54c0e63df7
<!-- Thanks for sending a pull request! BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html --> ### What this PR does / why we need it? As custom deepseek modeling do some changes to support graph mode in https://github.com/vllm-project/vllm-ascend/pull/585, so i follow it to change custom deepseek_mtp modeling. And some modifications for k>1 were not carried over by the https://github.com/vllm-project/vllm-ascend/pull/429, now i add it. In order to better take care of the MTP feature in the vllm-ascend repository, I added cases related to graph mode(torchair), but i skip it since torchair can not correctly clean up memory in vllmrunner. Also i add some case for MTP quantization weights, but test weight is not ready, so i skip it and i will open it when test quant weights is ready. https://github.com/vllm-project/vllm-ascend/pull/648 did not completely fix the sample change(https://github.com/vllm-project/vllm-ascend/issues/660) issue, I added the relevant changes. ### Does this PR introduce _any_ user-facing change? now, u can use following method to use mtp in deepseek v3/r1 float or quant weights with eager mode. ```python llm = LLM( model="wemaster/deepseek_mtp_main_random_bf16", tensor_parallel_size=2, speculative_config={ "num_speculative_tokens": 1, }, enforce_eager=True, trust_remote_code=True, disable_log_stats=False, gpu_memory_utilization=0.8, max_model_len=64, ) ``` or use mtp in deepseek v3/r1 float or quant weights with graph mode（torchair） ```python llm = LLM( model="wemaster/deepseek_mtp_main_random_bf16", tensor_parallel_size=2, speculative_config={ "num_speculative_tokens": 1, }, trust_remote_code=True, additional_config={ 'enable_graph_mode': True, }, disable_log_stats=False, gpu_memory_utilization=0.8, max_model_len=64, ) ``` add notes: 1. now, we support k>1, so u can set num_speculative_tokens > 1 if there is sufficient redundant computing power; 2. MTP is not supported in V1, we will support it when vLLM does it in https://github.com/vllm-project/vllm/issues/13500. 3. if u run MTP failed by `segmentation fault`, u can follow v0.7.3 patch https://github.com/vllm-project/vllm-ascend/pull/236 file `vllm_ascend/patch/patch_metrics.py` method `__npu_async_metrics_collector_init__` ### How was this patch tested? local tested passed and test by CI Signed-off-by: mengwei805 <mengwei25@huawei.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [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-Expert, Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
Prerequisites
- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series
- OS: Linux
- Software:
- Python >= 3.9, < 3.12
- CANN >= 8.0.0
- PyTorch >= 2.5.1, torch-npu >= 2.5.1
- vLLM (the same version as vllm-ascend)
Getting Started
Please refer to QuickStart and Installation for more details.
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up 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 main branch and dev branch.
- main: main branch，corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- vX.Y.Z-dev: development branch, created with part of new releases of vLLM. For example,
v0.7.3-devis the dev branch for vLLMv0.7.3version.
Below is maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM 0.8.x branch |
| v0.7.1-dev | Unmaintained | Only doc fixed is allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version |
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.