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[torch.package] add utility for determining where bad modules may come from #74998

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Summary: this is a cleaned-up version of a util that I commonly use to figure out where new transitive (surprise!) dependencies come from when a model breaks, since it can be difficult in large models to tell exactly what code change indirectly added the depdendency. I tried to keep the opinionated bits out of OSS as much as possible.

Differential Revision: D35265017

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facebook-github-bot commented Mar 31, 2022 •
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This pull request was exported from Phabricator. Differential Revision: D35265017

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[torch.package] add utility for determining where bad modules may com…
4cebb7c 
…e from (pytorch#74998)

Summary:
Pull Request resolved: pytorch#74998

this is a cleaned-up version of a util that I commonly use to figure out where new transitive (surprise!) dependencies come from when a model breaks, since it can be difficult in large models to tell exactly what code change indirectly added the depdendency. I tried to keep the opinionated bits out of OSS as much as possible.

Reviewed By: PaliC

Differential Revision: D35265017

fbshipit-source-id: a9cc08df44b11e0f3c2cf5d3c7e672b4c8ec4e42
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This pull request was exported from Phabricator. Differential Revision: D35265017

facebook-github-bot pushed a commit that referenced this pull request Mar 31, 2022
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[torch.package] add utility for determining where bad modules may com…
ffa0f4b 
…e from (#74998)

Summary:
Pull Request resolved: #74998

this is a cleaned-up version of a util that I commonly use to figure out where new transitive (surprise!) dependencies come from when a model breaks, since it can be difficult in large models to tell exactly what code change indirectly added the depdendency. I tried to keep the opinionated bits out of OSS as much as possible.

Reviewed By: PaliC

Differential Revision: D35265017

fbshipit-source-id: e126e03aa113db6ab79d32d86a69bcbba844875e
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Hey @bradleyhd.
You've committed this PR, but it does not have both a 'release notes: ...' and 'topics: ...' label. Please add one of each to the PR. The 'release notes: ...' label should represent the part of PyTorch that this PR changes (fx, autograd, distributed, etc) and the 'topics: ...' label should represent the kind of PR it is (not user facing, new feature, bug fix, perf improvement, etc). The list of valid labels can be found here for the 'release notes: ...' and here for the 'topics: ...'.
For changes that are 'topic: not user facing' there is no need for a release notes label.

@WBobby WBobby mentioned this pull request Aug 17, 2022
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