### What this PR does / why we need it?
EPLB currently does not have CI related to aclgraph and redundancy
experts; this PR adds them.
release on #5529
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Tested the use cases to be added in this PR.
PASSED
====================================================== warnings summary
==========================================================
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type
SwigPyPacked has no __module__ attribute
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type
SwigPyObject has no __module__ attribute
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================================== 1 passed, 2
warnings in 272.24s (0:04:32)
=====================================================
- vLLM version: v0.13.0
- vLLM main:
8be6432bda
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
1. MagicMTP (paper: "Block Verification Accelerates Speculative
Decoding") was introduced to consider the influence among multiple draft
tokens, improving the acceptance rate without compromising accuracy.
2. Added Triton and PyTorch implementations, and added E2E test cases.
### Does this PR introduce _any_ user-facing change?
MagicMTP will automatically take effect when the parameter
"num_speculative_tokens" >= 3.
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: chenaoxuan <cax1165@163.com>
### What this PR does / why we need it?
1.replace moe_gating_top_k from torch_npu with custom op
2.enable the renorm function of moe_gating_top_k in softmax scenerio
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No need test
- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: ZCG12345 <2097562023@qq.com>
### What this PR does / why we need it?
1. Accuracy testing no longer compares eager and graph modes; instead,
it directly extracts the golden result under the graph mode
configuration (the implicit purpose of this case is to verify whether
modifications affect existing results)
2. Next step: finer-grained supervision of logits/sampler results
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Add qwen3-8b nightly test
- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
1. speed up e2e light test.
2. create `2-cards` and `4-cards` folder in multicard
3. move ops to nightly
4. run test in Alphabetical Order
- vLLM version: v0.13.0
- vLLM main:
8be6432bda
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
#### What this PR does / why we need it?
This PR adapt DispatchGmmCombineDecode operator to eplb tensor list and
expert token numbers.
This operator support gmm1, gmm2, gmm1Scale and gmm2Scale in format of
list.
This operator support couting how many token each local expert recieves
by expertTokensNum .
- vLLM version: v0.13.0
- vLLM main:
7157596103
More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476
# What this PR does / why we need it?
Add DeepSeek-V3.2-W8A8 dual-node nightly CI test and update A3 nightly
test configuration:
1. Add DeepSeek-V3.2-W8A8 dual-node test:
tests/e2e/nightly/multi_node/config/DeepSeek-V3_2-W8A8-A3-dual-nodes.yaml
- 2 nodes, 16 NPUs per node (32 NPUs total)
- Configuration: 2P+1D (data-parallel-size=4, tensor-parallel-size=8,
data-parallel-size-local=2)
- Includes performance and accuracy benchmarks with GSM8K dataset
2. Update A3 nightly workflow: .github/workflows/nightly_test_a3.yaml
- Added DeepSeek-V3.2-W8A8 dual-node test to the A3 nightly test matrix
- Test name: multi-node-dpsk3.2-2node
3. Improve test scripts: Updated
.github/workflows/_e2e_nightly_multi_node.yaml and related scripts for
better multi-node testing support
test on A3 instances
- Performance baseline: 1 (threshold: 0.97)
- Accuracy baseline: 95% (threshold: 5%)
- Test dataset: GSM8K with 512 prompts for performance, gsm8k-lite for
accuracy
---------
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
### What this PR does / why we need it?
[Bugfix] fix dcp_only bug and add e2e accuracy test for dcp only and pcp
only
this pr fix the bug of accuracy test when decode_parallel_size>1 and
prefill_context_parallel_size=1.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
### What this PR does / why we need it?
There was an accuracy issue with **Qwen3-Next-80B-A3B-Instruct-W8A8**
model in the old version of **Triton-Ascend**, so, we are now adding one
nightly test to maintain it.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: IncSec <1790766300@qq.com>
### What this PR does / why we need it?
mv ops to correct path
:`tests/e2e/nightly/single_node/ops/singlecard_ops/triton`
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Fix Smoke Testing Bug for DSR1 longseq
We need to make this change because the daily smoke test case is
throwing an error: "max_tokens or max_completion_tokens is too large:
32768.This model's maximum context length is 32768 tokens and your
request has 128 input tokens". We encounter this error due to
max-out-len equals to max-model-len. We can fix this error by increasing
max-model-len argument in the script.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: daishixun <dsxsteven@sina.com>
### What this PR does / why we need it?
Add nightly test for triton split_rmsnorm_rope
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
[UT]add triton ops ut : test_fused_qkvzba_split_reshape_cat
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
pytest -sv tests/ut/ops/test_fused_qkvzba_split_reshape_cat.py
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: ZT-AIA <1028681969@qq.com>
### What this PR does / why we need it?
Replace multiple PyTorch operations with a fused Triton kernel to
determine token indices for sampling during speculative decoding. This
reduces kernel launch overhead and memory traffic, improving overall
performance on Ascend hardware.
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Fix the bug in the PCP overlay feature
1、Fix the bug related to PCP and EPLB overlap by including PCP size in
the word_size calculation.
2、In the PCP pooling scenario, a prompt has been added for setting the
cp_kv_cache_interleave_size.
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
skip xlite-decode-only e2e test, since it's unstable
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
Signed-off-by: changdawei1 <changdawei3@huawei.com>
### What this PR does / why we need it?
Fix chunk prefill bug for long_sequence feature
When there are two requests with chunk prefill enabled in the
long-sequence scenario, if one request has only 1 token during
scheduling, it will be identified as a decode request and trigger an
error. This PR fixes the issue.
Closes: https://github.com/vllm-project/vllm-ascend/issues/5445
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: LookAround <lixushi@huawei.com>
### What this PR does / why we need it?
move longseq nightly CI to correct path due to #5479 [1/N] Refactor
nightly test structure
Signed-off-by: daishixun <dsxsteven@sina.com>
### What this PR does / why we need it?
Supported to use full-graph with Qwen3-Next-MTP.
In detail, we adatpted `AscendAttentionState.ChunkedPrefill` in main
model, and also adapted `AscendAttentionState.ChunkedPrefill` in mtp
model.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
We changed the test of Qwen3-Next-MTP in
`tests/e2e/multicard/test_qwen3_next.py` to make it a test of
`FULL_DECODE_ONLY`. Then run `pytest -s
tests/e2e/multicard/test_qwen3_next.py::test_qwen3_next_distributed_mp_eager_mtp_similarity_tp4`.
And this test passed.
```text
.
================================================================================================================================= warnings summary =================================================================================================================================
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================================================================================================== 1 passed, 2 warnings in 271.89s (0:04:31) =====================================================================================================================
```
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
add DeepSeek-R1-W8A8 and Qwen3-235B-W8A8 configs in multi-nodes and
longseq (PCP&DCP) scenario
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: daishixun <dsxsteven@sina.com>
Currently in the Fused MoE module, functions of classes like
MoECommMethod and MoETokenDispatcher output data in dictionary or tuple
format, which hampers code maintainability, readability, and
extensibility. This PR introduces dataclasses for these key output types
to address these issues.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
By converting the KV cache from ND to NZ format when the decode node
receives it, this PR ensures that the KV NZ feature works correctly
during the decoding phase in disagg-prefill scenario.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
Co-authored-by: ghphotoframe <854746559@qq.com>
Co-authored-by: alex101-ops <alex1015718386@gmail.com>
### What this PR does / why we need it?
Upgrade vllm commit to 1230
Affected by https://github.com/vllm-project/vllm/pull/27614 (and the
core PR https://github.com/vllm-project/vllm/pull/26866), we have to
make the following changes:
1. Modify `tests/e2e/multicard/test_aclgraph_capture_replay.py` to keep
compatible with both vllm version of `v0.13.0` and latest main commitID,
while vllm enables async scheduling by default
2. Skip `test_guided_decoding.py` due to xgrammar errors
(https://github.com/vllm-project/vllm-ascend/issues/5524)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: wjunLu <wjunlu217@gmail.com>
### What this PR does / why we need it?
1. Refactor the current test with mtp and eagle cases
2. Add new necessary cases with mtp and eagle
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
This patch is a series of refactoring actions, including clarifying the
directory structure of nightly tests, refactoring the config retrieval
logic, and optimizing the workflow, etc. This is the first step:
refactoring the directory structure of nightly to make it more readable
and logical.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: wangli <wangli858794774@gmail.com>
1. What this PR does / why we need it?
This PR supports the moe_gating_top_k operator, which enables
post-positioned renormalization (renorm) on the basis of softmax.
2. Does this PR introduce any user-facing change?
No user-facing changes are required.
3. How was this patch tested?
This patch was tested with the test_npu_moe_gating_top_k test case.
vLLM version: release/v0.13.0
vLLM main:
ad32e3e19c
---------
Signed-off-by: ZCG12345 <2097562023@qq.com>
Signed-off-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
Co-authored-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
### What this PR does / why we need it?
Refactor the `capture_model` method in model_runner to directly reuse
the method from vLLM.
Currently, most of the logic in the capture_model method is similar to
that in the vllm code. Directly using the vllm method can reduce the
maintenance cost of the vllm-ascend code. Modify as follows:
1、refactor capture_model function, directly inheriting community methods
2、refactor initialize_aclgraph_capture function, move to
initialize_attn_backend
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: weijinqian0 <1184188277@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 #
-->
This pull request introduces a new custom operator
`aclnnMoeInitRoutingCustom` for Mixture-of-Experts models.
It can be replaced by `aclnnMoeInitRoutingV3` once CANN 8.5 becomes
available.
### 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.
-->
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: jiazhengyi <jiazhengyi@huawei.com>
Signed-off-by: Chenxi Qian <chenxi.qian.cq@outlook.com>
Co-authored-by: jiazhengyi <jiazhengyi@huawei.com>
Co-authored-by: Chenxi Qian <chenxi.qian.cq@outlook.com>
### What this PR does / why we need it?
#5051 only implement a basic framework for model runner v2, but there
are still some bugs for e2e functionality, this PR aim to enable basic
functionality.
model runner v2 plans:
https://github.com/vllm-project/vllm-ascend/issues/5208
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
We support to use full graph with eagle.
Change list:
1. Distinguish between processing graph_params and draft_graph_params in
attention_v1.
2. Adapt the full-graph mode in eagle_proposer, include:
1). If use full graph, make Fullgraph Wrapper when load model.
2). Build a new meatadata, set running mode in FULL and mark attention
update in dummy_run when in Fullgraph mode.
3). Fixed and fill any attn_metadata, such as
attn_metadata.slot_mapping.
4). Add a descriptor.
5). Set running mode and triggered update metadata.
3. Trans is_mtp_model to is_draft_model, and add the update of
workspace.
NOTE:
When set async_scheduling=True, the draft model will enforce execution
in eager mode.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: Yizhou <136800916+yiz-liu@users.noreply.github.com>
### What this PR does / why we need it?
This pacth mainly do the following things:
1. Bugfix for multi_node_tests log, log names must be unique when
uploading logs.
2. Optimize `get_cluster_ips` logic, increase the max retry times for
robustness
3. Abandoned the existing gh-proxy temporarily until it is stable
enough.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Last month the interface of `OffloadingSpec` has
changed(https://github.com/vllm-project/vllm/pull/27743). This PR fixes
this bug and adds e2e test for cpu offloading.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
CI passed with new added test.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
This PR aims to add acceptance test for eagle/eagle3 via llama/qwen. We
obtained golden baselines by running several times (based on healthy
main), which is feasible and convincing.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: Zetong Li <slippersss@126.com>
### What this PR does / why we need it?
Qwen3-235B-A22B belongs to the TopN model, but there is currently a lack
of care for the test cases of the wen3-235B-A22B model on Atlas A2, and
most of the machines currently owned by users in the community are A2.
When users encounter problems, we currently have no way of knowing
whether the model runs normally on the corresponding version of the
code, so we added it. In addition, we currently see TopN models such as:
qwen-dense, qwen3-30b-a3b, Qwen3-Next, Qwen2.5-Omni, but Qwen3-235B-A22B
is missing.
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
Test with multi-node, result as following:
1. Accuracy test (Time for executing this test case: 25 minutes)
test running successfully, accuracy as following:
```
dataset version metric mode vllm-api-general-chat
--------- --------- -------- ------ -----------------------
gsm8k 7cd45e accuracy gen 95.68
```
2. Perf test (Time for executing this test case: 1h15 minutes)
test running successfully, throughput as following(This is the atlas A3,
for A2 the result about A3/1.3):
```
╒══════════════════════════╤═════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤════════════════╤══════╕
│ Performance Parameters │ Stage │ Average │ Min │ Max │ Median │ P75 │ P90 │ P99 │ N │
╞══════════════════════════╪═════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪════════════════╪══════╡
│ E2EL │ total │ 384086.3958 ms │ 214767.0486 ms │ 528014.771 ms │ 387621.5746 ms │ 388776.7492 ms │ 390164.3559 ms │ 488105.8512 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ TTFT │ total │ 159409.9868 ms │ 1849.4588 ms │ 302439.6965 ms │ 162183.7007 ms │ 162965.477 ms │ 164274.1936 ms │ 262578.6041 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ TPOT │ total │ 149.8842 ms │ 130.2175 ms │ 151.2625 ms │ 150.473 ms │ 150.6978 ms │ 150.9102 ms │ 151.2131 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ ITL │ total │ 149.6789 ms │ 0.0099 ms │ 283.0242 ms │ 150.3276 ms │ 156.8649 ms │ 168.1372 ms │ 199.378 ms │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ InputTokens │ total │ 3654.3079 │ 3108.0 │ 4280.0 │ 3629.0 │ 3728.0 │ 3842.1 │ 4079.0 │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ OutputTokens │ total │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 1500.0 │ 2800 │
├──────────────────────────┼─────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼────────────────┼──────┤
│ OutputTokenThroughput │ total │ 3.935 token/s │ 2.8408 token/s │ 6.9843 token/s │ 3.8698 token/s │ 3.8799 token/s │ 3.9916 token/s │ 6.2137 token/s │ 2800 │
╘══════════════════════════╧═════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧════════════════╧══════╛
╒══════════════════════════╤═════════╤═══════════════════╕
│ Common Metric │ Stage │ Value │
╞══════════════════════════╪═════════╪═══════════════════╡
│ Benchmark Duration │ total │ 4391524.3389 ms │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Requests │ total │ 2800 │
├──────────────────────────┼─────────┼───────────────────┤
│ Failed Requests │ total │ 0 │
├──────────────────────────┼─────────┼───────────────────┤
│ Success Requests │ total │ 2800 │
├──────────────────────────┼─────────┼───────────────────┤
│ Concurrency │ total │ 244.8903 │
├──────────────────────────┼─────────┼───────────────────┤
│ Max Concurrency │ total │ 256 │
├──────────────────────────┼─────────┼───────────────────┤
│ Request Throughput │ total │ 0.6376 req/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Input Tokens │ total │ 10232062 │
├──────────────────────────┼─────────┼───────────────────┤
│ Prefill Token Throughput │ total │ 22.924 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Total generated tokens │ total │ 4200000 │
├──────────────────────────┼─────────┼───────────────────┤
│ Input Token Throughput │ total │ 2329.9568 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Output Token Throughput │ total │ 956.3877 token/s │
├──────────────────────────┼─────────┼───────────────────┤
│ Total Token Throughput │ total │ 3286.3445 token/s │
╘══════════════════════════╧═════════╧═══════════════════╛
```
- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
Currently, MHA models (eg: minicpm-2b, Baichuan-7b) will encounter
errors when running in piecewise graph mode, with error messages similar
to:
```
(E89999): When layout is TND and PA not enabled, keyT(8) and valueT(8) must be equal to the last element of actualSeqenceLengthKV(5)[FUNC:CheckInputShapeWhenLayoutIsTND][FILE:prompt_flash_attention_tiling.cpp][LINE:3618]
```
The error occurs because the qkv in the Prefill stage is also padded,
causing the shape to be inconsistent with actual_seq_lengths.
Add unpadding logic for kv.
- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
### What this PR does / why we need it?
This PR adds the method for sending chat and non-chat request, we need
it to test much folloing cases.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
#4443 introduces a precision issue in scenarios where MTP >= 3 + deepseek v3.1, and this pr reverts it
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
Signed-off-by: GDzhu01 <809721801@qq.com>
### What this PR does / why we need it?
Add cudagraph_capture_sizes for E2E CI test.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: menogrey <1299267905@qq.com>
1. refresh additional config doc
2. move kv config logic to platform.
3. improve `dump_config` init logic and rename it to `dump_config_path`
this change is user impacted. dump_config is changed from dict to
string.
4. correct `enable_async_exponential` type
5. remove useless `chunked_prefill_for_mla`
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
- Fix vllm break in the pr:
1.[Drop v0.14 deprecations
]https://github.com/vllm-project/vllm/pull/31285
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: ZT-AIA <1028681969@qq.com>
