[Misc] Minimum requirements for SageMaker compatibility (#11576)

Dockerfile

@@ -234,8 +234,8 @@ RUN mv vllm test_docs/
 #################### TEST IMAGE ####################
 
 #################### OPENAI API SERVER ####################
-# openai api server alternative
-FROM vllm-base AS vllm-openai
+# base openai image with additional requirements, for any subsequent openai-style images
+FROM vllm-base AS vllm-openai-base
 
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/pip \
@@ -247,5 +247,14 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 
 ENV VLLM_USAGE_SOURCE production-docker-image
 
+# define sagemaker first, so it is not default from `docker build`
+FROM vllm-openai-base AS vllm-sagemaker
+
+COPY examples/sagemaker-entrypoint.sh .
+RUN chmod +x sagemaker-entrypoint.sh
+ENTRYPOINT ["./sagemaker-entrypoint.sh"]
+
+FROM vllm-openai-base AS vllm-openai
+
 ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
 #################### OPENAI API SERVER ####################

examples/sagemaker-entrypoint.sh

@@ -0,0 +1,24 @@
+#!/bin/bash
+
+# Define the prefix for environment variables to look for
+PREFIX="SM_VLLM_"
+ARG_PREFIX="--"
+
+# Initialize an array for storing the arguments
+# port 8080 required by sagemaker, https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-code-container-response
+ARGS=(--port 8080)
+
+# Loop through all environment variables
+while IFS='=' read -r key value; do
+    # Remove the prefix from the key, convert to lowercase, and replace underscores with dashes
+    arg_name=$(echo "${key#"${PREFIX}"}" | tr '[:upper:]' '[:lower:]' | tr '_' '-')
+
+    # Add the argument name and value to the ARGS array
+    ARGS+=("${ARG_PREFIX}${arg_name}")
+    if [ -n "$value" ]; then
+        ARGS+=("$value")
+    fi
+done < <(env | grep "^${PREFIX}")
+
+# Pass the collected arguments to the main entrypoint
+exec python3 -m vllm.entrypoints.openai.api_server "${ARGS[@]}"

vllm/entrypoints/openai/api_server.py

@@ -16,7 +16,7 @@ from http import HTTPStatus
 from typing import AsyncIterator, Optional, Set, Tuple
 
 import uvloop
-from fastapi import APIRouter, FastAPI, Request
+from fastapi import APIRouter, FastAPI, HTTPException, Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, Response, StreamingResponse
@@ -44,11 +44,15 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               CompletionResponse,
                                               DetokenizeRequest,
                                               DetokenizeResponse,
+                                              EmbeddingChatRequest,
+                                              EmbeddingCompletionRequest,
                                               EmbeddingRequest,
                                               EmbeddingResponse,
                                               EmbeddingResponseData,
                                               ErrorResponse,
                                               LoadLoraAdapterRequest,
+                                              PoolingChatRequest,
+                                              PoolingCompletionRequest,
                                               PoolingRequest, PoolingResponse,
                                               ScoreRequest, ScoreResponse,
                                               TokenizeRequest,
@@ -310,6 +314,12 @@ async def health(raw_request: Request) -> Response:
     return Response(status_code=200)
 
 
+@router.api_route("/ping", methods=["GET", "POST"])
+async def ping(raw_request: Request) -> Response:
+    """Ping check. Endpoint required for SageMaker"""
+    return await health(raw_request)
+
+
 @router.post("/tokenize")
 @with_cancellation
 async def tokenize(request: TokenizeRequest, raw_request: Request):
@@ -483,6 +493,54 @@ async def create_score_v1(request: ScoreRequest, raw_request: Request):
     return await create_score(request, raw_request)
 
 
+TASK_HANDLERS = {
+    "generate": {
+        "messages": (ChatCompletionRequest, create_chat_completion),
+        "default": (CompletionRequest, create_completion),
+    },
+    "embed": {
+        "messages": (EmbeddingChatRequest, create_embedding),
+        "default": (EmbeddingCompletionRequest, create_embedding),
+    },
+    "score": {
+        "default": (ScoreRequest, create_score),
+    },
+    "reward": {
+        "messages": (PoolingChatRequest, create_pooling),
+        "default": (PoolingCompletionRequest, create_pooling),
+    },
+    "classify": {
+        "messages": (PoolingChatRequest, create_pooling),
+        "default": (PoolingCompletionRequest, create_pooling),
+    },
+}
+
+
+@router.post("/invocations")
+async def invocations(raw_request: Request):
+    """
+    For SageMaker, routes requests to other handlers based on model `task`.
+    """
+    body = await raw_request.json()
+    task = raw_request.app.state.task
+
+    if task not in TASK_HANDLERS:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported task: '{task}' for '/invocations'. "
+            f"Expected one of {set(TASK_HANDLERS.keys())}")
+
+    handler_config = TASK_HANDLERS[task]
+    if "messages" in body:
+        request_model, handler = handler_config["messages"]
+    else:
+        request_model, handler = handler_config["default"]
+
+    # this is required since we lose the FastAPI automatic casting
+    request = request_model.model_validate(body)
+    return await handler(request, raw_request)
+
+
 if envs.VLLM_TORCH_PROFILER_DIR:
     logger.warning(
         "Torch Profiler is enabled in the API server. This should ONLY be "
@@ -687,6 +745,7 @@ def init_app_state(
         chat_template=resolved_chat_template,
         chat_template_content_format=args.chat_template_content_format,
     )
+    state.task = model_config.task
 
 
 def create_server_socket(addr: Tuple[str, int]) -> socket.socket: