Bound openai to under 2.25.0

Signed-off-by: khluu <khluu000@gmail.com>

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -44,7 +44,7 @@ docker run \
     python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2
     python3 examples/offline_inference/basic/generate.py --model ibm-research/PowerMoE-3b  --block-size 64 --enforce-eager -tp 2 --enable-expert-parallel
     cd tests
-    pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py
+    pytest -v -s v1/core --ignore=v1/core/test_reset_prefix_cache_e2e.py --ignore=v1/core/test_scheduler_e2e.py
     pytest -v -s v1/engine
     pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
     pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py

.buildkite/scripts/upload-release-wheels-pypi.sh

@@ -54,10 +54,13 @@ mkdir -p $DIST_DIR
 # include only wheels for the release version, ignore all files with "dev" or "rc" in the name (without excluding 'aarch64')
 aws s3 cp --recursive --exclude "*" --include "vllm-${PURE_VERSION}*.whl" --exclude "*dev*" --exclude "*rc[0-9]*" "$S3_COMMIT_PREFIX" $DIST_DIR
 echo "Wheels copied to local directory"
-# generate source tarball
+# generate source distribution using setup.py
-git archive --format=tar.gz --output="$DIST_DIR/vllm-${PURE_VERSION}.tar.gz" "$BUILDKITE_COMMIT"
+python setup.py sdist --dist-dir=$DIST_DIR
 ls -la $DIST_DIR
 
+SDIST_FILE=$(find $DIST_DIR -name "vllm*.tar.gz")
+echo "Found sdist: $SDIST_FILE"
+
 # upload wheels to PyPI (only default variant, i.e. files without '+' in the name)
 PYPI_WHEEL_FILES=$(find $DIST_DIR -name "vllm-${PURE_VERSION}*.whl" -not -name "*+*")
 if [[ -z "$PYPI_WHEEL_FILES" ]]; then
@@ -65,6 +68,6 @@ if [[ -z "$PYPI_WHEEL_FILES" ]]; then
   exit 1
 fi
 
-python3 -m twine check "$PYPI_WHEEL_FILES"
+python3 -m twine check "$PYPI_WHEEL_FILES" "$SDIST_FILE"
-python3 -m twine upload --non-interactive --verbose "$PYPI_WHEEL_FILES"
+python3 -m twine upload --non-interactive --verbose "$PYPI_WHEEL_FILES" "$SDIST_FILE"
-echo "Wheels uploaded to PyPI"
+echo "Wheels and source distribution uploaded to PyPI"

requirements/common.txt

@@ -12,7 +12,7 @@ tokenizers >= 0.21.1  # Required for fast incremental detokenization.
 protobuf >= 5.29.6, !=6.30.*, !=6.31.*, !=6.32.*, !=6.33.0.*, !=6.33.1.*, !=6.33.2.*, !=6.33.3.*, !=6.33.4.* # Required by LlamaTokenizer, gRPC. CVE-2026-0994
 fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
 aiohttp >= 3.13.3
-openai >= 1.99.1  # For Responses API with reasoning content
+openai >= 1.99.1, < 2.25.0  # For Responses API with reasoning content
 pydantic >= 2.12.0
 prometheus_client >= 0.18.0
 pillow  # Required for image processing

requirements/xpu.txt

@@ -15,4 +15,4 @@ torch==2.10.0+xpu
 torchaudio
 torchvision
 
-vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.2/vllm_xpu_kernels-0.1.2-cp312-cp312-linux_x86_64.whl
+vllm_xpu_kernels @ https://github.com/vllm-project/vllm-xpu-kernels/releases/download/v0.1.3/vllm_xpu_kernels-0.1.3-cp38-abi3-linux_x86_64.whl

tests/tokenizers_/test_basic.py

@@ -29,7 +29,8 @@ def test_tokenizer_like_protocol():
     _assert_tokenizer_like(tokenizer)
 
     tokenizer = get_tokenizer(
-        "mistralai/Mistral-7B-Instruct-v0.3", tokenizer_mode="mistral"
+        "mistralai/Mistral-7B-Instruct-v0.3",
+        tokenizer_mode="mistral",
     )
     assert isinstance(tokenizer, MistralTokenizer)
     _assert_tokenizer_like(tokenizer)
@@ -40,11 +41,20 @@ def test_tokenizer_like_protocol():
 
     tokenizer = get_tokenizer("deepseek-ai/DeepSeek-V3", tokenizer_mode="deepseek_v32")
     assert isinstance(tokenizer, HfTokenizer)
+
     # Verify it's a fast tokenizer (required for FastIncrementalDetokenizer)
     assert isinstance(tokenizer, PreTrainedTokenizerFast)
     assert "DSV32" in tokenizer.__class__.__name__
     _assert_tokenizer_like(tokenizer)
 
