diff --git a/python/aiffairness/uv.lock b/python/aiffairness/uv.lock index 0a089d794c3..fbe21a60c05 100644 --- a/python/aiffairness/uv.lock +++ b/python/aiffairness/uv.lock @@ -1166,7 +1166,7 @@ requires-dist = [ { name = "timing-asgi", specifier = ">=0.3.0,<1.0.0" }, { name = "urllib3", specifier = ">=2.6.0" }, { name = "uvicorn", extras = ["standard"], specifier = ">=0.30.6,<1.0.0" }, - { name = "vllm", marker = "extra == 'llm'", specifier = "==0.15.1" }, + { name = "vllm", marker = "extra == 'llm'", specifier = "==0.18.0" }, ] provides-extras = ["storage", "logging", "ray", "llm"] diff --git a/python/huggingface_server.Dockerfile b/python/huggingface_server.Dockerfile index 775535c6a65..2d0450fb308 100644 --- a/python/huggingface_server.Dockerfile +++ b/python/huggingface_server.Dockerfile @@ -52,9 +52,8 @@ WORKDIR ${WORKSPACE_DIR} FROM base AS build ARG WORKSPACE_DIR -ARG VLLM_VERSION=0.15.1 -ARG LMCACHE_VERSION=0.3.9 -ARG FLASHINFER_VERSION=0.6.1 +ARG VLLM_VERSION=0.20.0 +ARG LMCACHE_VERSION=0.4.4 WORKDIR ${WORKSPACE_DIR} @@ -87,22 +86,10 @@ RUN --mount=type=cache,target=/root/.cache/uv cd huggingfaceserver && uv sync -- # Install vllm # https://docs.vllm.ai/en/latest/models/extensions/runai_model_streamer.html, https://docs.vllm.ai/en/latest/models/extensions/tensorizer.html # https://docs.vllm.ai/en/latest/models/extensions/fastsafetensor.html -RUN --mount=type=cache,target=/root/.cache/pip pip install vllm[runai,tensorizer,fastsafetensors]==${VLLM_VERSION} +RUN --mount=type=cache,target=/root/.cache/uv uv pip install vllm[runai,tensorizer,fastsafetensors]==${VLLM_VERSION} # Install lmcache -RUN --mount=type=cache,target=/root/.cache/pip pip install lmcache==${LMCACHE_VERSION} - -# Use Bash with `-o pipefail` so we can leverage Bash-specific features (like `[[ … ]]` for glob tests) -# and ensure that failures in any part of a piped command cause the build to fail immediately. -SHELL ["/bin/bash", "-o", "pipefail", "-c"] - -# Install flashinfer -# https://docs.flashinfer.ai/installation.html -RUN --mount=type=cache,target=/root/.cache/pip \ - pip install flashinfer-cubin==${FLASHINFER_VERSION} && \ - pip install flashinfer-jit-cache==${FLASHINFER_VERSION} \ - --extra-index-url https://flashinfer.ai/whl/cu$(echo ${CUDA_VERSION} | cut -d. -f1,2 | tr -d '.') && \ - flashinfer show-config +RUN --mount=type=cache,target=/root/.cache/uv uv pip install lmcache==${LMCACHE_VERSION} # Generate third-party licenses COPY pyproject.toml pyproject.toml @@ -162,6 +149,10 @@ ENV VLLM_NCCL_SO_PATH="/lib/x86_64-linux-gnu/libnccl.so.2" # Set the multiprocess method to spawn to avoid issues with cuda initialization for `mp` executor backend. ENV VLLM_WORKER_MULTIPROC_METHOD="spawn" +ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/targets/x86_64-linux/lib:$(LD_LIBRARY_PATH) +# Default AWS region when using runai to download from S3 bucket. TODO: pass this at runtime. +ENV AWS_REGION=eu-west-1 + USER 1000 ENV PYTHONPATH=${WORKSPACE_DIR}/huggingfaceserver ENTRYPOINT ["python3", "-m", "huggingfaceserver"] diff --git a/python/huggingface_server_cpu.Dockerfile b/python/huggingface_server_cpu.Dockerfile index e59174f2f6c..89fd1490c96 100644 --- a/python/huggingface_server_cpu.Dockerfile +++ b/python/huggingface_server_cpu.Dockerfile @@ -83,7 +83,7 @@ RUN cd huggingfaceserver && \ rm -rf ~/.cache/uv # install vllm -ARG VLLM_VERSION=0.15.1 +ARG VLLM_VERSION=0.20.0 ARG VLLM_CPU_DISABLE_AVX512=true ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512} ARG VLLM_CPU_AVX512BF16=1 diff --git a/python/huggingfaceserver/huggingfaceserver/__main__.py b/python/huggingfaceserver/huggingfaceserver/__main__.py index 66deba1a320..6a51b57098e 100644 --- a/python/huggingfaceserver/huggingfaceserver/__main__.py +++ b/python/huggingfaceserver/huggingfaceserver/__main__.py @@ -50,22 +50,37 @@ def list_of_strings(arg): return arg.split(",") +_REMOTE_URI_PREFIXES = ( + "s3://", "gs://", "az://", "azure://", + "http://", "https://", "ftp://", "hdfs://", +) + + def get_model_id_or_path(args: argparse.Namespace) -> Union[str, Path]: # If --model_id is specified then pass model_id to HF API, otherwise load the model from /mnt/models if args.model_id: return cast(str, args.model_id) + # For remote URIs (s3://, gs://, etc.), pass directly to vLLM which handles + # streaming via load format (e.g., runai_streamer). Skip Storage.download + # to avoid attempting a full local download. + if isinstance(args.model_dir, str) and args.model_dir.startswith(_REMOTE_URI_PREFIXES): + return args.model_dir return Path(Storage.download(args.model_dir)) def is_vllm_backend_enabled( args: argparse.Namespace, model_id_or_path: Union[str, Path] ) -> bool: + # When backend is explicitly set to vllm, skip architecture validation. + # This allows using custom load formats (like runai_streamer) with remote URIs. + force_vllm = args.backend == Backend.vllm return ( (args.backend == Backend.vllm or args.backend == Backend.auto) and vllm_available() and infer_vllm_supported_from_model_architecture( model_id_or_path, trust_remote_code=args.trust_remote_code, + force_vllm=force_vllm, ) ) @@ -140,6 +155,12 @@ def is_vllm_backend_enabled( parser.add_argument( "--return_token_type_ids", action="store_true", help="Return token type ids" ) +parser.add_argument( + "--return_offsets_mapping", + action="store_true", + default=False, + help="Return start/end character offsets for each token (token_classification only).", +) # Create a mutually exclusive group for output format options # This group allows the user to choose between returning probabilities or disabling postprocessing. @@ -312,6 +333,7 @@ def load_model(): tensor_input_names=kwargs.get("tensor_input_names", None), return_token_type_ids=kwargs.get("return_token_type_ids", None), request_logger=request_logger, + return_offsets_mapping=kwargs.get("return_offsets_mapping", False), return_probabilities=kwargs.get("return_probabilities", False), return_raw_logits=kwargs.get("return_raw_logits", False), ) diff --git a/python/huggingfaceserver/huggingfaceserver/vllm/utils.py b/python/huggingfaceserver/huggingfaceserver/vllm/utils.py index e701484cb37..44d2d169c90 100644 --- a/python/huggingfaceserver/huggingfaceserver/vllm/utils.py +++ b/python/huggingfaceserver/huggingfaceserver/vllm/utils.py @@ -41,18 +41,47 @@ def vllm_available() -> bool: def infer_vllm_supported_from_model_architecture( model_config_path: Union[Path, str], trust_remote_code: bool = False, + force_vllm: bool = False, ) -> bool: if not _vllm: return False - model_config = AutoConfig.from_pretrained( - model_config_path, trust_remote_code=trust_remote_code - ) - for architecture in model_config.architectures: - if architecture not in ModelRegistry.get_supported_archs(): - logger.info("not a supported model by vLLM") - return False - return True + model_path_str = str(model_config_path) + + # Skip architecture validation for remote URIs (S3, GCS, Azure, HTTP). + # These are used with custom load formats like runai_streamer that bypass + # standard HuggingFace model loading. + is_remote_uri = model_path_str.startswith(( + "s3://", "gs://", "az://", "azure://", + "http://", "https://", "ftp://", "hdfs://", + )) + + if is_remote_uri: + logger.info( + f"Skipping architecture validation for remote URI: {model_path_str}. " + "Assuming vLLM can handle this with appropriate load-format settings." + ) + return True + + if force_vllm: + logger.info("vLLM backend explicitly