Move to a faster base64 implementation (#19984)

Signed-off-by: h-avsha <avshalom.manevich@hcompany.ai>

Author: h-avsha

requirements/common.txt

@@ -44,3 +44,4 @@ watchfiles # required for http server to monitor the updates of TLS files
 python-json-logger # Used by logging as per examples/others/logging_configuration.md
 scipy # Required for phi-4-multimodal-instruct
 ninja # Required for xgrammar, rocm, tpu, xpu
+pybase64 # fast base64 implementation

vllm/multimodal/image.py

@@ -1,10 +1,10 @@
 # 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 pybase64
 import torch
 from PIL import Image
 
@@ -55,7 +55,7 @@ def load_bytes(self, data: bytes) -> Image.Image:
         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))
+        return self.load_bytes(pybase64.b64decode(data, validate=True))
 
     def load_file(self, filepath: Path) -> Image.Image:
         image = Image.open(filepath)
@@ -75,7 +75,7 @@ def encode_base64(
             image.save(buffer, image_format)
             data = buffer.getvalue()
 
-        return base64.b64encode(data).decode('utf-8')
+        return pybase64.b64encode(data).decode('utf-8')
 
 
 class ImageEmbeddingMediaIO(MediaIO[torch.Tensor]):
@@ -88,10 +88,10 @@ def load_bytes(self, data: bytes) -> torch.Tensor:
         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))
+        return self.load_bytes(pybase64.b64decode(data, validate=True))
 
     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')
+        return pybase64.b64encode(media.numpy()).decode('utf-8')