265 lines
14 KiB
Python
265 lines
14 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
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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.nn as nn
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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_omni import OmniConfig
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from dataset.omni_dataset import OmniDataset
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from trainer.trainer_utils import get_lr, Logger, is_main_process, init_distributed_mode, setup_seed, init_omni_model, omni_checkpoint, SkipBatchSampler, log_model_params
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warnings.filterwarnings('ignore')
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def omni_collate_fn(batch):
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"""自定义collate函数，处理变长audio_inputs和pixel_values"""
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input_ids, labels, audio_labels, audio_inputs, audio_lens, pixel_values, spk_emb = zip(*batch)
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input_ids = torch.stack(input_ids)
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labels = torch.stack(labels)
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audio_labels = torch.stack(audio_labels)
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audio_lens = torch.tensor(audio_lens, dtype=torch.long)
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valid_audios = [a for a in audio_inputs if a is not None]
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if valid_audios:
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max_t = max(a.size(1) for a in valid_audios)
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padded = [a if a.size(1) == max_t else torch.nn.functional.pad(a, (0, 0, 0, max_t - a.size(1))) for a in valid_audios]
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audio_inputs = torch.cat(padded, dim=0)
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else:
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audio_inputs = None
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valid_images = [p for p in pixel_values if p is not None]
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if valid_images:
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if hasattr(valid_images[0], 'keys'):
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keys = set.intersection(*[set(d.keys()) for d in valid_images])
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pixel_values = {k: torch.cat([d[k] for d in valid_images], dim=0) for k in keys}
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else:
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pixel_values = torch.cat(valid_images, dim=0)
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else:
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pixel_values = None
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spk_emb = torch.stack(spk_emb)
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return input_ids, labels, audio_labels, audio_inputs, audio_lens, pixel_values, spk_emb
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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_step = start_step
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for step, (input_ids, labels, audio_labels, audio_inputs, audio_lens, pixel_values, spk_emb) in enumerate(loader, start=start_step + 1):
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input_ids = input_ids.to(args.device)
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labels = labels.to(args.device)
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audio_labels = audio_labels.to(args.device)
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audio_lens = audio_lens.to(args.device)
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if audio_inputs is not None:
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audio_inputs = audio_inputs.to(args.device)
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if pixel_values is not None:
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if hasattr(pixel_values, 'keys'):
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pixel_values = {k: v.to(args.device) for k, v in pixel_values.items()}
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else:
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pixel_values = pixel_values.to(args.device)
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spk_emb = spk_emb.to(args.device)
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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, audio_inputs=audio_inputs, audio_lens=audio_lens, pixel_values=pixel_values, spk_emb=spk_emb)
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loss_fct = nn.CrossEntropyLoss(reduction='none')
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# Text loss
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text_loss_raw = loss_fct(res.logits.view(-1, res.logits.size(-1)), labels.view(-1))
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text_mask = (labels.view(-1) != -100).float()
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text_loss = (text_loss_raw * text_mask).sum() / (text_mask.sum() + 1e-9)
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# Audio loss
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audio_loss = res.audio_logits[0].sum() * 0
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for i, al in enumerate(res.audio_logits):
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al_flat = al.view(-1, al.size(-1))
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target_flat = audio_labels[:, i, :].reshape(-1)
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layer_loss = loss_fct(al_flat, target_flat)
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valid_mask = (target_flat != -100).float()
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stop_mask = (target_flat == 2050).float()
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weighted_loss = layer_loss * valid_mask * (1 + stop_mask * 9)
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msum = valid_mask.sum()
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if msum > 0:
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audio_loss = audio_loss + weighted_loss.sum() / (msum + 1e-9)
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audio_loss = audio_loss / 8
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loss = (text_loss + audio_loss + res.aux_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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spend_time = time.time() - start_time
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current_loss = loss.item() * args.accumulation_steps
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text_loss_val = text_loss.item() if isinstance(text_loss, torch.Tensor) else 0
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audio_loss_val = audio_loss.item() if isinstance(audio_loss, torch.Tensor) else 0
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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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Logger(f'Epoch:[{epoch+1}/{args.epochs}]({step}/{iters}), loss: {current_loss:.4f}, text: {text_loss_val:.4f}, audio: {audio_loss_val:.4f}, lr: {current_lr:.8f}, epoch_time: {eta_min:.1f}min')
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if wandb:
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wandb.log({"loss": current_loss, "text_loss": text_loss_val,
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"audio_loss": audio_loss_val, "lr": current_lr, "epoch_time": eta_min})
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if (step % args.save_interval == 0 or step == iters) and is_main_process():
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model.eval()
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moe_suffix = '_moe' if omni_config.use_moe else ''
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ckp = f'{args.save_dir}/{args.save_weight}_{omni_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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clean_state_dict = {k: v for k, v in raw_model.state_dict().items() if not k.startswith('audio_encoder.')}
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torch.save({k: v.half().cpu() for k, v in clean_state_dict.items()}, ckp)
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omni_checkpoint(omni_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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model.train()
