[ONNX][TypePromo] Introduce ReductionTypePromotionRule #104491
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[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/104491
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 27ea2f3: This comment was automatically generated by Dr. CI and updates every 15 minutes. |
Introduce `ReductionTypePromotionRule` and rename `TypePromotionRule` as
`ElementwiseTypePromotionRule`. Created base abstract class `TypePromotionRule`.
Reduction rules are manually curated because the total number of ops is low, yet
most of them require some special treatment. The list that are covered in our unittest is
- "all", done
- "amax", done
- "amin", done
- "any", done
- "cumsum", done
- "cumprod", no torchlib impl
- "mean", done
- "std", no torchlib impl
- "std_mean", no torchlib impl
- "sum", done
- "sum_to_size", no torchlib impl
- "prod", no torchlib impl
- "var", no torchlib impl
- "var_mean", tricky. Node has multiple outputs. Follow up in separate PR.
[ghstack-poisoned]
…pePromo] Introduce ReductionTypePromotionRule"
Introduce `ReductionTypePromotionRule` and rename `TypePromotionRule` as
`ElementwiseTypePromotionRule`. Created base abstract class `TypePromotionRule`.
Reduction rules are manually curated because the total number of ops is low, yet
most of them require some special treatment. The list that are covered in our unittest is
- "all", done
- "amax", done
- "amin", done
- "any", done
- "cumsum", done
- "cumprod", no torchlib impl
- "mean", done
- "std", no torchlib impl
- "std_mean", no torchlib impl
- "sum", done
- "sum_to_size", no torchlib impl
- "prod", no torchlib impl
- "var", no torchlib impl
- "var_mean", tricky. Node has multiple outputs. Follow up in separate PR.
[ghstack-poisoned]
…Introduce ReductionTypePromotionRule"
Introduce `ReductionTypePromotionRule` and rename `TypePromotionRule` as
`ElementwiseTypePromotionRule`. Created base abstract class `TypePromotionRule`.
Reduction rules are manually curated because the total number of ops is low, yet
most of them require some special treatment. The list that are covered in our unittest is
- "all", done
- "amax", done
- "amin", done
- "any", done
- "cumsum", done
- "cumprod", no torchlib impl
- "mean", done
- "std", no torchlib impl
- "std_mean", no torchlib impl
- "sum", done
- "sum_to_size", no torchlib impl
- "prod", no torchlib impl
- "var", no torchlib impl
- "var_mean", tricky. Node has multiple outputs. Follow up in separate PR.
[ghstack-poisoned]
Introduce `ReductionTypePromotionRule` and rename `TypePromotionRule` as
`ElementwiseTypePromotionRule`. Created base abstract class `TypePromotionRule`.
Reduction rules are manually curated because the total number of ops is low, yet
most of them require some special treatment. The list that are covered in our unittest is
- "all", done
- "amax", done
- "amin", done
- "any", done
- "cumsum", done
- "cumprod", no torchlib impl
- "mean", done
- "std", no torchlib impl
- "std_mean", no torchlib impl
- "sum", done
- "sum_to_size", no torchlib impl
- "prod", no torchlib impl
- "var", no torchlib impl
- "var_mean", tricky. Node has multiple outputs. Follow up in separate PR.
[ghstack-poisoned]
Introduce `ReductionTypePromotionRule` and rename `TypePromotionRule` as
`ElementwiseTypePromotionRule`. Created base abstract class `TypePromotionRule`.
Reduction rules are manually curated because the total number of ops is low, yet
most of them require some special treatment. The list that are covered in our unittest is
- "all", done
- "amax", done
- "amin", done
- "any", done
- "cumsum", done
- "cumprod", no torchlib impl
- "mean", done
- "std", no torchlib impl
- "std_mean", no torchlib impl
- "sum", done
- "sum_to_size", no torchlib impl
- "prod", no torchlib impl
- "var", no torchlib impl
- "var_mean", tricky. Node has multiple outputs. Follow up in separate PR.
[ghstack-poisoned]
justinchuby
approved these changes
Jul 12, 2023
thiagocrepaldi
approved these changes
Jul 12, 2023
| "unflatten", | ||
| # "var_mean", # Segfault during onnx shape inference. Need to bump onnx version. |
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Is there a issue to track it? or at least add a TODO:
|
@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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Stack from ghstack (oldest at bottom):
Introduce
ReductionTypePromotionRuleand renameTypePromotionRuleasElementwiseTypePromotionRule. Created base abstract classTypePromotionRule.Reduction rules are manually curated because the total number of ops is low, yet
most of them require some special treatment. The list that are covered in our unittest is