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[BE] Switch TestConsistency to MPS device (#147893)
Which will eventually allow move decorators away more `common_mps.py` Adjust tolerances accordingly. XFAIL a bunch of tests on MacOS-13, which is going to be deprecated anyway Pull Request resolved: https://github.com/pytorch/pytorch/pull/147893 Approved by: https://github.com/atalman ghstack dependencies: #152204
This commit is contained in:
Nikita Shulga
PyTorch MergeBot
parent
73f11e3365
commit
3ef6d6924a
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@ -11706,23 +11706,24 @@ MPS_DTYPES = [t for t in get_all_dtypes() if t not in MPS_UNSUPPORTED_TYPES]
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MPS_GRAD_DTYPES = [torch.float32, torch.float16]
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def transform_opinfo_sample_to_mps(sample):
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"""Transforms opinfo.core.SampleInput from CPU to MPS"""
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def transform_opinfo_sample_to_cpu(sample):
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"""Transforms opinfo.core.SampleInput from MPS to CPU"""
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def transform_sample(x):
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if not isinstance(x, torch.Tensor):
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return x
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requires_grad = x.requires_grad
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conjugated = x.is_conj()
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rc = x.detach()
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rc = rc.to("mps") if not conjugated else x.conj().to("mps").conj()
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rc = rc.cpu() if not conjugated else x.conj().cpu().conj()
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return rc.requires_grad_(x.requires_grad)
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mps_sample = sample.transform(transform_sample)
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cpu_sample = sample.transform(transform_sample)
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# Transform kwargs `device="cpu"` to `device="mps"`
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if mps_sample.kwargs.get("device", "") == "cpu":
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mps_sample.kwargs["device"] = "mps"
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return mps_sample
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# Transform kwargs `device="mps:0"` to `device="cpu"`
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if cpu_sample.kwargs.get("device", "") == "mps:0":
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cpu_sample.kwargs["device"] = "cpu"
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return cpu_sample
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class TestConsistency(TestCaseMPS):
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# TODO: This is only used while some ops are being added.
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@ -11838,8 +11839,10 @@ class TestConsistency(TestCaseMPS):
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@ops(mps_ops_modifier(test_consistency_op_db), allowed_dtypes=MPS_DTYPES)
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def test_output_match(self, device, dtype, op):
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self.assertEqual(device, "cpu")
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self.assertEqual(device, "mps:0")
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include_conjugated_inputs = dtype.is_complex and op.test_conjugated_samples
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if op.name.endswith("svd") and MACOS_VERSION < 14.0 and dtype == torch.complex64:
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raise unittest.SkipTest("Can't even generate complex samples on MacOS-13")
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def get_samples():
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return op.sample_inputs(
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@ -11847,16 +11850,14 @@ class TestConsistency(TestCaseMPS):
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dtype,
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requires_grad=(dtype.is_floating_point or dtype.is_complex),
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include_conjugated_inputs=include_conjugated_inputs,
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# TODO: Enable per-sample seed setting and tweak tolerances / fix xfails
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set_seed=False,
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set_seed=True,
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)
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cpu_samples = get_samples()
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for cpu_sample in cpu_samples:
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for mps_sample in get_samples():
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#
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# Forward check
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#
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mps_sample = transform_opinfo_sample_to_mps(cpu_sample)
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cpu_sample = transform_opinfo_sample_to_cpu(mps_sample)
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cpu_args = [cpu_sample.input] + list(cpu_sample.args)
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cpu_kwargs = cpu_sample.kwargs
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@ -11896,7 +11897,7 @@ class TestConsistency(TestCaseMPS):
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@ops(mps_ops_grad_modifier(copy.deepcopy(test_consistency_op_db)), allowed_dtypes=MPS_GRAD_DTYPES)
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def test_output_grad_match(self, device, dtype, op):
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self.assertEqual(device, "cpu")
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self.assertEqual(device, "mps:0")
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def get_samples():
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return op.sample_inputs(
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@ -11906,13 +11907,12 @@ class TestConsistency(TestCaseMPS):
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# TODO: Enable per-sample seed setting and tweak tolerances / fix xfails
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set_seed=False,
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)
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cpu_samples = get_samples()
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for cpu_sample in cpu_samples:
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for mps_sample in get_samples():
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#
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# Forward check
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#
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mps_sample = transform_opinfo_sample_to_mps(cpu_sample)
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cpu_sample = transform_opinfo_sample_to_cpu(mps_sample)
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cpu_args = [cpu_sample.input] + list(cpu_sample.args)
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cpu_kwargs = cpu_sample.kwargs
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@ -12290,7 +12290,7 @@ class TestMetalLibrary(TestCaseMPS):
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# This requires mps to be properly registered in the device generic test framework which is not the
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# case right now. We can probably use `allow_mps` introduced in https://github.com/pytorch/pytorch/pull/87342
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# to achieve this.
