110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
# coding=utf-8
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# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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from collections import OrderedDict
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from typing import Any, Dict
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import fire
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import torch
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from safetensors.torch import save_file
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from tqdm import tqdm
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from transformers.modeling_utils import (
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SAFE_WEIGHTS_INDEX_NAME,
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SAFE_WEIGHTS_NAME,
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WEIGHTS_INDEX_NAME,
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WEIGHTS_NAME,
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shard_checkpoint,
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)
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CONFIG_NAME = "config.json"
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def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetensors: bool):
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baichuan2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
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for filepath in tqdm(os.listdir(input_dir), desc="Load weights"):
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if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".bin"):
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shard_weight = torch.load(os.path.join(input_dir, filepath), map_location="cpu")
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baichuan2_state_dict.update(shard_weight)
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llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
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for key, value in tqdm(baichuan2_state_dict.items(), desc="Convert format"):
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if "W_pack" in key:
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proj_size = value.size(0) // 3
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llama2_state_dict[key.replace("W_pack", "q_proj")] = value[:proj_size, :]
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llama2_state_dict[key.replace("W_pack", "k_proj")] = value[proj_size : 2 * proj_size, :]
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llama2_state_dict[key.replace("W_pack", "v_proj")] = value[2 * proj_size :, :]
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elif "lm_head" in key:
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llama2_state_dict[key] = torch.nn.functional.normalize(value)
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else:
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llama2_state_dict[key] = value
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weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
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shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
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for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
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if save_safetensors:
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save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
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else:
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torch.save(shard, os.path.join(output_dir, shard_file))
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if index is None:
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print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
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else:
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index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
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with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
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json.dump(index, f, indent=2, sort_keys=True)
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print("Model weights saved in {}".format(output_dir))
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def save_config(input_dir: str, output_dir: str):
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with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
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llama2_config_dict: Dict[str, Any] = json.load(f)
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llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
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llama2_config_dict.pop("auto_map", None)
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llama2_config_dict.pop("tokenizer_class", None)
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llama2_config_dict["model_type"] = "llama"
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with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
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json.dump(llama2_config_dict, f, indent=2)
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print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
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def llamafy_baichuan2(
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input_dir: str,
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output_dir: str,
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shard_size: str = "2GB",
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save_safetensors: bool = True,
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):
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r"""
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Converts the Baichuan2-7B model in the same format as LLaMA2-7B.
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Usage: python llamafy_baichuan2.py --input_dir input --output_dir output
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Converted model: https://huggingface.co/hiyouga/Baichuan2-7B-Base-LLaMAfied
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"""
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try:
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os.makedirs(output_dir, exist_ok=False)
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except Exception as e:
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raise print("Output dir already exists", e)
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save_weight(input_dir, output_dir, shard_size, save_safetensors)
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save_config(input_dir, output_dir)
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if __name__ == "__main__":
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fire.Fire(llamafy_baichuan2)
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