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BEFORE SUBMITTING, PLEASE READ
https://docs.vllm.ai/en/latest/contributing/overview.html
-->
### What this PR does / why we need it?
cherry-pick from #7675 .
The current RecomputeScheduler is aligned to Scheduler in vLLM v0.16.0.
Since upstream vLLM has upgraded to v0.18.0, we also need to upgrade
RecomputeScheduler to pick up missing updates.
### 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.
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why it was difficult to add.
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---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
This PR restores #7029, which adds W8A8C8 support for dsv3.2/glm5 using
the `lightning_indexer_quant` ops in the pd-mix stage.
The original PR was reverted by #7288 because the patch did not work
with the recompute scheduler.
This PR also fixes the patching issue so that it works correctly with
the recompute scheduler.
### Does this PR introduce _any_ user-facing change?
Yes. To enable LI C8, users need to set the `enable_sparse_c8` option to
`"true"` in `additional_config`.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
Fix the wrong usage of `model_type`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
Mooncake Layerwise Connector supports hybrid attention manager with
multiple kvcache groups.
### Does this PR introduce _any_ user-facing change?
Yes.
### How was this patch tested?
By CI.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
Adapt the recompute feature to vLLM 0.16.0, where the D node forwards
recompute requests to the P node.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
**BUG**
When using prefill-decode disaggregation + MTP + full graph
+asynchronous scheduling, the KV cache pulled by decode nodes from
prefill decodes does not include spec tokens. As a result, the
total_num_scheduled_tokens obtained by decode nodes from the scheduler
lacks spec tokens. When determining whether to enqueue the full graph on
decode nodes, the condition for uniform_decode `
scheduler_output.total_num_scheduled_tokens == self.input_batch.num_reqs
* max_query_len` is not met, leading to the current instance not being
enqueued into the full graph.
The above situation leads to both full graph and eagle mode instances
coexisting in the decode instances. Due to the synchronization wait of
MoeDispatch, the decode instances in full graph are significantly slowed
down by the instance in eagle mode.
**Solution**
The scenario is PD separation + MTP + Full Graph + asynchronous
scheduling.
On the decode nodes, the spec tokens of the request with KV cache from P
need be padded. Then, the padded spec tokens will be rejected by
sampling. This operation ensures that the uniform_decode condition is
satisfied when determining whether decode nodes are included in the full
graph, thereby guaranteeing that all decode instances are present in the
full graph and avoiding synchronous waiting for MoeDispatch.
- vLLM version: v0.15.0
- vLLM main:
5326c89803
Signed-off-by: chenmenglong <chenmenglong1@huawei.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?
layerwise connector support recompute scheduler.
NOTE：
Triggering recompute will invoke the tokenizer again, which may lead to
precision fluctuations.
[RFC]: CDCP Scheduling for Disaggregated Prefilling with KV Cache
Layerwise Push Support
https://github.com/vllm-project/vllm-ascend/issues/4842
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
bde38c11df
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
This PR rebases RecomputeScheduler codebase to vllm tags/v0.14.1 in
order to fix the incompatibility with vllm's original Scheduler and
AsyncScheduler. Main changes focus on multimodal model and speculative
decoding parts.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
We tested this PR with 2P1D E2E serving test case.
- vLLM version: v0.14.1
- vLLM main:
d68209402d
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
adapt to: https://github.com/vllm-project/vllm/pull/30475.
just change get_num_encoder_tokens() to get_num_encoder_embeds() in
recompute_schedule.py, which seems that it is currently not in use. The
get_num_encoder_tokens() function in VLLM no longer exists.
- vLLM version: v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: 01267596 <xiongkai123@cmbchina.com>
Co-authored-by: 01267596 <xiongkai123@cmbchina.com>
### What this PR does / why we need it?
Currently, the initialization and fundamental functions of
RecomputeScheduler are broken with `vLLM v0.12.0`. This PR fixes the
conflicts of `RecomputeScheduler` and refactor its implementations by
inheriting original `Scheduler` of vLLM. Meanwhile, this PR also
supports async cheduling with recompute scheduler by implementing
`AsyncRecomputeScheduler` which is simply inherited `AsncyScheduler` of
vLLM and `RecomputeScheduler` of vLLM-Ascend with python MRO.
