wrap more functions to complete a minimal viable feature set #65
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No longer WIP, ready for review. |
lezcano
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Feb 27, 2023
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All the complex max/min/argmax/argmin... we may be able to implement them via casting the complex tensor to a real one via view_as_real. Now, I think it's pretty low prio, as IMO, these should not even work.
| # For more than d=2, the strided formula is only valid for arrays with | ||
| # all dimensions equal, so we check first. | ||
| s = tensor.shape | ||
| if s[1:] != s[:-1]: |
|
Review comments addressed, other than those I'd rather do in a sequel |
lezcano
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Feb 27, 2023
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| def diagonal(tensor, offset=0, axis1=0, axis2=1): | ||
| axis1 = _util.normalize_axis_index(axis1, tensor.ndim) | ||
| axis2 = _util.normalize_axis_index(axis2, tensor.ndim) | ||
| result = torch.diagonal(tensor, offset, axis1, axis2) | ||
| return result |
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My point was that this could simply be
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| def diagonal(tensor, offset=0, axis1=0, axis2=1): | |
| axis1 = _util.normalize_axis_index(axis1, tensor.ndim) | |
| axis2 = _util.normalize_axis_index(axis2, tensor.ndim) | |
| result = torch.diagonal(tensor, offset, axis1, axis2) | |
| return result | |
| def diagonal(tensor, offset=0, axis1=0, axis2=1): | |
| return torch.diagonal(tensor, offset, axis1, axis2) |
No need to change it really.
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Ah, great. Made a note to self to go through normalize_axis_index invocations across the codebase.
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Thanks Mario for the review! |
This was referenced Feb 28, 2023
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are left for a tentative minimum viable functionality set.