mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
Enable additional tests for MPS CI runs (#134356)
As part of the follow up for https://github.com/pytorch/pytorch/issues/133520, adapting existing unused tests for use in MPS CI runs. Focusing on nhwc & other memory formatting tests Pull Request resolved: https://github.com/pytorch/pytorch/pull/134356 Approved by: https://github.com/malfet, https://github.com/eqy, https://github.com/huydhn
This commit is contained in:
Siddharth Kotapati
PyTorch MergeBot
parent
7c1d93944e
commit
e27c0048db
1
.github/workflows/lint.yml
vendored
1
.github/workflows/lint.yml
vendored
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@ -223,6 +223,7 @@ jobs:
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cache: pip
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- name: Install dependencies
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run: |
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python3 -m pip install --upgrade pip
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pip install pytest-rerunfailures==11.1.* pytest-flakefinder==1.1.* pytest-xdist==3.3.* expecttest==0.2.* fbscribelogger==0.1.* numpy==1.24.*
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pip install torch --pre --index-url https://download.pytorch.org/whl/nightly/cpu/
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- name: Run run_test.py (nonretryable)
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@ -22,6 +22,8 @@ from torch.testing._internal.common_device_type import (
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disableMkldnn,
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dtypes,
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dtypesIfCUDA,
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dtypesIfMPS,
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expectedFailureMPS,
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instantiate_device_type_tests,
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largeTensorTest,
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onlyCPU,
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@ -37,6 +39,7 @@ from torch.testing._internal.common_device_type import (
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skipCUDAIfRocm,
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skipCUDAIfRocmVersionLessThan,
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skipMeta,
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skipMPS,
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)
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from torch.testing._internal.common_dtype import (
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floating_and_complex_types_and,
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@ -50,6 +53,7 @@ from torch.testing._internal.common_utils import (
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GRADCHECK_NONDET_TOL,
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gradgradcheck,
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instantiate_parametrized_tests,
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IS_MACOS,
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parametrize as parametrize_test,
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run_tests,
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set_default_dtype,
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@ -68,6 +72,13 @@ if TEST_SCIPY:
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import scipy.ndimage
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import scipy.signal
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if IS_MACOS:
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import platform
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product_version = float(".".join(platform.mac_ver()[0].split(".")[:2]) or -1)
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else:
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product_version = 0.0
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class TestConvolutionNN(NNTestCase):
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_do_cuda_memory_leak_check = True
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@ -1677,6 +1688,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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)
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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@torch.backends.cudnn.flags(enabled=True, benchmark=False)
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def test_conv1d_same_padding(self, device, dtype):
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# Test padding='same' outputs the correct shape
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@ -1716,6 +1730,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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actual = F.conv1d(x, y, padding="same", dilation=3)
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self.assertEqual(expect, actual)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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@dtypes(torch.float, torch.cfloat)
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def test_conv2d_same_padding(self, device, dtype):
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if dtype is torch.cfloat:
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@ -1768,6 +1785,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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self.assertEqual(expect, actual, rtol=rtol, atol=atol)
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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def test_conv1d_valid_padding(self, device, dtype):
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# Test F.conv1d padding='valid' is the same as no padding
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x = torch.rand(1, 1, 10, device=device, dtype=dtype)
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@ -1777,6 +1797,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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self.assertEqual(expect, actual)
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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def test_conv2d_valid_padding(self, device, dtype):
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# Test F.conv2d padding='valid' is the same as no padding
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x = torch.rand(1, 1, 1, 10, device=device, dtype=dtype)
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@ -1795,6 +1818,7 @@ class TestConvolutionNNDeviceType(NNTestCase):
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self.assertEqual(expect, actual)
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(torch.float)
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def test_conv1d_same_padding_backward(self, device, dtype):
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# Test F.conv1d gradients work with padding='same'
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x = torch.rand(1, 1, 12, dtype=dtype, device=device, requires_grad=True)
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@ -1824,6 +1848,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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self.assertEqual(gy_expect, y.grad)
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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@tf32_on_and_off(0.001)
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def test_conv2d_same_padding_backward(self, device, dtype):
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# Test F.conv2d gradients work with padding='same'
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@ -1855,6 +1882,10 @@ class TestConvolutionNNDeviceType(NNTestCase):
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self.assertEqual(gy_expect, y.grad)
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@dtypes(torch.double, torch.cdouble)
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@dtypesIfMPS(
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torch.float, torch.cfloat
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) # Double, complex double not supported on MPS
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@expectedFailureMPS # https://github.com/pytorch/pytorch/issues/107214
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def test_conv3d_same_padding_backward(self, device, dtype):
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check_forward_ad = torch.device(device).type != "xla"
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@ -1915,6 +1946,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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)
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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def test_conv1d_valid_padding_backward(self, device, dtype):
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# Test F.conv1d gradients work with padding='valid'
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x = torch.rand(1, 1, 10, dtype=dtype, device=device, requires_grad=True)
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@ -1930,6 +1964,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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@unittest.skipIf(not TEST_SCIPY, "Scipy required for the test.")
