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