layernorm and ne constraints + tests #80909
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This was referenced Jul 5, 2022
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- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
jamesr66a
approved these changes
Jul 6, 2022
| return [Disj([c1, Disj(c2)])], counter | ||
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| # return [BinConstraintT(input, output, op_eq), | ||
| # BinConstraintT(input, normalized_shape, op_consistency)], counter |
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
This was referenced Jul 10, 2022
Closed
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
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@pytorchbot merge -g |
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@pytorchbot successfully started a merge job. Check the current status here |
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Merge failed due to Command Raised by https://github.com/pytorch/pytorch/actions/runs/2659862433 |
- The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` [ghstack-poisoned]
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@pytorchbot merge -g |
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@pytorchbot successfully started a merge job. Check the current status here |
|
Hey @migeed-z. |
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Summary: - The constraints for ne are the same as the ones for tensor addition - Constraints for layernorm ensure that the input has the form `(*, d1, ..., dn)` where `d1, ..., dn` are consistent with the normalized_dim of the form `d1', ..., dn'`. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form `(*, d1, ..., dn)` Pull Request resolved: #80909 Approved by: https://github.com/jamesr66a Test Plan: contbuild & OSS CI, see https://hud.pytorch.org/commit/pytorch/pytorch/27db2750ba3fd524e0a03013bbc1aa6f44165224 Reviewed By: DanilBaibak Differential Revision: D37847284 Pulled By: migeed-z fbshipit-source-id: b1d0bf933e272bd879140517ce3dddbeab0dd137
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Stack from ghstack (oldest at bottom):
(*, d1, ..., dn)whered1, ..., dnare consistent with the normalized_dim of the formd1', ..., dn'. Since we are using gradual types, they do not have to be equal. The final result is then equal to the input and of the form(*, d1, ..., dn)