350 lines
13 KiB
Python
350 lines
13 KiB
Python
import torch
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import torch.nn as nn
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from torch.optim import Adam
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from torch.utils.data import DataLoader, DistributedSampler
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from torch.nn.parallel import DistributedDataParallel as DDP
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import numpy as np
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import math
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import tqdm
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import matplotlib.pyplot as plt
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import os
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import time
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from models import TimeAwareGPT2, CombinedLoss
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from utils import PatientEventDataset
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# --- Configuration ---
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class TrainConfig:
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# Data parameters
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train_data_path = 'ukb_real_train.bin'
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val_data_path = 'ukb_real_val.bin'
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block_length = 24 # Sequence length
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# Model parameters
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n_embd = 256
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n_layer = 8
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n_head = 8
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pdrop = 0.1
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token_pdrop = 0.1
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# Training parameters
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max_epoch = 200
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batch_size = 512 # 增大总批次大小
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lr_initial = 6e-4
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lr_final = 6e-5
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warmup_epochs = 10
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early_stopping_patience = 5
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# Loss parameters
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ignored_token_ids = [0, 1]
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# Distributed training parameters
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world_size = torch.cuda.device_count()
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distributed = world_size > 1
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# --- Main Training Function ---
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def train_worker(local_rank, config):
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# Initialize distributed training
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if config.distributed:
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dist.init_process_group(
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backend='nccl',
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init_method='env://',
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rank=local_rank,
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world_size=config.world_size
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)
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torch.cuda.set_device(local_rank)
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device = torch.device('cuda', local_rank)
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print(f"Worker {local_rank} initialized on device {device}")
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else:
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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local_rank = 0
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# --- 1. Data Loading ---
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if local_rank == 0:
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print(f"Loading data from {config.train_data_path} and {config.val_data_path}...")
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train_data_arr = np.memmap(config.train_data_path, dtype=np.uint32, mode='r').reshape(-1, 3)
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val_data_arr = np.memmap(config.val_data_path, dtype=np.uint32, mode='r').reshape(-1, 3)
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vocab_size = int(max(train_data_arr[:, 2].max(), val_data_arr[:, 2].max())) + 1
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if local_rank == 0:
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print(f"Inferred vocabulary size: {vocab_size}")
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print(f"Using {config.world_size} GPU(s) for training")
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train_dataset = PatientEventDataset(train_data_arr, config.block_length)
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val_dataset = PatientEventDataset(val_data_arr, config.block_length)
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# 计算每个GPU的批次大小
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per_gpu_batch_size = config.batch_size // config.world_size
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# 优化数据加载器参数
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if config.distributed:
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train_sampler = DistributedSampler(train_dataset, num_replicas=config.world_size, rank=local_rank, shuffle=True)
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val_sampler = DistributedSampler(val_dataset, num_replicas=config.world_size, rank=local_rank, shuffle=False)
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else:
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train_sampler = None
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val_sampler = None
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# 增加num_workers，使用persistent_workers减少进程创建开销
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train_loader = DataLoader(
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train_dataset,
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batch_size=per_gpu_batch_size, # 使用每个GPU的批次大小
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sampler=train_sampler,
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shuffle=(train_sampler is None),
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num_workers=8, # 增加worker数量
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pin_memory=True,
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persistent_workers=True, # 保持worker进程
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prefetch_factor=2 # 预取批次
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)
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val_loader = DataLoader(
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val_dataset,
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batch_size=per_gpu_batch_size,
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sampler=val_sampler,
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shuffle=False,
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num_workers=8,
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pin_memory=True,
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persistent_workers=True,
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prefetch_factor=2
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)
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# --- 2. Model, Optimizer, and Loss Initialization ---
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if local_rank == 0:
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print(f"Initializing model on {device}...")
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model = TimeAwareGPT2(
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vocab_size=vocab_size,
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n_embd=config.n_embd,
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n_layer=config.n_layer,
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n_head=config.n_head,
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pdrop=config.pdrop,
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token_pdrop=config.token_pdrop
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).to(device)
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# 使用梯度累积来模拟更大的批次大小，减少通信频率
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if config.distributed:
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# 使用find_unused_parameters=False来加速
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model = DDP(model, device_ids=[local_rank], output_device=local_rank,
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find_unused_parameters=False)
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if local_rank == 0:
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if config.distributed:
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num_params = sum(p.numel() for p in model.module.parameters() if p.requires_grad)
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else:
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num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print(f"Model initialized with {num_params/1e6:.2f}M trainable parameters.")
