### What this PR does / why we need it?
fix(mtp): resolve MTP core bugs and enhance eager mode test cases
1. Resolved critical issues in eager mode MTP core execution logic;
2. Fixed functional bugs in the _update_states_after_model_execute
function;
3. Updated and released test_mtp_qwen3_next.py to validate eager mode
acceptance rate.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: Bowen-Leee <caoshankuangren@gmail.com>
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
The incorrect regular expression syntax `.*[UE4M3|ue4m3].*` actually
ignores all words containing any of the following characters: `u, e, 4,
m, 3, |`
```yaml
extend-ignore-identifiers-re = [".*Unc.*", ".*_thw",
".*UE8M0.*", ".*[UE4M3|ue4m3].*", ".*eles.*", ".*fo.*", ".*ba.*",
".*ot.*", ".*[Tt]h[rR].*"]
```
===fix===>
```yaml
extend-ignore-identifiers-re = [".*Unc.*", ".*_thw",
".*UE8M0.*", ".*(UE4M3|ue4m3]).*", ".*eles.*", ".*fo.*", ".*ba.*",
".*ot.*", ".*[Tt]h[rR].*"]
```
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: MrZ20 <2609716663@qq.com>
### What this PR does / why we need it?
upgrade vllm commit to `9562912cead1f11e8540fb91306c5cbda66f0007`
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
all tests passed
- vLLM version: v0.15.0
- vLLM main:
13397841ab
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
This PR adds a case of qwen3-30b w8a8 with mooncake mempool, we need to
test it regual
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test
- vLLM version: v0.14.1
- vLLM main:
d68209402d
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
This PR upgrades the core vLLM dependency to a newer version from the
main branch (`13397841ab469cecf1ed425c3f52a9ffc38139b5`). This is
necessary to keep our project up-to-date with the latest features and
fixes from upstream vLLM.
1.
ac32e66cf9
pass file is moved.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Co-authored-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
This PR adds DCP support to the SFA backend.
Please note that due to operator constraints, the current implementation
has to all-gather the entire KV cache and modify the block table to
satisfy the operator input requirements. This results in significantly
increased communication overhead and peak memory usage. Therefore, this
is only a temporary workaround and will be refactored once the operator
provides proper support.
Additionally, because of the above limitations,
`cp_kv_cache_interleave_size` is currently required to be equal to
`block_size`. This restriction will also be removed after the refactor.
#### Test
accuracy test using DeepSeek-V3.2-Exp-W8A8 with dp2tp8dcp8
| dataset | version | metric | mode | vllm-api-general-stream |
|----- | ----- | ----- | ----- | -----|
| gsm8kdataset | - | accuracy | gen | 96.35 |
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
This pull request significantly enhances the test suite by adding new
end-to-end test cases for Qwen3 models on the 310P hardware platform.
The primary goal is to ensure the stability and correctness of these
models under diverse operational conditions, including various
parallelism strategies, data types, and quantization methods.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
E2E test
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
### What this PR does / why we need it?
This PR adds disaggregated encoder tests for Qwen2.5-VL-7B-Instruct
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test
by running ci
- vLLM version: release/v0.12.0
---------
Signed-off-by: wangyu31577 <wangyu31577@hundsun.com>
Signed-off-by: wangyu <53896905+yenuo26@users.noreply.github.com>
Co-authored-by: wangyu31577 <wangyu31577@hundsun.com>
### What this PR does / why we need it?
- This PR removes several self-hosted runner labels from the
`actionlint.yaml` configuration file. These runners are likely no longer
in use, so this change cleans up the configuration and ensures
`actionlint` has an accurate list of available runners.
- Move all Action dockerfiles to one folder
- remove useless `runner` input for e2e test.
- update workflow option version
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This is a configuration change for the CI linter. The correctness will
be verified by `actionlint` running in CI on subsequent pull requests.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This patch bump the mooncake version to the latest
[release](https://github.com/kvcache-ai/Mooncake/releases/tag/v0.3.8.post1)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
test is locally
>>> from mooncake.engine import TransferEngine
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This PR updates the CI runner from `linux-aarch64-a2-*` to
`linux-aarch64-a2b3-*` in various test configuration files. This change
is necessary to adapt to updates in the CI infrastructure.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The changes are configuration updates for CI tests. The correctness will
be verified by the CI pipeline.
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
[CI] Update doctest from 0.9.1 to 0.13.0, and copy doc test workflow to
nightly CI for better monitor.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: menogrey <1299267905@qq.com>
### What this PR does / why we need it?
Add E2E for Prefix Caching cp & Chunked Prefill cp
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: F.Liu <liufeng248@huawei.com>
Signed-off-by: Feng Liu <46866849+ader47@users.noreply.github.com>
Co-authored-by: F.Liu <liufeng248@huawei.com>
### What this PR does / why we need it?
Introduced 310P W8A8 Quantization Support: New modules and methods have
been added to enable W8A8 static quantization specifically for the
Ascend 310P platform.
