[Bugfix][V1] Fix molmo text-only inputs

vllm/model_executor/models/molmo.py

@@ -1081,45 +1081,25 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
     else:
         out = processor.process(None, image, tokens=inputs["prompt_token_ids"])
 
-    image_processor = processor.image_processor
-    max_total_crops = 1 + image_processor.max_crops
-    if image is not None:
-        images, image_input_idx, image_masks = pad_images(
-            max_total_crops,
-            out["images"],
-            out["image_input_idx"],
-            out.get("image_masks"),
-        )
-    else:
-        base_image_input_size = image_processor.base_image_input_size
-        image_patch_size = image_processor.image_patch_size
-        image_num_patch = (
-            base_image_input_size[0] // image_patch_size,
-            base_image_input_size[1] // image_patch_size,
-        )
-        n_pixels = image_patch_size * image_patch_size * 3
-        n_patches = image_num_patch[0] * image_num_patch[1]
-
-        image_length_w = image_processor.image_token_length_w
-        image_length_h = image_processor.image_token_length_h
-        tokens_per_image = image_length_w * image_length_h
-        images = torch.full(
-            (max_total_crops, n_patches, n_pixels),
-            -1,
-            dtype=torch.float32,
-        )
-        image_input_idx = torch.full(
-            (max_total_crops, tokens_per_image),
-            -1,
-            dtype=torch.int32,
+    # If there is no image, return directly.
+    if image is None:
+        new_prompt_token_ids = out["input_ids"].tolist()
+        prompt = inputs.get("prompt")
+        if prompt is None:
+            prompt = tokenizer.decode(new_prompt_token_ids)
+        return token_inputs(
+            prompt_token_ids=new_prompt_token_ids,
+            prompt=prompt,
         )
-        if image_processor.image_padding_mask:
-            image_masks = torch.full(
-                (max_total_crops, n_patches),
-                -1,
-                dtype=torch.float32,
-            )
 
+    image_processor = processor.image_processor
+    max_total_crops = 1 + image_processor.max_crops
+    images, image_input_idx, image_masks = pad_images(
+        max_total_crops,
+        out["images"],
+        out["image_input_idx"],
+        out.get("image_masks"),
+    )
     image_data = dict(
         images=images,
         image_input_idx=image_input_idx,
@@ -1143,11 +1123,9 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
                 offset = i
             size += 1
     image_data["image_start_end"] = (offset, offset + size)
-
     prompt = inputs.get("prompt")
     if prompt is None:
         prompt = tokenizer.decode(new_prompt_token_ids)
-
     return token_inputs(
         prompt_token_ids=new_prompt_token_ids,
         prompt=prompt,