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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@parametrize_test("mode", ("valid", "same"))
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def test_conv1d_vs_scipy(self, device, dtype, mode):
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t = make_tensor((1, 10), device=device, dtype=dtype)
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@ -1969,6 +2006,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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@unittest.skipIf(not TEST_SCIPY, "Scipy required for the test.")
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@dtypes(torch.float, torch.cfloat)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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@parametrize_test("mode", ("valid", "same"))
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def test_conv2d_vs_scipy(self, device, dtype, mode):
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t = make_tensor((1, 5, 10), device=device, dtype=dtype)
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@ -2008,6 +2048,7 @@ class TestConvolutionNNDeviceType(NNTestCase):
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_test(t, weight_odd, mode)
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@unittest.skipIf(not TEST_SCIPY, "Scipy required for the test.")
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@skipMPS # Results in CI are inconsistent, forced to skip
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@dtypes(torch.float, torch.cfloat)
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@parametrize_test("mode", ("valid", "same"))
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def test_conv3d_vs_scipy(self, device, dtype, mode):
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@ -2061,6 +2102,9 @@ class TestConvolutionNNDeviceType(NNTestCase):
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_test(t, weight_odd, mode)
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@dtypes(torch.float, torch.complex64)
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@dtypesIfMPS(
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*([torch.float] if product_version < 14.0 else [torch.float, torch.cfloat])
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) # Complex not supported on MacOS13
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def test_conv2d_valid_padding_backward(self, device, dtype):
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# Test F.conv2d gradients work with padding='valid'
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x = torch.rand(1, 1, 1, 10, device=device, dtype=dtype, requires_grad=True)
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@ -2075,6 +2119,10 @@ class TestConvolutionNNDeviceType(NNTestCase):
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self.assertEqual(gy_expect, gy_actual)
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@dtypes(torch.double, torch.cdouble)
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@dtypesIfMPS(
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torch.float, torch.cfloat
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) # Double, complex double not supported on MPS
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@expectedFailureMPS # https://github.com/pytorch/pytorch/issues/107214
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def test_conv3d_valid_padding_backward(self, device, dtype):
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check_forward_ad = torch.device(device).type != "xla"
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@ -2101,7 +2149,15 @@ class TestConvolutionNNDeviceType(NNTestCase):
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check_fwd_over_rev=check_forward_ad,
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)
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@parametrize_test("N", range(2, 4), name_fn=lambda N: f"ConvTranspose{N}d")
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@parametrize_test(
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arg_str="N",
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arg_values=[
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subtest(arg_values=(2), name="ConvTranspose2d"),
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subtest(
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arg_values=(3), name="ConvTranspose3d", decorators=[expectedFailureMPS]
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),
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],
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)
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def test_conv_transpose_with_output_size_and_no_batch_dim(self, device, N):
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# For inputs with no batch dim, verify output is the correct shape when output_size is set.
