122 lines
4.8 KiB
Python
122 lines
4.8 KiB
Python
# coding=utf-8
|
|
# Converts the InternLM2 model in the same format as LLaMA2.
|
|
# Usage: python llamafy_internlm2.py --input_dir input --output_dir output --shard_size 10GB
|
|
|
|
import os
|
|
import fire
|
|
import json
|
|
import torch
|
|
from tqdm import tqdm
|
|
from collections import OrderedDict
|
|
from safetensors.torch import save_file
|
|
from transformers.modeling_utils import (
|
|
shard_checkpoint,
|
|
SAFE_WEIGHTS_NAME,
|
|
SAFE_WEIGHTS_INDEX_NAME,
|
|
WEIGHTS_NAME,
|
|
WEIGHTS_INDEX_NAME
|
|
)
|
|
from typing import Any, Dict, Optional
|
|
|
|
|
|
CONFIG_NAME = "config.json"
|
|
|
|
|
|
def save_weight(
|
|
input_dir: str,
|
|
output_dir: str,
|
|
shard_size: str,
|
|
save_safetensors: bool
|
|
):
|
|
with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
|
|
internlm2_config_dict: Dict[str, Any] = json.load(f)
|
|
|
|
internlm2_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")
|
|
internlm2_state_dict.update(shard_weight)
|
|
|
|
llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
|
|
for key, value in tqdm(internlm2_state_dict.items(), desc="Convert format"):
|
|
if "output" in key:
|
|
llama2_state_dict["lm_head"] = value
|
|
elif "tok_embeddings" in key:
|
|
llama2_state_dict["embed_tokens"] = value
|
|
elif "attention_norm" in key:
|
|
llama2_state_dict[key.replace("attention_norm", "input_layernorm")] = value
|
|
elif "wqkv" in key:
|
|
proj_size = value.size(0) // 3
|
|
num_q_heads = internlm2_config_dict["num_attention_heads"]
|
|
num_kv_heads = internlm2_config_dict["num_key_value_heads"]
|
|
q_size = proj_size // (num_q_heads + num_kv_heads) * num_q_heads
|
|
kv_size = proj_size // (num_q_heads + num_kv_heads) * num_kv_heads
|
|
llama2_state_dict[key.replace("attention.wqkv", "self_attn.q_proj")] = value[:q_size, ...]
|
|
llama2_state_dict[key.replace("attention.wqkv", "self_attn.k_proj")] = value[q_size:q_size+kv_size, ...]
|
|
llama2_state_dict[key.replace("attention.wqkv", "self_attn.v_proj")] = value[q_size+kv_size:, ...]
|
|
elif "wo" in key:
|
|
llama2_state_dict[key.replace("attention.wo", "self_attn.o_proj")] = value
|
|
elif "ffn_norm" in key:
|
|
llama2_state_dict[key.replace("ffn_norm", "post_attention_layernorm")] = value
|
|
elif "w1" in key:
|
|
llama2_state_dict[key.replace("feed_forward.w1", "mlp.gate_proj")] = value
|
|
elif "w2" in key:
|
|
llama2_state_dict[key.replace("feed_forward.w2", "mlp.down_proj")] = value
|
|
elif "w3" in key:
|
|
llama2_state_dict[key.replace("feed_forward.w3", "mlp.up_proj")] = 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))
|
|
|
|
|
|
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)
|
|
|
|
llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
|
|
llama2_config_dict.pop("auto_map", None)
|
|
llama2_config_dict.pop("bias", 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_internlm2(
|
|
input_dir: str,
|
|
output_dir: str,
|
|
shard_size: str,
|
|
save_safetensors: Optional[bool] = False
|
|
):
|
|
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_safetensors)
|
|
save_config(input_dir, output_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
fire.Fire(llamafy_internlm2)
|