Update Gemma to reflect upstream HF changes #596
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Thanks Chris! looks good. |
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* update activation function to tanh approximation * keep RMSNorm calcs in float32 and match cfg dtype for embedding scaling * formatting * keep mypy happy * formatting
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* update activation function to tanh approximation * keep RMSNorm calcs in float32 and match cfg dtype for embedding scaling * formatting * keep mypy happy * formatting
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* Initial Commit (add pyright + test by adding few annotations) * Slightly more typing added * more typing * Additional typing * Completed typing for hook_points.py file * todo clarifications * formatting changes to hook_points.py * Apply some suggestions from code review Co-authored-by: Alan <[email protected]> * Added typing for Literals and changed some assertions to if statements * formatting * update to accout for merged code * small typing issue * changing hookfunction protocol + more assertions * change the slice input * change from isinstance to callable checks * fix: Update Gemma to reflect upstream HF changes (#596) * update activation function to tanh approximation * keep RMSNorm calcs in float32 and match cfg dtype for embedding scaling * formatting * keep mypy happy * formatting * allow user to force trust_remote_code=true via from_pretrained kwargs (#597) * change + revert HookFunctionProtocol * format * module_output is now just a tensor * set module ouput to be any type --------- Co-authored-by: Alan <[email protected]> Co-authored-by: Bryce Meyer <[email protected]> Co-authored-by: cmathw <[email protected]> Co-authored-by: Clement Dumas <[email protected]>
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Description
Fixes #594.
Since Gemma was merged in March 14th, there have been a number of upstream HuggingFace changes that mean our activations/logits no longer matched this implementation well. This PR implements the changes described here, changes include:
A demo showing this agreement can be found here and summarised below. Note: There is currently a bug in various HF/transformer models where the default attention implementation used is not causally masking patterns, I opened an issue here but it is already being addressed here. For this reason it is important when comparing activations/logits to specify
attn_implementation="eager"when using HF/transformers'from_pretrainedmethod.Tolerances for both 2b and 7b models when running in float32 can be found below:
Tolerances when running float16 are much worse but I think this is commonly the case when adding models to TL with internal upcasting/downcasting, if this is not the case though and these tolerances should also be similarly close I can investigate this further.
Type of change
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Checklist: