Co-authored-by: kunpengW-code <1289706727@qq.com>
Co-authored-by: linsheng1 <1950916997@qq.com>
### What this PR does / why we need it?
Currently, chunked prefill is forcibly enabled. DeepSeek V3.1 W8A8C8
supports only the PD separation scenario. C8 refers to quantizing the KV
cache to int8, which aims to reduce the GPU memory usage of the KV cache
and improve the inference throughput.
Constraints:
1. Only the PD separation mode can be used and
MooncakeLayerwiseConnector can be used to run the model.
2. Currently, only the activation value supports dynamic quantization,
and the KV cache supports static quantization. C8 quantization with MTP
is not supported. You can use ModelSlim for quantization. The
quantization procedure is as follows:
pip install transformers==4.48.2
git clone https://gitcode.com/Ascend/msmodelslim.git
cd msmodelslim
bash install.sh
cd example/DeepSeek/
python3 quant_deepseek_w8a8.py --model_path <path/weight> --save_path
<path/quant_weight>
--anti_dataset../common/deepseek_anti_prompt_50_v3_1.json
--calib_dataset../common/deepseek_calib_prompt_50_v3_1.json --rot
--trust_remote_code True --fa_quant --dynamic --anti_method m6
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: pichangping <1337510399@qq.com>
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
Co-authored-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
This pull request addresses a bug related to the fused mc2 functionality
within the EPLB (Expert Parallelism Load Balancing) system, specifically
impacting quantization and MoE communication.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
Signed-off-by: Spicy-Stick <873805887@qq.com>
Signed-off-by: root <root@localhost.localdomain>
### What this PR does / why we need it?
This pull request is for quantization adaptation of Qwen3Omni, and it
achieves operator-level optimization and AUT (Auto-Quantization Tuning)
component optimization through patch-based modifications.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: tanhaoan333 <tanhaoan@huawei.com>
### What this PR does / why we need it?
`mxfp_compat` only provides dtype/symbol compatibility helpers for
different `torch_npu` versions, but it was placed under
`vllm_ascend.quantization`. Importing it from device/ops paths could
trigger `quantization/__init__.py` and pull in heavy quantization method
dependencies, increasing startup coupling and causing import-cycle risk
(especially on 310P paths).
### Does this PR introduce _any_ user-facing change?
No functional behavior change intended.
### How was this patch tested?
CI passed.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
## Summary
- Remove unused `set_rotation_config` and `apply_rotation` methods from
`AscendW4A4LaosDynamicLinearMethod`
- Remove unused `rotation_type` field and associated conditional
quantization parameters (`heads_rotation`, `kronecker_rotation_n`,
`kronecker_rotation_m`)
These rotation-related functions and parameters are never called in the
current W4A4 LAOS dynamic quantization workflow.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
Introduced 310P W8A8 Quantization Support: New modules and methods have
been added to enable W8A8 static quantization specifically for the
Ascend 310P platform.
Platform-Specific Quantization Configuration Loading: The system now
dynamically loads the appropriate quantization configurations
(AscendCompressedTensorsConfig, AscendModelSlimConfig) based on whether
the current hardware is an Ascend 310P device.
Implemented AscendW8A8LinearMethod310P: A dedicated linear quantization
method for 310P is provided, handling the specifics of weight and
activation quantization, including input parameter broadcasting and
weight data manipulation.
Extended AscendModelSlimConfig for 310P: A specialized configuration
class for 310P integrates the new W8A8 linear method for both standard
linear layers and vocabulary parallel embeddings, ensuring proper
quantization application.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>
### What this PR does / why we need it?
While using the LLM Compressor quantization tool from the VLLM community
to generate quantized weights, the VLLM Ascend engine needs to be
adapted to support the compressed tensors quantization format.
1. Support Moe model W4A8 dynamic weight.
- vLLM version: v0.13.0
- vLLM main:
bde38c11df
---------
Signed-off-by: LHXuuu <scut_xlh@163.com>
Signed-off-by: menogrey <1299267905@qq.com>
Co-authored-by: menogrey <1299267905@qq.com>
