[Feature] add session based streaming input support to v1 #28973

joshuadeng · 2025-11-19T00:50:39Z

Purpose

Proposal for session-based streaming with sequenced updates with minimal changes to the core scheduler and engine interfaces.

Latest design

AsyncLLM.generate can now optionally take an async generator that produces a stream of StreamingInput objects, instead of a single prompt:

@dataclass
class StreamingInput:
    prompt: PromptType
    sampling_params: SamplingParams | None = None

The SamplingParams for each input can differ, if omitted will use those provided in the original generate call.

The StreamingInput chunks are handled internally as separate requests, where the prompt of each request is the cumulative concatenation of all input prompts so far + their corresponding output tokens - excluding the final sampled token from each request. All generated tokens are returned to the caller in the session's output stream.

So for streaming inputs [A1, B1, C1], [A2, B2], [A3, B3]:

First prompt [A1, B1, C1], generates [D1]
Second prompt is [A1, B1, C1, A2, B2], generates [C2, D2, E2] (D1 discarded)
Third prompt is [A1, B1, C1, A2, B2, C2, D2], generates [C3, D3] (E2 discarded)

Streamed output tokens would be D1, C2, D2, E2, C3, D3. Note that we expect to generalize/parameterize the behaviour w.r.t. which output tokens to retain in successive prompts as a follow-on based on use case requirements.

Inputs are considered completed for a particular session when the provided async generator exits or is closed/garbage collected.

It is not necessary (but also not required) to wait for all of the outputs corresponding to a particular input chunk prior to sending the next one, they are queued internally.

Certain input types/parameters are not yet supported with streaming input:

pooling, prompt_embeds, n > 1, output_kind == FINAL_ONLY, and stop strings.

Original PR summary (superseded)

design (please request access):
https://docs.google.com/document/d/16iE0pUsjdlfEdcghiCSLlicKFdvVtxn2aiic3kL9RVY/edit?usp=sharing

Enable low‑latency, interactive workloads (e.g., ASR) where inputs arrive incrementally.
Reuse vLLM’s request‑centric architecture and KV blocks, minimizing scheduling changes and avoiding re‑prefill.

High‑level design

A session is opened by the first request with streaming_sequence_id = 0 for a given request_id.
While the session is RUNNING, new updates are queued to the session's queue (streaming_queue).
When a chunk finishes, the session transitions to WAITING_FOR_STREAMING_REQ:
Queued updates are applied from streaming_queue in place and the session goes back to WAITING to decode the next chunk.
- If the update sets close_session = True, we mark the session finished with stop_reason = "close_session" and free resources.
To keep KV and multimodal alignment correct, each updated session request sends the full prompt history. The unscheduled output token from the previous step, will be pruned before appending new prompt tokens (preserves num_new_tokens correctness and KV alignment).

Test Plan

pytest tests/v1/streaming/test_streaming_scheduler.py
pytest tests/v1/streaming/test_streaming_async_llm.py
pytest tests/v1/streaming/test_streaming_gpu_model_runner.py

Test Result

they pass

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
(Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

Note

^{Cursor Bugbot is generating a summary for commit e38b36d. Configure here.}

Note

Enables multi-turn, session-based streaming with resumable updates and correct KV/MM alignment across the stack.

Add StreamingInput and new APIs AsyncLLM.generate_streaming and AsyncLLM.generate_from_stream; update generate/add_request to support resumable and reuse request queues
Extend Request with resumable and streaming_queue plus StreamingUpdate; add RequestStatus.WAITING_FOR_STREAMING_REQ
Scheduler: queue streaming updates, _update_request_as_session (prunes last unscheduled output, merges mm offsets, updates tokens/params), propagate resumable, exclude waiting-for-streaming from unfinished counts, and build NewRequestData via _make_new_request_data with copied prompt_token_ids to avoid aliasing
GPUModelRunner: handle updated streaming sessions in scheduled_new_reqs via _update_streaming_request (remove from InputBatch, refresh fields, clear output_token_ids)
OutputProcessor: allow in-place streaming state updates, only free request when finished and not resumable
Minor: compute prompt/encoder properties via accessors; plumb resumable into EngineCoreOutput
Add targeted unit tests for AsyncLLM, Scheduler (including aliasing bug prevention), GPUModelRunner, and RequestStatus

^{Written by Cursor Bugbot for commit e38b36d. This will update automatically on new commits. Configure here.}

Note

Introduces resumable, session-based streaming that queues incremental inputs and preserves KV/MM alignment end-to-end.

