5f8b1699ae
### What this PR does / why we need it? **Problem Description:** The existing implementation for the w4a8-dynamic linear method only supports the old quantization format from msmodelslim. When attempting to load models quantized with the new version, vLLM encounters errors due to mismatched tensor shapes and unprocessed quantization parameters. Relavant issues: - https://github.com/vllm-project/vllm-ascend/issues/3192 - https://github.com/vllm-project/vllm-ascend/issues/3152 **Proposed Changes:** 1. Add support for w4a8 dynamic(new format) in AscendW4A8DynamicLinearMethod and TorchairAscendW4A8DynamicLinearMethod 2. Add unit tests and e2e tests for w4a8 dynamic new and old format models <details> <summary><b>details</b></summary> 1. **Support for new w4a8-dynamic format:** * Detects quantization format by reading the "version" field in quant_description to ensure backward compatibility. * Handles the new pre-packed weight format (`2x int4` in an `int8`), which has a halved dimension. It tells the vLLM loader how to unpack it using `_packed_dim` and `_packed_factor`. * Supports the new `scale_bias` parameter, setting its shape based on the layer type, as required by msmodelslim. For api consistency and future use, the `layer_type` parameter was also added to other quantization methods. * Updates the weight processing logic: new format weights are handled with `.view(torch.int32)` since they're pre-packed, while old ones are processed with `npu_convert_weight_to_int4pack`. 2. **New unit and E2E tests:** * Added unit tests that verify the logic for both the old and new formats. * Split the distributed E2E test to confirm that both old and new format models work correctly. </details> Theoretically, these changes will provide support for all common new version w4a8(dynamic) models from msmodelslim. ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? I implement relevant unit tests and e2e tests and test the changes with following commands: ```bash # unit tests python -m pytest tests/ut/quantization/test_w4a8_dynamic.py tests/ut/torchair/quantization/test_torchair_w4a8_dynamic.py -v # e2e tests pytest tests/e2e/singlecard/test_quantization.py -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_new_version -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_old_version -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC -v -s ``` I also tested Hunyuan-1.8B-Instruct quantized with the new w4a8-dynamic format: ``` vllm serve ./models/Hunyuan-1.8B-Instruct-quantized --gpu-memory-utilization 0.96 --quantization ascend --max-model-len 9600 --seed 0 --max-num-batched-tokens 16384 ``` All tests mentioned passed locally. **NOTE: I use quantization model from my own repo in test_offline_inference_distributed.py**. Here is the description: [Anionex/Qwen3-1.7B-W4A8-V1](https://modelscope.cn/models/Anionex/Qwen3-1.7B-W4A8-V1/summary) (including quantization steps).This should be replaced by a model in vllm-ascend ci modelscope repo. Thanks for reading! - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 --------- Signed-off-by: Anionex <1005128408@qq.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [2025/09] We released the new official version v0.9.1! Please follow the official guide to start deploy 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 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-Expert, 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 >= 3.9, < 3.12
- CANN >= 8.2.rc1 (Ascend HDK version refers to here)
- PyTorch >= 2.7.1, torch-npu >= 2.7.1.dev20250724
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.11.0rc0 | Latest release candidate | QuickStart and Installation for more details |
| v0.9.1 | Latest stable version | 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 v0.11.0 tag |
| v0.7.1-dev | Unmaintained | Only doc fixed is allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fix is allowed and no new release tag any more. |
| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 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.