a398fa6a0b
Set proper 'text/event-stream; charset=utf-8' media type for streaming
requests instead of hardcoded 'application/json'
### What this PR does / why we need it?
This PR fixes an issue in the disaggregated prefill proxy server where
streaming requests (`"stream": true`) were always returned with a
hardcoded `Content-Type: application/json`, even when the backend vLLM
servers correctly returned Server-Sent Events (SSE) with `Content-Type:
text/event-stream; charset=utf-8`.
Specifically, the proxy used `StreamingResponse` with a fixed
`media_type` of `application/json`, which caused FastAPI to override the
response headers and break proper SSE semantics. As a result, clients
(e.g. `curl -i`, EventSource, or OpenAI-compatible SDKs) could not
reliably receive token-by-token streaming output.
In addition, this incorrect response type causes compatibility issues
with benchmarking and load-testing tools such as **EvalScope**. When
streaming is enabled, these tools expect SSE-formatted responses to
correctly parse token usage information. With the incorrect
`application/json` content type, EvalScope fails to parse the response
and reports errors similar to:`2025-12-15 09:27:56 - evalscope - ERROR:
Failed to parse usage from response: list index out of range. Response:
[]`
This PR updates the proxy to:
- Detect whether the incoming request is a streaming request
(`stream=true`)
- Use `text/event-stream; charset=utf-8` for streaming responses
- Preserve `application/json` for non-streaming responses
This aligns the proxy behavior with native vLLM prefill/decoder servers
and the OpenAI-compatible streaming API contract.
Fixes incorrect streaming response headers that prevented proper
real-time token delivery.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
This change was tested manually using a disaggregated prefill + decode
setup
with the proxy server.
### Test Steps
1. Start prefiller and decoder vLLM servers:
```bash
vllm serve --host 0.0.0.0 --port 8001 ...
vllm serve --host 0.0.0.0 --port 8002 ...
```
2. Start the proxy server:
```bash
python load_balance_proxy_server_example.py \
--host 127.0.0.1 --port 8000 \
--prefiller-hosts 127.0.0.1 --prefiller-ports 8001 \
--decoder-hosts 127.0.0.1 --decoder-ports 8002
```
3. Send a streaming completion request through the proxy:
```bash
curl -i -X POST http://127.0.0.1:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "test",
"prompt": "hello",
"max_tokens": 3,
"stream": true
}'
```
4. Verify the following:
- The response header is Content-Type: text/event-stream; charset=utf-8
- Tokens are streamed incrementally as SSE data: events
- Non-streaming requests still return application/json
No automated tests were added because this change affects an example
proxy
server and is limited to HTTP response headers. The behavior is directly
verifiable using standard SSE-compatible clients.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: zrj026 <zhangrunjiang026@gmail.com>
Co-authored-by: zrj026 <zhangrunjiang026@gmail.com>
vLLM Ascend Plugin
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Latest News 🔥
- [2026/02] We released the new official version v0.13.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2025/12] We released the new official version v0.11.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2025/09] We released the new official version v0.9.1! Please follow the official guide to start deploying large-scale Expert Parallelism (EP) on Ascend.
- [2025/08] We hosted the vLLM Beijing Meetup with vLLM and Tencent! Please find the meetup slides here.
- [2025/06] User stories page is now live! It kicks off with LLaMA-Factory/verl/TRL/GPUStack to demonstrate how vLLM Ascend assists Ascend users in enhancing their experience across fine-tuning, evaluation, reinforcement learning (RL), and deployment scenarios.
- [2025/06] Contributors page is now live! All contributions deserve to be recorded, thanks for all contributors.
- [2025/05] We've released the first official version v0.7.3! We collaborated with the vLLM community to publish a blog post sharing our practice: Introducing vLLM Hardware Plugin, Best Practice from Ascend NPU.
- [2025/03] We hosted the vLLM Beijing Meetup with vLLM team! Please find the meetup slides here.
- [2025/02] vLLM community officially created vllm-project/vllm-ascend repo for running vLLM seamlessly on the Ascend NPU.
- [2024/12] We are working with the vLLM community to support [RFC]: Hardware pluggable.
Overview
vLLM Ascend (vllm-ascend) is a community maintained hardware plugin for running vLLM seamlessly on the Ascend NPU.
It is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [RFC]: Hardware pluggable, providing a hardware-pluggable interface that decouples the integration of the Ascend NPU with vLLM.
By using vLLM Ascend plugin, popular open-source models, including Transformer-like, Mixture-of-Experts (MoE), Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
Prerequisites
- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series, Atlas 800I A3 Inference series, Atlas A3 Training series, Atlas 300I Duo (Experimental)
- OS: Linux
- Software:
- Python >= 3.10, < 3.12
- CANN == 8.5.0 (Ascend HDK version refers to here)
- PyTorch == 2.9.0, torch-npu == 2.9.0
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.14.0rc1 | Latest release candidate | See QuickStart and Installation for more details |
| v0.13.0 | Latest stable version | See QuickStart and Installation for more details |
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up the development environment, build and test.
We welcome and value any contributions and collaborations:
- Please let us know if you encounter a bug by filing an issue
- Please use User forum for usage questions and help.
Branch
vllm-ascend has a main branch and a dev branch.
- main: main branch, corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- releases/vX.Y.Z: development branch, created alongside new releases of vLLM. For example,
releases/v0.13.0is the dev branch for vLLMv0.13.0version.
Below are the maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM v0.13.0 tag |
| v0.7.1-dev | Unmaintained | Only doc fixes are allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fixes are allowed, and no new release tags anymore. |
| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 version |
| v0.11.0-dev | Maintained | CI commitment for vLLM 0.11.0 version |
| releases/v0.13.0 | Maintained | CI commitment for vLLM 0.13.0 version |
| rfc/feature-name | Maintained | Feature branches for collaboration |
Please refer to Versioning policy for more details.
Weekly Meeting
- vLLM Ascend Weekly Meeting: https://tinyurl.com/vllm-ascend-meeting
- Wednesday, 15:00 - 16:00 (UTC+8, Convert to your timezone)
License
Apache License 2.0, as found in the LICENSE file.