### What this PR does / why we need it?
The condition for determining padding in the fullgraph overlay with MTP
and PCP has been modified to accommodate corner cases where the shape
capture size is manually specified.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut and tests
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
mlapo in deepseek is a huge performance improvement in decode, this pr
support pcp & dcp with mlapo
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
### What this PR does / why we need it?
This addresses the issue brought up by #5356 and #4963, and we believe
the unnecessary conditions are the root cause.
Change the unpad trigger to be driven by actual size mismatches
(num_reqs vs base_num_reqs or scheduled vs input token counts) rather
than specific speculative-method flags. Then remove brittle workarounds
that forced request counts and sliced query start locations.
This prevents incorrect indexing and length mismatches during
speculative decoding and makes metadata unpadding more robust across
scheduling modes.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Tested by existing cases.
- vLLM version: v0.13.0
- vLLM main:
8be6432bda
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Adapted sp to eagle3.
There may still be some problems, e.g., accuracy in some scenes,
`sp`+`dp`...
We will fix them later.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
We tested it mainly in a new `e2e`.
```shell
pytest -s tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py::test_llama_qwen_eagle_acceptance
```
```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
============= 3 passed, 1 skipped, 2 warnings in 142.05s (0:02:22) =============
```
It passed.
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: drslark <slarksblood@qq.com>
Currently, the vllm pull request
(https://github.com/vllm-project/vllm/pull/24252) is causing operator
fusion to fail. This issue was previously fixed by patching the backend.
The root cause has been identified, and the problem can be resolved with
this pull request.
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
Import global var form vllm instead of overwirte it, so that we could
use the correct global variant value
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
## What this PR does / why we need it?
This PR fixes the `AttentionMaskBuilder` singleton initialization issue
introduced in PR #4779 and removes the unused `pcp_prefill_mask` field.
### Background
After PR #4779 made `AttentionMaskBuilder` a singleton with `@singleton`
decorator, the class constructor now requires a `device` parameter.
However, two initialization sites were still using the old parameterless
constructor, causing failures.
### Changes
1. **Fix singleton initialization**
- Fixed `AttentionMaskBuilder()` → `AttentionMaskBuilder(self.device)`
in `AscendMLAMetadataBuilder.__init__()`
- Fixed `AttentionMaskBuilder()` → `AttentionMaskBuilder(self.device)`
in `AscendAttentionMetadataBuilder.__init__()`
2. **Remove unused field**
- Removed `pcp_prefill_mask` field from
`AscendPrefillContextParallelMetadata` (never used in codebase)
- Updated related test assertions
### Related
- Issue #5463
- PR #4779 (Unify all mask generation methods)
- PR #5389 (Make AttentionMaskBuilder singleton)
## Does this PR introduce _any_ user-facing change?
No. This is an internal refactoring.
## How was this patch tested?
- ✅ Local testing: No linter errors
- ✅ Unit tests for attention modules verified
- ⏳ CI pipeline
Signed-off-by: lico67373 <918688502@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
When launching the service in the scenario where the
cudagraph_mode is set to FULL and Eagle3 acceleration is enabled for
inference, an error in fia will cause graph capture to fail. This PR
fixes the issue.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: WithHades <244036962@qq.com>
### What this PR does / why we need it?
Purpose: initialize padded slot mapping buffer to prevent garbage
values.
In PCP mode, the `pcp_padded_slot_mapping` buffer is reused across
invocations. Without explicit initialization, this buffer retain stale
values from previous runs, which can lead to incorrect results.
This change ensures the buffer is filled with -1.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: F.Liu <liufeng248@huawei.com>
Co-authored-by: F.Liu <liufeng248@huawei.com>
### What this PR does / why we need it?
1. add `COMPILATION_PASS_KEY` constant
2. clean up useless platform interface `empty_cache`, `synchronize`,
`mem_get_info`, `clear_npu_memory`
3. rename `CUSTOM_OP_REGISTERED` to `_CUSTOM_OP_REGISTERED`
4. remove uesless env `VLLM_ENABLE_CUDAGRAPH_GC`
NPUPlatform is the interface called by vLLM. Do not call it inner
vllm-ascend.
### Does this PR introduce _any_ user-facing change?
This PR is just a cleanup. All CI should pass.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.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?
