### What this PR does / why we need it?
torch_npu.npu_grouped_matmul:
https://www.hiascend.com/document/detail/zh/Pytorch/710/apiref/torchnpuCustomsapi/context/torch_npu-npu_grouped_matmul.md
According to the document, when `split_item` is 2 or 3,
`torch_npu.npu_grouped_matmul` will return a list which has one element.
Therefore, the `torch.cat` after `torch_npu.npu_grouped_matmul` is
unnecessary.
### Does this PR introduce _any_ user-facing change?
not involved
### How was this patch tested?
ut and e2e covered: `tests/ut/ops/test_fused_ops.py`,
`tests/e2e/singlecard/ops/test_fused_moe.py`
**performance**:
(qwen3 30B, 2k->20k)
base:
Total Token throughput (tok/s): 667.76
remove cat:
Total Token throughput (tok/s): 680.82
- vLLM version: v0.10.0
- vLLM main:
fa00c5d75b
Signed-off-by: huangxialu <huangxialu1@huawei.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'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?
Updates the FusedMoE method to determine whether to use ACL Graph based
on the `torchair_graph_config`
This is equivalent to #2154 on v0.9.1-dev.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
None needed.
- vLLM version: v0.10.0
- vLLM main:
ad57f23f6a
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.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?
Fix vLLM startup failure when inferring DeepSeek R1 model in DP
scenario.
When running vLLM inference for the DeepSeek R1 model in DP32+TP1
configuration, the vLLM service fails to start with the following error.
<img width="1786" height="918" alt="21b2011042d4f77f36f5243fa64d9c18"
src="https://github.com/user-attachments/assets/df1963fe-587e-43ca-822e-a9094d0034fb"
/>
The root cause is a missing else branch after [this line of
code](d629f0b2b5/vllm_ascend/ops/fused_moe.py (L1411)).
This PR fixes the issue.
### 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:
5bbaf492a6
---------
Signed-off-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.com>
Co-authored-by: zhanghaiwen <zhanghaiwen@cmss.chinamobile.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>
### 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>
There is a lot torchair specified logic in common code. It results hard
code maintenance. We will create a new torchair module to launch
torchair related logic there. I plan to add 4 PR.
1. Refactor worker
2. Refactor utils (this PR)
- simple change that move all torchair related util function to torchair
module
3. Refactor model_runner
4. Refactor attention
- vLLM version: v0.9.2
- vLLM main:
8188196a1c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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?
The optimization solution for non-deepseek select_experts is to replace
gating_topk_softmax with softmax+topk+to, which is optimized from 37us
to 14us on bf16/fp16 of qwen3-235b
- vLLM version: v0.9.2
- vLLM main:
1a4f35e2ea
---------
Signed-off-by: ttanzhiqiang <389825161@qq.com>
### What this PR does / why we need it?
The previous code is
router_logits, _ = self.gate(hidden_states)
hidden_states = get_dp_group().all_gather(hidden_states, 0)
router_logits = get_dp_group().all_gather(router_logits, 0)
I want to change the two all_gathers to one, reduce one all_gather
communication, and make it
hidden_states = get_dp_group().all_gather(hidden_states, 0)
router_logits, _ = self.gate(hidden_states)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
bash examples/run_dp_attention_etp16.sh
bash examples/run_dp_attention_etp16_benmark.sh
gsm8k accuracy verification
<img width="1809" alt="截屏2025-06-24 21 53 24"
src="https://github.com/user-attachments/assets/47eace3b-a86b-41b4-9de8-773f57fea33b"
/>
- vLLM version: v0.9.2
- vLLM main:
77f77a951e
---------
Signed-off-by: ttanzhiqiang <389825161@qq.com>
### What this PR does / why we need it?
Follow vllm-project/vllm lint way:
https://github.com/vllm-project/vllm/blob/main/.pre-commit-config.yaml
Enable pre-commit to avoid some low level error AMAP.
This pr is one step of #1241, The purpose is make linting system more
clear and convenient, on this step, Mainly did the following things:
yapf, actionlint, ruff, typos, isort, mypy, png-lint, signoff-commit,
enforce-import-regex-instead-of-re.
TODO:
- clang-format(check for csrc with google style)
need clean code, disable for now
- pymarkdown
need clean code, disable for now
- shellcheck
need clean code, disable for now
### Does this PR introduce _any_ user-facing change?
Only developer UX change:
https://vllm-ascend--1256.org.readthedocs.build/en/1256/developer_guide/contributing.html#run-lint-locally
```
pip install -r requirements-lint.txt && pre-commit install
bash format.sh
```
### How was this patch tested?
CI passed with new added/existing test.
Co-authored-by: Yikun [yikunkero@gmail.com](mailto:yikunkero@gmail.com)
Co-authored-by: wangli
[wangli858794774@gmail.com](mailto:wangli858794774@gmail.com)
- vLLM version: v0.9.1
- vLLM main:
5358cce5ff
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
When all_reduce_merge is in progress, shared_experts does not do
all_reduce in mlp, but waits until shared_experts+router_experts are
completed before doing all_reduce
In prefill and decode, as long as shared_experts+router_experts are
all_reduce, there will be benefits.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
bash examples/run_dp_attention_etp16.sh
bash examples/run_dp_attention_etp16_benmark.sh
- vLLM version: v0.9.1
- vLLM main:
977180c912
---------
Signed-off-by: ttanzhiqiang <389825161@qq.com>
### What this PR does / why we need it?
In some cases, `dist._functional_collectives.reduce_scatter_tensor` can
cause its input tensor not to be released immediately after the current
layer ends. Instead, it will only be released when the GPU memory usage
of the current process reaches a certain threshold (approximately every
15 layers each time).
**Before Fix**
<img width="1441" alt="截屏2025-06-24 01 26 13"
src="https://github.com/user-attachments/assets/72d5dbb3-c8c8-4778-bf64-8db7bab8aff0"
/>
**After Fix**
<img width="1475" alt="截屏2025-06-24 01 23 43"
src="https://github.com/user-attachments/assets/6c69cfcd-a469-4ee5-b8c6-210aeb3a5bdf"
/>
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.9.1
- vLLM main:
9ff2af6d2b
---------
Signed-off-by: ApsarasX <apsarax@outlook.com>
vllm has released 0.9.2. This PR drop 0.9.1 support.
- vLLM version: v0.9.1
- vLLM main:
b942c094e3
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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>
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?
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?
<!--
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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.
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Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Add static build_info py file to show soc and sleep mode info. It helps
to make the code clean and the error info will be more friendly for
users
This PR also added the unit test for vllm_ascend/utils.py
This PR also added the base test class for all ut in tests/ut/base.py
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
support fused_moe_allgather_ep
### How was this patch tested?
It was tested by UT.
Signed-off-by: lyj-jjj <liuyingjun5@huawei.com>
### What this PR does / why we need it?
Add initial experimental support for Ascend 310P, this patch squash
below PR into one to help validation:
- https://github.com/vllm-project/vllm-ascend/pull/914
- https://github.com/vllm-project/vllm-ascend/pull/1318
- https://github.com/vllm-project/vllm-ascend/pull/1327
### Does this PR introduce _any_ user-facing change?
User can run vLLM on Altlas 300I DUO series
### How was this patch tested?
CI passed with:
- E2E image build for 310P
- CI test on A2 with e2e test and longterm test
- Unit test missing because need a real 310P image to have the test,
will add in a separate PR later.
- Manually e2e test:
- Qwen2.5-7b-instruct, Qwen2.5-0.5b, Qwen3-0.6B, Qwen3-4B, Qwen3-8B:
https://github.com/vllm-project/vllm-ascend/pull/914#issuecomment-2942989322
- Pangu MGoE 72B
The patch has been tested locally on Ascend 310P hardware to ensure that
the changes do not break existing functionality and that the new
features work as intended.
#### ENV information
CANN, NNAL version: 8.1.RC1
> [!IMPORTANT]
> PTA 2.5.1 version >= torch_npu-2.5.1.post1.dev20250528 to support NZ
format and calling NNAL operators on 310P
#### Code example
##### Build vllm-ascend from source code
```shell
# download source code as vllm-ascend
cd vllm-ascend
export SOC_VERSION=Ascend310P3
pip install -v -e .
cd ..
```
##### Run offline inference
```python
from vllm import LLM, SamplingParams
prompts = ["水的沸点是100摄氏度吗？请回答是或者否。", "若腋下体温为38摄氏度，请问这人是否发烧？请回答是或者否。",
"水的沸点是100摄氏度吗？请回答是或者否。", "若腋下体温为38摄氏度，请问这人是否发烧？请回答是或者否。"]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.0, top_p=0.95, max_tokens=10)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen2.5-7B-Instruct",
max_model_len=4096,
max_num_seqs=4,
dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 310P
disable_custom_all_reduce=True,
trust_remote_code=True,
tensor_parallel_size=2,
compilation_config={"custom_ops":['none', "+rms_norm", "+rotary_embedding"]},
)
# Generate texts from the prompts.
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}")
```
---------
Signed-off-by: Vincent Yuan <farawayboat@gmail.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: Vincent Yuan <farawayboat@gmail.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: shen-shanshan <467638484@qq.com>
### What this PR does / why we need it?
Support Pangu Pro MoE model (https://arxiv.org/abs/2505.21411)
### Does this PR introduce _any_ user-facing change?
Yes, new model supported
### How was this patch tested?
Test locally
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
<!-- Thanks for sending a pull request!
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### What this PR does / why we need it?
<!--
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section is to outline the changes and how this PR fixes the issue.
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### Does this PR introduce _any_ user-facing change?
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### How was this patch tested?
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Signed-off-by: zzzzwwjj <1183291235@qq.com>
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### What this PR does / why we need it?
This PR is used for resolved [issue
1147](https://github.com/vllm-project/vllm-ascend/issues/1147)
1. Move fused_moe code into one file `fused_moe.py`.
2. Integrate branch conditions into function `get_fused_moe_state`.
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### Does this PR introduce _any_ user-facing change?
1. This PR has removed the env `VLLM_ENABLE_MC2`, because I think this
env is useless, we can make judgments based on the current scenario
without this env, it will only increase complexity.
2. This PR has removed the env `USING_LCCL_COM`, because this env has
already expired.
3. `additional_config.expert_tensor_parallel_size` has already expired,
and now we also use parameter `enable_expert_parallel`, consistent with
the vLLM.
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### How was this patch tested?
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why it was difficult to add.
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Signed-off-by: zzzzwwjj <1183291235@qq.com>
Contains on #1111 for completeness.
<!-- Thanks for sending a pull request!
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### What this PR does / why we need it?
Implement multi-stream parallelism for MoE layers with shared experts,
where computation of shared experts will be overlapped with expert token
dispatch and combine. Also, when multi-stream is enabled, weights of
shared experts will be force to replicate across all cards, regardless
of any tensor parallelism configurations, to avoid AllReduce operations.
With the expected overlaping being:
```
| shared gate_up | shared act | | shared down |
| dispatch | routed gate_up, act, down | combine |
```
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### Does this PR introduce _any_ user-facing change?
No.
<!--
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### How was this patch tested?
Tested on 1x16 910 node, with tailored 2 layer DSKv2.
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---------
Signed-off-by: sdmyzlp <lrwei2@petalmail.com>
### What this PR does / why we need it?
Add EPLB expert map import capabilities
### Does this PR introduce _any_ user-facing change?
When importing the EPLB expert map you need import expert map file by
vllm args additional_config
### How was this patch tested?
1.You need to collect expert hotness and generate an expert placement
file based on the hotness and the EPLB algorithm, or you can directly
use an existing expert placement table.
2.When launching vLLM, enable EC2 and pass the configuration via the
command-line argument:
--additional-config '{"expert_map_path": "/xxx/xxx/xx.json"}
Co-authored-by: songshanhu07 <1763685535@qq.com>
---------
Signed-off-by: songshanhu07 <1763685535@qq.com>
Signed-off-by: Yuxiao-Xu <664988918@qq.com>
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: songshanhu07 <1763685535@qq.com>
Co-authored-by: Xu Yuxiao <xuyuxiao2@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Based on the design of dual-batch overlap proposed by Deepseek team and
also the implementation of fused moe in VLLM project, we implement the
multi-stream(also known as dual-batch) overlap for deepseek+mla on
Ascend NPU. We split the input batch of model into two microbatches and
then overlap the comp/comm ops in attention and moe layers using two
streams to improve the performance. Our approach can be easily extended
when adding dispatch/combine communications for moe layer.
Compared with the previously proposed
[draft](https://github.com/vllm-project/vllm-ascend/pull/842), we use
one stream for computation ops and the other for communication ops,
separately. In out opinions, it is beneficial for arranging the order of
executing different ops and thus avoiding the contention of
computation/communication resources.
ref: [overlap for
llama](https://github.com/vllm-project/vllm/pull/15787/files)
ref: [dbo in
sglang](https://github.com/sgl-project/sglang/pull/4068/files#diff-b4937569fc71f6ad215181b633b2f89c7183a2b4ac39e41fc22635599a9be7de)
### Does this PR introduce _any_ user-facing change?
Adding an env variable "VLLM_ENABLE_DBO". Users can enable dbo by
setting "VLLM_ASCEND_ENABLE_DBO=1"
See /examples/offline_dualbatch_overlap_npu.py for more info.
### How was this patch tested?
This patch can be tested with vllm-0.9.0 using its online service with
benchmark tests. We have decoupled the func of dbo from vllm and it
should be able to run without any modification to the code of vllm(some
modifications is better to implement in vllm though).
Any advice/discussion is welcome.
### Performance Benchmark
We have ran the benchmark_serving script of vllm to test the performance
after using dual-batch overlap.
`python -m vllm.entrypoints.openai.api_server \
--model=DeepSeek-R1-W8A8 \
--trust-remote-code \
--distributed-executor-backend=mp \
-tp=16 \
--port 8006 \
--max-num-seqs 390 \
--max-model-len 32768 \
--max-num-batched-tokens 65536 \
--block-size 128 \
--compilation_config 0 \
--gpu-memory-utilization 0.90 \
--disable-log-requests \
--additional-config
'{"expert_tensor_parallel_size":1,"enable_inter_dp_scheduling":true,"init_torchair_graph_batch_sizes":true,"trace_recompiles":true,"ascend_scheduler_config":{},"enable_graph_mode":false}'`
and run benchmark with the parameters of :
`--dataset-name random --random-input-len 4096 --random-output-len 1
--num-prompts 200 --max-concurrency 8 --request-rate 5
--metric-percentiles 90`
1. test with the version using allgather+allreduce in Ascend 910B (tp16
ep16 + deepseek r1 w8a8)
2. test with the version using alltoall:
prefill qps: 0.90 -> 1.01
Mean TTFT：8226->7432ms
The overlap approach when using alltoall communication can be further
optimized by overlapping micro-batch1's moe comp with micro-batch2's
dispatch a2a comm
---------
Signed-off-by: zhuohuan <zxdu1997@gmail.com>
[CI]Moe alltoall communication optimization
The DeepSeek V3/R1 model has 256 routing experts. During parallel
inference, if the load of an EP rank is high, the overall communication
and computing time is slowed down, which becomes a weakness of parallel
inference because the load is unevenly distributed. However, the data
volume in the prefill phase is large, and the inter-card communication
time consumption/calculation time consumption and the data volume are
closely related to each other. Therefore, less non-linear precision loss
can be used to obtain a near-linear performance improvement.
