401 lines
14 KiB
Python
401 lines
14 KiB
Python
# train.py (DDP-ready)
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import os
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import math
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import argparse
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import numpy as np
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import tqdm
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import matplotlib.pyplot as plt
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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 torch.optim import Adam
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from torch.utils.data import DataLoader, DistributedSampler
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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 = 128
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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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# 0 = padding, 1 = "no event"
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ignored_token_ids = [0, 1]
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# System parameters (device 将在 main() 内按 local_rank 动态设置)
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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def setup_distributed(backend: str = "nccl"):
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"""
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如果由 torchrun 启动且 WORLD_SIZE>1，则初始化分布式。
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返回 (is_distributed, world_size, rank, local_rank)
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"""
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world_size = int(os.environ.get("WORLD_SIZE", "1"))
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is_distributed = world_size > 1
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if is_distributed:
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if not dist.is_initialized():
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dist.init_process_group(backend=backend, init_method="env://")
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rank = dist.get_rank()
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local_rank = int(os.environ.get("LOCAL_RANK", "0"))
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torch.cuda.set_device(local_rank)
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else:
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rank = 0
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local_rank = 0
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return is_distributed, world_size, rank, local_rank
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def cleanup_distributed():
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if dist.is_available() and dist.is_initialized():
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dist.destroy_process_group()
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def all_reduce_mean(value: float, device, world_size: int):
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"""
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value 是 Python float（本进程的和/均值），返回所有进程平均后的 float。
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"""
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tensor = torch.tensor([value], dtype=torch.float32, device=device)
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dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
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tensor /= world_size
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return float(tensor.item())
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--backend", type=str, default="nccl", choices=["nccl", "gloo", "mpi"])
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parser.add_argument("--seed", type=int, default=42)
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args = parser.parse_args()
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# 分布式初始化
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is_dist, world_size, rank, local_rank = setup_distributed(args.backend)
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# 基本环境
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torch.manual_seed(args.seed + rank)
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np.random.seed(args.seed + rank)
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torch.backends.cudnn.benchmark = True
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config = TrainConfig()
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device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu")
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config.device = device
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is_main = (rank == 0)
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# --- 1. Data Loading ---
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if is_main:
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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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# Infer vocab_size from the data (max label + 1)
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vocab_size = int(max(train_data_arr[:, 2].max(), val_data_arr[:, 2].max())) + 1
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if is_main:
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print(f"Inferred vocabulary size: {vocab_size}")
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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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# 分布式采样器
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if is_dist:
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train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=rank, shuffle=True, drop_last=False)
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val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=rank, shuffle=False, drop_last=False)
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else:
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train_sampler = None
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val_sampler = None
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train_loader = DataLoader(
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train_dataset,
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batch_size=config.batch_size,
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shuffle=(train_sampler is None),
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sampler=train_sampler,
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num_workers=4,
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pin_memory=True,
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drop_last=False,
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persistent_workers=True if 4 > 0 else False,
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)
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val_loader = DataLoader(
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val_dataset,
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batch_size=config.batch_size,
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shuffle=False,
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sampler=val_sampler,
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num_workers=4,
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pin_memory=True,
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drop_last=False,
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persistent_workers=True if 4 > 0 else False,
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)
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# --- 2. Model, Optimizer, and Loss Initialization ---
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if is_main:
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print(f"Initializing model on {config.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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if is_main and hasattr(model, "get_num_params"):
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print(f"Model initialized with {model.get_num_params():.2f}M trainable parameters.")
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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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# DDP 包装
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if is_dist:
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model = nn.parallel.DistributedDataParallel(
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model,
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device_ids=[local_rank] if device.type == "cuda" else None,
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output_device=local_rank if device.type == "cuda" else None,
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find_unused_parameters=False,
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)
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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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# 只在主进程收集与画图
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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 is_main:
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print("Starting training...")