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# See https://github.com/pytorch/pytorch/issues/75889
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@ -3067,6 +3123,7 @@ class TestConvolutionNNDeviceType(NNTestCase):
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input_large = torch.randn(1, 1, 2048, 1024, dtype=dtype, device=device)
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conv2(input_large)
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@expectedFailureMPS # ConvTranspose 3D is not supported on MPS
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def test_conv_noncontig_weights(self, device):
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for dim in (1, 2, 3):
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for grouped in (False, True):
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@ -3383,6 +3440,8 @@ class TestConvolutionNNDeviceType(NNTestCase):
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)
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@dtypes(torch.double, torch.cdouble)
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@dtypesIfMPS(torch.float, torch.cfloat)
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@expectedFailureMPS # https://github.com/pytorch/pytorch/issues/107214
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def test_Conv2d_backward_depthwise(self, device, dtype):
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x = torch.randn(2, 2, 4, 20, device=device, dtype=dtype, requires_grad=True)
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weight = torch.randn(2, 1, 3, 5, device=device, dtype=dtype, requires_grad=True)
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@ -4032,7 +4091,7 @@ class TestConvolutionNNDeviceType(NNTestCase):
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self.assertEqual(yref, y)
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instantiate_device_type_tests(TestConvolutionNNDeviceType, globals())
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instantiate_device_type_tests(TestConvolutionNNDeviceType, globals(), allow_mps=True)
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instantiate_parametrized_tests(TestConvolutionNN)
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if __name__ == "__main__":
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@ -9,6 +9,10 @@ import torch.nn as nn
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import torch.nn.functional as F
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from torch.testing._internal.common_cuda import TEST_CUDA
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from torch.testing._internal.common_device_type import (
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dtypes,
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dtypesIfMPS,
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expectedFailureMPS,
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expectedFailureMPSPre15,
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expectedFailureXLA,
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instantiate_device_type_tests,
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)
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@ -169,6 +173,7 @@ class TestDropoutNNDeviceType(NNTestCase):
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else:
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self.assertNotEqual(permuted_inp, out)
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@expectedFailureMPSPre15
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def test_Dropout(self, device):
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input = torch.empty(1000)
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self._test_dropout(nn.Dropout, device, input)
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@ -207,8 +212,11 @@ class TestDropoutNNDeviceType(NNTestCase):
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self.assertTrue(result[b, c].count_nonzero() in (0, channel_numel))
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@expectedFailureXLA # seems like freeze_rng_state is not honoured by XLA
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def test_Dropout1d(self, device):
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with set_default_dtype(torch.double):
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@dtypes(torch.double)
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@dtypesIfMPS(torch.float32)
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@expectedFailureMPS
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def test_Dropout1d(self, device, dtype):
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with set_default_dtype(dtype):
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N, C, L = (
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random.randint(10, 15),
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random.randint(10, 15),
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@ -279,6 +287,7 @@ class TestDropoutNNDeviceType(NNTestCase):
