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Merging QLoRA weights with quantized model
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""" | |
The code below combines approaches published by both @eugene-yh and @jinyongyoo on Github. | |
Thanks for the contributions guys! | |
""" | |
import torch | |
import peft | |
import json | |
import shutil | |
from peft.utils import _get_submodules | |
import os | |
import bitsandbytes as bnb | |
from bitsandbytes.functional import dequantize_4bit | |
from peft import PeftModel | |
from transformers import AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, BitsAndBytesConfig, CodeLlamaTokenizer | |
import gc | |
import copy | |
def save_model(model, tokenizer, to): | |
print(f"Saving dequantized model to {to}...") | |
model.save_pretrained(to) | |
tokenizer.save_pretrained(to) | |
config_data = json.loads(open(os.path.join(to, 'config.json'), 'r').read()) | |
config_data.pop("quantization_config", None) | |
config_data.pop("pretraining_tp", None) | |
with open(os.path.join(to, 'config.json'), 'w') as config: | |
config.write(json.dumps(config_data, indent=2)) | |
def dequantize_model(model, tokenizer, to='./dequantized_model', dtype=torch.bfloat16, device="cpu"): | |
""" | |
'model': the peftmodel you loaded with qlora. | |
'tokenizer': the model's corresponding hf's tokenizer. | |
'to': directory to save the dequantized model | |
'dtype': dtype that the model was trained using | |
'device': device to load the model to | |
""" | |
# Delete the model object if it exists | |
if os.path.exists(to): | |
shutil.rmtree(to) | |
os.makedirs(to, exist_ok=True) | |
cls = bnb.nn.Linear4bit | |
with torch.no_grad(): | |
for name, module in model.named_modules(): | |
if isinstance(module, cls): | |
print(f"Dequantizing `{name}`...") | |
quant_state = copy.deepcopy(module.weight.quant_state) | |
quant_state[2] = dtype | |
weights = dequantize_4bit(module.weight.data, quant_state=quant_state, quant_type="nf4").to(dtype) | |
new_module = torch.nn.Linear(module.in_features, module.out_features, bias=None, dtype=dtype) | |
new_module.weight = torch.nn.Parameter(weights) | |
new_module.to(device=device, dtype=dtype) | |
parent, target, target_name = _get_submodules(model, name) | |
setattr(parent, target_name, new_module) | |
model.is_loaded_in_4bit = False | |
save_model(model, tokenizer, to) | |
return model | |
model_path = 'Huggingface-base-model/path-goes-here' | |
adapter_path = 'Huggingface-adapter/path-goes-here' | |
quantization_config=BitsAndBytesConfig( | |
load_in_4bit=True, | |
bnb_4bit_compute_dtype=torch.bfloat16, | |
bnb_4bit_use_double_quant=True, | |
bnb_4bit_quant_type="nf4", | |
) | |
try: | |
print(f"Starting to load the model {model_path} into memory") | |
model = LlamaForCausalLM.from_pretrained( | |
model_path, | |
load_in_4bit=True, | |
torch_dtype=torch.bfloat16, | |
quantization_config=quantization_config, | |
device_map="auto" | |
) | |
print(model) | |
tok = LlamaTokenizer.from_pretrained(model_path) | |
# Note: This function outputs the dequantized model without merging the adapter yet | |
# The code below it will merge the adapter and then save it to disk | |
model = dequantize_model(model, tok, to='output-folder-for-dequantized-model-here') | |
print(model) | |
model = PeftModel.from_pretrained(model = model, model_id = adapter_path) | |
print(model) | |
model = model.merge_and_unload() | |
print(model) | |
print(f"Successfully loaded the model {model_path} into memory") | |
# Note that the output folder here should be different than the one you used for dequantize_model | |
# This save will output the model merged with LoRA weights | |
save_model(model, tok, "put-output-folder-here") | |
print(f"Successfully saved merged model {model_path} to disk") | |
except Exception as e: | |
print(f"An error occurred: {e}") | |
# Delete the model object if it exists | |
if 'model' in locals(): | |
del model | |
# Clear the GPU cache | |
torch.cuda.empty_cache() | |
# Run the garbage collection | |
gc.collect() | |
print("Model, GPU cache, and garbage have been cleared.") |
Thanks for sharing. I found AutoModel has a dequantize
function to dequantize the whole model and it works as expected after lora adapter merging (same ppl). So you don't have to do it yourself. But quantization_config
should still be removed manually from saved config.json
of the dequantized model.
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Thank you for sharing this helpful script. I've noticed that the current script ignores
bias
. To deal with it,