[Core] Do not copy array during hashing (#19484)

Signed-off-by: Lukas Geiger <lukas.geiger94@gmail.com>

tests/multimodal/test_hasher.py

@@ -60,3 +60,15 @@ def test_hash_collision_array_shape():
 
     hasher = MultiModalHasher
     assert hasher.hash_kwargs(data=arr1) != hasher.hash_kwargs(data=arr2)
+
+
+def test_hash_non_contiguous_array():
+    arr = np.arange(24).reshape(4, 6).T
+    assert not arr.flags.c_contiguous
+
+    arr_c = np.ascontiguousarray(arr)
+    assert arr_c.flags.c_contiguous
+
+    hasher = MultiModalHasher
+    # Both should be hashable and produce the same hashes
+    assert hasher.hash_kwargs(data=arr) == hasher.hash_kwargs(data=arr_c)

vllm/multimodal/hasher.py

@@ -3,6 +3,7 @@
 
 import pickle
 from collections.abc import Iterable, Mapping
+from typing import Union
 
 import numpy as np
 import torch
@@ -23,11 +24,11 @@ A dictionary containing hashes for items in each modality.
 class MultiModalHasher:
 
     @classmethod
-    def serialize_item(cls, obj: object) -> bytes:
+    def serialize_item(cls, obj: object) -> Union[bytes, memoryview]:
         # Simple cases
         if isinstance(obj, str):
             return obj.encode("utf-8")
-        if isinstance(obj, bytes):
+        if isinstance(obj, (bytes, memoryview)):
             return obj
         if isinstance(obj, (int, float)):
             return np.array(obj).tobytes()
@@ -38,12 +39,13 @@ class MultiModalHasher:
         if isinstance(obj, torch.Tensor):
             return cls.item_to_bytes("tensor", obj.numpy())
         if isinstance(obj, np.ndarray):
-            return cls.item_to_bytes(
-                "ndarray", {
-                    "dtype": obj.dtype.str,
-                    "shape": obj.shape,
-                    "data": obj.tobytes(),
-                })
+            # If the array is non-contiguous, we need to copy it first
+            arr_data = obj.data if obj.flags.c_contiguous else obj.tobytes()
+            return cls.item_to_bytes("ndarray", {
+                "dtype": obj.dtype.str,
+                "shape": obj.shape,
+                "data": arr_data,
+            })
 
         logger.warning(
             "No serialization method found for %s. "
@@ -64,7 +66,7 @@ class MultiModalHasher:
         cls,
         key: str,
         obj: object,
-    ) -> Iterable[tuple[bytes, bytes]]:
+    ) -> Iterable[tuple[bytes, Union[bytes, memoryview]]]:
         # Recursive cases
         if isinstance(obj, (list, tuple)):
             for i, elem in enumerate(obj):
@@ -73,7 +75,7 @@ class MultiModalHasher:
             for k, v in obj.items():
                 yield from cls.iter_item_to_bytes(f"{key}.{k}", v)
         else:
-            key_bytes = cls.serialize_item(key)
+            key_bytes = key.encode("utf-8")
             value_bytes = cls.serialize_item(obj)
             yield key_bytes, value_bytes
 

vllm/v1/serial_utils.py

@@ -140,7 +140,7 @@ class MsgpackEncoder:
     ) -> tuple[str, tuple[int, ...], Union[int, memoryview]]:
         assert self.aux_buffers is not None
         # If the array is non-contiguous, we need to copy it first
-        arr_data = obj.data if obj.data.c_contiguous else obj.tobytes()
+        arr_data = obj.data if obj.flags.c_contiguous else obj.tobytes()
         if not obj.shape or obj.nbytes < self.size_threshold:
             # Encode small arrays and scalars inline. Using this extension type
             # ensures we can avoid copying when decoding.