Revert PR 5253 to fix the smoking problem
### Does this PR introduce _any_ user-facing change?
Does not.
### How was this patch tested?
It was tested in the failure case.
Signed-off-by: Rifa <865071616@qq.com>
### What this PR does / why we need it?
This fixes a bug that occurred when running `test_camem.py` in the
triton-ascend environment `NPU function error:
aclrtGetMemInfo(ACL_HBM_MEM, &device_free, &device_total)`
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Currently, the vllm pull request
(https://github.com/vllm-project/vllm/pull/24252) is causing operator
fusion to fail. This issue was previously fixed by patching the backend.
The root cause has been identified, and the problem can be resolved with
this pull request.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
1. Refactor eagle and mtp function: load_model and generate_token_ids
2. Remove redundant code in mtp and eagle file
3. Refactor the UT of file
2/N of Refactor and merge mtp and eagle
Relational RFC: https://github.com/vllm-project/vllm-ascend/issues/5467
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut and tests
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
Since the _npu_ring_mla operator deteriorates in long-sequencescenarios,
the long sequence is split into shorter sequences for input to improve
performance.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: pichangping <1337510399@qq.com>
### What this PR does / why we need it?
In the training-inference switching scenario, there is no need to resume
the model weights during KV cache resumption, as this would lead to
format mismatch.
### 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: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@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?
Since the [PR](https://github.com/vllm-project/vllm/pull/28988) for PCP
modifications to `GPUModelRunner` has not yet been merged into vLLM,
this PR temporarily requires adjustments to certain buffer sizes. These
changes can be reverted once the original
[PR](https://github.com/vllm-project/vllm/pull/28988) is merged.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
This PR adds multi-stream for GQA to enable computation-communication
overlap. For chunked prefill, we reduce TTFT by approximately 4%.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.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 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?
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?
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?
#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?
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?
In the speculative decoding scenario, the original code performs
Host-Device synchronization, which slows down the main model's execution
speed.
### 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: hwhaokun <haokun0405@163.com>
Co-authored-by: realliujiaxu <realliujiaxu@163.com>
We'll release 0.13.0 soon. The main branch is freeze. Let's revert the
newest change and redo it once 0.13.0 is released
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
### What this PR does / why we need it?
Since the _npu_ring_mla operator deteriorates in long-sequencescenarios,
the long sequence is split into shorter sequences for input to improve
performance.
### 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: pichangping <1337510399@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.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?
The contiguous() operation temporarily increases memory usage, leading
to higher peak GPU memory, which necessitates reducing
gpu_memory_utilization. However, making tensors contiguous in
modelrunnerv1 significantly enhances operator performance, resulting in
greater end-to-end model benefits despite the memory overhead.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
### What this PR does / why we need it?
[Bugfix] Fixing the issue where 128K context does not work in long
sequence scenarios.
This issue is caused by not splitting num_token according to pcp_size
during profile_run.
During `profile_run`, a warm-up is performed based on
`self.max_num_tokens`. When PCP is enabled, each PCP group will only
schedule up to `self.max_num_tokens / pcp_size`. After `profile_run` is
completed, the original scheduling size needs to be restored.
This is a temporary workaround; once
https://github.com/vllm-project/vllm/pull/28988/files is implemented,
this part can be removed.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
The variable `self.num_pcp_pads` was incorrectly truncated during
assignment, causing errors in certain scenarios such as PD
disaggregated. This issue has now been resolved.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Co-author by: QiuChunshuo <qiuchunshuo@huawei.com>
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: daishixun <dsxsteven@sina.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
Revert [KV-Sharing] Support KV-Sharing feature in CLA models (#4138) as
it causes deepseek v3.2 hang error
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
Support KV-Sharing feature in CLA (cross layer attention) models, which
sharing kv cache in some layers.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
This patch add handling of `XDRotaryEmbedding` in modelrunner to support
for `hunyuan-vl`
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
CI passed with added/exist tests
Closes: https://github.com/vllm-project/vllm-ascend/issues/4992
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Remove unnecessary attributes from set_ascend_forward_context
1.prefetch_stream
2.weight_prefetch_method
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: Wang Kunpeng <1289706727@qq.com>
