enable 7 cases on XPU #11503
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enable 7 cases on XPU #11503
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Signed-off-by: Yao Matrix <[email protected]>
yao-matrix
commented
May 6, 2025
| id_vit_hidden = [torch.ones([1, 2, 2])] * 1 | ||
| id_cond = torch.ones(1, 2) | ||
| id_vit_hidden = [torch.ones([1, 577, 1024])] * 5 | ||
| id_cond = torch.ones(1, 1280) |
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the size of the id_vit_hidden and id_cond is wrong, both A100 and XPU will report below error
def forward(self, input: Tensor) -> Tensor: return F.linear(input, self.weight, self.bias) E RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x2 and 1280x1024)
I checked the needed size for these 2 tensors and use the correct tensor
| @@ -357,5 +358,5 @@ def test_consisid(self): | |||
| video = videos[0] | |||
| expected_video = torch.randn(1, 16, 480, 720, 3).numpy() | |||
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expected_video here is generated by RNG, so suppose this is a in-complete case, so for this PR, I enable the case to run on XPU, need re-check the numerical correctness after expected_video correctly set.
| @@ -357,5 +358,5 @@ def test_consisid(self): | |||
| video = videos[0] | |||
| expected_video = torch.randn(1, 16, 480, 720, 3).numpy() | |||
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| max_diff = numpy_cosine_similarity_distance(video, expected_video) | |||
| max_diff = numpy_cosine_similarity_distance(video.cpu(), expected_video) | |||
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need to cpu, since expected_video is in cpu, numpy_cosine_similarity_distance only supports 2 tensors in same device.
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@DN6 @a-r-r-o-w , pls help review and comment, thx very much. |
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@a-r-r-o-w @DN6 , pls help review, thx.