### What this PR does / why we need it?
Pin the xlite version to avoid CI failures during its upgrade.
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: changdawei1 <changdawei3@huawei.com>
### What this PR does / why we need it?
Revert [KV-Sharing] Support KV-Sharing feature in CLA models (#4138) as
it causes deepseek v3.2 hang error
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Support KV-Sharing feature in CLA (cross layer attention) models, which
sharing kv cache in some layers.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
This PR introduces the initial integration of **UCM (Unified Cache
Management)** into the vllm-ascend distributed KV-cache system.
Specifically, it adds:
- A new `UCMConnector` implementation under the distributed KV-transfer
framework.
- Support for offloading KV-cache blocks to external UCM backends (DRAM
/ NFS / Localdisk), depending on UCM configuration).
- Integration with vLLM V1 KV connector interface, including metadata
handling and role registration.
**Why it is needed:**
- UCM provides a unified, high-performance storage layer for KV-cache
externalization.
- This enables vllm-ascend to support out-of-core KV-cache workloads,
improve memory efficiency, and leverage hardware-accelerated storage
paths (RDMA / NFS / hybrid modes).
- This connector is a required component to allow future work on
multi-node inference + UCM-based scaling.
---
### Does this PR introduce _any_ user-facing change?
Yes, but limited:
- A new `kv_connector=UCMConnector` option becomes available through the
configuration interface.
- When selected, vllm-ascend workers may initialize UCM and offload
KV-cache blocks externally.
- No default behaviors are changed. Users must explicitly enable this
connector.
This PR does **not** modify:
- existing APIs,
- default execution paths,
- model runner behavior,
- user workflow unless `UCMConnector` is configured.
---
### How was this patch tested?
---
### Prefix Caching Benchmark
We provide preliminary measurements for TTFT (ms) under VLLM benchmark.
Tests run on 2 * Ascend 910B3, vllm-ascend 0.11.0, Tensor Parallel size
2, with UCM (Localdisk) enabled.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: UnifiedCacheManager <unifiedcachem@163.com>
### What this PR does / why we need it?
This patch adds support for the xlite graph wrapper to vllm_ascend.
Xlite provides operator implementations of the transformer network on
Ascend hardware. For details about xlite, please refer to the following
link: https://gitee.com/openeuler/GVirt/blob/master/xlite/README.md
The latest performance comparison data between xlite and the default
aclgraph mode is as follows:
## Qwen3 32B TPS 910B3(A2) Online Inference Performance Comparison
- aclgraph: main(c4a71fc6)
- xlite-full: main(c4a71fc6) + xlite-full
- xlite-decode-only: main(c4a71fc6) + xlite-decode-only
- diff1: Performance comparison between xlite-full and aclgraph
- diff2: Performance comparison between xlite-decode-only and aclgraph
### Does this PR introduce _any_ user-facing change?
Enable the xlite graph mode by setting xlite_graph_config:
--additional-config='{"xlite_graph_config": {"enabled": true}}' #
Enabled for decode only
--additional-config='{"xlite_graph_config": {"enabled": true,
"full_mode": true}}' # Enabled for prefill and decode
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: lulina <lina.lulina@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR integrate suffix decoding (https://arxiv.org/abs/2411.04975)
from vllm (https://github.com/vllm-project/vllm/pull/25784)
#
Suffix Decoding is a dynamic n-gram matching method that:
1. Uses suffix trees to generate speculative tokens quickly using branch
frequency counts.
2. Can keep a history of prior model responses, which tends to work very
well with repetitive agentic use cases.
3. Can be dynamically updated with newly generated tokens, and FIFO
eviction of older requests.
#
### Does this PR introduce _any_ user-facing change?
This feature should be implemented as opt-in and remain seamless for
users who do not require suffix speculative decoding.
For users who wish to enable it, they must first install
arctic-inference:
`pip install arctic-inference
`
After installation, the suffix speculative decoding feature can be
enabled using the following speculative config:
`--speculative_config '{"method": "suffix", "num_speculative_tokens":
5}'
`
### How was this patch tested?
This PR is currently being tested on vLLM
main:83f478bb19
with PR https://github.com/vllm-project/vllm/pull/25784
In our previous testing, suffix decoding achieved a 13%-30% throughput
improvement over n-gram on the sonnet dataset, tested on vllm-ascend
v0.9.1 with concurrency ranging from 2 to 40.
- vLLM version: v0.11.2
---------
Signed-off-by: fluctlux <38945811+fluctlux@users.noreply.github.com>
### What this PR does / why we need it?
vllm-ascend need to dump data during model execution to debug some
precision problems, here msprobe provide the corresponding abilities, so
msprobe will join vllm-ascend to make debug easier
### Does this PR introduce _any_ user-facing change?
```
'dump_config': '/path/to/config.json'
```
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: Tjh-UKN <2559659915@qq.com>
### What this PR does / why we need it?
To support the data collection capabilities of the msServiceProfiler on
vLLM-ascned framework and enable customization of data collection points
via configuration file, a default profiling configuration has been added
to vllm-ascend, facilitating debugging and optimization for developers
and users.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
Signed-off-by: minghangc <29514143@qq.com>
### What this PR does / why we need it?
This PR enabled pytest and yaml style accuracy test, users now can
enable accuracy test by running:
```bash
cd ~/vllm-ascend
pytest -sv ./tests/e2e/singlecard/models/test_lm_eval_correctness.py \
--config ./tests/e2e/singlecard/models/configs/Qwen3-8B-Base.yaml \
--report_output ./benchmarks/accuracy/Qwen3-8B-Base.md
pytest -sv ./tests/e2e/singlecard/models/test_lm_eval_correctness.py \
--config-list-file ./tests/e2e/singlecard/models/configs/accuracy.txt
```
Closes: https://github.com/vllm-project/vllm-ascend/issues/1970
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
2836dd73f1
---------
Signed-off-by: Icey <1790571317@qq.com>
### What this PR does / why we need it?
Fix protobuf version in Dockerfile to resolve `AttributeError: 'str'
object has no attribute 'DESCRIPTOR' when packaging message to dict`
using protobuf. will remove version specification after
https://github.com/ray-project/ray/pull/54910 is merged
### 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:
0e36abf993
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Fix num_hidden_layers when Qwen2-Audio 7B and #1760 ：
```
INFO 07-15 04:38:53 [platform.py:174] PIECEWISE compilation enabled on NPU. use_inductor not supported - using only ACL Graph mode
Traceback (most recent call last):
File "/workspace/test1.py", line 58, in <module>
main(audio_count)
File "/workspace/test1.py", line 38, in main
llm = LLM(model="Qwen/Qwen2-Audio-7B-Instruct",
File "/vllm-workspace/vllm/vllm/entrypoints/llm.py", line 271, in __init__
self.llm_engine = LLMEngine.from_engine_args(
File "/vllm-workspace/vllm/vllm/engine/llm_engine.py", line 494, in from_engine_args
vllm_config = engine_args.create_engine_config(usage_context)
File "/vllm-workspace/vllm/vllm/engine/arg_utils.py", line 1286, in create_engine_config
config = VllmConfig(
File "/usr/local/python3.10.17/lib/python3.10/site-packages/pydantic/_internal/_dataclasses.py", line 123, in __init__
s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)
File "/vllm-workspace/vllm/vllm/config.py", line 4624, in __post_init__
current_platform.check_and_update_config(self)
File "/vllm-workspace/vllm-ascend/vllm_ascend/platform.py", line 180, in check_and_update_config
update_aclgraph_sizes(vllm_config)
File "/vllm-workspace/vllm-ascend/vllm_ascend/utils.py", line 307, in update_aclgraph_sizes
num_hidden_layers = vllm_config.model_config.hf_config.num_hidden_layers
File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/configuration_utils.py", line 211, in __getattribute__
return super().__getattribute__(key)
AttributeError: 'Qwen2AudioConfig' object has no attribute 'num_hidden_layers'
```
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Closes: https://github.com/vllm-project/vllm-ascend/issues/1780https://github.com/vllm-project/vllm-ascend/issues/1760https://github.com/vllm-project/vllm-ascend/issues/1276https://github.com/vllm-project/vllm-ascend/issues/359
- vLLM version: v0.10.0
- vLLM main:
7728dd77bb
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
Support pipeline parallel with ray backend in V1Engine.
Fixes#1751
### Does this PR introduce _any_ user-facing change?
Users could specify ray as distributed backend when inferencing with pp
### How was this patch tested?
CI passed with new added test.
- vLLM version: v0.9.2
- vLLM main:
32142b3c62
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
V1 is enabled by default, no need to set it by hand now. This PR remove
the useless setting in example and tests
- vLLM version: v0.9.2
- vLLM main:
9ad0a4588b
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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?
- Enable code cov for V1
- Enable push triggered job
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
Signed-off-by: Yikun Jiang <yikunkero@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?
<!--
- 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 #
-->
1.add static EPLB unit test
2.fix bug: Tensor cannot be directly judged by if statements
### 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.
-->
Run the unit test.
---------
Signed-off-by: songshanhu07 <1763685535@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?
<!--
- 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 #
-->
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>
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>
<!-- 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?
<!--
- 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 #
-->
This PR supports the access of vllm-acend to the piecewise_graph feature
provided by the v1 engine.
1. register unifiled_ascend_attention_with_output for piecewise_graph to
split graph.
2. support NPUGraph to accelerate kernel launch.
### 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.
-->
support npugraph to default， Users can disenable the npugraph feature by
configuring enforce_eager.
This has corresponding requirements for the versions of torch_npu and
CANN, and they need to support graph capture.
### 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.
-->
it turn to default
---------
Signed-off-by: Bug Hunter Yan <yanpq@zju.edu.cn>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
After extensive testing, we are happy to say that guided_decoding is
fully supported by npu, in this pr, we add guided_decoding integrated
with our test, mainly does the following things:
1. test v0 supported backends including ` "outlines",
"lm-format-enforcer","xgrammar"`
2. test v1 supported backends including ` "guidance", "xgrammar"`
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Backport: https://github.com/vllm-project/vllm-ascend/pull/252
This support speculative decoding in Ascend, including speculating with
a draft model、by matching n-grams in the prompt、using MLP speculators
and using EAGLE based draft models.
Backport: https://github.com/vllm-project/vllm-ascend/pull/423
spec decode MultiStepWorker support TP1DraftModelRunner fully, support
run the draft_model_runner with multi-step prepare on the NPU directly
and support draft_model_runner use MLA.
1. before this pr, `MultiStepWorker` would not step into the branch
using NPU prepare, but only into the branch using CPU prepare (`line 52`
of `vllm_ascend/patch/patch_multi_step_worker.py`). Although this has
`no effect` on the `correct operation` of speculative decoding and the
performance of the two branches is basically the same as of the current
version, I support entering this branch in this PR. In general, there
are two main changes in `patch_multi_step_worker.py`: first, the
`is_cuda_like()` check is removed and the `TP1DraftModelRunner`
rewritten in vllm_ascend is used; second, the
`supports_gpu_multi_step()` function is made to return true on NPU
devices when outer Multi_step_worker could work correct.
3. before this pr, `TP1DraftModelRunner` only supports Attention on NPU,
but not MLA. The relevant adaptation is in
`vllm_ascend/worker/draft_model_runner.py`. Although I don’t know why
the `input_positions` of `model_input.attn_metadata` in vllm-ascend
needs to be added in `execute_model`, it is done in `model_runner.py`,
so I also made corresponding changes. Otherwise, when atten_backend is
MLA, it will prompt that input_positions cannot be found.
4. I commented out two lines in `draft_model_runner.py` in `line118` to
support the scenario of K>1.
```
# lora_mapping=model_input.lora_mapping,
# lora_requests=model_input.lora_requests,
```
I added comments. In the future, when vllm-ascend supports lora feature,
the changes here can be restored.
TODO：
- [ ] revert the patch when the related issues are addressed in vllm
### How was this patch tested?
CI passed with new added test.
- e2e test for medusa proposer:
tests/singlecard/spec_decode/e2e/test_medusa_correctness.py
- e2e test for mlp proposer:
tests/singlecard/spec_decode/e2e/test_mlp_correctness.py
- e2e test for n-gram proposer:
tests/singlecard/spec_decode/e2e/test_ngram_correctness.py
Tests for patched files:
- tests/singlecard/spec_decode/test_dynamic_spec_decode.py
- tests/singlecard/spec_decode/test_multi_step_worker.py
- tests/singlecard/spec_decode/test_ngram_worker.py
- tests/singlecard/spec_decode/test_spec_decode_worker.py
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
Co-authored-by: mengwei805 <mengwei25@huawei.com>
Enable CI on all branch.
Installing with the torch-npu-2.5.1.dev20250218 so that we could enable
CI on all branch and prepare for merging 0.7.1-dev to main
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
This patch enables the doc build for vllm-ascend
- Add sphinx build for vllm-ascend
- Enable readthedocs for vllm-ascend
- Fix CI:
- exclude vllm-empty/tests/mistral_tool_use to skip `You need to agree
to share your contact information to access this model` which introduce
in
314cfade02
- Install test req to fix
https://github.com/vllm-project/vllm-ascend/actions/runs/13304112758/job/37151690770:
```
vllm-empty/tests/mistral_tool_use/conftest.py:4: in <module>
import pytest_asyncio
E ModuleNotFoundError: No module named 'pytest_asyncio'
```
- exclude docs PR
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
1. test locally:
```bash
# Install dependencies.
pip install -r requirements-docs.txt
# Build the docs and preview
make clean; make html; python -m http.server -d build/html/
```
Launch browser and open http://localhost:8000/.
2. CI passed with preview:
https://vllm-ascend--55.org.readthedocs.build/en/55/
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Use `pytest.ini` to manage vllm native tests.
This will convert the original test script whitelist to a blacklist to
prevent missing the newly added test scripts of the upstream vLLM.
**note**: _we do **not** manage the test scripts of vLLM-Ascend in
`pytest.ini`, because if we do so, there will be conflicts between vLLM
and vLLM-Ascend's `conftest.py`._
### Does this PR introduce _any_ user-facing change?
N/A
### 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?
vLLM Ascend plugin (vllm-ascend) is a backend plugin for running vLLM on
the Ascend NPU.
This plugin is the recommended approach for supporting the Ascend
backend within the vLLM community. It adheres to the principles outlined
in the [RFC]: Hardware pluggable, providing a hardware-pluggable
interface that decouples the integration of the Ascend NPU with vLLM.
This patch also include changes to make CI work and use cache speed up
e2e test, including:
1. Change push (post merge ci) and pull_request (pr ci) trigger branch
to main
2. Make mypy work by ignore base_communicator and clear unused deps
3. Several improvements for vllm_ascend_test:
- use cache (pip, ms, hf) speed up e2e test (25mins --> 5mins)
- switch `git clone` command to `action/checkout` to speedup checkout
and
- Enable sv for pytest for better info dump
- Remove network host to resole `docker: conflicting ontions: cannot
attach both user-defined and non-user-definednetwork-modes`, which is a
problem on docker 1.45 but not on 1.39.
4. Adapt MLA decode optimizations:
cabaf4eff3
### Does this PR introduce _any_ user-facing change?
Yes, init the PR.
### How was this patch tested?
- This is the first PR to make ascend NPU work on vLLM. All code is
tested on ascend with vLLM V0 Engine.
- CI passed
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: MengqingCao <cmq0113@163.com>
Co-authored-by: wangshuai09 <391746016@qq.com>
Co-authored-by: Shanshan Shen <467638484@qq.com>
Co-authored-by: wangli <wangli858794774@gmail.com>