Platform-Specific Quantization Configuration Loading: The system now
dynamically loads the appropriate quantization configurations
(AscendCompressedTensorsConfig, AscendModelSlimConfig) based on whether
the current hardware is an Ascend 310P device.
Implemented AscendW8A8LinearMethod310P: A dedicated linear quantization
method for 310P is provided, handling the specifics of weight and
activation quantization, including input parameter broadcasting and
weight data manipulation.
Extended AscendModelSlimConfig for 310P: A specialized configuration
class for 310P integrates the new W8A8 linear method for both standard
linear layers and vocabulary parallel embeddings, ensuring proper
quantization application.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>
### What this PR does / why we need it?
1. Disable the feature to exit early upon encountering an error in order
to complete all tests.
2. Within each partition, tests are re-sorted by `estimated_time` in
ascending order. This allows the CI to cover as many test cases as
possible in the early stages.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
### What this PR does / why we need it?
While using the LLM Compressor quantization tool from the VLLM community
to generate quantized weights, the VLLM Ascend engine needs to be
adapted to support the compressed tensors quantization format.
1. Support Moe model W4A8 dynamic weight.
- vLLM version: v0.13.0
- vLLM main:
bde38c11df
---------
Signed-off-by: LHXuuu <scut_xlh@163.com>
Signed-off-by: menogrey <1299267905@qq.com>
Co-authored-by: menogrey <1299267905@qq.com>
### What this PR does / why we need it?
This PR upgrades the vLLM dependency from `v0.14.1` to `v0.15.0`. This
involves:
- Updating the `VLLM_TAG` in all `Dockerfile`.
- Updating the vLLM version in `docs/source/conf.py`.
- Removing conditional code paths specific to `v0.14.1` across the
codebase, which simplifies maintenance.
- Fix `TypeError: MMEncoderAttention.__init__() got an unexpected
keyword argument 'multimodal_config'` due to
https://github.com/vllm-project/vllm/pull/31972.
- Fix `_shared_experts: 'NoneType' object is not callable` due to
https://github.com/vllm-project/vllm/pull/32082 by
https://github.com/vllm-project/vllm-ascend/pull/6335.
- Fix `ReshapeAndCacheOperation setup failed!` due to
https://github.com/vllm-project/vllm/pull/25954 by overriding attention
metadata slots.
This upgrade is necessary to keep the project aligned with the latest
features, bug fixes, and API changes in the vLLM project.
### Does this PR introduce _any_ user-facing change?
No, this is an internal dependency update and does not introduce any
user-facing changes.
### How was this patch tested?
CI is expected to pass with these changes, ensuring that all existing
tests are successful with the new vLLM version.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
co-authored-by: shen-shanshan <467638484@qq.com>
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Make image and wheel build CI job work with workflow_dispatch way
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR removes the custom `ProfileExecuteDuration` utility and its
usages across the codebase. This utility was used for profiling
execution duration of different stages in the inference process. It is
replaced by the standard `vllm.v1.utils.record_function_or_nullcontext`,
which integrates with PyTorch's profiler.
This change simplifies the code by removing a custom implementation in
favor of an upstream utility, improving maintainability. Associated
documentation and tests for `ProfileExecuteDuration` are also removed.
### Does this PR introduce _any_ user-facing change?
`VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` env is removed now.
### How was this patch tested?
CI passed. The changes are a cleanup and replacement with a standard
utility. Existing tests cover the functionality. The removed feature had
its own tests which are also removed.
Related RFC: #5304
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
The underlying tags for nightly image builds have been corrected, and
some useless and confusing workflow fields have been removed.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Specify tensorflow version in accuracy test to avoid segmentation fault
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
We found the custom passes of NPUGraphEX have implemented fusion
operator features, which still require E2E test case validation and
guard. This PR implements E2E test cases for the AddRMSNormQuant and
SplitQKVNormRope operator fusions under NPUGraphEX that are already in
the codebase.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: cjian <2318164299@qq.com>
### What this PR does / why we need it?
Enable the tests that were skipped due to an outdated driver version:
- tests/e2e/multicard/4-cards/long_sequence/test_accuracy.py
- tests/e2e/multicard/4-cards/long_sequence/test_basic.py
- tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill.py
and some cases in
- tests/e2e/multicard/2-cards/spec_decode/test_spec_decode.py
- tests/e2e/multicard/2-cards/test_external_launcher.py
- tests/e2e/multicard/2-cards/test_offline_weight_load.py
- tests/e2e/multicard/2-cards/test_quantization.py
- tests/e2e/multicard/4-cards/test_data_parallel_tp2.py
TODO:
- tests/e2e/multicard/4-cards/spec_decode/test_mtp_qwen3_next.py
- tests/e2e/multicard/4-cards/long_sequence/test_mtp.py
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: wjunLu <wjunlu217@gmail.com>
### What this PR does / why we need it?
This patch add auto-partition feat for tests, for example, before this
pr, we are running e2e single card test for 2h40min, after the auto
partition, test case is automatically allocated into the required n
parts based on its test duration (greedy strategy) and run in parallel.
