chore: 0.7.1 + vllm 0.12.0

.github/actions/pytest/action.yml

@@ -130,4 +130,4 @@ runs:
         path: |
           test-results/pytest_test_report_${{ inputs.framework }}_${{ env.STR_TEST_TYPE }}_${{ inputs.platform_arch }}.xml
           test-results/test_metadata_${{ inputs.framework }}_${{ env.STR_TEST_TYPE }}_${{ inputs.platform_arch }}.json
-        retention-days: 7
+        retention-days: 7

components/src/dynamo/vllm/args.py

@@ -192,6 +192,24 @@ def parse_args() -> Config:
     args = parser.parse_args()
     engine_args = AsyncEngineArgs.from_cli_args(args)
 
+    # Workaround for vLLM GIL contention bug with NIXL connector when using UniProcExecutor.
+    # With TP=1, vLLM defaults to UniProcExecutor which runs scheduler and worker in the same
+    # process. This causes a hot loop in _process_engine_step that doesn't release the GIL,
+    # blocking NIXL's add_remote_agent from completing. Using "mp" backend forces separate
+    # processes, avoiding the GIL contention.
+    # Note: Only apply for NIXL - other connectors (kvbm, lmcache) work fine with UniProcExecutor
+    # and forcing mp can expose race conditions in vLLM's scheduler.
+    # See: https://github.com/vllm-project/vllm/issues/29369
+    connector_list = [c.lower() for c in args.connector] if args.connector else []
+    uses_nixl = "nixl" in connector_list
+    tp_size = getattr(engine_args, "tensor_parallel_size", None) or 1
+    if uses_nixl and tp_size == 1 and engine_args.distributed_executor_backend is None:
+        logger.info(
+            "Setting --distributed-executor-backend=mp for TP=1 to avoid "
+            "UniProcExecutor GIL contention with NIXL connector"
+        )
+        engine_args.distributed_executor_backend = "mp"
+
     if engine_args.enable_prefix_caching is None:
         logger.debug(
             "--enable-prefix-caching or --no-enable-prefix-caching not specified. Defaulting to True (vLLM v1 default behavior)"
@@ -443,7 +461,7 @@ def overwrite_args(config):
         # skip tokenizer initialisation.  Setting this to **False** avoids
         # a NoneType error when the processor accesses the tokenizer.
         "skip_tokenizer_init": False,
-        "disable_log_requests": True,
+        "enable_log_requests": False,
         "disable_log_stats": False,
     }
 

components/src/dynamo/vllm/main.py

@@ -328,7 +328,7 @@ def setup_vllm_engine(config, stat_logger=None):
         vllm_config=vllm_config,
         usage_context=usage_context,
         stat_loggers=factory,
-        disable_log_requests=engine_args.disable_log_requests,
+        enable_log_requests=engine_args.enable_log_requests,
         disable_log_stats=engine_args.disable_log_stats,
     )
     if ENABLE_LMCACHE:

components/src/dynamo/vllm/multimodal_handlers/processor_handler.py

@@ -11,7 +11,7 @@
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.entrypoints.openai.protocol import ChatCompletionRequest, CompletionRequest
 from vllm.outputs import RequestOutput
-from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.tokenizers import TokenizerLike as AnyTokenizer
 
 from dynamo.runtime import Client
 

components/src/dynamo/vllm/multimodal_utils/chat_processor.py

@@ -28,9 +28,22 @@
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
 from vllm.entrypoints.openai.serving_engine import RequestPrompt
+from vllm.entrypoints.openai.serving_models import BaseModelPath, OpenAIServingModels
 from vllm.inputs.data import TokensPrompt
 from vllm.sampling_params import SamplingParams
-from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.tokenizers import TokenizerLike as AnyTokenizer
+
+
+class StubEngineClient:
+    """
+    Stub EngineClient for preprocessing-only use of OpenAIServingChat/Completion.
+    Provides the minimal attributes required by OpenAIServingModels.
+    """
+
+    def __init__(self, model_config: ModelConfig):
+        self.model_config = model_config
+        self.input_processor = None
+        self.io_processor = None
 
