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[release/2.9] skip convolution tests on Navi4x (#2775)
Cherry-pick of #2675 (original commit #2055), for Navi4x only these testcases skipped until support of next kernels will be added (progress can be tracked here ROCm/rocm-libraries#2237): for test_freeze_conv_relu_fusion_not_forward and test_freeze_conv_relu_fusion: ConvBinWinogradRxSf2x3g1Fused for test_cudnn_convolution_relu: ConvBinWinogradRxSf2x3g1, ConvBinWinogradRxSf2x3g1Fused and ConvWinoFuryRxS<2-3> Fixes #SWDEV-555401 Co-authored-by: Divin Honnappa <[email protected]> Co-authored-by: Dmitry Nikolaev <[email protected]>
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test/jit/test_freezing.py

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@@ -16,8 +16,10 @@
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from torch.testing._internal.common_quantized import override_quantized_engine
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from torch.testing._internal.common_utils import (
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raise_on_run_directly,
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NAVI4_ARCH,
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set_default_dtype,
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skipCUDAMemoryLeakCheckIf,
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skipIfRocmArch,
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skipIfTorchDynamo,
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TEST_WITH_ROCM,
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)
@@ -2967,6 +2969,7 @@ def test_conv_to_mkldnn_no_mkldnn(self):
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self.assertEqual(frozen(inp), mod(inp))
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@unittest.skipIf(not (TEST_CUDNN or TEST_WITH_ROCM), "requires CUDNN")
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@skipIfRocmArch(NAVI4_ARCH) # not supported by MIOPEN on NAVI4x
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def test_freeze_conv_relu_fusion(self):
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with set_default_dtype(torch.float):
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conv_bias = [True, False]
@@ -3029,6 +3032,7 @@ def forward(self, x):
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self.assertEqual(mod_eager(inp), frozen_mod(inp))
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@unittest.skipIf(not (TEST_CUDNN or TEST_WITH_ROCM), "requires CUDNN")
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@skipIfRocmArch(NAVI4_ARCH) # not supported by MIOPEN on NAVI4x
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def test_freeze_conv_relu_fusion_not_forward(self):
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with set_default_dtype(torch.float):
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test/nn/test_convolution.py

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@@ -48,10 +48,12 @@
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gradgradcheck,
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instantiate_parametrized_tests,
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MACOS_VERSION,
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NAVI4_ARCH,
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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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skipIfNotMiopenSuggestNHWC,
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skipIfRocmArch,
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skipIfRocmVersionLessThan,
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subtest,
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TEST_SCIPY,
@@ -3874,6 +3876,7 @@ def test_conv2d_no_grad(self, device, dtype):
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@onlyCUDA
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@skipCUDAIfNoCudnn
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@skipIfRocmArch(NAVI4_ARCH) # not supported by MIOPEN on NAVI4x
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@dtypes(torch.float, torch.float16)
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@precisionOverride({torch.half: 0.002, torch.float: 1e-4})
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def test_cudnn_convolution_relu(self, device, dtype):

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