130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
import argparse
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import json
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import os
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import torch
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from pathlib import Path
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from tqdm import tqdm
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data_abs_dir = Path(__file__).parent / "data"
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from utils.utils import extract_generation_code, languge_settings
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from human_eval.evaluation import evaluate_functional_correctness
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def build_deepseekcoder_instruction(languge: str, question: str):
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return '''
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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:
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```{}
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{}
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```
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'''.strip().format(languge.lower(), question.strip())
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def generate_one(example, lang, tokenizer, model):
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prompt = build_deepseekcoder_instruction(languge_settings[lang]['full_name'], example['prompt'])
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inputs = tokenizer.apply_chat_template(
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[{'role': 'user', 'content': prompt }],
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return_tensors="pt",
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add_generation_prompt=True
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).to(model.device)
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stop_id = tokenizer.convert_tokens_to_ids("<|EOT|>")
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assert isinstance(stop_id, int), "Invalid tokenizer, EOT id not found"
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outputs = model.generate(
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inputs,
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max_new_tokens=1024,
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do_sample=False,
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# top_p=0.95,
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# temperature=temperature,
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pad_token_id=stop_id,
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eos_token_id=stop_id
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)
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output = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
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example['output'] = output
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return extract_generation_code(example, lang_code=lang)
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def generate_main(args):
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model_name_or_path = args.model
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lang = args.language
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saved_path = args.output_path
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temp_dir = args.temp_dir
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os.makedirs(temp_dir, exist_ok=True)
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problem_file = os.path.join(data_abs_dir, f"humaneval-{lang}.jsonl")
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print("model", model_name_or_path)
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
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print("load tokenizer {} from {} over.".format(tokenizer.__class__, model_name_or_path))
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model = AutoModelForCausalLM.from_pretrained(
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model_name_or_path,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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#use_flash_attention_2=True
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)
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model.eval()
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examples = [json.loads(x) for x in open(problem_file) if x.strip()]
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print("Read {} examples for evaluation over.".format(len(examples)))
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generated_examples = []
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for ex in tqdm(examples, desc='Generating'):
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gen_example = generate_one(ex, args.language, tokenizer, model)
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generated_examples.append(gen_example)
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print("Generate all over!!!")
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with open(saved_path, 'w', encoding='utf-8') as fw:
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for ex in generated_examples:
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fw.write(json.dumps(ex) + '\n')
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print("Save {} processed examples into {} over!".format(len(generated_examples), saved_path))
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result = evaluate_functional_correctness(
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input_file=saved_path,
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tmp_dir=temp_dir,
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n_workers=8,
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timeout=3.0,
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problem_file=problem_file,
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language=lang
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)
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print(lang, result, model_name_or_path)
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pass
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def evaluation_only(args):
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lang = args.language
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temp_dir = args.temp_dir
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assert os.path.exists(args.output_path), "Not fond output file: {}".format(args.output_path)
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os.makedirs(temp_dir, exist_ok=True)
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output_name = os.path.basename(args.output_path)
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output_examples = [json.loads(x) for x in open(args.output_path) if x.strip()]
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processed_examples = [extract_generation_code(ex, lang) for ex in tqdm(output_examples, "Processing")]
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processed_path = os.path.join(temp_dir, output_name)
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with open(processed_path, 'w', encoding='utf-8') as fw:
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for ex in processed_examples:
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fw.write(json.dumps(ex) + '\n')
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print("Save {} processed examples into {} over!".format(len(processed_examples), processed_path))
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problem_file = os.path.join(data_abs_dir, f"humaneval-{lang}.jsonl")
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from human_eval.evaluation import evaluate_functional_correctness
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result = evaluate_functional_correctness(
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input_file=processed_path,
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tmp_dir=temp_dir,
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n_workers=8,
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timeout=3.0,
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problem_file=problem_file,
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language=lang
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)
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print(lang, result)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, help="model name or path")
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parser.add_argument('--output_path', type=str, help="output path of your generation")
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parser.add_argument('--language', type=str, help="langauge")
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parser.add_argument('--temp_dir', type=str, help="temp dir for evaluation", default="tmp")
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args = parser.parse_args()
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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generate_main(args)
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pass
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