[Core] Reduce mm scheduler, get_num_embed overhead (#40143)

Signed-off-by: milesial <milesial@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

tests/multimodal/test_inputs.py

@@ -26,11 +26,8 @@ def test_placeholder_range_get_num_embeds(is_embed, expected):
     "is_embed,expected",
     [
         (None, None),
-        (
-            torch.tensor([False, True, False, True, True]),
-            torch.tensor([0, 1, 1, 2, 3]),
-        ),
-        (torch.tensor([True, True, True]), torch.tensor([1, 2, 3])),
+        (torch.tensor([False, True, False, True, True]), [0, 1, 1, 2, 3]),
+        (torch.tensor([True, True, True]), [1, 2, 3]),
     ],
 )
 def test_placeholder_range_embeds_cumsum(is_embed, expected):
@@ -41,6 +38,6 @@ def test_placeholder_range_embeds_cumsum(is_embed, expected):
         assert pr.embeds_cumsum is None
         return
 
-    assert torch.equal(pr.embeds_cumsum, expected)
+    assert pr.embeds_cumsum == expected
     # cached_property should return the same object on repeated access
     assert pr.embeds_cumsum is pr.embeds_cumsum

vllm/multimodal/inputs.py

@@ -145,14 +145,15 @@ class PlaceholderRange:
     """
 
     @cached_property
-    def embeds_cumsum(self) -> torch.Tensor | None:
-        return None if self.is_embed is None else self.is_embed.cumsum(dim=0)
+    def embeds_cumsum(self) -> list[int] | None:
+        # python list so python indexing avoids torch C++ overhead/conversions/deallocs
+        return None if self.is_embed is None else self.is_embed.cumsum(dim=0).tolist()
 
     def get_num_embeds(self) -> int:
         if self.embeds_cumsum is None:
             return self.length
 
-        return int(self.embeds_cumsum[-1])
+        return self.embeds_cumsum[-1] if self.embeds_cumsum else 0
 
     def get_embeds_indices_in_range(
         self, start_idx: int, end_idx: int
@@ -170,10 +171,8 @@ class PlaceholderRange:
         if self.embeds_cumsum is None:
             return start_idx, end_idx
 
-        embeds_start_idx = (
-            int(self.embeds_cumsum[start_idx - 1]) if start_idx > 0 else 0
-        )
-        embeds_end_idx = int(self.embeds_cumsum[end_idx - 1])
+        embeds_start_idx = self.embeds_cumsum[start_idx - 1] if start_idx > 0 else 0
+        embeds_end_idx = self.embeds_cumsum[end_idx - 1] if end_idx > 0 else 0
 
         return embeds_start_idx, embeds_end_idx