Add StreamingInput and extend AsyncLLM.generate to accept async generators; plumb resumable through add_request and reuse per-request RequestOutputCollector
Extend Request with resumable, streaming_queue, and StreamingUpdate; add RequestStatus.WAITING_FOR_STREAMING_REQ and expose it in __str__
Scheduler: queue and apply streaming updates via _update_request_as_session (prunes last unscheduled output, merges MM offsets, updates tokens/params), propagate resumable, exclude waiting-for-streaming from unfinished counts, and build NewRequestData via _make_new_request_data using a copied prompt_token_ids to avoid aliasing
GPUModelRunner: handle updated streaming sessions in scheduled_new_reqs via _update_streaming_request (remove from InputBatch, refresh fields, clear output_token_ids)
OutputProcessor: support in-place streaming state updates and only free requests when finished and not resumable; propagate resumable in EngineCoreOutput
Minor: compute prompt/encoder properties via accessors; add unit tests covering AsyncLLM streaming, scheduler lifecycle (incl. aliasing bug prevention), GPUModelRunner updates, and new status

^{Written by Cursor Bugbot for commit eba8018. This will update automatically on new commits. Configure here.}

github-actions · 2025-11-19T00:51:41Z

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mergify · 2025-11-21T17:00:42Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @joshuadeng.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: Joshua Deng <[email protected]>

…sion to true Signed-off-by: Joshua Deng <[email protected]>

Signed-off-by: Joshua Deng <[email protected]>

vllm/v1/engine/__init__.py

vllm/v1/engine/async_llm.py

Signed-off-by: Joshua Deng <[email protected]>

zhuohan123 · 2025-12-02T03:38:15Z

vllm/v1/request.py

    RequestStatus.FINISHED_LENGTH_CAPPED: FinishReason.LENGTH,
    RequestStatus.FINISHED_ABORTED: FinishReason.ABORT,
    RequestStatus.FINISHED_IGNORED: FinishReason.LENGTH,
+    RequestStatus.WAITING_FOR_STREAMING_REQ: FinishReason.STOP,


Why do we count RequestStatus.WAITING_FOR_STREAMING_REQ as a finished state?

we need to provide a finish reason when creating EngineCoreOutput, otherwise it would be none. the client will expect a finish reason to know generation stopped (temporarily for streaming), so we can consume the output chunk, and it won't hang.

zhuohan123 · 2025-12-02T03:48:01Z

vllm/v1/engine/output_processor.py

+    def _update_streaming_request_state(
+        self, request: EngineCoreRequest, prompt: str | None
+    ) -> None:
+        req_state = self.request_states[request.request_id]
+        if req_state.prompt and prompt:
+            req_state.prompt += prompt
+        if req_state.prompt_token_ids is not None and request.prompt_token_ids:
+            req_state.prompt_token_ids.extend(request.prompt_token_ids)
+        req_state.prompt_embeds = request.prompt_embeds
+        if req_state.stats is not None:
+            req_state.stats.arrival_time = request.arrival_time
+        req_state.is_prefilling = True


We need to raise error if we are updating a non-streaming/close_session=True request.

zhuohan123 · 2025-12-02T04:07:56Z

vllm/v1/engine/processor.py

+                data_parallel_rank=data_parallel_rank,
+                close_streaming_session=True,
+            )
+


