[V1] [2/n] Logging and Metrics - OutputProcessor Abstraction (#11973)

Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>

tests/v1/engine/test_async_llm.py

@@ -1,5 +1,5 @@
 import asyncio
-from typing import Tuple
+from typing import List, Tuple
 
 import pytest
 
@@ -13,6 +13,7 @@ if not current_platform.is_cuda():
                 allow_module_level=True)
 
 ENGINE_ARGS = AsyncEngineArgs(model="meta-llama/Llama-3.2-1B",
+                              enforce_eager=True,
                               disable_log_requests=True)
 
 
@@ -53,17 +54,63 @@ async def test_load(monkeypatch):
                     generate(engine, request_id, NUM_EXPECTED_TOKENS)))
 
         # Confirm that we got all the EXPECTED tokens from the requests.
-        failed_request_id = None
-        tokens = None
         for task in tasks:
             num_generated_tokens, request_id = await task
-            if (num_generated_tokens != NUM_EXPECTED_TOKENS
+            assert num_generated_tokens == NUM_EXPECTED_TOKENS, (
-                    and failed_request_id is None):
+                f"{request_id} generated {num_generated_tokens} but "
-                failed_request_id = request_id
+                f"expected {NUM_EXPECTED_TOKENS}")
-                tokens = num_generated_tokens
 
-        assert failed_request_id is None, (
+        assert not engine.output_processor.has_unfinished_requests()
-            f"{failed_request_id} generated {tokens} but "
+        engine.shutdown()
-            f"expected {NUM_EXPECTED_TOKENS}")
+
+
+@pytest.mark.asyncio
+async def test_abort(monkeypatch):
+
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+
+        engine = AsyncLLM.from_engine_args(ENGINE_ARGS)
+
+        NUM_REQUESTS = 100
+        NUM_EXPECTED_TOKENS = 100
+        REQUEST_IDS_TO_ABORT = range(1, 100, 10)
+
+        request_ids = [f"request-{i}" for i in range(NUM_REQUESTS)]
+
+        # Create concurrent requests.
+        tasks: List[asyncio.Task] = []
+        for request_id in request_ids:
+            tasks.append(
+                asyncio.create_task(
+                    generate(engine, request_id, NUM_EXPECTED_TOKENS)))
+
+        # API server cancels requests when they disconnect.
+        for idx in REQUEST_IDS_TO_ABORT:
+            tasks[idx].cancel()
+            await asyncio.sleep(0.1)
+
+        # Confirm the other requests are okay.
+        for idx, task in enumerate(tasks):
+            # Confirm that it was actually canceled.
+            if idx in REQUEST_IDS_TO_ABORT:
+                with pytest.raises(asyncio.CancelledError):
+                    await task
+            else:
+                # Otherwise, make sure the request was not impacted.
+                num_generated_tokens, request_id = await task
+                assert num_generated_tokens == NUM_EXPECTED_TOKENS, (
+                    f"{request_id} generated {num_generated_tokens} but "
+                    f"expected {NUM_EXPECTED_TOKENS}")
+
+        assert not engine.output_processor.has_unfinished_requests()
+
+        # Confirm we can do another generation.
+        request_id = f"request-{REQUEST_IDS_TO_ABORT[0]}"
+        task = asyncio.create_task(
+            generate(engine, request_id, NUM_EXPECTED_TOKENS))
+        num_generated_tokens, request_id = await task
+        assert num_generated_tokens == NUM_EXPECTED_TOKENS
+        assert not engine.output_processor.has_unfinished_requests()
 
         engine.shutdown()

tests/v1/engine/test_output_processor.py

@@ -3,11 +3,18 @@ from typing import List
 import pytest
 from transformers import AutoTokenizer
 
+from vllm.engine.arg_utils import EngineArgs
 from vllm.sampling_params import RequestOutputKind, SamplingParams
+from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
 from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest
-from vllm.v1.engine.detokenizer import Detokenizer
+from vllm.v1.engine.output_processor import OutputProcessor
 
 TOKENIZER_NAME = "mistralai/Mistral-7B-Instruct-v0.3"
+VLLM_CONFIG = EngineArgs(model=TOKENIZER_NAME).create_engine_config()
+TOKENIZER_GROUP = init_tokenizer_from_configs(VLLM_CONFIG.model_config,
+                                              VLLM_CONFIG.scheduler_config,
+                                              VLLM_CONFIG.parallel_config,
+                                              VLLM_CONFIG.lora_config)
 tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
 
 FULL_STRINGS = [
@@ -66,7 +73,7 @@ class MockEngineCore:
     "request_output_kind",
     [RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY])
 def test_incremental_detokenization(request_output_kind: RequestOutputKind):
-    detokenizer = Detokenizer(TOKENIZER_NAME)
+    output_processor = OutputProcessor(TOKENIZER_GROUP, log_stats=False)
     engine_core = MockEngineCore(GENERATION_TOKENS)
 
