202 lines
8.5 KiB
Python
202 lines
8.5 KiB
Python
"""
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训练工具函数集合
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"""
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import os
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import random
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import math
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import numpy as np
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import datasets
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import torch
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import torch.distributed as dist
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from torch.utils.data import Sampler
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from transformers import AutoTokenizer
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from model.model_omni import MiniMindOmni
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def is_main_process():
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return not dist.is_initialized() or dist.get_rank() == 0
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def Logger(content):
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if is_main_process():
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print(content)
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def get_lr(current_step, total_steps, lr):
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# 与 mmv 保持一致：初始 lr=1.0*base_lr，最终 lr=0.1*base_lr
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return lr * (0.1 + 0.45 * (1 + math.cos(math.pi * current_step / total_steps)))
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def init_distributed_mode():
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if int(os.environ.get("RANK", -1)) == -1:
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return 0 # 非DDP模式
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dist.init_process_group(backend="nccl")
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local_rank = int(os.environ["LOCAL_RANK"])
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torch.cuda.set_device(local_rank)
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return local_rank
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def setup_seed(seed: int):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def log_model_params(model, ignore_patterns=['audio_encoder', 'vision_encoder']):
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def should_count(n): return not any(p in n for p in ignore_patterns)
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total = sum(p.numel() for n, p in model.named_parameters() if should_count(n)) / 1e6
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cfg = model.config
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n_routed = getattr(cfg, 'n_routed_experts', getattr(cfg, 'num_experts', 0))
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n_active = getattr(cfg, 'num_experts_per_tok', 0)
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n_shared = getattr(cfg, 'n_shared_experts', 0)
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expert = sum(p.numel() for n, p in model.named_parameters() if 'mlp.experts.0.' in n and should_count(n)) / 1e6
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shared_expert = sum(p.numel() for n, p in model.named_parameters() if 'mlp.shared_experts.0.' in n and should_count(n)) / 1e6
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base = total - (expert * n_routed) - (shared_expert * n_shared)
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active = base + (expert * n_active) + (shared_expert * n_shared)
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if active < total: Logger(f'Model Params: {total:.2f}M-A{active:.2f}M')
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else: Logger(f'Model Params: {total:.2f}M')
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def init_omni_model(omni_config, from_weight='full_sft', tokenizer_path='../model', audio_encoder_path='../model/SenseVoiceSmall', vision_model_path='../model/siglip2-base-p32-256-ve', save_dir='../out', device='cuda', freeze_backbone='none', from_resume=0):
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
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model = MiniMindOmni(omni_config, audio_encoder_path=audio_encoder_path, vision_model_path=vision_model_path)
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if from_weight != 'none':
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moe_suffix = '_moe' if omni_config.use_moe else ''
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weight_path = f'{save_dir}/{from_weight}_{omni_config.hidden_size}{moe_suffix}.pth'
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if os.path.exists(weight_path):
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weights = torch.load(weight_path, map_location=device)
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param_shapes = {k: v.shape for k, v in model.named_parameters()}
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incompatible = {k for k, v in weights.items() if k in param_shapes and v.shape != param_shapes[k]}
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if incompatible:
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Logger(f'跳过shape不匹配的权重: {incompatible}')
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weights = {k: v for k, v in weights.items() if k not in incompatible}
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model.load_state_dict(weights, strict=False)
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Logger(f'已加载权重: {weight_path}')
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if from_resume == 0 and omni_config.talker_hidden_size == omni_config.hidden_size:
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n_talker = omni_config.num_talker_hidden_layers
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n_thinker = len(model.thinker.layers)
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has_talker = any(k.startswith('talker.layers.') for k in weights)
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if not has_talker and n_talker > 0:
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for i in range(n_talker):
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src = n_thinker - n_talker + i
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model.talker.layers[i].load_state_dict(model.thinker.layers[src].state_dict())
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Logger(f'Talker层初始化: 复制thinker layers[{n_thinker-n_talker}:{n_thinker}] → talker layers[0:{n_talker}]')
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# 冻结策略
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if freeze_backbone == 'all':
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# 冻结整个主干模型
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for param in model.model.parameters():
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param.requires_grad = False
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elif freeze_backbone == 'last1':
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# 冻结除了最后1层之外的所有层
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for param in model.model.parameters():
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param.requires_grad = False
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# 打开最后1层
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if hasattr(model.model, 'layers') and len(model.model.layers) > 0:
