[gpt-oss] Add gpt-oss bf16 support

vllm/multimodal/hasher.py

@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pickle
+from collections.abc import Iterable, Mapping
+from typing import TYPE_CHECKING, Optional
+
+import numpy as np
+import torch
+from blake3 import blake3
+from PIL import Image
+
+from vllm.logger import init_logger
+from vllm.multimodal.image import convert_image_mode
+
+if TYPE_CHECKING:
+    from vllm.inputs import TokensPrompt
+
+logger = init_logger(__name__)
+
+MultiModalHashDict = Mapping[str, list[str]]
+"""
+A dictionary containing hashes for items in each modality.
+"""
+
+
+class MultiModalHasher:
+
+    @classmethod
+    def serialize_item(cls, obj: object) -> bytes:
+        # Simple cases
+        if isinstance(obj, str):
+            return obj.encode("utf-8")
+        if isinstance(obj, bytes):
+            return obj
+        if isinstance(obj, (int, float)):
+            return np.array(obj).tobytes()
+
+        if isinstance(obj, Image.Image):
+            return cls.item_to_bytes(
+                "image", np.asarray(convert_image_mode(obj, "RGBA")))
+        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(),
+                })
+
+        logger.warning(
+            "No serialization method found for %s. "
+            "Falling back to pickle.", type(obj))
+
+        return pickle.dumps(obj)
+
+    @classmethod
+    def item_to_bytes(
+        cls,
+        key: str,
+        obj: object,
+    ) -> bytes:
+        return b''.join(kb + vb for kb, vb in cls.iter_item_to_bytes(key, obj))
+
+    @classmethod
+    def iter_item_to_bytes(
+        cls,
+        key: str,
+        obj: object,
+    ) -> Iterable[tuple[bytes, bytes]]:
+        # Recursive cases
+        if isinstance(obj, (list, tuple)):
+            for i, elem in enumerate(obj):
+                yield from cls.iter_item_to_bytes(f"{key}.{i}", elem)
+        elif isinstance(obj, dict):
+            for k, v in obj.items():
+                yield from cls.iter_item_to_bytes(f"{key}.{k}", v)
+        else:
+            key_bytes = cls.serialize_item(key)
+            value_bytes = cls.serialize_item(obj)
+            yield key_bytes, value_bytes
+
+    @classmethod
+    def hash_kwargs(cls, **kwargs: object) -> str:
+        hasher = blake3()
+
+        for k, v in kwargs.items():
+            for k_bytes, v_bytes in cls.iter_item_to_bytes(k, v):
+                hasher.update(k_bytes)
+                hasher.update(v_bytes)
+
+        return hasher.hexdigest()
+
+    @classmethod
+    def hash_prompt_mm_data(
+            cls, prompt: "TokensPrompt") -> Optional["MultiModalHashDict"]:
+        """Hash multimodal data in the user input prompt if they exist."""
+
+        if "multi_modal_data" not in prompt:
+            return None
+
+        mm_data = prompt["multi_modal_data"]
+        if not mm_data:
+            # mm_data can be None or an empty dict.
+            return None
+
+        mm_items = {
+            modality: items if isinstance(items, list) else [items]
+            for modality, items in mm_data.items()
+        }
+
+        mm_hashes = {
+            modality: [cls.hash_kwargs(**{modality: item}) for item in items]
+            for modality, items in mm_items.items()
+        }
+
+        return mm_hashes