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import argparse
import random
import string
from pathlib import Path
import torch.nn.functional as F
import os, math, torch, pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from accelerate import Accelerator, DeepSpeedPlugin
from accelerate.utils import set_seed
from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, AdamW
parser = argparse.ArgumentParser()
parser.add_argument("--temperature", type=float, default=0.5)
parser.add_argument("--lr", type=float, default=2e-5)
parser.add_argument("--bsz", type=int)
parser.add_argument("--seed", type=int, default=15)
args = parser.parse_args()
set_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
temp_str = str(args.temperature).replace(".", "")
lr_str = f"{args.lr:.0e}"
bsz_str = str(args.effective_batch)
save_root = f"/volume/ailab4sci/ztgao/em/checkpoints/qwen25math7b/t{temp_str}_lr{lr_str}_bsz{bsz_str}_seed{args.seed}"
temperature = args.temperature
learning_rate = args.lr
effective_batch = args.effective_batch
micro_batch_size = 2
world_size = int(os.environ.get("WORLD_SIZE", 1))
accum_steps = max(1, effective_batch // (micro_batch_size * world_size))
DEEPSPEED_CONFIG = {
"train_micro_batch_size_per_gpu": micro_batch_size,
"train_batch_size": effective_batch,
"gradient_accumulation_steps": accum_steps,
"bf16": {"enabled": True},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {"device": "cpu"},
"offload_param": {"device": "none"}
},
"gradient_clipping": 1.0,
}
ds_plugin = DeepSpeedPlugin(hf_ds_config=DEEPSPEED_CONFIG)
accelerator = Accelerator(
mixed_precision="bf16",
gradient_accumulation_steps=accum_steps,
deepspeed_plugin=ds_plugin,
)
print = accelerator.print
model_name = "Qwen2.5-Math-7B"
model_path = f"/volume/ailab4sci/models/{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")
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
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 apply_chat_template(problem: str) -> str:
return tokenizer.apply_chat_template(
[{"role": "user", "content": problem}],
tokenize=False, add_generation_prompt=True
)
df = pd.read_parquet("/volume/ailab4sci/ztgao/em/dataset/1shot_rlvr/pi1_r1280.parquet")
data = [{"input": apply_chat_template(p)} for p in df["problem"].dropna().tolist()]
dataset = FTDataset(data)
data_loader = DataLoader(
dataset,
batch_size=micro_batch_size,
shuffle=True,
collate_fn=custom_collate,
)
optimizer = AdamW(model.parameters(), lr=learning_rate)
model, optimizer, data_loader = accelerator.prepare(model, optimizer, data_loader)
model.train()
for step, batch in enumerate(data_loader, start=1):
if step > 30:
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=2048,
do_sample=True,
top_p=0.95,
temperature=temperature,
synced_gpus=True,
repetition_penalty=1.15,
pad_token_id=151643,
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 / temperature, dim=-1)
H_tok = -(probs * torch.log(probs + 1e-12)).sum(-1)
loss = (H_tok * gen_mask).sum() / gen_mask.sum().clamp_min(1)
accelerator.backward(loss)
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step(); optimizer.zero_grad()
print(f"Step {step} | loss={loss.item():.8f}")
if accelerator.is_main_process:
ckpt_dir = Path(save_root) / f"step_{step}"
ckpt_dir.mkdir(parents=True, exist_ok=True)
accelerator.unwrap_model(model).save_pretrained(ckpt_dir, safe_serialization=True)
tokenizer.save_pretrained(ckpt_dir)
accelerator.wait_for_everyone()
print(f"Checkpoint saved to {ckpt_dir}")
if accelerator.is_main_process:
final_dir = Path(save_root) / "final"
final_dir.mkdir(parents=True, exist_ok=True)
accelerator.unwrap_model(model).save_pretrained(final_dir, safe_serialization=True)
tokenizer.save_pretrained(final_dir)
print(f"Final checkpoint saved to {final_dir}")
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