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merge_model.py
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merge_model.py
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import argparse
import json
import os
import gc
import torch
import peft
from transformers import LlamaTokenizer
from transformers.modeling_utils import dtype_byte_size
from huggingface_hub import snapshot_download
import re
parser = argparse.ArgumentParser(description='Script to merge Llama2-7b-chat and LoRA weights')
parser.add_argument('--base_model', default=None, required=True,
type=str, help="Base model path (basically Llama2-7b-chat)")
parser.add_argument('--lora_model', default=None, required=True,
type=str, help="LoRA model path")
parser.add_argument('--output_type', default='huggingface', choices=['huggingface', 'pth'],
type=str, help="Output model type can be 'huggingface' (default) or 'pth' format")
parser.add_argument('--output_dir', default='./merged_model',
type=str, help="Output path for the merged model")
parser.add_argument('--verbose', default=False, action='store_true',
help="Show detailed debugging messages")
emb_to_model_size = {
4096: '7B',
5120: '13B',
8192: '70B',
}
num_shards_of_models = {'7B': 1, '13B': 2, '70B': 8}
params_of_models = {
'7B':
{
"dim": 4096,
"multiple_of": 256,
"n_heads": 32,
"n_layers": 32,
"norm_eps": 1e-05,
"vocab_size": -1,
},
'13B':
{
"dim": 5120,
"multiple_of": 256,
"n_heads": 40,
"n_layers": 40,
"norm_eps": 1e-05,
"vocab_size": -1,
},
'70B':
{
"dim": 8192,
"multiple_of": 4096,
"ffn_dim_multiplier": 1.3,
"n_heads": 64,
"n_kv_heads": 8,
"n_layers": 80,
"norm_eps": 1e-05,
"vocab_size": -1,
},
}
def transpose(weight, fan_in_fan_out):
return weight.T if fan_in_fan_out else weight
def translate_state_dict_key(k):
k = k.replace("base_model.model.", "")
if k == "model.embed_tokens.weight":
return "tok_embeddings.weight"
elif k == "model.norm.weight":
return "norm.weight"
elif k == "lm_head.weight":
return "output.weight"
elif k.startswith("model.layers."):
layer = k.split(".")[2]
if k.endswith(".self_attn.q_proj.weight"):
return f"layers.{layer}.attention.wq.weight"
elif k.endswith(".self_attn.k_proj.weight"):
return f"layers.{layer}.attention.wk.weight"
elif k.endswith(".self_attn.v_proj.weight"):
return f"layers.{layer}.attention.wv.weight"
elif k.endswith(".self_attn.o_proj.weight"):
return f"layers.{layer}.attention.wo.weight"
elif k.endswith(".mlp.gate_proj.weight"):
return f"layers.{layer}.feed_forward.w1.weight"
elif k.endswith(".mlp.down_proj.weight"):
return f"layers.{layer}.feed_forward.w2.weight"
elif k.endswith(".mlp.up_proj.weight"):
return f"layers.{layer}.feed_forward.w3.weight"
elif k.endswith(".input_layernorm.weight"):
return f"layers.{layer}.attention_norm.weight"
elif k.endswith(".post_attention_layernorm.weight"):
return f"layers.{layer}.ffn_norm.weight"
elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
return None
else:
print(layer, k)
raise NotImplementedError
else:
print(k)
raise NotImplementedError
def unpermute(w):
return (
