### What this PR does / why we need it?
fix import error for get_ip() in vllm main branch
### Does this PR introduce _any_ user-facing change?
N
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: pz1116 <zpbzpb123123@gmail.com>
This PR is used to fix mooncake_connector in pcp/dcp case. When
executing function update_done_task_count, it is necessary to ensure
that both pcp/dcp and TP ranks have finished transferring KV cache.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: wangxiaochao <w00642655@china.huawei.com>
Co-authored-by: wangxiaochao <w00642655@china.huawei.com>
### What this PR does / why we need it?
fix proxy when host ip using domain name
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
pd proxy support ipv6, mooncake connector check whether the IPv6 address
is used and notify the user.
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
Supports generalized FlashComm2 optimization, which reduces
communication overhead, decreases RmsNorm computation, and saves one
AllGather step by replacing Allreduce operations in the Attention module
with pre-AlltoAll and post-AllGather operations (used in combination
with FlashComm1). This feature is enabled during the Prefill phase and
is recommended to be used together with FlashComm1, delivering broad
performance improvements, especially in long sequence scenarios with
large tensor parallelism (TP) configurations. Benchmark tests show that
under TP16DP1 configuration, it can improve the prefill performance of
the DeepSeek model by 8% on top of FlashComm1.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: zzhxx <2783294813@qq.com>
Signed-off-by: Levi-JQ <yujinqi2@huawei.com>
Co-authored-by: Levi-JQ <yujinqi2@huawei.com>
Co-authored-by: zzhxx <2783294813@qq.com>
### What this PR does / why we need it?
1. Fix proxy format processing errors.
2. Layer-wise connector performance optimization.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
- global_segment_size and local_buffer_size use constants for unified
management.
- Newly added support for input formats ending with GB, MB, KB, and B,
while being compatible with existing input methods.
### Does this PR introduce _any_ user-facing change?
- Users can use new input methods
- The documentation has also been modified
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: 李子琦 <liziqi_ing@163.com>
### What this PR does / why we need it?
Make kv-transfer env variable take effect and Fix load-balance proxy.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
Set adxl engine as the default Mooncake backend, because Ascend
Transport is no longer maintained.
Update README to include instructions for installing the adxl backend
Mooncake.
### Does this PR introduce _any_ user-facing change?
Users need to compile and install the mooncake backend for adxl
according to the revised README instructions.
### How was this patch tested?
By CI.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
cancel tokenize for layerwise_proxy
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
by ci
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
Refactored the layerwise code to send to the D node first, preventing
P-node hangs due to communication timeouts when DP > 1.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
When using multi connector, the multi connector does not define
get_finished_count, which will cause the kv cache to be released
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0rc3
- vLLM main:
83f478bb19
---------
Signed-off-by: baxingpiaochong <771405853@qq.com>
### What this PR does / why we need it?
fix a typo in mooncake layerwise connector. There is only `requests`,
instead of `request` in `connector_metadata`. This pr fixes this typo
- vLLM version: v0.11.0rc3
- vLLM main:
83f478bb19
Signed-off-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
force with_prefill true after allreduce in kv producer
- vLLM version: v0.11.0rc3
- vLLM main:
c9461e05a4
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
In multi-Tensor Parallel (TP) scenarios, the KV pool only queries the
first GPU card. When keys on other cards are released, the query result
still returns as successful, introducing accuracy issues. This PR
modifies the KV pool's query logic to check all cards, resolving this
problem.
### Does this PR introduce _any_ user-facing change?
### 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: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
In certain scenarios, the performance of synchronously loading data from
the pool is better than that of asynchronously loading data. Therefore,
a control logic (or switch) for asynchronous loading from the pool has
been added.
### Does this PR introduce _any_ user-facing change?
### 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: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
fix bug: In the mooncake pooling scenario, when the client closes the
request, the server fails to locate the request, leading to the server
hanging.oling scenario, when the client closes the request, the server
fails to locate the request, leading to the server hanging.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Pull up the PD separated pooling service, send requests using aisbench,
press CTRL+C twice, and check if the vllm_ascend service exit.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: linhebiwen <linhebiwen@gmail.com>
### What this PR does / why we need it?
Fix mooncake connector. In scenarios where TP is not equal, when the
prefill TP size is less than the number of key-value heads,
_get_remote_tp_ranks_for_req will return a list of np.arrays. Performing
an operation like int in list of np.arrays will cause an error.
Converting the list of np.arrays into a single np.array resolves this
issue.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
qwen235B
P tp16, D tp1
P tp8, D tp1
P tp4, D tp1
P tp8, D tp2
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: underfituu <hzhucong@163.com>
Co-authored-by: underfituu <hzhucong@163.com>
### What this PR does / why we need it?
mooncake connector support external dp & update readme
### Does this PR introduce _any_ user-facing change?
### 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: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
The GPQA dataset accuracy in the PD separation scenario of testing is
33.2, which does not meet the paper's requirement of 70. Resolve this
accuracy issue.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
qpqa has accuracy issues, but modifying the code can ensure the accuracy
meets the standard
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: fjw <2270923832@qq.com>
### What this PR does / why we need it?
Adapt deepseek-v3.2 to vllm 0.11.0, removing the useless patch.