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print(f"Per GPU batch size: {per_gpu_batch_size}")
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loss_fn = CombinedLoss(config.ignored_token_ids)
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optimizer = Adam(model.parameters(), lr=config.lr_initial)
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# --- 3. Training Loop ---
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best_val_loss = float('inf')
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patience_counter = 0
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if local_rank == 0:
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train_losses_ce, train_losses_surv, train_losses_total = [], [], []
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val_losses_ce, val_losses_surv, val_losses_total = [], [], []
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if local_rank == 0:
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print("Starting training...")
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for epoch in range(config.max_epoch):
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if config.distributed:
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train_sampler.set_epoch(epoch)
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# --- Learning Rate Scheduling ---
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if epoch < config.warmup_epochs:
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lr = config.lr_initial
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else:
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progress = (epoch - config.warmup_epochs) / (config.max_epoch - config.warmup_epochs)
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lr = config.lr_final + 0.5 * (config.lr_initial - config.lr_final) * (1 + math.cos(math.pi * progress))
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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# --- Training Phase ---
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model.train()
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train_loss_ce_acc, train_loss_surv_acc = 0.0, 0.0
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train_steps = 0
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# 只在rank 0显示进度条
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if local_rank == 0:
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pbar = tqdm.tqdm(train_loader, desc=f"Epoch {epoch+1}/{config.max_epoch} [Train]")
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else:
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pbar = train_loader
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batch_start_time = time.time()
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for batch_idx, (event_seq, time_seq) in enumerate(pbar):
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event_seq, time_seq = event_seq.to(device, non_blocking=True), time_seq.to(device, non_blocking=True)
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# Prepare inputs and targets
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input_events = event_seq[:, :-1]
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input_times = time_seq[:, :-1]
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target_events = event_seq[:, 1:]
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target_wait_times = (time_seq[:, 1:] - time_seq[:, :-1]).float()
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# Forward pass
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logits = model(input_events, input_times)
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loss_ce, loss_survival = loss_fn(logits, target_events, target_wait_times)
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loss = loss_ce + loss_survival
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# Backward pass and optimization
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optimizer.zero_grad()
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loss.backward()
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# 梯度同步在DDP中自动处理
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optimizer.step()
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# 异步记录损失，避免同步阻塞
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train_loss_ce_acc += loss_ce.item()
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train_loss_surv_acc += loss_survival.item()
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train_steps += 1
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if local_rank == 0 and batch_idx % 10 == 0: # 每10个批次更新一次
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batch_time = time.time() - batch_start_time
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pbar.set_postfix({
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'loss_ce': f'{loss_ce.item():.4f}',
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'loss_surv': f'{loss_survival.item():.4f}',
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'lr': f'{lr:.2e}',
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'batch_time': f'{batch_time:.3f}s'
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})
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batch_start_time = time.time()
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# 只在epoch结束时同步一次损失，减少通信
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if config.distributed:
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# 使用all_reduce同步损失
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train_loss_ce_tensor = torch.tensor([train_loss_ce_acc], device=device)
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train_loss_surv_tensor = torch.tensor([train_loss_surv_acc], device=device)
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train_steps_tensor = torch.tensor([train_steps], device=device)
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dist.all_reduce(train_loss_ce_tensor)
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dist.all_reduce(train_loss_surv_tensor)
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dist.all_reduce(train_steps_tensor)
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avg_train_loss_ce = (train_loss_ce_tensor.item() / train_steps_tensor.item())
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avg_train_loss_surv = (train_loss_surv_tensor.item() / train_steps_tensor.item())
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else:
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avg_train_loss_ce = train_loss_ce_acc / train_steps
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avg_train_loss_surv = train_loss_surv_acc / train_steps
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# --- Validation Phase ---
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model.eval()
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val_loss_ce_acc, val_loss_surv_acc = 0.0, 0.0
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val_steps = 0
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with torch.no_grad():
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for event_seq, time_seq in val_loader:
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event_seq, time_seq = event_seq.to(device, non_blocking=True), time_seq.to(device, non_blocking=True)
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input_events = event_seq[:, :-1]
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input_times = time_seq[:, :-1]
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target_events = event_seq[:, 1:]
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target_wait_times = (time_seq[:, 1:] - time_seq[:, :-1]).float()
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logits = model(input_events, input_times)
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loss_ce, loss_survival = loss_fn(logits, target_events, target_wait_times)
