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import argparse
import os
import random
import time
from pathlib import Path
import psutil
import torch
import torch.nn.functional as F
from torch.optim import AdamW
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
# import wandb
from accelerate import Accelerator, DeepSpeedPlugin
from accelerate.utils import set_seed
from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
os.environ.setdefault("NCCL_TIMEOUT", "2700")
os.environ.setdefault("TORCH_NCCL_HEARTBEAT_TIMEOUT_SEC", "2700")
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', type=str, default='Qwen3-8B', help='Model name')
parser.add_argument('--model_path', type=str, default=None, help='Local model path')
parser.add_argument('--train_data', type=str, default='dataset/1shot_rlvr/pi1_r1280.parquet', help='Training data file path')
parser.add_argument('--save_root', type=str, default=None, help='Checkpoint save root directory')
parser.add_argument('--effective_batch', type=int, default=64, help='Global batch size')
parser.add_argument('--micro_batch_size', type=str, default=2, help='Micro batch size or "auto"')
parser.add_argument('--temperature', type=float, default=0.9, help='Temperature coefficient')
parser.add_argument('--learning_rate', type=float, default=2e-5, help='Learning rate')
parser.add_argument('--log_steps', type=int, default=1, help='Logging step interval')
parser.add_argument('--save_steps', type=int, default=1, help='Checkpoint saving step interval')
parser.add_argument('--max_steps', type=int, default=1000, help='Maximum training steps')
parser.add_argument('--sample_temp', type=float, default=0.5, help='Generation temperature parameter')
parser.add_argument('--run_name', type=str, default=None, help='Experiment run name')
# parser.add_argument('--wandb_project', type=str, default='entropy-maximization-ft', help='W&B project name')
# parser.add_argument('--wandb_name', type=str, default=None, help='W&B run name')
parser.add_argument('--seed', type=int, default=42, help='Random seed')
return parser.parse_args()
class FTDataset(Dataset):
def __init__(self, rows): self.rows = rows
def __len__(self): return len(self.rows)
def __getitem__(self, idx): return self.rows[idx]
def custom_collate(batch):
return {"input": [item["input"] for item in batch]}
def get_optimal_micro_batch_size(model_name: str, world_size: int = 1) -> int:
model_configs = {
"1.5B": {"base_batch": 4, "keywords": ["1.5B", "1B"]},
"2B": {"base_batch": 4, "keywords": ["2B"]},
"3B": {"base_batch": 2, "keywords": ["3B"]},
"7B": {"base_batch": 2, "keywords": ["7B"]},
"8B+": {"base_batch": 1, "keywords": ["8B", "9B", "10B", "11B", "12B", "13B", "14B"]},
}
model_name_upper = model_name.upper()
detected = next((cfg for cfg in model_configs.values() if any(k in model_name_upper for k in cfg["keywords"])), None)
base_batch = detected["base_batch"] if detected else 2
if world_size > 1:
return min(base_batch + 1, int(base_batch * 1.5))
return base_batch
def apply_chat_template(tokenizer, problem: str) -> str:
return tokenizer.apply_chat_template(
[{"role": "user", "content": problem}],
tokenize=False, add_generation_prompt=True
)
def main():
args = parse_args()
set_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
world_size = int(os.getenv("WORLD_SIZE", "1"))
micro_bs = int(args.micro_batch_size)
eff_bs = args.effective_batch
accum_steps = max(1, eff_bs // (micro_bs * world_size))
temp = args.temperature
lr = args.learning_rate
save_root = args.save_root or (f"checkpoints/{args.model_name}/{args.run_name}" if args.run_name else f"checkpoints/{args.model_name}")
ds_config = {
"train_micro_batch_size_per_gpu": micro_bs,
"train_batch_size": eff_bs,
"gradient_accumulation_steps": accum_steps,
"bf16": {"enabled": True},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {"device": "cpu"},
"offload_param": {"device": "none"}
},
"gradient_clipping": 1.0,
}
accelerator = Accelerator(mixed_precision="bf16",
gradient_accumulation_steps=accum_steps,
deepspeed_plugin=DeepSpeedPlugin(hf_ds_config=ds_config))
print = accelerator.print
model_path = args.model_path or f"/volume/pt-train/models/{args.model_name}"
config = AutoConfig.from_pretrained(model_path)
config.use_cache = False
model = AutoModelForCausalLM.from_pretrained(model_path, config=config)
model.gradient_checkpointing_enable()
tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token
# if accelerator.is_main_process:
# wandb.init(project=args.wandb_project, name=args.run_name or args.wandb_name or args.model_name, config=vars(args))
df = pd.read_parquet(args.train_data)
train_data = [{"input": apply_chat_template(tokenizer, p)} for p in df["problem"].dropna().tolist()]
train_loader = DataLoader(FTDataset(train_data), batch_size=micro_bs, shuffle=True, collate_fn=custom_collate)
optimizer = AdamW(model.parameters(), lr=lr)
model, optimizer, train_loader = accelerator.prepare(model, optimizer, train_loader)
prev_logits = None
model.train()
for step, batch in enumerate(train_loader, start=1):
if step > args.max_steps:
print(f"Exceed max step {args.max_steps}, training stopped.")
break
with accelerator.accumulate(model):
enc = tokenizer(batch["input"],
return_tensors="pt",
padding="longest",
truncation=True,
max_length=2048).to(accelerator.device)
with torch.no_grad():
gen_ids = accelerator.unwrap_model(model).generate(**enc,
max_new_tokens=512,
do_sample=True,
top_p=0.95,
temperature=args.sample_temp,
synced_gpus=True,
pad_token_id=tokenizer.pad_token_id,
use_cache=False)
seq = torch.cat([enc.input_ids, gen_ids[:, enc.input_ids.shape[1]:]], dim=1)[:, :4096]
pad_mask = seq.ne(tokenizer.pad_token_id)
prompt_len = pad_mask[:, :enc.input_ids.shape[1]].sum(-1)
token_idx = torch.arange(seq.size(1), device=seq.device)
gen_mask = (token_idx.unsqueeze(0) >= prompt_len.unsqueeze(1)) & pad_mask
logits = model(seq, attention_mask=pad_mask).logits
probs = F.softmax(logits / temp, dim=-1)
H_tok = -(probs * torch.log(probs + 1e-12)).sum(-1)
loss = (H_tok * gen_mask).sum() / gen_mask.sum().clamp_min(1)
prev_logits = logits.detach()
accelerator.backward(loss)
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
optimizer.zero_grad()
if accelerator.is_main_process:
if step % args.log_steps == 0:
print(f"Step {step} | loss={loss.item():.6f}")
# wandb.log({"step": step, "loss": loss.item()})
if step % args.save_steps == 0:
ckpt = Path(save_root) / f"step_{step}"
ckpt.mkdir(parents=True, exist_ok=True)
accelerator.unwrap_model(model).save_pretrained(ckpt, safe_serialization=True)
tokenizer.save_pretrained(ckpt)
print(f"Checkpoint saved to {ckpt}")
if accelerator.is_main_process:
final = Path(save_root) / "final"
final.mkdir(parents=True, exist_ok=True)
accelerator.unwrap_model(model).save_pretrained(final, safe_serialization=True)
tokenizer.save_pretrained(final)
print(f"Final checkpoint saved to {final}")
# wandb.finish()
if __name__ == "__main__":
main()
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