feat: Add load_model function and update training script

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.

train_iter.py

@@ -23,8 +23,8 @@ class TrainConfig:
     n_embd = 120
     n_layer = 12
     n_head = 12
-    pdrop = 0.1
+    pdrop = 0.0
-    token_pdrop = 0.1
+    token_pdrop = 0.0
 
     # Training parameters
     max_iter = 200000

utils.py

@@ -2,6 +2,9 @@ import torch
 import numpy as np
 import random
 from collections import defaultdict
+import json
+from models import TimeAwareGPT2
+
 
 class PatientEventDataset(torch.utils.data.Dataset):
     """
@@ -102,3 +105,46 @@ class PatientEventDataset(torch.utils.data.Dataset):
         time_tensor = torch.tensor(time_stamps, dtype=torch.long)
 
         return event_tensor, time_tensor
+
+def load_model(config_path, model_path, vocab_size, device='cpu'):
+    """
+    Loads a trained TimeAwareGPT2 model from a configuration file and a state dictionary.
+
+    Args:
+        config_path (str): Path to the JSON configuration file.
+        model_path (str): Path to the saved model state dictionary (.pt file).
+        vocab_size (int): The vocabulary size used during training.
+        device (str): The device to load the model onto ('cpu' or 'cuda').
+
+    Returns:
+        (TimeAwareGPT2): The loaded and initialized model.
+    """
+    with open(config_path, 'r') as f:
+        config_dict = json.load(f)
+
+    # Create a config object from the dictionary
+    class AttrDict(dict):
+        def __init__(self, *args, **kwargs):
+            super(AttrDict, self).__init__(*args, **kwargs)
+            self.__dict__ = self
+
+    config = AttrDict(config_dict)
+
+    # Initialize the model with parameters from the config
+    model = TimeAwareGPT2(
+        vocab_size=vocab_size,
+        n_embd=config.n_embd,
+        n_layer=config.n_layer,
+        n_head=config.n_head,
+        pdrop=config.pdrop,
+        token_pdrop=config.token_pdrop
+    ).to(device)
+
+    # Load the saved state dictionary
+    model.load_state_dict(torch.load(model_path, map_location=device))
+
+    # Set the model to evaluation mode
+    model.eval()
+
+    print(f"Model loaded from {model_path} with {model.get_num_params():.2f}M parameters.")
+    return model