uncomment some code

Signed-off-by: ruit <ruit@nvidia.com>

Author: RayenTian

tests/functional/test_converter_roundtrip.py

@@ -630,7 +630,7 @@ def main():
         with torch.no_grad():
             lora_output = lora_merged_model(test_input_lora)
         print("✓ LoRA merged model can perform forward pass")
-        # del lora_merged_model
+        del lora_merged_model
         gc.collect()
 
         # Adapter-only (PEFT) export assertions
@@ -652,12 +652,6 @@ def main():
             "✓ PEFT adapter directory has expected files (adapter_config.json + weights)"
         )
 
-        # Forward pass using the already-merged model from Step 7c.
-        test_input_peft = torch.randint(0, 1000, (1, 10))
-        with torch.no_grad():
-            lora_merged_model(test_input_peft)
-        print("✓ LoRA merged model can perform a forward pass")
-
         # Verify the adapter-only export produces the same merged weights as Step 7c
         # by calling merge_lora_to_hf again with the same Megatron adapter. This
         # avoids tied-weight complications from PeftModel.merge_and_unload().
@@ -682,7 +676,7 @@ def main():
         )
         print("✓ adapter-only merge via merge_lora_to_hf matches Step 7c")
 
-        del adapter_only_merged_model, lora_merged_model
+        del adapter_only_merged_model
         gc.collect()
 
         # Verify that both converted models have the expected structure
@@ -713,7 +707,7 @@ def main():
             megatron_output = megatron_converted_model(test_input)
 
         print(
-            "✓ Dtensor V1 and Dtensor V2 DCP, Megatron, and LoRA models can perform forward passes"
+            "✓ Dtensor V1 and Dtensor V2 DCP, Megatron, and LoRA merged models can perform forward passes"
         )
 
         print("\n" + "=" * 80)