### Does this PR introduce _any_ user-facing change?
No. The switch naming is the same as v0.11.0 :
`recompute_scheduler_enable`
### How was this patch tested?
E2E serving with 2P1D dsv3.1 passed. The performance was the same as
original vllm scheduler with `async_scheduling` and preempted requests
in D Nodes are successfully transfered to Proxy and further to P Node.
This significantly improves the performance and robustness of PD
disaggregation deployments.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
It's safe to drop ascend scheduler now. The related test and doc has
been removed already
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Ascend scheduler was added for non chunk prefill case before, since that
the npu ops didn't work well with chunked prefill.
Now the ops with chunked prefill work better, it's time to remove the
ascend scheduler to use vLLM default scheduler.
- vLLM version: v0.11.2
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
There is a lot hack code for v0.11.0, which makes the code hard to
upgrade to newer vLLM version. Since v0.11.0 will release soon. Let's
drop v0.11.0 support first. Then we'll upgrade to v0.11.2 soon.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This PR fix the bug related with running multi-modal models with
AscendScheduler. This bug was introduced by PR #2372 by using the same
parameter names as vLLM with different default values.
Currently I fix this bug by changing the default values of these two
parameters to align with vLLM.
- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993
Signed-off-by: hw_whx <wanghexiang7@huawei.com>
Co-authored-by: hw_whx <wanghexiang7@huawei.com>
### What this PR does / why we need it?
Modify the recalculation logic to prevent waiting requests from filling
up the D node KVCache
- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993
Signed-off-by: underfituu <hzhucong@163.com>
### What this PR does / why we need it?
This PR is aimed to fix the recomputing out of memory bug in decode
instance. When recomputing happens in decode, kv cache usage may exceed
the pre-allocated memory, and it will cause OOM.
So we propose a new scheduling strategy, when decode instance cannot
allocate new block for running requests, we will stop the request that
will be preempted. These stopped request will be recognied by proxy, and
they will be send to prefill instance again to calculate kvc and then
direct to decode instance.
This is a temporary plan to fix the bug. The long-term stratege is to
use CPU offload in decode instance.
### Does this PR introduce _any_ user-facing change?
An extra ascend configuration option **-- recompute_scheduler_enable =
True** is added to enable this strategy. The default value is False
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: CHEN <116010019@link.cuhk.edu.cn>
### What this PR does / why we need it?
Running multimodal model with ascend scheduler may cause assert error
【assert (request.num_tokens - request.num_computed_tokens) == 1】
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
17b4c6685c
---------
Signed-off-by: fan2956 <zhoufan53@huawei.com>
# What this PR does / why we need it?
When processing a mix of large and small requests, the TTFT of responses
is significantly reduc\ed. Please refer to
https://github.com/vllm-project/vllm/pull/10235, which achieves the same
effect by simply limiting the number of prompt fills for long requests.
This solution can be applied to both AscendScheduler (V0) and vLLM
Scheduler (V1). Tests show that TTFT can be significantly improved when
handling such mixed requests. However, This capability is currently
missing when Ascend Scheduler is enabled.
This benchmark used the Qwen3-8B model, with a context length of 128K,
running on a single card.
Regarding dataset selection, the sharegpt_clean dataset is used, with
its content concatenated and cropped. Small requests with token=50 and
medium requests with token=10240 were constructed (there were also large
requests with token=102400, but these were ignored because when using
the Prefill First scheduling strategy, max_num_batched_tokens will not
be set to such a large value). When loading vLLM, set
max_num_batched_tokens=22000. This length can accommodate two
medium-sized requests and some short requests, reflecting an extreme
scenario where the budget is almost entirely occupied by longer
requests.
Next, we mix 990 small requests and 100 medium requests into one type of
load scenario (hereinafter referred to as 10%), and similarly generate
load scenarios with 5% medium requests and 1% load scenarios.
Performance tests were conducted separately for enabling vLLMScheduler,
AscendScheduler, and AscendScheduler (long prompt concurrency set to 1).