+    tokenizer = get_tokenizer(
+        "Qwen/Qwen-VL",
+        tokenizer_mode="qwen_vl",
+        trust_remote_code=True,
+    )
+    assert isinstance(tokenizer, HfTokenizer)
+    assert "WithoutImagePad" in tokenizer.__class__.__name__
+
 
 @pytest.mark.parametrize("tokenizer_name", ["facebook/opt-125m", "gpt2"])
 def test_tokenizer_revision(tokenizer_name: str):

vllm/benchmarks/serve.py

@@ -1321,6 +1321,7 @@ def add_cli_args(parser: argparse.ArgumentParser):
         - "slow" will always use the slow tokenizer.\n
         - "mistral" will always use the tokenizer from `mistral_common`.\n
         - "deepseek_v32" will always use the tokenizer from `deepseek_v32`.\n
+        - "qwen_vl" will always use the tokenizer from `qwen_vl`.\n
         - Other custom values can be supported via plugins.""",
     )
     parser.add_argument("--use-beam-search", action="store_true")

vllm/config/model.py

@@ -126,6 +126,7 @@ class ModelConfig:
     - "slow" will always use the slow tokenizer.\n
     - "mistral" will always use the tokenizer from `mistral_common`.\n
     - "deepseek_v32" will always use the tokenizer from `deepseek_v32`.\n
+    - "qwen_vl" will always use the tokenizer from `qwen_vl`.\n
     - Other custom values can be supported via plugins."""
     trust_remote_code: bool = False
     """Trust remote code (e.g., from HuggingFace) when downloading the model

vllm/model_executor/models/qwen2_5_omni_thinker.py

@@ -353,6 +353,39 @@ class Qwen2_5OmniThinkerProcessingInfo(
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None, "image": None, "video": None}
 
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int] | None = None,
+    ) -> Mapping[str, int] | None:
+        mm_counts = mm_counts or {}
+        requested_modalities = {m for m, c in mm_counts.items() if c > 0}
+        mm_max_tokens: dict[str, int] = {}
+
+        if requested_modalities & {"image", "video"}:
+            vl_tokens = Qwen2_5_VLProcessingInfo.get_mm_max_tokens_per_item(
+                self,
+                seq_len=seq_len,
+                mm_counts=mm_counts,
+            )
+            mm_max_tokens.update(
+                {
+                    m: vl_tokens[m]
+                    for m in ["image", "video"]
+                    if m in requested_modalities
+                }
+            )
+
+        if "audio" in requested_modalities:
+            audio_tokens = Qwen2AudioProcessingInfo.get_mm_max_tokens_per_item(
+                self,
+                seq_len=seq_len,
+                mm_counts=mm_counts,
+            )
+            mm_max_tokens["audio"] = audio_tokens["audio"]
+
+        return mm_max_tokens
+
 
 class Qwen2_5OmniThinkerDummyInputsBuilder(
     BaseDummyInputsBuilder[Qwen2_5OmniThinkerProcessingInfo]

vllm/model_executor/models/qwen2_audio.py

@@ -179,6 +179,26 @@ class Qwen2AudioProcessingInfo(BaseProcessingInfo):
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None}
 
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int] | None = None,
+    ) -> Mapping[str, int]:
+        mm_counts = mm_counts or {}
+        if mm_counts.get("audio", 0) <= 0:
+            return {}
+
+        feature_extractor = self.get_feature_extractor()
+        chunk_length = min(feature_extractor.chunk_length, 30)
+        audio_len = int(chunk_length * feature_extractor.sampling_rate)
+        hop_length = feature_extractor.hop_length
+        max_mel_seq_len = audio_len // hop_length
+
+        input_lengths = torch.tensor([max_mel_seq_len], dtype=torch.long)
+        _, output_lengths = _get_feat_extract_output_lengths(input_lengths)
+
+        return {"audio": int(output_lengths.item())}
+
 
 class Qwen2AudioDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2AudioProcessingInfo]):
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:

vllm/model_executor/models/qwen3_omni_moe_thinker.py

@@ -1163,6 +1163,39 @@ class Qwen3OmniMoeThinkerProcessingInfo(
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": None, "image": None, "video": None}
 
