Fix checkpointable_layers Logic
#6881
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Quentin-Anthony
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loadams
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tests/unit/runtime/activation_checkpointing/test_activation_checkpointing.py
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Feb 7, 2025
**Problem** There's an edge-case in DeepSpeed, where if all three of the following are true: 1. Deepspeed activation checkpointing is applied 2. The user passes `checkpointable_layers` (e.g. https://github.com/EleutherAI/gpt-neox/blob/f5325805678c2b9e35aae4528283e0132c5f5bbc/megatron/model/gpt2_model.py#L175) 3. The user's model class contains `GPT2ModelPipe` or GPTModelPipe` Then the `checkpointable_layers` will not be activation checkpointed. **Reason** This is because in the current logic, `_is_checkpointable` will short-circuit to just return layers matching `ParallelTransformerLayerPipe` in the case of `self.__class__.__name__ in ('GPTModelPipe', 'GPT2ModelPipe')`. See https://github.com/microsoft/DeepSpeed/blob/da771ed42e41a44d5047813ca4672f1cfe9d1731/deepspeed/runtime/pipe/module.py#L653 **Proposed Fixes** I think that `checkpointable_layers` should always be checked for, and added logic to this effect. I also found the documentation for `checkpointable_layers` confusing and contradictory, so I updated the docstring. Lastly, I added a unit test for `checkpointable_layers`. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Signed-off-by: siqi <siqi@tecorigin.com>
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Feb 9, 2025
**Problem** There's an edge-case in DeepSpeed, where if all three of the following are true: 1. Deepspeed activation checkpointing is applied 2. The user passes `checkpointable_layers` (e.g. https://github.com/EleutherAI/gpt-neox/blob/f5325805678c2b9e35aae4528283e0132c5f5bbc/megatron/model/gpt2_model.py#L175) 3. The user's model class contains `GPT2ModelPipe` or GPTModelPipe` Then the `checkpointable_layers` will not be activation checkpointed. **Reason** This is because in the current logic, `_is_checkpointable` will short-circuit to just return layers matching `ParallelTransformerLayerPipe` in the case of `self.__class__.__name__ in ('GPTModelPipe', 'GPT2ModelPipe')`. See https://github.com/microsoft/DeepSpeed/blob/da771ed42e41a44d5047813ca4672f1cfe9d1731/deepspeed/runtime/pipe/module.py#L653 **Proposed Fixes** I think that `checkpointable_layers` should always be checked for, and added logic to this effect. I also found the documentation for `checkpointable_layers` confusing and contradictory, so I updated the docstring. Lastly, I added a unit test for `checkpointable_layers`. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
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Problem
There's an edge-case in DeepSpeed, where if all three of the following are true:
checkpointable_layers(e.g. https://github.com/EleutherAI/gpt-neox/blob/f5325805678c2b9e35aae4528283e0132c5f5bbc/megatron/model/gpt2_model.py#L175)GPT2ModelPipeor GPTModelPipe`Then the
checkpointable_layerswill not be activation checkpointed.Reason
This is because in the current logic,
_is_checkpointablewill short-circuit to just return layers matchingParallelTransformerLayerPipein the case ofself.__class__.__name__ in ('GPTModelPipe', 'GPT2ModelPipe'). See https://github.com/microsoft/DeepSpeed/blob/da771ed42e41a44d5047813ca4672f1cfe9d1731/deepspeed/runtime/pipe/module.py#L653Proposed Fixes
I think that
checkpointable_layersshould always be checked for, and added logic to this effect. I also found the documentation forcheckpointable_layersconfusing and contradictory, so I updated the docstring. Lastly, I added a unit test forcheckpointable_layers.