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Includes test_torch, test_autograd and docs changes.
torch/functional.py
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| return torch.mm(tensor1, tensor2) | ||
| else: | ||
| return torch.mm(tensor1, tensor2, out=out) | ||
| elif dim_tensor1 >= 2 and dim_tensor2 >= 2: |
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test/test_autograd.py
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| # if non-Variable torch function returns a scalar, compare to scalar | ||
| if not torch.is_tensor(unpacked_result): | ||
| assert(packed_result.dim() == 1) | ||
| assert(packed_result.nelement() == 1) |
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torch/functional.py
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| try: | ||
| dim_tensor2 = tensor2.dim() | ||
| except AttributeError: # not a tensor | ||
| return NotImplemented |
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| if out is None: | ||
| return torch.mm(tensor1.unsqueeze(0), tensor2).squeeze(0) | ||
| else: | ||
| return torch.mm(tensor1.unsqueeze(0), tensor2, out=out).squeeze_(0) |
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| return torch.mm(tensor1.unsqueeze(0), tensor2).squeeze(0) | ||
| else: | ||
| return torch.mm(tensor1.unsqueeze(0), tensor2, out=out).squeeze_(0) | ||
| elif dim_tensor1 == 2 and dim_tensor2 == 2: |
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| else: | ||
| return torch.mm(tensor1.unsqueeze(0), tensor2, out=out).squeeze_(0) | ||
| elif dim_tensor1 == 2 and dim_tensor2 == 2: | ||
| if out is None: |
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torch/functional.py
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| return torch.mm(tensor1, tensor2) | ||
| else: | ||
| return torch.mm(tensor1, tensor2, out=out) | ||
| elif dim_tensor1 >= 2 and dim_tensor2 >= 2: |
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torch/functional.py
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| # But 1) is inconsistent with other functions (e.g. torch.bmm) that will maintain | ||
| # output non-contiguity if the size is correct (perhaps we should change this globally?) | ||
| # And 3) is a surprising output to accept if we aren't accepting 1). | ||
| # So let's just force accepting contiguous tensors. |
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|
Latest push should have addressed the review comments. |
Includes test_torch, test_autograd and docs changes.