New activation checkpointing #343
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epwalsh
requested changes
Oct 26, 2023
| @@ -853,6 +854,11 @@ class TrainConfig(BaseConfig): | |||
| Stop at a specific step. | |||
| """ | |||
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| activation_checkpointing: bool = False | |||
olmo/config.py
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| @@ -404,6 +404,12 @@ class ModelConfig(BaseConfig): | |||
| See :data:`TrainConfig.precision` instead. | |||
| """ | |||
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| activation_checkpointing: bool = False | |||
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When this gets set to true during training this could cause issues later when loading the model for inference since. Instead maybe we have a method on the model like Olmo.enable_activation_checkpointing()? The trainer calls that when TrainConfig.activation_checkpointing is true, so we don't need to add a configuration option to ModelConfig`.
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I don't see why it would cause issues for inference, as the resulting checkpoint files should be 100% identical.
But I like this other design anyways.
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What I mean is that it would enable activation checkpointing when the model is loaded for inference
# Conflicts: # olmo/model.py
This is untested.
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The checkpoint wrapper stuff is undocumented in torch. It breaks the way we construct parameter groups. This new way is documented, and, I think, easier to understand.