requested, skipping architecture validation.") + return True + + try: + model_config = AutoConfig.from_pretrained( + model_config_path, trust_remote_code=trust_remote_code + ) + for architecture in model_config.architectures: + if architecture not in ModelRegistry.get_supported_archs(): + logger.info("not a supported model by vLLM") + return False + return True + except Exception as e: + logger.warning( + f"Failed to load model config from {model_path_str}: {e}. " + "If using a remote URI with custom load format, consider setting --backend=vllm explicitly." + ) + return False def maybe_add_vllm_cli_parser(parser: ArgumentParser) -> ArgumentParser: @@ -70,26 +99,16 @@ def build_vllm_engine_args(args) -> "AsyncEngineArgs": @asynccontextmanager async def build_async_engine_client_from_engine_args( engine_args: AsyncEngineArgs, - disable_frontend_multiprocessing: bool = False, ) -> AsyncIterator[EngineClient]: """ - Create EngineClient, either: - - V1 AsyncLLM (default) - - V0 AsyncLLMEngine (legacy) + Create V1 AsyncLLM EngineClient. Returns the Client or None if the creation failed. """ - # Create the EngineConfig (determines if we can use V1). usage_context = UsageContext.OPENAI_API_SERVER vllm_config = engine_args.create_engine_config(usage_context=usage_context) - if disable_frontend_multiprocessing: - logger.warning( - "V1 is enabled, but got --disable-frontend-multiprocessing. " - "To disable frontend multiprocessing, set VLLM_USE_V1=0." - ) - from vllm.v1.engine.async_llm import AsyncLLM async_llm: Optional[AsyncLLM] = None diff --git a/python/huggingfaceserver/huggingfaceserver/vllm/vllm_model.py b/python/huggingfaceserver/huggingfaceserver/vllm/vllm_model.py index af04fe7d1c7..8206f88d205 100644 --- a/python/huggingfaceserver/huggingfaceserver/vllm/vllm_model.py +++ b/python/huggingfaceserver/huggingfaceserver/vllm/vllm_model.py @@ -23,8 +23,8 @@ from vllm.engine.protocol import EngineClient from vllm.entrypoints.openai.completion.serving import OpenAIServingCompletion from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat -from vllm.entrypoints.pooling.embed.serving import OpenAIServingEmbedding -from vllm.entrypoints.pooling.score.serving import ServingScores +from vllm.entrypoints.pooling.embed.serving import ServingEmbedding +from vllm.entrypoints.pooling.scoring.serving import ServingScores from vllm.tool_parsers import ToolParserManager from vllm.entrypoints.openai.models.protocol import BaseModelPath from vllm.entrypoints.openai.models.serving import OpenAIServingModels @@ -60,7 +60,7 @@ class VLLMModel(OpenAIEncoderModel, OpenAIGenerativeModel): # pylint:disable=c- openai_serving_models: Optional[OpenAIServingModels] = None openai_serving_completion: Optional[OpenAIServingCompletion] = None openai_serving_chat: Optional[OpenAIServingChat] = None - openai_serving_embedding: Optional[OpenAIServingEmbedding] = None + openai_serving_embedding: Optional[ServingEmbedding] = None serving_reranking: Optional[ServingScores] = None def __init__( @@ -107,7 +107,7 @@ async def start_engine(self): self.vllm_engine_args.tensor_parallel_size = torch.cuda.device_count() async with build_async_engine_client_from_engine_args( - self.vllm_engine_args, self.args.disable_frontend_multiprocessing + self.vllm_engine_args, ) as engine_client: self.engine_client = engine_client vllm_config = self.engine_client.vllm_config @@ -137,11 +137,29 @@ async def start_engine(self): ) await self.openai_serving_models.init_static_loras() + from vllm.entrypoints.serve.render.serving import OpenAIServingRender + + openai_serving_render = OpenAIServingRender( + model_config=vllm_config.model_config, + renderer=self.engine_client.renderer, + model_registry=self.openai_serving_models.registry, + request_logger=self.request_logger, + chat_template=resolved_chat_template, + chat_template_content_format=self.args.chat_template_content_format, + trust_request_chat_template=self.args.trust_request_chat_template, + enable_auto_tools=self.args.enable_auto_tool_choice, + exclude_tools_when_tool_choice_none=self.args.exclude_tools_when_tool_choice_none, + tool_parser=self.args.tool_call_parser, + reasoning_parser=self.args.structured_outputs_config.reasoning_parser, + log_error_stack=self.args.log_error_stack, + ) + self.openai_serving_chat = ( OpenAIServingChat( self.engine_client, self.openai_serving_models, self.args.response_role, + openai_serving_render=openai_serving_render, request_logger=self.request_logger, chat_template=resolved_chat_template, chat_template_content_format=self.args.chat_template_content_format, @@ -154,7 +172,6 @@ async def start_engine(self): enable_prompt_tokens_details=self.args.enable_prompt_tokens_details, enable_force_include_usage=self.args.enable_force_include_usage, enable_log_outputs=self.args.enable_log_outputs, - log_error_stack=self.args.log_error_stack, ) if "generate" in supported_tasks else None @@ -164,18 +181,18 @@ async def start_engine(self): OpenAIServingCompletion( self.engine_client, self.openai_serving_models, + openai_serving_render=openai_serving_render, request_logger=self.request_logger, return_tokens_as_token_ids=self.args.return_tokens_as_token_ids, enable_prompt_tokens_details=self.args.enable_prompt_tokens_details, enable_force_include_usage=self.args.enable_force_include_usage, - log_error_stack=self.args.log_error_stack, ) if "generate" in supported_tasks else None ) self.openai_serving_embedding = ( - OpenAIServingEmbedding( + ServingEmbedding( self.engine_client, self.openai_serving_models, request_logger=self.request_logger, @@ -195,7 +212,7 @@ async def start_engine(self): request_logger=self.request_logger, log_error_stack=self.args.log_error_stack, ) - if ("embed" in supported_tasks or "score" in supported_tasks) + if ("embed" in supported_tasks or "classify" in supported_tasks) else None ) @@ -281,9 +298,7 @@ async def create_embedding( message="The model does not support Embeddings API", status_code=HTTPStatus.BAD_REQUEST, ) - response = await self.openai_serving_embedding.create_embedding( - request, raw_request - ) + response = await self.openai_serving_embedding(request, raw_request) if isinstance(response, engineError): return create_error_response( @@ -306,7 +321,7 @@ async def create_rerank( message="The model does not support Rerank API", status_code=HTTPStatus.BAD_REQUEST, ) - response = await self.serving_reranking.do_rerank(request, raw_request) + response = await self.serving_reranking(request, raw_request) if isinstance(response, engineError): return create_error_response( diff --git a/python/huggingfaceserver/pyproject.toml b/python/huggingfaceserver/pyproject.toml index 30ba481f807..a445d2123dd 100644 --- a/python/huggingfaceserver/pyproject.toml +++ b/python/huggingfaceserver/pyproject.toml @@ -14,10 +14,16 @@ dependencies = [ "setuptools>=70.0.0", ] name = "huggingfaceserver" -version = "0.17.0" +version = "0.18.0" description = "Model Server implementation for huggingface. Not intended for use outside KServe Frameworks Images." readme = "README.md" +# fixes CVE-2026-40192 +[tool.uv] +override-dependencies = [ + "pillow>=12.2.0", +] + [dependency-groups] test = [ "pytest<8.0.0,>=7.4.4", @@ -26,7 +32,7 @@ test = [ "pytest-asyncio<1.0.0,>=0.23.4", "pytest-httpx<1.0.0,>=0.30.0", "einops<1.0.0,>=0.8.0", - "openai<2.0.0,>=1.59.9", + "openai>=2.0.0", ] dev = [ "black[colorama]~=24.3.0", diff --git a/python/kserve/kserve/protocol/rest/openai/types/__init__.py