 
 @runtime_checkable
@@ -120,12 +133,19 @@ class ChatProcessor:
     def __init__(self, tokenizer: AnyTokenizer, model_config: ModelConfig):
         self.tokenizer = tokenizer
         self.model_config = model_config
+        # Create stub engine client and models for preprocessing-only usage
+        stub_engine = StubEngineClient(model_config)
+        serving_models = OpenAIServingModels(
+            engine_client=stub_engine,
+            base_model_paths=[
+                BaseModelPath(name=model_config.model, model_path=model_config.model)
+            ],
+        )
         self.openai_serving = OpenAIServingChat(
-            engine_client=None,
-            model_config=model_config,
-            models=None,
-            request_logger=None,
+            engine_client=stub_engine,
+            models=serving_models,
             response_role="assistant",
+            request_logger=None,
             chat_template=None,
             chat_template_content_format="auto",
         )
@@ -186,7 +206,6 @@ async def stream_response(
                 conversation,
                 self.tokenizer,
                 request_metadata,
-                enable_force_include_usage=False,
             ):
                 if raw_response.startswith("data: [DONE]"):
                     yield raw_response
@@ -220,7 +239,6 @@ async def stream_response(
                 conversation,
                 self.tokenizer,
                 request_metadata,
-                enable_force_include_usage=False,
             ):
                 if raw_response.startswith("data: [DONE]"):
                     break
@@ -267,10 +285,17 @@ class CompletionsProcessor:
     def __init__(self, tokenizer: AnyTokenizer, model_config: ModelConfig):
         self.tokenizer = tokenizer
         self.model_config = model_config
+        # Create stub engine client and models for preprocessing-only usage
+        stub_engine = StubEngineClient(model_config)
+        serving_models = OpenAIServingModels(
+            engine_client=stub_engine,
+            base_model_paths=[
+                BaseModelPath(name=model_config.model, model_path=model_config.model)
+            ],
+        )
         self.openai_serving = OpenAIServingCompletion(
-            engine_client=None,
-            model_config=model_config,
-            models=None,
+            engine_client=stub_engine,
+            models=serving_models,
             request_logger=None,
         )
 

components/src/dynamo/vllm/multimodal_utils/protocol.py

@@ -26,7 +26,7 @@
 from vllm.multimodal.inputs import MultiModalUUIDDict  # noqa: F401
 from vllm.outputs import CompletionOutput
 from vllm.sampling_params import SamplingParams
-from vllm.sequence import RequestMetrics
+from vllm.v1.metrics.stats import RequestStateStats
 
 import dynamo.nixl_connect as connect
 
@@ -156,7 +156,7 @@ class MyRequestOutput(BaseModel):
     https://github.com/vllm-project/vllm/blob/a4c402a756fa3213caf9d2cde0e4ceb2d57727f2/vllm/outputs.py#L85
 
     This class is used to serialize the RequestOutput and any recursively defined types
-    We can do this because PromptLogprobs, RequestMetrics, and CompletionOutput are all serializable dataclasses
+    We can do this because PromptLogprobs, RequestStateStats, and CompletionOutput are all serializable dataclasses
     """
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -167,7 +167,7 @@ class MyRequestOutput(BaseModel):
     prompt_logprobs: Optional[PromptLogprobs] = None
     outputs: List[CompletionOutput]
     finished: bool
-    metrics: Optional[RequestMetrics] = None
+    metrics: Optional[RequestStateStats] = None
     kv_transfer_params: Optional[dict[str, Any]] = None
     # lora_request: Optional[LoRARequest] = None
     # encoder_prompt: Optional[str] = None

container/Dockerfile.vllm

@@ -10,17 +10,17 @@ ARG BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
 ARG BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
 ARG ENABLE_KVBM=false
 ARG RUNTIME_IMAGE="nvcr.io/nvidia/cuda"
-ARG RUNTIME_IMAGE_TAG="12.8.1-runtime-ubuntu24.04"
-ARG CUDA_VERSION="12.8"
+ARG RUNTIME_IMAGE_TAG="12.9.0-runtime-ubuntu24.04"
+ARG CUDA_VERSION="12.9"
 
 # Make sure to update the dependency version in pyproject.toml when updating this
-ARG VLLM_REF="v0.11.0"
-# FlashInfer only respected when building vLLM from source, ie when VLLM_REF does not start with 'v' or for arm64 builds
-ARG FLASHINF_REF="v0.3.1"
-ARG TORCH_BACKEND="cu128"
+ARG VLLM_REF="v0.12.0"
+# FlashInfer Ref used to install flashinfer-cubin and flashinfer-jit-cache
+ARG FLASHINF_REF="v0.5.3"
 
 # If left blank, then we will fallback to vLLM defaults
 ARG DEEPGEMM_REF=""
+ARG LMCACHE_REF="0.3.10"
 
 # sccache configuration - inherit from base build
 ARG USE_SCCACHE
@@ -109,7 +109,7 @@ ARG VLLM_REF
 ARG VLLM_GIT_URL
 ARG DEEPGEMM_REF
 ARG FLASHINF_REF
-ARG TORCH_BACKEND
+ARG LMCACHE_REF
 ARG CUDA_VERSION
 
 ARG MAX_JOBS=16
@@ -143,7 +143,7 @@ RUN --mount=type=bind,source=./container/deps/,target=/tmp/deps \
     export SCCACHE_S3_KEY_PREFIX=${SCCACHE_S3_KEY_PREFIX:-${ARCH}} && \
         cp /tmp/deps/vllm/install_vllm.sh /tmp/install_vllm.sh && \
         chmod +x /tmp/install_vllm.sh && \
-        /tmp/install_vllm.sh --editable --vllm-ref $VLLM_REF --max-jobs $MAX_JOBS --arch $ARCH --installation-dir /opt ${DEEPGEMM_REF:+--deepgemm-ref "$DEEPGEMM_REF"} ${FLASHINF_REF:+--flashinf-ref "$FLASHINF_REF"} --torch-backend $TORCH_BACKEND --cuda-version $CUDA_VERSION && \
+        /tmp/install_vllm.sh --vllm-ref $VLLM_REF --max-jobs $MAX_JOBS --arch $ARCH --installation-dir /opt ${DEEPGEMM_REF:+--deepgemm-ref "$DEEPGEMM_REF"} ${FLASHINF_REF:+--flashinf-ref "$FLASHINF_REF"} ${LMCACHE_REF:+--lmcache-ref "$LMCACHE_REF"} --cuda-version $CUDA_VERSION && \
         /tmp/use-sccache.sh show-stats "vLLM";
 
 ENV LD_LIBRARY_PATH=\
@@ -206,7 +206,7 @@ RUN apt-get update && \
         # prometheus dependencies
         ca-certificates \
         # DeepGemm uses 'cuobjdump' which does not come with CUDA image
-        cuda-command-line-tools-12-8 && \
+        cuda-command-line-tools-12-9 && \
     rm -rf /var/lib/apt/lists/*
 
 # Copy CUDA development tools (nvcc, headers, dependencies, etc.) from base devel image
@@ -287,8 +287,14 @@ RUN --mount=type=bind,source=./container/deps/requirements.txt,target=/tmp/requi
         --requirement /tmp/requirements.txt \
         --requirement /tmp/requirements.test.txt
 
-# Copy benchmarks, examples, and tests for CI with correct ownership
-COPY --chown=dynamo: . /workspace/
+# Copy tests, benchmarks, deploy and components for CI
+COPY --chown=dynamo: benchmarks /workspace/benchmarks
+COPY --chown=dynamo: tests /workspace/tests
+COPY --chown=dynamo: examples /workspace/examples
+COPY --chown=dynamo: deploy /workspace/deploy
+COPY --chown=dynamo: recipes/ /workspace/recipes/
+COPY --chown=dynamo: components/ /workspace/components/
+COPY --chown=dynamo: lib/ /workspace/lib/
 
 # Copy attribution files
 COPY --chown=dynamo: ATTRIBUTION* LICENSE /workspace/
@@ -373,6 +379,7 @@ COPY --from=dynamo_base /usr/local/cargo /usr/local/cargo
 
 # Install maturin, for maturin develop
 # Editable install of dynamo
+COPY pyproject.toml README.md hatch_build.py /workspace/
 RUN uv pip install maturin[patchelf] && \
     uv pip install --no-deps -e .
 

container/build.sh

@@ -106,7 +106,7 @@ VLLM_BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
 # Please check https://github.com/ai-dynamo/dynamo/pull/1065
 # for details and reproducer to manually test if the image
 # can be updated to later versions.
-VLLM_BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"
+VLLM_BASE_IMAGE_TAG="25.04-cuda12.9-devel-ubuntu24.04"
 
 NONE_BASE_IMAGE="nvcr.io/nvidia/cuda-dl-base"
 NONE_BASE_IMAGE_TAG="25.01-cuda12.8-devel-ubuntu24.04"