[Frontend] Use MQLLMEngine for embeddings models too (#8584)

vllm/engine/multiprocessing/__init__.py

@@ -2,6 +2,7 @@ from dataclasses import dataclass
 from enum import Enum
 from typing import List, Mapping, Optional, Union
 
+from vllm import PoolingParams
 from vllm.inputs import PromptInputs
 from vllm.lora.request import LoRARequest
 from vllm.outputs import RequestOutput
@@ -21,9 +22,9 @@ class MQEngineDeadError(RuntimeError):
 
 
 @dataclass
-class RPCGenerateRequest:
+class RPCProcessRequest:
     inputs: PromptInputs
-    sampling_params: SamplingParams
+    params: Union[SamplingParams, PoolingParams]
     request_id: str
     lora_request: Optional[LoRARequest] = None
     trace_headers: Optional[Mapping[str, str]] = None
@@ -55,7 +56,7 @@ class RPCStartupResponse:
     tracing_enabled: bool
 
 
-RPC_REQUEST_T = Union[RPCGenerateRequest, RPCAbortRequest, RPCHealthRequest,
+RPC_REQUEST_T = Union[RPCProcessRequest, RPCAbortRequest, RPCHealthRequest,
                       RPCStartupRequest]
 
 REQUEST_OUTPUTS_T = Union[List[RequestOutput], RPCError]

vllm/engine/multiprocessing/client.py

@@ -11,6 +11,7 @@ import zmq.asyncio
 from zmq import Frame  # type: ignore[attr-defined]
 from zmq.asyncio import Socket
 
+from vllm import PoolingParams
 from vllm.config import DecodingConfig, EngineConfig, ModelConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
 # yapf conflicts with isort for this block
@@ -19,8 +20,8 @@ from vllm.engine.multiprocessing import (ENGINE_DEAD_ERROR, IPC_DATA_EXT,
                                          IPC_HEALTH_EXT, IPC_INPUT_EXT,
                                          IPC_OUTPUT_EXT, RPC_REQUEST_T,
                                          VLLM_RPC_SUCCESS_STR, RPCAbortRequest,
-                                         RPCError, RPCGenerateRequest,
-                                         RPCHealthRequest, RPCStartupRequest,
+                                         RPCError, RPCHealthRequest,
+                                         RPCProcessRequest, RPCStartupRequest,
                                          RPCStartupResponse)
 # yapf: enable
 from vllm.envs import VLLM_RPC_TIMEOUT
@@ -111,20 +112,8 @@ class MQLLMEngineClient:
 
     @staticmethod
     def is_unsupported_config(engine_args: AsyncEngineArgs):
-        if engine_args.pipeline_parallel_size > 1:
-            return True
-
-        is_embedding = ModelConfig(
-            model=engine_args.model,
-            revision=engine_args.revision,
-            tokenizer=engine_args.model,
-            tokenizer_mode="auto",
-            trust_remote_code=engine_args.trust_remote_code,
-            quantization=engine_args.quantization,
-            seed=0,
-            dtype="auto").embedding_mode
-
-        return is_embedding
+        # Pipeline parallel not yet supported
+        return engine_args.pipeline_parallel_size > 1
 
     @contextmanager
     def get_data_socket(self) -> Iterator[Socket]:
@@ -382,12 +371,9 @@ class MQLLMEngineClient:
 
     @property
     def dead_error(self) -> BaseException:
-        if self._errored_with is not None:
         return ENGINE_DEAD_ERROR(self._errored_with)
-        else:
-            return ENGINE_DEAD_ERROR()
 
-    async def generate(
+    def generate(
         self,
         inputs: PromptInputs,
         sampling_params: SamplingParams,
@@ -396,6 +382,67 @@ class MQLLMEngineClient:
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None
     ) -> AsyncGenerator[RequestOutput, None]:
+        """Generate outputs for a request.
+
+        Generate outputs for a request. This method is a coroutine. It adds the
+        request into the waiting queue of the LLMEngine and streams the outputs
+        from the LLMEngine to the caller.
+
+        Args:
+            inputs: The inputs to the LLM. See
+                :class:`~vllm.inputs.PromptInputs`
+                for more details about the format of each input.
+            sampling_params: The sampling parameters of the request.
+            request_id: The unique id of the request.
+            lora_request: LoRA request to use for generation, if any.
+            trace_headers: OpenTelemetry trace headers.
+            prompt_adapter_request: Prompt Adapter request to use
+                                            for generation, if any.
+        """
+        return self._process_request(inputs, sampling_params, request_id,
+                                     lora_request, trace_headers,
+                                     prompt_adapter_request)
+
+    def encode(
+        self,
+        inputs: PromptInputs,
+        pooling_params: PoolingParams,
+        request_id: str,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+    ) -> AsyncGenerator[EmbeddingRequestOutput, None]:
+        """Generate outputs for a request from an embedding model.
+
+        Generate outputs for a request. This method is a coroutine. It adds the
+        request into the waiting queue of the LLMEngine and streams the outputs
+        from the LLMEngine to the caller.
+
+        Args:
+            inputs: The inputs to the LLM. See
+                :class:`~vllm.inputs.PromptInputs`
+                for more details about the format of each input.
+            pooling_params: The pooling parameters of the request.
+            request_id: The unique id of the request.
+            lora_request: LoRA request to use for generation, if any.
+            trace_headers: OpenTelemetry trace headers.
+
+        Yields:
+            The output `EmbeddingRequestOutput` objects from the LLMEngine
+            for the request.
+        """
+        return self._process_request(inputs, pooling_params, request_id,
+                                     lora_request, trace_headers)
+
+    async def _process_request(
+        self,
+        inputs: PromptInputs,
+        params: Union[SamplingParams, PoolingParams],
+        request_id: str,
+        lora_request: Optional[LoRARequest] = None,
+        trace_headers: Optional[Mapping[str, str]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None
+    ) -> Union[AsyncGenerator[RequestOutput, None], AsyncGenerator[
+            EmbeddingRequestOutput, None]]:
         """Send an RPCGenerateRequest to the RPCServer and stream responses."""
 
