### What this PR does / why we need it?
Fix attn_group initialization timing so that fix qwen3-next model
### 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: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Add new npu_fused_infer_attention_score op to improve perfomance in
flash attention case.
### 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: ZYang6263 <zy626375@gmail.com>
### What this PR does / why we need it?
In memory of https://github.com/vllm-project/vllm-ascend/pull/2610 and
#3449 Fix Mtp torchair pd bug.
In the pd Disaggregation scenario, the first token of the inference
after the d node receives the kv follows the eager mode.
Fixes:
Running with MTP torchair graph mode with Prefilling Decoding
Disaggregation , if all requests processed by the D node are requests
just transmitted from the P node, it will break the torchair graph.
Reason: During PD Disaggregation , the P node only transmits the KV
cache and prompt to the D node, not the actual tokens inferred (neither
the main model tokens nor the MTP tokens are transmitted). Therefore,
the D node will treat this request as one without MTP tokens for
inference (seq_len=1).
The community does not have graph mode issues because the community's
attention has a seq_len=1 for each batch during the decode phase.
We have issues because the graph mode pads according to processing 2
tokens per request. When there are some seq_len=1 and some seq_len=2,
padding is done at the end. If all requests received by the D node are
seq_len=1, padding cannot be performed normally according to the
attention's fia operator constraints.
Solution:
The kv consumer uses extra torchair graph padding to avoid breaking FIA
graph constrains (The one this PR implemented).
### 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: xuyexiong <xuyexiong@huawei.com>
### What this PR does / why we need it?
This PR aims to add padding logic to seq_lens、block_tables when running
in full decode scenario. Before this PR, the number of input tokens with
padding might exceeds corresponding seq_lens. For example, when running
in full decode scenario:
```
input_ids : [1, 3, 0, 0]
seq_lens: [2, 1]
query_start_loc: [0, 1, 2]
```
Here, `input_ids` is padded by 2 tokens while
`seq_lens`/`query_start_loc` are not. The mismatch between `input_ids`
and `seq_lens`/`query_start_loc` might cause some potential bugs. This
PR would change it into :
```
input_ids : [1, 3, 0, 0]
seq_lens: [2, 1, 1, 1]
query_start_loc: [0, 1, 2, 3, 4]
```
### Does this PR introduce _any_ user-facing change?
No.
### 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: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
Fix 3 bugs in flash comm1 of Allgather
EP(https://github.com/vllm-project/vllm-ascend/pull/3334):
1. call `enable_sp()` with argument `vllm_config` trigger a lot of
warning log, this PR caches its return value.
2. `num_tokens_after_padding` should be cpu tensor as it will used as
`num_tokens_across_dp_cpu` in `DPMetadata`. It will causes may d2h copy
when running model.
3. In PD, model runner will execute `kv_connector_no_forward`，where
`num_tokens` is None
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: realliujiaxu <realliujiaxu@163.com>
### What this PR does / why we need it?
Make the Full Graph mode can run with MTP.
### 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: anon189Ty <Stari_Falcon@outlook.com>
### What this PR does / why we need it?
Currently, MTP Model in deepseek can not be capture in ACLGraph. This PR
is use to allow MTP to be captured in ACLGraph mode.
### 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: anon189Ty <Stari_Falcon@outlook.com>
### What this PR does / why we need it?
MLA and GQA use different computation logic: MLA slice batches and only
compute on the actually valid tokens. That means outer padding must be
handled carefully — the accuracy issue this PR fixes was caused by stale
data in `slot_mapping` being reused by subsequent inference steps.
So we zeros out the portion of the slot mapping tensor that is not used
by the currently scheduled tokens.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Working on it.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.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?
1.Record expert map without dynamic eplb.
2.Add export PYTHONOPTIMIZE=1 when using dynamic eplb.
3.change eplb doc
Does this PR introduce any user-facing change?
How was this patch tested?
Qwen3_moe in A3.
