata properly.
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# See https://github.com/pytorch/pytorch/issues/78050 for a discussion of stride propagation.
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@skipXPU
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops(python_ref_db)
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@skipIfTorchInductor("Takes too long for inductor")
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@ -580,6 +585,7 @@ class TestCommon(TestCase):
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# Tests that experimental Python References perform the same computation
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# as the operators they reference, when operator calls in the torch
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# namespace are remapped to the refs namespace (torch.foo becomes refs.foo).
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@skipXPU
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops(python_ref_db)
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@skipIfTorchInductor("Takes too long for inductor")
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@ -598,6 +604,7 @@ class TestCommon(TestCase):
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# Tests that experimental Python References perform the same computation
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# as the operators they reference, when operator calls in the torch
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# namespace are preserved (torch.foo remains torch.foo).
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@skipXPU
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops(python_ref_db)
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@skipIfTorchInductor("Takes too long for inductor")
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@ -633,6 +640,7 @@ class TestCommon(TestCase):
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op.op = partial(make_traced(op.op), executor=executor)
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self._ref_test_helper(contextlib.nullcontext, device, dtype, op)
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@skipXPU
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@skipMeta
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops([op for op in op_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
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@ -644,6 +652,7 @@ class TestCommon(TestCase):
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out = op(si.input, *si.args, **si.kwargs)
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self.assertFalse(isinstance(out, type(NotImplemented)))
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@skipXPU
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@skipMeta
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops(
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@ -667,6 +676,7 @@ class TestCommon(TestCase):
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out = op(si.input, *si.args, **si.kwargs)
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self.assertFalse(isinstance(out, type(NotImplemented)))
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@skipXPU
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@skipMeta
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops(
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@ -693,6 +703,7 @@ class TestCommon(TestCase):
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# Tests that the function produces the same result when called with
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# noncontiguous tensors.
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@skipXPU
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@with_tf32_off
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@onlyNativeDeviceTypesAnd(["hpu"])
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@suppress_warnings
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@ -785,6 +796,7 @@ class TestCommon(TestCase):
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# incorrectly sized out parameter warning properly yet
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# Cases test here:
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# - out= with the correct dtype and device, but the wrong shape
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@skipXPU
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@ops(ops_and_refs, dtypes=OpDTypes.none)
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def test_out_warning(self, device, op):
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if TEST_WITH_TORCHDYNAMO and op.name == "_refs.clamp":
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@ -923,6 +935,7 @@ class TestCommon(TestCase):
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# Case 3 and 4 are slightly different when the op is a factory function:
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# - if device, dtype are NOT passed, any combination of dtype/device should be OK for out
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# - if device, dtype are passed, device and dtype should match
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@skipXPU
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@ops(ops_and_refs, dtypes=OpDTypes.any_one)
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def test_out(self, device, dtype, op):
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# Prefers running in float32 but has a fallback for the first listed supported dtype
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@ -1126,6 +1139,7 @@ class TestCommon(TestCase):
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with self.assertRaises(exc_type, msg=msg_fail):
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op_out(out=out)
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@skipXPU
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@ops(
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[
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op
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@ -1164,6 +1178,7 @@ class TestCommon(TestCase):
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with self.assertRaises(RuntimeError, msg=msg), maybe_skip_size_asserts(op):
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op(sample.input, *sample.args, **sample.kwargs, out=out)
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@skipXPU
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@ops(filter(reduction_dtype_filter, ops_and_refs), dtypes=(torch.int16,))
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def test_out_integral_dtype(self, device, dtype, op):
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def helper(with_out, expectFail, op_to_test, inputs, *args, **kwargs):
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@ -1207,6 +1222,7 @@ class TestCommon(TestCase):
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# Tests that the forward and backward passes of operations produce the
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# same values for the cross-product of op variants (method, inplace)
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# against eager's gold standard op function variant
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@skipXPU
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@_variant_ops(op_db)
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def test_variant_consistency_eager(self, device, dtype, op):
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# Acquires variants (method variant, inplace variant, operator variant, inplace_operator variant, aliases)
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@ -1387,6 +1403,7 @@ class TestCommon(TestCase):
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# Reference testing for operations in complex32 against complex64.
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# NOTE: We test against complex64 as NumPy doesn't have a complex32 equivalent dtype.
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@skipXPU
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@ops(op_db, allowed_dtypes=(torch.complex32,))
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def test_complex_half_reference_testing(self, device, dtype, op):
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if not op.supports_dtype(torch.complex32, device):
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@ -1422,6 +1439,7 @@ class TestCommon(TestCase):
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# `cfloat` input -> `float` output
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self.assertEqual(actual, expected, exact_dtype=False)
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@skipXPU
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@ops(op_db, allowed_dtypes=(torch.bool,))
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def test_non_standard_bool_values(self, device, dtype, op):
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# Test boolean values other than 0x00 and 0x01 (gh-54789)
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@ -1450,6 +1468,7 @@ class TestCommon(TestCase):
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# Validates that each OpInfo specifies its forward and backward dtypes
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# correctly for CPU and CUDA devices
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@skipXPU
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@skipMeta
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops(ops_and_refs, dtypes=OpDTypes.none)
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@ -1656,6 +1675,7 @@ class TestCommon(TestCase):
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self.fail(msg)
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# Validates that each OpInfo that sets promotes_int_to_float=True does as it says
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@skipXPU
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@skipMeta
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@onlyNativeDeviceTypesAnd(["hpu"])
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@ops(
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@ -2845,7 +2865,7 @@ class TestFakeTensor(TestCase):
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self.assertEqual(strided_result.layout, torch.strided)
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instantiate_device_type_tests(TestCommon, globals())
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instantiate_device_type_tests(TestCommon, globals(), allow_xpu=True)
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instantiate_device_type_tests(TestCompositeCompliance, globals())
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instantiate_device_type_tests(TestMathBits, globals())
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instantiate_device_type_tests(TestRefsOpsInfo, globals(), only_for="cpu")
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@ -628,8 +628,17 @@ class XPUTestBase(DeviceTypeTestBase):
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@classmethod
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def get_all_devices(cls):
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# currently only one device is supported on MPS backend
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primary_device_idx = int(cls.get_primary_device().split(":")[1])
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num_devices = torch.xpu.device_count()
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prim_device = cls.get_primary_device()
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return [prim_device]
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xpu_str = "xpu:{0}"
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non_primary_devices = [
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xpu_str.format(idx)
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for idx in range(num_devices)
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if idx != primary_device_idx
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]
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return [prim_device] + non_primary_devices
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@classmethod
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def setUpClass(cls):
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@ -1395,13 +1404,13 @@ class expectedFailure:
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class onlyOn:
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def __init__(self, device_type):
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def __init__(self, device_type: Union[str, list]):
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self.device_type = device_type
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def __call__(self, fn):
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@wraps(fn)
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def only_fn(slf, *args, **kwargs):
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if self.device_type != slf.device_type:
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if slf.device_type not in self.device_type:
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reason = f"Only runs on {self.device_type}"
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raise unittest.SkipTest(reason)
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@ -1960,6 +1969,10 @@ def skipHPU(fn):
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return skipHPUIf(True, "test doesn't work on HPU backend")(fn)
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def skipXPU(fn):
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return skipXPUIf(True, "test doesn't work on XPU backend")(fn)
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def skipPRIVATEUSE1(fn):
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return skipPRIVATEUSE1If(True, "test doesn't work on privateuse1 backend")(fn)
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@ -28,7 +28,7 @@ from torch.testing._internal.common_device_type import \
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(onlyCPU, onlyCUDA, onlyNativeDeviceTypes, disablecuDNN, skipCUDAIfNoMagma, skipCUDAIfNoMagmaAndNoCusolver,
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skipCUDAIfNoCusolver, skipCPUIfNoLapack, skipCPUIfNoFFT, skipCUDAIf, precisionOverride,
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skipCPUIfNoMklSparse,
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toleranceOverride, tol)
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toleranceOverride, tol, skipXPU)
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from torch.testing._internal.common_cuda import (
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PLATFORM_SUPPORTS_FLASH_ATTENTION, PLATFORM_SUPPORTS_MEM_EFF_ATTENTION,
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SM53OrLater, SM80OrLater, SM89OrLater, with_tf32_off, TEST_CUDNN, _get_torch_cuda_version,
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@ -39,7 +39,7 @@ from torch.testing._internal.common_utils import (
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TEST_WITH_ROCM, IS_FBCODE, IS_WINDOWS, IS_MACOS, IS_S390X, TEST_SCIPY,
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torch_to_numpy_dtype_dict, numpy_to_torch_dtype, TEST_WITH_ASAN,
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GRADCHECK_NONDET_TOL, slowTest, TEST_WITH_SLOW,
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TEST_WITH_TORCHINDUCTOR, MACOS_VERSION
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TEST_WITH_TORCHINDUCTOR, MACOS_VERSION,
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)
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from torch.testing._utils import wrapper_set_seed
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@ -13646,7 +13646,8 @@ op_db: list[OpInfo] = [
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skipCUDAIf(not ((_get_torch_cuda_version() >= (11, 3))
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or (_get_torch_rocm_version() >= (5, 2))),
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"cusparseSDDMM was added in 11.2.1"),
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skipCPUIfNoMklSparse, ],
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skipCPUIfNoMklSparse,
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skipXPU],
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skips=(
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# NotImplementedError: Tensors of type SparseCsrTensorImpl do not have is_contiguous
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DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_noncontiguous_samples'),
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@ -16373,7 +16374,7 @@ op_db: list[OpInfo] = [
