update dpp
This commit is contained in:
478
train_dpp.py
478
train_dpp.py
@@ -1,16 +1,16 @@
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# train_ddp.py
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import os
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import math
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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.nn.parallel import DistributedDataParallel as DDP
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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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@@ -19,8 +19,8 @@ from utils import PatientEventDataset
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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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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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@@ -31,117 +31,90 @@ class TrainConfig:
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# Training parameters
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max_epoch = 200
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batch_size = 128
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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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# 0 = padding, 1 = "no event"
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ignored_token_ids = [0, 1]
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# System parameters
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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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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def ddp_is_active():
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return dist.is_available() and dist.is_initialized() and dist.get_world_size() > 1
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def ddp_setup():
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"""Initialize process group if launched by torchrun."""
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if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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backend = 'nccl' if torch.cuda.is_available() else 'gloo'
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dist.init_process_group(backend=backend, init_method='env://')
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return True
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return False
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def ddp_cleanup():
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if dist.is_initialized():
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dist.barrier()
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dist.destroy_process_group()
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def all_reduce_mean(value: torch.Tensor):
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"""Mean across processes (no-op if not DDP)."""
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if ddp_is_active():
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dist.all_reduce(value, op=dist.ReduceOp.SUM)
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value /= dist.get_world_size()
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return value
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def bcast_bool(stop: bool):
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if not ddp_is_active():
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return stop
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t = torch.tensor(1 if stop else 0, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
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dist.broadcast(t, src=0)
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return bool(t.item())
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def main():
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config = TrainConfig()
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# --- DDP setup ---
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ddp_enabled = ddp_setup()
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local_rank = int(os.environ.get("LOCAL_RANK", 0))
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global_rank = int(os.environ.get("RANK", 0)) if ddp_enabled else 0
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world_size = int(os.environ.get("WORLD_SIZE", 1)) if ddp_enabled else 1
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if torch.cuda.is_available():
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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(config.device)
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# Seed per-rank (different but deterministic)
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torch.manual_seed(1337 + global_rank)
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np.random.seed(1337 + global_rank)
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is_main = (global_rank == 0)
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if is_main:
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print(f"DDP enabled: {ddp_enabled} | world_size={world_size} | device={device}")
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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 is_main:
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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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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 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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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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val_dataset = PatientEventDataset(val_data_arr, config.block_length)
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# DDP samplers (fall back to regular shuffle when not DDP)
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if ddp_enabled:
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train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=global_rank, shuffle=True, drop_last=False)
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val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=global_rank, shuffle=False, drop_last=False)
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shuffle_flag = False
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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, val_sampler = None, None
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shuffle_flag = True
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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=config.batch_size,
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shuffle=shuffle_flag,
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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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num_workers=4,
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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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drop_last=False,
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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=config.batch_size,
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shuffle=False,
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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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num_workers=4,
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shuffle=False,
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num_workers=8,
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pin_memory=True,
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drop_last=False,
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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, Loss ---
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if is_main:
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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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@@ -151,167 +124,175 @@ def main():
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token_pdrop=config.token_pdrop
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).to(device)
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if is_main:
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# If your model has get_num_params
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try:
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print(f"Model initialized with {model.get_num_params():.2f}M trainable parameters.")
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except Exception:
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total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print(f"Model initialized with {total_params/1e6:.2f}M trainable parameters.")
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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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loss_fn = CombinedLoss(config.ignored_token_ids)
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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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# Wrap with DDP
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if ddp_enabled:
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model = DDP(model, 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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# AMP
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use_amp = (device.type == 'cuda')
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scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
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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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# store losses only on main to plot
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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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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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# Ensure different shuffles per epoch under DDP
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if ddp_enabled:
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if config.distributed:
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train_sampler.set_epoch(epoch)
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val_sampler.set_epoch(epoch)
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# --- LR scheduling (same as original) ---
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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 pg in optimizer.param_groups:
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pg['lr'] = lr
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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# --- Training ---
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if is_main:
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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 # silent on non-main ranks
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model.train()
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train_ce_sum = torch.tensor(0.0, device=device)
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train_surv_sum = torch.tensor(0.0, device=device)
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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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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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optimizer.zero_grad(set_to_none=True)
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with torch.cuda.amp.autocast(enabled=use_amp):
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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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# 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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if use_amp:
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scaler.scale(loss).backward()
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scaler.step(optimizer)
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scaler.update()
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else:
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loss.backward()
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optimizer.step()
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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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train_ce_sum += loss_ce.detach()
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train_surv_sum += loss_survival.detach()
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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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if is_main and isinstance(pbar, tqdm.tqdm):
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pbar.set_postfix({'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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# Average train losses across ranks
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train_ce_avg = train_ce_sum / max(1, train_steps)
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train_surv_avg = train_surv_sum / max(1, train_steps)
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train_ce_avg = all_reduce_mean(train_ce_avg)
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train_surv_avg = all_reduce_mean(train_surv_avg)
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train_total_avg = train_ce_avg + train_surv_avg
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# --- Validation ---
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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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# 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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pbar_val = val_loader
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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_ce_sum = torch.tensor(0.0, device=device)
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val_surv_sum = torch.tensor(0.0, device=device)
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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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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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with torch.cuda.amp.autocast(enabled=use_amp):
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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_ce_sum += loss_ce.detach()
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val_surv_sum += loss_survival.detach()
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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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|
||||
if is_main and isinstance(pbar_val, tqdm.tqdm):
|
||||
pbar_val.set_postfix({'loss_ce': f'{loss_ce.item():.4f}',
|
||||
'loss_surv': f'{loss_survival.item():.4f}'})
|
||||
# 同步验证损失
|
||||
if config.distributed:
|
||||
val_loss_ce_tensor = torch.tensor([val_loss_ce_acc], device=device)
|
||||
val_loss_surv_tensor = torch.tensor([val_loss_surv_acc], device=device)
|
||||
val_steps_tensor = torch.tensor([val_steps], device=device)
|
||||
|
||||
dist.all_reduce(val_loss_ce_tensor)
|
||||
dist.all_reduce(val_loss_surv_tensor)
|
||||
dist.all_reduce(val_steps_tensor)
|
||||
|
||||
avg_val_loss_ce = (val_loss_ce_tensor.item() / val_steps_tensor.item())
|
||||
avg_val_loss_surv = (val_loss_surv_tensor.item() / val_steps_tensor.item())
|
||||
else:
|
||||
avg_val_loss_ce = val_loss_ce_acc / val_steps
|
||||
avg_val_loss_surv = val_loss_surv_acc / val_steps
|
||||
|
||||
# Average val losses across ranks
|
||||
val_ce_avg = val_ce_sum / max(1, val_steps)
|
||||
val_surv_avg = val_surv_sum / max(1, val_steps)
|
||||
val_ce_avg = all_reduce_mean(val_ce_avg)
|
||||
val_surv_avg = all_reduce_mean(val_surv_avg)
|
||||
val_total_avg = val_ce_avg + val_surv_avg
|
||||
total_val_loss = avg_val_loss_ce + avg_val_loss_surv
|
||||
|
||||
# 只在rank 0进行打印和保存
|
||||
if local_rank == 0:
|
||||
train_losses_ce.append(avg_train_loss_ce)
|
||||
train_losses_surv.append(avg_train_loss_surv)
|
||||
train_losses_total.append(avg_train_loss_ce + avg_train_loss_surv)
|
||||
val_losses_ce.append(avg_val_loss_ce)
|
||||
val_losses_surv.append(avg_val_loss_surv)
|
||||
val_losses_total.append(total_val_loss)
|
||||
|
||||
# --- Logging & Early Stopping (rank 0) ---
|
||||
stop_now = False
|
||||
if is_main:
|
||||
print(f"Epoch {epoch+1} Summary:\n"
|
||||
f" Train Loss: {float(train_total_avg):.4f} (CE: {float(train_ce_avg):.4f}, Surv: {float(train_surv_avg):.4f})\n"
|
||||
f" Val Loss: {float(val_total_avg):.4f} (CE: {float(val_ce_avg):.4f}, Surv: {float(val_surv_avg):.4f})\n"
|
||||
print(f"Epoch {epoch+1} Summary: \n"
|
||||
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"
|
||||
f" Val Loss: {total_val_loss:.4f} (CE: {avg_val_loss_ce:.4f}, Surv: {avg_val_loss_surv:.4f})\n"
|
||||
f" Learning Rate: {lr:.6f}")
|
||||
|
||||
# Record for curves
|
||||
train_losses_ce.append(float(train_ce_avg))
|
||||
train_losses_surv.append(float(train_surv_avg))
|
||||
train_losses_total.append(float(train_total_avg))
|
||||
val_losses_ce.append(float(val_ce_avg))
|
||||
val_losses_surv.append(float(val_surv_avg))
|
||||
val_losses_total.append(float(val_total_avg))
|
||||
|
||||
if val_total_avg < best_val_loss:
|
||||
best_val_loss = float(val_total_avg)
|
||||
# Early stopping check
|
||||
if total_val_loss < best_val_loss:
|
||||
best_val_loss = total_val_loss
|
||||
patience_counter = 0
|
||||
print(f"Validation loss improved to {best_val_loss:.4f}. Saving checkpoint (rank0)...")
|
||||
# unwrap DDP
|
||||
to_save = model.module if isinstance(model, DDP) else model
|
||||
torch.save(to_save.state_dict(), 'best_model_checkpoint.pt')
|
||||
print(f"Validation loss improved to {best_val_loss:.4f}. Saving checkpoint...")
|
||||
if config.distributed:
|
||||
torch.save(model.module.state_dict(), 'best_model_checkpoint.pt')
|
||||
else:
|
||||
torch.save(model.state_dict(), 'best_model_checkpoint.pt')
|
||||
else:
|
||||
if epoch >= config.warmup_epochs:
|
||||
patience_counter += 1
|
||||
@@ -319,58 +300,51 @@ def main():
|
||||
|
||||
if patience_counter >= config.early_stopping_patience:
|
||||
print("\nEarly stopping triggered due to no improvement in validation loss.")
|
||||
stop_now = True
|
||||
|
||||
# Broadcast stop flag so all ranks exit together
|
||||
stop_now = bcast_bool(stop_now)
|
||||
if stop_now:
|
||||
break
|
||||
|
||||
# --- Save Best Model at the End (rank 0 only) ---
|
||||
if is_main:
|
||||
if best_val_loss != float('inf'):
|
||||
print(f"\nTraining finished. Loading best model from checkpoint with validation loss {best_val_loss:.4f}.")
|
||||
# unwrap for loading & final save
|
||||
to_load = model.module if isinstance(model, DDP) else model
|
||||
to_load.load_state_dict(torch.load('best_model_checkpoint.pt', map_location='cpu'))
|
||||
print("Saving final best model to best_model.pt")
|
||||
torch.save(to_load.state_dict(), 'best_model.pt')
|
||||
if config.distributed:
|
||||
stop_signal = torch.tensor(1, device=device)
|
||||
dist.broadcast(stop_signal, 0)
|
||||
break
|
||||
else:
|
||||
print("\nTraining finished. No best model to save as validation loss never improved.")
|
||||
# 非rank 0进程检查停止信号
|
||||
if config.distributed:
|
||||
stop_signal = torch.tensor(0, device=device)
|
||||
dist.broadcast(stop_signal, 0)
|
||||
if stop_signal.item() == 1:
|
||||
break
|
||||
|
||||
# --- Plot and Save Loss Curves ---
|
||||
num_epochs = len(train_losses_total)
|
||||
if num_epochs > 0:
|
||||
epochs = range(1, num_epochs + 1)
|
||||
plt.figure(figsize=(18, 5))
|
||||
# 清理和保存
|
||||
if local_rank == 0 and best_val_loss != float('inf'):
|
||||
print(f"\nTraining finished. Loading best model from checkpoint with validation loss {best_val_loss:.4f}.")
|
||||
if config.distributed:
|
||||
model.module.load_state_dict(torch.load('best_model_checkpoint.pt'))
|
||||
torch.save(model.module.state_dict(), 'best_model.pt')
|
||||
else:
|
||||
model.load_state_dict(torch.load('best_model_checkpoint.pt'))
|
||||
torch.save(model.state_dict(), 'best_model.pt')
|
||||
print("Final best model saved to best_model.pt")
|
||||
|
||||
plt.subplot(1, 3, 1)
|
||||
plt.plot(epochs, train_losses_ce, label='Train CE')
|
||||
plt.plot(epochs, val_losses_ce, label='Val CE')
|
||||
plt.title('Cross-Entropy Loss')
|
||||
plt.xlabel('Epochs'); plt.ylabel('Loss')
|
||||
plt.legend(); plt.grid(True)
|
||||
if config.distributed:
|
||||
dist.destroy_process_group()
|
||||
|
||||
plt.subplot(1, 3, 2)
|
||||
plt.plot(epochs, train_losses_surv, label='Train Survival')
|
||||
plt.plot(epochs, val_losses_surv, label='Val Survival')
|
||||
plt.title('Survival Loss')
|
||||
plt.xlabel('Epochs'); plt.ylabel('Loss')
|
||||
plt.legend(); plt.grid(True)
|
||||
|
||||
plt.subplot(1, 3, 3)
|
||||
plt.plot(epochs, train_losses_total, label='Train Total')
|
||||
plt.plot(epochs, val_losses_total, label='Val Total')
|
||||
plt.title('Total Loss')
|
||||
plt.xlabel('Epochs'); plt.ylabel('Loss')
|
||||
plt.legend(); plt.grid(True)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig('loss_curves.png')
|
||||
print("\nLoss curves saved to loss_curves.png")
|
||||
|
||||
# Clean up DDP
|
||||
ddp_cleanup()
|
||||
def main():
|
||||
config = TrainConfig()
|
||||
|
||||
# 设置环境变量优化
|
||||
os.environ['CUDA_LAUNCH_BLOCKING'] = '0' # 减少同步
|
||||
os.environ['NCCL_DEBUG'] = 'WARN' # 减少NCCL日志
|
||||
os.environ['NCCL_SOCKET_IFNAME'] = '^lo,docker' # 选择正确的网络接口
|
||||
|
||||
if config.distributed:
|
||||
print(f"Starting distributed training with {config.world_size} GPUs")
|
||||
mp.spawn(
|
||||
train_worker,
|
||||
args=(config,),
|
||||
nprocs=config.world_size,
|
||||
join=True
|
||||
)
|
||||
else:
|
||||
print("Starting single GPU training")
|
||||
train_worker(0, config)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
main()
|
||||
Reference in New Issue
Block a user