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Are Lora weights loaded?

#4
by syncdoth - opened
Discussion
syncdoth
  1. I am not sure if the model = LlamaForCausalLM.from_pretrained("chainyo/alpaca-lora-7b") is enough to load to lora weights in adapter_model.bin too. I have loaded the model using the example, and looking at the state_dict:
model = LlamaForCausalLM.from_pretrained("chainyo/alpaca-lora-7b")
state_dict = model.state_dict()
print([k for k in state_dict.keys() if 'lora' in k])
>>> []

While the state_dict in adapter_model.bin are:

adapter_dict = torch.load('adapter_model.bin')
print(adapter_dict.keys())
>>> dict_keys(['base_model.model.model.layers.0.self_attn.q_proj.lora_A.weight', 'base_model.model.model.layers.0.self_attn.q_proj.lora_B.weight',  ...

If this is the case, then the loading script should be sth like:

from transformers import LlamaTokenizer, LlamaForCausalLM
from peft import PeftModel, PeftConfig

peft_model_id = "chainyo/alpaca-lora-7b"
config = PeftConfig.from_pretrained(peft_model_id)
model = LlamaForCausalLM.from_pretrained(config.base_model_name_or_path,
                                         load_in_8bit=True,
                                         torch_dtype=torch.float16,
                                         device_map="auto")
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = LlamaTokenizer.from_pretrained(peft_model_id)

Also, the base_model_name_or_path in adapter_config.json might need to be corrected so it doesn't download original llama weights again.

  1. On second thought, are lora weights required to perform as expected? Maybe the LLaMa weights themselves are tuned... (then why the name Lora?)

Thanks!

syncdoth

On a deeper investigation, I found that only 'self_attn.q_proj.weight', 'self_attn.rotary_emb.inv_freq', 'self_attn.v_proj.weight' diverge from the original llama weight. Since lora is applied to q_proj and v_proj, I suppose these weights are re-constructed from lora A and B matrices (found PEFT's merge_and_unload function).

syncdoth changed discussion status to closed
chainyo
 Owner
•
edited Apr 5, 2023

I chose to share the adapters and the fully merged Lora weights with the base model.

You can choose to load only the adapters or the full model.

  • Full model:
tokenizer = LlamaTokenizer.from_pretrained("chainyo/alpaca-lora-7b")
model = LlamaForCausalLM.from_pretrained(
    "chainyo/alpaca-lora-7b",
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
)
  • Using the adapters:
model = LlamaForCausalLM.from_pretrained(
    "decapoda-research/llama-7b-hf",
    load_in_8bit=load_8bit,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(
    model,
    "chainyo/alpaca-lora-7b",
    torch_dtype=torch.float16,
)

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