w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim)
)
def save_shards(model_sd, num_shards: int, prefix="", verbose=False):
"""
Convert and save the HF format weights to PTH format weights
"""
with torch.no_grad():
if num_shards == 1:
new_state_dict = {}
for k, v in model_sd.items():
new_k = translate_state_dict_key(k)
if new_k is not None:
if "wq" in new_k or "wk" in new_k:
new_state_dict[new_k] = unpermute(v)
else:
new_state_dict[new_k] = v
os.makedirs(output_dir, exist_ok=True)
print(f"Saving shard 1 of {num_shards} into {output_dir}/{prefix}consolidated.00.pth")
torch.save(new_state_dict, output_dir + f"/{prefix}consolidated.00.pth")
else:
new_state_dicts = [dict() for _ in range(num_shards)]
for k in list(model_sd.keys()):
v = model_sd[k]
new_k = translate_state_dict_key(k)
if new_k is not None:
if new_k == 'tok_embeddings.weight':
assert v.size(1) % num_shards == 0
splits = v.split(v.size(1) // num_shards, dim=1)
elif new_k == 'output.weight':
if v.size(0) % num_shards == 0:
splits = v.split(v.size(0) // num_shards, dim=0)
else:
size_list = [v.size(0) // num_shards] * num_shards
size_list[-1] += v.size(0) % num_shards
splits = v.split(size_list, dim=0) # 13B: size_list == [24976,24977]
elif new_k == 'norm.weight':
splits = [v] * num_shards
elif 'ffn_norm.weight' in new_k:
splits = [v] * num_shards
elif 'attention_norm.weight' in new_k:
splits = [v] * num_shards
elif 'w1.weight' in new_k:
splits = v.split(v.size(0) // num_shards, dim=0)
elif 'w2.weight' in new_k:
splits = v.split(v.size(1) // num_shards, dim=1)
elif 'w3.weight' in new_k:
splits = v.split(v.size(0) // num_shards, dim=0)
elif 'wo.weight' in new_k:
splits = v.split(v.size(1) // num_shards, dim=1)
elif 'wv.weight' in new_k:
splits = v.split(v.size(0) // num_shards, dim=0)
elif "wq.weight" in new_k or "wk.weight" in new_k:
v = unpermute(v)
splits = v.split(v.size(0) // num_shards, dim=0)
else:
print(f"Unexpected key {new_k}")
raise ValueError
if verbose:
print(f"Processing {new_k}")
for sd, split in zip(new_state_dicts, splits):
sd[new_k] = split.clone()
del split
del splits
del model_sd[k], v
gc.collect()
os.makedirs(output_dir, exist_ok=True)
for i, new_state_dict in enumerate(new_state_dicts):
print(f"Saving shard {i + 1} of {num_shards} into {output_dir}/{prefix}consolidated.0{i}.pth")
torch.save(new_state_dict, output_dir + f"/{prefix}consolidated.0{i}.pth")
def merge_shards(output_dir, num_shards: int):
ckpt_filenames = sorted([f for f in os.listdir(output_dir) if re.match('L(\d+)-consolidated.(\d+).pth', f)])
for i in range(num_shards):
shards_filenames = sorted([f for f in ckpt_filenames if re.match(f'L(\d+)-consolidated.0{i}.pth', f)])
print(f"Loading {shards_filenames} ...")
shards_dicts = [torch.load(os.path.join(output_dir, fn)) for fn in shards_filenames]
shards_merged = {}
for d in shards_dicts:
shards_merged |= d
print(f"Saving the merged shard to " + os.path.join(output_dir, f"consolidated.0{i}.pth"))
torch.save(shards_merged, os.path.join(output_dir, f"consolidated.0{i}.pth"))
print("Cleaning up...")
