Initial NJT testing over dim type / views #140161
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/140161
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Skylion007
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This PR introduces `ExtraOpData`, a structure that contains op metadata regarding whether the op is a view and the dim-related args it accepts. It also populates a huge database for dim-wise / view ops with this info. Test logic (sample input generation, references) have been updated to utilize this data. It allows for a fairly generic set of sample inputs & a reference for the class of ops that accept a single NJT and operate dim-wise (AKA "unary dimwise ops"). Testing is added over the following ops: * `chunk()` * `narrow()` * `select()` * `split()` * `split_with_sizes()` * `squeeze()` * `unflatten()` * `unsqueeze()` Most of the above do not operate on the ragged / batch dims or on non-contiguous NJTs, so the proper xfails are added as needed. I also slipped in a couple minor fixes (sorry): 1. The `_wrap_jagged_dim()` helper now avoids assuming the `nt._ragged_idx == 1` and allows for a batch dim to be a valid input, disambiguating the converted inner dim as necessary through an additional `operating_on_batch` return value (i.e. both dim=0 and dim=1 map to dim=0 on the inner values tensor, since that dim represents a packed ragged dim for all batch items) 2. Padded dense -> NJT conversion requires shape gymnastics to operate with the restrictive FBGEMM kernel. The gymnastics were slightly wrong for the transposed NJT case, and this PR fixes that [ghstack-poisoned]
This PR introduces `ExtraOpData`, a structure that contains op metadata regarding whether the op is a view and the dim-related args it accepts. It also populates a huge database for dim-wise / view ops with this info. Test logic (sample input generation, references) have been updated to utilize this data. It allows for a fairly generic set of sample inputs & a reference for the class of ops that accept a single NJT and operate dim-wise (AKA "unary dimwise ops"). Testing is added over the following ops: * `chunk()` * `narrow()` * `select()` * `split()` * `split_with_sizes()` * `squeeze()` * `unflatten()` * `unsqueeze()` Most of the above do not operate on the ragged / batch dims or on non-contiguous NJTs, so the proper xfails are added as needed. I also slipped in a couple minor fixes (sorry): 1. The `_wrap_jagged_dim()` helper now avoids assuming the `nt._ragged_idx == 1` and allows for a batch dim to be a valid input, disambiguating the converted inner dim as necessary through an additional `operating_on_batch` return value (i.e. both dim=0 and dim=1 map to dim=0 on the inner values tensor, since that dim represents a packed ragged dim for all batch items) 2. Padded dense -> NJT conversion requires shape gymnastics to operate with the restrictive FBGEMM kernel. The gymnastics were slightly wrong for the transposed NJT case, and this PR fixes that [ghstack-poisoned]
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Merge startedYour change will be merged while ignoring the following 1 checks: trunk / linux-focal-rocm6.2-py3.10 / test (distributed, 1, 1, linux.rocm.gpu) Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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This PR contains three `unsqueeze()`-related fixes for NJT: 1. Adjusts the output's `_ragged_idx` when `unsqueeze()` inserts a dim before the ragged dim 2. Corrects the unbind reference for `unsqueeze()` after the last input dim. For this case, the dim kwarg canonicalization logic needs to be applied wrt `inp.dim() + 1` to account for `dim=-1` properly 3. Adds ragged dim support to `unsqueeze()`, allowing for e.g. `(B, j1, D) -> (B, 1, j1, D)`. This is okay now after #137125 Note that `unsqueeze()` still doesn't support batch dim operation, and arguably should never support this. Pull Request resolved: #141392 Approved by: https://github.com/cpuhrsch ghstack dependencies: #141500, #140736, #140161
cyyever
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) This fixes some bugs when performing reductions / select() on dims before the ragged dim. In this case, the output NJT has a smaller number of dims, and its ragged_idx should reflect that correctly. Pull Request resolved: pytorch#141506 Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer ghstack dependencies: pytorch#141500, pytorch#140736, pytorch#140161, pytorch#141392
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…141604) Old logic was completely wrong, returning `chunk_size` chunks instead of the intended number. The original test didn't catch this because `chunk_size == num_chunks` :p New OpInfo-based testing covers it though. Pull Request resolved: #141604 Approved by: https://github.com/soulitzer ghstack dependencies: #141500, #140736, #140161, #141392, #141506
pobin6
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This PR introduces `ExtraOpData`, a structure that contains op metadata regarding whether the op is a view and the dim-related args it accepts. It also populates a huge database for dim-wise / view ops with this info. Test logic (sample input generation, references) have been updated to utilize this data. It allows for a fairly generic set of sample inputs & a reference for the class of ops that accept a single NJT and operate dim-wise (AKA "unary dimwise ops"). Testing is added over the following ops: * `chunk()` * `narrow()` * `select()` * `split()` * `split_with_sizes()` * `squeeze()` * `unflatten()` * `unsqueeze()` Most of the above do not operate on the ragged / batch dims or on non-contiguous NJTs, so the proper xfails are added as needed. I also slipped in a couple minor fixes (sorry): 1. The `_wrap_jagged_dim()` helper now avoids assuming the `nt._ragged_idx == 1` and allows for a batch dim to be a valid input, disambiguating the converted inner dim as necessary through an additional `operating_on_batch` return value (i.e. both dim=0 and dim=1 map to dim=0 on the inner values tensor, since that dim represents a packed ragged dim for all batch items) 2. Padded dense -> NJT conversion requires shape gymnastics to operate with the restrictive FBGEMM kernel. The gymnastics were slightly wrong for the transposed NJT case, and this PR fixes that Pull Request resolved: pytorch#140161 Approved by: https://github.com/Skylion007, https://github.com/cpuhrsch ghstack dependencies: pytorch#140736