The final goal is to remove all the patches and align the code arch to
vllm, thus we need to do the following work in next prs.
TODO:
- [x] remove patch on attention spec
- [ ] refactor the kvcache creation logic
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
1. CI passed with existing test.
2. Test pass with deepseek-v3.2-exp
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Fix ZeroDivisionError when prefill_tp_size > num_kv_head, in this
situation, num_head_replica can be 0 and used to divide another value,
this PR restricts the minimum value of a to be 1. And this PR fix
tp_resharding README.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
Refactor KVCache as page_size_bytes is ineffective.
1. Currently the `AttentionSpec` is patched, but the `page_size_bytes`
is still using that in vLLM in runtime, thus the patch is not working
actually. Thus this pr removes the patch on `AttentionSpec`, and will do
the final fix in vLLM.
2. Use `MLAAttentionSpec` instead of `FullAttentionSpec` to reduce
`page_size_bytes` of spec, so that num_blocks in spec could double
### How was this patch tested?
Test pass with Qwen3-Next and DeepSeek-V3.2-Exp
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
This PR fixes the port conflict bug of running external dp in
disaggregated-prefill scenario.
- vLLM version: v0.11.0
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
The previous implementation of the Mooncake connector only supported
scenarios where the Tensor Parallel sizes for the Prefill and Decode
phases were the same for MLA and GQA/MHA.
For heterogeneous TP scenarios, a single rank on a decode node needs to
pull the KV cache from multiple ranks on the prefill nodes and then
merge them (only support prefill TP >= decode TP now). During this
merge, a transpose operation is required because the layouts of the KV
caches are different. To minimize transpose overhead, we use the
npu_paged_cache_load operation to extract the blocks corresponding to
the request from the KV cache. After performing the transpose, we use
_npu_reshape_and_cache to write the blocks back to their original
positions.
This process is illustrated in the diagram below.
b means block_size, this diagram illustrates transpose kv cache layout
for one block. In the implementation, we transpose kv cache by layer for
one request.
<img width="1464" height="916" alt="image"
src="https://github.com/user-attachments/assets/09d96a98-e41c-4733-9535-05544163081a"
/>
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested
- vLLM version: v0.11.0
---------
Signed-off-by: chenxiao <Jaychou1620@Gmail.com>
Signed-off-by: zzy-ContiLearn <1831242919@qq.com>
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Signed-off-by: Kurumi5210 <jaychou1620@gmail.com>
Co-authored-by: zzy-ContiLearn <1831242919@qq.com>
Co-authored-by: chenxiao <cx02308786@antgroup.com>
Co-authored-by: chenxiao <Jaychou1620@Gmail.com>
Co-authored-by: zzhx1 <zzh_201018@outlook.com>
### What this PR does / why we need it?
Resolve the issue where, in the case of unequal TP (Tensor Parallelism),
the TP size is larger than the number of model attention kvcache heads,
causing the KV cache to generate duplicates, which leads to transmission
errors in the original code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
Register the connector in the plugin
### Does this PR introduce _any_ user-facing change?
### 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: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
In the P node timeout release mechanism during PD separation, the req_id
that requires timeout release is transmitted from the scheduler to the
worker. If the KV cache between PDs is transferred too quickly, the P
node's req_id may be released twice. The first release is when the D
node notifies the P node that the KV cache has been pulled, and the
second release is when the scheduler transmits the timeout release to
the worker.
To address this bug, an intermediate component is introduced to manage
the release of req_ids.
Pull kv and forward2 may occur one after the other in timing. The
previous timeout defaulted to forward2 being before pull_kv.
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
---------
Signed-off-by: baxingpiaochong <771405853@qq.com>
### What this PR does / why we need it?
This PR addresses a critical issue where Node D (Device) failures cause
Node P (Processor) to hang due to inability to release KV cache.
**Trigger Scenarios:**
1. Node D fails mid-inference (e.g., network disconnection)
2. Node D rejects requests at a certain stage (e.g., via API server)
3. Load-test script termination causes Node P or D to abort queued
requests
**Root Cause Analysis:**
1. Currently, Node D sends a "KV cache pull complete, release approved"
message to Node P
2. This message is transmitted via the worker connector. If PD
connection breaks or requests are rejected upstream, Node D cannot send
the message
3. Node P will never release KV cache without receiving this message
**Solution:**
Following VLLM community's approach (NIXL connector timeout mechanism),
we're implementing:
- A timeout mechanism with comprehensive warnings
- Updated README documentation
- Reference: VLLM's optimization PR
[#20139](https://github.com/vllm-project/vllm/pull/20139)
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
None
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
---------
Signed-off-by: underfituu <hzhucong@163.com>
### What this PR does / why we need it?
A new kv_role "kv_both" is added to run mixed deployment scenarios. The
mixed deployment will involve a decode phase, where with_prefill should
be false.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
c60e6137f0
Signed-off-by: fems14 <1804143737@qq.com>
### What this PR does / why we need it?
Added a new connector for Mooncake store integration to enable kvcache
reuse in scenarios with system prompts or multi-turn dialogues.
