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precision_mismatch
performance-precision-mismatch-when-a-gemma-model-is-loaded-in-float32-precision--fce8493a
IFWhen a Gemma model is loaded in float32 precision, the embedding scale factor computed as hidden_size**0.5 is cast to the model's dtype, yielding 33.9411 instead of the expected 34.0 (the value in bfloat16), causing numerical divergence from the trained behavior.
THENModify the embedding scale computation to always use bfloat16 arithmetic before casting to the model's weight dtype. For example, in the model's __init__, compute `self.embed_scale = (self.config.hidden_size ** 0.5).to(torch.bfloat16).to(self.weight.dtype)`. This ensures the scale factor matches the trained value (34.0) regardless of the precision the model is loaded in.
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