During parallel inference, global synchronization occurs during
communication. As a result, the card with low load completes the
calculation first and waits for the card with the highest load to
complete the calculation. Therefore, if the load is unbalanced, the card
with high load slows down the overall time consumption. Significant
performance gains can be achieved by discarding a small number of
tokens, which is unacceptable in some precision-sensitive scenarios.
However, similar to quantification, it is a solution that uses an
acceptable precision loss in some scenarios for performance. In
addition, a trade-off between performance and precision can be achieved
by configuring a proportion of discarded tokens.
Perform the test on A3. The batch size is 8 (B), the prompt length is
3.5K tokens (S), and the parallel configuration is as follows: AttnDP=2,
AttnTP=8, MoeTP=1, and MoeEP=16. In this sence, we got a 10%-15%
performance gain.
Plus, the next version, we'll have an alltoallv moe.
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
### What this PR does / why we need it remove cumsum operator in MOE to improve performance
### How was this patch tested?
it should be tested on a case with mc2 operator and graph mode enabled
Signed-off-by: zhky <hahazhky@163.com>
Co-authored-by: 洪炜杰 <hongweijie1@huawei.com>
### What this PR does / why we need it?
Support MOE inner Multi-stream for Deepseek.
This feature requires graph mode with mc2 enabled.
---------
Signed-off-by: David9857 <985700846@qq.com>
### What this PR does / why we need it?
Optimize the performance of calculation logic in sampler and deepseekv2.
### Does this PR introduce _any_ user-facing change?
Added VLLM_ENABLE_TOPK_OPTIMZE config in sampler
### How was this patch tested?
pytest test_sampler.py
Signed-off-by: wangxiaoxin (A) <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin (A) <wangxiaoxin7@huawei.com>
Co-authored-by: ZhengWG <zwg0606@gmail.com>
More and more config options are added to additional_config. This PR
provide a new AscendConfig to manage these config options by an easier
way to make code cleaner and readable.
This PR also added the `additional_config` doc for users.
Added the test_ascend_config.py to make sure the new AscendConfig works
as expect.
TODO: Add e2e test with torchair and deepseek once the CI resource is
available.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Solve the bug that the graph mode is the same as p and d, and some other
bugs.
### Does this PR introduce _any_ user-facing change?
Wouldn't be
### How was this patch tested?
Follow the end-to-end test
Signed-off-by: ningbenzhe1 <ningbenzhe@huawei.com>
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
This PR fix accuracy issues incurred by codes that adapt to
`FusedMoEParallelConfig` in vLLM 0.9.0 version. The `tp_size` used to
split weights are wrongly passed. The root cause is that vLLM community
and vLLM-Ascend are using different methods to decide whether to use
Expert Parallel.
vLLM:
vLLM use a flag `enable_expert_parallel` to indicate whether to use EP
and use the following codes to decide `ep_size`:
```
use_ep = (dp_size_ * tp_size_ > 1
and vllm_parallel_config.enable_expert_parallel)
dp_size = dp_size_
dp_rank = get_dp_group().rank_in_group if dp_size > 1 else 0
tp_size, tp_rank = flatten_tp_across_dp(dp_rank)
if not use_ep:
return FusedMoEParallelConfig(tp_size=tp_size,
tp_rank=tp_rank,
dp_size=dp_size,
dp_rank=dp_rank,
ep_size=1,
ep_rank=0,
use_ep=False)
# DP + EP / TP + EP / DP + TP + EP
assert use_ep
# In EP, each device owns a set of experts fully. There is no tensor
# parallel update tp_size, tp_rank, ep_size and ep_rank to reflect that.
ep_size = tp_size
ep_rank = tp_rank
return FusedMoEParallelConfig(tp_size=1,
tp_rank=0,
dp_size=dp_size,
dp_rank=dp_rank,
ep_size=ep_size,
ep_rank=ep_rank,
use_ep=True)
```
vLLM-Ascend:
vLLM-Ascend uses `etp` to specify Tensor Parallel in MoE.