     # Make N requests.
@@ -93,7 +100,7 @@ def test_incremental_detokenization(request_output_kind: RequestOutputKind):
 
     # Add requests to the detokenizer.
     for request in requests:
-        detokenizer.add_request(request)
+        output_processor.add_request(request)
 
     gen_strings = {}
     gen_tokens = {}
@@ -104,7 +111,9 @@ def test_incremental_detokenization(request_output_kind: RequestOutputKind):
             break
 
         # Step the Detokenizer.
-        request_outputs, requests_to_abort = detokenizer.step(outputs)
+        processed_outputs = output_processor.process_outputs(outputs, )
+        request_outputs = processed_outputs.request_outputs
+        requests_to_abort = processed_outputs.reqs_to_abort
         assert len(requests_to_abort) == 0
 
         # Update tracking.
@@ -128,13 +137,13 @@ def test_incremental_detokenization(request_output_kind: RequestOutputKind):
         assert gen_str == ref_gen_str, f"{gen_str=}, {ref_gen_str=}"
         assert gen_toks == ref_gen_toks, f"{gen_toks=}, {ref_gen_toks=}"
 
-    assert detokenizer.get_num_unfinished_requests() == 0
+    assert output_processor.get_num_unfinished_requests() == 0
-    assert not detokenizer.has_unfinished_requests()
+    assert not output_processor.has_unfinished_requests()
 
 
 @pytest.mark.parametrize("include_stop_str_in_output", [True, False])
 def test_stop_string(include_stop_str_in_output: bool):
-    detokenizer = Detokenizer(TOKENIZER_NAME)
+    output_processor = OutputProcessor(TOKENIZER_GROUP, log_stats=False)
     engine_core = MockEngineCore(GENERATION_TOKENS)
 
     # Make N requests.
@@ -162,7 +171,7 @@ def test_stop_string(include_stop_str_in_output: bool):
 
     # Add requests to the detokenizer.
     for request in requests:
-        detokenizer.add_request(request)
+        output_processor.add_request(request)
 
     gen_strings = {}
     aborted = []
@@ -173,7 +182,9 @@ def test_stop_string(include_stop_str_in_output: bool):
             break
 
         # Step the Detokenizer.
-        request_outputs, requests_to_abort = detokenizer.step(outputs)
+        processed_outputs = output_processor.process_outputs(outputs)
+        request_outputs = processed_outputs.request_outputs
+        requests_to_abort = processed_outputs.reqs_to_abort
         for request_output in request_outputs:
             # If aborted, we should not get a request output.
             assert request_output.request_id not in aborted
@@ -214,5 +225,71 @@ def test_stop_string(include_stop_str_in_output: bool):
             assert gen_str == ref_str_exc_stop, (
                 f"{gen_str=}, {ref_str_exc_stop=}")
 
-    assert detokenizer.get_num_unfinished_requests() == 0
+    assert output_processor.get_num_unfinished_requests() == 0
-    assert not detokenizer.has_unfinished_requests()
+    assert not output_processor.has_unfinished_requests()
+
+
+def test_iteration_stats():
+    output_processor = OutputProcessor(TOKENIZER_GROUP, log_stats=True)
+    engine_core = MockEngineCore(GENERATION_TOKENS)
+
+    # Make N requests.
+    requests = [
+        EngineCoreRequest(
+            request_id=f"request-{idx}",
+            prompt=prompt,
+            prompt_token_ids=prompt_tokens,
+            arrival_time=0,
+            mm_inputs=None,
+            mm_hashes=None,
+            mm_placeholders=None,
+            eos_token_id=None,
+            lora_request=None,
+            sampling_params=SamplingParams(),
+        ) for idx, (
+            prompt,
+            prompt_tokens) in enumerate(zip(PROMPT_STRINGS, PROMPT_TOKENS))
+    ]
+
+    # Add all requests except one to the OutputProcessor.
+    num_active = len(GENERATION_TOKENS) - 1
+    for request in requests[:num_active]:
+        output_processor.add_request(request)
+    inactive_request = requests[num_active]
+
+    # First iteration has 2 prefills.
+    outputs = engine_core.get_outputs()[:num_active]
+    processed_outputs = output_processor.process_outputs(outputs)
+    iteration_stats = processed_outputs.iteration_stats
+    total_prompt_tokens = sum(
+        [len(prompt_tokens) for prompt_tokens in PROMPT_TOKENS[:num_active]])
+
+    assert iteration_stats.num_prompt_tokens == total_prompt_tokens
+    assert iteration_stats.num_generation_tokens == num_active
+
+    # Just decodes in this step.
+    outputs = engine_core.get_outputs()[:num_active]
+    processed_outputs = output_processor.process_outputs(outputs)
+    iteration_stats = processed_outputs.iteration_stats
+
+    assert iteration_stats.num_prompt_tokens == 0
+    assert iteration_stats.num_generation_tokens == num_active
+
+    # Add a new request - prefill and 2 decodes in this step.
+    output_processor.add_request(inactive_request)
+    num_active += 1
+    outputs = engine_core.get_outputs()[:num_active]
+    processed_outputs = output_processor.process_outputs(outputs)
+    iteration_stats = processed_outputs.iteration_stats
+    total_prompt_tokens = len(PROMPT_TOKENS[num_active - 1])
+
+    assert iteration_stats.num_prompt_tokens == total_prompt_tokens
+    assert iteration_stats.num_generation_tokens == num_active
+
+    # Just decodes in this step.
+    outputs = engine_core.get_outputs()[:num_active]
+    processed_outputs = output_processor.process_outputs(outputs)
+    iteration_stats = processed_outputs.iteration_stats
+
+    assert iteration_stats.num_prompt_tokens == 0
+    assert iteration_stats.num_generation_tokens == num_active

vllm/v1/engine/async_llm.py

@@ -1,6 +1,6 @@
 import asyncio
 import os
-from typing import AsyncGenerator, Dict, List, Mapping, Optional, Type, Union
+from typing import AsyncGenerator, List, Mapping, Optional, Type, Union
 
 from vllm.config import ModelConfig, VllmConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
@@ -18,11 +18,11 @@ from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import kill_process_tree
 from vllm.v1.engine.core_client import EngineCoreClient
-from vllm.v1.engine.detokenizer import Detokenizer
+from vllm.v1.engine.output_processor import OutputProcessor
 from vllm.v1.engine.processor import Processor
 from vllm.v1.executor.abstract import Executor
 from vllm.v1.metrics.loggers import LoggingStatLogger, StatLoggerBase
-from vllm.v1.metrics.stats import SchedulerStats
+from vllm.v1.metrics.stats import IterationStats, SchedulerStats
 
 logger = init_logger(__name__)
 
@@ -59,9 +59,6 @@ class AsyncLLM(EngineClient):
             lora_config=vllm_config.lora_config)
         self.tokenizer.ping()
 
-        # Request streams (map of request_id -> queue).
-        self.rid_to_queue: Dict[str, asyncio.Queue] = {}
-
         # Processor (converts Inputs --> EngineCoreRequests).
         self.processor = Processor(
             model_config=vllm_config.model_config,
@@ -71,13 +68,9 @@ class AsyncLLM(EngineClient):
             input_registry=input_registry,
         )
 
-        # Detokenizer (converts EngineCoreOutputs --> RequestOutput).
+        # OutputProcessor (converts EngineCoreOutputs --> RequestOutput).
-        self.detokenizer = Detokenizer(
+        self.output_processor = OutputProcessor(self.tokenizer,
-            tokenizer_name=vllm_config.model_config.tokenizer,
+                                                log_stats=self.log_stats)
-            tokenizer_mode=vllm_config.model_config.tokenizer_mode,
-            trust_remote_code=vllm_config.model_config.trust_remote_code,
-            revision=vllm_config.model_config.tokenizer_revision,
-        )
 
         # EngineCore (starts the engine in background process).
         self.engine_core = EngineCoreClient.make_client(
@@ -140,9 +133,9 @@ class AsyncLLM(EngineClient):
         """Add new request to the AsyncLLM."""
 
         # 1) Create a new output queue for the request.
-        if request_id in self.rid_to_queue:
+        if self.output_processor.is_request_active(request_id):
             raise ValueError(f"Request id {request_id} already running.")
-        self.rid_to_queue[request_id] = asyncio.Queue()
+        queue: asyncio.Queue[RequestOutput] = asyncio.Queue()
 
         # 2) Convert Input --> Request.
         request = self.processor.process_inputs(request_id, prompt, params,
@@ -151,8 +144,8 @@ class AsyncLLM(EngineClient):
                                                 prompt_adapter_request,
                                                 priority)
 
-        # 3) Add the request to Detokenizer (this process).
+        # 3) Add the request to OutputProcessor (this process).
-        self.detokenizer.add_request(request)
+        self.output_processor.add_request(request, queue)
 
         # 4) Add the EngineCoreRequest to EngineCore (separate process).
         await self.engine_core.add_request_async(request)
@@ -160,7 +153,7 @@ class AsyncLLM(EngineClient):
         if self.log_requests:
             logger.info("Added request %s.", request_id)
 