b/python/kserve/kserve/protocol/rest/openai/types/__init__.py index 9cd889870fe..d70e25806fb 100644 --- a/python/kserve/kserve/protocol/rest/openai/types/__init__.py +++ b/python/kserve/kserve/protocol/rest/openai/types/__init__.py @@ -43,7 +43,7 @@ EmbeddingResponseData, EmbeddingCompletionRequest, ) -from vllm.entrypoints.pooling.score.protocol import ( +from vllm.entrypoints.pooling.scoring.protocol import ( RerankRequest, RerankResponse as Rerank, ) diff --git a/python/kserve/pyproject.toml 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a/python/lgbserver/uv.lock b/python/lgbserver/uv.lock index c262cfa7d78..89455ae8bfc 100644 --- a/python/lgbserver/uv.lock +++ b/python/lgbserver/uv.lock @@ -1101,7 +1101,7 @@ requires-dist = [ { name = "timing-asgi", specifier = ">=0.3.0,<1.0.0" }, { name = "urllib3", specifier = ">=2.6.0" }, { name = "uvicorn", extras = ["standard"], specifier = ">=0.30.6,<1.0.0" }, - { name = "vllm", marker = "extra == 'llm'", specifier = "==0.15.1" }, + { name = "vllm", marker = "extra == 'llm'", specifier = "==0.18.0" }, ] provides-extras = ["storage", "logging", "ray", "llm"] diff --git a/python/paddleserver/uv.lock b/python/paddleserver/uv.lock index f47e6b4515d..84863e8e1cc 100644 --- a/python/paddleserver/uv.lock +++ b/python/paddleserver/uv.lock @@ -1117,7 +1117,7 @@ requires-dist = [ { name = "timing-asgi", specifier = ">=0.3.0,<1.0.0" }, { name = "urllib3", specifier = ">=2.6.0" }, { name = "uvicorn", extras = ["standard"], specifier = ">=0.30.6,<1.0.0" }, - { name = "vllm", marker = "extra == 'llm'", specifier = "==0.15.1" }, + { name = "vllm", marker = "extra == 'llm'", specifier = "==0.18.0" }, ] provides-extras = ["storage", "logging", "ray", "llm"] diff --git a/python/pyproject.toml b/python/pyproject.toml index 72bd625f973..d4303c5a9e5 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -7,22 +7,39 @@ with-urls = true output-path = "third_party/library" # Requires manual review ignore-packages = [ - "nvidia-cublas-cu12", - "nvidia-cuda-cupti-cu12", - "nvidia-cuda-nvrtc-cu12", - "nvidia-cuda-runtime-cu12", + # cu12-suffixed variants (older vLLM) + "nvidia-cublas-cu12", + "nvidia-cuda-cupti-cu12", + "nvidia-cuda-nvrtc-cu12", + "nvidia-cuda-runtime-cu12", "nvidia-cudnn-cu12", - "nvidia-cufft-cu12", - "nvidia-cufile-cu12", - "nvidia-curand-cu12", - "nvidia-cusolver-cu12", - "nvidia-cusparse-cu12", - "nvidia-nccl-cu12", - "nvidia-nvjitlink-cu12", - "nvidia-nvtx-cu12", + "nvidia-cufft-cu12", + "nvidia-cufile-cu12", + "nvidia-curand-cu12", + "nvidia-cusolver-cu12", + "nvidia-cusparse-cu12", + "nvidia-nccl-cu12", + "nvidia-nvjitlink-cu12", + "nvidia-nvtx-cu12", + "nvidia-cusparselt-cu12", + # suffix-free variants (vLLM 0.20+) + "nvidia-cublas", + "nvidia-cuda-cupti", + "nvidia-cuda-nvrtc", + "nvidia-cuda-runtime", + "nvidia-cudnn", + "nvidia-cufft", + "nvidia-cufile", + "nvidia-curand", + "nvidia-cusolver", + "nvidia-cusparse", + "nvidia-nccl", + "nvidia-nvjitlink", + "nvidia-nvtx", + "nvidia-cusparselt", + # non-versioned nvidia packages "nvidia-cutlass-dsl", "nvidia-cutlass-dsl-libs-base", - "nvidia-cusparselt-cu12", "nvidia-cudnn-frontend", "nvidia-nvshmem-cu12", # Intel EULA diff --git a/python/xgbserver/uv.lock b/python/xgbserver/uv.lock index 4d353cb6046..8e1cbe12add 100644 --- a/python/xgbserver/uv.lock +++ b/python/xgbserver/uv.lock @@ -1101,7 +1101,7 @@ requires-dist = [ { name = "timing-asgi", specifier = ">=0.3.0,<1.0.0" }, { name = "urllib3", specifier = ">=2.6.0" }, { name = "uvicorn", extras = ["standard"], specifier = ">=0.30.6,<1.0.0" }, - { name = "vllm", marker = "extra == 'llm'", specifier = "==0.15.1" }, + { name = "vllm", marker = "extra == 'llm'", specifier = "==0.18.0" }, ] provides-extras = ["storage", "logging", "ray", "llm"]