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
5963b98b46
---------
Signed-off-by: LCAIZJ <leichao139636@163.com>
Signed-off-by: fems14 <1804143737@qq.com>
Co-authored-by: fems14 <1804143737@qq.com>
Co-authored-by: Dreamerleader <2270923832@qq.com>
Co-authored-by: Pz1116 <zpbzpb123123@gmail.com>
Co-authored-by: lizy124 <1950471827@qq.com>
Co-authored-by: zouyida2052 <zouyida2002@gmail.com>
### What this PR does / why we need it?
This PR is used to adapt the hostname format for Mooncake when using
adxl. When Mooncake uses adxl, it is necessary to set
```USE_ASCEND_DIRECT``` to True in the file
```/Mooncake/mooncake-common/common.cmake``` during compilation. The
mooncake_connector obtains this config by calling
```vllm_config.kv_transfer_config.get_from_extra_config```, determines
whether Mooncake is using adxl, and selects the corresponding hostname
format.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: main
- vLLM main:
d21a36f5f9
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
1. Move ops/comm_utils to ops/moe/comm_utils
2. Move distributed/tensor_parallel/gather_from_sequence_parallel_region
to ops/moe/comm_utils
3. Delete distributed/tensor_parallel
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e & ut
- vLLM version: main
- vLLM main:
a1213fae5f
---------
Signed-off-by: wuweiqiang24 <1005334931@qq.com>
Signed-off-by: wuweiqiang24 <wuweiqiang11@huawei.com>
### What this PR does / why we need it?
LLMdatadist connector adapt the distributed KV aggregation for the main
branch. Change the P node from returning "finish sending" only when TP0
responds to returning "finish sending" as soon as each NPU receives it.
The D node will send a finish receive signal to the corresponding tp
rank of the P node.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
gsm8k test
2*A3 1P 1D
P: dp2 tp8 D:dp 4 tp4
P: dp2 tp8 D:dp 2 tp8
- vLLM version: main
- vLLM main:
cc99baf14d
Signed-off-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
1. Move prepare/finalize operation from moe_comm_method to
/ops/moe/fused_moe_prepare_and_finalize
2. Adapt to token_dispatcher in moe_comm_method
3. Move
moe_comm_method/experts_selector/token_dispatcher/fused_moe_prepare_and_finalize
to /ops/moe
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e & ut
- vLLM version: v0.10.1.1
- vLLM main:
f4962a6d55
Signed-off-by: weichen <calvin_zhu0210@outlook.com>
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
### What this PR does / why we need it?
This PR introduces Oproj matrix tensor model parallel to achieve
decreasing of memory consumption. It only support graph mode in pure DP
scenario.
In deepseek r1 w8a8 PD disagregated Decode instance, using pure DP, with
oproj_tensor_parallel_size = 8, we have 1 ms TPOT increasing, saved 5.8
GB NPU memory per RANK. We got best performance when
oproj_tensor_parallel_size=4 without TPOT increasing.
performance data:
<img width="1442" height="442" alt="image"
src="https://github.com/user-attachments/assets/83270fc5-868a-4387-b0a9-fac29b4a376d"
/>
### Does this PR introduce _any_ user-facing change?
This PR introduces one new config in `additional_config`.
| Name | Effect | Required | Type | Constraints |
| :---------------------------- |
:--------------------------------------- | :------- | :--- |
:----------------- |
| oproj_tensor_parallel_size | Split the o_proj matrix along the row
dimension (head num * head dim) into oproj_tensor_parallel_size pieces.
| No | int | default value is None, once this value is set, the feature
will be enabled, head num * head dim must be divisible by this value. |
example
`--additional_config={"oproj_tensor_parallel_size": 8}`
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
eddaafc1c7
---------
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: zzh <zzh_201018@outlook.com>
### What this PR does / why we need it?
Delete redundant codes related to communication
### Does this PR introduce _any_ user-facing change?
not involve
### How was this patch tested?
not involve
- vLLM version: v0.10.1.1
- vLLM main:
6c7af8110a
---------
Signed-off-by: 刘哲续 <liuzhexu1@huawei.com>
Co-authored-by: 刘哲续 <liuzhexu1@huawei.com>
### What this PR does / why we need it?
In vllm version 0.10.1, a new KVOutputAggregator was added to the
executor, moving aggregation to the
executor(https://github.com/vllm-project/vllm/pull/19555). This caused
mooncake_connector to break. This change aims to fix this bug and also
adds a policy to forcibly release the KV cache when the prefill node
times out.
This PR is currently linked to a PR in vllm
(https://github.com/vllm-project/vllm/pull/23917). The vllm PR aims to
modify the finish and send count confirmation in heterogeneous TP
situations.
The reason for deleting many UTs is that a lot of communication codes
have been deleted, so the UT as a whole will appear more concise.
- vLLM version: v0.10.1.1
- vLLM main:
fa4311d85f
---------
Signed-off-by: baxingpiaochong <771405853@qq.com>