-        return self.rid_to_queue[request_id]
+        return queue
 
     # TODO: we should support multiple prompts in one call, as you
     # can do with LLM.generate. So that for multi-prompt completion
@@ -217,10 +210,9 @@ class AsyncLLM(EngineClient):
                 # task switching under load which helps performance).
                 out = q.get_nowait() if q.qsize() > 0 else await q.get()
 
-                # Note: both Detokenizer and EngineCore handle their
+                # Note: both OutputProcessor and EngineCore handle their
                 # own request cleanup based on finished.
                 if out.finished:
-                    del self.rid_to_queue[request_id]
                     yield out
                     break
 
@@ -233,57 +225,51 @@ class AsyncLLM(EngineClient):
             await self.abort(request_id)
             raise
 
-    def _process_request_outputs(self, request_outputs: List[RequestOutput]):
-        """Process outputs by putting them into per-request queues."""
-
-        for request_output in request_outputs:
-            request_id = request_output.request_id
-
-            # Note: it is possible a request was aborted and removed from
-            # the state due to client cancellations, so if we encounter a
-            # request id not in the state, we skip.
-            if request_id in self.rid_to_queue:
-                self.rid_to_queue[request_id].put_nowait(request_output)
-
     async def _run_output_handler(self):
         """Background loop: pulls from EngineCore and pushes to AsyncStreams."""
 
         try:
             while True:
-                # 1) Pull EngineCoreOutput from the EngineCore.
+                # 1) Pull EngineCoreOutputs from the EngineCore.
                 outputs = await self.engine_core.get_output_async()
 
-                # 2) Detokenize based on the output.
+                # 2) Process EngineCoreOutputs.
-                request_outputs, reqs_to_abort = self.detokenizer.step(
+                processed_outputs = self.output_processor.process_outputs(
                     outputs.outputs)
+                # NOTE: RequestOutputs are pushed to their queues.
+                assert len(processed_outputs.request_outputs) == 0
 
-                # 3) Put the RequestOutputs into the per-request queues.
+                # 3) Abort any reqs that finished due to stop strings.
-                self._process_request_outputs(request_outputs)
+                await self.engine_core.abort_requests_async(
+                    processed_outputs.reqs_to_abort)
 
-                # 4) Abort any requests that finished due to stop strings.
+                # 4) Logging.
-                await self.engine_core.abort_requests_async(reqs_to_abort)
+                # TODO(rob): make into a coroutine and launch it in
-
+                # background thread once we add Prometheus.
-                # 5) Log any stats.
+                self._log_stats(
-                await self._log_stats(scheduler_stats=outputs.scheduler_stats)
+                    scheduler_stats=outputs.scheduler_stats,
+                    iteration_stats=processed_outputs.iteration_stats,
+                )
 
         except Exception as e:
             logger.exception("EngineCore output handler hit an error: %s", e)
             kill_process_tree(os.getpid())
 
     async def abort(self, request_id: str) -> None:
-        """Abort RequestId in self, detokenizer, and engine core."""
+        """Abort RequestId in OutputProcessor and EngineCore."""
 
         request_ids = [request_id]
         await self.engine_core.abort_requests_async(request_ids)
-        self.detokenizer.abort_requests(request_ids)
+        self.output_processor.abort_requests(request_ids)
 
-        # If a request finishes while we await then the request_id
+        if self.log_requests:
-        # will be removed from the tracked queues before we get here.
+            logger.info("Aborted request %s.", request_id)
-        if request_id in self.rid_to_queue:
-            del self.rid_to_queue[request_id]
 
-    async def _log_stats(self, scheduler_stats: SchedulerStats):
+    def _log_stats(
-        """Log stats to the stat loggers."""
+        self,
+        scheduler_stats: SchedulerStats,
+        iteration_stats: IterationStats,
+    ):
         if not self.log_stats:
             return
 
@@ -314,8 +300,7 @@ class AsyncLLM(EngineClient):
         self,
         lora_request: Optional[LoRARequest] = None,
     ) -> AnyTokenizer:
-        assert lora_request is None
+        return self.tokenizer.get_lora_tokenizer(lora_request)
-        return self.detokenizer.tokenizer
 
     async def is_tracing_enabled(self) -> bool:
         return False

vllm/v1/engine/core_client.py

@@ -105,7 +105,8 @@ class InprocClient(EngineCoreClient):
         self.engine_core.add_request(request)
 
     def abort_requests(self, request_ids: List[str]) -> None:
-        self.engine_core.abort_requests(request_ids)
+        if len(request_ids) > 0:
+            self.engine_core.abort_requests(request_ids)
 
     def shutdown(self):
         self.engine_core.shutdown()
@@ -221,7 +222,8 @@ class SyncMPClient(MPClient):
         self._send_input(EngineCoreRequestType.ADD, request)
 
     def abort_requests(self, request_ids: List[str]) -> None:
-        self._send_input(EngineCoreRequestType.ABORT, request_ids)
+        if len(request_ids) > 0:
+            self._send_input(EngineCoreRequestType.ABORT, request_ids)
 
     def profile(self, is_start: bool = True) -> None:
         self._send_input(EngineCoreRequestType.PROFILE,

vllm/v1/engine/detokenizer.py

@@ -1,18 +1,25 @@
 from dataclasses import dataclass
-from typing import Dict, Iterable, List, Optional, Tuple, Union
+from typing import List, Optional, Union
 
 from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.logger import init_logger
-from vllm.outputs import RequestOutput
 from vllm.sampling_params import RequestOutputKind
 from vllm.transformers_utils.detokenizer_utils import (
     AnyTokenizer, convert_prompt_ids_to_tokens, detokenize_incrementally)
-from vllm.transformers_utils.tokenizer import get_tokenizer
 from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest
 
 logger = init_logger(__name__)
 
 
+@dataclass
+class DetokenizerOutput:
+    output_text: str
+    token_ids: List[int]
+    finished: bool
+    finish_reason: Optional[str] = None
+    stop_reason: Union[int, str, None] = None
+
+
 @dataclass
 class IncrementalDetokenizer:
 
@@ -20,6 +27,7 @@ class IncrementalDetokenizer:
     output_text: str
     tokens: List[str]
     token_ids: List[int]
+    prompt_len: int
 
     # Stop strings
     stop: List[str]
@@ -34,11 +42,6 @@ class IncrementalDetokenizer:
     spaces_between_special_tokens: bool
     output_kind: RequestOutputKind
 
-    # TODO: Probably decouple these
-    request_id: str
-    prompt: Optional[str]
-    prompt_token_ids: List[int]
-
     # Tokenizer for this request
     tokenizer: AnyTokenizer
 
@@ -48,8 +51,7 @@ class IncrementalDetokenizer:
 
     @property
     def output_token_ids(self) -> List[int]:
-        assert len(self.token_ids) >= len(self.prompt_token_ids)
+        return self.token_ids[self.prompt_len:]
-        return self.token_ids[len(self.prompt_token_ids):]
 
     @classmethod
     def from_new_request(
@@ -87,25 +89,25 @@ class IncrementalDetokenizer:
             spaces_between_special_tokens=request.sampling_params.
             spaces_between_special_tokens,
             output_kind=request.sampling_params.output_kind,
-            request_id=request.request_id,
+            prompt_len=len(request.prompt_token_ids),
-            prompt=request.prompt,
-            prompt_token_ids=request.prompt_token_ids,
             tokenizer=tokenizer,
             stop_buffer_length=stop_buffer_length,
         )
 
-    def add_tokens(
+    def update_from_output(
         self,
-        new_token_ids: List[int],
+        output: EngineCoreOutput,
-        finish_reason: Optional[str],
+    ) -> Optional[DetokenizerOutput]:
-        stop_reason: Optional[Union[int, str, None]],
-    ) -> Optional[RequestOutput]:
         """
         Update RequestState for the request_id by:
             1) Detokenize the new token ids incrementally.
             2) Update the RequestOutput with the new text.
         """
 
+        new_token_ids = output.new_token_ids
+        finish_reason = output.finish_reason
+        stop_reason = output.stop_reason
+
         # 1) Detokenize the new token ids incrementally.
         # TODO(woosuk): This method becomes very inefficient when the number of
         # new_token_ids is more than 1. We need to optimize this.
@@ -158,21 +160,8 @@ class IncrementalDetokenizer:
         output_text = self._get_next_output_text(finished, delta)
         token_ids = new_token_ids if delta else self.output_token_ids
 
-        request_output = RequestOutput.new(
+        return DetokenizerOutput(output_text, token_ids, finished,
-            self.request_id,
+                                 finish_reason, stop_reason)
-            self.prompt,
-            self.prompt_token_ids,
-            output_text,
-            token_ids,
-            finished,
-        )
-
-        if finished:
-            completion_output = request_output.outputs[0]
-            completion_output.finish_reason = finish_reason
-            completion_output.stop_reason = stop_reason
-
-        return request_output
 