pobin6
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Dec 5, 2024
This PR contains three `unsqueeze()`-related fixes for NJT: 1. Adjusts the output's `_ragged_idx` when `unsqueeze()` inserts a dim before the ragged dim 2. Corrects the unbind reference for `unsqueeze()` after the last input dim. For this case, the dim kwarg canonicalization logic needs to be applied wrt `inp.dim() + 1` to account for `dim=-1` properly 3. Adds ragged dim support to `unsqueeze()`, allowing for e.g. `(B, j1, D) -> (B, 1, j1, D)`. This is okay now after pytorch#137125 Note that `unsqueeze()` still doesn't support batch dim operation, and arguably should never support this. Pull Request resolved: pytorch#141392 Approved by: https://github.com/cpuhrsch ghstack dependencies: pytorch#140736, pytorch#140161
pobin6
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Dec 5, 2024
This reverts commit 730caf0. Reverted pytorch#140161 on behalf of https://github.com/malfet due to Sorry for reverting your change but its tests are failing in trunk ([comment](pytorch#140736 (comment)))
pobin6
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Dec 5, 2024
This PR introduces `ExtraOpData`, a structure that contains op metadata regarding whether the op is a view and the dim-related args it accepts. It also populates a huge database for dim-wise / view ops with this info. Test logic (sample input generation, references) have been updated to utilize this data. It allows for a fairly generic set of sample inputs & a reference for the class of ops that accept a single NJT and operate dim-wise (AKA "unary dimwise ops"). Testing is added over the following ops: * `chunk()` * `narrow()` * `select()` * `split()` * `split_with_sizes()` * `squeeze()` * `unflatten()` * `unsqueeze()` Most of the above do not operate on the ragged / batch dims or on non-contiguous NJTs, so the proper xfails are added as needed. I also slipped in a couple minor fixes (sorry): 1. The `_wrap_jagged_dim()` helper now avoids assuming the `nt._ragged_idx == 1` and allows for a batch dim to be a valid input, disambiguating the converted inner dim as necessary through an additional `operating_on_batch` return value (i.e. both dim=0 and dim=1 map to dim=0 on the inner values tensor, since that dim represents a packed ragged dim for all batch items) 2. Padded dense -> NJT conversion requires shape gymnastics to operate with the restrictive FBGEMM kernel. The gymnastics were slightly wrong for the transposed NJT case, and this PR fixes that Pull Request resolved: pytorch#140161 Approved by: https://github.com/Skylion007, https://github.com/cpuhrsch ghstack dependencies: pytorch#141500, pytorch#140736
pobin6
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Dec 5, 2024
This PR contains three `unsqueeze()`-related fixes for NJT: 1. Adjusts the output's `_ragged_idx` when `unsqueeze()` inserts a dim before the ragged dim 2. Corrects the unbind reference for `unsqueeze()` after the last input dim. For this case, the dim kwarg canonicalization logic needs to be applied wrt `inp.dim() + 1` to account for `dim=-1` properly 3. Adds ragged dim support to `unsqueeze()`, allowing for e.g. `(B, j1, D) -> (B, 1, j1, D)`. This is okay now after pytorch#137125 Note that `unsqueeze()` still doesn't support batch dim operation, and arguably should never support this. Pull Request resolved: pytorch#141392 Approved by: https://github.com/cpuhrsch ghstack dependencies: pytorch#141500, pytorch#140736, pytorch#140161
pobin6
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Dec 5, 2024
) This fixes some bugs when performing reductions / select() on dims before the ragged dim. In this case, the output NJT has a smaller number of dims, and its ragged_idx should reflect that correctly. Pull Request resolved: pytorch#141506 Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer ghstack dependencies: pytorch#141500, pytorch#140736, pytorch#140161, pytorch#141392
pobin6
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Dec 5, 2024
…ytorch#141604) Old logic was completely wrong, returning `chunk_size` chunks instead of the intended number. The original test didn't catch this because `chunk_size == num_chunks` :p New OpInfo-based testing covers it though. Pull Request resolved: pytorch#141604 Approved by: https://github.com/soulitzer ghstack dependencies: pytorch#141500, pytorch#140736, pytorch#140161, pytorch#141392, pytorch#141506
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This PR introduces
ExtraOpData, a structure that contains op metadata regarding whether the op is a view and the dim-related args it accepts. It also populates a huge database for dim-wise / view ops with this info.Test logic (sample input generation, references) have been updated to utilize this data. It allows for a fairly generic set of sample inputs & a reference for the class of ops that accept a single NJT and operate dim-wise (AKA "unary dimwise ops").
Testing is added over the following ops:
chunk()narrow()select()split()split_with_sizes()squeeze()unflatten()unsqueeze()Most of the above do not operate on the ragged / batch dims or on non-contiguous NJTs, so the proper xfails are added as needed.
I also slipped in a couple minor fixes (sorry):
_wrap_jagged_dim()helper now avoids assuming thent._ragged_idx == 1and allows for a batch dim to be a valid input, disambiguating the converted inner dim as necessary through an additionaloperating_on_batchreturn value (i.e. both dim=0 and dim=1 map to dim=0 on the inner values tensor, since that dim represents a packed ragged dim for all batch items)