### What this PR does / why we need it?
Fixes#3096
1. Fix kv cache initialization error of attention layer. There are some
models with layer name like `attn.attn`, instead of `self_attn`, but the
initialization of kv cache tensors only check for `self_attn` and
`attn.attn`, which leding to the error `AssertionError: Some layers are
not correctly initialized`
2. Set the default value of input arg `sampling_metadata` in
`compute_logits` for the modeling files in vllm-ascend. Thus fixing the
error `Qwen3NextForCausalLM.compute_logits() missing 1 required
positional argument: 'sampling_metadata'`
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
test locally with internlm
- vLLM version: v0.10.2
- vLLM main:
5aeb925452
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
What this PR does / why we need it?
there are two sets of sp implementations for moe and dense models. One
is called sequence_parallelism, and the other is flashcomm_v1.
We did the following things：
Merge two sets of code with the same implementation into one.
Remove the implementation of sequence_parallelism, as this solution
cannot support aclgraph.
Does this PR introduce any user-facing change?
No
How was this patch tested?
e2e&ut
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
### What this PR does / why we need it?
Some custom models in vllm-ascend define packed_modules_mapping, which
prevent keeping same model class with vllm community. So move these
custom packed_modules_mapping to quant utils.py. After this pr, some
custom models can be removed.
### Does this PR introduce _any_ user-facing change?
tested by CI
### How was this patch tested?
tested by CI
- vLLM version: v0.10.2
- vLLM main:
5089fd749c
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
This PR fused addrmsnorm op and w8a8 quant op to get better perf.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.2
- vLLM main:
0faf3cc3e8
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
In memory of #677 , a long overdue milestone. Now DeepSeek V3/R1 should
be OK with ACL Graph.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Working on it.
- vLLM version: v0.10.2
- vLLM main:
68dbde5dbb
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
This PR deletes ~2K lines of code about deepseek modeling. It falls back
CustomDeepseekV2 modules to original vllm implementations and adapts
some modifications in vllm about deepseek and moe.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E vllm serving with torchair graph mode and eager mode.
- vLLM version: v0.10.2
- vLLM main:
759ef49b15
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: yiz-liu <136800916+yiz-liu@users.noreply.github.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
support qwen25 vl w8a8 quantization
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
62f66be1f7
---------
Signed-off-by: lijiaojiao <lijiaojiao990304@163.com>
Co-authored-by: lijiaojiao <lijiaojiao990304@163.com>
### What this PR does / why we need it?
Adjust the position of warm_up_atb.
### Does this PR introduce _any_ user-facing change?
not involved
### How was this patch tested?
CI passed with existing test.
- vLLM version: main
- vLLM main:
b23fb78623
Signed-off-by: huangxialu <huangxialu1@huawei.com>
### What this PR does / why we need it?
Register AscendMultiHeadLatentAttention as CustomOP, following vllm changes
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: main
- vLLM main:
b23fb78623
---------
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
Remove compatibility maintenance for vllm v0.10.1 and v0.10.1.1
### Does this PR introduce _any_ user-facing change?
branch main of vllm-ascend will not be compatible with vllm v0.10.1 and
v0.10.1.1
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.1.1
- vLLM main:
6fb2788163
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Optimize rope by caching sin and cos at the first layer in Qwen 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:
562663a044
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: ZYang6263 <zy626375@gmail.com>
Signed-off-by: rjg-lyh <1318825571@qq.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
Co-authored-by: ZYang6263 <51255902183@stu.ecnu.edu.cn>
Co-authored-by: ZYang6263 <zy626375@gmail.com>
vllm-ascend won't contain model file anymore. Now pangu model file has
been moved to torchair module. The origin one can be removed.
Note: After this PR, pangu only works with torchair mode then.
- vLLM version: v0.10.1.1
- vLLM main:
8c892b1831
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Remove useless PD check in deepseek
- vLLM version: v0.10.1.1
- vLLM main:
6c7af8110a
---------
Signed-off-by: liziyu <liziyu16@huawei.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?
