Merge branch 'serve-v1' into lkchen-ray_data_llm

Signed-off-by: Linkun Chen <github@lkchen.net>

Author: lk-chen

python/ray/llm/_internal/serve/deployments/llm/vllm/vllm_engine.py

@@ -305,12 +305,43 @@ async def start(self):
         logger.info("Started vLLM engine.")
 
     async def _start_engine(self) -> "EngineClient":
+        from vllm import envs
+
+        # Since vLLM 0.8.0, the logic to determine v0/v1 engine is as follows:
+        # 1. If VLLM_USE_V1 is not set, then it tries to use v1 engine. However,
+        #    if any feature specified in the engine config is not supported, then
+        #    it falls back to v0. Note that launching vLLM on a non-main thread
+        #    is an experimental feature, so vLLM will fall back to v0 in this case.
+        # 2. If VLLM_USE_V1 is set to 1, then it will use v1 engine even with
+        #    experimental features (such as launching vLLM on a non-main thread).
+        # 3. If VLLM_USE_V1 is set to 0, force using v0 engine.
+        if not envs.VLLM_USE_V1:
+            return await self._start_engine_v0()
+        return await self._start_engine_v1()
+
+    async def _start_engine_v1(self) -> "EngineClient":
+        """Start the vLLM v1 engine. Note that we only use _get_async_engine_args
+        to get the engine args and don't use _get_vllm_engine_config, because
+        we integrate vLLM v1 using the highest-level async engine API.
+        TODO: Refactor vLLM v0 integration to use the same async engine API
+        to simplify the code.
+        """
+        from vllm import AsyncLLMEngine
+
+        await self.initialize_node(self.llm_config)
+        engine_args = _get_async_engine_args(self.llm_config)
+
+        return AsyncLLMEngine.from_engine_args(
+            engine_args=engine_args,
+        )
+
+    async def _start_engine_v0(self) -> "EngineClient":
         from vllm.engine.multiprocessing.client import MQLLMEngineClient
 
         args: InitializeNodeOutput = await self.initialize_node(self.llm_config)
         engine_args, engine_config = _get_vllm_engine_config(self.llm_config)
 
-        if MQLLMEngineClient.is_unsupported_config(engine_args):
+        if MQLLMEngineClient.is_unsupported_config(engine_config):
             # If the engine is not supported, we fall back to the legacy async engine.
             #
             # Note (genesu): as of 2025-02-11, this code path is only triggered when
@@ -502,20 +533,36 @@ async def _generate(
                 )
 
             if request_output is not None:
-                time_in_queue_histogram.observe(request_output.metrics.time_in_queue)
                 total_request_time = time.perf_counter() - start
-                generation_time = (
-                    total_request_time - request_output.metrics.time_in_queue
-                )
+                if request_output.metrics is None:
+                    # vLLM V1 metrics are not included in the request output yet.
+                    queue_time = "N/A"
+                    generation_time_str = "N/A"
+                    tokens_s = "N/A"
+                    generated_tokens_s = "N/A"
+                else:
+                    time_in_queue_histogram.observe(
+                        request_output.metrics.time_in_queue
+                    )
+                    queue_time = f"{request_output.metrics.time_in_queue}s"
+                    generation_time = (
+                        total_request_time - request_output.metrics.time_in_queue
+                    )
+                    generation_time_str = f"{generation_time}s"
+                    tokens_s = (
+                        num_input_tokens + all_tokens_collected
+                    ) / generation_time
+                    generated_tokens_s = all_tokens_collected / generation_time
+
                 logger.info(
                     f"Request {vllm_generation_request.request_id} finished ({finish_reason}). "
                     f"Total time: {total_request_time}s, "
-                    f"Queue time: {request_output.metrics.time_in_queue}s, "
-                    f"Generation+async time: {generation_time}s, "
+                    f"Queue time: {queue_time}, "
+                    f"Generation+async time: {generation_time_str}, "
                     f"Input tokens: {num_input_tokens}, "
                     f"Generated tokens: {all_tokens_collected}, "
-                    f"tokens/s: {(num_input_tokens + all_tokens_collected) / generation_time}, "
-                    f"generated tokens/s: {all_tokens_collected / generation_time}."
+                    f"tokens/s: {tokens_s}, "
+                    f"generated tokens/s: {generated_tokens_s}."
                 )
             else:
                 logger.warning(