rebase 2026/02/11

vllm: 4a1550d22d7058e129d0e1257e726b3bf4a77025

Signed-off-by: tzhouam <tzhouam@connect.ust.hk>

docker/Dockerfile.ci

@@ -18,18 +18,18 @@ RUN apt-get update && \
 # Note: --prerelease=allow is required because nightly/commit wheels use
 # pre-release version numbers (e.g. 2.10.0.dev...) which uv ignores by default,
 # causing it to fall back to the older stable release from PyPI.
-RUN uv pip install --system --upgrade --force-reinstall --prerelease=allow vllm --extra-index-url https://wheels.vllm.ai/e94ec597334d9a3e9b0d04bc17152e2747c83d51
+RUN uv pip install --system --upgrade --force-reinstall --prerelease=allow vllm --extra-index-url https://wheels.vllm.ai/4a1550d22d7058e129d0e1257e726b3bf4a77025
 
 # Keep flashinfer runtime packages in version sync.
 # We derive the version from installed flashinfer-python (pulled by vllm)
 # and enforce the same version for flashinfer-cubin / flashinfer-jit-cache.
-RUN FLASHINFER_VERSION="$(python3 -c 'import importlib.metadata as m; print(m.version("flashinfer-python"))')" && \
-    FLASHINFER_CUDA_TAG="$(python3 -c 'import torch; print((torch.version.cuda or "12.4").replace(".", ""))')" && \
-    uv pip install --system --upgrade --force-reinstall \
-      "flashinfer-cubin==${FLASHINFER_VERSION}" \
-      "flashinfer-jit-cache==${FLASHINFER_VERSION}" \
-      --extra-index-url "https://flashinfer.ai/whl/cu${FLASHINFER_CUDA_TAG}" && \
-    flashinfer show-config
+# RUN FLASHINFER_VERSION="$(python3 -c 'import importlib.metadata as m; print(m.version("flashinfer-python"))')" && \
+#     FLASHINFER_CUDA_TAG="$(python3 -c 'import torch; print((torch.version.cuda or "12.4").replace(".", ""))')" && \
+#     uv pip install --system --upgrade --force-reinstall \
+#       "flashinfer-cubin==${FLASHINFER_VERSION}" \
+#       "flashinfer-jit-cache==${FLASHINFER_VERSION}" \
+#       --extra-index-url "https://flashinfer.ai/whl/cu${FLASHINFER_CUDA_TAG}" && \
+#     flashinfer show-config
 
 # Install vllm-omni into the same uv-managed Python environment used by the base image.
 RUN uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" --no-cache-dir ".[dev]"

vllm_omni/core/sched/omni_ar_scheduler.py

@@ -245,10 +245,10 @@ class OmniARScheduler(VLLMScheduler):
                 # Skip requests that were recovered from KV load failure
                 continue
             request = self.requests.get(req_id)
-            if request is None:
+            if request is None or request.is_finished():
                 # The request is already finished. This can happen if the
                 # request is aborted while the model is executing it (e.g.,
-                # in pipeline parallelism).
+                # in pipeline parallelism or async scheduling).
                 continue
 
             req_index = model_runner_output.req_id_to_index[req_id]
@@ -475,15 +475,16 @@ class OmniARScheduler(VLLMScheduler):
 
         return engine_core_outputs
 
-    def _free_request(self, request: Request) -> dict[str, Any] | None:
+    def _free_request(self, request: Request, delay_free_blocks: bool = False) -> dict[str, Any] | None:
         # TODO(wzliu)! for offline mode, we should not end process until all data is transferred
         """Mark a request as finished and free its resources."""
+        assert request.is_finished()
 
         # 1. Standard cleanup parts from base _free_request
-        delay_free_blocks = False
+        connector_delay_free_blocks = False
         kv_xfer_params = None
         if self.connector is not None:
-            delay_free_blocks, kv_xfer_params = self._connector_finished(request)
+            connector_delay_free_blocks, kv_xfer_params = self._connector_finished(request)
 
         self.encoder_cache_manager.free(request)
         request_id = request.request_id
@@ -543,6 +544,7 @@ class OmniARScheduler(VLLMScheduler):
                 return kv_xfer_params
 
         # 3. Standard Freeing
+        delay_free_blocks |= connector_delay_free_blocks
         if not delay_free_blocks:
             self._free_blocks(request)
 

vllm_omni/core/sched/omni_generation_scheduler.py

@@ -317,10 +317,10 @@ class OmniGenerationScheduler(VLLMScheduler):
                 # Skip requests that were recovered from KV load failure
                 continue
             request = self.requests.get(req_id)
-            if request is None:
+            if request is None or request.is_finished():
                 # The request is already finished. This can happen if the
                 # request is aborted while the model is executing it (e.g.,
-                # in pipeline parallelism).
+                # in pipeline parallelism or async scheduling).
                 continue
 
             req_index = model_runner_output.req_id_to_index[req_id]

vllm_omni/engine/arg_utils.py

@@ -1,8 +1,10 @@
 from dataclasses import dataclass, field
 from typing import Any
 
+import vllm.envs as envs
 from vllm.engine.arg_utils import EngineArgs
 from vllm.logger import init_logger
+from vllm.transformers_utils.gguf_utils import is_gguf
 from vllm.v1.engine.async_llm import AsyncEngineArgs
 
 from vllm_omni.config import OmniModelConfig
@@ -89,6 +91,19 @@ class OmniEngineArgs(EngineArgs):
         Returns:
             OmniModelConfig instance with all configuration fields set
         """
+        # GGUF files need a specific model loader path in vLLM.
+        if is_gguf(self.model):
+            self.quantization = self.load_format = "gguf"
+
+        if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
+            logger.warning(
+                "The global random seed is set to %d. Since "
+                "VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
+                "affect the random state of the Python process that "
+                "launched vLLM.",
+                self.seed,
+            )
+
         # register omni models to avoid model not found error
         self._ensure_omni_models_registered()
 
@@ -215,6 +230,19 @@ class AsyncOmniEngineArgs(AsyncEngineArgs):
         Returns:
             OmniModelConfig instance with all configuration fields set
         """
+        # GGUF files need a specific model loader path in vLLM.
+        if is_gguf(self.model):
+            self.quantization = self.load_format = "gguf"
+
+        if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
+            logger.warning(
+                "The global random seed is set to %d. Since "
+                "VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
+                "affect the random state of the Python process that "
+                "launched vLLM.",
+                self.seed,
+            )
+
         # register omni models to avoid model not found error
         self._ensure_omni_models_registered()
 

vllm_omni/entrypoints/openai/api_server.py

@@ -33,7 +33,13 @@ from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.mcp.tool_server import DemoToolServer, MCPToolServer, ToolServer
 from vllm.entrypoints.openai.api_server import build_app as build_openai_app
 from vllm.entrypoints.openai.api_server import setup_server as setup_openai_server
-from vllm.entrypoints.openai.basic.api_router import base
+
+# vLLM moved `base` from openai.basic.api_router to serve.instrumentator.basic.
+# Keep a fallback for older/newer upstream layouts during rebase windows.
+try:
+    from vllm.entrypoints.serve.instrumentator.basic import base
+except ModuleNotFoundError:
+    from vllm.entrypoints.openai.basic.api_router import base
 from vllm.entrypoints.openai.chat_completion.protocol import (
     ChatCompletionRequest,
     ChatCompletionResponse,