028b8cabc4
### What this PR does / why we need it? Fix a bug in the GLM tool call parser where the `function.name` field was incorrectly included in the final (non-first) chunks of streaming tool calls. Per OpenAI streaming semantics, `id`, `type`, and `function.name` must only appear in the **first** chunk for a given tool call index. When `_create_remaining_args_delta` was called for continuing/finishing chunks, it was incorrectly reading the function name from `delta_message.tool_calls` and re-emitting it, causing clients to see a duplicate/extra function name in the final chunk. **Root cause**: The original code always looked up the tool call in `delta_message.tool_calls` to get the name, id, and type — even when this was not the first chunk being streamed. This caused the function name to appear again in the final argument-completion chunk. **Fix**: - Track whether arguments have already been streamed (`already_streamed_args`) for each tool call index. - Only populate `fallback_tool_call_id`, `fallback_tool_call_type`, and `fallback_tool_call_name` when `already_streamed_args` is empty (i.e., this is genuinely the first chunk). - Refactored `_create_remaining_args_delta` to omit header fields entirely when all fallback values are `None`, which is the correct behavior for continuing/finishing chunks. ### Does this PR introduce _any_ user-facing change? Yes. Clients consuming the streaming tool call response will no longer receive a duplicate `function.name` in the final chunk. This fixes incorrect behavior visible in the OpenAI-compatible streaming API output for GLM models using tool calls. ### How was this patch tested? - Code review and logic analysis of the streaming tool call path in `patch_glm_tool_call_parser.py`. - Existing unit tests in `tests/ut/platform/test_patch_glm_tool_call_parser.py`. --------- Signed-off-by: chen-weipeng12 <chen-weipeng12@noreply.gitcode.com> Signed-off-by: chenweiqiang11 <chenweiqiang11@noreply.github.com> Co-authored-by: chen-weipeng12 <chen-weipeng12@noreply.gitcode.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 |
|---|---|---|
| v0.17.0rc1 | 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 v0.17.0 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.