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LLM: Fix baichuan pre-normalize model tensor assigning issue when loading (#9481)

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* No need to normalized when loading
This commit is contained in:
Zhao Changmin 2023-11-16 21:57:28 +08:00 committed by GitHub
parent bc06bec90e
commit 30abd304a7
1 changed files with 7 additions and 3 deletions
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8
python/llm/src/bigdl/llm/transformers/convert.py
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@ -309,8 +309,12 @@ def _optimize_pre(model):
if hasattr(model, 'lm_head') and model.lm_head is not None: if hasattr(model, 'lm_head') and model.lm_head is not None:
# do we need to check the class instance? # do we need to check the class instance?
vocab_size, hidden_size = model.lm_head.weight.shape vocab_size, hidden_size = model.lm_head.weight.shape
norm_weight = nn.functional.normalize(model.lm_head.weight.data) lm_head_weight_data = model.lm_head.weight.data
model.lm_head = nn.Linear(hidden_size, vocab_size, bias=False) model.lm_head = nn.Linear(hidden_size, vocab_size, bias=False,
device=lm_head_weight_data.device)
# In which case we are NOT loading the normalized weights
if model.lm_head.weight.data.device != "meta":
norm_weight = nn.functional.normalize(lm_head_weight_data)
model.lm_head.weight.data = norm_weight model.lm_head.weight.data = norm_weight
return model return model