163 lines
7.2 KiB
Python
163 lines
7.2 KiB
Python
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
|
|
|