Add warnings if training args differ from checkpoint trainer state #29255
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jonflynng
commented
Feb 25, 2024
| @@ -1370,6 +1370,29 @@ def ipex_optimize_model(self, model, training=False, dtype=torch.float32): | |||
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| return model | |||
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| def compare_trainer_and_checkpoint_args(self, training_args, trainer_state): | |||
| attributes_map = { | |||
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These are some parameters I expected to be overridden via the training_args when resuming from a checkpoint.
4 tasks
jonflynng
changed the title
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cc @muellerzr ! |
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This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread. Please note that issues that do not follow the contributing guidelines are likely to be ignored. |
ArthurZucker
approved these changes
Mar 25, 2024
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
muellerzr
approved these changes
Mar 25, 2024
5 tasks
hovnatan
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to hovnatan/transformers
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Mar 27, 2024
…uggingface#29255) * add warnings if training args differ from checkpoint args stored in trainer_state.json * run formatting and styling * add a test * format and styling --------- Co-authored-by: Jonathan Flynn <jonl.flynn@guardian.co.uk>
itazap
pushed a commit
that referenced
this pull request
May 14, 2024
…29255) * add warnings if training args differ from checkpoint args stored in trainer_state.json * run formatting and styling * add a test * format and styling --------- Co-authored-by: Jonathan Flynn <jonl.flynn@guardian.co.uk>
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What does this PR do?
Resolves #28867
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Pull Request section?
to it if that's the case.
Training arguments are not applied when resuming from a checkpoint #28867
documentation guidelines, and
here are tips on formatting docstrings.
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@ArthurZucker
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