LLaMA-Efficient-Tuning/tests/llamafy_baichuan2.py

# coding=utf-8
# Converts the Baichuan2-7B model in the same format as LLaMA2-7B.
# Usage: python llamafy_baichuan2.py --llama2_json llama2.index.json --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 os
import fire
import json
import torch
from collections import OrderedDict


SHARD_A = "pytorch_model-00001-of-00002.bin"
SHARD_B = "pytorch_model-00002-of-00002.bin"


def llamafy_baichuan2(
    llama2_json: str,
    input_dir: str,
    output_dir: str
):
    baichuan2_state_dict = OrderedDict()
    for filepath in os.listdir(input_dir):
        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 = OrderedDict()
    total_size = 0
    for key, value in baichuan2_state_dict.items():
        total_size += 2 * value.numel() # half precision
        if "W_pack" in key:
            llama2_state_dict[key.replace("W_pack", "q_proj")] = value[:4096, :]
            llama2_state_dict[key.replace("W_pack", "k_proj")] = value[4096:2*4096, :]
            llama2_state_dict[key.replace("W_pack", "v_proj")] = value[2*4096:, :]
        elif "lm_head" in key:
            llama2_state_dict[key] = torch.nn.functional.normalize(value)
        else:
            llama2_state_dict[key] = value

    with open(os.path.join(input_dir, llama2_json), "r", encoding="utf-8") as f:
        llama2_index = json.load(f)

    merged_index = OrderedDict()
    merged_index["metadata"] = {"total_size": total_size}
    merged_index["weight_map"] = llama2_index["weight_map"]

    state_dict_a, state_dict_b = OrderedDict(), OrderedDict()
    for key, value in llama2_state_dict.items():
        if merged_index["weight_map"][key] == SHARD_A:
            state_dict_a[key] = value
        else:
            state_dict_b[key] = value

    os.makedirs(output_dir, exist_ok=True)
    torch.save(state_dict_a, os.path.join(output_dir, SHARD_A))
    torch.save(state_dict_b, os.path.join(output_dir, SHARD_B))
    with open(os.path.join(output_dir, "pytorch_model.bin.index.json"), "w", encoding="utf-8") as f:
        json.dump(merged_index, f, indent=2)
    print("Completed!")


if __name__ == "__main__":
    fire.Fire(llamafy_baichuan2)