| import argparse
|
| import json
|
| import os
|
| import torch
|
| import re
|
| from pathlib import Path
|
| from tqdm import tqdm
|
|
|
| data_abs_dir = Path(__file__).parent / "data"
|
|
|
| from transformers import AutoTokenizer, AutoModelForCausalLM
|
| from human_eval.evaluation import evaluate_functional_correctness
|
|
|
| def read_test_examples(data_path: str):
|
| def format_test_example(q, tests, code: str=None):
|
| prompt = ">>> Problem:\n{}\n>>> Test Cases:\n{}\n".format(q.strip(), "\n".join(tests))
|
| if code:
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| code = code.replace("\r", "").replace("\t", " ")
|
| prompt += "\n>>> Code:\n```python\n{}\n```".format(code)
|
| return prompt
|
|
|
| examples = [json.loads(x) for x in open(data_path)]
|
| print("Read all {} examples from {} over!".format(len(examples), data_path))
|
|
|
|
|
| examples_str = []
|
| for i in range(1, 4):
|
| ex = examples[i]
|
| q, test, code = ex['text'], ex['test_list'], ex['code']
|
| ex_prompt = format_test_example(q, test, code)
|
| example_prompt = '- Example {}:\n{}'.format(i, ex_prompt)
|
| examples_str += [example_prompt]
|
|
|
| for i in range(10, 510):
|
| ex = examples[i]
|
| q, test, code = ex['text'], ex['test_list'], ex['code']
|
|
|
| prompt = format_test_example(q, test, code=None)
|
|
|
| prompt_with_shots = '''
|
| Please refer the given examples and generate a python function for my problem.
|
| Examples are listed as follows:
|
| {}
|
|
|
| Here is my problem:
|
| {}
|
| '''.strip().format('\n\n'.join(examples_str), prompt)
|
| yield {
|
| 'task_id': ex['task_id'],
|
| 'prompt': prompt_with_shots
|
| }
|
|
|
| def convert_for_evaluation(example):
|
| gpt_completion = example['gpt_completion']
|
| generation = gpt_completion
|
| try:
|
| code_block: str = re.findall(f'```python\n(.*?)```', gpt_completion, re.DOTALL | re.IGNORECASE)[0]
|
| generation = code_block
|
| except Exception as ex:
|
| print("Failed to extract codeblock:\n{}".format(gpt_completion))
|
|
|
| example['generation'] = generation
|
| return example
|
|
|
| def generate_one(example, tokenizer, model):
|
| prompt = 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=512,
|
| 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['gpt_completion'] = output
|
| return convert_for_evaluation(example)
|
|
|
| def generate_main(args):
|
| model_name_or_path = args.model
|
| 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"mbpp.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 = list(read_test_examples(problem_file))
|
| print("Read {} examples for evaluation over.".format(len(examples)))
|
|
|
| generated_examples = []
|
| for ex in tqdm(examples, desc='Generating'):
|
| gen_example = generate_one(ex, tokenizer, model)
|
| generated_examples.append(gen_example)
|
| print("Generate {}/{} over...".format(len(generated_examples), len(examples)))
|
|
|
| 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,
|
| problem_file=os.path.join(data_abs_dir, f"mbpp_test.jsonl"),
|
| language='python',
|
| is_mbpp=True
|
| )
|
| print(result, model_name_or_path)
|
| pass
|
|
|
| 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('--temp_dir', type=str, help="temp dir for evaluation", default="tmp")
|
| args = parser.parse_args()
|
|
|
| os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
| generate_main(args)
|
| pass |
