since we support self-defined pass manager now, it's no need to override
the pass config. Let's clean up it.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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>
### What this PR does / why we need it?
We should also trigger image build when nightly test related files are
changed to ensure the image is valid for nightly tests. Please note that
this only applies to image with the tag `main*`(which means build
triggered by PR).
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Currently, when the MooncakeConnector interacts via ZeroMQ, it throws
the following exception upon send/receive failure:
**Issue 1:** The currently used `zmq.REQ` socket follows a strict
request-reply pattern, requiring an alternating sequence of send →
receive → send → receive... If either a send() or receive() operation
fails, the ZeroMQ socket becomes unusable.
**Solution:** When a send() or receive() exception occurs, close and
delete the ZeroMQ socket, and recreate it upon next use.
**Issue 2:** In `_handle_request`, if `_send_done_recv_signal` raises an
exception, the exception is thrown immediately and subsequent code is
not executed, causing the decode logic to fail to properly release the
request.
**Solution:** Move the call to `_send_done_recv_signal` to the end of
the function.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: LCAIZJ <leichao139636@163.com>
### What this PR does / why we need it?
This PR builds upon PR #5011 and aims to further enhance the
npu_graph_ex_passes module. Based on prior work, we have added graph
optimization support for the add_rms_quant fused operator in scenarios
where a bias term is present—ensuring the fusion pattern is correctly
registered and matched into the computation graph.
For validation, we switched to the Qwen3-235B-A22B-W8A8 model. Benchmark
results show that, compared to the unfused baseline, enabling this
fusion pass significantly improves inference throughput for W8A8
quantized models.
For more details can refer to the
RFC:https://github.com/vllm-project/vllm-ascend/issues/4715
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
```
llm = LLM(
model=model,
tensor_parallel_size=GPUs_per_dp_rank,
enforce_eager=False,
enable_expert_parallel=enable_expert_parallel,
trust_remote_code=trust_remote_code,
gpu_memory_utilization=0.98,
max_num_batched_tokens=512,
# load_format="dummy",
max_model_len=2048,
max_num_seqs=16,
quantization="ascend",
additional_config={
"refresh": True,
"enable_npugraph_ex": True
},
compilation_config={
"cudagraph_capture_sizes": [8, 16],
"cudagraph_mode": "FULL_DECODE_ONLY",
},
)
if profile_dir:
llm.start_profile()
outputs = llm.generate(prompts, sampling_params)
if profile_dir:
llm.stop_profile()
for i, output in enumerate(outputs):
if i >= 5:
break
prompt = output.prompt
generated_text = output.outputs[0].text
print(
f"DP rank {global_dp_rank}, Prompt: {prompt!r}, "
f"Generated text: {generated_text!r}"
)
```
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: cjian <2318164299@qq.com>
### What this PR does / why we need it?
Add LongCat-Flash support.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed
- vLLM version: v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: chuyuelin <923822139@qq.com>
Co-authored-by: chuyuelin <chuyuelin1@huawei.com>
### What this PR does / why we need it?
In the current process of implementing attention updates, the FIA
operator shares a single workspace among different layers within the
same computation graph. To enable memory reuse, we adopt the
weak_ref_tensor mechanism. However, this approach may lead to precision
anomalies in certain scenarios. To address this issue, different layers
in the same computation graph are assigned independent workspaces.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: WithHades <244036962@qq.com>
### What this PR does / why we need it?
Improve the performance of Layerwise Connector, mainly includes the
following points:
1. Use event synchronize to replace stream synchronize.
2. Access metaserver when scheduling.
3. Transfer kvcache each Chunk prefill segmentation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.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?
1. This PR is proposed to support complicated pcp/dcp parallelisms in
Prefill and Decode nodes in Mooncake, such as Prefill: TP8/PCP2DCP8 and
Decode: TP8/DCP4/DP2, which is not supported now. We establish the link
mappings to transfer KVCache between prefill and decode nodes. The main
function is realized in Function of `_get_kv_split_metadata` in
Mooncake_connector.py
2. After a prefill rank is pulled KVCache by a decode rank, the decode
rank will send `DONE_RECVING_MSG` to the prefill rank and the prefill
rank will free its KVCache blocks. If a prefill rank is pulled KVCache
more than one time by several decode ranks and it surely could happen in
complicated pcp/dcp parallelisms, it will cause the prefill rank free
its KVCache blocks for several times, which could cause memory issue.