Correct `AscendQwen2_5_VLForConditionalGeneration_Without_Padding`
override methods
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
42dc59dbac
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This patch also supports v0.10.1
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- CI passed
- test 0.10.1: https://github.com/vllm-project/vllm-ascend/pull/2583
- vLLM version: v0.10.1.1
- vLLM main:
321938e9ac
Signed-off-by: Yikun Jiang <yikunkero@gmail.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>
### 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?
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?
Remove redundant parentheses in pangu_moe.py.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Local.
- vLLM version: v0.10.0
- vLLM main:
099c046463
Signed-off-by: xleoken <xleoken@163.com>
### What this PR does / why we need it?
1. update `CachedRequestState` as `NewRequestData` changed in
https://github.com/vllm-project/vllm/pull/22570
2. drop maintenance of vllm v0.10.0 in the branch main
### 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:
92ff41abea
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Remove redundant imported `envs`, using `envs_ascend` instead.
```python
import vllm.envs as envs_vllm
import vllm_ascend.envs as envs_ascend
```
- vLLM version: v0.10.0
- vLLM main:
71683ca6f6
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
1.Shared Expert Sharding Strategy Update: Switched from TP-aligned to
pure DP for shared experts, enabling more efficient execution.
2.O_Proj AllReduce → ReduceScatter: Reduced communication overhead by
using ReduceScatter, made possible by pure DP sharding.
3.AllGather Postponed: Delayed to after QKV down projection to reduce
synchronization impact during prefill.
### How was this patch tested?
Adding ut case in `tests/ut/attention/test_mla_v1.py`
#### How to run
use parameter `--additional_config='{"enable_shared_expert_dp": true}'`
##### a.How to run eager mode
eg:
python -m vllm.entrypoints.openai.api_server --model=/model_path
--trust-remote-code -tp 8 -dp 2 --enable_expert_parallel --port 8002
--max-model-len 5120 --max-num-batched-tokens 16384 --enforce-eager
--disable-log-requests
--additional_config='{"ascend_scheduler_config":{"enabled":true},"enable_shared_expert_dp":
true,"chunked_prefill_for_mla":true}'
##### b.How to run graph mode
eg:
python -m vllm.entrypoints.openai.api_server --model=/model_path
--trust-remote-code -tp 8 -dp 2 --enable_expert_parallel --port 8002
--max-model-len 5120 --max-num-batched-tokens 16384
--disable-log-requests
--additional_config='{"ascend_scheduler_config":{"enabled":true},"enable_shared_expert_dp":
true,"chunked_prefill_for_mla":true,"torchair_graph_config":{"enabled":true}}'
- vLLM version: v0.10.0
- vLLM main:
9edd1db02b
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Co-authored-by: SlightwindSec <slightwindsec@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?
Support MTP with：
- [x] V0 Scheduler
- [x] TorchAir
- [x] Single DP
- [x] Multi DP
- [x] Disaggregate PD
Known issues：
- [ ] Not support V1 Scheduler (chunked prefill), will be supported in a
few weeks
- [ ] vllm v0.10.0 does not support metrics with `DP > 1` right now,
need to comment out the line 171-175 in file
`vllm/vllm/v1/metrics/loggers.py`
```
if (len(self.engine_indexes) > 1
and vllm_config.speculative_config is not None):
raise NotImplementedError("Prometheus metrics with Spec Decoding "
"with >1 EngineCore per AsyncLLM is not "
"supported yet.")
```
To start an online server with torchair enabled, here is an example:
```
python -m vllm.entrypoints.openai.api_server \
--model="/weights/DeepSeek-R1_w8a8/" \
--trust-remote-code \
--max-model-len 40000 \
--tensor-parallel-size 4 \
--data_parallel_size 4 \
--max-num-seqs 16 \
--no-enable-prefix-caching \
--enable_expert_parallel \
--served-model-name deepseekr1 \
--speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}' \
--quantization ascend \
--host 0.0.0.0 \
--port 1234 \
--additional-config '{"ascend_scheduler_config":{"enabled":true,"enable_chunked_prefill":false},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]},"enable_weight_nz_layout":true}' \
--gpu_memory_utilization 0.9
```
offline example with torchair enabled
```
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=16, temperature=0)
