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Llama3-BioYouri-8B-instruct-chatvector-mergetest

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Llama3-BioYouri-8B-instruct-chatvector-mergetest
File size: 3,090 Bytes 
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#coding:utf-8

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
from safetensors.torch import save_file, load_file

DIR_CACHE = r"E:\llm_baack\cache"
DIR_OFFLOAD = r"E:\llm_baack\offload"
DIR_SAVE = r"E:\llm_baack\safetensors"

for _dir in [DIR_CACHE, DIR_OFFLOAD, DIR_SAVE]:
    if not os.path.exists(_dir):
        os.makedirs(_dir)

MODEL_SUBJ = "aaditya/Llama3-OpenBioLLM-8B"
MODEL_VECTOR = "aixsatoshi/Llama-3-youko-8b-instruct-chatvector"
MODEL_BASE = "NousResearch/Meta-Llama-3-8B"


def download_model(model_name):
    s_name_offload = model_name.replace("/", "-")
    dir_offload = os.path.join(DIR_OFFLOAD, s_name_offload)
    if not os.path.exists(dir_offload):
        os.makedirs(dir_offload)

    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        cache_dir=DIR_CACHE,
        torch_dtype=torch.bfloat16,
        device_map="cpu",
        offload_folder=dir_offload,
        offload_state_dict=True,
        trust_remote_code=True,
    )
    model.eval()
    model.hf_device_map
    
    model_state_dict = model.state_dict().copy()

    for key in model_state_dict.keys():
        model_value = model_state_dict[key].clone().to("cpu")
        print(key, model_value.dtype, model_value.shape, model_value)
        break

    s_name = model_name.replace("/", "-")
    dir_save_safe = os.path.join(DIR_SAVE, f"{s_name}.safetensors")
    save_file(model_state_dict, dir_save_safe)

    # modelを解放
    del model
    del model_state_dict

    return dir_save_safe, s_name


DIR_MODEL_SUBJ, s_name_subj = download_model(MODEL_SUBJ)
DIR_MODEL_VECTOR, s_name_vect = download_model(MODEL_VECTOR)
DIR_MODEL_BASE, s_name_base = download_model(MODEL_BASE)


d_state_subj = load_file(DIR_MODEL_SUBJ, device="cpu")
d_state_vector = load_file(DIR_MODEL_VECTOR, device="cpu")
new_state_dict = d_state_subj

with torch.no_grad():
    for key in d_state_subj.keys():
        print(key)

        new_state_dict[key] = (
            new_state_dict[key].to("cuda") + d_state_vector[key].to("cuda")
        ).to("cpu")

new_state_dict
del d_state_subj, d_state_vector
torch.cuda.empty_cache()
dir_save_subjpvect = os.path.join(DIR_SAVE, f"{s_name_subj}+{s_name_vect}.safetensors")
save_file(new_state_dict, dir_save_subjpvect)

# モデルの読み込み
d_state_subj_subjpvect = load_file(dir_save_subjpvect, device="cpu")
d_state_base = load_file(DIR_MODEL_BASE, device="cpu")

# キー名が同じことを確認
for key_subjpvect, key_base in zip(
    d_state_subj_subjpvect.keys(), d_state_base.keys()
):
    assert key_subjpvect == key_base

new_state_dict = d_state_subj_subjpvect

with torch.no_grad():
    for key in new_state_dict.keys():
        print(key)

        new_state_dict[key] = (
            new_state_dict[key].to("cuda") - d_state_base[key].to("cuda")
        ).to("cpu")

new_state_dict
save_file(new_state_dict, os.path.join(DIR_SAVE, f"{s_name_subj}+{s_name_vect}-{s_name_base}.safetensors"))