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stop_training = False
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for epoch in range(config.max_epoch):
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# 设置 epoch 给分布式采样器，确保跨 epoch shuffle
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if is_dist:
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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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if is_main:
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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 # 非主进程禁用 tqdm
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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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for batch in pbar:
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event_seq, time_seq = batch
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event_seq = event_seq.to(device, non_blocking=True)
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time_seq = 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(set_to_none=True)
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loss.backward()
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optimizer.step()
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train_loss_ce_acc += float(loss_ce.item())
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train_loss_surv_acc += float(loss_survival.item())
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train_steps += 1
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if is_main and isinstance(pbar, tqdm.tqdm):
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pbar.set_postfix({'loss_ce': f'{loss_ce.item():.4f}', 'loss_surv': f'{loss_survival.item():.4f}', 'lr': f'{lr:.2e}'})
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# 进程内均值
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avg_train_loss_ce_local = train_loss_ce_acc / max(train_steps, 1)
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avg_train_loss_surv_local = train_loss_surv_acc / max(train_steps, 1)
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# 所有进程平均
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if is_dist:
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avg_train_loss_ce = all_reduce_mean(avg_train_loss_ce_local, device, world_size)
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avg_train_loss_surv = all_reduce_mean(avg_train_loss_surv_local, device, world_size)
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else:
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avg_train_loss_ce = avg_train_loss_ce_local
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avg_train_loss_surv = avg_train_loss_surv_local
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if is_main:
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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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# --- Validation Phase ---
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if is_main:
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pbar_val = tqdm.tqdm(val_loader, desc=f"Epoch {epoch+1}/{config.max_epoch} [Val]")
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else:
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pbar_val = val_loader
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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 batch in pbar_val:
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event_seq, time_seq = batch
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event_seq = event_seq.to(device, non_blocking=True)
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time_seq = 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 += float(loss_ce.item())
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val_loss_surv_acc += float(loss_survival.item())
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val_steps += 1
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if is_main and isinstance(pbar_val, tqdm.tqdm):
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pbar_val.set_postfix({'loss_ce': f'{loss_ce.item():.4f}', 'loss_surv': f'{loss_survival.item():.4f}'})
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avg_val_loss_ce_local = val_loss_ce_acc / max(val_steps, 1)
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avg_val_loss_surv_local = val_loss_surv_acc / max(val_steps, 1)
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if is_dist:
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avg_val_loss_ce = all_reduce_mean(avg_val_loss_ce_local, device, world_size)
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avg_val_loss_surv = all_reduce_mean(avg_val_loss_surv_local, device, world_size)
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else:
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avg_val_loss_ce = avg_val_loss_ce_local
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avg_val_loss_surv = avg_val_loss_surv_local
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total_val_loss = avg_val_loss_ce + avg_val_loss_surv
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# 主进程打印与记录
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if is_main:
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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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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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# --- Early Stopping Check (基于聚合后的 total_val_loss) ---
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improved = False
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if is_main:
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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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improved = True
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print(f"Validation loss improved to {best_val_loss:.4f}. Saving checkpoint...")
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# DDP: 保存 module.state_dict()
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state_dict = model.module.state_dict() if isinstance(model, nn.parallel.DistributedDataParallel) else model.state_dict()
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torch.save(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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stop_training = patience_counter >= config.early_stopping_patience
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# 把 improved/stop 广播到所有进程，确保一致退出
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if is_dist:
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flag_tensor = torch.tensor([1 if stop_training else 0], device=device, dtype=torch.int32)
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dist.broadcast(flag_tensor, src=0)
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stop_training = bool(int(flag_tensor.item()))
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if stop_training:
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if is_main:
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print("\nEarly stopping triggered due to no improvement in validation loss.")
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break
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# --- Save Best Model at the End (只主进程) ---
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if is_main:
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if 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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# 为了易用，这里在主进程上重新构建单卡模型加载权重再保存
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model_single = 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('cpu')
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model_single.load_state_dict(torch.load('best_model_checkpoint.pt', map_location='cpu'))
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print("Saving final best model to best_model.pt")
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torch.save(model_single.state_dict(), 'best_model.pt')
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else:
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print("\nTraining finished. No best model to save as validation loss never improved.")
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# --- Plot and Save Loss Curves ---
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num_epochs = len(train_losses_total)
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if num_epochs > 0:
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epochs = range(1, num_epochs + 1)
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plt.figure(figsize=(18, 5))
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# Plot CE Loss
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plt.subplot(1, 3, 1)
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plt.plot(epochs, train_losses_ce, label='Train CE')
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plt.plot(epochs, val_losses_ce, label='Val CE')
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plt.title('Cross-Entropy Loss')
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plt.xlabel('Epochs')
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plt.ylabel('Loss')
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plt.legend()
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plt.grid(True)
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# Plot Survival Loss
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plt.subplot(1, 3, 2)
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plt.plot(epochs, train_losses_surv, label='Train Survival')
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plt.plot(epochs, val_losses_surv, label='Val Survival')
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plt.title('Survival Loss')
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plt.xlabel('Epochs')
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plt.ylabel('Loss')
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plt.legend()
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plt.grid(True)
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# Plot Total Loss
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plt.subplot(1, 3, 3)
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plt.plot(epochs, train_losses_total, label='Train Total')
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plt.plot(epochs, val_losses_total, label='Val Total')
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plt.title('Total Loss')
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plt.xlabel('Epochs')
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plt.ylabel('Loss')
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plt.legend()
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plt.grid(True)
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plt.tight_layout()
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plt.savefig('loss_curves.png')
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print("\nLoss curves saved to loss_curves.png")
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# 清理分布式
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cleanup_distributed()
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if __name__ == '__main__':
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main()
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