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https://github.com/deepseek-ai/DeepSeek-Coder
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Dejian
204
Evaluation/MBPP/mbpp.py
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204
Evaluation/MBPP/mbpp.py
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import time
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import string
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import multiprocessing
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import os
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import numpy as np
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import json
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import re
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import torch
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import datetime
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import subprocess
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import torch.distributed as dist
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from attrdict import AttrDict
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from tqdm import tqdm
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from human_eval.evaluation import evaluate_functional_correctness
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from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList
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from utils.dataset import MBPPDataset
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from utils.utils import cleanup_code
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class KeywordsStoppingCriteria(StoppingCriteria):
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def __init__(self, keywords_str, tokenizer):
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StoppingCriteria.__init__(self)
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self.current_context = []
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self.tokenizer = tokenizer
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self.keywords_str = keywords_str
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
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self.current_context.append(input_ids[0][-1].item())
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current_context = self.tokenizer.decode(self.current_context)
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for word in self.keywords_str:
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if word in current_context:
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return True
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return False
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class MBPP:
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"""
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MBPP evaluation class.
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"""
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def __init__(self, data_root, max_seq_len=2048,
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language="python", max_gen_len=200, batch_size=512,
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log_dir=None, temperature=0, issft=False, top_p=0.95,
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model_name="", inference_increment=True,
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tokenizer_cfg=None, n_sample=40, k_sample=1):
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self.data_root = data_root
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self.max_seq_len = max_seq_len
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self.max_gen_len = max_gen_len
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self.batch_size = batch_size
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self.k = k_sample
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self.n_sample = n_sample
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self.language = language
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self.log_dir = log_dir
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self.sft = issft
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self.temperature = temperature
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self.top_p = top_p
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self.model_name = tokenizer_cfg["model_path"].replace("/", "_")
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self.inference_increment = inference_increment
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os.makedirs(self.log_dir, exist_ok=True)
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tokenizer_cls = tokenizer_cfg.pop('cls')
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try:
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self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_cfg.pop("model_path"), trust_remote_code=True)
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except Exception as e:
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print(e)
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assert False
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@torch.no_grad()
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def eval_model(self, gpt, accelerator):
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"""
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Evaluate the model.
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"""
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assert self.log_dir is not None, "log_dir should not be None when evaluating MBPP"
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dataset = MBPPDataset(self.data_root, samplenum=self.n_sample)
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nprompt = len(dataset) // self.n_sample
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dp_rank = accelerator.process_index
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dp_size = accelerator.num_processes
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if self.k > 1:
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assert self.n_sample >= 80, "MBPP PASS@80 needs n_sample >= 80"
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gpt.eval()
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prompt_indices_split = np.array_split(range(nprompt), dp_size)
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prompt_indices = prompt_indices_split[dp_rank]
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indices = []
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for x in prompt_indices:
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for j in range(self.n_sample):
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indices.append(x * self.n_sample + j)
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all_num = len(indices)
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processed_num = 0
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log_file = os.path.join(self.log_dir,
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f'{self.model_name}_rank{dp_rank}_bs{self.batch_size}_shot_log_{self.language}.json')
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tmpfile = open(log_file, "w")
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totoalnum = 0
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start_time = time.time()
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for idx in tqdm(range(0, len(indices), self.batch_size)):
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prompt_list = []
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prompt_lens = []
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answers_list = []
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test_list = []
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taskid = []
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tokenized_prompt_lens = []
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for j in indices[idx:idx + self.batch_size]:
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data = dataset[j]
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prompt = dataset.prompt
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prompt1 = data["prompt"]
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tests = "\n".join(data["test"])
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test_list.append(data["test"])