     def _get_next_output_text(self, finished: bool, delta: bool) -> str:
         """If delta is True, only new text since the last call to
@@ -189,85 +178,3 @@ class IncrementalDetokenizer:
             self._last_output_text_offset = length
             return self.output_text[last_offset:length]
         return ""
-
-
-class Detokenizer:
-
-    def __init__(self,
-                 tokenizer_name: str,
-                 tokenizer_mode: str = "auto",
-                 trust_remote_code: bool = False,
-                 revision: Optional[str] = None):
-        # TODO: once we support LoRA, we should should pass the tokenizer
-        # here. We currently have two copies (this + in the LLMEngine).
-        self.tokenizer = get_tokenizer(tokenizer_name=tokenizer_name,
-                                       tokenizer_mode=tokenizer_mode,
-                                       trust_remote_code=trust_remote_code,
-                                       revision=revision)
-
-        # Request id -> IncrementalDetokenizer
-        self.request_states: Dict[str, IncrementalDetokenizer] = {}
-
-    def is_request_active(self, request_id: str):
-        return request_id in self.request_states
-
-    def get_num_unfinished_requests(self):
-        return len(self.request_states)
-
-    def has_unfinished_requests(self) -> bool:
-        return len(self.request_states) > 0
-
-    def abort_requests(
-        self,
-        request_ids: Iterable[str],
-    ) -> None:
-        """Remove the request_ids from the Detokenizer."""
-
-        for request_id in request_ids:
-            self.request_states.pop(request_id, None)
-
-    def add_request(
-        self,
-        request: EngineCoreRequest,
-    ):
-        """Add new request to the Detokenizer."""
-
-        assert (request.request_id not in self.request_states)
-
-        request_state = IncrementalDetokenizer.from_new_request(
-            self.tokenizer, request)
-        self.request_states[request.request_id] = request_state
-
-    def step(
-        self, encore_core_outputs: List[EngineCoreOutput]
-    ) -> Tuple[List[RequestOutput], List[str]]:
-        """Update state and request the RequestOutputs to the LLMEngine."""
-
-        request_outputs: List[RequestOutput] = []
-        requests_to_abort: List[str] = []
-        for engine_core_output in encore_core_outputs:
-            request_id = engine_core_output.request_id
-            detokenizer = self.request_states.get(request_id)
-            if detokenizer is None:
-                # Ignore output for already-aborted request.
-                continue
-
-            # Detokenize and update state.
-            request_output = detokenizer.add_tokens(
-                new_token_ids=engine_core_output.new_token_ids,
-                finish_reason=engine_core_output.finish_reason,
-                stop_reason=engine_core_output.stop_reason,
-            )
-
-            if request_output is not None:
-                # Add to RequestOutputs list.
-                request_outputs.append(request_output)
-
-                # Free completed requests.
-                if request_output.finished:
-                    self.request_states.pop(request_id)
-                    if not engine_core_output.finished:
-                        requests_to_abort.append(request_id)
-
-        # Return to EngineClient.
-        return request_outputs, requests_to_abort

vllm/v1/engine/llm_engine.py

@@ -18,7 +18,7 @@ from vllm.transformers_utils.tokenizer_group import (
     BaseTokenizerGroup, init_tokenizer_from_configs)
 from vllm.usage.usage_lib import UsageContext
 from vllm.v1.engine.core_client import EngineCoreClient
-from vllm.v1.engine.detokenizer import Detokenizer
+from vllm.v1.engine.output_processor import OutputProcessor
 from vllm.v1.engine.processor import Processor
 from vllm.v1.executor.abstract import Executor
 
@@ -60,13 +60,9 @@ class LLMEngine:
                                    input_registry=input_registry,
                                    mm_registry=mm_registry)
 
-        # Detokenizer (converts EngineCoreOutputs --> RequestOutput)
+        # OutputProcessor (convert EngineCoreOutputs --> RequestOutput).
-        self.detokenizer = Detokenizer(
+        self.output_processor = OutputProcessor(self.tokenizer,
-            tokenizer_name=vllm_config.model_config.tokenizer,
+                                                log_stats=False)
-            tokenizer_mode=vllm_config.model_config.tokenizer_mode,
-            trust_remote_code=vllm_config.model_config.trust_remote_code,
-            revision=vllm_config.model_config.tokenizer_revision,
-        )
 
         # EngineCore (gets EngineCoreRequests and gives EngineCoreOutputs)
         self.engine_core = EngineCoreClient.make_client(
@@ -103,10 +99,10 @@ class LLMEngine:
                    multiprocess_mode=enable_multiprocessing)
 
     def get_num_unfinished_requests(self) -> int:
-        return self.detokenizer.get_num_unfinished_requests()
+        return self.output_processor.get_num_unfinished_requests()
 
     def has_unfinished_requests(self) -> bool:
-        return self.detokenizer.has_unfinished_requests()
+        return self.output_processor.has_unfinished_requests()
 
     @classmethod
     def validate_outputs(cls, outputs, output_type):
@@ -116,7 +112,7 @@ class LLMEngine:
         """Remove request_ids from EngineCore and Detokenizer."""
 