# Create an LLM.
llm = LLM(
model="/home/data/DeepSeek-R1_w8a8/",
tensor_parallel_size=16,
max_num_seqs=16,
gpu_memory_utilization=0.9,
distributed_executor_backend="mp",
enable_expert_parallel=True,
speculative_config={
"method": "deepseek_mtp",
"num_speculative_tokens": 1,
},
trust_remote_code=True,
enforce_eager=False,
max_model_len=2000,
additional_config = {
'torchair_graph_config': {
'enabled': True,
"graph_batch_sizes": [16],
'enable_multistream_shared_expert': False,
},
"ascend_scheduler_config": {
"enabled": True
},
# 'expert_tensor_parallel_size': 16,
}
)
# Generate texts from the prompts.
# llm.start_profile()
outputs = llm.generate(prompts, sampling_params)
# llm.stop_profile()
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
- vLLM version: v0.10.0
- vLLM main:
302962e806
---------
Signed-off-by: xuyexiong <xuyexiong@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?
This pr fix broken CI:
1. Fix the
ee2eb6ecd8
changes, in this commit, they fused the gate and up projections in the
vision MLP, This can improve performance by reducing one matrix
multiplication. so, this pr do the following things:
- Specify that the two linear layers are fused as `mlp.gate_up_proj`
when loading the weights.
- Use a SiluAndMul activation function.
2. Fix
aefeea0fde,
Update ModelRunnerOutput parameters to adapt to its changes
3. Fix
[vllm-commit](https://github.com/vllm-project/vllm/pull/20815/files#diff-3ffb829a39ab2b3e4706aa28f5e476815f36c3a87b98d6a66514ebedc8f3ffb4R354-R356),
fix qwen moe
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
fed5849d3f
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
cherry-pick #1675 to main
This PR adds validation checking to torchair_graph_config for better
reliability.
Co-authored-by: whx-sjtu <2952154980@qq.com>
### 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: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
Cherry pick #1291 from v0.9.1-dev, This pr implement the synchronization
of whether `dbo` is enabled across all dp ranks. specifically, it
performed allreduce op across multiple DP ranks, only when all the dp
rank is `enable_dbo`, it is enabled
Co-authored-by: shikang-hangzhou <459956190@qq.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Cherry pick #1705 from v0.9.1-dev
Compared qwen2_5_vl.py, qwen2_5_vl_without_padding.py missing some
funtions. The purpose of this PR is to supplement these.
add:
- rot_pos_emb(self, grid_thw: torch.Tensor)
- get_window_index(self, grid_thw)
- _process_image_input(self, image_input)
- _process_video_input(self, video_input)
Co-authored-by: zheliuyu
[15750543867@163.com](mailto:15750543867@163.com)
Co-authored-by: wangli
[wangli858794774@gmail.com](mailto:wangli858794774@gmail.com)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
207b750e19
Signed-off-by: wangli <wangli858794774@gmail.com>
bugfix cherry-pick from v0.9.1-dev
https://github.com/vllm-project/vllm-ascend/pull/2007
### What this PR does / why we need it?
Minimum reproducing code：
```python
# test.py
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="Qwen2.5-VL-7B-Instruct", max_model_len=26240)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
```bash
export USE_OPTIMIZED_MODEL=0
python test.py
```
exception as follow:
```
[rank0]: File "/home/xxx/vllm_ascend/models/qwen2_5_vl_without_padding.py", line 84, in forward
[rank0]: q = torch_npu.npu_rotary_mul(q, cos, sin)
[rank0]: File "/home/anaconda3/envs/xxx/lib/python3.10/site-packages/torch/_ops.py", line 1116, in __call__
[rank0]: return self._op(*args, **(kwargs or {}))
[rank0]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, npu:0 and cpu! (when checking argument for argument r1 in method wrapper__npu_rotary_mul)
```
In `AscendQwen2_5_VisionAttention_Without_Padding`,
`torch_npu.npu_rotary_mul(q, cos, sin)`， `cos`/`sin` on cpu, but `q` on
npu, so there will be an error.
`qwen2_5_vl_without_padding.py` need this bugfix, because
`AscendQwen2_5_VisionTransformer_Without_Padding.rot_pos_emb` in
wen2_5_vl_without_padding.py is from vllm and `inv_freq` will create on
cpu.
