260 lines
13 KiB
Python
260 lines
13 KiB
Python
import os
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import sys
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__package__ = "trainer"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import datasets # noqa: F401 # Windows pyarrow/torch DLL conflict workaround (issue #771)
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import argparse
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import time
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import warnings
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import torch
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import torch.distributed as dist
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from contextlib import nullcontext
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from torch import optim
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from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data import DataLoader, DistributedSampler
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from model.model_minimind import MiniMindConfig
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from model.quant import prepare_qat, DEFAULT_SKIP_PATTERNS
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from dataset.lm_dataset import SFTDataset
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from trainer.trainer_utils import (
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get_lr, Logger, is_main_process, lm_checkpoint,
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init_distributed_mode, setup_seed, init_model, SkipBatchSampler,
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detect_gpu_peak_tflops_bf16, compute_model_flops_per_token,
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)
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warnings.filterwarnings('ignore')
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def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
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start_time = time.time()
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last_log_time, last_log_step = start_time, start_step
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last_step = start_step
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for step, (input_ids, labels) in enumerate(loader, start=start_step + 1):
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# torch.compile + CUDA graph 每步必须 mark step 边界，见 train_pretrain.py 同处注释。
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# QAT 默认 --use_compile 0（fake-quant 阻断 fusion，即使开也少受益），此处保护性写入。
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if args.use_compile == 1:
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torch.compiler.cudagraph_mark_step_begin()
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input_ids = input_ids.to(args.device, non_blocking=True)
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labels = labels.to(args.device, non_blocking=True)
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last_step = step
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lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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with autocast_ctx:
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res = model(input_ids, labels=labels)
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loss = res.loss + res.aux_loss
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loss = loss / args.accumulation_steps
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scaler.scale(loss).backward()
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if step % args.accumulation_steps == 0:
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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if step % args.log_interval == 0 or step == iters:
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now = time.time()
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spend_time = now - start_time
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window_dt = max(now - last_log_time, 1e-6)
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window_steps = step - last_log_step
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window_its = window_steps / window_dt
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# MFU 报的是 dense 6P 等价吞吐；QAT 会因 fake-quant 逐 Linear 加 round/clamp/dequant
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# 有额外内存搬运，MFU 通常比同 seq 下的 SFT 低 5-15%——这本身就是我们要观测的量。
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tokens_window = window_steps * args.batch_size * args.max_seq_len * world_size
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mfu = (flops_per_token * tokens_window / window_dt) / (gpu_peak_tflops * 1e12) * 100
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last_log_time, last_log_step = now, step
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current_loss = loss.item() * args.accumulation_steps
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current_aux_loss = res.aux_loss.item() if res.aux_loss is not None else 0.0
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current_logits_loss = current_loss - current_aux_loss
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current_lr = optimizer.param_groups[-1]['lr']
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eta_min = spend_time / max(step - start_step, 1) * (iters - step) // 60
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# observer_warmup 期内 act_fq 只统计不量化，日志上会看到一个"200 步后 loss 抖一下"的转折点。
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qat_phase = 'observe' if step <= args.quant_observer_steps else 'fake-quant'
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Logger(
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f'[{time.strftime("%H:%M:%S")}] Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), '
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f'it/s: {window_its:.2f}, MFU: {mfu:.1f}%, phase: {qat_phase}, '
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f'loss: {current_loss:.4f}, logits_loss: {current_logits_loss:.4f}, '
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f'aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, '
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f'epoch_time: {eta_min:.1f}min'
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)
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if wandb:
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wandb.log({
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"loss": current_loss, "logits_loss": current_logits_loss,
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"aux_loss": current_aux_loss, "learning_rate": current_lr,
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"epoch_time": eta_min, "it_per_s": window_its, "mfu_pct": mfu,
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})
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if step % args.save_interval == 0 or step == iters:
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if is_main_process():
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model.eval()
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moe_suffix = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
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raw_model = model.module if isinstance(model, DistributedDataParallel) else model
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raw_model = getattr(raw_model, '_orig_mod', raw_model)
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state_dict = raw_model.state_dict()
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ckp_tmp = ckp + '.tmp'
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torch.save({k: v.half().cpu() for k, v in state_dict.items()}, ckp_tmp)
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os.replace(ckp_tmp, ckp)
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lm_checkpoint(
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lm_config, weight=args.save_weight, model=model, optimizer=optimizer,
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epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints', scaler=scaler,
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)
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model.train()
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del state_dict
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if dist.is_initialized():
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dist.barrier()
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del input_ids, labels, res, loss
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if last_step > start_step and last_step % args.accumulation_steps != 0:
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind QAT (W8A8 fake-quant fine-tune)")
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parser.add_argument("--save_dir", type=str, default="../out")
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parser.add_argument('--save_weight', default='qat', type=str)
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parser.add_argument("--epochs", type=int, default=1, help="QAT 通常 1~2 epoch 即可")
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parser.add_argument("--batch_size", type=int, default=16)
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parser.add_argument("--learning_rate", type=float, default=2e-6,
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help="QAT 用比 SFT 更小的 lr（默认 1/5），防止打乱已收敛的权重分布")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
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parser.add_argument("--dtype", type=str, default="bfloat16")
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parser.add_argument("--num_workers", type=int, default=8)
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parser.add_argument("--accumulation_steps", type=int, default=1)
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parser.add_argument("--grad_clip", type=float, default=1.0)
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parser.add_argument("--log_interval", type=int, default=100)
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parser.add_argument("--save_interval", type=int, default=1000)
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parser.add_argument('--hidden_size', default=768, type=int)
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parser.add_argument('--num_hidden_layers', default=8, type=int)
