ac963f1519959f61074359926efaf2f4e4fc19fb
13 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Cao Yi
|
a69ef10c3a |
[Refactor] Quantization Module Refactor (#5738)
### Summary
This PR refactors the `vllm_ascend/quantization` module to improve code
organization, maintainability, and extensibility. The refactoring
introduces a clear separation of concerns with a registry-based scheme
discovery pattern, abstract base classes for quantization schemes, and
dedicated wrapper classes.
### Key Changes
#### 1. **Modular Directory Structure**
| Before | After |
|--------|-------|
| Flat file structure with mixed responsibilities | Organized into
`methods/` subpackage for schemes |
| Single `quant_config.py` (600+ lines) | Separate config files:
`modelslim_config.py`, `compressed_tensors_config.py` |
| `utils.py` with scheme lookup logic | `methods/registry.py` with
decorator-based registration |
#### 2. **Registry-Based Scheme Discovery**
Replaced hardcoded `ASCEND_QUANTIZATION_METHOD_MAP` dictionary with a
decorator-based registry pattern:
```python
# Before: Manual dictionary mapping
ASCEND_QUANTIZATION_METHOD_MAP = {
"W8A8_DYNAMIC": {"linear": AscendW8A8DynamicLinearMethod, ...},
...
}
# After: Decorator-based registration
@register_scheme("W8A8_DYNAMIC", "linear")
class AscendW8A8DynamicLinearMethod(AscendLinearScheme):
...
```
#### 3. **Abstract Base Classes**
Introduced three abstract base classes in `methods/base.py`:
- `AscendLinearScheme` - Base for linear layer quantization
- `AscendMoEScheme` - Base for MoE layer quantization
- `AscendAttentionScheme` - Base for attention layer quantization
#### 4. **Separated Config and Wrapper Classes**
- **Config classes** (`AscendModelSlimConfig`,
`AscendCompressedTensorsConfig`): Handle config parsing and scheme
selection
- **Wrapper classes** (`AscendLinearMethod`, `AscendFusedMoEMethod`,
etc.): Implement vLLM interfaces and delegate to schemes
#### 5. **Cleaner Public API**
```python
# New clean module interface
from vllm_ascend.quantization import (
AscendModelSlimConfig,
AscendCompressedTensorsConfig,
)
from vllm_ascend.quantization.methods import get_scheme_class
```
### Architecture Diagram
```mermaid
classDiagram
direction TB
class QuantizationConfig {
<<vLLM Interface>>
+get_quant_method()
}
class AscendModelSlimConfig {
+quant_description
+get_quant_method()
-create_scheme_for_layer()
}
class AscendCompressedTensorsConfig {
+target_scheme_map
+get_quant_method()
-_get_scheme_from_parts()
}
class AscendLinearMethod {
<<Wrapper>>
+quant_method: AscendLinearScheme
+create_weights()
+apply()
}
class AscendFusedMoEMethod {
<<Wrapper>>
+quant_method: AscendMoEScheme
+create_weights()
+apply()
}
class AscendLinearScheme {
<<Abstract>>
+get_weight()*
+apply()*
+get_pertensor_param()
+get_perchannel_param()
}
class AscendMoEScheme {
<<Abstract>>
+get_weight()*
+get_dynamic_quant_param()*
+apply()*
}
class W8A8DynamicLinear {
+get_weight()
+apply()
}
class W8A8DynamicMoE {
+get_weight()
+apply()
}
QuantizationConfig <|-- AscendModelSlimConfig
QuantizationConfig <|-- AscendCompressedTensorsConfig
AscendModelSlimConfig ..> AscendLinearMethod : creates
AscendModelSlimConfig ..> AscendFusedMoEMethod : creates
AscendCompressedTensorsConfig ..> AscendLinearMethod : creates
AscendCompressedTensorsConfig ..> AscendFusedMoEMethod : creates
AscendLinearMethod o-- AscendLinearScheme : delegates to
AscendFusedMoEMethod o-- AscendMoEScheme : delegates to
AscendLinearScheme <|-- W8A8DynamicLinear
AscendMoEScheme <|-- W8A8DynamicMoE
```
### Scheme Registration Flow
```mermaid
sequenceDiagram
participant Module as Scheme Module
participant Registry as _SCHEME_REGISTRY
participant Config as QuantConfig
participant Wrapper as Wrapper Class
Note over Module: At import time
Module->>Registry: @register_scheme("W8A8_DYNAMIC", "linear")
Registry->>Registry: Store (quant_type, layer_type) -> Class
Note over Config: At runtime
Config->>Config: Determine quant_type from description
Config->>Registry: get_scheme_class(quant_type, layer_type)
Registry-->>Config: Return scheme class
Config->>Config: scheme = scheme_cls()
Config->>Wrapper: Create wrapper with scheme
Wrapper-->>Config: Return wrapper instance
```
### File Changes Summary
| Original Files | Refactored Files |
|----------------|------------------|
| `__init__.py` (empty) | `__init__.py` (exports public API) |
| `quant_config.py` | `modelslim_config.py` + `wrappers.py` |
| `compressed_tensors/` | `compressed_tensors_config.py` |
| `utils.py` | `methods/registry.py` |
| `w8a8_dynamic.py` | `methods/w8a8_dynamic.py` |
| `w8a8.py` | `methods/w8a8_static.py` |
| `w4a4_flatquant_dynamic.py` | `methods/w4a4_flatquant.py` |
| ... | `methods/base.py` (new) |
### Benefits
1. **Extensibility**: Adding new quantization schemes only requires
implementing the base class and adding `@register_scheme` decorator
2. **Maintainability**: Clear separation between config parsing, wrapper
logic, and scheme implementation
3. **Testability**: Abstract base classes enable easier unit testing and
mocking
4. **Discoverability**: Registry pattern makes it easy to list all
supported schemes
5. **Reduced Coupling**: Config classes no longer need to know about all
scheme implementations
___
- vLLM version: v0.13.0
- vLLM main:
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|
LI SHENGYONG
|
da958ee386 |
[EPLB]Eplb Config Renaming (#5533)
### What this PR does / why we need it?
1. Rename num_iterations_eplb_update to expert_heat_collection_interval.
2. Rename num_wait_worker_iterations to algorithm_execution_interval.
3. Rename init_redundancy_expert to num_redundant_experts because the
variable with the same meaning in vLLM is named this way.
4. Delete gate_eplb because we don't need this feature.
5. Move eplb config into a dict in additional config.
6. Depend on pr5817
### Does this PR introduce _any_ user-facing change?
before this pr：
`--additional-config '{"dynamic_eplb":true,
"num_iterations_eplb_update": 4000, "num_wait_worker_iterations": 150,
"init_redundancy_expert": 16, "expert_map_path": "xxx.json"}'`
after this pr:
`--additional-config
'{"eplb_config":{"dynamic_eplb":true,"expert_heat_collection_interval":4000,
"algorithm_execution_interval":150,"num_redundant_experts": 16,
"expert_map_path": "xxx.json"}}'`
### How was this patch tested?
#### test qwen3-235b eplb num_redundant_experts=16
without pr5817
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 83.33 |
with pr5817
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
- vLLM version: v0.13.0
- vLLM main:
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|
zzzzwwjj
|
cc23067f1e |
[refactor] refactor weight trans nz and transpose (#4878)
### What this PR does / why we need it?
Now `VLLM_ASCEND_ENABLE_NZ` will have three options:
0: disable nz;
1: only quant case enable nz;
2: enable nz as long as possible;
And `VLLM_ASCEND_ENABLE_NZ`=1 by default.
All cases are shown in the table below:
| | W4A4 | W4A8 | W8A8 | fp16/bf16 | fp32 |
|---|---|---|---|---|---|
| trans nz | can't support nz | trans nz by default | trans nz by
default | trans nz when VLLM_ASCEND_ENABLE_NZ is 2 | can't support nz |
| transpose | only support not transpose case | only support transpose
case | only support transpose case | linear: only support not transpose
case<br>gmm: only support transpose case | same to fp16/bf16 |
Some exceptional cases:
1. MLAPO op need to do some additional processing on the weights,
including trans nz. If use MLAPO op, some weight will be transformed to
nz forcely;
2. MLA/SFA's weight `W_UV` will be used by op
`torch.ops._C_ascend.batch_matmul_transpose`, and this op can't support
nz currently;
### Does this PR introduce _any_ user-facing change?
Now fp16/bf16 weight will not trans nz by default.
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
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|
InSec
|
5a4e8cdeba |
[Feat][BugFix]Support the Qwen3-Next-80B-A3B-Instruct quantization model&Fix the NZ issue (#4245)
### What this PR does / why we need it?
Support the Qwen3-Next-80B-A3B-Instruct quantization model and Fix the
NZ issue. Triton kernel doesn't support data format nz, thus we skip
converting weight to nz on layer `conv1d`
- vLLM version: v0.11.0
- vLLM main:
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|
Icey
|
e04a87f4be |
[BugFix] Fixes Qwen3-Next enable nz accuracy problem (#4058)
### What this PR does / why we need it?
- Fixes Qwen3-Next enable nz accuracy problem
### Does this PR introduce _any_ user-facing change?
N/A
- vLLM version: v0.11.0
- vLLM main:
|
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|
Anion
|
5f8b1699ae |
[Feat][quantization] Support new version w4a8 dynamic quantization for Linear layers (#3311)
### 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> |
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|
Wang Kunpeng
|
859e861d92 |
[main][quantization] Support deepseek w4a8 per-channel quantization (#3011)
### What this PR does / why we need it?
1.Support deepseek w4a8 per-channel quantization
2.The eager mode supports converting weights to the NZ format
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
#### How to get weights using Modelslim
##### Installation steps
git clone https://gitcode.com/Ascend/msit.git
cd msit/msmodelslim
bash install.sh
##### Generate w4a8 per-channel weights
cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md
- vLLM version: v0.10.2
- vLLM main:
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|
weichen
|
37a0715eda |
[Refactor] Adjustments to moe_comm_method selection process (#3001)
### What this PR does / why we need it?
Fix issues mentioned in
https://github.com/vllm-project/vllm-ascend/pull/2791 and some minor
refactoring.
1. Use Enum instead of string.
2. Avoid setting a new property to forward_context in
AscendFusedMoE.forward().
3. Enabling TokenDispatcherWithMoge.
4. Remove redundant code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Qwen3-30B-A3B/Qwen3-30B-A3B-W8A8/DeepSeek-V3-W4A8-Pruing/deepseek-mtp/pangu-pro-moe-pruing:
1. Enable/Disable EP
2. Aclgraph & eager
- vLLM version: v0.10.2
- vLLM main:
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lidenghui1110
|
5a7181569c |
[feat]: oproj tensor parallelism in pure DP and graph-mode scenarios. (#2167)
### What this PR does / why we need it?
This PR introduces Oproj matrix tensor model parallel to achieve
decreasing of memory consumption. It only support graph mode in pure DP
scenario.
In deepseek r1 w8a8 PD disagregated Decode instance, using pure DP, with
oproj_tensor_parallel_size = 8, we have 1 ms TPOT increasing, saved 5.8
GB NPU memory per RANK. We got best performance when
oproj_tensor_parallel_size=4 without TPOT increasing.
performance data:
<img width="1442" height="442" alt="image"
src="https://github.com/user-attachments/assets/83270fc5-868a-4387-b0a9-fac29b4a376d"
/>
### Does this PR introduce _any_ user-facing change?
This PR introduces one new config in `additional_config`.
| Name | Effect | Required | Type | Constraints |
| :---------------------------- |
:--------------------------------------- | :------- | :--- |
:----------------- |
| oproj_tensor_parallel_size | Split the o_proj matrix along the row
dimension (head num * head dim) into oproj_tensor_parallel_size pieces.
| No | int | default value is None, once this value is set, the feature
will be enabled, head num * head dim must be divisible by this value. |
example
`--additional_config={"oproj_tensor_parallel_size": 8}`
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
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Wang Yixuan
|
a955e5d404 |
[4/N][refactor]delete torchair from quantization (#2535)
### What this PR does / why we need it? After moved torchair related quantization section into torchair_quantization, split the torchair from the origin quantization ### Does this PR introduce _any_ user-facing change? NO ### How was this patch tested? vLLM version: main vLLM main: |
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|
Wang Kunpeng
|
c40d4171bc |
[main][quantization] Adapt to the new format of ds w4a8 weight (#2392)
### What this PR does / why we need it?
The deepseek w4a8 weights we supported before were in mindie-format
format. It uses int8 to represent int4, so the weight size is similar to
w8a8, and we need to do a few extra steps to make vllm-ascend load it
normally.
Now we can directly use the new weight format, which uses two int4 packs
to save the weight, the weight size is reduced, and there is no need to
do many extra operations to directly use it on vllm-ascend, but we are
also compatible with the weights of the previous mindie format.
The weight changes in the new version:
1. The weight is packed （2 int4 pack to int8）
2. The bias required in the apply method is directly generated by
modelslim
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py`
#### 1.How to get weights using Modelslim
##### Installation steps
we can use the branch br_release_MindStudio_8.1.RC2_TR5_20260624
git clone -b br_release_MindStudio_8.1.RC2_TR5_20260624
https://gitee.com/ascend/msit.git
cd msit/msmodelslim
bash install.sh
##### Generate w4a8 weights
cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md Execute the
[pre-check](https://gitee.com/ascend/msit/blob/br_release_MindStudio_8.1.RC2_TR5_20260624/msmodelslim/example/DeepSeek/README.md#%E8%BF%90%E8%A1%8C%E5%89%8D%E5%BF%85%E6%A3%80)
and [DeepSeek-R1 w4a8 mix
quantization](https://gitee.com/ascend/msit/blob/br_release_MindStudio_8.1.RC2_TR5_20260624/msmodelslim/example/DeepSeek/README.md#deepseek-r1-w4a8-%E6%B7%B7%E5%90%88%E9%87%8F%E5%8C%96%E5%89%8D%E4%B8%89%E5%B1%82-mlpw8a8-dynamic-%E9%87%8F%E5%8C%96mla%E5%85%B1%E4%BA%AB%E4%B8%93%E5%AE%B6w8a8%E9%87%8F%E5%8C%96%E8%B7%AF%E7%94%B1%E4%B8%93%E5%AE%B6w4a8-dynamic%E9%87%8F%E5%8C%96)
chapter
Reference command：python3 quant_deepseek_w4a8.py --model_path {Original
weight path} --save_path {Generate weight path}
##### Adapt to vllm-ascend
Modification in `config.json`：`"model_type":deepseekv2` is changed to
`"model_type":deepseek_v3`;
#### 2.How to run w4a8
##### a.How to run eager mode
export VLLM_ASCEND_MLA_PA=1
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5 --max-num-seqs $6
--enforce-eager
eg: python -m vllm.entrypoints.openai.api_server
--model=/weightpath/w4a8_4_layer --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120 --max-num-seqs 128 --enforce-eager
##### b.How to run graph mode
export HCCL_BUFFSIZE=1024
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
eg: python -m vllm.entrypoints.openai.api_server
--model=/weight/dsr1_w4a8_vllm --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
- vLLM version: v0.10.0
- vLLM main:
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Wang Kunpeng
|
8a59367d0c |
[main][Feature] Support deepseek w4a8 quantization (#2172)
### What this PR does / why we need it?
Supports Deepseek-R1 w4a8 quantization.