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int] | None = None,
+    ) -> Mapping[str, int] | None:
+        mm_counts = mm_counts or {}
+        requested_modalities = {m for m, c in mm_counts.items() if c > 0}
+        mm_max_tokens: dict[str, int] = {}
+
+        if requested_modalities & {"image", "video"}:
+            vl_tokens = Qwen2_5_VLProcessingInfo.get_mm_max_tokens_per_item(
+                self,
+                seq_len=seq_len,
+                mm_counts=mm_counts,
+            )
+            mm_max_tokens.update(
+                {
+                    m: vl_tokens[m]
+                    for m in ["image", "video"]
+                    if m in requested_modalities
+                }
+            )
+
+        if "audio" in requested_modalities:
+            audio_tokens = Qwen2AudioProcessingInfo.get_mm_max_tokens_per_item(
+                self,
+                seq_len=seq_len,
+                mm_counts=mm_counts,
+            )
+            mm_max_tokens["audio"] = audio_tokens["audio"]
+
+        return mm_max_tokens
+
 
 Qwen3OmniMoeThinkerDummyInputsBuilder = Qwen2_5OmniThinkerDummyInputsBuilder
 

vllm/model_executor/models/qwen_vl.py

@@ -6,11 +6,9 @@
 # Copyright (c) Alibaba Cloud.
 """Inference-only Qwen-VL model compatible with HuggingFace weights."""
 
-import copy
 import math
-import unicodedata
+from collections.abc import Callable, Mapping, Sequence
-from collections.abc import Callable, Collection, Mapping, Sequence, Set
+from functools import partial
-from functools import lru_cache, partial
 from typing import Annotated, Literal, TypeAlias
 
 import regex as re
@@ -436,60 +434,6 @@ class QwenVLModel(QWenModel):
         )
 
 
-@lru_cache(maxsize=1)
-def _get_tokenizer_without_image_pad(
-    tokenizer: PreTrainedTokenizer,
-) -> PreTrainedTokenizer:
-    """
-    The logic of adding image pad tokens should only be applied in
-    [`QwenVLProcessor`][vllm.model_executor.models.qwen_vl.QwenVLProcessor],
-    so they are patched out here.
-
-    The definition of the wrapped tokenizer can be found here:
-    https://huggingface.co/Qwen/Qwen-VL/blob/main/tokenization_qwen.py
-    """
-    new_tokenizer = copy.deepcopy(tokenizer)
-
-    class TokenizerWithoutImagePad(tokenizer.__class__):  # type: ignore
-        def tokenize(
-            self,
-            text: str,
-            allowed_special: Set[str] | str = "all",
-            disallowed_special: Collection[str] | str = (),
-            **kwargs,
-        ) -> list[bytes | str]:
-            text = unicodedata.normalize("NFC", text)
-
-            return [
-                self.decoder[t]
-                for t in self.tokenizer.encode(
-                    text,
-                    allowed_special=allowed_special,
-                    disallowed_special=disallowed_special,
-                )
-            ]
-
-        def _decode(
-            self,
-            token_ids: int | list[int],
-            skip_special_tokens: bool = False,
-            errors: str | None = None,
-            **kwargs,
-        ) -> str:
-            if isinstance(token_ids, int):
-                token_ids = [token_ids]
-
-            return self.tokenizer.decode(
-                token_ids,
-                errors=errors or self.errors,
-            )
-
-    TokenizerWithoutImagePad.__name__ = f"{tokenizer.__class__.__name__}WithoutImagePad"
-
-    new_tokenizer.__class__ = TokenizerWithoutImagePad
-    return new_tokenizer
-
-
 class QwenVLProcessor:
     """
     This model doesn't define its own HF processor,
@@ -574,12 +518,6 @@ class QwenVLProcessor:
 
 
 class QwenVLProcessingInfo(BaseProcessingInfo):
-    def get_tokenizer(self) -> PreTrainedTokenizer:
-        tokenizer = self.ctx.get_tokenizer()
-        assert isinstance(tokenizer, PreTrainedTokenizer)
-
-        return _get_tokenizer_without_image_pad(tokenizer)
-
     def get_hf_processor(self, **kwargs: object) -> QwenVLProcessor:
         return self.ctx.init_processor(
             QwenVLProcessor,