```
self.ep_size = get_ep_group().world_size
self.tp_size = get_etp_group().world_size
self.dp_size = (dp_size if dp_size is not None else
get_dp_group().world_size)
```
So there will be conflicts if we simply combine these codes together.
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->
### 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?
This PR fixes two accuracy bugs incurred by PR #819 when running
deepseekv3 series models:
1. #819 adds `all_to_all` communication in quantized cases, but
`all_gather` && `reduce_scatter` are removed in both of quantized and
unquantized cases. When running unquantized deepseekv3 models with
`ep_size == world_size`, the moe modules fail to communicate. Therefore,
this PR adds `all_to_all` communication on unquantized situation to
solve this accuracy issue.
2. Use `ep_size` rather than `dp_size` to decide whether to use
`all_to_all` in moe.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
this PR fix CI failure broken by vllm.
1. add moe_config for fused_moe
2. adjust the change for kv cache group from vllm. currently vllm-ascend
doesn't support this feature. this is just a quick fix for backward
compatibility
fix: #872
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
<!-- Thanks for sending a pull request!
BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
1. This PR introduces native `all_to_all` communication operator to fix
`allgather` bugs when dp_size > 1. Besides, it adds a naive
implementation of force-load-balance when doing profile runs.
2. The operator `npu_dequant_swiglu_quant` only supports input
hidden_states with dtype `torch.int32`. This tensor occupies space of
`global_bs * seq_len * topk * hidden_size`, which might be very large as
`ep_size` grows. Therefore we need to disable this operator and use
original `swiglu` && `quantize`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By performing offline inference:
---------
Signed-off-by: angazenn <zengyanjia@huawei.com>
Co-authored-by: angazenn <zengyanjia@huawei.com>
### What this PR does / why we need it?
moe support for llama4 and mllama4 in vllm-ascend
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
start sever:
python -m vllm.entrypoints.openai.api_server --model
/data/nfs/benchmark/tokenizer/Llama-4-Scout-17B-16E-Instruct \
--max-num-seqs=256 \
--max-model-len=8192 \
--tensor-parallel-size=8 \
--block-size=128 \
--dtype bfloat16 \
--host=0.0.0.0 \
--port=8000 \
--gpu-memory-utilization=0.9 \
--trust-remote-code
client:
python online_server.py --model-path
/data/nfs/benchmark/tokenizer/Llama-4-Scout-17B-16E-Instruct
--image-path /data/nfs/w60040464/cherry_blossom.jpg --docker-ip
7.242.108.253 --served-port 8000 --text "what is the content of this
image?"
result:
{'id': 'chatcmpl-2b709a5d2e1a4017991ec4ba8248686a', 'object':
'chat.completion', 'created': 1747056823, 'model':
'/data/nfs/benchmark/tokenizer/Llama-4-Scout-17B-16E-Instruct',
'choices': [{'index': 0, 'message': {'role': 'assistant',
'reasoning_content': None, 'content': 'The image depicts a tower, likely
Tokyo Skytree, framed by branches of a cherry blossom tree. The tower is
white and has a distinctive shape, with a large sphere at the top and a
long, thin spire extending from it. The branches of the cherry blossom
tree are in the foreground, with pink flowers blooming on them. The
background is a clear blue sky.\n\n**Key Features:**\n\n* **Tower:**
White, spherical shape at the top, long thin spire\n', 'tool_calls':
[]}, 'logprobs': None, 'finish_reason': 'length', 'stop_reason': None}],
'usage': {'prompt_tokens': 2340, 'total_tokens': 2440,
'completion_tokens': 100, 'prompt_tokens_details': None},
'prompt_logprobs': None}
Signed-off-by: chenxu <chenxu68@huawei.com>
Co-authored-by: chenxu <chenxu68@huawei.com>
Co-authored-by: evian <eviantai@u.nus.edu>
### What this PR does / why we need it?
Fix the method of importing environment variables in DeepSeek model to
support successful compilation via aclgraph.
Signed-off-by: rjg-lyh <1318825571@qq.com>
