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#!/usr/bin/python3
# -*- coding: utf-8 -*-
from dataclasses import dataclass, field
import os
from pathlib import Path
import platform
import re
from typing import Dict, List, Optional, Union
if platform.system() == "Windows":
from project_settings import project_path
else:
project_path = os.path.abspath("./")
project_path = Path(project_path)
hf_hub_cache = (project_path / "cache/huggingface/hub").as_posix()
os.environ["HUGGINGFACE_HUB_CACHE"] = hf_hub_cache
from datasets import load_dataset
import huggingface_hub
import torch
import torch.multiprocessing as mp
from transformers import HfArgumentParser
from transformers.data.data_collator import DataCollatorForLanguageModeling
from transformers.models.auto import AutoModelForCausalLM, AutoTokenizer
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
from transformers.trainer import Trainer
from transformers.trainer_callback import EarlyStoppingCallback
from transformers.training_args import TrainingArguments
@dataclass
class ScriptArguments:
# dataset
dataset_path: str = field(default="qgyd2021/lip_service_4chan")
dataset_name: str = field(default=None)
dataset_split: str = field(default=None)
dataset_cache_dir: str = field(default=(project_path / "hub_datasets").as_posix())
dataset_streaming: bool = field(default=False)
num_workers: int = field(default=None if platform.system() == "Windows" else os.cpu_count() // 2)
# model
pretrained_model_name_or_path: str = field(
default="uer/gpt2-chinese-cluecorpussmall"
)
# pretrained_model_name_or_path: str = field(
# default=(project_path / "pretrained_models/gpt2-chinese-cluecorpussmall").as_posix()
# )
hf_token: str = field(default=None)
def get_args():
parser = HfArgumentParser(ScriptArguments)
args = parser.parse_args_into_dataclasses(return_remaining_strings=True)[0]
return args
def train_model(local_rank, world_size, args):
os.environ["RANK"] = f"{local_rank}"
os.environ["LOCAL_RANK"] = f"{local_rank}"
os.environ["WORLD_SIZE"] = f"{world_size}"
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
huggingface_hub.login(token=args.hf_token)
# dataset
dataset_dict = load_dataset(
path=args.dataset_path,
name=args.dataset_name,
split=args.dataset_split,
cache_dir=args.dataset_cache_dir,
# num_proc=args.num_workers if not args.dataset_streaming else None,
streaming=args.dataset_streaming,
)
print(dataset_dict)
dataset = dataset_dict["train"]
if args.dataset_streaming:
valid_dataset = dataset.take(args.valid_dataset_size)
train_dataset = dataset.skip(args.valid_dataset_size)
train_dataset = train_dataset.shuffle(buffer_size=args.shuffle_buffer_size, seed=None)
else:
dataset = dataset.train_test_split(test_size=4000, seed=None)
train_dataset = dataset["train"]
valid_dataset = dataset["test"]
# pretrained model
model: GPT2LMHeadModel = AutoModelForCausalLM.from_pretrained(args.pretrained_model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_name_or_path)
# map
def encode(examples: dict):
questions_ = examples.pop("question")
answers_ = examples.pop("answer")
utterances = list()
for question, answer in zip(questions_, answers_):
if not isinstance(question, str):
continue
if not isinstance(answer, str):
continue
utterance = question + tokenizer.sep_token + answer
utterances.append(utterance)
utterances = tokenizer.__call__(
text=utterances,
truncation=True,
padding="longest",
max_length=512,
return_special_tokens_mask=True,
)
return utterances
train_dataset = train_dataset.map(
encode,
batched=True,
drop_last_batch=True,
batch_size=10,
num_proc=None,
cache_file_name="train.cache"
)
valid_dataset = valid_dataset.map(
encode,
batched=True,
drop_last_batch=True,
batch_size=10,
num_proc=None,
cache_file_name="valid.cache"
)
dataset_info = f"""
train dataset: {len(train_dataset)}
valid dataset: {len(valid_dataset)}
"""
dataset_info = re.sub(r"[\u0020]{4,}", "", dataset_info)
print(dataset_info)
# for k, v in model.named_parameters():
# if k.__contains__(".bias"):
# v.requires_grad = True
# else:
# v.requires_grad = False
# for k, v in model.named_parameters():
# if v.requires_grad is True:
# print(k)
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer, mlm=False
)
# training_args
training_args = TrainingArguments(
output_dir="output_dir",
evaluation_strategy="steps",
per_device_train_batch_size=8,
gradient_accumulation_steps=4,
learning_rate=2e-4,
weight_decay=0,
max_grad_norm=1.0,
num_train_epochs=1.0,
warmup_steps=1000,
logging_steps=100,
save_strategy="steps",
save_steps=100,
save_total_limit=2,
no_cuda=False,
fp16=True if torch.cuda.is_available() else False,
local_rank=local_rank,
ddp_backend="nccl",
remove_unused_columns=True,
load_best_model_at_end=True,
metric_for_best_model="loss",
greater_is_better=False,
report_to="tensorboard",
push_to_hub=True,
hub_model_id="lib_service_4chan",
hub_strategy="every_save",
gradient_checkpointing=True,
)
partial_state_str = f"""
distributed_type: {training_args.distributed_state.distributed_type}
local_process_index: {training_args.distributed_state.local_process_index}
num_processes: {training_args.distributed_state.num_processes}
process_index: {training_args.distributed_state.process_index}
device: {training_args.distributed_state.device}
"""
partial_state_str = re.sub(r"[\u0020]{4,}", "", partial_state_str)
print(partial_state_str)
environ = f"""
RANK: {os.environ.get("RANK", -1)}
WORLD_SIZE: {os.environ.get("WORLD_SIZE", -1)}
LOCAL_RANK: {os.environ.get("LOCAL_RANK", -1)}
"""
environ = re.sub(r"[\u0020]{4,}", "", environ)
print(environ)
callbacks = [
EarlyStoppingCallback(early_stopping_patience=5)
]
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=valid_dataset,
tokenizer=tokenizer,
callbacks=callbacks
)
train_result = trainer.train()
# 保存最好的 checkpoint
final_save_path = os.path.join(training_args.output_dir, "final")
trainer.save_model(final_save_path) # Saves the tokenizer too
# 保存训练指标
metrics = train_result.metrics
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
tokenizer.save_pretrained(final_save_path)
return
def train_on_cpu():
args = get_args()
train_model(0, 1, args)
return
def train_on_kaggle_notebook():
"""
train on kaggle notebook with GPU T4 x2
from shutil import copyfile
copyfile(src = "../input/tempdataset/step_2_train_model.py", dst = "../working/step_2_train_model.py")
import step_2_train_model
step_2_train_model.train_on_kaggle_notebook()
"""
args = get_args()
world_size = torch.cuda.device_count()
print("world_size: {}".format(world_size))
mp.spawn(train_model,
args=(world_size, args),
nprocs=world_size,
join=True)
return
if __name__ == '__main__':
train_on_cpu()
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