Since R1 w4a8 uses mixed quantization, only the MOE layer uses
w4a8_dynamic quantization, so we added the w4a8_dynamic.py file, which
includes the AscendW4A8DynamicFusedMoEMethod class.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py` and
`tests/ut/quantization/test_quantizer.py`
Adding e2e case in
`tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC`
to test deepseek w4a8_dynamic quantized model
#### 1.How to get weights using Modelslim
##### Installation steps
Use the branch master, the commit id is:
298e175d69b3b855111a1e09bbe2fcd12fdb4e24
git clone https://gitee.com/ascend/msit.git
cd msit/msmodelslim
bash install.sh
##### The required transformers environment
transformers>=4.48.2
##### Generate w4a8 weights
cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md Execute the
[pre-check](https://gitee.com/ascend/msit/blob/master/msmodelslim/example/DeepSeek/README.md#%E8%BF%90%E8%A1%8C%E5%89%8D%E5%BF%85%E6%A3%80)
and [DeepSeek-R1 w4a8 mix
quantization](https://gitee.com/ascend/msit/blob/master/msmodelslim/example/DeepSeek/README.md#deepseek-r1-w4a8-%E6%B7%B7%E5%90%88%E9%87%8F%E5%8C%96%E5%89%8D%E4%B8%89%E5%B1%82-mlpw8a8-dynamic-%E9%87%8F%E5%8C%96mla%E5%85%B1%E4%BA%AB%E4%B8%93%E5%AE%B6w8a8%E9%87%8F%E5%8C%96%E8%B7%AF%E7%94%B1%E4%B8%93%E5%AE%B6w4a8-dynamic%E9%87%8F%E5%8C%96)
chapter
Reference command：python3 quant_deepseek_w4a8.py --model_path {Original
weight path} --save_path {Generate weight path} --mindie_format
##### Adapt to vllm-ascend
Since mindie_format generates mindie format, some adaptation
modifications are needed for vllm-ascend to use it:
`quant_model_description_w8a8_dynamic.json` rename to
`quant_model_description.json`, and add `"group_size": 256`
Modification in `config.json`：`"model_type":deepseekv2` is changed to
`"model_type":deepseek_v3`; `quantization_config` is removed;
tips:The group_size and weights match. If the w4a8 weights are not
generated using msmodelslim, you can check the group_size in
quantization_config in config.json.
#### 2.How to run w4a8
##### a.How to run eager mode
export VLLM_USE_V1=1 # v1
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5 --max-num-seqs $6
--enforce-eager
eg: python -m vllm.entrypoints.openai.api_server
--model=/weightpath/w4a8_4_layer --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120 --max-num-seqs 128 --enforce-eager
##### b.How to run graph mode
export VLLM_USE_V1=1 # v1
export HCCL_BUFFSIZE=1024
python -m vllm.entrypoints.openai.api_server --model=$1
--trust-remote-code -tp $2 -dp $3 --enable_expert_parallel
--quantization ascend --port $4 --max-model-len $5
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
eg: python -m vllm.entrypoints.openai.api_server
--model=/weight/dsr1_w4a8_vllm --trust-remote-code -tp 4 -dp 4
--enable_expert_parallel --quantization ascend --port 8002
--max-model-len 5120
--additional_config='{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
- vLLM version: v0.10.0
- vLLM main:
|
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|
Ruri
|
4fcca137a7 |
[main][Feature] Support Qwen3 W4A8 quantization (#2060)
### What this PR does / why we need it?
Adding `W4A8_DYNAMIC` quantization support for linear.
Dense models like Qwen3 can infer with `W4A8_DYNAMIC` quantization.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
Adding ut case in `tests/ut/quantization/test_w4a8_dynamic.py`
Adding e2e case in
`tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC`
to test qwen3 w4a8_dynamic quantized model
Note the w4a8_dynamic quantized model is quantized by `msit/msmodelslim`
of commit `d0abb0a47e1f1a473b866ad41b737fbc28fb1409`
1. Generate `W4A8_DYNAMIC` quantization weights using `msmodelslim`
```shell
git clone https://gitee.com/ascend/msit.git
cd msit/msmodelslim
git checkout d0abb0a47e1f1a473b866ad41b737fbc28fb1409
bash install.sh
```
2. Serve model using `vllm`
```shell
VLLM_USE_V1=1 python -m vllm.entrypoints.openai.api_server \
--model vllm-ascend/Qwen3-8B-W4A8 \
--port 8000 \
--quantization ascend \
--tensor_parallel_size 2 \
--enforce-eager
```
- vLLM version: v0.10.0
- vLLM main:
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