For non-streaming requests, do we default close_streaming_session to None or True? I personally prefer True and not distinguishing between streaming and non-streaming requests. We can view a non-streaming request as a request that a user has finished their input in the first round.

right now it's defaulted to None, per Patrick's comment, but i'll change it to True

The reason I think None is better is because it cleanly declares close_streaming_session as irrelevant for non-streaming use case (which it is I think). We can see non-streaming requests as "single-round" streaming requests, but I think we don't do this really at the moment (for example because we're abstracting the scheduler into a specialized streaming_scheduler design that should never be used for non-streaming requests)

So streaming requests have to go trough the new streaming_scheduler and are not allowed to call certain functionality from the non-streaming scheduler. By declaring the variable close_streaming_session "not used" with None I think we have an easy mechanism to verify that something doesn't accidentally ends up in the wrong path.

For example we can nicely assert everywhere in streaming_scheduler that close_streaming_session is not None (similar for functions that are used for both streaming and non-streaming assert close_streaming_session is None can be useful for statements / functions that are not relevant for streaming.

But obviously no strong opinion and I'm not super familiar with general design logic of vllm.

Ah I see now that you also proposed to merge the streaming_scheduler here: https://github.com/vllm-project/vllm/pull/28973/files#r2579527152 => if we're able to merge everything into one then yeah agree

zhuohan123 · 2025-12-02T04:09:16Z

vllm/v1/engine/async_llm.py

+                # For streaming sessions, generator completion is normal
+                return


Can you elaborate a bit for the behavior here?

so this is needed because let's say for caller side we have this code

output_generator = await generate() async for request_output in output_generator: client_handle_output --> here triggers GeneratorExit do_more_things

for non-streaming before, in GeneratorExit, it's fine to just do abort(request_id) which will free everything.
Now, for streaming session, we shouldn't abort(request_id) because client hasn't close session yet.

This change wouldn't be needed with the alternative proposed API

zhuohan123 · 2025-12-02T04:15:45Z

vllm/v1/core/sched/scheduler.py

+    def _handle_non_stopped(
+        self,
+        request: Request,
+        status_before_stop: RequestStatus,
+        mark_running_stopped: Callable[[Request], None],
+        model_runner_output: ModelRunnerOutput,
+    ) -> None:
+        pass
+
+    def _handle_finished(
+        self,
+        finished_req_ids: set[str],
+        outputs: dict[int, list[EngineCoreOutput]],
+    ) -> None:
+        pass
+


what are these functions for?

for our internal use case of streaming, we want to be able to mutate request for sessions that are runnning still, and it's handled by overriding this function _handle_non_stopped

I guess we can remove this for now to avoid confusion?

yeah also I think this seems to be a bit model specific and it's weird to have that kind of logic in the shared scheduler.

zhuohan123 · 2025-12-02T04:19:03Z

vllm/v1/engine/__init__.py

    priority: int = 0

    trace_headers: Mapping[str, str] | None = None
+    close_streaming_session: bool | None = None


Nit: We can maybe call this field something like more_tokens_coming (probably also not the best name but you got the point)? Then we can default this field to False for all requests but only set this to True for requests that have more streaming inputs coming.

Should we maybe re-use resumable that was used in another PR: #25463 and which I think is quite intuitive

zhuohan123 · 2025-12-02T04:20:27Z

vllm/v1/streaming/streaming_scheduler.py

+from vllm.v1.request import Request, RequestStatus
+
+
+class StreamingScheduler(Scheduler):


What's the difficulty of merging StreamingScheduler and normal scheduler?

