[WIP] Add vllm Dynamo support

sdks/python/apache_beam/ml/inference/vllm_inference.py

@@ -109,13 +109,20 @@ def getAsyncVLLMClient(port) -> AsyncOpenAI:
 
 
 class _VLLMModelServer():
-  def __init__(self, model_name: str, vllm_server_kwargs: dict[str, str]):
+  def __init__(
+      self,
+      model_name: str,
+      vllm_server_kwargs: dict[str, str],
+      vllm_executable: Optional[str] = None):
     self._model_name = model_name
     self._vllm_server_kwargs = vllm_server_kwargs
     self._server_started = False
     self._server_process = None
     self._server_port: int = -1
     self._server_process_lock = threading.RLock()
+    self._vllm_executable = 'vllm.entrypoints.openai.api_server'
+    if vllm_executable is not None:
+      self._vllm_executable = vllm_executable
 
     self.start_server()
 
@@ -125,7 +132,7 @@ def start_server(self, retries=3):
         server_cmd = [
             sys.executable,
             '-m',
-            'vllm.entrypoints.openai.api_server',
+            self._vllm_executable,
             '--model',
             self._model_name,
             '--port',
@@ -175,7 +182,8 @@ class VLLMCompletionsModelHandler(ModelHandler[str,
   def __init__(
       self,
       model_name: str,
-      vllm_server_kwargs: Optional[dict[str, str]] = None):
+      vllm_server_kwargs: Optional[dict[str, str]] = None,
+      use_dynamo: bool = False):
     """Implementation of the ModelHandler interface for vLLM using text as
     input.
 
@@ -194,13 +202,22 @@ def __init__(
         `{'echo': 'true'}` to prepend new messages with the previous message.
         For a list of possible kwargs, see
         https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#extra-parameters-for-completions-api
+      use_dynamo: Whether to use Nvidia Dynamo as the underlying vLLM engine.
+        Requires installing dynamo in your runtime environment
+        (`pip install ai-dynamo[vllm]`)
     """
     self._model_name = model_name
     self._vllm_server_kwargs: dict[str, str] = vllm_server_kwargs or {}
     self._env_vars = {}
+    self._vllm_executable = None
+    if use_dynamo:
+      self._vllm_executable = 'dynamo.vllm'
 
   def load_model(self) -> _VLLMModelServer:
-    return _VLLMModelServer(self._model_name, self._vllm_server_kwargs)
+    return _VLLMModelServer(
+      self._model_name,
+      self._vllm_server_kwargs,
+      self._vllm_executable)
 
   async def _async_run_inference(
       self,
@@ -253,7 +270,8 @@ def __init__(
       self,
       model_name: str,
       chat_template_path: Optional[str] = None,
-      vllm_server_kwargs: Optional[dict[str, str]] = None):
+      vllm_server_kwargs: Optional[dict[str, str]] = None,
+      use_dynamo: bool = False):
     """ Implementation of the ModelHandler interface for vLLM using previous
     messages as input.
 
@@ -277,12 +295,17 @@ def __init__(
         `{'echo': 'true'}` to prepend new messages with the previous message.
         For a list of possible kwargs, see
         https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#extra-parameters-for-chat-api
+      use_dynamo: Whether to use Nvidia Dynamo as the underlying vLLM engine.
+        Requires installing dynamo in your runtime environment
+        (`pip install ai-dynamo[vllm]`)
     """
     self._model_name = model_name
     self._vllm_server_kwargs: dict[str, str] = vllm_server_kwargs or {}
     self._env_vars = {}
     self._chat_template_path = chat_template_path
     self._chat_file = f'template-{uuid.uuid4().hex}.jinja'
+    if use_dynamo:
+      self._vllm_executable = 'dynamo.vllm'
 
   def load_model(self) -> _VLLMModelServer:
     chat_template_contents = ''
@@ -295,7 +318,10 @@ def load_model(self) -> _VLLMModelServer:
           f.write(chat_template_contents)
       self._vllm_server_kwargs['chat_template'] = local_chat_template_path
 
-    return _VLLMModelServer(self._model_name, self._vllm_server_kwargs)
+    return _VLLMModelServer(
+      self._model_name,
+      self._vllm_server_kwargs,
+      self._vllm_executable)
 
   async def _async_run_inference(
       self,

sdks/python/apache_beam/ml/inference/vllm_tests_requirements.txt

@@ -20,3 +20,4 @@ pillow>=8.0.0
 transformers>=4.18.0
 google-cloud-monitoring>=2.27.0
 openai>=1.52.2
+ai-dynamo[vllm]>=0.1.1