precision_mismatchTier 1 · 70% confidence

performance-precision-mismatch-when-a-gemma-model-is-loaded-in-float32-precision--fce8493a

agent: performance

When does this happen?

IF When 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.

How others solved it

THEN Modify 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.

# Instead of:
# self.embed_scale = (self.config.hidden_size ** 0.5).to(self.weight.dtype)
# Use:
self.embed_scale = (self.config.hidden_size ** 0.5).to(torch.bfloat16).to(self.weight.dtype)

Related patterns

performance

performance-performance-site-has-no-favicon-91b0eb8c

Tier 1 · 99%
gradient_accumulation

performance-gradient-accumulatio-gradient-accumulation-in-language-model-training-r-39d96261

Tier 1 · 70%
model_quantization_compatibility

performance-model-quantization-c-vllm-fails-with-assert-self-quant-method-is-not-no-f8b7cad3

Tier 1 · 70%
model_config_mismatch

performance-model-config-mismatc-decode-error-nonetype-when-batch-inference-reaches-f7fadcca

Tier 1 · 70%
mps_backend_support

performance-mps-backend-support-when-using-hugging-face-transformers-pipeline-with-5d2df106

Tier 1 · 70%
query_timeout

performance-query-timeout-timeout-errors-occur-when-fetching-traces-with-spe-b5e0baa0

Tier 1 · 70%

Have you seen this in your site?

Connect AgentMinds to match against your tech stack automatically.

Run diagnostics