         # If already dead, error out.
@@ -410,19 +457,19 @@ class MQLLMEngineClient:
         try:
             # 2) Detach logits processors so that they can be pickled
             # separately (may require cloudpickle which is slower)
-            if sampling_params.logits_processors:
+            if isinstance(params, SamplingParams) and params.logits_processors:
                 # Defensive shallow copy
-                sampling_params = copy.copy(sampling_params)
-                logits_processors = sampling_params.logits_processors
-                sampling_params.logits_processors = None
+                params = copy.copy(params)
+                logits_processors = params.logits_processors
+                params.logits_processors = None
                 lp_bytes = cloudpickle.dumps(logits_processors)
             else:
                 lp_bytes = None
 
             request_bytes = pickle.dumps(
-                RPCGenerateRequest(
+                RPCProcessRequest(
                     inputs=inputs,
-                    sampling_params=sampling_params,
+                    params=params,
                     request_id=request_id,
                     lora_request=lora_request,
                     trace_headers=trace_headers,
@@ -452,8 +499,3 @@ class MQLLMEngineClient:
                     await self.abort(request_id)
         finally:
             self.output_queues.pop(request_id)
-
-    async def encode(self, *args,
-                     **kwargs) -> AsyncGenerator[EmbeddingRequestOutput, None]:
-        raise NotImplementedError(
-            "Embeddings not supported with multiprocessing backend")

vllm/engine/multiprocessing/engine.py

@@ -6,7 +6,7 @@ from typing import Iterator, List, Optional, Union
 import cloudpickle
 import zmq
 
-from vllm import AsyncEngineArgs, LLMEngine
+from vllm import AsyncEngineArgs, LLMEngine, SamplingParams
 from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig)
 # yapf conflicts with isort for this block
@@ -15,8 +15,8 @@ from vllm.engine.multiprocessing import (ENGINE_DEAD_ERROR, IPC_DATA_EXT,
                                          IPC_HEALTH_EXT, IPC_INPUT_EXT,
                                          IPC_OUTPUT_EXT, REQUEST_OUTPUTS_T,
                                          VLLM_RPC_SUCCESS_STR, RPCAbortRequest,
-                                         RPCError, RPCGenerateRequest,
-                                         RPCHealthRequest, RPCStartupRequest,
+                                         RPCError, RPCHealthRequest,
+                                         RPCProcessRequest, RPCStartupRequest,
                                          RPCStartupResponse)
 # yapf: enable
 from vllm.logger import init_logger
@@ -39,8 +39,8 @@ class MQLLMEngine:
     in concurrnet manner. It runs a background loop and uses zeromq to 
     receive new requests and stream outputs incrementally via ipc.
     
-    The :class:`LLMEngine.generate` is kicked off when a new 
-    RPCGenerateRequest is received by the input_socket.
+    The :class:`LLMEngine` generate or encode process is kicked off when a new
+    RPCProcessRequest is received by the input_socket.
     
     The self.engine_loop checks the input_socket for new requests,
     adds them to the LLMEngine if there are any, calls the internal
@@ -213,12 +213,13 @@ class MQLLMEngine:
                 frames = self.input_socket.recv_multipart(copy=False)
                 request = pickle.loads(frames[0].buffer)
 
-                if isinstance(request, RPCGenerateRequest):
+                if isinstance(request, RPCProcessRequest):
                     if len(frames) > 1:
                         # Use cloudpickle for logits processors
+                        assert isinstance(request.params, SamplingParams)
                         lprocs = cloudpickle.loads(frames[1].buffer)
-                        request.sampling_params.logits_processors = lprocs
-                    self._handle_generate_request(request)
+                        request.params.logits_processors = lprocs
+                    self._handle_process_request(request)
                 elif isinstance(request, RPCAbortRequest):
                     self._handle_abort_request(request)
                 elif isinstance(request, RPCHealthRequest):
@@ -231,8 +232,8 @@ class MQLLMEngine:
             self._send_unhealthy(e)
             raise e
 
-    def _handle_generate_request(self, request: RPCGenerateRequest):
-        """Handle RPCGenerateRequest by adding it to the LLMEngine."""
+    def _handle_process_request(self, request: RPCProcessRequest):
+        """Handle RPCProcessRequest by adding it to the LLMEngine."""
         request_id = request.request_id
 
         if self._errored_with is not None:
@@ -245,7 +246,7 @@ class MQLLMEngine:
             self.engine.add_request(
                 request_id=request_id,
                 inputs=request.inputs,
-                params=request.sampling_params,
+                params=request.params,
                 lora_request=request.lora_request,
                 trace_headers=request.trace_headers,
                 prompt_adapter_request=request.prompt_adapter_request)