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val_loss_ce_acc += loss_ce.item()
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val_loss_surv_acc += loss_survival.item()
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val_steps += 1
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# 同步验证损失
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if config.distributed:
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val_loss_ce_tensor = torch.tensor([val_loss_ce_acc], device=device)
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val_loss_surv_tensor = torch.tensor([val_loss_surv_acc], device=device)
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val_steps_tensor = torch.tensor([val_steps], device=device)
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dist.all_reduce(val_loss_ce_tensor)
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dist.all_reduce(val_loss_surv_tensor)
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dist.all_reduce(val_steps_tensor)
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avg_val_loss_ce = (val_loss_ce_tensor.item() / val_steps_tensor.item())
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avg_val_loss_surv = (val_loss_surv_tensor.item() / val_steps_tensor.item())
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else:
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avg_val_loss_ce = val_loss_ce_acc / val_steps
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avg_val_loss_surv = val_loss_surv_acc / val_steps
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total_val_loss = avg_val_loss_ce + avg_val_loss_surv
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# 只在rank 0进行打印和保存
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if local_rank == 0:
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train_losses_ce.append(avg_train_loss_ce)
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train_losses_surv.append(avg_train_loss_surv)
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train_losses_total.append(avg_train_loss_ce + avg_train_loss_surv)
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val_losses_ce.append(avg_val_loss_ce)
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val_losses_surv.append(avg_val_loss_surv)
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val_losses_total.append(total_val_loss)
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print(f"Epoch {epoch+1} Summary: \n"
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f" Train Loss: {avg_train_loss_ce + avg_train_loss_surv:.4f} (CE: {avg_train_loss_ce:.4f}, Surv: {avg_train_loss_surv:.4f})\n"
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f" Val Loss: {total_val_loss:.4f} (CE: {avg_val_loss_ce:.4f}, Surv: {avg_val_loss_surv:.4f})\n"
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f" Learning Rate: {lr:.6f}")
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# Early stopping check
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if total_val_loss < best_val_loss:
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best_val_loss = total_val_loss
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patience_counter = 0
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print(f"Validation loss improved to {best_val_loss:.4f}. Saving checkpoint...")
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if config.distributed:
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torch.save(model.module.state_dict(), 'best_model_checkpoint.pt')
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else:
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torch.save(model.state_dict(), 'best_model_checkpoint.pt')
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else:
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if epoch >= config.warmup_epochs:
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patience_counter += 1
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print(f"Validation loss did not improve. Patience: {patience_counter}/{config.early_stopping_patience}")
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if patience_counter >= config.early_stopping_patience:
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print("\nEarly stopping triggered due to no improvement in validation loss.")
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if config.distributed:
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stop_signal = torch.tensor(1, device=device)
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dist.broadcast(stop_signal, 0)
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break
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else:
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# 非rank 0进程检查停止信号
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if config.distributed:
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stop_signal = torch.tensor(0, device=device)
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dist.broadcast(stop_signal, 0)
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if stop_signal.item() == 1:
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break
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# 清理和保存
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if local_rank == 0 and best_val_loss != float('inf'):
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print(f"\nTraining finished. Loading best model from checkpoint with validation loss {best_val_loss:.4f}.")
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if config.distributed:
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model.module.load_state_dict(torch.load('best_model_checkpoint.pt'))
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torch.save(model.module.state_dict(), 'best_model.pt')
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else:
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model.load_state_dict(torch.load('best_model_checkpoint.pt'))
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torch.save(model.state_dict(), 'best_model.pt')
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print("Final best model saved to best_model.pt")
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if config.distributed:
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dist.destroy_process_group()
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def main():
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config = TrainConfig()
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# 设置环境变量优化
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os.environ['CUDA_LAUNCH_BLOCKING'] = '0' # 减少同步
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os.environ['NCCL_DEBUG'] = 'WARN' # 减少NCCL日志
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os.environ['NCCL_SOCKET_IFNAME'] = '^lo,docker' # 选择正确的网络接口
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if config.distributed:
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print(f"Starting distributed training with {config.world_size} GPUs")
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mp.spawn(
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train_worker,
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args=(config,),
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nprocs=config.world_size,
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join=True
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)
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else:
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print("Starting single GPU training")
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train_worker(0, config)
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if __name__ == '__main__':
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main() |