### What this PR does / why we need it?
This patch upgrade vLLM version to v0.9.2, this patch didn't remove the
v0.9.1 compatible code to easy review.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.9.1
- vLLM main:
14601f5fba
- Accuracy test with 0.9.2:
https://github.com/vllm-project/vllm-ascend/actions/runs/16121612087
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
1、Sometimes loading torchair cache will fail because of the floating of
npu memory, so this pr add a new cache to save the old kv cache bytes to
avoid the possible crash while loading the torchair graph cache.
2、When caching is enabled and does not exist, the first compilation
introduces the overhead of Dynamo Gurad. So in this case, we will
compile them directly twice to skip them (This will bring 3-4 ms of tpot
optimization)
### Does this PR introduce _any_ user-facing change?
Add a new env `VLLM_ASCEND_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE` to
control kv cache floating tolerance
### How was this patch tested?
- vLLM version: v0.9.1
- vLLM main:
1fd471e957
Signed-off-by: boying <897013703@qq.com>
### What this PR does / why we need it?
perf: use multicast to avoid padding decode request to prefill size
### How was this patch tested?
- vLLM version: v0.9.1
- vLLM main:
1fd471e957
Signed-off-by: boying <897013703@qq.com>
### What this PR does / why we need it?
This PR fixes a bug that is caused by max_num_tokens_across_dp
calculation. In earlier version, we compute this by graph_pad_size plus
max_num_tokens(actual). This will result in different
max_num_tokens_across_dp across dp ranks. If padding related is
required, this might cause a wrong padding.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed normally.
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
This PR implements the DeepSeek Expert Parallel Load Balancing (EPLB)
strategy to optimize expert distribution in vllm-ascend. The
implementation:
- Adapts the expert-map format to work with vllm-ascend's architecture
- Provides DeepSeek-provided mechanism to balance expert workload across
devices
### Does this PR introduce _any_ user-facing change?
This PR adds a new script that allows users to:
- Generate expert map configurations based on workload analysis
- Optimize expert distribution for their specific use case
### How was this patch tested?
To use this feature:
1. First collect expert heat information during model execution
2. Run the provided script to generate the expert map configuration
3. Apply the generated configuration to your vllm-ascend deployment
User example:
```bash
# expert_load_view.pt: dumped expert heat info file
python3 examples/eplb/eplb_strategy.py --exp_name 'deepseek_demo' \
--input_path expert_load_view.pt --output_path examples/eplb/results/demo \
--num_nodes 4
```
---------
Signed-off-by: ZhengWG <zwg0606@gmail.com>
### What this PR does / why we need it?
Add ut for test_pooling_model_runner.py
### Does this PR introduce _any_ user-facing change? N/A
### How was this patch tested?
python -m unittest test_pooling_model_runner.py
- vLLM version: v0.9.1
- vLLM main:
2e610deb72
---------
Signed-off-by: wangyanhui-cmss <wangyanhui_yewu@cmss.chinamobile.com>
### What this PR does / why we need it?
This patch enables the vllm commits recording and also cleanup unused
commit msg note in PR.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- CI passed
- Test on https://github.com/Yikun/vllm-ascend/pull/33 and vllm commit
refreshed as expected.
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Add the resource clear logic to fix oom issue when testing
`tests/e2e/singlecard/core/ascend_scheduler`.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test.
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Update accuracy test
1. remove accuarcy report on V0
2. add parallel and execution mode
3. add Qwen/Qwen3-30B-A3B and remove Qwen/Qwen2.5-7B-Instruct
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
Use Base test and cleanup all manaul patch code
- Cleanup EPLB config to avoid tmp test file
- Use BaseTest with global cache
- Add license
- Add a doc to setup unit test in local env
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Since running on Altlas 300I Duo was initial supported after #1333 ,
this PR will disable the JIT compiler for the 310P and changed the data
format to NZ for the weight in the vocabulary embedding and QKV
projection layers, which help improving performance.
See #1563
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Test manually:
https://github.com/vllm-project/vllm-ascend/pull/1591#issuecomment-3028352339
Signed-off-by: Vincent Yuan <farawayboat@gmail.com>
This commit
78fe77534b
from vllm reverted the change for FusedMoEParallelConfig
This PR do the same to fix the CI error
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Unify Model Usage via ModelScope
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
This PR supports torchair graph mode with non-mla backend on both 800IA2
and 300I Duo platforms. The main change is to add
`attention_v1_torchair.py` to support specific attention related
operations that are required by torchair.
### Does this PR introduce _any_ user-facing change?
Before this PR, vLLM-Ascend only allows deepseek to use torchair. Now we
can also use it with pangu. Besides, we add a support model list to
control which type of models that can use torchair.
### How was this patch tested?
We have test it with PanguProMoE on both 800IA2 and 300I Duo platforms,
and model generates answer normally.
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Signed-off-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>
### What this PR does / why we need it?
This pr supports w8a8 on 300I Duo platform. The main change is to use
`npu_quant_grouped_matmul_dequant` to replace `npu_grouped_matmul`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
offline inference on 310p runs normally.
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Signed-off-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>
### What this PR does / why we need it?
Fix lint
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
The `os.environ["VLLM_USE_MODELSCOPE"] = "True"` should be placed before
module imports
if not
```
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/xleoken/projects/vllm-ascend/examples/offline_embed.py", line 48, in <module>
model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
File "/usr/local/python3.10.17/lib/python3.10/site-packages/vllm/entrypoints/llm.py", line 243, in __init__
self.llm_engine = LLMEngine.from_engine_args(
File "/usr/local/python3.10.17/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 494, in from_engine_args
vllm_config = engine_args.create_engine_config(usage_context)
File "/usr/local/python3.10.17/lib/python3.10/site-packages/vllm/engine/arg_utils.py", line 1018, in create_engine_config
model_config = self.create_model_config()
File "/usr/local/python3.10.17/lib/python3.10/site-packages/vllm/engine/arg_utils.py", line 910, in create_model_config
return ModelConfig(
File "/usr/local/python3.10.17/lib/python3.10/site-packages/pydantic/_internal/_dataclasses.py", line 120, in __init__
s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
File "/usr/local/python3.10.17/lib/python3.10/site-packages/vllm/config.py", line 528, in __post_init__
hf_config = get_config(self.hf_config_path or self.model,
File "/usr/local/python3.10.17/lib/python3.10/site-packages/vllm/transformers_utils/config.py", line 321, in get_config
config_dict, _ = PretrainedConfig.get_config_dict(
File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/configuration_utils.py", line 590, in get_config_dict
config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/configuration_utils.py", line 649, in _get_config_dict
resolved_config_file = cached_file(
File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/utils/hub.py", line 266, in cached_file
file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)
File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/utils/hub.py", line 491, in cached_files
raise OSError(
OSError: We couldn't connect to 'https://huggingface.co' to load the files, and couldn't find them in the cached files.
Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.
[ERROR] 2025-07-03-15:27:10 (PID:333665, Device:-1, RankID:-1) ERR99999 UNKNOWN applicaiton exception
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Local.
Signed-off-by: xleoken <xleoken@163.com>
### What this PR does / why we need it?
Fix word spelling in DOC.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
No.
Signed-off-by: paulyu12 <507435917@qq.com>
### What this PR does / why we need it?
mla attention still using the gpu_input_batch's attr:`swap_states`, which will lead to
an error `AttributeError: 'InputBatch' object has no attribute 'swap_states'`
This PR fixed the mla input patch error
### How was this patch tested?
will be tested by #1136
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Add test for chunked prefill and prefix cache on v1/AscendScheduler
Covered scenarios:
- `Qwen/Qwen3-0.6B-Base` and `deepseek-ai/DeepSeek-V2-Lite-Chat` ---
multicard CI time increased by 19 min
- `V1 + default scheduler` vs `V1 + default scheduler + enable prefix
cache`
- `V1 + Ascend scheduler` vs `V1 + Ascend scheduler + enable prefix
cache` vs `V1 + Ascend scheduler + enable prefix cache + enable chunked
prefill`
- `Qwen/Qwen3-0.6B-Base` --- singlecard CI time increased by 8 min
- `V1 + Ascend scheduler` vs `V1 + Ascend scheduler + enable chunked
prefill`
should rebase after #1498 and #1446
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added test.
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
1. drop some useless code for w8a8 fusedmoe
2. Add in8 kv cache check
3. Add more ut.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with new added test.
---------
Signed-off-by: zhuyilin <809721801@qq.com>
Signed-off-by: tianyitang <tangtianyi4@huawei.com>
Co-authored-by: tianyitang <tangtianyi4@huawei.com>
### What this PR does / why we need it?
test kv data transfer contains connect,pipe,buffer
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with new added test.
---------
Signed-off-by: lixudong <lixudong@cmss.chinamobile.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: lixudong <lixudong@cmss.chinamobile.com>
Co-authored-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Only enable single version for wheel pr build to speedup PR triggered CI
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Add LoRA user guide to DOC. The content refers to [LoRA
Adapters](https://docs.vllm.ai/en/latest/features/lora.html).
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No
---------
Signed-off-by: paulyu12 <507435917@qq.com>
### What this PR does / why we need it?
Make sure that None parameters are not passed in for `--error`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed locally
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This PR (adapted from
2863befce3)
updates the CachedRequestData definition to use a single instance shared
across all requests in a batch, instead of creating a new instance per
request.
Found ci boken by the vllm's model_runner change: `ERROR 07-01 09:53:53
[core.py:521] TypeError: 'CachedRequestData' object is not iterable`,
Modify the model_runner to fix it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
pass ci will verify this.
---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Since, `vllm bench` cli has optimized enough for production use(support
more datasets), we are now do not need to copy vllm codes, now , with
vllm installed, we can easily use the benchmark cli
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
- Update Altlas 300I series doc: cleanup unused parameters and enable
optimized ops
- Fix code spell CI
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.com>
Previous, the DeepSeek V3 Pruning weight is not correct, the moe layer
is not tested. We update a new Pruning model to enable moe layer
compute.
This PR fix the CI to address the new weight.
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
When use AscendScheduler with prefix-cache enabled and chunk-prefill
disabled, there will be accuray problem because there is no branch in
mla_v1 to process this scenario. This PR fixes it.
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Change as little existing code as possible to add v1 pooling task's
support, notice that i move down the `vllm.v1.worker.gpu_input_batch` to
vllm-ascend, Considering the frequent changes in upstream interfaces, in
order to decouple, so i move it here
### How was this patch tested?
CI passed with new added/existing test, and I have a simple test was
first conducted locally which is adapted from
https://www.modelscope.cn/models/Qwen/Qwen3-Embedding-0.6B, just like
bellow：
```python
import os
import torch
from vllm import LLM
os.environ["VLLM_USE_MODELSCOPE"]="True"
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery:{query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'What is the capital of China?'),
get_detailed_instruct(task, 'Explain gravity')
]
# No need to add instruction for retrieval documents
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
input_texts = queries + documents
model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
outputs = model.embed(input_texts)
embeddings = torch.tensor([o.outputs.embedding for o in outputs])
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
```
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
Signed-off-by: wangli <858794774@qq.com>
Co-authored-by: wangli <858794774@qq.com>
### What this PR does / why we need it?
Add Pangu MoE Pro for 300I series docs
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
This fix the shape of block_table which was introduced by hybrid kv
groups several weeks ago.
Error will be raised when enable prefix-cache (eager or not) and Ascend
Scheduler at the same time, just send two identical requests and it will
reproduce.
v0.9.1: https://github.com/vllm-project/vllm-ascend/pull/1297
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Test manually
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
support pangu moe w8a8c8
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with new added test.
Signed-off-by: zhuyilin <809721801@qq.com>
### What this PR does / why we need it?
This PR introduces an expert rearrange algorithm for PanguProMoE model.
Different from the original grouped topk, it filters out the top experts
that are allocated more tokens. Therefore, we can load less experts when
calculating gmm.
We have test this algorithm for PanguProMoE-72B on 300I Duo platform and
800I A2 platform. On 300I Duo platform, we find that `num_voted_experts`
set to 5 achieves both good performance and accuracy. While on 800I A2,
we still set it to 8 to use original pangu grouped topk.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
<!--
CI passed with new added/existing test.
If it was tested in a way different from regular unit tests, please
clarify how you tested step by step, ideally copy and paste-able, so
that other reviewers can test and check, and descendants can verify in
the future.
If tests were not added, please describe why they were not added and/or
why it was difficult to add.
-->
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
In this PR, we support H2P communication optimization when running
PanguProMoE with dp_size > 1. H2P use `reduce_scatter` and `all_gather`
to replace `all_reduce` to improve performance:
original layer:
input_layernorm --> attn --> tp all_reduce --> post_attention_layernorm
--> dp all_gather --> moe/mlp --> dp reduce_scatter --> tp all_reduce
now:
input_layernorm --> tp all_gather --> attn --> tp reduce_scatter -->
post_attention_layernorm --> all_rank all_gather --> moe/mlp -->
all_rank reduce_scatter
Besides, because `reduce_scatter` requires num_tokens that can be
divided by group size, we need pad the seqs based on
`max_tokens_across_dp`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This PR has been tested with both offline and online inference using
PanguProMoE-72B.
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
This PR fixes a bug that use broadcast with cpu_group when running dp.
The `broadcast310p` patch will take effects for both cpu_group and
device group, but we only need it for device group. Hence a wrapper is
added to allow cpu_group use native torch broadcast and it solves the
bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
With this PR, DP on 310p runs normally and generates reasonable answers.
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
Fix version conflict on transformers:
`pip._vendor.pkg_resources.ContextualVersionConflict: (transformers
4.53.0 (/usr/local/python3.10.17/lib/python3.10/site-packages),
Requirement.parse('transformers<4.53.0'), {'vllm-ascend'})`
Fix
https://github.com/vllm-project/vllm-ascend/actions/runs/15933263325/job/44947231642
### Does this PR introduce _any_ user-facing change?
Fix broken build
### How was this patch tested?
CI passed with new existing test.
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Support prompt logprobs in V1. This also enable lm_eval to test accuracy
on V1
### Does this PR introduce _any_ user-facing change?
support prompt logprobs output
### How was this patch tested?
CI passed with accuracy test.
Using lm_eval, which use prompt logprobs as output to test accuracy, to
test:
```python
VLLM_USE_V1=1 lm_eval \
--model vllm \
--model_args pretrained=Qwen/Qwen2.5-7B-Instruct,max_model_len=4096,block_size=4 \
--tasks ceval-valid_computer_network \
--batch_size 8
```
After this pr, the accuracy test results of `Qwen/Qwen2.5-7B-Instruct`
on V1 is:
```bash
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|----------------------------|------:|------|-----:|--------|---|-----:|---|-----:|
|ceval-valid_computer_network| 2|none | 0|acc |↑ |0.7368|± |0.1038|
| | |none | 0|acc_norm|↑ |0.7368|± |0.1038|
```
Closes: https://github.com/vllm-project/vllm-ascend/issues/1043
Signed-off-by: MengqingCao <cmq0113@163.com>
