""" 训练工具函数集合 """ import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import random import math import numpy as np import torch import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel from torch.utils.data import Sampler from transformers import AutoTokenizer from model.model_vlm import MiniMindVLM def get_model_params(model, config, ignore_patterns=['vision_encoder']): def should_count(n): return not any(p in n for p in ignore_patterns) total = sum(p.numel() for n, p in model.named_parameters() if should_count(n)) / 1e6 n_routed = getattr(config, 'n_routed_experts', getattr(config, 'num_experts', 0)) n_active = getattr(config, 'num_experts_per_tok', 0) n_shared = getattr(config, 'n_shared_experts', 0) expert = sum(p.numel() for n, p in model.named_parameters() if 'mlp.experts.0.' in n and should_count(n)) / 1e6 shared_expert = sum(p.numel() for n, p in model.named_parameters() if 'mlp.shared_experts.0.' in n and should_count(n)) / 1e6 base = total - (expert * n_routed) - (shared_expert * n_shared) active = base + (expert * n_active) + (shared_expert * n_shared) if active < total: Logger(f'Model Params: {total:.2f}M-A{active:.2f}M') else: Logger(f'Model Params: {total:.2f}M') def is_main_process(): return not dist.is_initialized() or dist.get_rank() == 0 def Logger(content): if is_main_process(): print(content) def get_lr(current_step, total_steps, lr): return lr*(0.1 + 0.45*(1 + math.cos(math.pi * current_step / total_steps))) def init_distributed_mode(): if int(os.environ.get("RANK", -1)) == -1: return 0 # 非DDP模式 dist.init_process_group(backend="nccl") local_rank = int(os.environ["LOCAL_RANK"]) torch.cuda.set_device(local_rank) return local_rank def setup_seed(seed: int): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def init_vlm_model(vlm_config, from_weight='pretrain_vlm', tokenizer_path='../model', vision_model_path='../model/siglip2-base-p16-ve', save_dir='../out', device='cuda', freeze_llm=0): tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) model = MiniMindVLM(vlm_config, vision_model_path=vision_model_path) if from_weight != 'none': moe_suffix = '_moe' if vlm_config.use_moe else '' weight_path = f'{save_dir}/{from_weight}_{vlm_config.hidden_size}{moe_suffix}.pth' weights = torch.load(weight_path, map_location=device) model.load_state_dict(weights, strict=False) # 1、全部冻结，只打开vision_proj梯度 for name, param in model.named_parameters(): if 'vision_proj' not in name: param.requires_grad = False # 2、判断策略 if freeze_llm == 0: for name, param in model.named_parameters(): if 'vision_encoder' not in name: param.requires_grad = True elif freeze_llm == 1: for name, param in model.model.named_parameters(): if 'layers.0.' in name: param.requires_grad = True elif freeze_llm == 2: pass get_model_params(model, vlm_config) Logger(f'Trainable Params: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f}M') preprocess = model.processor return model.to(device), tokenizer, preprocess def vlm_checkpoint(vlm_config, weight='pretrain_vlm', model=None, optimizer=None, epoch=0, step=0, wandb=None, save_dir='../checkpoints', **kwargs): os.makedirs(save_dir, exist_ok=True) moe_path = '_moe' if vlm_config.use_moe else '' ckp_path = f'{save_dir}/{weight}_{vlm_config.hidden_size}{moe_path}.pth' resume_path = f'{save_dir}/{weight}_{vlm_config.hidden_size}{moe_path}_resume.pth' if model is not None: raw_model = model.module if isinstance(model, DistributedDataParallel) else model raw_model = getattr(raw_model, '_orig_mod', raw_model) state_dict = raw_model.state_dict() # 移除vision_encoder参数（不需要保存，因为是预训练的） clean_state_dict = {k: v for k, v in state_dict.items() if not k.startswith('vision_encoder.')} ckp_tmp = ckp_path + '.tmp' torch.save({k: v.half().cpu() for k, v in clean_state_dict.items()}, ckp_tmp) os.replace(ckp_tmp, ckp_path) wandb_id = None if wandb: if hasattr(wandb, 'get_run'): run = wandb.get_run() wandb_id = getattr(run, 'id', None) if run else None else: wandb_id = getattr(wandb, 'id', None) resume_data = { 'model': state_dict, 'optimizer': optimizer.state_dict(), 'epoch': epoch, 'step': step, 'world_size': dist.get_world_size() if dist.is_initialized() else 1, 'wandb_id': wandb_id } for key, value in kwargs.items(): if value is not None: if hasattr(value, 'state_dict'): raw_value = value.module if isinstance(value, DistributedDataParallel) else value raw_value = getattr(raw_value, '_orig_mod', raw_value) resume_data[key] = raw_value.state_dict() else: resume_data[key] = value resume_tmp = resume_path + '.tmp' torch.save(resume_data, resume_tmp) os.replace(resume_tmp, resume_path) del state_dict, clean_state_dict, resume_data torch.cuda.empty_cache() else: # 加载模式 if os.path.exists(resume_path): ckp_data = torch.load(resume_path, map_location='cpu') saved_ws = ckp_data.get('world_size', 1) current_ws = dist.get_world_size() if dist.is_initialized() else 1 if saved_ws != current_ws: ckp_data['step'] = ckp_data['step'] * saved_ws // current_ws Logger(f'GPU数量变化({saved_ws}→{current_ws})，step已自动转换为{ckp_data["step"]}') return ckp_data return None def vlm_collate_fn(batch): input_ids = torch.stack([b[0] for b in batch]) labels = torch.stack([b[1] for b in batch]) pixel_data = [b[2] for b in batch] if hasattr(pixel_data[0], 'keys'): pixel_values = {k: torch.stack([d[k] for d in pixel_data]) for k in pixel_data[0].keys()} else: pixel_values = torch.stack(pixel_data) return input_ids, labels, pixel_values class SkipBatchSampler(Sampler): def __init__(self, sampler, batch_size, skip_batches=0): self.sampler = sampler self.batch_size = batch_size self.skip_batches = skip_batches def __iter__(self): batch = [] skipped = 0 for idx in self.sampler: batch.append(idx) if len(batch) == self.batch_size: if skipped < self.skip_batches: skipped += 1 batch = [] continue yield batch batch = [] if len(batch) > 0 and skipped >= self.skip_batches: yield batch def __len__(self): total_batches = (len(self.sampler) + self.batch_size - 1) // self.batch_size return max(0, total_batches - self.skip_batches)