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prompt_curr = f"You are an expert Python programmer, and here is your task: {prompt1} Your code should pass these tests:\n\n{tests}\n[BEGIN]"
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fprompt = ""
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for i in range(len(prompt) - 1, -1, -1):
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finalprompt = prompt[i] + prompt_curr
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curr_seq_len = len(self.tokenizer.encode(finalprompt))
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if curr_seq_len >= self.max_seq_len - self.max_gen_len:
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continue
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else:
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fprompt = finalprompt
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break
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if fprompt == "":
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fprompt = prompt_curr
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encodelist = self.tokenizer.encode(fprompt)
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while True:
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try:
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fprompt = self.tokenizer.decode(encodelist[:self.max_seq_len - self.max_gen_len])
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break
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except:
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encodelist.pop(-1)
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prompt_list.append(fprompt)
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answers_list.append(data['code'])
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prompt_lens.append(len(fprompt))
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taskid.append(data["task_id"])
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tokenized_prompt = self.tokenizer(prompt_list, padding=True, return_tensors="pt")
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inputids = tokenized_prompt["input_ids"].to(gpt.device)[:, -self.max_seq_len:]
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attenion_mask = tokenized_prompt["attention_mask"].to(gpt.device)[:, -self.max_seq_len:]
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if self.temperature == 0:
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stop_criteria = KeywordsStoppingCriteria(["[DONE]"], self.tokenizer)
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decoded = gpt.generate(
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input_ids=inputids,
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attention_mask=attenion_mask,
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max_new_tokens=self.max_gen_len,
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top_p=self.top_p,
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eos_token_id=self.tokenizer.eos_token_id,
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do_sample=False,
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stopping_criteria=StoppingCriteriaList([stop_criteria]),
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pad_token_id=self.tokenizer.eos_token_id,
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)
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else:
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decoded = gpt.generate(
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tokenized_prompt_lens,
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max_new_tokens=self.max_gen_len,
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temperature=self.temperature,
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top_p=0.95,
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inference_increment=True,
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stopping_criteria=StoppingCriteriaList([stop_criteria]),
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pad_token_id=self.tokenizer.eos_token_id,
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)
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for local_idx, text in enumerate(decoded):
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prediction = decoded[local_idx]
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prediction = self.tokenizer.decode(prediction, skip_special_tokens=True)
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#print(prediction)
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suffixprediction = prediction[prompt_lens[local_idx]:]
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suffixprediction = suffixprediction.split("[DONE]")[0].strip()
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res = {"task_id": taskid[local_idx], "generation": suffixprediction}
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tmpfile.write(json.dumps(res) + "\n")
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tmpfile.flush()
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totoalnum += 1
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self.log_score(dp_rank, totoalnum, all_num, start_time, self.batch_size)
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tmpfile.close()
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accelerator.wait_for_everyone()
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self._calculate_final_score(accelerator)
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def log_score(self, dp_rank, processed_num, all_num, start_time, bs):
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"""
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Log the score.
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"""
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mem = torch.cuda.max_memory_allocated() / (1 << 30)
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avg_time = (time.time() - start_time) / processed_num * bs
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print(
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f'DP RANK:{dp_rank} process_num/all_num:{int(processed_num)}/{all_num} '
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f'avg_time_per_batch:{avg_time:.2f} s '
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f'still_need:{((all_num - processed_num) // bs + 1) * avg_time / 60:.2f} m',
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f'mem:{mem:.3f} GiB bs:{bs}',
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flush=True
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)
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if processed_num == all_num:
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print(f'EVAL DONE! Process time {(time.time() - start_time) / 60:.2f} m', flush=True)
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def _calculate_final_score(self, accelerator):
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"""
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Calculate the final score.
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"""
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if accelerator.is_local_main_process:
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logfilepath = os.path.join(self.log_dir, f'final_{self.model_name}.jsonl')
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logfile = open(logfilepath, "w")
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for i in range(accelerator.num_processes):
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tmplogfile = os.path.join(self.log_dir, f'{self.model_name}_rank{i}_bs{self.batch_size}_shot_log_{self.language}.json')
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logfile.write(open(tmplogfile).read().strip() + "\n")
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os.remove(tmplogfile)
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logfile.close()
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timeout = 10
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runlang = self.language
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res = evaluate_functional_correctness(input_file=logfilepath, problem_file=os.path.join(self.data_root, f"mbpp_test.jsonl"), tmp_dir=self.log_dir, timeout=timeout, language=runlang)
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print("score is", res['pass@%d' % self.k])
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os.remove(logfilepath)
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return
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