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parser.add_argument('--max_seq_len', default=768, type=int)
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parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1])
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parser.add_argument("--data_path", type=str, default="../dataset/sft_t2t.jsonl")
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parser.add_argument("--qat_subset_size", type=int, default=0,
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help="QAT 数据子集大小（0=用全量）；64M dry run 建议 100000，1B 正式建议 200000")
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parser.add_argument("--gpu_peak_tflops", type=float, default=0,
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help="GPU bf16 理论峰值 TFLOPS，用于 MFU 计算；0=按 GPU 名自动检测")
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parser.add_argument('--from_weight', default='full_sft', type=str,
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help="QAT 起点权重前缀（部署走 full_sft；1.12 dry run 走 pretrain）")
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parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1])
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parser.add_argument("--use_wandb", action="store_true")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-QAT")
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parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1])
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# ---- QAT-specific ----
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parser.add_argument("--w_bits", type=int, default=8, help="weight 量化位宽")
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parser.add_argument("--a_bits", type=int, default=8, help="activation 量化位宽")
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parser.add_argument("--quant_observer_steps", type=int, default=200,
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help="activation observer 预热步数（仅采集 EMA，不做 fake-quant）")
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parser.add_argument("--quant_skip", type=str, default=",".join(DEFAULT_SKIP_PATTERNS),
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help="名称中包含这些子串的 Linear 跳过量化（逗号分隔）"
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"；默认跳过 lm_head 和 MoE 路由 mlp.gate")
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args = parser.parse_args()
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# 1. dist + seed
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local_rank = init_distributed_mode()
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if dist.is_initialized():
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args.device = f"cuda:{local_rank}"
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setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0))
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# 2. config + checkpoint probe
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os.makedirs(args.save_dir, exist_ok=True)
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lm_config = MiniMindConfig(
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hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
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use_moe=bool(args.use_moe),
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)
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ckp_data = lm_checkpoint(
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lm_config, weight=args.save_weight, save_dir='../checkpoints',
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) if args.from_resume == 1 else None
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# 3. autocast
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device_type = "cuda" if "cuda" in args.device else "cpu"
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dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
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autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
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# 4. wandb
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wandb = None
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if args.use_wandb and is_main_process():
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import swanlab as wandb
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wandb_id = ckp_data.get('wandb_id') if ckp_data else None
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resume = 'must' if wandb_id else None
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wandb_run_name = (
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f"MiniMind-QAT-W{args.w_bits}A{args.a_bits}-Epoch-{args.epochs}-"
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f"BS-{args.batch_size}-LR-{args.learning_rate}"
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)
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wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
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# 5. model: load fp weights -> wrap with QATLinear -> dataset/optimizer
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if args.from_weight == 'none':
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raise SystemExit("--from_weight=none is not supported for QAT; load a pretrained/SFT ckpt.")
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model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
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skip_patterns = tuple(s.strip() for s in args.quant_skip.split(',') if s.strip())
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replaced = prepare_qat(
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model, w_bits=args.w_bits, a_bits=args.a_bits,
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skip_patterns=skip_patterns, a_observer_steps=args.quant_observer_steps,
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)
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Logger(f'QAT: wrapped {replaced} Linear modules '
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f'(W{args.w_bits}A{args.a_bits}, skip={skip_patterns}, '
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f'observer_warmup={args.quant_observer_steps} steps)')
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train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len,
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subset_size=args.qat_subset_size)
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Logger(f'QAT dataset: {len(train_ds)} samples '
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f'(subset_size={args.qat_subset_size}, seq_len={args.max_seq_len})')
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train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
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optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate, fused=True)
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# MFU 测量准备
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flops_per_token = compute_model_flops_per_token(model, lm_config)
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gpu_peak_tflops = detect_gpu_peak_tflops_bf16(override=args.gpu_peak_tflops)
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world_size = dist.get_world_size() if dist.is_initialized() else 1
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Logger(f'[MFU] params_flops/token={flops_per_token/1e9:.3f} GFLOPS, '
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f'GPU peak={gpu_peak_tflops:.0f} TFLOPS bf16, world_size={world_size}')
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# 6. resume
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start_epoch, start_step = 0, 0
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if ckp_data:
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model.load_state_dict(ckp_data['model']) # state dict already contains QAT buffers
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optimizer.load_state_dict(ckp_data['optimizer'])
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scaler.load_state_dict(ckp_data['scaler'])
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start_epoch = ckp_data['epoch']
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start_step = ckp_data.get('step', 0)
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# 7. compile + DDP
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if args.use_compile == 1:
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model = torch.compile(model)
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Logger('torch.compile enabled')
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if dist.is_initialized():
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model = DistributedDataParallel(model, device_ids=[local_rank])
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# 8. train
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for epoch in range(start_epoch, args.epochs):
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train_sampler and train_sampler.set_epoch(epoch)
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setup_seed(42 + epoch)
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indices = torch.randperm(len(train_ds)).tolist()
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skip = start_step if (epoch == start_epoch and start_step > 0) else 0
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batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
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loader = DataLoader(train_ds, batch_sampler=batch_sampler,
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num_workers=args.num_workers, pin_memory=True,
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persistent_workers=args.num_workers > 0)
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if skip > 0:
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Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
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train_epoch(epoch, loader, len(loader) + skip, start_step, wandb)
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else:
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train_epoch(epoch, loader, len(loader), 0, wandb)
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# 9. cleanup
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if dist.is_initialized():
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dist.barrier()
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dist.destroy_process_group()
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