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instantiate_device_type_tests(TestConsistency, globals(), only_for="cpu")
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instantiate_device_type_tests(TestConsistency, globals(), allow_mps=True, only_for="mps")
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instantiate_device_type_tests(TestErrorInputs, globals(), allow_mps=True, only_for="mps")
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instantiate_device_type_tests(TestCommon, globals(), allow_mps=True, only_for="mps")
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instantiate_device_type_tests(TestLinalgMPS, globals(), allow_mps=True, only_for="mps")
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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
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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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@ -12172,6 +12172,8 @@ op_db: list[OpInfo] = [
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'TestSchemaCheckModeOpInfo',
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'test_schema_correctness',
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dtypes=(torch.complex64, torch.complex128)),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-3, rtol=2e-3)}),
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"TestConsistency", "test_output_grad_match", device_type="mps"),
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)),
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OpInfo('addmm',
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# When alpha=beta=1 as compile-time constants, JIT will decompose addmm into mm and add.
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@ -12206,6 +12208,10 @@ op_db: list[OpInfo] = [
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DecorateInfo(
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toleranceOverride({torch.half: tol(atol=1e-5, rtol=3e-3)}),
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'TestInductorOpInfo', 'test_comprehensive', device_type='cpu'),
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DecorateInfo(toleranceOverride({torch.float32: tol(atol=2e-5, rtol=3e-6)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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DecorateInfo(toleranceOverride({torch.float32: tol(atol=2e-5, rtol=3e-6)}),
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"TestConsistency", "test_output_grad_match", device_type="mps"),
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],
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sample_inputs_func=sample_inputs_addmv),
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OpInfo('addbmm',
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@ -12232,7 +12238,8 @@ op_db: list[OpInfo] = [
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torch.complex64: tol(atol=1e-05, rtol=1.2e-03)}),
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'TestCommon', 'test_numpy_ref_mps'),
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DecorateInfo(
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toleranceOverride({torch.float32: tol(atol=1e-5, rtol=1e-5)}),
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toleranceOverride({torch.float32: tol(atol=1e-5, rtol=1e-5),
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torch.bfloat16: tol(atol=2e-1, rtol=6e-1)}),
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'TestConsistency',
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'test_output_match',
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),
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@ -12328,7 +12335,15 @@ op_db: list[OpInfo] = [
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# NVIDIA only assures that bfloat16 is supported by bmm if SM >= 5.3
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DecorateInfo(unittest.skip("Skipped!"), 'TestCommon', 'test_dtypes', device_type='cuda', active_if=not SM53OrLater),
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DecorateInfo(toleranceOverride({torch.float32: tol(atol=1e-5, rtol=1e-5)}),
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"TestCommon", "test_out")
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"TestCommon", "test_out"),
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# Fast math on MacOS-13?
