[Minor] fix the style for multimodal models (#2257)

python/sglang/srt/managers/schedule_batch.py

@@ -149,8 +149,8 @@ class ImageInputs:
 
         # Use image hash as fake token_ids. We use this as the key for prefix matching in the radix cache.
         # Please note that if the `input_ids` is later used in the model forward,
-        # you also need to clamp the values within the range of [0, vocab_size) to avoid illegal
-        # cuda memory access.
+        # you also need to clamp the values within the range of [0, vocab_size) to avoid out-of-bound
+        # errors in cuda kernels. See also llava.py for example.
         ret.pad_values = [x % (1 << 30) for x in ret.image_hashes]
 
         optional_args = [
@@ -172,8 +172,8 @@ class ImageInputs:
 
         # Use image hash as fake token_ids. We use this as the key for prefix matching in the radix cache.
         # Please note that if the `input_ids` is later used in the model forward,
-        # you also need to clamp the values within the range of [0, vocab_size) to avoid illegal
-        # cuda memory access.
+        # you also need to clamp the values within the range of [0, vocab_size) to avoid out-of-bound
+        # errors in cuda kernels. See also llava.py for example.
         self.image_hashes += other.image_hashes
         self.pad_values = [x % (1 << 30) for x in self.image_hashes]
 

python/sglang/srt/managers/scheduler.py

@@ -568,15 +568,17 @@ class Scheduler:
             )
             req.extend_image_inputs(image_inputs)
 
-            if len(req.origin_input_ids) > self.max_req_input_len:
-                req.finished_reason = FINISH_ABORT(
-                    "Image request length is longer than the KV cache pool size or "
-                    "the max context length. "
-                    "Abort this request because you cannot truncate the image embeds"
+            if len(req.origin_input_ids) >= self.max_req_input_len:
+                logger.error(
+                    "Multimodal prompt is too long after expanding multimodal tokens. "
+                    f"After expanding {len(req.origin_input_ids_unpadded)=} => {len(req.origin_input_ids)} >= {self.max_req_input_len}. "
                 )
-                req.image_inputs = None
                 req.origin_input_ids = [0]
+                req.image_inputs = None
                 req.sampling_params.max_new_tokens = 0
+                req.finished_reason = FINISH_ABORT(
+                    "Multimodal prompt is too long. Check server logs for details."
+                )
                 self.waiting_queue.append(req)
                 return
 

python/sglang/srt/models/llava.py

@@ -134,7 +134,6 @@ class LlavaBaseForCausalLM(nn.Module):
         image_inputs = forward_batch.image_inputs
 
         if forward_batch.forward_mode.is_extend():
-            bs = forward_batch.batch_size
             # Got List[List[str]] extend it to List[str]
             # The length of the List should be equal to batch size
             modalities_list = []
@@ -142,7 +141,7 @@ class LlavaBaseForCausalLM(nn.Module):
             for im in image_inputs:
                 if im and im.modalities is not None:
                     modalities_list.extend(im.modalities)
-                if im and im.image_offsets is not None:
+                if im and im.image_offsets:
                     max_image_offset.append(max(im.image_offsets))
                 else:
                     max_image_offset.append(-1)
@@ -159,6 +158,7 @@ class LlavaBaseForCausalLM(nn.Module):
             need_vision = start_positions <= np.array(max_image_offset)
 
             if need_vision.any():
+                bs = forward_batch.batch_size
                 pixel_values = [
                     image_inputs[i].pixel_values for i in range(bs) if need_vision[i]
                 ]