for stopped requests (normally we would finish and free request) for streaming we make status WAITING_FOR_STREAMING_REQ

for streaming requests, we create NewRequestData with _all_token_ids (not just prompt), to provide all tokens for the prompt field to include all past inputs (including decoded outputs). Updated streaming requests will create new entries in InputBatch, so we need the full input history to ensure alignment

these are the main 2 issues, however I think if we leverage the more_tokens_coming field we can gate this logic from non streaming behavior and merge StreamingScheduler into normal scheduler

mergify · 2025-12-02T04:21:26Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @joshuadeng.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

joshuadeng · 2025-12-02T19:07:43Z

vllm/v1/streaming/streaming_scheduler.py

+                session_request._all_token_ids[-1]
+                == session_request._output_token_ids[-1]
+            )
+            del session_request._all_token_ids[-1]


I also want to highlight this behavior. This removes the last output token (which hasn't been scheduled) from _all_token_ids, as the new request's prompt tokens will replace it. The last output token (unscheduled) thus will not go in the kv cache as it will be replaced by the streaming update request's prompt, but will be returned to the client.

We needed this behavior for our internal use case, but we can handle it another way for oss, i.e. make the new scheduled tokens the last output token + new prompt tokens

njhill · 2026-01-16T05:05:32Z

Thanks @joshuadeng for all of the updates. I've some more in another branch based on top of this one, which should address the remaining concerns I had. Here is a summary:

Pushed down the streaming input logic into the AsyncLLM.add_request method, so that generate itself remains largely unchanged.
Removed the resumable flag completed from the external API, input stream completion is now communicated by just finishing/closing the input generator, including if it's garbage collected.
To handle outputs having inconsistent metadata I've introduced a queue in the output processor too, so that the updates happen when each sub request completed.
To address the issue related to stop checking happening in the output processor, I've blocked the use of stop strings with streaming input requests for now.
Also blocked for n > 1 case, pooling models and output_kind == FINAL_ONLY since they will require some additional consideration I think.
Removed the num_prompt_tokens @property methods (update the field when num tokens change rather than having the logic in the getter).

Other notes:

I haven't tested this yet (!) nor updated your tests.
I haven't looked closely at all the metrics/tracing aspects. This isn't a blocker from my pov but we may want to make sure various request-level metrics make sense and are recorded in a coherent way for streaming input reqs.
I think the current logic is sound w.r.t. preemption, but not optimal in that we won't preempt streaming-input requests in WAITING_FOR_STREAMING_REQ state. These should probably be high on the preemption list given they are (temporarily) idle. We may also want different resumption logic so that they can be re-prefilled if kvcache space frees up, even if the next input chunk still hasn't arrived. In any case I don't consider that a blocker either.

Please take a look and let me know what you think, in the meantime I'll try to test too.

njhill · 2026-01-20T04:49:08Z

@joshuadeng I have now tested/fixed the changes, and added a bunch of e2e tests (in that same branch).

joshuadeng · 2026-01-20T07:13:53Z

@joshuadeng I have now tested/fixed the changes, and added a bunch of e2e tests (in that same branch).

thanks, this is great! will take a closer look and merge your changes if it all looks good

joshuadeng · 2026-01-21T05:14:22Z

@njhill looks good overall, left a comment in joshuadeng#1

mergify · 2026-01-22T18:47:06Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @joshuadeng.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: Patrick von Platen <[email protected]>

Signed-off-by: Nick Hill <[email protected]>

and don't support prompt_embeds with input streaming for now Signed-off-by: Nick Hill <[email protected]>

Signed-off-by: Nick Hill <[email protected]>

nick's streaming changes 2

Signed-off-by: Nick Hill <[email protected]>

njhill

@joshuadeng @patrickvonplaten @zhuohan123 @ErickLuo90 @ywang96 thanks for all of the work / input / reviews of this! And thanks for the patience while iterating.

I've updated the PR summary above with the final design (though we expect there will be follow-on changes, especially w.r.t. handling of generated tokens).

