### What this PR does / why we need it?
This PR prefetchs the weight of mlp layers in Qwen Dense Models to
optimize the performance in Decode phase mainly.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: main
- vLLM main:
a1213fae5f
Signed-off-by: rjg-lyh <1318825571@qq.com>
Co-authored-by: Shuming19 <313093131@qq.com>
### What this PR does / why we need it?
[Feat]support dynamic quantization in allgather
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: main
- vLLM main:
5931b7e5d9
Signed-off-by: withHades <244036962@qq.com>
Signed-off-by: WithHades <244036962@qq.com>
This PR is based on top of
[#23569](https://github.com/vllm-project/vllm/pull/23569) and
[#24219](https://github.com/vllm-project/vllm/pull/24219).
### What this PR does / why we need it?
This PR allows the model runner to function asynchronously when using
async scheduling. This allows full overlap of the cpu operations
(including prepare_inputs) and the model forward pass. This diff is
functional and does not support speculative decoding, PP, or guided
decoding.
Expected speedup is 5-10% over the current async scheduling.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
server
```
python -m vllm.entrypoints.openai.api_server --model=Qwen3-32B\
--trust-remote-code --enforce-eager \
--distributed-executor-backend=mp \
-tp=4 \
--port 8006 \
--max-model-len 32000 \
--block-size 128 \
--gpu-memory-utilization 0.99
```
client
```
python $TEST_PY --backend vllm --trust-remote-code --model Qwen3-32B \
--dataset-name random --random-input-len 2048 --random-output-len 2048 \
--ignore-eos\
--num-prompts 48 --max-concurrency 48 --request-rate inf --temperature 0 \
--metric-percentiles 90 --base-url http://localhost:8006 --save-result \
--result-dir $PROFILER_DIR
```
benchmark test based on Qwen3-32B TPOT result:
||forward async| scheduler async |sync|
|-|-|-|-|
|avg|41.73|41.86|44.20|
|improve0|0.3%|0|0|
|improve1|5.58%|0|0|
benchmark test based on Qwen2___5-VL-7B-Instruct TPOT result:
||forward async|sync|
|-|-|-|
|avg|23.22|29.16|
|improve|20.3%|0|
- vLLM version: main
- vLLM main:
e93f4cc9e3
Signed-off-by: jiangpeng36 <jiangpeng36@huawei.com>
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
Co-authored-by: jiangpeng36 <jiangpeng36@huawei.com>
Co-authored-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
Flashcomm_v1 optim in Qwen Dense Models.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.1.1
- vLLM main:
5e537f45b4
Co-authored-by: 1024daniel <xxltju324@gmail.com>
### What this PR does / why we need it?
1. Move prepare/finalize operation from moe_comm_method to
/ops/moe/fused_moe_prepare_and_finalize
2. Adapt to token_dispatcher in moe_comm_method
3. Move
moe_comm_method/experts_selector/token_dispatcher/fused_moe_prepare_and_finalize
to /ops/moe
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e & ut
- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55
Signed-off-by: weichen <calvin_zhu0210@outlook.com>
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
### What this PR does / why we need it?
1. Similar to #2384 , this PR add a torchair-specific modeling for
pangu.
2. Fixes a bug introduced by routed_scaling_factor in #2675 .
3. remove eager test case for pangu since there has already been a
torchair test case.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6
---------
Signed-off-by: zengyanjia <z00883269@china.huawei.com>
Signed-off-by: Angazenn <supperccell@163.com>
Co-authored-by: zengyanjia <z00883269@china.huawei.com>
### What this PR does / why we need it?
Fix MTP torchair bug caused by torchair refactor and moe refactor
Depends on PRs:
fused moe fix: https://github.com/vllm-project/vllm-ascend/pull/2627
torchair multi DP fix:
https://github.com/vllm-project/vllm-ascend/pull/2626
### Does this PR introduce _any_ user-facing change?
when dp is enabled, to run mtp online server, need to disable server log
due to the current metrics does not support multi dp
`--disable-log-stats`
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
7c8271cd1e
Signed-off-by: xuyexiong <xuyexiong@huawei.com>
Refactor E2E CI to make it clear and faster
1. remove some uesless e2e test
2. remove some uesless function
3. Make sure all test runs with VLLMRunner to avoid oom error
4. Make sure all ops test end with torch.empty_cache to avoid oom error
5. run the test one by one to avoid resource limit error
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
There are a lot of redundant codes related to moe here, and the
structure is not very clear.
We did the following things：
we have placed the relatively independent code related to apply_mlp into
a separate file;
removed the environment variables of alltoall_buffer and alltoall_seq.
Remove the code related to alltoall_buffer and alltoall_seq, and retain
the sole TokenDispatcher inheritance class.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e&ut
- vLLM version: v0.10.1.1
- vLLM main:
4071c76cf3
---------
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
### What this PR does / why we need it?
* **Unify execution paths:** Consolidates the quantized and
non-quantized execution paths into a single `fused_experts` function,
removing duplicated logic and making the control flow clearer and easier
to maintain.
* **W8A8 dynamic quantization:** Adds support for W8A8 dynamic
quantization inside the unified MoE kernel. Communication routines are
updated to correctly handle dynamic quantization scales for activations.
* **Weight pre-processing:** Prae-transpose the `w13` and `w2` weight
matrices (as implemented in PR #2025) so that quantized and
non-quantized models follow the same code path for the MoE gating,
up-projection, and down-projection operations.
* **All-to-all communication:** Adds an `all-to-all` collective
communication pattern. For large token counts on modern hardware,
`all-to-all` is more efficient than the previous `all-gather` strategy.
However, `all-to-all` is not really captured and replayed due to
multiple D2H operations which will trigger synchronization, and thus
raise error when capture graphs. We only use `all-to-all` when fallback
to `compiled_graph_for_general_shape`.
* **Dynamic communication selection:** The model runner now selects the
optimal MoE communication method (`mc2`, `allgather`, or `alltoall`) at
runtime based on token count and the Ascend SoC version.
* **Limitation:** `all-gather` is not yet supported for quantized
models, which means there is still something left to do on A2.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
No further test cases needed.
- vLLM version: v0.10.1.1
- vLLM main:
d660c98c1b
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
1. quickfix mc2 operator error in aclgraph + ep<16 scenario to recover
CI, will be refactorred in the future
2. disable aclgraph when testing w8a8
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.1.1
- vLLM main:
95089607fa
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Integrate the arange operator to reduce the time spent and improve
performance
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
56dcf4e7e9
---------
Signed-off-by: s30076806 <songjiayang2@h-partners.com>
### What this PR does / why we need it?
This method replaces the previous all-gather approach for small numbers
of tokens.
The key changes include:
- A new `AscendFusedMoE` layer that handles token splitting, local
computation, and final aggregation via all-gather.
- Logic in the model runner to dynamically select between the new MC2
method and the existing all-gather method based on the number of input
tokens.
- Sharding the MoE communication mask across tensor-parallel ranks.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Test case fixed.
- vLLM version: v0.10.1.1
- vLLM main:
b00e69f8ca
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
[Bugfix]Support Qwen3-MOE on aclgraph mode in sizes capture and add new
ut
What this PR does / why we need it?
This PR solves the problem of sizes capture and stream error caused by
using ACLgraph on the Qwen3-30B MOE model.
Add new ut.
Does this PR introduce any user-facing change?
no
How was this patch tested?
ut
- vLLM version: v0.10.1.1
- vLLM main:
6fad29b11b
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
Add test for `outlines` backend for structured output in CI.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tests have all passed with:
```bash
pytest -sv tests/e2e/singlecard/test_guided_decoding.py
```
- vLLM version: v0.10.0
- vLLM main:
53415653ff
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
Accuracy report formatting
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
53415653ff
---------
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
Add configuration check logic for ascend scheduler: if chunked_prefill
is disabled, max_num_batched_tokens couldn't be less than max_model_len,
following vLLM;
### Does this PR introduce _any_ user-facing change?
users cannot set max_num_batched_tokens smaller than max_model_len with
ascend scheduler
### How was this patch tested?
CI and vllm serving passed
- vLLM version: v0.10.0
- vLLM main:
f77a0802b7
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
Fix mtp mode ut
### Does this PR introduce _any_ user-facing change?
Nothing
### How was this patch tested?
This can be tested in the same way as a unit test.
- vLLM version: v0.10.0
- vLLM main:
53415653ff
Signed-off-by: 赵江江 <zhaojiangjiang1@h-partners.com>
Co-authored-by: 赵江江 <zhaojiangjiang1@h-partners.com>
### What this PR does / why we need it?
Skip failed ut to recover CI quickly
related ut:
- `test_embed_models_correctness`: revert me when pooler is adapted with
the latest vllm main
- `test_check_and_update_config_enforce_eager_mode`: revert me when the
occasional failed is fixed
- vLLM version: v0.10.0
- vLLM main:
8896eb72eb
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
1. use action/checkout@v5 instead of v4
2. remove dbo test case because there is issue with it and will be
refactored later
3. make vllm-ascend compatible with vllm v0.10.1.1 and add CI for it
4. fix sampler api changes introduced by
https://github.com/vllm-project/vllm/pull/22387
6. fix qwen3 moe config changes intruoduced by
https://github.com/vllm-project/vllm/pull/20562
7. fix kvcache block changes introduced by
https://github.com/vllm-project/vllm/pull/23262
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
0c6e40bbaa
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### 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:
103f1ec8d3
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
Fix some ci issue and refactor modelrunner
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.0
- vLLM main:
4d9c61993a
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
Co-authored-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
1. Remove the return statement, it will always skip following logic.
2. Update `deepseek` to `Qwen2.5-Instruct` for OOM in github e2e test
env.
3. Fix the comparison logic
### Does this PR introduce _any_ user-facing change?
NO.
### How was this patch tested?
Local Test.
- vLLM version: v0.10.0
- vLLM main:
0933f9d518
Signed-off-by: xleoken <xleoken@163.com>
### What this PR does / why we need it?
this pr refactor select_experts of moe module
i merge implementations of quantitative and non-quantitative method in a
new class
use such as vllm like ExpertsSelector.select_experts
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
test in qwen3-moe and all ut.
- vLLM version: v0.10.0
- vLLM main:
e18859298d
Signed-off-by: yangcheng <yangcheng104@huawei.com>
Co-authored-by: yangcheng (AJ) <y00806874@china.huawei.com>
### What this PR does / why we need it?
This PR refactors the MoE (Mixture of Experts) communication logic by
introducing a strategy pattern. It defines an abstract base class,
`MoECommMethod`, which encapsulates different communication strategies
for MoE layers. By decoupling the MoE implementation from any single
communication method, this change makes it simpler to add, replace, or
optimize communication strategies in the future.
Plan / Roadmap
1. Introduce `MoECommMethod`, implement `AllGatherImpl`, and adapt ACL
Graph handling to cover all scenarios (this PR).
2. Implement `MC2CommImpl` and `AllToAllCommImpl` to optimize
performance in specific scenarios.
3. Enable W8A8 / Int8 models to use `unified_fused_experts`.
Other notes
* Data-parallel (DP) communication currently does not work with vLLM's
dispatch/combine mechanisms; an alternative approach is required to
resolve this incompatibility.
- vLLM version: v0.10.0
- vLLM main:
f7ad6a1eb3
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
This PR port optimization in PR #2002 to main and makes it cleaner.
- vLLM version: v0.10.0
- vLLM main:
afa5b7ca0b
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Thanks to the PR https://github.com/vllm-project/vllm-ascend/pull/426
make vllm-ascend support the aclgraph inference to reduce the host
overhead. However, the capability of aclgraph strongly relies on the
functionality provided by `torch.compile`, which is the key feature
supported in torch 2.x . Therefore, capture custom op into aclgraph is
only possible when it can be recognize and captured by `torch.compile`.
In this PR, we register the meta implementation of current custom ops to
enable the fx graph capture. And by doing that, insert those custom ops
into aclgraph become a natural thing to the ascend runtime.
### Does this PR introduce _any_ user-facing change?
No user face change.
### How was this patch tested?
Tested in unittest, we will integrate the `rotary_embedding` op into a
small custom model and use `torch.compile` and aclgraph to capture and
replay it to verify its functionality.
