[gpt-oss] Add gpt-oss bf16 support

vllm/multimodal/image.py

@@ -0,0 +1,97 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import base64
+from io import BytesIO
+from pathlib import Path
+
+import torch
+from PIL import Image
+
+from .base import MediaIO
+
+
+def rescale_image_size(image: Image.Image,
+                       size_factor: float,
+                       transpose: int = -1) -> Image.Image:
+    """Rescale the dimensions of an image by a constant factor."""
+    new_width = int(image.width * size_factor)
+    new_height = int(image.height * size_factor)
+    image = image.resize((new_width, new_height))
+    if transpose >= 0:
+        image = image.transpose(Image.Transpose(transpose))
+    return image
+
+
+# TODO: Support customizable background color to fill in.
+def rgba_to_rgb(
+    image: Image.Image, background_color=(255, 255, 255)) -> Image.Image:
+    """Convert an RGBA image to RGB with filled background color."""
+    assert image.mode == "RGBA"
+    converted = Image.new("RGB", image.size, background_color)
+    converted.paste(image, mask=image.split()[3])  # 3 is the alpha channel
+    return converted
+
+
+def convert_image_mode(image: Image.Image, to_mode: str):
+    if image.mode == to_mode:
+        return image
+    elif image.mode == "RGBA" and to_mode == "RGB":
+        return rgba_to_rgb(image)
+    else:
+        return image.convert(to_mode)
+
+
+class ImageMediaIO(MediaIO[Image.Image]):
+
+    def __init__(self, *, image_mode: str = "RGB") -> None:
+        super().__init__()
+
+        self.image_mode = image_mode
+
+    def load_bytes(self, data: bytes) -> Image.Image:
+        image = Image.open(BytesIO(data))
+        image.load()
+        return convert_image_mode(image, self.image_mode)
+
+    def load_base64(self, media_type: str, data: str) -> Image.Image:
+        return self.load_bytes(base64.b64decode(data))
+
+    def load_file(self, filepath: Path) -> Image.Image:
+        image = Image.open(filepath)
+        image.load()
+        return convert_image_mode(image, self.image_mode)
+
+    def encode_base64(
+        self,
+        media: Image.Image,
+        *,
+        image_format: str = "JPEG",
+    ) -> str:
+        image = media
+
+        with BytesIO() as buffer:
+            image = convert_image_mode(image, self.image_mode)
+            image.save(buffer, image_format)
+            data = buffer.getvalue()
+
+        return base64.b64encode(data).decode('utf-8')
+
+
+class ImageEmbeddingMediaIO(MediaIO[torch.Tensor]):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def load_bytes(self, data: bytes) -> torch.Tensor:
+        buffer = BytesIO(data)
+        return torch.load(buffer, weights_only=True)
+
+    def load_base64(self, media_type: str, data: str) -> torch.Tensor:
+        return self.load_bytes(base64.b64decode(data))
+
+    def load_file(self, filepath: Path) -> torch.Tensor:
+        return torch.load(filepath, weights_only=True)
+
+    def encode_base64(self, media: torch.Tensor) -> str:
+        return base64.b64encode(media.numpy()).decode('utf-8')