Remove index_put from MM embeddings merging (#22105)

Co-authored-by: Chenxi Yang <cxyang@meta.com>

vllm/model_executor/models/utils.py

@@ -393,7 +393,7 @@ def merge_multimodal_embeddings_from_map(
         inputs_embeds: torch.Tensor, multimodal_embeddings: NestedTensors,
         placeholder_map: MultiModalPlaceholderMap.IndexMap) -> torch.Tensor:
     """
-    Merge ``multimodal_embeddings`` into ``inputs_embeds`` using the provided 
+    Merge ``multimodal_embeddings`` into ``inputs_embeds`` using the provided
     placeholder map .
 
     Note:
@@ -418,17 +418,23 @@ def _merge_multimodal_embeddings(
     Note:
         This updates ``inputs_embeds`` in place.
     """
-    num_expected_tokens = is_multimodal.sum().item()
-    assert isinstance(num_expected_tokens, int)
-
     flattened = _flatten_embeddings(multimodal_embeddings)
-    if flattened.shape[0] != num_expected_tokens:
-        expr = _embedding_count_expression(multimodal_embeddings)
-        raise ValueError(
-            f"Attempted to assign {expr} = {flattened.shape[0]} "
-            f"multimodal tokens to {num_expected_tokens} placeholders")
+    try:
+        # This is equivalent to: inputs_embeds[is_multimodal] = flattened.
+        inputs_embeds.masked_scatter_(is_multimodal.unsqueeze(-1), flattened)
+    except RuntimeError as e:
+        num_expected_tokens = is_multimodal.sum().item()
+        assert isinstance(num_expected_tokens, int)
+
+        if flattened.shape[0] != num_expected_tokens:
+            expr = _embedding_count_expression(multimodal_embeddings)
+            raise ValueError(
+                f"Attempted to assign {expr} = {flattened.shape[0]} "
+                f"multimodal tokens to {num_expected_tokens} placeholders"
+            ) from e
+        else:
+            raise ValueError("Error during masked scatter operation") from e
 
-    inputs_embeds[is_multimodal] = flattened
     return inputs_embeds
 
 
@@ -478,11 +484,11 @@ def merge_multimodal_embeddings(
     Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the
     positions in ``inputs_embeds`` corresponding to placeholder tokens in
     ``input_ids``.
-    
-    ``placeholder_token_id`` can be a list of token ids (e.g, token ids 
-    of img_start, img_break, and img_end tokens) when needed: This means 
-    the order of these tokens in the ``input_ids`` MUST MATCH the order of 
-    their embeddings in ``multimodal_embeddings`` since we need to 
+
+    ``placeholder_token_id`` can be a list of token ids (e.g, token ids
+    of img_start, img_break, and img_end tokens) when needed: This means
+    the order of these tokens in the ``input_ids`` MUST MATCH the order of
+    their embeddings in ``multimodal_embeddings`` since we need to
     slice-merge instead of individually scattering.
 
     For example, if input_ids is "TTTTTSIIIBIIIBIIIETTT", where
@@ -491,9 +497,9 @@ def merge_multimodal_embeddings(
     - I is image embedding token
     - B is image break token
     - E is image end token.
-    
-    Then the image embeddings (that correspond to I's) from vision encoder 
-    must be padded with embeddings of S, B, and E in the same order of 
+
+    Then the image embeddings (that correspond to I's) from vision encoder
+    must be padded with embeddings of S, B, and E in the same order of
     input_ids for a correct embedding merge.
 
     Note: