Allow linspace and logspace with steps=1 and start != end like numpy#14748
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f0k wants to merge 2 commits into
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Allow linspace and logspace with steps=1 and start != end like numpy#14748f0k wants to merge 2 commits into
f0k wants to merge 2 commits into
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The failures are not my fault, as far as I see. The first one says: The second one says: |
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Yeah, not sure what happened. Push merge with master, let's see if it's clean this time. |
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…(#14748) Summary: `torch.linspace(0, 1, 1)` fails with `RuntimeError: invalid argument 3: invalid number of points at ../aten/src/TH/generic/THTensorMoreMath.cpp:2119`, while `np.linspace(0, 1, 1)` works fine. Looking at the code, there is even a comment by gchanan asking: "NumPy allows you to pass different points even if n <= 1 -- should we?" I would say "yes". Currently, I would need to handle the case of `steps == 1` or `steps == 0` separately, making sure to change the `end` when calling `torch.linspace`. This is impractical. If we support `start != end`, there are two possibilities for the result: Either we ensure the first value in the resulting sequence always equals `start`, or we ensure the last value in the resulting sequence always equals `end`. Numpy chose the former, which also allows it to support a boolean `endpoint` flag. I'd say we should follow numpy. This PR adapts `linspace` and `logspace` to mimic the behavior of numpy, adapts the tests accordingly, and extends the docstrings to make clear what happens when passing `steps=1`. If you decide against this PR, the error message should become explicit about what I did wrong, and the documentation should be extended to mention this restriction. Pull Request resolved: pytorch/pytorch#14748 Differential Revision: D13356136 Pulled By: ezyang fbshipit-source-id: db85b8f0a98a5e24b3acd766132ab71c91794a82
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This diff was landed but it didn't actually add endpoint. File an issue request for it please! |
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…ytorch#14748) Summary: `torch.linspace(0, 1, 1)` fails with `RuntimeError: invalid argument 3: invalid number of points at ../aten/src/TH/generic/THTensorMoreMath.cpp:2119`, while `np.linspace(0, 1, 1)` works fine. Looking at the code, there is even a comment by gchanan asking: "NumPy allows you to pass different points even if n <= 1 -- should we?" I would say "yes". Currently, I would need to handle the case of `steps == 1` or `steps == 0` separately, making sure to change the `end` when calling `torch.linspace`. This is impractical. If we support `start != end`, there are two possibilities for the result: Either we ensure the first value in the resulting sequence always equals `start`, or we ensure the last value in the resulting sequence always equals `end`. Numpy chose the former, which also allows it to support a boolean `endpoint` flag. I'd say we should follow numpy. This PR adapts `linspace` and `logspace` to mimic the behavior of numpy, adapts the tests accordingly, and extends the docstrings to make clear what happens when passing `steps=1`. If you decide against this PR, the error message should become explicit about what I did wrong, and the documentation should be extended to mention this restriction. Pull Request resolved: pytorch#14748 Differential Revision: D13356136 Pulled By: ezyang fbshipit-source-id: db85b8f0a98a5e24b3acd766132ab71c91794a82
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torch.linspace(0, 1, 1)fails withRuntimeError: invalid argument 3: invalid number of points at ../aten/src/TH/generic/THTensorMoreMath.cpp:2119, whilenp.linspace(0, 1, 1)works fine.Looking at the code, there is even a comment by @gchanan asking: "NumPy allows you to pass different points even if n <= 1 -- should we?"
I would say "yes". Currently, I would need to handle the case of
steps == 1orsteps == 0separately, making sure to change theendwhen callingtorch.linspace. This is impractical. If we supportstart != end, there are two possibilities for the result: Either we ensure the first value in the resulting sequence always equalsstart, or we ensure the last value in the resulting sequence always equalsend. Numpy chose the former, which also allows it to support a booleanendpointflag. I'd say we should follow numpy.This PR adapts
linspaceandlogspaceto mimic the behavior of numpy, adapts the tests accordingly, and extends the docstrings to make clear what happens when passingsteps=1.If you decide against this PR, the error message should become explicit about what I did wrong, and the documentation should be extended to mention this restriction.