- vLLM version: v0.11.0
---------
Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
### What this PR does / why we need it?
[Feat] Supports Aclgraph for bge-m3
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
```
pytest -s tests/e2e/singlecard/test_embedding.py
pytest -s tests/e2e/singlecard/test_embedding_aclgraph.py
```
to start an online server with bs 10, each batch's seq length=8192, we
set --max-num-batched-tokens=8192*10 to ensure encoder is not chunked:
```
vllm serve /home/data/bge-m3 --max_model_len 1024 --served-model-name "bge-m3" --task embed --host 0.0.0.0 --port 9095 --max-num-batched-tokens 81920 --compilation-config '{"cudagraph_capture_sizes":[8192, 10240, 20480, 40960, 81920]}'
```
For bs10, each batch's seq length=8192, QPS is improved from 85 to 104,
which is a 22% improvement, lots of host bound is reduced.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: xuyexiong <xuyexiong@huawei.com>
Co-authored-by: wangyongjun <1104133197@qq.com>
### What this PR does / why we need it?
Because the update_attn_params function requires passing the num_tokens
parameter, and num_tokens is obtained via postions.shape[0]. However,
the multimodal model uses mrope (Multidimensional Rotary Position
Embedding), which results in the postions having a shape of 2.
Consequently, postions.shape[0] retrieves an incorrect value.We resolve
this issue by replacing positions.shape[0] with maybe_padded_num_tokens.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
Signed-off-by: fan2956 <zhoufan53@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>
### What this PR does / why we need it?
Currently, when executing to the Linear layer of models in vLLM-Ascend,
the weights format is ND in unquantized case and skipped ascend case.
This PR supplements the execution logic for Linear layer. We use a new
global variable: VLLM_ASCEND_ENABLE_NZ. When VLLM_ASCEND_ENABLE_NZ=1 and
CANN version is 8.3, the weights of the Linear layer will be converted
to FRACTAL_NZ, in both unquantized case and skipped ascend case. We also
use VLLM_ASCEND_ENABLE_NZ to control the existing NZ conversion, such as
w8a8-quantized case.
### Does this PR introduce _any_ user-facing change?
Add a new global variable VLLM_ASCEND_ENABLE_NZ. If you want to use NZ
format, you should set VLLM_ASCEND_ENABLE_NZ=1.
### 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: anon189Ty <Stari_Falcon@outlook.com>
### What this PR does / why we need it?
Replaces the hardcoded `mc2_tokens_capacity` with the max graph capture
size for a more accurate allocation.
This change ensures the capacity is correctly sized relative to the
graph capture configuration, removing a magic number and making the
setup more robust.
This PR fixes two issues:
1. <del>MC2 op restrictions differ between SoCs.</del> @Angazenn This
requires an overhaul, hence removed from this PR, please commit another
PR.
2. The hardcoded value `512` allocates too much buffer for large models.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Tested in daily checks.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
This PR primarily focuses on two key changes:
1. Adjusts internal interface calls to optimize the interaction logic
between related modules.
2. Exposes an interface that allows users to select the EPLB algorithm,
enabling more flexible configuration based on specific usage scenarios.
These changes aim to enhance the usability of the system while ensuring
the stability of internal operations. Relevant unit tests have been
updated to cover the modified logic.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: Che Ruan <cr623@ic.ac.uk>
Co-authored-by: Che Ruan <cr623@ic.ac.uk>
### What this PR does / why we need it?
Adds support for capturing the Multi-Layer Attention (MLA) decode
operation into an ACL graph. This improves performance by compiling the
attention kernel for single-token decoding.
Key changes include:
- Implementing the graph capture logic for the MLA kernel, including
workspace management and parameter updates.
- Modifying the rotary embedding (RoPE) handling to use pre-allocated
tensors, which is a requirement for graph capture.
- Adding a `build_for_graph_capture` method to the MLA metadata builder
to create dummy metadata during the graph compilation phase.
Known issues:
- Currently, MTP is not supported in FULL_DECEDE_ONLY mode -- we're
working on a fix
- We are preparing to remove update_mla_attn_params with
auto_dispatch_capture
### Does this PR introduce _any_ user-facing change?
compilation_config={
"cudagraph_mode": "FULL_DECODE_ONLY",
},
### How was this patch tested?
- vLLM version: v0.11.0
---------
Signed-off-by: panchao-hub <315134829@qq.com>
Signed-off-by: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@huawei.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `qkv_proj.weight` and `o_proj.weight` in quantized Attention
modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency
### Does this PR introduce _any_ user-facing change?
Add a new config in `--additional-config` for configuration:
```json
{
"weight_prefetch_config": {
"enabled": false,
"prefetch_ratio": {
"attn": {
"qkv": 1.0,
"o": 1.0,
},
},
},
}
```
This feature is enabled by default, and can be disabled through this
configuration
### How was this patch tested?
- vLLM version: v0.11.0
---------
Signed-off-by: yuzhup <15705211260@163.com>
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
Co-authored-by: yuzhup <15705211260@163.com>
### What this PR does / why we need it?
When running DP in a non-equilibrium scenario, which means there is some
dp groups executing `dummy_run`, we need to make sure it running the
same mode as other dp, thus improving then performance in dp scenario
### How was this patch tested?
Tested by adding log in `_dummy_run`
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Currently we run an extra profile_run with `num_tokens ==
self.mc2_tokens_capacity`. However, when setting `max_num_batched_tokens
< self.mc2_tokens_capacity`, this will trigger an assertion error that
requires num_tokens in `_dummy_run` to be smaller than
`max_num_batched_tokens`. This PR skips this extra `profile_run` if
`self.max_num_tokens <= self.mc2_tokens_capacity` so as to avoid this
bug.
This PR fixes a bug that `kernel_block_sizes` never equals to
`[self.cache_config.block_size]`. `kernel_block_sizes` is type of
List[List[int]], so the condition should be `kernel_block_sizes !=
[[self.cache_config.block_size]]`. This also helps to resolve a issue
that cpu_offload_gb cannot be enabled.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
Fix the error "cur batch_size is invalid" during profile_run in the
torchair scenario.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
Signed-off-by: WithHades <244036962@qq.com>
### What this PR does / why we need it?
1. Solved the issue where sizes capture failed for the Qwen3-32b-int8
model when aclgraph, dp1, and tp4 were enabled.
2. Added the exception thrown when sizes capture fails and provided a
solution
3. Add this common problem to the FAQ doc
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut
- vLLM version: v0.10.2
- vLLM main:
https://github.com/vllm-project/vllm/commit/releases/v0.11.0
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
fix oom in aclgraph.
1. In the current token dispatch implementation, tensors are mounted on
class instances to facilitate parameter passing between different
methods. This approach prevents automatic recycling of these tensors. In
some cases, it may lead to out-of-memory error. To address this issue,
we manually set these tensors to None to release corresponding memory.
2. The `profile_run` method is designed to accurately estimate the
maximum NPU memory usage during vLLM inference. However, in certain
scenarios, MoE models perform inference via MC2, which includes
communication and consumes additional NPU memory. This leads to
inaccurate estimation by the profile run. We address this by actively
triggering the MC2 during profile run for initialization.```.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
52d0cb8458
Signed-off-by: WithHades <244036962@qq.com>
### What this PR does / why we need it?
Upgrade vLLM to newest commit
- Fix the aclgraph doesn't work problem, caused by
24fab45d96
- Fix PoolerOutput import error, caused by
755ed7b05b
- Fix the aclgraph weight load error to keep the same with torchair fix.
4492e3a554
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
All test should pass
- vLLM version: v0.10.2
- vLLM main:
52d0cb8458
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
…to avoid unintentional copy ops blocking across different NPU streams,
improving disagg TTIT/TTFT (#2788)"
### What this PR does / why we need it?
This reverts commit 6995a7bc5b. We'll add
it back once the issue is fixed.
related issue: https://github.com/vllm-project/vllm-ascend/issues/3195
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
52d0cb8458
### What this PR does / why we need it?
vllm-ascend support [msMonitor
](https://gitcode.com/Ascend/mstt/tree/master/msmonitor)tool to collect
performance of vllm-ascend
### Does this PR introduce _any_ user-facing change?
1.add env MSMONITOR_USE_DAEMON；
2.user cann enable msMonitor tool by setting MSMONITOR_USE_DAEMON=1
before run vllm-ascend model；
3.MSMONITOR_USE_DAEMON and VLLM_TORCH_PROFILER_DIR cannot both set
### How was this patch tested?
1.run vllm-ascend model while not set MSMONITOR_USE_DAEMON=1 or set
MSMONITOR_USE_DAEMON=0, model will run successfully;
2.run vllm-ascend model while set MSMONITOR_USE_DAEMON=1, run msMonitor
tool to collect profile data;
3.run vllm-ascend model while set MSMONITOR_USE_DAEMON=1 and
VLLM_TORCH_PROFILER_DIR, will raise error
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
Signed-off-by: mei-feiyao <1332490378@qq.com>
### What this PR does / why we need it?
Fixes#3096
1. Fix kv cache initialization error of attention layer. There are some
models with layer name like `attn.attn`, instead of `self_attn`, but the
initialization of kv cache tensors only check for `self_attn` and
`attn.attn`, which leding to the error `AssertionError: Some layers are
not correctly initialized`
2. Set the default value of input arg `sampling_metadata` in
`compute_logits` for the modeling files in vllm-ascend. Thus fixing the
error `Qwen3NextForCausalLM.compute_logits() missing 1 required
positional argument: 'sampling_metadata'`
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
test locally with internlm
- vLLM version: v0.10.2
- vLLM main:
5aeb925452
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
What this PR does / why we need it?
there are two sets of sp implementations for moe and dense models. One
is called sequence_parallelism, and the other is flashcomm_v1.
We did the following things：
Merge two sets of code with the same implementation into one.
Remove the implementation of sequence_parallelism, as this solution
cannot support aclgraph.
Does this PR introduce any user-facing change?
No
How was this patch tested?
e2e&ut
- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
### What this PR does / why we need it?
When we copy the sampled valid token ids from device to host, avoid
using tolist which would trigger a CUDA wise stream sync if the source
is on device. We change it to use non-blocking copy followed by an
explicit CUDA event sync.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Bring up vLLM server
```bash
VLLM_USE_V1=1 vllm serve Qwen/Qwen2.5-14B-Instruct --disable-l
og-requests -tp 8 --max-num-seqs 64 --no-enable-prefix-caching --max_num_batched_tokens=8000
```
## Before：
## After
As shown in the figure, the TTFT decreased
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
---------
Signed-off-by: jesse <szxfml@gmail.com>
### What this PR does / why we need it?
Updates the `cudagraph_support` attribute to `aclgraph_support` to use
terminology appropriate for the Ascend platform (ACL graphs instead of
CUDA graphs).
This change also explicitly disables graph support for the MLA attention
backend.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
None needed.
- vLLM version: v0.10.2
- vLLM main:
5aeb925452
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Fix the impact to LoRA that
https://github.com/vllm-project/vllm/pull/25249 brought.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
pytest -sv tests/e2e/singlecard/test_ilama_lora.py
pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
---------
Signed-off-by: paulyu12 <507435917@qq.com>
### What this PR does / why we need it?
This is the follow-up PR of #2128 .
Moves graph parameter management components, including `GraphParams`,
`get_graph_params`, and `set_graph_params`, from the generic `utils.py`
to the more specific `compilation/acl_graph.py`.
Additionally, extracts the `update_attn_params` logic from the
`NPUModelRunner` class into a standalone function within the `acl_graph`
module.
This refactoring improves code organization by centralizing ACL
graph-related logic into its own dedicated module, enhancing modularity
and clarity.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
None needed.
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.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?
Fix issues mentioned in
https://github.com/vllm-project/vllm-ascend/pull/2791 and some minor
refactoring.
1. Use Enum instead of string.
2. Avoid setting a new property to forward_context in
AscendFusedMoE.forward().
3. Enabling TokenDispatcherWithMoge.
4. Remove redundant code.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Qwen3-30B-A3B/Qwen3-30B-A3B-W8A8/DeepSeek-V3-W4A8-Pruing/deepseek-mtp/pangu-pro-moe-pruing:
1. Enable/Disable EP
2. Aclgraph & eager
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <12153182+weijinqian0@users.noreply.github.com>
Note: This depends on [vLLM
#25161](https://github.com/vllm-project/vllm/pull/25161) and the
torch\_npu release from September 30.
### What this PR does / why we need it?
This pull request adds `FULL_DECODE_ONLY` mode for GQA/MHA models (MLA
models like DeepSeek V3/R1 are not included). Key improvements include:
* **Reduced dispatch latency:** By replaying the entire model execution
graph at once, we cut overhead compared with multiple smaller replays.
* **Stabilized multi-device performance:** Captureing the whole model as
one static graph also mitigates the dispatch fluctuations across
devices.