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for param in model.model.layers[-1].parameters():
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param.requires_grad = True
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return model.to(device), tokenizer
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def omni_checkpoint(omni_config, weight='pretrain_omni', model=None, optimizer=None, epoch=0, step=0, wandb=None, save_dir='../checkpoints', **kwargs):
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os.makedirs(save_dir, exist_ok=True)
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moe_path = '_moe' if omni_config.use_moe else ''
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ckp_path = f'{save_dir}/{weight}_{omni_config.hidden_size}{moe_path}.pth'
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resume_path = f'{save_dir}/{weight}_{omni_config.hidden_size}{moe_path}_resume.pth'
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if model is not None:
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from torch.nn.parallel import DistributedDataParallel
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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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# 移除冻结的 audio_encoder / vision_encoder 参数（不需要保存，从预训练路径重新加载）
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clean_state_dict = {k: v for k, v in raw_model.state_dict().items() if not k.startswith('audio_encoder.') and not k.startswith('vision_encoder.')}
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state_dict = {k: v.half().cpu() for k, v in clean_state_dict.items()}
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ckp_tmp = ckp_path + '.tmp'
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torch.save(state_dict, ckp_tmp)
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os.replace(ckp_tmp, ckp_path)
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wandb_id = None
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if wandb:
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if hasattr(wandb, 'get_run'):
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run = wandb.get_run()
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wandb_id = getattr(run, 'id', None) if run else None
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else:
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wandb_id = getattr(wandb, 'id', None)
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resume_data = {
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'model': state_dict,
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'optimizer': optimizer.state_dict(),
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'epoch': epoch,
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'step': step,
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'world_size': dist.get_world_size() if dist.is_initialized() else 1,
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'wandb_id': wandb_id
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}
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for key, value in kwargs.items():
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if value is not None:
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if hasattr(value, 'state_dict'):
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if isinstance(value, DistributedDataParallel):
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resume_data[key] = value.module.state_dict()
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else:
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resume_data[key] = value.state_dict()
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else:
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resume_data[key] = value
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resume_tmp = resume_path + '.tmp'
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torch.save(resume_data, resume_tmp)
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os.replace(resume_tmp, resume_path)
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else: # 加载模式
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if os.path.exists(resume_path):
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ckp_data = torch.load(resume_path, map_location='cpu')
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saved_ws = ckp_data.get('world_size', 1)
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current_ws = dist.get_world_size() if dist.is_initialized() else 1
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if saved_ws != current_ws:
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ckp_data['step'] = ckp_data['step'] * saved_ws // current_ws
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Logger(f'GPU数量变化({saved_ws}→{current_ws})，step已自动转换为{ckp_data["step"]}')
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return ckp_data
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return None
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def vlm_collate_fn(batch):
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input_ids = torch.stack([b[0] for b in batch])
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labels = torch.stack([b[1] for b in batch])
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pixel_data = [b[2] for b in batch]
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if hasattr(pixel_data[0], 'keys'):
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pixel_values = {k: torch.stack([d[k] for d in pixel_data]) for k in pixel_data[0].keys()}
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else:
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pixel_values = torch.stack(pixel_data)
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return input_ids, labels, pixel_values
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class SkipBatchSampler(Sampler):
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def __init__(self, sampler, batch_size, skip_batches=0):
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self.sampler = sampler
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self.batch_size = batch_size
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self.skip_batches = skip_batches
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def __iter__(self):
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batch = []
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skipped = 0
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for idx in self.sampler:
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batch.append(idx)
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if len(batch) == self.batch_size:
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if skipped < self.skip_batches:
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skipped += 1
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batch = []
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continue
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yield batch
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batch = []
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if len(batch) > 0 and skipped >= self.skip_batches:
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yield batch
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def __len__(self):
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total_batches = (len(self.sampler) + self.batch_size - 1) // self.batch_size
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return max(0, total_batches - self.skip_batches)
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