Jiarui Li
a631ac6d59
Added a `load_model` function to `utils.py` to allow loading of trained models from configuration and state dictionary files. The `train_iter.py` script was also modified, likely to incorporate or test this new functionality.
219 lines
7.6 KiB
Python
219 lines
7.6 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 itertools
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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 = 0.0
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token_pdrop = 0.0
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# Training parameters
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max_iter = 200000
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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_iter = 1000
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# Loss parameters
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# 0 = padding, 1 = "no event"
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ignored_token_ids = [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] # Example ignored token IDs
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# System parameters
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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# --- Main Training Script ---
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def main():
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config = TrainConfig()
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model_filename = f"best_model_n_embd_{config.n_embd}_n_layer_{config.n_layer}_n_head_{config.n_head}_iter.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}_iter.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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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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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_loader = DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True, num_workers=4, pin_memory=True)
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val_loader = DataLoader(val_dataset, batch_size=config.batch_size, shuffle=False, num_workers=4, pin_memory=True)
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train_iter_loader = iter(itertools.cycle(train_loader))
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# --- 2. Model, Optimizer, and Loss Initialization ---
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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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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 = AdamW(model.parameters(), lr=config.lr_initial, weight_decay=config.weight_decay, betas=(0.9, 0.99))
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# --- 3. Training Loop ---
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# Lists to store losses
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train_losses_ce, train_losses_surv, train_losses_total = [], [], []
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print("Starting training...")
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pbar = tqdm.tqdm(range(1, config.max_iter + 1), desc="Training")
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for iter_num in pbar:
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# --- Learning Rate Scheduling ---
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if iter_num < config.warmup_iter:
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lr = config.lr_initial
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else:
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progress = (iter_num - config.warmup_iter) / (config.max_iter - config.warmup_iter)
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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 Step ---
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model.train()
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event_seq, time_seq = next(train_iter_loader)
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event_seq, time_seq = event_seq.to(config.device), time_seq.to(config.device)
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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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optimizer.step()
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train_losses_ce.append(loss_ce.item())
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train_losses_surv.append(loss_survival.item())
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train_losses_total.append(loss.item())
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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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print("\nTraining finished.")
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# --- 4. Final Validation ---
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print("Running final validation...")
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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="Final Validation")
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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 = 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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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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print(f"Final Validation Summary: \n"
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f" Val Loss: {total_val_loss:.4f} (CE: {avg_val_loss_ce:.4f}, Surv: {avg_val_loss_surv:.4f})")
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# --- 5. Save Model ---
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print(f"Saving final model to {model_filename}")
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torch.save(model.state_dict(), model_filename)
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# --- 6. Save and Plot Losses ---
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losses_filename = f"losses_n_embd_{config.n_embd}_n_layer_{config.n_layer}_n_head_{config.n_head}_iter.txt"
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with open(losses_filename, 'w') as f:
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f.write("iteration,train_loss_ce,train_loss_surv,train_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]}\n")
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print(f"\nLosses saved to {losses_filename}")
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# Plot and Save Loss Curves
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iterations = range(1, len(train_losses_total) + 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(iterations, train_losses_ce, label='Train CE')
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plt.title('Cross-Entropy Loss')
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plt.xlabel('Iterations')
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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(iterations, train_losses_surv, label='Train Survival')
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plt.title('Survival Loss')
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plt.xlabel('Iterations')
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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(iterations, train_losses_total, label='Train Total')
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plt.title('Total Loss')
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plt.xlabel('Iterations')
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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_iter.png')
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print("\nLoss curves saved to loss_curves_iter.png")
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if __name__ == '__main__':
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main()
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