The advantage of doing this is that our overall test duration will
become 1/n of the original.
### Does this PR introduce _any_ user-facing change?
Before:
e2e single card test spend 2h40min
After:
e2e single card test spend 1h13min
### How was this patch tested?
```shell
python .github/workflows/scripts/run_suite.py --auto-partition-size 2 --auto-partition-id 0
args=Namespace(timeout_per_file=2000, suite='e2e-singlecard', auto_partition_id=0, auto_partition_size=2, continue_on_error=False, enable_retry=False, max_attempts=2, retry_wait_seconds=60, retry_timeout_increase=600)
+----------------+--------------------+
| Suite | Partition |
|----------------+--------------------|
| e2e-singlecard | 1/2 (0-based id=0) |
+----------------+--------------------+
✅ Enabled 13 test(s) (est total 4020.0s):
- tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py (est_time=1800)
- tests/e2e/singlecard/test_aclgraph_accuracy.py (est_time=480)
- tests/e2e/singlecard/test_guided_decoding.py (est_time=354)
- tests/e2e/singlecard/test_batch_invariant.py (est_time=320)
- tests/e2e/singlecard/pooling/test_embedding.py (est_time=270)
- tests/e2e/singlecard/test_quantization.py (est_time=200)
- tests/e2e/singlecard/test_llama32_lora.py (est_time=162)
- tests/e2e/singlecard/test_cpu_offloading.py (est_time=132)
- tests/e2e/singlecard/pooling/test_classification.py (est_time=120)
- tests/e2e/singlecard/test_camem.py (est_time=77)
- tests/e2e/singlecard/compile/test_norm_quant_fusion.py (est_time=70)
- tests/e2e/singlecard/test_auto_fit_max_mode_len.py (est_time=25)
- tests/e2e/singlecard/test_profile_execute_duration.py (est_time=10)
(base) wangli@Mac-mini vllm-ascend % python .github/workflows/scripts/run_suite.py --auto-partition-size 2 --auto-partition-id 1
args=Namespace(timeout_per_file=2000, suite='e2e-singlecard', auto_partition_id=1, auto_partition_size=2, continue_on_error=False, enable_retry=False, max_attempts=2, retry_wait_seconds=60, retry_timeout_increase=600)
+----------------+--------------------+
| Suite | Partition |
|----------------+--------------------|
| e2e-singlecard | 2/2 (0-based id=1) |
+----------------+--------------------+
✅ Enabled 13 test(s) (est total 4025.0s):
- tests/e2e/singlecard/spec_decode/test_mtp_eagle_correctness.py (est_time=1500)
- tests/e2e/singlecard/pooling/test_scoring.py (est_time=500)
- tests/e2e/singlecard/test_aclgraph_batch_invariant.py (est_time=410)
- tests/e2e/singlecard/test_vlm.py (est_time=354)
- tests/e2e/singlecard/test_models.py (est_time=300)
- tests/e2e/singlecard/test_multistream_overlap_shared_expert.py (est_time=200)
- tests/e2e/singlecard/test_sampler.py (est_time=200)
- tests/e2e/singlecard/test_async_scheduling.py (est_time=150)
- tests/e2e/singlecard/test_aclgraph_mem.py (est_time=130)
- tests/e2e/singlecard/test_ilama_lora.py (est_time=95)
- tests/e2e/singlecard/test_completion_with_prompt_embeds.py (est_time=76)
- tests/e2e/singlecard/test_qwen3_multi_loras.py (est_time=65)
- tests/e2e/singlecard/test_xlite.py (est_time=45)
```
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
The pre-requirement pr is
https://github.com/vllm-project/vllm-ascend/pull/6353, this patch aims
to transfer nightly tests to `releases/v0.13.0`, what we need to do is
just use the branch built image for nightly
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Qwen3-Next nightly test fix. Temporarily avoid the accuracy issue in the
**full graph** mode.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
d68209402d
Signed-off-by: InSec <1790766300@qq.com>
### What this PR does / why we need it?
Currently, the nightly image is built at 20 PM and 23 PM UTC+8. Due to
some timeliness requirements, we need to add a new trigger method for
nightly image builds.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
d68209402d
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
After removing codepsell a while, we discovered that typo had a problem
correctly recognizing certain misspelled words, so I suggested adding it
back.
- vLLM version: v0.14.1
- vLLM main:
d68209402d
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This patch purpose to add the `update_max_model_len` interface.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This patch add new runner labels for the HK region, and e2e single-card
testing has been migrated to this runner.
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
* Refactor the LayerNorm and activation operator classes to decouple the
310P device implementation from the main branch.
* Refactor `mm_encoder_attention` on 310P to use the
`torch_npu._npu_flash_attention_unpad` operator.
* Refactor the QKV inputs in the prefill stage of `attention_v1` on 310P
so they are no longer padded to 16× alignment.
* Refactor `model_runner` on 310P to align the KV-cache initialization
logic with the mainline implementation.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
use the e2e tests.
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
