compile eval model also

scripts/base_eval.py

@@ -75,6 +75,7 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
     # Evaluate each task
     results = {}
     centered_results = {}
+    task_times = []
     for task in tasks:
         start_time = time.time()
         label = task['label']
@@ -106,13 +107,20 @@ def evaluate_model(model, tokenizer, device, max_per_task=-1):
         centered_result = (accuracy - 0.01 * random_baseline) / (1.0 - 0.01 * random_baseline)
         centered_results[label] = centered_result
         end_time = time.time()
+        task_times.append(end_time - start_time)
         print0(f"accuracy: {accuracy:.4f} | centered: {centered_result:.4f} | time: {end_time - start_time:.2f}s")
 
     core_metric = sum(centered_results.values()) / len(centered_results)
+    total_time = sum(task_times)
+    print0(f"Task timing stats: total={total_time:.2f}s"
+           f"| avg={total_time/len(task_times):.2f}s |"
+           "+ min={min(task_times):.2f}s | max={max(task_times):.2f}s")
     out = {
         "results": results,
         "centered_results": centered_results,
-        "core_metric": core_metric
+        "core_metric": core_metric,
+        "dt": total_time
+        
     }
     return out
 
@@ -169,7 +177,8 @@ def main():
         model, tokenizer, meta = load_model("base", device, phase="eval")
         model_name = f"base_model (step {meta['step']})" # just for logging
         model_slug = f"base_model_{meta['step']:06d}" # for the output csv file
-
+    
+    model = torch.compile(model)
     # Evaluate the model
     with autocast_ctx:
         out = evaluate_model(model, tokenizer, device, max_per_task=args.max_per_task)

scripts/base_train.py

@@ -112,7 +112,9 @@ with torch.device("meta"):
 model.to_empty(device=device)
 model.init_weights()
 orig_model = model # original, uncompiled model, for saving raw model state_dict
-model = torch.compile(model, dynamic=False) # TODO: dynamic True/False think through
+eval_model = model
+eval_model = torch.compile(eval_model, fullgraph=True, dynamic=True)
+model = torch.compile(model, fullgraph=True, dynamic=False) # TODO: dynamic True/False think through
 num_params = sum(p.numel() for p in model.parameters())
 print0(f"Number of parameters: {num_params:,}")
 num_flops_per_token = model.estimate_flops()
@@ -206,8 +208,9 @@ for step in range(num_iterations + 1):
     if core_metric_every > 0 and (last_step or (step > 0 and step % core_metric_every == 0)):
         model.eval()
         with autocast_ctx:
-            results = evaluate_model(orig_model, tokenizer, device, max_per_task=core_metric_max_per_task)
+            results = evaluate_model(eval_model, tokenizer, device, max_per_task=core_metric_max_per_task)
-        print0(f"Step {step:05d} | CORE metric: {results['core_metric']:.4f}")
+        print0(f"Step {step:05d} | CORE metric: {results['core_metric']:.4f} Eval time: {results['dt']:.4f}s")
+        
         wandb_run.log({
             "step": step,
             "total_training_flops": flops_so_far,
@@ -229,7 +232,7 @@ for step in range(num_iterations + 1):
             "My favorite color is",
             "If 5*x + 3 = 13, then x is",
         ]
-        engine = Engine(orig_model, tokenizer) # use orig_model to avoid recompilation
+        engine = Engine(eval_model, tokenizer) # use eval_model to avoid recompilation
         for prompt in prompts:
             tokens = tokenizer(prompt, prepend="<|bos|>")
             with autocast_ctx: