1. remove some useless test func and file
2. fix format.sh problem
3. enable full test for singlecard and multicard
4. move long term test to long_term folder. For this kind of test, it
only runs by labeled and daily test. Include: spec decode、accuracy test
## After refactor:
There are 4 test modules
- `singlecard`: contains the test running on one NPU. It'll be run for
each PR and daily test.
- `multicard`: contains the test running on multi NPUs. It'll be run for
each PR and daily test.
- `long_term`: contains the test that cost much time(Now include `spec
decode` and `accuracy` test). It'll be run for the PR with
`long-term-test` labeled and daily test.
- `e2e`: contains the test for doc and pd feature. It'll be run for the
PR with `pd-test` labeled and daily test.
## Todo:
1. some test are skipped, they should be fixed and reenabled in the
future.
2. pyhccl test for multicard doesn't work at all. It should be enabled
as well.
3. ensure long-term-test pass by daily test.
### Know issue
Now, `ready` labels is required to start pd test or long term test. And
when `long-term-test` or `pd-test` is labeled after another one, the old
labeled test will be re-run again. So the labeled test should be ran in
the following step:
1. decide which test need run, then label it. `long-term-test` or
`pd-test` or both.
2. add `ready-for-test` label, then the test will be ran.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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 is a continuing work of #716.
This PR add workflow to build and release wheel, and also release source
to PYPI.
We have 3 conditions to trigger the workflow:
1. PR to `main` and `*-dev`
2. push to `main` and `*-dev`
3. push tag with name of `v*`
Release to PYPI will only be done under condition 3. Under condition 1
and 2, it will generate .tar.gz and build .whl, upload to github
artifacts but will not release.
update:
Will build .whl and upload to github artifacts with scheduled task.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
All triggered conditions are well tested with my fork repo.
---------
Signed-off-by: Shuqiao Li <celestialli@outlook.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?
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>
Tweak packed_modules_mapping to support W8A8 weights.
<!-- Thanks for sending a pull request!
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https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
<!--
- Please clarify what changes you are proposing. The purpose of this
section is to outline the changes and how this PR fixes the issue.
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- Please clarify why the changes are needed. For instance, the use case
and bug description.
- Fixes #
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### Does this PR introduce _any_ user-facing change?
<!--
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as API, interface or other behavior changes.
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### How was this patch tested?
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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: Yizhou Liu <liu_yizhou@outlook.com>
<!-- Thanks for sending a pull request!
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https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
<!--
- Please clarify what changes you are proposing. The purpose of this
section is to outline the changes and how this PR fixes the issue.
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- Please clarify why the changes are needed. For instance, the use case
and bug description.
- Fixes #
-->
Make spec decode support for V1 Engine
- Currently, Ascend does not support the triton kernel. PyTorch is used
to rewrite the `rejection_sampler.py` triton kernel. However, PyTorch is
not as good as Triton. Therefore, ascend c is used to implement the
function in the future.
- Currently, spec decode supports only the ngram algorithm. The eagle
algorithm needs to be further adapted.
### 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.
-->
Not change user facing.
### 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.
-->
test by `tests/singlecard/spec_decode/e2e/test_v1_spec_decode.py` and
`tests/sample/test_rejection_sampler.py`, test base function of
rejection sampler and e2e function of spec decode.
Signed-off-by: ponix-j <657511300@qq.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?
Currently, the implementation for MLA V1 pads q, k, v to `head_dim` 256
to conform to early MLA kernel. But the new MLA kernel supports
`head_dim` that can't be devided by 128. Therefore we can remove those
unnecessary paddings to boost the performance
### 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?
Update attention nz and mla nz modules to improve TPOP 6ms performance
Convert W_UV and W_UK_T to NPU format in mla_v1.py
Convert layer.weight to NPU format in w8a8.py
Signed-off-by: ttanzhiqiang <389825161@qq.com>
Implement save kv cache logic for v1 disaggregated prefill in ascend
scheduler
This PR adds support for saving kv cache in the ascend scheduler, which
is part of the v1 disaggregated prefill design. The load functionality
is not yet implemented.
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
Revert the default value of enable_chunked_prefill to 'False' in
additional_scheduler_config. In engine v1, enable_chunked_prefill is
forcibly set to True in VllmConfig, which causes it to be perceived as
True in check_and_update_config(). As a result, when the v0 scheduler is
enabled, the chunked prefill feature remains active, leading to the
failure of the v0 scheduler and causing it to fall back to the native v1
scheduling logic.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
Fix the bug of #703, where vllm wrong raised the ERROR : Failed to
import vllm_ascend_C:No module named 'vllm_ascend.vllm_ascend_C'. The
format for reporting import vllm_ascend_C failure is unified by warning
("Failed to import vllm_ascend_C:%s", e).
### Does this PR introduce _any_ user-facing change?
No
---------
Signed-off-by: yangpuPKU <604425840@qq.com>
### What this PR does / why we need it?
Update feature support for lora
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
preview
Signed-off-by: paulyu <paulyu0307@gmail.com>
Co-authored-by: paulyu <paulyu0307@gmail.com>
### What this PR does / why we need it?
Add V1Engine LoRA support.
Add LoRA e2e test on single card and multiple cards.
### Does this PR introduce _any_ user-facing change?
support lora for V1
### How was this patch tested?
CI passed with new added test
---------
Signed-off-by: jesse <szxfml@gmail.com>
Signed-off-by: paulyu <paulyu0307@gmail.com>
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: jesse <szxfml@gmail.com>
Co-authored-by: paulyu <paulyu0307@gmail.com>
### What this PR does / why we need it?
Fix the bugs when run deepseek model in engine v1.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
<!-- Thanks for sending a pull request!
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https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
<!--
- Please clarify what changes you are proposing. The purpose of this
section is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR.
- Please clarify why the changes are needed. For instance, the use case
and bug description.
- Fixes #
-->
Set div_mode to False to use the ACLNN kernel, which is crucial when
using ACL Graph.
### 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: Yizhou Liu <liu_yizhou@outlook.com>
1. update modelslim version to fix deepseek related issues
2. add note for "--quantization ascend"
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
### What this PR does / why we need it?
- According to https://github.com/vllm-project/vllm-ascend/issues/807,
we pull request for customer ascendc kernel of multi-step.
- also a bug we found in multi_step_runner.py is fixed when we use
multi-step on V0 Engine.
### Does this PR introduce _any_ user-facing change?
no user-facing change
### How was this patch tested?
we add Unit Test file and offline inference file to test the custom
ascendc kernel. See test/ops/test_multi_step.py and
examples/offline_multi_step.py
---------
Signed-off-by: wan_danfeng <wonderful199082@126.com>
For online serving, "ascend" quantization method is not a choice
natively, so we need to add "ascend" quantization method to quantization
methods list and the user can enable quantization using "vllm serve
--quantization ascend" command.
---------
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
1. replace deepseek-v2-lite model with more pratical model QwQ 32B
2. fix some incorrect commands
3. replase modelslim version with a more formal tag
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
The [vllm
commit](67da5720d4)
changed the input and rotary position embedding for qwen 2.5 vl which
break CI. This PR fix the CI failure for qwen2.5 vl in quick
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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>
make sure pytorch infer_schema check is patched before some case which
using fused moe ops:
1. model register
2. quantization loading
3. fused moe ut
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Add basic CI for PD disaggregation, and enable it when schedule and
label with `module:pd`
- Updated `.github/actionlint.yaml` to add a new self-hosted runner
configuration: `linux-arm64-npu-static-8`.
- Introduced a new GitHub Actions workflow
`.github/workflows/vllm_ascend_test_pd.yaml` for PD disaggregation
testing:
- Scheduled to run daily at 23:00 UTC and triggered by pull request
label `module:pd`.
- Added steps for baisci installation and other steps will add in
followup PR
Related: https://github.com/vllm-project/vllm-ascend/issues/841
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- CI passed
- No trigger by default
<img width="847" alt="image"
src="https://github.com/user-attachments/assets/23aa128f-526d-447f-91c8-8ebf6be8400f"
/>
- Trigger only if we tag with pd
<img width="930" alt="image"
src="https://github.com/user-attachments/assets/aef1caca-2029-48e8-a6e6-860136adcd37"
/>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Add quickstart doctest CI
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- CI passed
- Run `/vllm-ascend/tests/e2e/run_doctests.sh`
Related: https://github.com/vllm-project/vllm-ascend/issues/725
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Patch torch.library.infer_schema for torch 2.5 backward compatibility
- Introduced a new module `patch_utils` under
`vllm_ascend/patch/worker/patch_common/`.
- Added a function `ascend_direct_register_custom_op` to handle custom
operator registration with backward compatibility for PyTorch < 2.7
(such as torch 2.5.1).
- Implemented type conversion logic for annotations to ensure
compatibility across different PyTorch versions.
- Registered the function `ascend_direct_register_custom_op` to
`utils.direct_register_custom_op`.
- Updated `__init__.py` to include `patch_utils` as the first import.
- Ensured `patch_utils` is available for use in other patch files and
skipped isort checks for `patch_utils` import.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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>
1. Fix format check error to make format.sh work
2. Add codespell check CI
3. Add the missing required package for vllm-ascend.
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This PR fixes two bugs in AscendScheduler:
1. When running with high concurrency, the length of running queue may
exceed the limit of max_num_seqs
2. When some requests are prempted and recomputing is activated, the
logic of computing new tokens is wrong.
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Add padding for ACL Graph and refactor graph batch size adjustments to
utils.py
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
1. update cann version to 8.1.0 for multimodal
2. fix huggingface-hub version to adapt to qwen3
3. change Qwen3-8B to Qwen-8B-Base,
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
- Add qwen2.5-7b test
- Optimize the documentation to be more developer-friendly
Signed-off-by: xuedinge233 <damow890@gmail.com>
Co-authored-by: xuedinge233 <damow890@gmail.com>
### What this PR does / why we need it?
#### 1. fix spec ut in vllm-ascend main and vllm main
As https://github.com/vllm-project/vllm-ascend/pull/694 and
https://github.com/vllm-project/vllm-ascend/pull/749 verify, Now,
vllm-ascend main and vllm 0.8.5, spec UT is happy, but vllm-ascend main
and vllm main, CI is fail.
I found the reason is a triton bug
https://github.com/triton-lang/triton/issues/2266, but i I didn't figure
it out that why the bug did not effect vllm-ascend main and vllm 0.8.5,
maybe the usage of triton have changed when vllm 0.8.5 to latest main
As the bug describe, I changed the minimum block_size in UT from 8 to
16, and the modification is verified locally to be effective.
#### 2. modify some case skip form.
I modified some commented out cases to skipif form, which is more
standardized.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
CI
Signed-off-by: mengwei805 <mengwei25@huawei.com>
### What this PR does / why we need it?
This PR aims to fix nightly ci
[broken](https://github.com/vllm-project/vllm-ascend/actions/runs/14848150987)
We have a workflow containing multiple triggers:
- push events (to the default branch)
- pull requests (against the default branch)
- scheduled events
Our paths-filter action works great for the first two use-cases,
detecting the context and base to compare against. However, it fails for
scheduled events giving the error `This action requires 'base' input to
be configured or 'repository.default_branch' to be set in the event
payload.`
For the scheduling trigger event, we choose to skip this filter
because we don't need its results:
```
- name: Check for changes in Speculative Decode
if: github.event_name != 'schedule'
```
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
- Move Release Compatibility Matrix to top
- Remove v0.7.x rc info because v0.7.3 final release alread published
- Rename vllm-ascend to vLLM Ascend
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Preview
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Support the features of prefix cache and chunked prefill in v0/v1.
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
