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Diffstat (limited to 'train.py')
| -rw-r--r-- | train.py | 186 |
1 files changed, 186 insertions, 0 deletions
diff --git a/train.py b/train.py new file mode 100644 index 0000000..ae29205 --- /dev/null +++ b/train.py @@ -0,0 +1,186 @@ +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='Qwen2.5-Math-7B', 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=0.5, 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=15, 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, + repetition_penalty=1.15, + 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() |