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self._test_dropoutNd_channel_zero(nn.Dropout2d(p=0.5, inplace=True), input)
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@expectedFailureXLA # seems like freeze_rng_state is not honoured by XLA
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@expectedFailureMPS # Failing on current pytorch MPS
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def test_Dropout3d(self, device):
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b = random.randint(1, 5)
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w = random.randint(1, 5)
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@ -315,7 +324,7 @@ class TestDropoutNNDeviceType(NNTestCase):
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self.assertEqual(out.size(), x.size())
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instantiate_device_type_tests(TestDropoutNNDeviceType, globals())
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instantiate_device_type_tests(TestDropoutNNDeviceType, globals(), allow_mps=True)
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instantiate_parametrized_tests(TestDropoutNN)
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if __name__ == "__main__":
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@ -20,7 +20,9 @@ from torch.testing._internal.common_cuda import TEST_CUDA
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from torch.testing._internal.common_device_type import (
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dtypes,
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dtypesIfCUDA,
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dtypesIfMPS,
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expectedFailureMeta,
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expectedFailureMPS,
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instantiate_device_type_tests,
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largeTensorTest,
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onlyCPU,
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@ -41,7 +43,6 @@ from torch.testing._internal.common_utils import (
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parametrize as parametrize_test,
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run_tests,
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set_default_dtype,
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skipIfMps,
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skipIfTorchDynamo,
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slowTest,
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subtest,
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@ -818,6 +819,7 @@ torch.cuda.synchronize()
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inp = torch.randn(16, 0, 20, 32, device=device)
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avgpool(inp)
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@expectedFailureMPS # max_pool3d_with_indices not supported on MPS
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def test_pooling_shape(self, device):
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"""Test the output shape calculation for pooling functions"""
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@ -1328,6 +1330,8 @@ torch.cuda.synchronize()
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helper(1, 19, 20, 10, 8, 2, torch.channels_last)
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@dtypes(torch.float, torch.double)
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@dtypesIfMPS(torch.float)
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@expectedFailureMPS # test_adaptive_pooling_max_nhwc currently fails on MPS - ISSUE#
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def test_adaptive_pooling_max_nhwc(self, device, dtype):
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def helper(input_size, output_plane_size, contig):
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n_plane_dims = len(output_plane_size)
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@ -1379,6 +1383,8 @@ torch.cuda.synchronize()
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helper((2, 1, 3, 3, 3), (1, 1, 1), contig)
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@dtypes(torch.float, torch.double)
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@dtypesIfMPS(torch.float)
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@expectedFailureMPS # test_pooling_max_nhwc currently fails on MPS - ISSUE#
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def test_pooling_max_nhwc(self, device, dtype):
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def helper(n, c, h, w, kernel_size, stride, padding, dilation, contig, device):
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output_height = math.floor(
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@ -1585,32 +1591,30 @@ torch.cuda.synchronize()
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def test_MaxPool2d_indices(self, device, dtype):
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self._test_maxpool_indices(2, device=device, dtype=dtype)