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DecorateInfo(
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toleranceOverride({torch.float32: tol(atol=2e-5, rtol=5e-6)}),
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'TestConsistency',
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'test_output_match',
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active_if=lambda _: MACOS_VERSION < 14.0,
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device_type='mps',
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dtypes=(torch.float32,)),
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),
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sample_inputs_func=sample_inputs_bmm),
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OpInfo('mv',
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@ -12507,6 +12522,8 @@ op_db: list[OpInfo] = [
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"test_comprehensive",
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device_type="cuda"
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),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-3, rtol=2e-3)}),
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"TestConsistency", "test_output_grad_match", device_type="mps"),
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],
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supports_inplace_autograd=False,
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supports_forward_ad=True,
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@ -13030,6 +13047,8 @@ op_db: list[OpInfo] = [
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DecorateInfo(unittest.expectedFailure, 'TestJit', 'test_variant_consistency_jit'),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=8e-3, rtol=1.4e-3)}),
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"TestInductorOpInfo", "test_comprehensive", device_type="cpu"),
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DecorateInfo(toleranceOverride({torch.float32: tol(atol=3e-4, rtol=1e-4)}),
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"TestConsistency", "test_output_grad_match", device_type="mps"),
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)),
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OpInfo('cross',
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dtypes=all_types_and_complex_and(torch.half, torch.bfloat16),
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@ -13181,6 +13200,12 @@ op_db: list[OpInfo] = [
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'test_fn_grad',
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dtypes=(torch.float64,),
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device_type='cpu'),
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DecorateInfo(unittest.skip("Broken on MacOS13"),
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'TestConsistency',
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'test_output_match',
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device_type='mps',
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dtypes=(torch.float16,),
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active_if=lambda _: MACOS_VERSION < 14.0),
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)),
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BinaryUfuncInfo('true_divide',
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dtypes=all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16),
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@ -13409,6 +13434,12 @@ op_db: list[OpInfo] = [
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"test_comprehensive",
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device_type="cuda"
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),
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DecorateInfo(unittest.skip("Broken on MacOS13"),
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'TestConsistency',
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'test_output_match',
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device_type='mps',
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dtypes=(torch.float16,),
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active_if=lambda _: MACOS_VERSION < 14.0),
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)),
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UnaryUfuncInfo('frac',
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ref=lambda x: np.modf(x)[0],
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@ -15048,6 +15079,8 @@ op_db: list[OpInfo] = [
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'test_variant_consistency_jit',
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dtypes=(torch.float32,)
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),
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DecorateInfo(toleranceOverride({torch.float32: tol(atol=2e-5, rtol=3e-6)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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),
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),
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UnaryUfuncInfo(
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@ -16905,6 +16938,14 @@ op_db: list[OpInfo] = [
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'TestSchemaCheckModeOpInfo',
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'test_schema_correctness',
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dtypes=(torch.complex64, torch.complex128)),
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# Fast math on MacOS-13?
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DecorateInfo(
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toleranceOverride({torch.float32: tol(atol=2e-5, rtol=5e-6)}),
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'TestConsistency',
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'test_output_match',
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active_if=lambda _: MACOS_VERSION < 14.0,
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device_type='mps',
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dtypes=(torch.float32,)),
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)),
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OpInfo('mode',
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op=torch.mode,
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@ -17334,6 +17375,8 @@ op_db: list[OpInfo] = [
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dtypes=(torch.cfloat, torch.cdouble,), device_type='cpu', active_if=IS_WINDOWS),
