From edbeaf3b88f774f51a74e0a13f4c7be53ca0a454 Mon Sep 17 00:00:00 2001
From: Byron Hsu <byronhsu1230@gmail.com>
Date: Mon, 1 Dec 2025 11:36:51 -0800
Subject: [PATCH] [MM][style] rename inputs_embeds to input_embeds for
 consistency (#14240)

---
 python/sglang/srt/managers/mm_utils.py | 30 +++++++++++++-------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/python/sglang/srt/managers/mm_utils.py b/python/sglang/srt/managers/mm_utils.py
index f55f2e1d9..05de30dd3 100644
--- a/python/sglang/srt/managers/mm_utils.py
+++ b/python/sglang/srt/managers/mm_utils.py
@@ -598,20 +598,20 @@ def embed_mm_inputs(
     # filled with the hash values of the multimodal for the prefix matching in the radix attention.
     # There values are useless because their embeddings will be replaced by vision embeddings anyway.
     input_ids.clamp_(min=0, max=vocab_size - 1)
-    inputs_embeds = input_embedding(input_ids)
+    input_embeds = input_embedding(input_ids)
 
     # deepstack embedding
     if use_deepstack:
         num_deepstack_embeddings = len(multimodal_model.deepstack_visual_indexes)
 
-        deepstack_embedding_shape = inputs_embeds.shape[:-1] + (
-            inputs_embeds.shape[-1] * num_deepstack_embeddings,
+        deepstack_embedding_shape = input_embeds.shape[:-1] + (
+            input_embeds.shape[-1] * num_deepstack_embeddings,
         )
-        # a zero-filled embedding, with the same length of inputs_embeds, but different hidden_size
+        # a zero-filled embedding, with the same length of input_embeds, but different hidden_size
         input_deepstack_embeds = torch.zeros(
             deepstack_embedding_shape,
-            device=inputs_embeds.device,
-            dtype=inputs_embeds.dtype,
+            device=input_embeds.device,
+            dtype=input_embeds.dtype,
         )
 
         other_info["input_deepstack_embeds"] = input_deepstack_embeds
@@ -624,13 +624,13 @@ def embed_mm_inputs(
             continue
         # in-place update
         indices = torch.where(mask.squeeze(dim=-1))[0]
-        inputs_embeds[indices] = embedding.to(inputs_embeds.device, inputs_embeds.dtype)
+        input_embeds[indices] = embedding.to(input_embeds.device, input_embeds.dtype)
         if use_deepstack.get(modality, None):
             input_deepstack_embeds[indices] = deepstack_embeddings[i].to(
-                inputs_embeds.device, inputs_embeds.dtype
+                input_embeds.device, input_embeds.dtype
             )
 
-    return inputs_embeds, other_info
+    return input_embeds, other_info
 
 
 def general_mm_embed_routine(
@@ -692,7 +692,7 @@ def general_mm_embed_routine(
                     for i, seq_len in enumerate(forward_batch.extend_seq_lens_cpu)
                     if forward_batch.mm_inputs[i] is not None
                 ]
-                inputs_embeds, other_info = embed_mm_inputs(
+                input_embeds, other_info = embed_mm_inputs(
                     mm_inputs_list=mm_inputs_list,
                     extend_prefix_lens=extend_prefix_lens,
                     extend_seq_lens=extend_seq_lens,
@@ -712,18 +712,18 @@ def general_mm_embed_routine(
                 # just being defensive here
                 forward_batch.mm_inputs = None
             else:
-                inputs_embeds = embed_tokens(input_ids)
+                input_embeds = embed_tokens(input_ids)
             # Copy to pre-allocated buffer if available (for CUDA graph address stability)
             if forward_batch.input_embeds is not None:
-                forward_batch.input_embeds.copy_(inputs_embeds)
-                inputs_embeds = forward_batch.input_embeds
+                forward_batch.input_embeds.copy_(input_embeds)
+                input_embeds = forward_batch.input_embeds
         else:
-            inputs_embeds = None
+            input_embeds = None
 
     hidden_states = language_model(
         input_ids=None,
         forward_batch=forward_batch,
-        input_embeds=inputs_embeds,
+        input_embeds=input_embeds,
         **kwargs,
     )
     return hidden_states