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del input_ids, labels, audio_labels, audio_inputs, audio_lens, pixel_values, spk_emb, 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-O SFT")
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parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
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parser.add_argument('--save_weight', default='sft_omni', type=str, help="保存权重的前缀名")
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parser.add_argument("--epochs", type=int, default=15, help="训练轮数")
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parser.add_argument("--batch_size", type=int, default=32, help="batch size")
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parser.add_argument("--learning_rate", type=float, default=5e-4, help="初始学习率")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
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parser.add_argument("--num_workers", type=int, default=4, help="数据加载线程数")
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parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
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parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
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parser.add_argument("--log_interval", type=int, default=100, help="日志打印间隔")
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parser.add_argument("--save_interval", type=int, default=1000, help="模型保存间隔")
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parser.add_argument('--hidden_size', default=768, type=int, help="隐藏层维度")
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parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
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parser.add_argument('--max_seq_len', default=512, type=int, help="训练的最大截断长度")
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parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构")
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parser.add_argument("--data_path", type=str, default="../dataset/train_t2a_mini.parquet", help="训练数据路径（parquet格式）")
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parser.add_argument("--audio_encoder_dir", type=str, default="../model/SenseVoiceSmall", help="音频encoder路径(SenseVoice)")
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parser.add_argument("--vision_dir", type=str, default="../model/siglip2-base-p32-256-ve", help="CLIP视觉模型路径")
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parser.add_argument('--from_weight', default='llm', type=str, help="基于哪个权重训练，为none则不基于任何权重训练")
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parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1], help="是否自动检测&续训（0=否，1=是）")
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parser.add_argument('--freeze_backbone', default='none', type=str, choices=['none', 'all', 'last1'], help="冻结主干模型: none=全量训练, all=只训练audio层, last1=只训练最后1层+audio层")
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parser.add_argument('--mode', default='all', type=str, choices=['all', 'audio_proj', 'vision_proj'], help="训练模式: all=全量训练, audio_proj=只训练audio_proj, vision_proj=只训练vision_proj")
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parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-O-SFT", help="wandb项目名")
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parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1], help="是否使用torch.compile加速（0=否，1=是）")
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args = parser.parse_args()
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# ========== 1. 初始化环境和随机种子 ==========
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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. 配置目录、模型参数、检查ckp ==========
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os.makedirs(args.save_dir, exist_ok=True)
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omni_config = OmniConfig(
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hidden_size=args.hidden_size,
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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 = omni_checkpoint(omni_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
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# ========== 3. 设置混合精度 ==========
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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. 配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 = f"MiniMind-O-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LR-{args.learning_rate}"
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wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
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# ========== 5. 定义模型、数据、优化器 ==========
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model, tokenizer = init_omni_model(omni_config, from_weight=args.from_weight,
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audio_encoder_path=args.audio_encoder_dir,
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vision_model_path=args.vision_dir,
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save_dir=args.save_dir, device=args.device,
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freeze_backbone=args.freeze_backbone, from_resume=args.from_resume)
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if args.use_compile == 1:
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model = torch.compile(model)
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if model.audio_encoder is not None: model.audio_encoder.to(args.device)
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if model.vision_encoder is not None: model.vision_encoder.to(args.device)
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if args.mode == 'audio_proj':
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for p in model.parameters(): p.requires_grad = False
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for p in model.audio_proj.parameters(): p.requires_grad = True
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elif args.mode == 'vision_proj':
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for p in model.parameters(): p.requires_grad = False
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for p in model.vision_proj.parameters(): p.requires_grad = True
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log_model_params(model)
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trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6
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Logger(f'Trainable: {trainable:.2f}M | Mode: {args.mode} | Freeze: {args.freeze_backbone} | Compile: {"on" if args.use_compile else "off"}')
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# scheduled_sampling 现在会自动保护 image/audio token 的连续性
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train_ds = OmniDataset(
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args.data_path,
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tokenizer,
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audio_processor=model.audio_processor,
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vision_processor=model.vision_processor,
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max_length=args.max_seq_len,
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image_token_len=model.config.image_token_len
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)
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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)
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# ========== 6. 从ckp恢复状态 ==========
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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'], strict=False)
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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. DDP包模型 ==========
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if dist.is_initialized():
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model = DistributedDataParallel(model, device_ids=[local_rank])
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# ========== 8. 开始训练 ==========
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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); 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, collate_fn=omni_collate_fn, num_workers=args.num_workers, pin_memory=True)
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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. 清理分布进程 ==========
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if dist.is_initialized(): dist.destroy_process_group()
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