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@skipIfMps
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@expectedFailureMPS
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@dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
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@dtypes(torch.float)
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def test_MaxPool3d_indices(self, device, dtype):
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self._test_maxpool_indices(3, device=device, dtype=dtype)
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@skipIfMps
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@dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
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@dtypes(torch.float)
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def test_AdaptiveMaxPool1d_indices(self, device, dtype):
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self._test_maxpool_indices(1, adaptive=True, device=device, dtype=dtype)
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@dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
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@skipIfMps
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@dtypes(torch.float)
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def test_AdaptiveMaxPool2d_indices(self, device, dtype):
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self._test_maxpool_indices(2, adaptive=True, device=device, dtype=dtype)
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@dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
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@skipIfMps
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@expectedFailureMPS
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@dtypes(torch.float)
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def test_AdaptiveMaxPool3d_indices(self, device, dtype):
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self._test_maxpool_indices(3, adaptive=True, device=device, dtype=dtype)
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@dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
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@skipIfMps
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@expectedFailureMPS
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@dtypes(torch.float)
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def test_maxpool_indices_no_batch_dim(self, device, dtype):
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"""Check that indices with no batch dim is consistent with a single batch."""
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@ -1831,7 +1835,7 @@ torch.cuda.synchronize()
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)
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@dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
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@skipIfMps
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@expectedFailureMPS
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@dtypes(torch.float)
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def test_pool_large_size(self, device, dtype):
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for op in ("max", "avg"):
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|
|
@ -1864,7 +1868,7 @@ torch.cuda.synchronize()
|
|||
helper(nn.AdaptiveAvgPool2d((2**6, 2**6)))
|
||||
|
||||
@dtypesIfCUDA(*floating_types_and(torch.half, torch.bfloat16))
|
||||
@skipIfMps
|
||||
@expectedFailureMPS
|
||||
@dtypes(torch.float)
|
||||
def test_pool_invalid_size(self, device, dtype):
|
||||
for op in ("max", "avg"):
|
||||
|
|
@ -1926,6 +1930,7 @@ torch.cuda.synchronize()
|
|||
prec=0.05,
|
||||
)
|
||||
|
||||
@expectedFailureMPS # max_pool3d_with_indices not supported on MPS device
|
||||
def test_maxpool3d_non_square_backward(self, device):
|
||||
# previous CUDA routine of this backward calculates kernel launch grid size
|
||||
# with last two dimensions interchanged, so the tailing along the longer dim
|
||||
|
|
@ -1950,7 +1955,7 @@ torch.cuda.synchronize()
|
|||
imgs_ = F.adaptive_max_pool3d(imgs, (Od, Oh, Ow))
|
||||
|
||||
|
||||
instantiate_device_type_tests(TestPoolingNNDeviceType, globals())
|
||||
instantiate_device_type_tests(TestPoolingNNDeviceType, globals(), allow_mps=True)
|
||||
instantiate_parametrized_tests(TestPoolingNN)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -1417,7 +1417,16 @@ def get_selected_tests(options) -> List[str]:
|
|||
options.exclude.extend(CPP_TESTS)
|
||||
|
||||
if options.mps:
|
||||
selected_tests = ["test_mps", "test_metal", "test_modules", "test_nn"]
|
||||
selected_tests = [
|
||||
"test_mps",
|
||||
"test_metal",
|
||||
"test_modules",
|
||||
"nn/test_convolution",
|
||||
"nn/test_dropout",
|
||||
"nn/test_pooling",
|
||||
"test_view_ops",
|
||||
"test_nn",
|
||||
]
|
||||
else:
|
||||
# Exclude all mps tests otherwise
|
||||
options.exclude.extend(["test_mps", "test_metal"])
|
||||
|
|
|
|||
|
|
@ -8970,8 +8970,8 @@ class TestNNDeviceType(NNTestCase):
|
|||
else:
|
||||
self.assertEqual(hx.grad, hx_device.grad)
|
||||
|
||||
@dtypesIfMPS(torch.float)
|
||||
@dtypes(torch.double)
|
||||
@dtypesIfMPS(torch.float)
|
||||
def test_BatchNorm_empty(self, device, dtype):
|
||||
mod = torch.nn.BatchNorm2d(3).to(device)
|
||||
inp = torch.randn(0, 3, 2, 2, device=device, dtype=dtype)
|
||||
|
|
@ -9000,8 +9000,12 @@ class TestNNDeviceType(NNTestCase):
|
|||
|
||||
def test_one_hot(self, device):
|
||||
# cuda throws device assert for invalid data
|
||||
# xla ignores out of bound indices
|
||||
if self.device_type not in ('cuda', 'mps', 'xla'):
|
||||
# xla & mps ignore out of bound indices
|
||||
if (
|
||||
self.device_type != 'cuda'
|
||||
and self.device_type != 'xla'
|
||||
and self.device_type != 'mps'
|
||||
):
|
||||
with self.assertRaises(RuntimeError):
|
||||
torch.nn.functional.one_hot(torch.tensor([3, 4, -1, 0], device=device), -1)
|
||||
|
||||
|
|
@ -12721,6 +12725,8 @@ if __name__ == '__main__':
|
|||
def test_clip_grad_value(self, foreach, device):
|
||||
if torch.device(device).type == 'xla' and foreach:
|
||||
raise SkipTest('foreach not supported on XLA')
|
||||
if torch.device(device).type == 'mps' and foreach:
|
||||
raise SkipTest('foreach not supported on MPS')
|
||||
|
||||
l = nn.Linear(10, 10).to(device)
|
||||
clip_value = 2.5
|
||||
|
|
@ -12750,6 +12756,8 @@ if __name__ == '__main__':
|
|||
def test_clip_grad_norm(self, norm_type, foreach, device):
|
||||
if torch.device(device).type == 'xla' and foreach:
|
||||
raise SkipTest('foreach not supported on XLA')
|
||||
if torch.device(device).type == 'mps' and foreach:
|
||||
raise SkipTest('foreach not supported on MPS')
|
||||
|
||||
l = nn.Linear(10, 10).to(device)
|
||||
max_norm = 2
|
||||
|
|
|
|||
|
|
@ -10,9 +10,11 @@ import torch
|
|||
from torch.testing import make_tensor
|
||||
from torch.testing._internal.common_device_type import (
|
||||
dtypes,
|
||||
dtypesIfMPS,
|
||||
instantiate_device_type_tests,
|
||||
onlyCPU,
|
||||
onlyNativeDeviceTypes,
|
||||
onlyNativeDeviceTypesAnd,
|
||||
skipLazy,
|
||||
skipMeta,
|
||||
skipXLA,
|
||||
|
|
@ -22,6 +24,7 @@ from torch.testing._internal.common_dtype import (
|
|||
all_types_and_complex_and,
|
||||
complex_types,
|
||||
floating_and_complex_types_and,
|
||||
integral_types_and,
|
||||
)
|
||||
from torch.testing._internal.common_utils import (
|
||||
gradcheck,
|
||||
|
|
@ -395,6 +398,9 @@ class TestViewOps(TestCase):
|
|||
|
||||
@onlyNativeDeviceTypes
|
||||
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
|
||||
@dtypesIfMPS(
|
||||
*integral_types_and(torch.half, torch.bfloat16, torch.bool, torch.float32)
|
||||
)
|
||||
def test_view_tensor_split(self, device, dtype):
|
||||
a = make_tensor((40, 30), dtype=dtype, device=device, low=-9, high=9)
|
||||
a_split_dim0 = a.tensor_split(7, 0)
|
||||
|
|
@ -434,8 +440,9 @@ class TestViewOps(TestCase):
|
|||
t[2, 2, 2] = 7
|
||||
self.assertEqual(t_dsplit[1][2, 2, 0], t[2, 2, 2])
|
||||
|
||||
@onlyNativeDeviceTypes
|
||||
@onlyNativeDeviceTypesAnd("mps")
|
||||
@dtypes(*all_types_and(torch.half, torch.bfloat16))
|
||||
@dtypesIfMPS(*integral_types_and(torch.half, torch.bool, torch.float32))
|
||||
def test_imag_noncomplex(self, device, dtype):
|
||||
t = torch.ones((5, 5), dtype=dtype, device=device)
|
||||
|
||||
|
|
@ -2030,7 +2037,7 @@ class TestOldViewOps(TestCase):
|
|||
t.col_indices()
|
||||
|
||||
|
||||
instantiate_device_type_tests(TestViewOps, globals(), include_lazy=True)
|
||||
instantiate_device_type_tests(TestViewOps, globals(), include_lazy=True, allow_mps=True)
|
||||
instantiate_device_type_tests(TestOldViewOps, globals())
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -1646,10 +1646,6 @@ def expectedFailureMeta(fn):
|
|||
return skipIfTorchDynamo()(expectedFailure("meta")(fn))
|
||||
|
||||
|
||||
def expectedFailureMPS(fn):
|
||||
return expectedFailure("mps")(fn)
|
||||
|
||||
|
||||
def expectedFailureXLA(fn):
|
||||
return expectedFailure("xla")(fn)
|
||||
|
||||
|
|
@ -1658,6 +1654,32 @@ def expectedFailureHPU(fn):
|
|||
return expectedFailure("hpu")(fn)
|
||||
|
||||
|
||||
def expectedFailureMPS(fn):
|
||||
return expectedFailure("mps")(fn)
|
||||
|
||||
|
||||
def expectedFailureMPSPre15(fn):
|
||||
import platform
|
||||
|
||||
version = float(".".join(platform.mac_ver()[0].split(".")[:2]) or -1)
|
||||
if not version or version < 1.0: # cpu or other unsupported device
|
||||
return fn
|
||||
if version < 15.0:
|
||||
return expectedFailure("mps")(fn)
|
||||
return fn
|
||||
|
||||
|
||||
def expectedFailureMPSPre14(fn):
|
||||
import platform
|
||||
|
||||
version = float(".".join(platform.mac_ver()[0].split(".")[:2]) or -1)
|
||||
if not version or version < 1.0: # cpu or other unsupported device
|
||||
return fn
|
||||
if version < 14.0:
|
||||
return expectedFailure("mps")(fn)
|
||||
return fn
|
||||
|
||||
|
||||
# Skips a test on CPU if LAPACK is not available.
|
||||
def skipCPUIfNoLapack(fn):
|
||||
return skipCPUIf(not torch._C.has_lapack, "PyTorch compiled without Lapack")(fn)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user