Jiarui Li
d760c45baf
Add train_multigpu.py for distributed data parallel training. Update train.py to save the training configuration to a JSON file. Generalize .gitignore to exclude all *.pt checkpoint files. Delete obsolete train_dpp.py file.
274 lines
11 KiB
Python
274 lines
11 KiB
Python
import torch
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import torch.nn as nn
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from torch.optim import AdamW
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from torch.utils.data import DataLoader
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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 json
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import os
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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.utils.data.distributed import 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 = 48 # Sequence length
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# Model parameters
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n_embd = 120
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n_layer = 12
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n_head = 12
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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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weight_decay = 2e-1
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warmup_epochs = 10
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early_stopping_patience = 10
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# Loss parameters
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ignored_token_ids = [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
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# System parameters (will be updated by DDP setup)
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device = 'cuda'
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# --- DDP Setup ---
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def setup_ddp():
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"""Initializes the distributed data parallel environment."""
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dist.init_process_group(backend='nccl')
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rank = dist.get_rank()
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local_rank = int(os.environ['LOCAL_RANK'])
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torch.cuda.set_device(local_rank)
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return rank, local_rank
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def cleanup_ddp():
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"""Cleans up the distributed data parallel environment."""
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dist.destroy_process_group()
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# --- Main Training Script ---
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def main():
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rank, local_rank = setup_ddp()
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is_main_process = (rank == 0)
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config = TrainConfig()
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config.device = f'cuda:{local_rank}'
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if is_main_process:
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model_filename = f"best_model_n_embd_{config.n_embd}_n_layer_{config.n_layer}_n_head_{config.n_head}.pt"
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checkpoint_filename = f"best_model_checkpoint_n_embd_{config.n_embd}_n_layer_{config.n_layer}_n_head_{config.n_head}.pt"
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# --- 0. Save Configuration ---
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config_filename = f"config_n_embd_{config.n_embd}_n_layer_{config.n_layer}_n_head_{config.n_head}.json"
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config_dict = {k: v for k, v in vars(config).items() if not k.startswith('__')}
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with open(config_filename, 'w') as f:
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json.dump(config_dict, f, indent=4)
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print(f"Configuration saved to {config_filename}")
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# --- 1. Data Loading ---
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if is_main_process:
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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 is_main_process:
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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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train_sampler = DistributedSampler(train_dataset)
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val_sampler = DistributedSampler(val_dataset, shuffle=False)
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train_loader = DataLoader(train_dataset, batch_size=config.batch_size, sampler=train_sampler, num_workers=4, pin_memory=True)
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val_loader = DataLoader(val_dataset, batch_size=config.batch_size, sampler=val_sampler, num_workers=4, pin_memory=True)
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# --- 2. Model, Optimizer, and Loss Initialization ---
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if is_main_process:
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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(config.device)
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model = DDP(model, device_ids=[local_rank])
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if is_main_process:
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print(f"Model initialized with {model.module.get_num_params():.2f}M trainable parameters.")
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loss_fn = CombinedLoss(config.ignored_token_ids)
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optimizer = AdamW(model.parameters(), lr=config.lr_initial, weight_decay=config.weight_decay)
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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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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_process:
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print("Starting training...")
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for epoch in range(config.max_epoch):
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train_sampler.set_epoch(epoch) # Important for shuffling
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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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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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pbar = tqdm.tqdm(train_loader, desc=f"Epoch {epoch+1}/{config.max_epoch} [Train]", disable=not is_main_process)
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for event_seq, time_seq in pbar:
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event_seq, time_seq = event_seq.to(config.device), time_seq.to(config.device)
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input_events, input_times = event_seq[:, :-1], time_seq[:, :-1]
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target_events, target_wait_times = event_seq[:, 1:], (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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loss = loss_ce + loss_survival
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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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 is_main_process:
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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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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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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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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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pbar_val = tqdm.tqdm(val_loader, desc=f"Epoch {epoch+1}/{config.max_epoch} [Val]", disable=not is_main_process)
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for event_seq, time_seq in pbar_val:
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event_seq, time_seq = event_seq.to(config.device), time_seq.to(config.device)
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input_events, input_times = event_seq[:, :-1], time_seq[:, :-1]
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target_events, target_wait_times = event_seq[:, 1:], (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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if is_main_process:
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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 = 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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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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if is_main_process:
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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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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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torch.save(model.module.state_dict(), checkpoint_filename)
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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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if is_main_process:
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print("\nEarly stopping triggered due to no improvement in validation loss.")
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break
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if is_main_process:
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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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# Load the best weights into the module before saving the final model
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model.module.load_state_dict(torch.load(checkpoint_filename))
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print(f"Saving final best model to {model_filename}")
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torch.save(model.module.state_dict(), model_filename)
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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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losses_filename = f"losses_n_embd_{config.n_embd}_n_layer_{config.n_layer}_n_head_{config.n_head}.txt"
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with open(losses_filename, 'w') as f:
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f.write("epoch,train_loss_ce,train_loss_surv,train_loss_total,val_loss_ce,val_loss_surv,val_loss_total\n")
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for i in range(len(train_losses_total)):
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f.write(f"{i+1},{train_losses_ce[i]},{train_losses_surv[i]},{train_losses_total[i]},{val_losses_ce[i]},{val_losses_surv[i]},{val_losses_total[i]}\n")
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print(f"\nLosses saved to {losses_filename}")
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num_epochs = len(train_losses_total)
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epochs = range(1, num_epochs + 1)
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plt.figure(figsize=(18, 5))
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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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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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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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cleanup_ddp()
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if __name__ == '__main__':
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main()
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