del shards_merged
for d in shards_dicts:
del d
del shards_dicts
gc.collect()
for fn in shards_filenames:
os.remove(os.path.join(output_dir, fn))
if __name__ == '__main__':
args = parser.parse_args()
base_model_path = args.base_model
lora_model_path = args.lora_model
output_dir = args.output_dir
output_type = args.output_type
os.makedirs(output_dir, exist_ok=True)
print(f"=" * 80)
print(f"Base model: {base_model_path}")
print(f"LoRA model: {lora_model_path}")
tokenizers_and_loras = []
print(f"Loading {lora_model_path}")
if not os.path.exists(lora_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
lora_model_path = snapshot_download(repo_id=lora_model_path)
tokenizer = LlamaTokenizer.from_pretrained(lora_model_path, legacy=True)
lora_config = peft.LoraConfig.from_pretrained(lora_model_path)
lora_state_dict = torch.load(os.path.join(lora_model_path, 'adapter_model.bin'), map_location='cpu')
if 'base_model.model.model.embed_tokens.weight' in lora_state_dict:
lora_vocab_size = lora_state_dict['base_model.model.model.embed_tokens.weight'].shape[0]
assert lora_vocab_size == len(tokenizer), \
(
f"The vocab size of the tokenizer {len(tokenizer)} does not match the vocab size of the LoRA weight {lora_vocab_size}!\n")
tokenizers_and_loras.append(
{
"tokenizer": tokenizer,
"state_dict": lora_state_dict,
"config": lora_config,
"scaling": lora_config.lora_alpha / lora_config.r,
"fan_in_fan_out": lora_config.fan_in_fan_out,
})
if not os.path.exists(base_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
base_model_path = snapshot_download(repo_id=base_model_path)
ckpt_filenames = sorted([f for f in os.listdir(base_model_path) if re.match('pytorch_model-(\d+)-of-(\d+).bin', f)])
embedding_size = None
model_size = None
total_size = 0
for index, filename in enumerate(ckpt_filenames):
print(f"Loading ckpt {filename}")
state_dict = torch.load(os.path.join(base_model_path, filename), map_location='cpu')
if index == 0:
embedding_size = state_dict['model.embed_tokens.weight'].shape[1]
model_size = emb_to_model_size[embedding_size]
if output_type == 'pth':
params = params_of_models[model_size]
num_shards = num_shards_of_models[model_size]
n_layers = params["n_layers"]
n_heads = params["n_heads"]
dim = params["dim"]
dims_per_head = dim // n_heads
base = 10000.0
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
print("Merging...")
for k in state_dict:
for tl_idx, t_and_l in enumerate(tokenizers_and_loras):
saved_key = 'base_model.model.' + k
lora_key_A = saved_key.replace('.weight', '.lora_A.weight')
if saved_key in t_and_l['state_dict']:
if args.verbose:
print(f"copying {saved_key} from {tl_idx}-th LoRA weight to {k}")
state_dict[k] = t_and_l['state_dict'][saved_key].half().clone() # do we need half()?
if lora_key_A in t_and_l['state_dict']:
lora_key_B = lora_key_A.replace('lora_A.weight', 'lora_B.weight')
if args.verbose:
print(f"merging {lora_key_A} and lora_B.weight form {tl_idx}-th LoRA weight to {k}")
state_dict[k] += (
transpose(
t_and_l['state_dict'][lora_key_B].float()
@ t_and_l['state_dict'][lora_key_A].float(), t_and_l['fan_in_fan_out']) * t_and_l[
'scaling']
)
weight_size = state_dict[k].numel() * dtype_byte_size(state_dict[k].dtype)
total_size += weight_size
if output_type == 'huggingface':
print(f"Saving ckpt {filename} to {output_dir} in HF format...")
torch.save(state_dict, os.path.join(output_dir, filename))
elif output_type == 'pth':
print(f"Converting to pth format...")
save_shards(model_sd=state_dict, num_shards=num_shards, prefix=f"L{index + 1}-", verbose=args.verbose)
del state_dict
gc.collect()
print(f"Saving tokenizer")
tokenizers_and_loras[-1]['tokenizer'].save_pretrained(output_dir)
if output_type == 'pth':
with open(output_dir + "/params.json", "w") as f:
print(f"Saving params.json into {output_dir}/params.json")
json.dump(params, f)
merge_shards(output_dir, num_shards=num_shards)
if output_type == 'huggingface':
configs = ('config.json', 'generation_config.json', 'pytorch_model.bin.index.json')
for config in configs:
if os.path.exists(os.path.join(base_model_path, config)):
print(f"Saving {config}")
with open(os.path.join(base_model_path, config), 'r') as f:
obj = json.load(f)
if config == 'config.json':
obj['vocab_size'] = len(tokenizers_and_loras[-1]['tokenizer'])
if config == 'pytorch_model.bin.index.json':
obj['metadata']['total_size'] = total_size
with open(os.path.join(output_dir, config), 'w') as f:
json.dump(obj, f, indent=2)
print("Done.")
print(f"Check output dir: {output_dir}")