- vLLM version: v0.10.2
- vLLM main:
1dfea5f4a9
---------
Signed-off-by: Csrayz <jover@cmbchina.com>
### What this PR does / why we need it?
On main, AscendScheduler does not support Multimodels, becuse of lacking
of scheduled_encoder_inputs which is need on multimodels inference
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
vLLM version: main@93e28e6862669e3b5cf47cea9f782a65ec47e155
- vLLM version: v0.10.2rc2
- vLLM main:
15b8fef453
---------
Signed-off-by: fan2956 <zhoufan53@huawei.com>
Co-authored-by: zhoufan2956 <zhoufan2956@163.com>
### What this PR does / why we need it?
This PR enforces the forcible disabling of the chunked prefill feature
in Non-MLA models, as the performance of operators supporting this
functionality is currently suboptimal. Unless the user has enabled
chunked prefill in the ascend_scheduler_config, we would allow this
feature.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
Related: https://github.com/vllm-project/vllm-ascend/pull/2659
- vLLM version: main
- vLLM main:
d21a36f5f9
Signed-off-by: rjg-lyh <1318825571@qq.com>
### What this PR does / why we need it?
Remove compatibility maintenance for vllm v0.10.1 and v0.10.1.1
### Does this PR introduce _any_ user-facing change?
branch main of vllm-ascend will not be compatible with vllm v0.10.1 and
v0.10.1.1
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.10.1.1
- vLLM main:
6fb2788163
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Fix accuracy issue on prefix caching with AscendScheduler
### How was this patch tested?
CI passed with `test_prefix_cache_with_ascend_scheduler`
- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
bugfix ascend schedule encountered an incorrect req block length in the
check_watermark_for_prefill function
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
426cc8629f
Signed-off-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
This patch also supports v0.10.1
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- CI passed
- test 0.10.1: https://github.com/vllm-project/vllm-ascend/pull/2583
- vLLM version: v0.10.1.1
- vLLM main:
321938e9ac
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Add configuration check logic for ascend scheduler: if chunked_prefill
is disabled, max_num_batched_tokens couldn't be less than max_model_len,
following vLLM;
### Does this PR introduce _any_ user-facing change?
users cannot set max_num_batched_tokens smaller than max_model_len with
ascend scheduler
### How was this patch tested?
CI and vllm serving passed
- vLLM version: v0.10.0
- vLLM main:
f77a0802b7
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
1. use action/checkout@v5 instead of v4
2. remove dbo test case because there is issue with it and will be
refactored later
3. make vllm-ascend compatible with vllm v0.10.1.1 and add CI for it
4. fix sampler api changes introduced by
https://github.com/vllm-project/vllm/pull/22387
6. fix qwen3 moe config changes intruoduced by
https://github.com/vllm-project/vllm/pull/20562
7. fix kvcache block changes introduced by
https://github.com/vllm-project/vllm/pull/23262
### 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:
0c6e40bbaa
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
- Upgrade to v0.10.0
- Drop v0.9.2 version compatibility
- Add patch for
`vllm_ascend/patch/worker/patch_common/patch_sampler_gather_logprobs.py`
as workaround of
f3a683b7c9
for v0.10.0 and also add e2e test `test_models_prompt_logprobs`
- Pin transformers<4.54.0 as workaround of
https://github.com/vllm-project/vllm-ascend/issues/2034
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Test locally:
`VLLM_USE_MODELSCOPE=true pytest -sv
tests/e2e/singlecard/test_offline_inference.py::test_models_prompt_logprobs`
- CI passed
- vLLM version: v0.9.2
- vLLM main:
7728dd77bb
---------
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
### What this PR does / why we need it?
Now there is no need to calculate `num_draft_tokens` when allocating
slots.
This PR follows the changes in vllm:
https://github.com/vllm-project/vllm/pull/20701
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed with existing test
- vLLM version: v0.9.2
- vLLM main:
cc876d0f29
---------
Signed-off-by: MengqingCao <cmq0113@163.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>
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?
This PR (adapted from
2863befce3)
updates the CachedRequestData definition to use a single instance shared
across all requests in a batch, instead of creating a new instance per
request.
Found ci boken by the vllm's model_runner change: `ERROR 07-01 09:53:53
[core.py:521] TypeError: 'CachedRequestData' object is not iterable`,
Modify the model_runner to fix it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
pass ci will verify this.
---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
Co-authored-by: Yikun Jiang <yikunkero@gmail.com>
This PR adds support for speculative decoding in AsecendScheduler.
Also inculde part of support for disaggregated prefill, full support
will be merged in follow-up PR.
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
1. upgrade vllm to 0.9.1. 0.9.0 is not supported for main branch now.
keep doc to 0.9.0 until we release the first 0.9.1 release.
2. disable V0 test for PR
3. move actionlint check to lint job
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