40d86ee412/vllm/model_executor/models/qwen2_5_vl.py (L482)
```python
inv_freq = 1.0 / (theta**(torch.arange(0, dim, 2, dtype=torch.float, device='cpu') / dim))
```
`qwen2_5_vl.py` do not need, because
`AscendQwen2_5_VisionRotaryEmbedding` in qwen2_5_vl.py rewrite
`AscendQwen2_5_VisionRotaryEmbedding` and `inv_freq` will create on
device.
```python
inv_freq = 1.0 / (theta**(torch.arange(0, dim, 2, dtype=torch.float) / dim))
```
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.10.0
- vLLM main:
18cc33dd60
Signed-off-by: pjgao <gaopengju3@huawei.com>
Co-authored-by: pjgao <gaopengju3@huawei.com>
This PR designs the shared expert multi-stream parallelism of
w8a8-dynamic-quantized MoE stage in more detail to achieve better
performance.
- vLLM version: v0.10.0
- vLLM main:
2cc571199b
Signed-off-by: whx-sjtu <2952154980@qq.com>
Support the inference of the Deepseekr1-w8a8-mtp model with
statically-quantized shared_head in MTP layers.
- vLLM version: v0.9.2
- vLLM main:
6eca337ce0
Signed-off-by: curryliu <120010041@link.cuhk.edu.cn>
### What this PR does / why we need it?
A refactoring of forward_context and model_runner_v1, add some context
which is necessary in model inference into forward_context, and refactor
dummy_run logic, make it more reasonable.
Some details for this PR:
Add `ascend_forward_context`;
Update mc2_v2 op, and support `active_mask` param;
Update scripts in examples dir;
refactor `dummy_run` logic;
Add soc_version for A2 and A3;
### Does this PR introduce _any_ user-facing change?
No change at user-facing.
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
57c22e57f9
Signed-off-by: zzzzwwjj <1183291235@qq.com>
Signed-off-by: wuzhongjian <wuzhongjian_yewu@cmss.chinamobile.com>
### What this PR does / why we need it?
Fix duplicate 'torch.' prefix in qwen2-vl, qwen2.5-vl
- vLLM version: v0.9.2
- vLLM main:
dde295a934
Signed-off-by: wuzhongjian <wuzhongjian_yewu@cmss.chinamobile.com>
### What this PR does / why we need it?
Add prefix parameter to parent class initialization to avoid parameter
naming conflicts
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.9.2
- vLLM main:
32142b3c62
### What this PR does / why we need it?
Optimizes the performance of the Qwen3 quantization model by registering
a custom model and adding the AddRmsNormQuant operation. Subsequent PRs
will focus on performance optimizations based on this custom model.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.9.2
- vLLM main:
8d0a01a5f2
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
Remove ETP/EP maintained in branch main. We drop this as there is no
relevant scenarios to use ETP now, and we may subsequently advocate
implementing expert tensor parallelism in vLLM to support scenarios
where the expert is needed to be sliced
This is a part of #1422 backport.
Fixes https://github.com/vllm-project/vllm-ascend/issues/1396https://github.com/vllm-project/vllm-ascend/issues/1154
### Does this PR introduce _any_ user-facing change?
We'll not maintain etp/ep in vllm-ascend anymore, and use the tp/ep in
vllm instead.
### How was this patch tested?
CI passed with new added and existing test.
- vLLM version: v0.9.2
- vLLM main:
fe8a2c544a
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Before patch, we can see
`vllm_ascend.models.deepseek_v2:CustomDeepseekV3ForCausalLM`, it seems
not friendly format.
```
WARNING 07-14 23:57:34 [registry.py:413] Model architecture DeepseekV2ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV2ForCausalLM.
WARNING 07-14 23:57:34 [registry.py:413] Model architecture DeepseekV3ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.deepseek_v2:CustomDeepseekV3ForCausalLM.
WARNING 07-14 23:57:34 [registry.py:413] Model architecture Qwen3MoeForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend.models.qwen3_moe:CustomQwen3MoeForCausalLM.
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Local Test.
- vLLM version: v0.9.2
- vLLM main:
bcdfb2a330
Signed-off-by: xleoken <xleoken@163.com>