         self.engine_core.abort_requests(request_ids)
-        self.detokenizer.abort_requests(request_ids)
+        self.output_processor.abort_requests(request_ids)
 
     def add_request(
         self,
@@ -137,8 +133,8 @@ class LLMEngine:
                                                 prompt_adapter_request,
                                                 priority)
 
-        # 2) Add the request to Detokenizer.
+        # 2) Make a new RequestState and queue.
-        self.detokenizer.add_request(request)
+        self.output_processor.add_request(request)
 
         # 3) Add the request to EngineCore.
         self.engine_core.add_request(request)
@@ -148,15 +144,14 @@ class LLMEngine:
         # 1) Get EngineCoreOutput from the EngineCore.
         outputs = self.engine_core.get_output()
 
-        # 2) Detokenizer the EngineCoreOutput.
+        # 2) Process EngineCoreOutputs.
-        request_outputs, requests_to_abort = self.detokenizer.step(
+        processed_outputs = self.output_processor.process_outputs(
             outputs.outputs)
 
-        # 3) Abort requests that finished due to stopping criteria.
+        # 3) Abort any reqs that finished due to stop strings.
-        if requests_to_abort:
+        self.engine_core.abort_requests(processed_outputs.reqs_to_abort)
-            self.abort_request(requests_to_abort)
 
-        return request_outputs
+        return processed_outputs.request_outputs
 
