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 human_eval.evaluation import evaluate_functional_correctness
from transformers import AutoTokenizer
from utils.dataset import HumanEvalDataset
from utils.utils import cleanup_code
class HumanEval:
"""
HumanEval 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 on HumanEval.
"""
assert self.log_dir is not None, "log_dir should not be None when evaluating humaneval"
dataset = HumanEvalDataset(self.data_root, sample_num=self.n_sample, language=self.language, issft=self.sft)
nprompt = len(dataset) // self.n_sample
dp_rank = accelerator.process_index
dp_size = accelerator.num_processes
if self.k > 1:
assert self.n_sample >= 100, "HumanEval PASS@100 needs n_sample >= 100"
gpt.eval()
# each process will process a subset of the dataset
prompt_indices_split = np.array_split(range(nprompt), dp_size)
prompt_indices = prompt_indices_split[dp_rank]
indices = [x * self.n_sample + j for x in prompt_indices for j in range(self.n_sample)]
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")
start_time = time.time()
# split the dataset into batches and construct a list of inputs
for idx in range(0, len(indices), self.batch_size):
prompt_list = []
prompt_lens = []
orriginal_prompt_list = []
tokenized_prompt_lens = []
taskid = []
# get the prompts from the dataset
for j in indices[idx:idx + self.batch_size]:
data = dataset[j]
fprompt = data["prompt"].strip()
prompt_list.append(fprompt)
tmp = self.tokenizer.encode(fprompt)
orriginal_prompt_list.append(data["original_prompt"])
prompt_lens.append(len(fprompt))
tokenized_prompt_lens.append(tmp)
taskid.append(data["task_id"])
input_ids = torch.tensor(tokenized_prompt_lens).to(accelerator.device)
# generate the code
if self.temperature != 0:
decoded = gpt.generate(
input_ids=input_ids,
max_new_tokens=self.max_gen_len,
do_sample=True,
eos_token_id=self.tokenizer.eos_token_id,
temperature=self.temperature,
top_p=self.top_p,
pad_token_id=self.tokenizer.eos_token_id,
)
else:
decoded = gpt.generate(
input_ids=input_ids,
max_new_tokens=self.max_gen_len,
do_sample=False,
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=self.tokenizer.eos_token_id,
)
# save the results to a file
for local_idx, text in enumerate(decoded):
prediction = decoded[local_idx]
prediction = self.tokenizer.decode(prediction, skip_special_tokens=True)
suffixprediction = prediction[prompt_lens[local_idx]:]
suffixprediction = cleanup_code(suffixprediction, self.language, "humaneval", self.sft, dataset.stopwords)
# sft mode does not need original prompt
if not self.sft:
suffixprediction = orriginal_prompt_list[local_idx] + "\n" + suffixprediction
res = {"task_id": taskid[local_idx], "generation": suffixprediction, "prompt": orriginal_prompt_list[local_idx], "wholecode":prediction}
tmpfile.write(json.dumps(res) + "\n")
tmpfile.flush()
processed_num += 1
self.log_score(dp_rank, processed_num, all_num, start_time, self.batch_size)
tmpfile.close()
accelerator.wait_for_everyone()
# calculate the final score of pass@k
self._calculate_final_score(accelerator)
accelerator.wait_for_everyone()
return
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"humaneval-{self.language}.jsonl"), tmp_dir=self.log_dir, timeout=timeout, language=runlang)
print("score is", res['pass@%d' % self.k])
os.remove(logfilepath)
return