add llamafy_internlm2

This commit is contained in:
hiyouga 2024-01-18 00:17:41 +08:00
parent 42859f0734
commit f1d7ca77b1
2 changed files with 123 additions and 1 deletions

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@ -64,7 +64,8 @@ class Evaluator:
name=subject, name=subject,
cache_dir=self.model_args.cache_dir, cache_dir=self.model_args.cache_dir,
download_mode=self.eval_args.download_mode, download_mode=self.eval_args.download_mode,
token=self.model_args.hf_hub_token token=self.model_args.hf_hub_token,
trust_remote_code=True
) )
pbar.set_postfix_str(categorys[subject]["name"]) pbar.set_postfix_str(categorys[subject]["name"])
inputs, outputs, labels = [], [], [] inputs, outputs, labels = [], [], []

121
tests/llamafy_internlm2.py Normal file
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@ -0,0 +1,121 @@
# 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)