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I'm ambivalent about this. In Python land, this positional call is invalid: We don't have keyword arguments in C++, so it's hard to do this in a clear way. The existing positional booleans already make my skin crawl. |
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cc @gchanan |
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@ezyang Overloading it under the same name |
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I think part of the problem is that Python's torch.unique has a different argument ordering than _unique_dim, in the first, the dimension comes last. |
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Edit: sorry, comment on wrong pr |
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So, what is the best way to provide a C++ API of unique? |
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PS: I would suggest resolving this before release of 1.0, otherwise the user would start using |
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Closing this in favor of #15256 |
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Summary: This PR does three things: ~~Allow `int64_t?` in function schema, which provide an elegant way of implementing null-able int arguments, as discussed in #15208 (review) ~~Originally implemented in #15235 ~~Example:~~ ```yaml - func: myop(Tensor self, int64_t? dim=None) -> Tensor variants: function ``` ~~cc: zou3519~~ Edit: implemented in #15234 Previously tried in #12064. There was a problem that C++ does not have kwarg support, which makes it confusing to know whether `unique(t, 1)` actually means `unique(t, dim=1)` or `unique(t, sorted=1)`. Now I think I have a better idea on how to implement this: there are two ATen operators: `unique` and `unique_dim`. `unique` has the same signature as in python, and exported to both python and C++. `unique_dim` has signature `unique_dim(tensor, dim, sorted=False, return_inverse=False)`, and only exported to C++, which could be used more naturally for a C++ user. Differential Revision: D13540278 Pulled By: wanchaol fbshipit-source-id: 3768c76a90b0881f565a1f890459ebccbdfe6ecd
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Summary: This PR does three things: ~~Allow `int64_t?` in function schema, which provide an elegant way of implementing null-able int arguments, as discussed in pytorch/pytorch#15208 (review) ~~Originally implemented in pytorch/pytorch#15235 ~~Example:~~ ```yaml - func: myop(Tensor self, int64_t? dim=None) -> Tensor variants: function ``` ~~cc: zou3519~~ Edit: implemented in pytorch/pytorch#15234 Previously tried in pytorch/pytorch#12064. There was a problem that C++ does not have kwarg support, which makes it confusing to know whether `unique(t, 1)` actually means `unique(t, dim=1)` or `unique(t, sorted=1)`. Now I think I have a better idea on how to implement this: there are two ATen operators: `unique` and `unique_dim`. `unique` has the same signature as in python, and exported to both python and C++. `unique_dim` has signature `unique_dim(tensor, dim, sorted=False, return_inverse=False)`, and only exported to C++, which could be used more naturally for a C++ user. Differential Revision: D13540278 Pulled By: wanchaol fbshipit-source-id: 3768c76a90b0881f565a1f890459ebccbdfe6ecd
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Currently we only have
at::_uniqueandat::_unique_dim. It would be convenient to have a overloadedat::uniquethat handle both cases.