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DecorateInfo(unittest.skip("Skipped! sparse backward not supported"),
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'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-3, rtol=2e-3)}),
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"TestConsistency", "test_output_grad_match", device_type="mps"),
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),
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decorators=(precisionOverride({torch.bfloat16: 1e-2}),)),
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UnaryUfuncInfo('sinc',
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@ -17704,6 +17747,10 @@ op_db: list[OpInfo] = [
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dtypes=(torch.float16,),
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device_type="cuda",
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),
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DecorateInfo(toleranceOverride({torch.complex64: tol(atol=3e-5, rtol=7e-6)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=1e-3, rtol=2e-3)}),
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"TestConsistency", "test_output_grad_match", device_type="mps"),
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),
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# tan(pi/2 * odd_number) is nan
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reference_numerics_filter=NumericsFilter(
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@ -17738,6 +17785,8 @@ op_db: list[OpInfo] = [
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active_if=(IS_MACOS or IS_WINDOWS)),
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DecorateInfo(unittest.skip("Skipped! sparse backward not supported"),
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'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'),
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DecorateInfo(toleranceOverride({torch.complex64: tol(atol=3e-5, rtol=7e-6)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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),
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# tan(j * pi/2 * odd_number) is nan
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reference_numerics_filter=NumericsFilter(
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@ -17941,6 +17990,8 @@ op_db: list[OpInfo] = [
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active_if=IS_MACOS),
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DecorateInfo(unittest.skip("Skipped! sparse backward not supported"),
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'TestSparseUnaryUfuncs', 'test_sparse_fn_grad'),
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DecorateInfo(toleranceOverride({torch.complex64: tol(atol=2e-5, rtol=3e-6)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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)),
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UnaryUfuncInfo('square',
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ref=np.square,
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@ -19619,6 +19670,8 @@ op_db: list[OpInfo] = [
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# Decomp max diff: 1.8187482915266173e-06
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DecorateInfo(unittest.skip("Inconsistent accuracy"), 'TestDecomp', 'test_comprehensive',
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device_type='cpu', dtypes=(torch.float16,)),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=3e-4, rtol=3e-6)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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)),
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ShapeFuncInfo('repeat',
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op=lambda x, dims: x.repeat(dims),
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@ -21066,6 +21119,8 @@ op_db: list[OpInfo] = [
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device_type='cuda', dtypes=[torch.float16]),
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DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_extremal_values',
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device_type='cuda', dtypes=[torch.complex64]),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=2e-5, rtol=4e-2)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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),
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),
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ReductionOpInfo(
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@ -21253,6 +21308,8 @@ op_db: list[OpInfo] = [
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# possibly bad low precision reference in numpy
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DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input',
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dtypes=[torch.float16]),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=3e-3, rtol=4e-2)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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),
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),
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OpInfo(
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@ -21352,6 +21409,10 @@ op_db: list[OpInfo] = [
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# INTERNAL ASSERT FAILED at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270,
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# please report a bug to PyTorch.
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DecorateInfo(unittest.skip("Skipped!"), "TestJit", "test_variant_consistency_jit", dtypes=(torch.float32,),),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=8e-3, rtol=2e-3)}),
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"TestConsistency", "test_output_match", device_type="mps"),
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DecorateInfo(toleranceOverride({torch.float16: tol(atol=8e-3, rtol=2e-3)}),
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"TestConsistency", "test_output_grad_match", device_type="mps"),
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),
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),
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OpInfo(
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|
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@ -282,6 +282,8 @@ if torch.backends.mps.is_available():
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}
