File size: 3,621 Bytes
8a506a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
import argparse
import os
import sys
from typing import List
import torch
import transformers
from transformers import LlamaForCausalLM, LlamaTokenizer, LlamaConfig
from utils import *
from collator import Collator
def train(args):
set_seed(args.seed)
ensure_dir(args.output_dir)
device_map = "auto"
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
local_rank = int(os.environ.get("LOCAL_RANK") or 0)
if local_rank == 0:
print(vars(args))
if ddp:
device_map = {"": local_rank}
config = LlamaConfig.from_pretrained(args.base_model)
tokenizer = LlamaTokenizer.from_pretrained(
args.base_model,
model_max_length = args.model_max_length,
padding_side="right",
)
tokenizer.pad_token_id = 0
gradient_checkpointing = True
train_data, valid_data = load_datasets(args)
add_num = tokenizer.add_tokens(train_data.datasets[0].get_new_tokens())
config.vocab_size = len(tokenizer)
if local_rank == 0:
print("add {} new token.".format(add_num))
print("data num:", len(train_data))
tokenizer.save_pretrained(args.output_dir)
config.save_pretrained(args.output_dir)
collator = Collator(args, tokenizer)
model = LlamaForCausalLM.from_pretrained(
args.base_model,
# torch_dtype=torch.float16,
device_map=device_map,
)
model.resize_token_embeddings(len(tokenizer))
if not ddp and torch.cuda.device_count() > 1:
model.is_parallelizable = True
model.model_parallel = True
trainer = transformers.Trainer(
model=model,
train_dataset=train_data,
eval_dataset=valid_data,
args=transformers.TrainingArguments(
seed=args.seed,
per_device_train_batch_size=args.per_device_batch_size,
per_device_eval_batch_size=args.per_device_batch_size,
gradient_accumulation_steps=args.gradient_accumulation_steps,
warmup_ratio=args.warmup_ratio,
num_train_epochs=args.epochs,
learning_rate=args.learning_rate,
weight_decay=args.weight_decay,
lr_scheduler_type=args.lr_scheduler_type,
fp16=args.fp16,
bf16=args.bf16,
logging_steps=args.logging_step,
optim=args.optim,
gradient_checkpointing=gradient_checkpointing,
evaluation_strategy=args.save_and_eval_strategy,
save_strategy=args.save_and_eval_strategy,
eval_steps=args.save_and_eval_steps,
save_steps=args.save_and_eval_steps,
output_dir=args.output_dir,
save_total_limit=5,
load_best_model_at_end=True,
deepspeed=args.deepspeed,
ddp_find_unused_parameters=False if ddp else None,
report_to=None,
eval_delay= 1 if args.save_and_eval_strategy=="epoch" else 2000,
dataloader_num_workers = args.dataloader_num_workers,
dataloader_prefetch_factor = args.dataloader_prefetch_factor
),
tokenizer=tokenizer,
data_collator=collator,
)
model.config.use_cache = False
trainer.train(
resume_from_checkpoint=args.resume_from_checkpoint,
)
trainer.save_state()
trainer.save_model(output_dir=args.output_dir)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='LLMRec')
parser = parse_global_args(parser)
parser = parse_train_args(parser)
parser = parse_dataset_args(parser)
args = parser.parse_args()
train(args)
|