forked from p04798526/LLaMA-Factory-Mirror
add llamafy_qwen.py
This commit is contained in:
hiyouga
parent
de5523449e
commit
f9769cff8a
|
|
@ -128,7 +128,7 @@ def load_model_and_tokenizer(
|
|||
else:
|
||||
logger.warning("Current model does not support RoPE scaling.")
|
||||
|
||||
# Set FlashAttention-2
|
||||
# Set FlashAttention-2 and S^2-Attn
|
||||
if model_args.flash_attn:
|
||||
if getattr(config, "model_type", None) == "llama":
|
||||
LlamaModule.LlamaAttention = LlamaPatches.LlamaFlashAttention2
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# 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
|
||||
# Usage: python llamafy_baichuan2.py --input_dir input --output_dir output --shard_size 10GB
|
||||
# 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
|
||||
|
||||
|
|
@ -9,56 +9,77 @@ import fire
|
|||
import json
|
||||
import torch
|
||||
from collections import OrderedDict
|
||||
from transformers.modeling_utils import shard_checkpoint, WEIGHTS_NAME, WEIGHTS_INDEX_NAME
|
||||
from typing import Any, Dict
|
||||
|
||||
|
||||
SHARD_A = "pytorch_model-00001-of-00002.bin"
|
||||
SHARD_B = "pytorch_model-00002-of-00002.bin"
|
||||
CONFIG_NAME = "config.json"
|
||||
|
||||
|
||||
def llamafy_baichuan2(
|
||||
llama2_json: str,
|
||||
def save_weight(
|
||||
input_dir: str,
|
||||
output_dir: str
|
||||
output_dir: str,
|
||||
shard_size: str
|
||||
):
|
||||
baichuan2_state_dict = OrderedDict()
|
||||
baichuan2_state_dict: Dict[str, torch.Tensor] = 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
|
||||
llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
|
||||
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:, :]
|
||||
proj_size = value.size(0) // 3
|
||||
llama2_state_dict[key.replace("W_pack", "q_proj")] = value[:proj_size, :]
|
||||
llama2_state_dict[key.replace("W_pack", "k_proj")] = value[proj_size:2*proj_size, :]
|
||||
llama2_state_dict[key.replace("W_pack", "v_proj")] = value[2*proj_size:, :]
|
||||
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)
|
||||
shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=WEIGHTS_NAME)
|
||||
for shard_file, shard in shards.items():
|
||||
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:
|
||||
with open(os.path.join(output_dir, WEIGHTS_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))
|
||||
|
||||
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
|
||||
def save_config(
|
||||
input_dir: str,
|
||||
output_dir: str
|
||||
):
|
||||
with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
|
||||
llama2_config_dict: Dict[str, Any] = json.load(f)
|
||||
|
||||
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!")
|
||||
llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
|
||||
llama2_config_dict.pop("auto_map", None)
|
||||
llama2_config_dict.pop("tokenizer_class", None)
|
||||
llama2_config_dict["model_type"] = "llama"
|
||||
|
||||
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_baichuan2(
|
||||
input_dir: str,
|
||||
output_dir: str,
|
||||
shard_size: str
|
||||
):
|
||||
try:
|
||||
os.makedirs(output_dir, exist_ok=False)
|
||||
except Exception as e:
|
||||
raise print("Output dir already exists", e)
|
||||
|
||||
save_weight(input_dir, output_dir, shard_size)
|
||||
save_config(input_dir, output_dir)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -0,0 +1,135 @@
|
|||
# coding=utf-8
|
||||
# Converts the Qwen models in the same format as LLaMA2.
|
||||
# Usage: python llamafy_qwen.py --input_dir input --output_dir output --shard_size 10GB
|
||||
|
||||
import os
|
||||
import fire
|
||||
import json
|
||||
import torch
|
||||
from collections import OrderedDict
|
||||
from safetensors import safe_open
|
||||
from transformers.modeling_utils import shard_checkpoint, WEIGHTS_NAME, WEIGHTS_INDEX_NAME
|
||||
from transformers.utils import check_min_version
|
||||
from typing import Any, Dict
|
||||
|
||||
try:
|
||||
check_min_version("4.34.0")
|
||||
except:
|
||||
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
|
||||
) -> str:
|
||||
qwen_state_dict: Dict[str, torch.Tensor] = OrderedDict()
|
||||
for filepath in os.listdir(input_dir):
|
||||
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 qwen_state_dict.items():
|
||||
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))
|
||||
|
||||
shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=WEIGHTS_NAME)
|
||||
for shard_file, shard in shards.items():
|
||||
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:
|
||||
with open(os.path.join(output_dir, WEIGHTS_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
|
||||
):
|
||||
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_config(input_dir, output_dir, torch_dtype)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(llamafy_qwen)
|
||||
Loading…
Reference in New Issue