text stringlengths 1 93.6k |
|---|
# 每 gradient_accumulation_steps / (batch_size / mini_batch_size) 个global_steps反向传播一次
|
num_epochs = 300
|
log_steps = 1
|
save_steps = 10
|
max_grad_norm = 1
|
seed = 1024
|
max_save = 3
|
resume = False
|
# wandb
|
wandb_project = "grpo_training"
|
wandb_run_name = f"{wandb_project.split('/')[-1]}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
wandb_dir = f"./wandb/{wandb_run_name}"
|
# config
|
config = GRPOConfig(
|
learning_rate=learning_rate,
|
group_num=group_num, # 每个输入采样group_num个候选回复
|
mini_batch_size=mini_batch_size,
|
gradient_accumulation_steps=gradient_accumulation_steps,
|
max_grad_norm=max_grad_norm,
|
seed=seed
|
)
|
# accelerator init
|
accelerator = Accelerator(gradient_accumulation_steps=config.gradient_accumulation_steps)
|
# wandb init
|
if accelerator.is_main_process:
|
if not os.path.exists(wandb_dir):
|
os.makedirs(wandb_dir)
|
wandb.init(
|
project=wandb_project,
|
name=wandb_run_name,
|
dir=wandb_dir,
|
config={
|
"model_name_or_path": model_name_or_path,
|
"dataset": dataset_dir,
|
"batch_size": batch_size,
|
"mini_batch_size": mini_batch_size,
|
"num_epochs": num_epochs,
|
"learning_rate": learning_rate,
|
"group_num": group_num,
|
"gradient_accumulation_steps": gradient_accumulation_steps,
|
"max_grad_norm": max_grad_norm,
|
"seed": seed,
|
"wandb_dir": wandb_dir,
|
}
|
)
|
logger.info(f"Wandb local dir: {wandb_dir}")
|
# model
|
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True)
|
model.gradient_checkpointing_enable()
|
# 注意padding区分left/right
|
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, padding_side="left")
|
if tokenizer.pad_token is None:
|
tokenizer.pad_token = tokenizer.eos_token
|
ref_model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True).cpu()
|
for param in ref_model.parameters():
|
param.requires_grad = False
|
# # dataprocess
|
# dataset = load_dataset(path=dataset_dir, split="train")
|
# # dataset = dataset.select(range(100))
|
# dataset = convert_tldr(dataset)
|
dataset = get_demo_data()
|
column_names = list(next(iter(dataset)).keys())
|
preprocess_func = partial(
|
preprocess_rl_dataset_v1,
|
tokenizer=tokenizer,
|
)
|
with accelerator.main_process_first():
|
dataset = dataset.map(
|
preprocess_func,
|
batched=True,
|
num_proc=1,
|
remove_columns=column_names,
|
desc="Preprocessing dataset"
|
)
|
dataloader = DataLoader(dataset, batch_size=batch_size)
|
# optimizer & lr_scheduler
|
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
|
num_training_steps = num_epochs * len(dataloader) * (batch_size // mini_batch_size) // gradient_accumulation_steps
|
lr_scheduler = get_linear_schedule_with_warmup(optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps)
|
# resume training state from checkpoint
|
if resume:
|
training_state = torch.load(os.path.join(model_name_or_path, "training_state.pt"), map_location="cpu" )
|
optimizer.load_state_dict(training_state['optimizer_state_dict'])
|
lr_scheduler.load_state_dict(training_state['scheduler_state_dict'])
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.