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