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eval.py
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eval.py
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import fire
from typing import Optional
import jsonlines
import os
from tqdm import tqdm
import numpy as np
from glob import glob
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
AutoModelForSequenceClassification,
DataCollatorForLanguageModeling,
)
from prompt_templates import PROMPT_TEMPLATES
from question_datasets import DATASETS
from copy import deepcopy
import torch
DTYPES = {
"float16": torch.float16,
"bfloat16": torch.float16,
"float32": torch.float32,
}
def build_model_types(dtype: str, device: str):
if dtype == "int8":
return {"load_in_8bit": True, "device_map": "auto"}
elif dtype == "int4":
return {"load_in_4bit": True, "device_map": "auto"}
else:
return {"torch_dtype": DTYPES[dtype], "device_map": device}
def clean_text(text):
text = text.replace("<[!newline]>", "\n") # KT/midm
return text
@torch.no_grad()
def main(
name: str,
reward_model_id: str,
filename: Optional[str] = None,
device: str = "auto",
batch_size: int = 1,
dtype: str = "float16",
reward_prompt_template: Optional[str] = None,
model_revision: Optional[str] = None,
peft_model_id: Optional[str] = None,
peft_model_revision: Optional[str] = None,
):
if device == "auto":
device = "cuda:0" if torch.cuda.is_available() else "cpu"
else:
print(device)
tokenizer = AutoTokenizer.from_pretrained(reward_model_id, revision=model_revision)
model = AutoModelForSequenceClassification.from_pretrained(
reward_model_id, revision=model_revision, **build_model_types(dtype, device)
).eval()
collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
if peft_model_id:
from peft import PeftModel
model = PeftModel.from_pretrained(
model, peft_model_id, revision=peft_model_revision
)
if getattr(tokenizer, "chat_template"):
tokenizer.chat_template = PROMPT_TEMPLATES[
reward_prompt_template or reward_model_id
]
model_args = dict(
model_id=reward_model_id,
model_revision=model_revision,
peft_model_id=peft_model_id,
peft_model_revision=peft_model_revision,
)
device = model.device
files = list(glob(filename, recursive=True))
print("evaluation targets:", files)
def eval_rewards(filename):
dirname = os.path.dirname(filename)
basename = os.path.basename(filename)
reward_output_filename = os.path.join(
dirname, os.path.splitext(basename)[0] + f"_{name}.json"
)
with jsonlines.open(filename) as fin:
dataset = list(fin)
dataset_len = len(dataset)
if len(dataset) == 0 or "score" in dataset[0]:
print(f"pass {filename}")
return
all_scores = []
if os.path.exists(reward_output_filename):
with jsonlines.open(reward_output_filename) as fin:
evaluated_items = list(fin)
skip_lines = len(evaluated_items)
all_scores = [x["score"] for x in evaluated_items]
if skip_lines >= dataset_len:
print(f"pass {filename}")
return
print(f"파일이 이미 존재하며 {skip_lines}개가 이미 평가되어있습니다.")
else:
skip_lines = 0
all_scores = []
with jsonlines.open(reward_output_filename, "a") as fout:
progress = tqdm(total=skip_lines // batch_size, desc=filename)
for i in range(0, len(dataset), batch_size):
if i < skip_lines:
continue
# 우선 아이템들을 인코딩합니다.
end_i = min(dataset_len, i + batch_size)
input_ids = []
for j in range(i, end_i):
item = dataset[j]
conv = item["conversations"] + [
{"role": "assistant", "content": clean_text(item["response"])}
]
input_id = tokenizer.apply_chat_template(conv)
if input_id[-1] != tokenizer.eos_token_id:
input_id.append(tokenizer.eos_token_id)
input_ids.append(input_id)
# 점수 측정
inputs = collator(input_ids)
inputs = {k: v.to(device) for k, v in inputs.items() if k != "labels"}
scores = model(**inputs).logits.cpu()[:, 0].tolist()
# 결과 저장
for j in range(i, end_i):
item = dataset[j]
item["score"] = scores[j - i]
item["reward_model_args"] = model_args
fout.write(item)
all_scores.extend(scores)
progress.update(batch_size)
print(filename)
print("average score:", np.mean(all_scores))
print("average std:", np.std(all_scores))
for file in files:
eval_rewards(file)
if __name__ == "__main__":
fire.Fire(main)