Minor cleanup in torchchat/cli/builder.py

torchchat/cli/builder.py

@@ -79,19 +79,16 @@ def __post_init__(self):
         if self.dso_path and self.pte_path:
             raise RuntimeError("specify either DSO path or PTE path, but not both")
 
-        if self.checkpoint_path and (self.dso_path or self.pte_path):
-            print(
-                "Warning: checkpoint path ignored because an exported DSO or PTE path specified"
-            )
-        if self.checkpoint_dir and (self.dso_path or self.pte_path):
-            print(
-                "Warning: checkpoint dir ignored because an exported DSO or PTE path specified"
-            )
-        if self.gguf_path and (self.dso_path or self.pte_path):
-            print(
-                "Warning: GGUF path ignored because an exported DSO or PTE path specified"
-            )
-        if not (self.dso_path) and not (self.pte_path):
+        if self.dso_path or self.pte_path:
+            ignored_params = [
+                (self.checkpoint_path, "checkpoint path"),
+                (self.checkpoint_dir, "checkpoint dir"),
+                (self.gguf_path, "GGUF path"),
+            ]
+            for param, param_msg in ignored_params:
+                if param:
+                    print(f"Warning: {param_msg} ignored because an exported DSO or PTE path was specified")
+        else:
             self.prefill_possible = True
 
     @classmethod
@@ -446,7 +443,7 @@ def _maybe_init_distributed(
     return world_mesh, parallel_dims
 
 
-def _maybe_parellelize_model(
+def _maybe_parallelize_model(
     model: nn.Module,
     builder_args: BuilderArgs,
     world_mesh: DeviceMesh,
@@ -486,7 +483,7 @@ def _load_model(builder_args: BuilderArgs) -> Model:
         model = _init_model_on_meta_device(builder_args)
     else:
         model = _load_model_default(builder_args)
-    model = _maybe_parellelize_model(model, builder_args, world_mesh, parallel_dims)
+    model = _maybe_parallelize_model(model, builder_args, world_mesh, parallel_dims)
 
     model = model.to(device=builder_args.device, dtype=builder_args.precision)
     return model.eval()