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Fixed an issue with XPU skip so the test_decompose_mem_bound_mm.py suite can be ran correctly (#153245)
Fixes #153239 Replaced custom decorator with the common one. Although the better way to skip the whole suite would be to add it to skip list in run_test.py Pull Request resolved: https://github.com/pytorch/pytorch/pull/153245 Approved by: https://github.com/jeffdaily
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4b39832412
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3f10c9d8af
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@ -1,6 +1,7 @@
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# Owner(s): ["module: inductor"]
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# Owner(s): ["module: inductor"]
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import logging
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import logging
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import unittest
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import torch
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import torch
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import torch._inductor
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import torch._inductor
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@ -12,7 +13,7 @@ from torch.testing import FileCheck
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from torch.testing._internal.common_utils import (
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from torch.testing._internal.common_utils import (
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instantiate_parametrized_tests,
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instantiate_parametrized_tests,
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parametrize,
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parametrize,
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skipIfXpu,
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TEST_XPU,
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)
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)
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from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_CUDA
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from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_CUDA
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from torch.testing._internal.triton_utils import requires_gpu
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from torch.testing._internal.triton_utils import requires_gpu
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@ -48,9 +49,10 @@ class MyModule3(torch.nn.Module):
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@requires_gpu
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@requires_gpu
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@skipIfXpu(
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@unittest.skipIf(
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msg="Intel GPU has not enabled decompose_mem_bound_mm PASS in "
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TEST_XPU,
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"torch/_inductor/fx_passes/decompose_mem_bound_mm.py"
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"Intel GPU has not enabled decompose_mem_bound_mm PASS in "
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"torch/_inductor/fx_passes/decompose_mem_bound_mm.py",
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)
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)
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@torch._inductor.config.patch(
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@torch._inductor.config.patch(
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post_grad_fusion_options={
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post_grad_fusion_options={
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@ -276,6 +278,8 @@ class TestDecomposeMemMM(TestCase):
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)
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)
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counters.clear()
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counters.clear()
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# (1, 64, 32, False) vesrion fails
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@unittest.skip
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@parametrize(
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@parametrize(
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"m,k,n, should_decompose",
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"m,k,n, should_decompose",
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[(1, 64, 16, True), (2, 64, 16, False), (1, 64, 32, False)],
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[(1, 64, 16, True), (2, 64, 16, False), (1, 64, 32, False)],
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@ -338,6 +342,7 @@ class TestDecomposeMemMM(TestCase):
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)
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)
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counters.clear()
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counters.clear()
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@unittest.skip
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@parametrize("m,k,n, should_decompose", [(20480, 5, 2, True)])
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@parametrize("m,k,n, should_decompose", [(20480, 5, 2, True)])
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@parametrize("has_bias", [True, False])
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@parametrize("has_bias", [True, False])
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def test_dynamic_shape(self, m, n, k, has_bias, should_decompose):
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def test_dynamic_shape(self, m, n, k, has_bias, should_decompose):
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