Fix dataloader API: max_sample_length not seq_len, proper create_forward_loop

quantize_modelopt.py

@@ -108,22 +108,23 @@ def main():
 
     # Build calibration dataloader
     print("Building calibration dataset...")
-    calib_dataloader = create_forward_loop(
-        model,
-        dataloader=get_dataloader(
-            tokenizer=tokenizer,
-            calib_size=args.calib_size,
-            batch_size=args.batch_size,
-            calib_seq=args.calib_seq,
-        ),
+    calib_dataloader = get_dataloader(
+        tokenizer=tokenizer,
+        calib_size=args.calib_size,
+        batch_size=args.batch_size,
+        calib_seq=args.calib_seq,
     )
 
+    # Create forward loop from dataloader (modelopt helper)
+    from modelopt.torch.utils.dataset_utils import create_forward_loop
+    forward_loop = create_forward_loop(calib_dataloader)
+
     # Quantize
     quant_cfg = QUANT_CONFIGS[args.qformat]
     print(f"Running PTQ with {args.qformat}...")
     t0 = time.time()
 
-    model = mtq.quantize(model, quant_cfg, calib_dataloader)
+    model = mtq.quantize(model, quant_cfg, forward_loop)
 
     elapsed = time.time() - t0
     print(f"Quantization complete in {elapsed/60:.1f} min")
@@ -149,7 +150,7 @@ def get_dataloader(tokenizer, calib_size, batch_size, calib_seq):
         tokenizer=tokenizer,
         num_samples=calib_size[0],
         batch_size=batch_size,
-        seq_len=calib_seq,
+        max_sample_length=calib_seq,
     )