This PR solve this issue by counting the times of prefill rank would be
pulled KVCache and in the last time, it will free the prefill rank
KVCache blocks. The related code is in Function of `run_busy_loop` in
Mooncake_connector.py
3. If a prefill rank is not pulled KVCache by any decode ranks, the
first rank in decode node will send "DONE_RECVING_MSG" to free its
blocks. The related code is in Function of
`_send_done_signal_to_free_remote_port` in Mooncake_connector.py
### How was this patch tested?
This PR is tested in many pcp/dcp parallelisms, and the accuracy are all
correct.
MLA model:
Prefill node: TP8/DP2, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP8, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP8, Decode node: TP8/DCP4/DP2
Prefill node: TP8/PCP2/DCP4, Decode node: TP4/DCP2/DP4
Prefill node: TP8/PCP2/DCP2, Decode node: TP4/DCP4/DP4
Prefill node: TP8/PCP2, Decode node: TP4/DCP2
GQA model:
Prefill node: TP8/DP2, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP2, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP2, Decode node: TP8/DCP2/DP2
Prefill node: TP8/PCP2/DCP2, Decode node: TP4/DP4
Prefill node: TP16/DCP2/PCP1, Decode node: TP8/DCP2/DP2
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
- Co-author by: Daishixun dsxtsteven@sina.com
---------
Signed-off-by: wangxiaochao <w00642655@china.huawei.com>
Co-authored-by: wangxiaochao <w00642655@china.huawei.com>
Co-authored-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
We should transfer the mm_embed to the dtype of input_embed before
performing the in-place assignment
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangli <wangli858794774@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?
Refactor pcp& dcp related code. we use pcp_manager class to Unifiy
Manage pcp & dcp . as we do this , many code can be deleted from
model_runner, and can avoid break pcp & dcp by other developments.
RFC：https://github.com/vllm-project/vllm-ascend/issues/5449
### 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: zhenwenqi2024 <zhenwenqi_2022@qq.com>
Co-authored-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.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?
The float kernel of MOE_init_routing_v2 in the dispatch allgather
operation does not support tensor format for active_expert_range; it
only supports int.
PR5311 To unify the variables `local_num_experts` and
`self.local_num_experts`, `self.local_num_experts` was used
consistently, which led to the subsequent integer type parameter being
converted to a tensor type.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
gsm8k | exact_match,strict-match: ground_truth=0.89 | measured=0.8939 |
success=✅
gsm8k | exact_match,flexible-extract: ground_truth=0.85 | measured=0.856
| success=✅
ceval-valid | acc,none: ground_truth=0.84 | measured=0.8373 | success=✅
Model Parameters:
{'pretrained': 'Qwen/Qwen3-30B-A3B', 'tensor_parallel_size': 2, 'dtype':
'auto', 'trust_remote_code': False, 'max_model_len': 4096,
'gpu_memory_utilization': 0.6, 'enable_expert_parallel': True}
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
Update new contributors.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: menogrey <1299267905@qq.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>
Fixes#3376
- Remove --task embed from vllm serve command in Qwen3_embedding.md
- Remove task='embed' parameter from LLM constructor in Python example
The --task parameter has been deprecated in recent vLLM versions
in favor of automatic model type detection.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: hu-qi <huqi1024@gmail.com>
### What this PR does / why we need it?
update triton-ascend version to 1229 and bisheng version in 1225;
- vLLM version: release/v0.13.0
- vLLM main:
254f6b9867
---------
Signed-off-by: Meihan-chen <jcccx.cmh@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>
### What this PR does / why we need it?
- Fixes vllm break:
1. [[BugFix] register quant scale tensors as buffer #31395]
(https://github.com/vllm-project/vllm/pull/31395)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
Using open-source CANN, installation errors may occur due to changes in
the path of the aclnn directory. So we add the header file.
- Fixes #
-->
Using open-source CANN, installation errors may occur due to changes in
the path of the base/dlog_pub.h for aclnn.
### Does this PR introduce _any_ user-facing change?
Does not.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: Rifa <865071616@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?
This PR aims to refactor eagle-related modules in vllm-ascend.
