utils.load_model: take only config; infer checkpoint name from config (with legacy fallback) and vocab from checkpoint

utils.py

@@ -1,10 +1,10 @@
+import os
 import torch
 import numpy as np
 import random
 from collections import defaultdict
 import json
 from models import TimeAwareGPT2, TimeAwareGPT2Learnable
-from typing import Optional
 
 
 class PatientEventDataset(torch.utils.data.Dataset):
@@ -112,21 +112,21 @@ class PatientEventDataset(torch.utils.data.Dataset):
 
         return event_tensor, time_tensor
 
-def load_model(config_path: str, model_path: str, vocab_size: Optional[int] = None, device: str = 'cpu'):
+def load_model(config_path: str, device: str = 'cpu'):
     """
-    Load a trained model based on the training configuration and checkpoint.
+        Load a trained model based on the training configuration, inferring the
+        checkpoint filename from the configuration.
 
     According to train.py, models may be either 'TimeAwareGPT2' or
     'TimeAwareGPT2Learnable'. This function:
       - Reads the config JSON to get architecture hyperparameters
       - Selects the model class using config.model_name (defaults to TimeAwareGPT2 if absent)
-      - Infers vocab_size from the checkpoint if not provided
+            - Infers the checkpoint path from the config values
+            - Infers vocab_size from the checkpoint
       - Loads weights and returns the model in eval mode on the requested device
 
     Args:
         config_path: Path to the JSON configuration file saved during training.
-        model_path: Path to the saved model state dict (.pt).
-        vocab_size: Optional. If not provided, inferred from checkpoint weight shapes.
         device: 'cpu' or 'cuda'.
 
     Returns:
@@ -153,32 +153,47 @@ def load_model(config_path: str, model_path: str, vocab_size: Optional[int] = No
         'TimeAwareGPT2Learnable': TimeAwareGPT2Learnable,
     }.get(model_name, TimeAwareGPT2)
 
-    # 3) Infer vocab_size from checkpoint if not provided
+    # 3) Infer checkpoint filename from config
-    if vocab_size is None:
-        state = torch.load(model_path, map_location='cpu')
-        # Try typical parameter names first
-        if 'wte.weight' in state:
-            vocab_size = state['wte.weight'].shape[0]
-        elif 'head.weight' in state:
-            vocab_size = state['head.weight'].shape[0]
-        else:
-            # Fallback: try to find any (V, D) weight that likely encodes vocab
-            candidate = None
-            for k, v in state.items():
-                if isinstance(v, torch.Tensor) and v.ndim == 2:
-                    # Heuristic: the larger dim is probably vocab
-                    V = max(v.shape)
-                    if candidate is None or V > candidate:
-                        candidate = V
-            if candidate is None:
-                raise ValueError("Unable to infer vocab_size from checkpoint. Please pass vocab_size explicitly.")
-            vocab_size = candidate
-
-    # 4) Build model from config
-    # Be tolerant to configs from earlier runs that did not save some fields
     n_embd = getattr(config, 'n_embd')
     n_layer = getattr(config, 'n_layer')
     n_head = getattr(config, 'n_head')
+
+    # Newer naming (includes model_name) used by train.py when model_name is present
+    suffix_with_model = f"{model_name}_n_embd_{n_embd}_n_layer_{n_layer}_n_head_{n_head}"
+    ckpt_with_model = f"best_model_{suffix_with_model}.pt"
+
+    # Older naming (without model_name) matches existing repo files
+    suffix_legacy = f"n_embd_{n_embd}_n_layer_{n_layer}_n_head_{n_head}"
+    ckpt_legacy = f"best_model_{suffix_legacy}.pt"
+
+    # Prefer file that exists on disk
+    if os.path.exists(ckpt_with_model):
+        model_path = ckpt_with_model
+    elif os.path.exists(ckpt_legacy):
+        model_path = ckpt_legacy
+    else:
+        # Fall back to including model_name; if not present in config earlier, user may still have saved this way
+        model_path = ckpt_with_model
+        print(f"Warning: Could not find checkpoint on disk. Expected one of: {ckpt_with_model}, {ckpt_legacy}")
+
+    # 4) Infer vocab_size from checkpoint
+    state_preview = torch.load(model_path, map_location='cpu')
+    if 'wte.weight' in state_preview:
+        vocab_size = state_preview['wte.weight'].shape[0]
+    elif 'head.weight' in state_preview:
+        vocab_size = state_preview['head.weight'].shape[0]
+    else:
+        candidate = None
+        for k, v in state_preview.items():
+            if isinstance(v, torch.Tensor) and v.ndim == 2:
+                V = max(v.shape)
+                if candidate is None or V > candidate:
+                    candidate = V
+        if candidate is None:
+            raise ValueError("Unable to infer vocab_size from checkpoint. Unknown tensor shapes.")
+        vocab_size = candidate
+
+    # 5) Build model from config (tolerant to missing fields)
     pdrop = getattr(config, 'pdrop', 0.1)
     token_pdrop = getattr(config, 'token_pdrop', 0.1)
 
@@ -191,7 +206,7 @@ def load_model(config_path: str, model_path: str, vocab_size: Optional[int] = No
         token_pdrop=token_pdrop,
     ).to(device)
 
-    # 5) Load weights
+    # 6) Load weights
     state_dict = torch.load(model_path, map_location=device)
     missing, unexpected = model.load_state_dict(state_dict, strict=False)