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# coding=utf-8
# Converts the 2nd version of the Qwen models in the same format as LLaMA2.
# Usage: python convert_qwen2_to_llama.py --input_dir magnum-72b-v1 --output_dir magnum-72b-v1-llamaify --save_safetensors --continue_conversion
# Original script: https://github.com/Minami-su/character_AI_open/blob/main/llamafy_qwen_v2.py
import json
import os
from collections import OrderedDict
from typing import Any, Dict, Optional
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 load_existing_shards(
output_dir: str, save_safetensors: bool
) -> Dict[str, torch.Tensor]:
existing_state_dict = OrderedDict()
weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
if os.path.exists(os.path.join(output_dir, index_name)):
with open(os.path.join(output_dir, index_name), "r", encoding="utf-8") as f:
index = json.load(f)
for shard_file in tqdm(
index["weight_map"].values(), desc="Loading existing shards"
):
if os.path.exists(os.path.join(output_dir, shard_file)):
if save_safetensors:
with safe_open(
os.path.join(output_dir, shard_file),
framework="pt",
device="cpu",
) as f:
for key in f.keys():
existing_state_dict[key] = f.get_tensor(key)
else:
shard = torch.load(
os.path.join(output_dir, shard_file), map_location="cpu"
)
existing_state_dict.update(shard)
return existing_state_dict
def save_weight(
input_dir: str,
output_dir: str,
shard_size: str,
save_safetensors: bool,
continue_conversion: 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()
if continue_conversion:
llama2_state_dict = load_existing_shards(output_dir, save_safetensors)
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 "self_attn.o_proj" in key:
llama2_state_dict[key] = value
bias_key = key.replace(".weight", ".bias")
if bias_key not in llama2_state_dict:
llama2_state_dict[bias_key] = torch.zeros_like(value[:, 0]).squeeze()
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(f"Model weights saved in {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(f"Model weights saved in {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["attention_bias"] = True
llama2_config_dict["attention_dropout"] = qwen_config_dict["attention_dropout"]
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"]
llama2_config_dict["max_position_embeddings"] = 32767 # Qwen2-72B-Instruct
llama2_config_dict["max_window_layers"] = qwen_config_dict["max_window_layers"]
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["num_key_value_heads"]
llama2_config_dict["pretraining_tp"] = 1
llama2_config_dict["rms_norm_eps"] = qwen_config_dict["rms_norm_eps"]
llama2_config_dict["rope_theta"] = qwen_config_dict["rope_theta"]
llama2_config_dict["rope_scaling"] = None
llama2_config_dict["sliding_window"] = qwen_config_dict["sliding_window"]
llama2_config_dict["tie_word_embeddings"] = qwen_config_dict["tie_word_embeddings"]
llama2_config_dict["torch_dtype"] = torch_dtype
llama2_config_dict["transformers_version"] = "4.37.0"
llama2_config_dict["use_cache"] = True
llama2_config_dict["use_sliding_window"] = qwen_config_dict["use_sliding_window"]
llama2_config_dict["vocab_size"] = qwen_config_dict["vocab_size"]
with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
json.dump(llama2_config_dict, f, indent=2)
print(f"Model config saved in {os.path.join(output_dir, CONFIG_NAME)}")
def llamafy_qwen_v2(
input_dir: str,
output_dir: str,
shard_size: Optional[str] = "4GB",
save_safetensors: Optional[bool] = False,
continue_conversion: Optional[bool] = False,
):
if not continue_conversion:
try:
os.makedirs(output_dir, exist_ok=False)
except Exception as e:
raise ValueError(
"Output dir already exists. Use --continue_conversion to resume."
) from e
else:
os.makedirs(output_dir, exist_ok=True)
torch_dtype = save_weight(
input_dir, output_dir, shard_size, save_safetensors, continue_conversion
)
save_config(input_dir, output_dir, torch_dtype)
if __name__ == "__main__":
fire.Fire(llamafy_qwen_v2)
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