     def get_model_config(self):
         return self.model_config

vllm/v1/engine/output_processor.py

@@ -0,0 +1,200 @@
+import asyncio
+from dataclasses import dataclass
+from typing import Dict, List, Optional
+
+from vllm.outputs import RequestOutput
+from vllm.transformers_utils.detokenizer_utils import AnyTokenizer
+from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
+from vllm.v1.engine import EngineCoreOutput, EngineCoreRequest
+from vllm.v1.engine.detokenizer import (DetokenizerOutput,
+                                        IncrementalDetokenizer)
+from vllm.v1.metrics.stats import IterationStats
+
+
+@dataclass
+class OutputProcessorOutput:
+
+    request_outputs: List[RequestOutput]
+    reqs_to_abort: List[str]
+    iteration_stats: IterationStats
+
+
+class RequestState:
+
+    def __init__(
+        self,
+        request_id: str,
+        prompt: Optional[str],
+        prompt_token_ids: List[int],
+        detokenizer: IncrementalDetokenizer,
+        queue: Optional[asyncio.Queue[RequestOutput]],
+    ):
+        self.request_id = request_id
+        self.prompt = prompt
+        self.prompt_token_ids = prompt_token_ids
+        self.prompt_len = len(prompt_token_ids)
+        self.detokenizer = detokenizer
+        self.is_prefilling = True
+        self.queue = queue
+
+    @classmethod
+    def from_new_request(
+        cls,
+        tokenizer: AnyTokenizer,
+        request: EngineCoreRequest,
+        queue: Optional[asyncio.Queue[RequestOutput]] = None,
+    ) -> "RequestState":
+        return cls(
+            request_id=request.request_id,
+            prompt=request.prompt,
+            prompt_token_ids=request.prompt_token_ids,
+            detokenizer=IncrementalDetokenizer.from_new_request(
+                tokenizer=tokenizer,
+                request=request,
+            ),
+            queue=queue,
+        )
+
+
+class OutputProcessor:
+    """Process EngineCoreOutputs into RequestOutputs."""
+
+    def __init__(
+        self,
+        tokenizer: BaseTokenizerGroup,
+        log_stats: bool,
+    ):
+        self.log_stats = log_stats
+        self.tokenizer = tokenizer
+        self.request_states: Dict[str, RequestState] = {}
+
+    def is_request_active(self, request_id: str) -> bool:
+        return request_id in self.request_states
+
+    def get_num_unfinished_requests(self):
+        return len(self.request_states)
+
+    def has_unfinished_requests(self) -> bool:
+        return len(self.request_states) > 0
+
+    def abort_requests(
+        self,
+        request_ids: List[str],
+    ) -> None:
+        for request_id in request_ids:
+            self.request_states.pop(request_id, None)
+
+    def add_request(
+        self,
+        request: EngineCoreRequest,
+        queue: Optional[asyncio.Queue[RequestOutput]] = None,
+    ) -> None:
+        request_id = request.request_id
+        if request_id in self.request_states:
+            raise ValueError(f"Request id {request_id} already running.")
+
+        self.request_states[request_id] = RequestState.from_new_request(
+            tokenizer=self.tokenizer.get_lora_tokenizer(request.lora_request),
+            request=request,
+            queue=queue)
+
+    def process_outputs(
+        self,
+        engine_core_outputs: List[EngineCoreOutput],
+    ) -> OutputProcessorOutput:
+        """
+        Process the EngineCoreOutputs:
+        1) Compute stats for logging
+        2) Detokenize
+        3) Create and handle RequestOutput objects:
+            * If there is a queue (for usage with AsyncLLM), 
+              put the RequestOutput objects into the queue for
+              handling by the per-request generate() tasks.
+
+            * If there is no queue (for usage with LLMEngine), 
+              return a list of RequestOutput objects.
+
+        ****************** NOTE FOR DEVELOPERS ******************
+
+        VLLM V1 minimizes the number of python loops over the full
+        batch to ensure system overheads are minimized. This is the 
+        only function that should loop over EngineCoreOutputs.
+
+        If you need to touch every element of the batch, implement a
+        method called XXXClass.update_from_output() to be called
+        within the loop below. For examples, see:
+            * IterationStats.update_from_output()
+            * Detokenizer.update_from_output()
+        
+        TODO(rob): add Protocol makes update_from_output explicit.
+        
+        **********************************************************
+        """
+
+        request_outputs: List[RequestOutput] = []
+        reqs_to_abort: List[str] = []
+        iteration_stats = IterationStats(self.log_stats)
+        for engine_core_output in engine_core_outputs:
+            req_id = engine_core_output.request_id
+            req_state = self.request_states.get(req_id)
+            if req_state is None:
+                # Ignore output for already-aborted request.
+                continue
+
+            # 1) Compute stats for this iteration.
+            iteration_stats.update_from_output(engine_core_output,
+                                               req_state.is_prefilling,
+                                               req_state.prompt_len)
+            req_state.is_prefilling = False
+
+            # 2) Detokenize the token ids into text.
+            detokenizer_output = req_state.detokenizer.update_from_output(
+                engine_core_output)
+
+            # 3) Create and handle RequestOutput objects.
+            if request_output := self._make_request_output(
+                    req_state, detokenizer_output):
+                if req_state.queue is not None:
+                    # AsyncLLM: put into queue for handling by generate().
+                    req_state.queue.put_nowait(request_output)
+                else:
+                    # LLMEngine: return list of RequestOutputs.
+                    request_outputs.append(request_output)
+
+                # Free completed requests.
+                if request_output.finished:
+                    self.request_states.pop(req_id)
+                    if not engine_core_output.finished:
+                        # If req not finished in EngineCore, but Detokenizer
+                        # detected stop string, abort needed in EngineCore.
+                        reqs_to_abort.append(req_id)
+
+        return OutputProcessorOutput(
+            request_outputs=request_outputs,
+            reqs_to_abort=reqs_to_abort,
+            iteration_stats=iteration_stats,
+        )
+
+    def _make_request_output(
+        self,
+        request_state: RequestState,
+        detokenizer_output: Optional[DetokenizerOutput],
+    ) -> Optional[RequestOutput]:
+
+        if detokenizer_output is None:
+            return None
+
+        request_output = RequestOutput.new(
+            request_state.request_id,
+            request_state.prompt,
+            request_state.prompt_token_ids,
+            detokenizer_output.output_text,
+            detokenizer_output.token_ids,
+            detokenizer_output.finished,
+        )
+        if detokenizer_output.finished:
+            completion_output = request_output.outputs[0]
+            completion_output.finish_reason = detokenizer_output.finish_reason
+            completion_output.stop_reason = detokenizer_output.stop_reason
+
+        return request_output

vllm/v1/metrics/stats.py

@@ -1,4 +1,8 @@
 from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from vllm.v1.engine import EngineCoreOutput
 
 
 @dataclass
@@ -10,3 +14,26 @@ class SchedulerStats:
 
     # gpu_cache_usage: float = 0.0
     # gpu_prefix_cache_hit_rate: float = 0.0
+
+
+class IterationStats:
+    """Stats associated with a single set of EngineCoreOutputs."""
+
+    def __init__(self, log_stats: bool):
+        self.log_stats = log_stats
+        self.num_generation_tokens = 0
+        self.num_prompt_tokens = 0
+
+    def update_from_output(self, output: "EngineCoreOutput",
+                           is_prefilling: bool, prompt_len: int):
+        if not self.log_stats:
+            return
+
+        self.num_generation_tokens += len(output.new_token_ids)
+        if is_prefilling:
+            # This relies on the invariant that EngineCore does
+            # not stream outputs for partially completed prefills
+            # (scheduler.update_from_output makes EngineCoreOutput
+            # iff num_computed_tokens == num_tokens).
+            assert (len(output.new_token_ids) > 0)
+            self.num_prompt_tokens += prompt_len