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import argparse
import json
import os
import shutil
from pathlib import Path
import torch
import transformers
from human_eval.evaluation import evaluate_functional_correctness
from tqdm import tqdm
from transformers import AutoTokenizer
from utils.utils import extract_generation_code, languge_settings
from vllm import LLM, SamplingParams
data_abs_dir = Path(__file__).parent / "data"
def build_deepseekcoder_instruction(languge: str, question: str):
return """
Please continue to complete the function. You are not allowed to modify the given code and do the completion only. Please return all completed function in a codeblock. Here is the given code to do completion:
```{}
{}
```
""".strip().format(
languge.lower(), question.strip()
)
def create_dir(output_dir):
if os.path.exists(output_dir):
if not os.access(output_dir, os.W_OK):
shutil.rmtree(output_dir)
os.makedirs(output_dir)
os.chmod(output_dir, 0o777)
print("not write permission, makedir:", output_dir)
else:
print(f"{output_dir} exists!")
else:
os.makedirs(output_dir)
os.chmod(output_dir, 0o777)
print("makedir:", output_dir)
def get_client_res(messages, example, output_key, open_ai_key=False):
try:
if open_ai_key:
from openai import AzureOpenAI, OpenAI
try:
api_key = os.environ["OPENAI_API_KEY"]
except KeyError:
print("环境变量 OPENAI_API_KEY 未设置")
api_key = "default_value"
client = AzureOpenAI(
api_key=api_key,
api_version="2024-07-01-preview",
azure_endpoint="https://zju-tablegpt.openai.azure.com/",
)
chat_response = client.chat.completions.create(
model="gpt-4o",
# model="gpt-4o-mini",
messages=messages,
top_p=0.95,
temperature=0,
max_tokens=1024,
timeout=40,
)
else:
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8080/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
chat_response = client.chat.completions.create(
model="qwen2-7b-sft",
messages=messages,
top_p=0.3,
temperature=0.1,
max_tokens=1024,
)
example[output_key] = chat_response.choices[0].message.content
except Exception as e:
print(f"An unexpected error occurred: {e}")
example[output_key] = None
example["input"] = messages
return example
def generate_main(args):
model_name_or_path = args.model_path
lang = args.language
temp_dir = args.temp_dir
create_dir(temp_dir)
# os.makedirs(temp_dir, exist_ok=True)
problem_file = os.path.join(data_abs_dir, f"humaneval-{lang}.jsonl")
if not args.api:
print("model", model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
print(
"load tokenizer {} from {} over.".format(
tokenizer.__class__, model_name_or_path
)
)
llm_args = {
"model": model_name_or_path,
"gpu_memory_utilization": 0.95,
"trust_remote_code": True,
"tensor_parallel_size": args.gpus_num,
"dtype": "half",
"max_model_len": 8192,
"enforce_eager": True,
}
llm = LLM(**llm_args)
sampling_params = SamplingParams(
temperature=0,
max_tokens=1024,
top_p=0.95,
stop_token_ids=[tokenizer.eos_token_id],
)
examples = [json.loads(x) for x in open(problem_file) if x.strip()]
print("Read {} examples for evaluation over.".format(len(examples)))
messages_list = []
for example in tqdm(examples, desc="Generating"):
prompt = build_deepseekcoder_instruction(
languge_settings[lang]["full_name"], example["prompt"]
)
message = [{"role": "user", "content": prompt}]
if args.api:
messages_list.append(message)
else:
messages_list.append(
tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
)
if args.api:
from joblib import Parallel, delayed
examples_ = Parallel(n_jobs=24)(
delayed(get_client_res)(inp, examples[i], "output",open_ai_key=True)
for i, inp in enumerate(tqdm(messages_list))
)
# 请求错误的重新请求
examples = []
for example in examples_:
if example["output"] == None:
example = get_client_res(
example["input"], example, "output", open_ai_key=True
)
del example["input"]
examples.append(example)
generated_examples = []
for example in examples:
example = extract_generation_code(example, lang_code=lang)
generated_examples.append(example)
else:
outputs = llm.generate(messages_list, sampling_params=sampling_params)
generated_examples = []
for i, output in enumerate(tqdm(outputs)):
output = output.outputs[0].text
example = examples[i]
example["output"] = output
example = extract_generation_code(example, lang_code=lang)
generated_examples.append(example)
print("Generate all over!!!")
# os.makedirs(args.save_dir, exist_ok=True)
create_dir(args.save_dir)
saved_path = os.path.join(args.save_dir, "results_humaneval.json")
with open(saved_path, "w", encoding="utf-8") as fw:
for ex in generated_examples:
fw.write(json.dumps(ex) + "\n")
print(
"Save {} processed examples into {} over!".format(
len(generated_examples), saved_path
)
)
result = evaluate_functional_correctness(
input_file=saved_path,
tmp_dir=temp_dir,
n_workers=8,
timeout=3.0,
problem_file=problem_file,
language=lang,
out_path=saved_path,
)
print(lang, result, model_name_or_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
help="model name or path",
default="/data4/sft_output/qwen2-instruct-0709/checkpoint-1400",
)
parser.add_argument(
"--gpus_num", type=int, default=1, help="the number of GPUs you want to use."
)
parser.add_argument(
"--save_dir",
type=str,
help="output path of your generation",
default="output",
)
parser.add_argument("--api", action="store_true", help="infer api type")
parser.add_argument("--language", type=str, help="langauge", default="python")
parser.add_argument(
"--temp_dir", type=str, help="temp dir for evaluation", default="output/tmp"
)
parser.add_argument("--seed", type=int, help="seed", default=42)
args = parser.parse_args()
os.environ["TOKENIZERS_PARALLELISM"] = "false"
transformers.set_seed(args.seed)
generate_main(args)
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