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supports_out=True,
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supports_forward_ad=False,
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supports_autograd=False,
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decorators=[skipCUDAIf(not SM89OrLater or TEST_WITH_ROCM, 'Requires CUDA SM >= 8.9')],
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decorators=[skipXPU, skipCUDAIf(not SM89OrLater or TEST_WITH_ROCM, 'Requires CUDA SM >= 8.9')],
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skips=(
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# Sample inputs isn't really parametrized on dtype
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DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes'),
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@ -16500,7 +16501,8 @@ op_db: list[OpInfo] = [
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# FIXME: mask_type == 2 (LowerRight)
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decorators=[
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skipCUDAIf(not PLATFORM_SUPPORTS_MEM_EFF_ATTENTION, "This platform doesn't support efficient attention"),
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skipCUDAIf(TEST_WITH_ROCM, "Efficient attention on ROCM doesn't support custom_mask_type==2")],
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skipCUDAIf(TEST_WITH_ROCM, "Efficient attention on ROCM doesn't support custom_mask_type==2"),
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skipXPU],
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skips=(
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# Checking the scaler value of the philox seed and offset
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DecorateInfo(unittest.expectedFailure, 'TestCompositeCompliance', 'test_operator', device_type='cuda'),
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@ -20863,6 +20865,8 @@ op_db: list[OpInfo] = [
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# AssertionError: Tensor-likes are not close!
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# Fails in cuda11.7
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DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_compare_cpu', device_type='cuda'),
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# AssertionError: Tensor-likes are not close!
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DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_compare_cpu', device_type='xpu'),
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DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),),),
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# In training mode, feature_alpha_dropout currently doesn't support inputs of complex dtype
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# unlike when `train=False`, it supports complex inputs, hence 2 OpInfos to cover all cases
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@ -41,6 +41,7 @@ from torch.testing._internal.common_utils import (
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skipIfSlowGradcheckEnv,
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slowTest,
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TEST_WITH_ROCM,
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TEST_XPU,
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)
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from torch.testing._internal.opinfo.core import (
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clone_sample,
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@ -1766,7 +1767,12 @@ op_db: list[OpInfo] = [
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decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack],
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skips=(
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# linalg.lu_factor: LU without pivoting is not implemented on the CPU
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DecorateInfo(unittest.expectedFailure, "TestCommon", "test_compare_cpu"),
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DecorateInfo(
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unittest.expectedFailure,
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"TestCommon",
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"test_compare_cpu",
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active_if=(not TEST_XPU),
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),
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),
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),
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OpInfo(
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@ -1782,7 +1788,12 @@ op_db: list[OpInfo] = [
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decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack],
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skips=(
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# linalg.lu_factor: LU without pivoting is not implemented on the CPU
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DecorateInfo(unittest.expectedFailure, "TestCommon", "test_compare_cpu"),
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DecorateInfo(
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unittest.expectedFailure,
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"TestCommon",
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"test_compare_cpu",
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active_if=(not TEST_XPU),
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),
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),
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),
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OpInfo(
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@ -1799,7 +1810,12 @@ op_db: list[OpInfo] = [
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decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack],
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skips=(
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# linalg.lu_factor: LU without pivoting is not implemented on the CPU
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DecorateInfo(unittest.expectedFailure, "TestCommon", "test_compare_cpu"),
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DecorateInfo(
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unittest.expectedFailure,
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"TestCommon",
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"test_compare_cpu",
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active_if=(not TEST_XPU),
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),
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),
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),
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OpInfo(
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@ -448,6 +448,10 @@ op_db: list[OpInfo] = [
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DecorateInfo(unittest.skip("Skipped!"), "TestNNCOpInfo"),
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# Greatest absolute difference: inf
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DecorateInfo(unittest.expectedFailure, "TestCommon", "test_compare_cpu"),
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# Too slow
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DecorateInfo(
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unittest.skip, "TestCommon", "test_compare_cpu", device_type="xpu"
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),
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),
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supports_one_python_scalar=True,
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supports_autograd=False,
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@ -474,6 +478,10 @@ op_db: list[OpInfo] = [
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DecorateInfo(unittest.skip("Skipped!"), "TestNNCOpInfo"),
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# Greatest absolute difference: nan
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DecorateInfo(unittest.expectedFailure, "TestCommon", "test_compare_cpu"),
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# Too slow
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DecorateInfo(
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unittest.skip, "TestCommon", "test_compare_cpu", device_type="xpu"
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),
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),
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supports_one_python_scalar=True,
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supports_autograd=False,
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