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# coding=utf-8
# Converts the Baichuan2-7B model in the same format as LLaMA2-7B.
# Usage: python llamafy_baichuan2.py --input_dir input --output_dir output
# Inspired by: https://huggingface.co/fireballoon/baichuan-llama-7b/blob/main/convert_baichuan_to_llama.py
# Converted model: https://huggingface.co/hiyouga/Baichuan2-7B-Base-LLaMAfied
import json
import os
from collections import OrderedDict
from typing import Any, Dict, Optional
import fire
import torch
from safetensors.torch import save_file
from tqdm import tqdm
from transformers.modeling_utils import (
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
shard_checkpoint,
)
CONFIG_NAME = "config.json"
def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetensors: bool):
baichuan2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
for filepath in tqdm(os.listdir(input_dir), desc="Load weights"):
if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".bin"):
shard_weight = torch.load(os.path.join(input_dir, filepath), map_location="cpu")
baichuan2_state_dict.update(shard_weight)
llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
for key, value in tqdm(baichuan2_state_dict.items(), desc="Convert format"):
if "W_pack" in key:
proj_size = value.size(0) // 3
llama2_state_dict[key.replace("W_pack", "q_proj")] = value[:proj_size, :]
llama2_state_dict[key.replace("W_pack", "k_proj")] = value[proj_size : 2 * proj_size, :]
llama2_state_dict[key.replace("W_pack", "v_proj")] = value[2 * proj_size :, :]
elif "lm_head" in key:
llama2_state_dict[key] = torch.nn.functional.normalize(value)
else:
llama2_state_dict[key] = value
weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
if save_safetensors:
save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
else:
torch.save(shard, os.path.join(output_dir, shard_file))
if index is None:
print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
else:
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
json.dump(index, f, indent=2, sort_keys=True)
print("Model weights saved in {}".format(output_dir))
def save_config(input_dir: str, output_dir: str):
with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
llama2_config_dict: Dict[str, Any] = json.load(f)
llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
llama2_config_dict.pop("auto_map", None)
llama2_config_dict.pop("tokenizer_class", None)
llama2_config_dict["model_type"] = "llama"
with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
json.dump(llama2_config_dict, f, indent=2)
print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
def llamafy_baichuan2(
input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
):
try:
os.makedirs(output_dir, exist_ok=False)
except Exception as e:
raise print("Output dir already exists", e)
save_weight(input_dir, output_dir, shard_size, save_safetensors)
save_config(input_dir, output_dir)
if __name__ == "__main__":
fire.Fire(llamafy_baichuan2)
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