- vLLM version: v0.10.0
- vLLM main:
1b99028069
---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
### What this PR does / why we need it?
This PR fix accuracy test related to
https://github.com/vllm-project/vllm-ascend/pull/2073, users can now
perform accuracy tests on multiple models simultaneously and generate
different report files by running:
```bash
cd ~/vllm-ascend
pytest -sv ./tests/e2e/models/test_lm_eval_correctness.py \
--config-list-file ./tests/e2e/models/configs/accuracy.txt
```
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
<img width="1648" height="511" alt="image"
src="https://github.com/user-attachments/assets/1757e3b8-a6b7-44e5-b701-80940dc756cd"
/>
- vLLM version: v0.10.0
- vLLM main:
766bc8162c
---------
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
This PR is to add e2e test for using npu_mm_all_reduce_base fusion
kernel.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
not involved
- vLLM version: v0.10.0
- vLLM main:
5d5d419ca6
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
Remove transformer pins for v0.9.1-dev, because we already release the
v0.9.1rc2 with right transformer version
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
doctest CI passed
- vLLM version: v0.10.0
- vLLM main:
7e6544c797
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Qwen3 MoE supports SP. In scenarios like AlltoAll, AlltoAllv, and MC2,
replacing AllReduce with Reduce-Scatter and AllGather achieves
computational benefits in norm operations while saving one AllGather
communication. This feature is enabled during the P-phase and delivers
notable gains in long-sequence scenarios (e.g., 16k–25k), with
performance improvements reaching 5%–10%.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
```
compilation_config={
"pass_config":{
"enable_sequence_parallelism": True
}
},
enable_expert_parallel=True,
```
- vLLM version: v0.10.0
- vLLM main:
9edd1db02b
---------
Signed-off-by: libaokui <libaokui@huawei.com>
Co-authored-by: libaokui <libaokui@huawei.com>
### What this PR does / why we need it?
This pr add e2e testcase to make sure sleep mode in external_launcher is
ok.
### Does this PR introduce _any_ user-facing change?
not involved
### How was this patch tested?
not involved
- vLLM version: v0.10.0
- vLLM main:
74333ae2f6
Signed-off-by: huangxialu <huangxialu1@huawei.com>
### 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:
c494f96fbc
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
Fixes unable to load `qwen3_moe` quantized weights issue due to #1994
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
Add a `qwen3_moe` W8A8 quantized model in
`tests/e2e/multicard/test_qwen3_moe.py`
- vLLM version: v0.10.0
- vLLM main:
c494f96fbc
---------
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
What's the PR does:
1. Move AscendSparseMoeBlock to qwen3 model, since it's only used by
qwen3 model.
2. Disable AscendSparseMoeBlock if aclgraph is enabled,
AscendSparseMoeBlock doesn't work with aclgraph currently.
- vLLM version: v0.10.0
- vLLM main:
cdfd6871a5
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Fix Qwen MoE accuracy bug in DP scenario.
Now the implentment of `FusedMoE` in vLLM use `All2AllManager` to
manager different all2all algorithm branch. And the default branch use
`Multicast` in `dispatch` phase and `all_reduce` in `combine` phase,
which are not implented in vLLM-Ascend. This leading to invoking into a
default implentment in `base_communicator`, with empty `dispatch` and
`combine` operations, thus causing the accuracy issue on it.
This pr is a temporary workaround, refacting all2all in vLLM-Ascend
could be a better way.
- vLLM version: v0.10.0
- vLLM main:
ad57f23f6a
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Add qwen-vl model and sampling feature UT for 310I series
- vLLM version: v0.10.0
- vLLM main:
e0f63e4a35
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
cherry-pick #1501 from 0.9.1-dev to main
Currently, Ray is not compatible with ACL Graph, so we need to fall back
to eager mode when using the Ray backend.
co-authored: Yizhou Liu <liu_yizhou@outlook.com>
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
This PR enabled pytest and yaml style accuracy test, users now can
enable accuracy test by running:
```bash
cd ~/vllm-ascend
pytest -sv ./tests/e2e/singlecard/models/test_lm_eval_correctness.py \
--config ./tests/e2e/singlecard/models/configs/Qwen3-8B-Base.yaml \
--report_output ./benchmarks/accuracy/Qwen3-8B-Base.md
pytest -sv ./tests/e2e/singlecard/models/test_lm_eval_correctness.py \
--config-list-file ./tests/e2e/singlecard/models/configs/accuracy.txt
```
Closes: https://github.com/vllm-project/vllm-ascend/issues/1970
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
---------
Signed-off-by: Icey <1790571317@qq.com>