* **Stream/resource savings:** Consolidating graph captures frees up
streams, allowing more graphs to be captured.
**Known issues:**
1. `_npu_paged_attention` currently manages its own workspace in
`torch_npu`, which can deadlock when synchronizing during graph replay —
we’re working on a fix.
There may be other corner cases. This PR is the first in a planned
series; we’ll continue to iterate and address remaining issues in
follow-ups.
This is essentially a port of #1503 and #1677, but includes two major
changes:
1. Let `graph_dispatcher` decide the graph mode instead of hard-coding
it in the backend, which decouples Full Graph and Piecewise Graph and
could make it possible to remove dynamo.
2. Adapt to the new `attn_group` logic, but leave a small hack in
`update_graph_params`; multi-attention models may or may not be fully
supported yet.
### Does this PR introduce _any_ user-facing change?
```python
compilation_config={
"cudagraph_mode": "FULL_DECODE_ONLY",
},
```
### How was this patch tested?
Tests included.
- vLLM version: v0.10.2
- vLLM main:
9607d5eb44
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Follow up `UniformTypeKVCacheSpecs` changes introduced by
https://github.com/vllm-project/vllm/pull/25101, which support different
hidden size in uniform type kvcache specs
This also fix the CI issue about `TypeError: AttentionGroup.__init__()
missing 1 required positional argument: 'kv_cache_spec'`
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
Tests passed with exsiting e2e tests.
- vLLM version: v0.10.2
- vLLM main:
c60e6137f0
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Add new npu_fused_infer_attention_score op to improve perfomance in
splitfuse cases and resolve long-seq mask problems .
1. The original op's performance is suboptimal in certain scenarios,
necessitating optimization through the _new op_
(npu_fused_infer_attention_score)。
2. For ultra-long sequences (128k), the original operator will allocate
a large attn_mask, which consumes excessive CPU memory. In contrast, the
_new op_ supports a fixed-size compressed mask, effectively resolving
this issue.
NOTE1: The current PR retains the original logic and uses a version
check of the CANN package to determine whether the _new op_ can be
enabled. This ensures no impact on existing users. In future versions,
this version check and the original logic will be deprecated, and the
_new op_ scheduling will be uniformly adopted.
NOTE2: This pr relies on future CANN version, which is not available
now.
NOTE3: To enable the new op in chunked prefill, the parameter
additional_config should be set like `--additional-config
'{"ascend_scheduler_config":
{"enabled":true,"enable_chunked_prefill":true}}' \` at least.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed
- vLLM version: v0.10.2
- vLLM main:
6c5f82e5aa
---------
Signed-off-by: tangtianyi <tangtianyi4@huawei.com>
Signed-off-by: Angazenn <supperccell@163.com>
Co-authored-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
based on the https://github.com/vllm-project/vllm/pull/23770,
fix Async scheduling and PP compatibility with DP, also fixes issue with
finished requests not being processed in async scheduling and PP cases,
and possible worker race conditions.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
544fe76b95
---------
Signed-off-by: jesse <szxfml@gmail.com>
### What this PR does / why we need it?
This pr fixes a few issues on prefill disaggregation:
1. Fix prefill disaggregation kvcache addr alignment issue, llmdatadist
needs the addr of tensors to be aligned with 2M
2. Fix prefill disaggregation kvcache shape error, llmdatadist requires
k/v tensors with shape [num_blocks, ...], however the implentment before
this pr is [2, num_blocks, ...], which will break prefill disaggregation
3. Use hybrid kv cache only when running qwen3_next to fix accuracy
issue on prefill disaggregation.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
Tested locally by @liziyu179
- vLLM version: v0.10.2
- vLLM main:
4f02b77de4
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
For sleep mode level 2, we discarded model both weights and kv_cache,
but the problems is: When we discard weights, we also discard some
tensors representing the model state which we called
`model.named_buffers()`, such as: `running_mean / running_var` in
BatchNorm、rope cos-sin cache ... when we update weights, but forgot to
update buffers as well, this will lead to some unknown issue
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
5963b98b46
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This PR depends on the merge of #2707 and has adapted the aclgraph
functionality to support MTP.
### How was this patch tested?
- vLLM version: v0.10.2
- vLLM main:
2b85697031
---------
Signed-off-by: xuyexiong <xuyexiong@huawei.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>
