Initial version of async attribution with torch.futures #1295
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
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Jun 11, 2024
Summary: Pull Request resolved: pytorch#1295 Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
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Jun 11, 2024
Summary: Pull Request resolved: pytorch#1295 Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
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Jun 11, 2024
Summary: Pull Request resolved: pytorch#1295 Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
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Jun 11, 2024
Summary: Pull Request resolved: pytorch#1295 Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jun 11, 2024
Summary: Pull Request resolved: pytorch#1295 Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
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to yucu/captum
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this pull request
Jun 12, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jun 13, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jun 14, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
|
This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jun 14, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
|
This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jun 29, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
|
This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jun 29, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. PyTorch introduce future concepts ([link](https://pytorch.org/docs/stable/futures.html)) so we can adopt it for feature_ablation.py as the first step. Details: - Initial evaluation returns a future, save it. - Each evaluation for each feature for each input will returns an attribution result (plus corresponding weight if applicable), save all those result separately since futures cannot be added up directly. - When all futures above are done. we can add up the evaluation result to the final outcome as one Tensor per input. - Since common._run_forward is used by other attribution methods, need to do some type hacking over there. But if users attempt to use those methods async, they will end up in failure before Captum support async for those methods. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
|
This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jul 1, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. PyTorch introduce future concepts ([link](https://pytorch.org/docs/stable/futures.html)) so we can adopt it for feature_ablation.py as the first step. Details: - Initial evaluation returns a future, save it. - Each evaluation for each feature for each input will returns an attribution result (plus corresponding weight if applicable), save all those result separately since futures cannot be added up directly. - When all futures above are done. we can add up the evaluation result to the final outcome as one Tensor per input. - Since common._run_forward is used by other attribution methods, need to do some type hacking over there. But if users attempt to use those methods async, they will end up in failure before Captum support async for those methods. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
|
This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jul 1, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. PyTorch introduce future concepts ([link](https://pytorch.org/docs/stable/futures.html)) so we can adopt it for feature_ablation.py as the first step. Details: - Initial evaluation returns a future, save it. - Each evaluation for each feature for each input will returns an attribution result (plus corresponding weight if applicable), save all those result separately since futures cannot be added up directly. - When all futures above are done. we can add up the evaluation result to the final outcome as one Tensor per input. - Since common._run_forward is used by other attribution methods, need to do some type hacking over there. But if users attempt to use those methods async, they will end up in failure before Captum support async for those methods. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
|
This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jul 1, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. PyTorch introduce future concepts ([link](https://pytorch.org/docs/stable/futures.html)) so we can adopt it for feature_ablation.py as the first step. Details: - Initial evaluation returns a future, save it. - Each evaluation for each feature for each input will returns an attribution result (plus corresponding weight if applicable), save all those result separately since futures cannot be added up directly. - When all futures above are done. we can add up the evaluation result to the final outcome as one Tensor per input. - Since common._run_forward is used by other attribution methods, need to do some type hacking over there. But if users attempt to use those methods async, they will end up in failure before Captum support async for those methods. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
|
This pull request was exported from Phabricator. Differential Revision: D56764316 |
yucu
added a commit
to yucu/captum
that referenced
this pull request
Jul 1, 2024
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. PyTorch introduce future concepts ([link](https://pytorch.org/docs/stable/futures.html)) so we can adopt it for feature_ablation.py as the first step. Details: - Initial evaluation returns a future, save it. - Each evaluation for each feature for each input will returns an attribution result (plus corresponding weight if applicable), save all those result separately since futures cannot be added up directly. - When all futures above are done. we can add up the evaluation result to the final outcome as one Tensor per input. - Since common._run_forward is used by other attribution methods, need to do some type hacking over there. But if users attempt to use those methods async, they will end up in failure before Captum support async for those methods. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
Summary: Pull Request resolved: pytorch#1295 Currently Captum doesn't support async forward functions. Ads R&P team would like this feature in order to replace their custom variant (D56655643) of Feature Ablation with Captum and maintain similar performance. PyTorch introduce future concepts ([link](https://pytorch.org/docs/stable/futures.html)) so we can adopt it for feature_ablation.py as the first step. Details: - Initial evaluation returns a future, save it. - Each evaluation for each feature for each input will returns an attribution result (plus corresponding weight if applicable), save all those result separately since futures cannot be added up directly. - When all futures above are done. we can add up the evaluation result to the final outcome as one Tensor per input. - Since common._run_forward is used by other attribution methods, need to do some type hacking over there. But if users attempt to use those methods async, they will end up in failure before Captum support async for those methods. TODO: Extend FeatureAttributor to support `torch.futures` Differential Revision: D56764316
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This pull request was exported from Phabricator. Differential Revision: D56764316 |
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This pull request has been merged in 3543414. |
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Differential Revision: D56764316