This is the starting PR of eagle refactoring. Provided with vllm-eagle,
ascend-eagle and ascend-mtp, we first let ascend-mtp inherit from
ascend-eagle and let ascend-eagle inherit from vllm-eagle. As a
initialization, we just delete `__init__` in mtp_proposer and simplify
the corresponding logic in eagle_proposer.
Based on "vllm-eagle <----- ascend-eagle <----- ascend-mtp", our target
is to gradually delete ascend-mtp and enable ascend-eagle to converge to
vllm-eagle. So the main workspace is eagle_proposer. In this way, we
hope that contributors can concurrently refactor eagle.
Incoming changes:
1. delete common methods in vllm-eagle & ascend-eagle & ascend-mtp
2. delete `load_model` in mtp_proposer
3. delete `dummy_run` and `propose` in mtp_proposer
4. ......
RFC: #5467
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Zetong Li <slippersss@126.com>
### What this PR does / why we need it?
This PR moves reject sample related triton kernels into `ops/triton`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with existing test.
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
---------
Signed-off-by: whx-sjtu <2952154980@qq.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>
Enable fia to support sliding window function and adapt to the Gemma3
model.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: nsdie <yeyifan@huawei.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?
Unify the loading logic for expert_map and log2phy.
1. The map generated when enabling the redundancy expert is incorrect.
The community generation map function only accepts the number of global
experts. When we pass in the number of logical experts plus redundant
experts, the local expert ID of the last card will index to an expert ID
that does not exist. Now we ensure that the index points to a real
existing expert ID, and each expert can be accessed. Moreover, when
redundant experts are not enabled, the output of our function remains
consistent with the community's function.
2. The map we generate is based on the length of the physical expert,
but in reality, we only need to use the length of the logical expert.
Later on, we will need to pad it accordingly, so we can simply generate
a map with the length of the logical [expert.]
3. Unify the initialization logic across different scenarios and
simplify the code for fused_moe.
**Before refactoring**
- map path is not None：
expert map: get_rank_placement_map from _'expert_load_balancer.py'_,
maintains the map for all ranks and all layers.
log2phy: get_rank_log2phy_map from _'expert_load_balancer.py'_,
maintains the map for all ranks and all layers.
- map path is None:
expert map: determine_expert_map from '_vllm.laye_r', The function does
not support the redundant experts of vllm-ascend.
log2phy: determine_default_log2phy_map from _'eplb_utils.py'_. The
function does not support the redundant experts of vllm-ascend.
**Refactoring**
eplb_utils.py
init_eplb_config
generate placement
generate expert map
generate log2phy
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Expert Mapping Test Generation:
ep size: 16, num of experts: 256, num of redundant experts: 16
+++++++++++++++++++++++++++++++++++++++++
Expert Mapping (Non-1 indicates the expert responsible for this rank)
for Rank 15:
vllm map：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16]
+++++++++++++++++++++++++++++++++++++++++
Improved map：
[16 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]
Expert Mapping Test Generation:
ep size: 16, num of experts: 256, num of redundant experts: 0
+++++++++++++++++++++++++++++++++++++++++
Expert Mapping (Non-1 indicates the expert responsible for this rank)
for Rank 15:
vllm map：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]
+++++++++++++++++++++++++++++++++++++++
Improved map：
[-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]
dsr1 baselie：
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| gsm8k-lite | 7cd45e | accuracy | gen | 100.00 |
dsr1 eplb：
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| gsm8k-lite | 7cd45e | accuracy | gen | 100.00 |
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
Add release note for v0.13.0rc1
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: MengqingCao <cmq0113@163.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?
fix typo of _skip_all_reduce_across_dp_group
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
Signed-off-by: jiangkuaixue123 <jiangxiaozhou111@163.com>
### What this PR does / why we need it?
modify pcp tutorial doc
Because some optimization points have been submitted as PRs and haven't
been merged yet, I'll update the performance data now and refresh it
again after the PRs are merged.
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
Use `set -euo pipefail` to exit if
tools/install_flash_infer_attention_score_ops_a2.sh failed in any line
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: Mengqing Cao <cmq0113@163.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
### What this PR does / why we need it?
The PR updates the documentation for DeepSeek-V3.1 and DeepSeek-R1 in
the scenario of prefill-decode disaggregation.
Updated some PD separation-related setting parameters and optimal
configurations. This script has been verified.
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
Signed-off-by: chenmenglong <chenmenglong1@huawei.com>
