Merging QLoRA weights with quantized model

merge_qlora_with_quantized_model.py

"""

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.")

Hi Chris. Thanks for looking into it. McGovern provides a fairly detailed way of reproducing it in huggingface/transformers#26492. I did similar things - except I actually use llama.cpp - and when i quiz the model with set questions - I get much worse answers than using a non-merged model with adapters applied (the model is loaded in 4 bit with nf4=true).

Benjamin Marie uses this function to measure perplexity:

def ppl_model(model, tokenizer, dataset):
  nlls= []
  max_length = 2048
  stride = 512
  for s in tqdm(range(len(dataset['text']))):
      encodings = tokenizer(dataset['text'][s], return_tensors="pt")
      seq_len = encodings.input_ids.size(1)
      prev_end_loc = 0
      for begin_loc in range(0, seq_len, stride):
          end_loc = min(begin_loc + max_length, seq_len)
          trg_len = end_loc - prev_end_loc
          input_ids = encodings.input_ids[:, begin_loc:end_loc].to("cuda")
          target_ids = input_ids.clone()
          target_ids[:, :-trg_len] = -100
          with torch.no_grad():
              outputs = model(input_ids, labels=target_ids)
              neg_log_likelihood = outputs.loss
          nlls.append(neg_log_likelihood)
          prev_end_loc = end_loc
          if end_loc == seq_len:
              break
  ppl = torch.exp(torch.stack(nlls).mean())
  return ppl

So when he loads the merged model in 4 bit and measure perplexity - he gets a bizarrely high value of 5.2509 - versus the baseline value of 3.7411 if he uses the merged model in fp16.

bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=compute_dtype,
        bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained("./drive/MyDrive/dqz_merge/",  quantization_config=bnb_config, device_map={"": 0})
ppl = ppl_model(model, tokenizer, dataset)
print(ppl)

As you can imagine - this is practically a breaking issue. If you can solve this the community will be very grateful ..

Hi Chris. Thanks for looking into it. McGovern provides a fairly detailed way of reproducing it in huggingface/transformers#26492. I did similar things - except I actually use llama.cpp - and when i quiz the model with set questions - I get much worse answers than using a non-merged model with adapters applied (the model is loaded in 4 bit with nf4=true). ..

Two possible explanations. First, make sure you are using the correct type inbnb_4bit_compute_dtype=compute_dtype , torch.bfloat16 or torch.float16, Chris's code uses torch.bfloat16 by default. Second, the merged model should not to use LoRA again while inferencing, i.e., comment out model = PeftModel(model, config)

Hi Chris. Thanks for looking into it. McGovern provides a fairly detailed way of reproducing it in huggingface/transformers#26492. I did similar things - except I actually use llama.cpp - and when i quiz the model with set questions - I get much worse answers than using a non-merged model with adapters applied (the model is loaded in 4 bit with nf4=true). ..

Two possible explanations. First, make sure you are using the correct type inbnb_4bit_compute_dtype=compute_dtype , torch.bfloat16 or torch.float16, Chris's code uses torch.bfloat16 by default. Second, the merged model should not to use LoRA again while inferencing, i.e., comment out model = PeftModel(model, config)

I am currently using the 'default' method in Peft now - but thanks for clarifying!

I get:

Dequantizing `model.layers.0.self_attn.q_proj`...
An error occurred: 'QuantState' object does not support item assignment
Model, GPU cache, and garbage have been cleared.

Edit: Fixed by downgrading bitsandbytes to 0.41.0

@sampbarrow @ChrisHayduk This error is also witnessed by me, how can we rectify it. Downgrading should not be a solution.

@sampbarrow @NeelMishra Just change quant_state[2] = dtype to quant_state.dtype = dtype

You can't pass load_in_4bitor load_in_8bit as a kwarg when passing quantization_config

Any recs

Thank you for sharing this helpful script. I've noticed that the current script ignores bias. To deal with it,

                    has_bias = module.bias is not None
                    new_module = torch.nn.Linear(module.in_features, module.out_features, bias=has_bias, dtype=dtype)
                    new_module.weight = torch.nn.Parameter(weights, requires_grad=False)
                    if has_bias:
                        new_module.bias.data = module.bias.data.detach().to(dtype)

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.

Can this be run on cpu? I am facing error when trying to run on cpu