…ct#28973) Signed-off-by: Joshua Deng <[email protected]> Signed-off-by: Patrick von Platen <[email protected]> Signed-off-by: Nick Hill <[email protected]> Signed-off-by: Roger Wang <[email protected]> Co-authored-by: Roger Wang <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> Co-authored-by: Nick Hill <[email protected]> Signed-off-by: 陈建华 <[email protected]>

…ct#28973) Signed-off-by: Joshua Deng <[email protected]> Signed-off-by: Patrick von Platen <[email protected]> Signed-off-by: Nick Hill <[email protected]> Signed-off-by: Roger Wang <[email protected]> Co-authored-by: Roger Wang <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> Co-authored-by: Nick Hill <[email protected]> Signed-off-by: Josephasafg <[email protected]>

…ct#28973) Signed-off-by: Joshua Deng <[email protected]> Signed-off-by: Patrick von Platen <[email protected]> Signed-off-by: Nick Hill <[email protected]> Signed-off-by: Roger Wang <[email protected]> Co-authored-by: Roger Wang <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> Co-authored-by: Nick Hill <[email protected]> Signed-off-by: rayleeku <[email protected]>

…ct#28973) Signed-off-by: Joshua Deng <[email protected]> Signed-off-by: Patrick von Platen <[email protected]> Signed-off-by: Nick Hill <[email protected]> Signed-off-by: Roger Wang <[email protected]> Co-authored-by: Roger Wang <[email protected]> Co-authored-by: Patrick von Platen <[email protected]> Co-authored-by: Nick Hill <[email protected]>

mergify bot added v1 tpu Related to Google TPUs labels Nov 19, 2025

ywang96 self-assigned this Nov 19, 2025

njhill changed the title ~~[Feature] add session based streaming support to v1~~ [Feature] add session based streaming input support to v1 Nov 19, 2025

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joshuadeng force-pushed the streaming_support branch from ed44071 to 3651e29 Compare November 24, 2025 17:04

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joshuadeng added 4 commits November 24, 2025 09:07

add streaming session support to vllm v1

c88d2eb

Signed-off-by: Joshua Deng <[email protected]>

use request directly and remove WAITING_FOR_SESSION_REQ

74667dd

Signed-off-by: Joshua Deng <[email protected]>

update tests and fix bugs

ff49738

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fix test_request

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joshuadeng force-pushed the streaming_support branch from a52700d to 17cbbec Compare November 24, 2025 17:07

joshuadeng and others added 5 commits November 24, 2025 10:34

fix model runner typing

aa69c81

Signed-off-by: Joshua Deng <[email protected]>

remove streaming async llm and output processor and default close ses…

2a6be20

…sion to true Signed-off-by: Joshua Deng <[email protected]>

add update streaming session in scheduler directly

92d3cd2

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fix mypy

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Merge branch 'main' into streaming_support

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remove utils EngineCoreProc streaming case

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refactor close_session to close_streaming_session

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Merge branch 'main' into streaming_support

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clean up old scheduler subclass logic

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joshuadeng requested a review from njhill January 13, 2026 19:54

Merge remote-tracking branch 'origin/main' into streaming_support

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simon-mo mentioned this pull request Jan 16, 2026

[Roadmap] vLLM Roadmap Q1 2026 #32455

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Merge branch 'main' into streaming_support

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njhill and others added 10 commits January 23, 2026 14:58

some updates

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update existing tests (used claude)

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fixes, add e2e tests

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update other tests

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update behavior to only discard final output token

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and don't support prompt_embeds with input streaming for now Signed-off-by: Nick Hill <[email protected]>

small refactor

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Merge pull request #2 from njhill/streaming_support_nick2

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nick's streaming changes 2

Merge branch 'main' into streaming_support

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small fix

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fix duplicate ids in scheduler tests

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patrickvonplaten mentioned this pull request Jan 26, 2026

[Voxtral] Streaming example #33042

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vklimkov-nvidia mentioned this pull request Jan 27, 2026

Picking up session-based streaming input from vLLM vllm-project/vllm-omni#986

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		# For streaming sessions, generator completion is normal
		return

		from vllm.v1.request import Request, RequestStatus


		class StreamingScheduler(Scheduler):

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Latest design

Original PR summary (superseded)

High‑level design

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