# coding=utf-8 # Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os from collections import OrderedDict from typing import Any, Dict import fire import torch from safetensors import safe_open 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, ) from transformers.utils import check_min_version try: check_min_version("4.34.0") except Exception: raise ValueError("Please upgrade `transformers` to 4.34.0") CONFIG_NAME = "config.json" def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetensors: bool) -> str: qwen_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(".safetensors"): with safe_open(os.path.join(input_dir, filepath), framework="pt", device="cpu") as f: for key in f.keys(): qwen_state_dict[key] = f.get_tensor(key) llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict() torch_dtype = None for key, value in tqdm(qwen_state_dict.items(), desc="Convert format"): if torch_dtype is None: torch_dtype = value.dtype if "wte" in key: llama2_state_dict["model.embed_tokens.weight"] = value elif "ln_f" in key: llama2_state_dict["model.norm.weight"] = value else: key = key.replace("transformer.h", "model.layers") if "attn.c_attn" in key: proj_size = value.size(0) // 3 llama2_state_dict[key.replace("attn.c_attn", "self_attn.q_proj")] = value[:proj_size, ...] llama2_state_dict[key.replace("attn.c_attn", "self_attn.k_proj")] = value[ proj_size : 2 * proj_size, ... ] llama2_state_dict[key.replace("attn.c_attn", "self_attn.v_proj")] = value[2 * proj_size :, ...] elif "attn.c_proj" in key: llama2_state_dict[key.replace("attn.c_proj", "self_attn.o_proj")] = value llama2_state_dict[key.replace("attn.c_proj.weight", "self_attn.o_proj.bias")] = torch.zeros_like( value[:, 0] ).squeeze() elif "ln_1" in key: llama2_state_dict[key.replace("ln_1", "input_layernorm")] = value elif "ln_2" in key: llama2_state_dict[key.replace("ln_2", "post_attention_layernorm")] = value elif "mlp.w1" in key: llama2_state_dict[key.replace("mlp.w1", "mlp.up_proj")] = value elif "mlp.w2" in key: llama2_state_dict[key.replace("mlp.w2", "mlp.gate_proj")] = value elif "mlp.c_proj" in key: llama2_state_dict[key.replace("mlp.c_proj", "mlp.down_proj")] = value elif "lm_head" in key: llama2_state_dict[key] = value else: raise KeyError("Unable to process key {}".format(key)) 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)) return str(torch_dtype).replace("torch.", "") def save_config(input_dir: str, output_dir: str, torch_dtype: str): with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f: qwen_config_dict: Dict[str, Any] = json.load(f) llama2_config_dict: Dict[str, Any] = OrderedDict() llama2_config_dict["architectures"] = ["LlamaForCausalLM"] llama2_config_dict["hidden_act"] = "silu" llama2_config_dict["hidden_size"] = qwen_config_dict["hidden_size"] llama2_config_dict["initializer_range"] = qwen_config_dict["initializer_range"] llama2_config_dict["intermediate_size"] = qwen_config_dict["intermediate_size"] // 2 llama2_config_dict["max_position_embeddings"] = qwen_config_dict["max_position_embeddings"] llama2_config_dict["model_type"] = "llama" llama2_config_dict["num_attention_heads"] = qwen_config_dict["num_attention_heads"] llama2_config_dict["num_hidden_layers"] = qwen_config_dict["num_hidden_layers"] llama2_config_dict["num_key_value_heads"] = qwen_config_dict["hidden_size"] // qwen_config_dict["kv_channels"] llama2_config_dict["pretraining_tp"] = 1 llama2_config_dict["rms_norm_eps"] = qwen_config_dict["layer_norm_epsilon"] llama2_config_dict["rope_scaling"] = None llama2_config_dict["tie_word_embeddings"] = qwen_config_dict["tie_word_embeddings"] llama2_config_dict["torch_dtype"] = torch_dtype llama2_config_dict["transformers_version"] = "4.34.0" llama2_config_dict["use_cache"] = True llama2_config_dict["vocab_size"] = qwen_config_dict["vocab_size"] llama2_config_dict["attention_bias"] = True 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_qwen( input_dir: str, output_dir: str, shard_size: str = "2GB", save_safetensors: bool = False, ): r""" Converts the Qwen models in the same format as LLaMA2. Usage: python llamafy_qwen.py --input_dir input --output_dir output Converted model: https://huggingface.co/hiyouga/Qwen-14B-Chat-LLaMAfied """ try: os.makedirs(output_dir, exist_ok=False) except Exception as e: raise print("Output dir already exists", e) torch_dtype = save_weight(input_dir, output_dir, shard_size, save_safetensors) save_config(input_dir, output_dir, torch_dtype) if __name__ == "__main__": fire.Fire(llamafy_qwen)