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import os
import sys
from typing import List
import argparse
import wandb
import torch
import transformers
from transformers import LlamaForCausalLM, LlamaTokenizer, LlamaConfig
from peft import (
TaskType,
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
)
from collator import VanillaCollator
from rq_llama import *
from utils import *
parser = argparse.ArgumentParser(description = 'rqllama-pretrain')
parser = parse_global_args(parser)
parser = parse_train_args(parser)
parser = parse_dataset_args(parser)
parser = parse_rqvae_args(parser)
args = parser.parse_args()
wandb.init(config = args, reinit = True)
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}
train_data, valid_data = load_datasets(args)
config = LlamaConfig.from_pretrained(args.base_model)
config.args = vars(args)
rqllama = LlamaWithRQ(config)
ckpt = torch.load(args.rqvae_model, map_location = torch.device('cpu'))
state_dict = ckpt["state_dict"]
rqllama.rqvae.load_state_dict(state_dict)
for i in range(len(args.num_emb_list)):
rqllama.rqvae.rq.vq_layers[i].initted = True
if local_rank == 0:
print("token num:", len(rqllama.tokenizer))
print("data num:", len(train_data))
rqllama.tokenizer.save_pretrained(args.output_dir)
rqllama.config.save_pretrained(args.output_dir)
if args.resume_from_checkpoint:
checkpoint_name = os.path.join(args.resume_from_checkpoint, "adapter_model.bin")
args.resume_from_checkpoint = False
if os.path.exists(checkpoint_name):
if local_rank == 0:
print(f"Restarting from {checkpoint_name}")
adapters_weights = torch.load(checkpoint_name)
rqllama.model = set_peft_model_state_dict(rqllama.model, adapters_weights)
else:
if local_rank == 0:
print(f"Checkpoint {checkpoint_name} not found")
if local_rank == 0:
rqllama.model.print_trainable_parameters()
if not ddp and torch.cuda.device_count() > 1:
rqllama.is_parallelizable = True
rqllama.model_parallel = True
collator = VanillaCollator(args, rqllama.tokenizer)
trainer = transformers.Trainer(
model = rqllama,
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 = True,
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,
remove_unused_columns = args.remove_unused_columns,
),
tokenizer = rqllama.tokenizer,
data_collator = collator,
)
rqllama.config.use_cache = False
if torch.__version__ >= "2" and sys.platform != "win32":
rqllama = torch.compile(rqllama)
trainer.train(resume_from_checkpoint = args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir = args.output_dir)
if local_rank == 0:
print('rqllama pre-train finished.') |