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Enable loading of chained PEFT models #10

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Merged
lewtun merged 4 commits into from
Jan 2, 2024
Merged

Enable loading of chained PEFT models #10

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11d22b9
Enable loading of chained PEFT models
lewtun Jan 2, 2024
99872d9
Fix deps
lewtun Jan 2, 2024
2524800
Fix LoRA loading
lewtun Jan 2, 2024
e536b51
Fix merge
lewtun Jan 2, 2024
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18 changes: 15 additions & 3 deletions fastchat/model/model_adapter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,7 +174,8 @@ def load_model(
revision: str = "main",
debug: bool = False,
trust_remote_code: bool=False,
base_model_revision: str ="main"
base_model_revision: str ="main",
base_model_path: str = None,
):
"""Load a model from Hugging Face."""
# get model adapter
Expand Down Expand Up @@ -297,6 +298,7 @@ def load_model(
kwargs["trust_remote_code"] = trust_remote_code
if is_adapter_model(model_path, revision=revision) is True:
kwargs["base_model_revision"] = base_model_revision
kwargs["base_model_path"] = base_model_path

# Load model
model, tokenizer = adapter.load_model(model_path, kwargs)
Expand Down Expand Up @@ -487,7 +489,10 @@ def load_model(self, model_path: str, from_pretrained_kwargs: dict):
from peft import PeftConfig, PeftModel
revision = from_pretrained_kwargs.get("revision", "main")
config = PeftConfig.from_pretrained(model_path, revision=revision)
base_model_path = config.base_model_name_or_path
if "base_model_path" in from_pretrained_kwargs and from_pretrained_kwargs["base_model_path"] is not None:
base_model_path = from_pretrained_kwargs["base_model_path"]
else:
base_model_path = config.base_model_name_or_path
if "peft" in base_model_path:
raise ValueError(
f"PeftModelAdapter cannot load a base model with 'peft' in the name: {config.base_model_name_or_path}"
Expand Down Expand Up @@ -524,16 +529,23 @@ def load_model(self, model_path: str, from_pretrained_kwargs: dict):
return model, tokenizer

# In the normal case, load up the base model weights again.
base_adapter = get_model_adapter(base_model_path)
base_model_from_pretrained_kwargs = {
"revision": from_pretrained_kwargs.get("base_model_revision", "main"),
"trust_remote_code": from_pretrained_kwargs.get("trust_remote_code", False),
"device_map": from_pretrained_kwargs.get("device_map", "auto"),
"torch_dtype": from_pretrained_kwargs.get("torch_dtype", torch.float16),
}
base_adapter = get_model_adapter(base_model_path, revision=base_model_from_pretrained_kwargs["revision"])
print(f"Loading base model for {base_model_path=} and {base_model_from_pretrained_kwargs=}")
base_model, tokenizer = base_adapter.load_model(
base_model_path, base_model_from_pretrained_kwargs,
)
# If the base model is also a LoRA adapter, we need to merge those weights **before** loading the second adapter
# Without this, you will get garbage outputs!
if is_adapter_model(base_model_path, base_model_from_pretrained_kwargs["revision"]) is True:
print("Base model is adapter, merging LoRA weights")
base_model.eval()
base_model = base_model.merge_and_unload()
print(f"Base model loaded on device {base_model.device} for {base_model_path=} and {base_model_from_pretrained_kwargs=}")
model = PeftModel.from_pretrained(base_model, model_path, revision=revision)
return model, tokenizer
Expand Down