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# Those ops worked on MacOS12, but broken on MacOS13, see https://github.com/pytorch/pytorch/issues/85758
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MACOS_BEFORE_13_3_XFAILLIST = {
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# float16 seems horribly wrong on MacOS13
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"floor_divide": [torch.float16],
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# Failures due to precision issues (due to fast-math). These has been fixed in MacOS 13.3+
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"tan": [torch.float32],
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"cdist": [torch.float32],
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@ -312,6 +314,15 @@ if torch.backends.mps.is_available():
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"masked.cumsum": [torch.int64],
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"masked.cumprod": [torch.int64],
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"linalg.vander": [torch.int64],
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# Fail with `Expected 1.0 but got nan.` for empty tensors
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# Caused by sample input at index 23: SampleInput(
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# input=Tensor[size=(), device="mps:0", dtype=torch.float32],
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# args=(0),
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# kwargs={'mask': 'Tensor[size=(), device="mps:0", dtype=torch.bool]'},
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# broadcasts_input=False, name='')
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"masked.softmin": [torch.float32, torch.float16],
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"masked.softmax": [torch.float32, torch.float16],
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"masked.log_softmax": [torch.float32, torch.float16],
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}
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MACOS_AFTER_13_1_XFAILLIST = {
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@ -455,6 +466,7 @@ if torch.backends.mps.is_available():
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"_segment_reducelengths": None,
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"_segment_reduceoffsets": None,
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"sparse.mm": None,
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"sparse.sampled_addmm": None,
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"sparse.mmreduce": None,
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"special.airy_ai": None,
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"special.erfcx": None,
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@ -586,10 +598,6 @@ if torch.backends.mps.is_available():
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# round not working properly for float16 and bfloat16
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"round": [torch.float16, torch.bfloat16],
|
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"rounddecimals_0": [torch.bfloat16],
|
||||
# bfloat16 have weird issues with rounding
|
||||
"divfloor_rounding": [torch.bfloat16],
|
||||
"floor_divide": [torch.bfloat16],
|
||||
"remainder": [torch.bfloat16],
|
||||
# atomic operations not supported
|
||||
"_unsafe_masked_index_put_accumulate": [
|
||||
torch.bool,
|
||||
|
|
@ -654,7 +662,6 @@ if torch.backends.mps.is_available():
|
|||
torch.int64,
|
||||
torch.uint8,
|
||||
torch.int8,
|
||||
torch.bfloat16,
|
||||
],
|
||||
# Failures due to random output that they generate using
|
||||
# Philox engine causing mismatch with CPU results
|
||||
|
|
@ -857,9 +864,6 @@ if torch.backends.mps.is_available():
|
|||
|
||||
def mps_ops_grad_modifier(ops: Sequence[OpInfo]) -> Sequence[OpInfo]:
|
||||
XFAILLIST_GRAD = {
|
||||
# precision issues
|
||||
"special.polygammaspecial_polygamma_n_0": [torch.float16],
|
||||
"polygammapolygamma_n_0": [torch.float16],
|
||||
# Unimplemented ops
|
||||
"_segment_reduce": [torch.float16, torch.float32],
|
||||
"_chunk_cat": [torch.float16, torch.float32],
|
||||
|
|
@ -911,8 +915,6 @@ if torch.backends.mps.is_available():
|
|||
"equal": [torch.float16, torch.float32],
|
||||
# 'float' object is not iterable
|
||||
"item": [torch.float16, torch.float32],
|
||||
# "mse_backward_cpu_out" not implemented for 'Half'
|
||||
"nn.functional.mse_loss": [torch.float16],
|
||||
# "smooth_l1_backward_cpu_out" not implemented for 'Half'
|
||||
"nn.functional.smooth_l1_loss": [torch.float16],
|
||||
# cpu error: grad requires non-empty inputs
|
||||
|
|
@ -934,13 +936,18 @@ if torch.backends.mps.is_available():
|
|||
"fmod": [torch.float16],
|
||||
# round not working properly for float16
|
||||
"round": [torch.float16],
|
||||
# topk fails with duplicate indices
|
||||
"topk": [torch.float16],
|
||||
}
|
||||
|
||||
MACOS_BEFORE_13_3_XFAILLIST_GRAD = {
|
||||
# Failures due to precision issues (due to fast-math). These has been fixed in MacOS 13.3+
|
||||
# Failures due to precision issues (may be fast-math). These has been fixed in MacOS 14
|
||||
"masked.softmin": [torch.float32, torch.float16],
|
||||
"masked.softmax": [torch.float32, torch.float16],
|
||||
"masked.log_softmax": [torch.float32, torch.float16],
|
||||
"atanh": [torch.float16],
|
||||
"__rmod__": [torch.float16],
|
||||
"triangular_solve": [torch.float32],
|
||||
# Unsupported Border padding mode, forward pass success as fallback to cpu
|
||||
"grid_sampler_2d": [torch.float32, torch.float16, torch.bfloat16],
|
||||
# Same issue as `argsort` and `sort` with duplicate elements (undefined behaviour).
|
||||
|
|
|
|||
|
|
@ -1554,6 +1554,14 @@ op_db: list[OpInfo] = [
|
|||
supports_fwgrad_bwgrad=True,
|
||||
check_batched_grad=False,
|
||||
decorators=[skipCUDAIfNoMagmaAndNoCusolver, skipCPUIfNoLapack, with_tf32_off],
|
||||
skips=(
|
||||
DecorateInfo(
|
||||
toleranceOverride({torch.float32: tol(atol=8e-5, rtol=2e-6)}),
|
||||
"TestConsistency",
|
||||
"test_output_grad_match",
|
||||
device_type="mps",
|
||||
),
|
||||
),
|
||||
sample_inputs_func=sample_inputs_linalg_matrix_power,
|
||||
),
|
||||
OpInfo(
|
||||
|
|
@ -2284,6 +2292,12 @@ op_db: list[OpInfo] = [
|
|||
"test_noncontiguous_samples",
|
||||
device_type="cpu",
|
||||
),
|
||||
DecorateInfo(
|
||||
toleranceOverride({torch.float32: tol(atol=2e-04, rtol=3e-06)}),
|
||||
"TestConsistency",
|
||||
"test_output_match",
|
||||
device_type="mps",
|
||||
),
|
||||
],
|
||||
skips=(
|
||||
DecorateInfo(
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user