vllm/renderers/qwen_vl.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from vllm.config import VllmConfig
+from vllm.tokenizers import cached_get_tokenizer
+from vllm.tokenizers.qwen_vl import QwenVLTokenizer
+
+from .base import BaseRenderer
+from .hf import HfRenderer
+
+
+class QwenVLRenderer(BaseRenderer[QwenVLTokenizer]):
+    @classmethod
+    def from_config(  # type: ignore[override]
+        cls,
+        config: VllmConfig,
+        tokenizer_kwargs: dict[str, Any],
+    ) -> "HfRenderer":
+        model_config = config.model_config
+        if model_config.skip_tokenizer_init:
+            tokenizer = None
+        else:
+            tokenizer = cached_get_tokenizer(
+                tokenizer_cls=QwenVLTokenizer,
+                **tokenizer_kwargs,
+            )
+
+        return HfRenderer(config, tokenizer)

vllm/renderers/registry.py

@@ -20,6 +20,7 @@ _VLLM_RENDERERS = {
     "hf": ("hf", "HfRenderer"),
     "grok2": ("grok2", "Grok2Renderer"),
     "mistral": ("mistral", "MistralRenderer"),
+    "qwen_vl": ("qwen_vl", "QwenVLRenderer"),
     "terratorch": ("terratorch", "TerratorchRenderer"),
 }
 

vllm/tokenizers/deepseek_v32.py

@@ -7,9 +7,9 @@ from transformers import AutoTokenizer
 
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 
-from . import TokenizerLike
 from .deepseek_v32_encoding import encode_messages
 from .hf import HfTokenizer, get_cached_tokenizer
+from .protocol import TokenizerLike
 
 
 def get_deepseek_v32_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:

vllm/tokenizers/qwen_vl.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import copy
+import unicodedata
+from collections.abc import Collection, Set
+
+from transformers import AutoTokenizer
+
+from .hf import HfTokenizer, get_cached_tokenizer
+from .protocol import TokenizerLike
+
+
+def get_qwen_vl_tokenizer(tokenizer: HfTokenizer) -> HfTokenizer:
+    """
+    The logic of adding image pad tokens should only be applied in
+    `QwenVLProcessor`, so they are patched out here.
+
+    The definition of the wrapped tokenizer can be found here:
+    https://huggingface.co/Qwen/Qwen-VL/blob/main/tokenization_qwen.py
+    """
+    new_tokenizer = copy.copy(tokenizer)
+
+    class TokenizerWithoutImagePad(tokenizer.__class__):  # type: ignore
+        def tokenize(
+            self,
+            text: str,
+            allowed_special: Set[str] | str = "all",
+            disallowed_special: Collection[str] | str = (),
+            **kwargs,
+        ) -> list[bytes | str]:
+            text = unicodedata.normalize("NFC", text)
+
+            return [
+                self.decoder[t]
+                for t in self.tokenizer.encode(
+                    text,
+                    allowed_special=allowed_special,
+                    disallowed_special=disallowed_special,
+                )
+            ]
+
+        def _decode(
+            self,
+            token_ids: int | list[int],
+            skip_special_tokens: bool = False,
+            errors: str | None = None,
+            **kwargs,
+        ) -> str:
+            if isinstance(token_ids, int):
+                token_ids = [token_ids]
+
+            return self.tokenizer.decode(
+                token_ids,
+                errors=errors or self.errors,
+            )
+
+    TokenizerWithoutImagePad.__name__ = f"{tokenizer.__class__.__name__}WithoutImagePad"
+
+    new_tokenizer.__class__ = TokenizerWithoutImagePad
+    return new_tokenizer
+
+
+class QwenVLTokenizer(TokenizerLike):
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs) -> HfTokenizer:
+        tokenizer = AutoTokenizer.from_pretrained(*args, **kwargs)
+        return get_cached_tokenizer(get_qwen_vl_tokenizer(tokenizer))

vllm/tokenizers/registry.py

@@ -36,6 +36,7 @@ _VLLM_TOKENIZERS = {
     "grok2": ("grok2", "Grok2Tokenizer"),
     "hf": ("hf", "CachedHfTokenizer"),
     "mistral": ("mistral", "MistralTokenizer"),
+    "qwen_vl": ("qwen_vl", "QwenVLTokenizer"),
 }
 
 
@@ -165,6 +166,10 @@ def resolve_tokenizer_args(
     ):
         tokenizer_mode = "grok2"
 
+    # Model-specific tokenizers
+    if tokenizer_mode == "auto" and "/Qwen-VL" in str(tokenizer_name):
+        tokenizer_mode = "qwen_vl"
+
     # Fallback to HF tokenizer
     if tokenizer_mode == "auto":
         tokenizer_mode = "hf"