### What this PR does / why we need it?
- Fix vllm break in the pr:
1.[Drop v0.14 deprecations
]https://github.com/vllm-project/vllm/pull/31285
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: ZT-AIA <1028681969@qq.com>
### What this PR does / why we need it?
Currently, our multi-node logs only show the master node's logs (via the
Kubernetes API), which is insufficient for effective problem
localization if other nodes experience issues. Therefore, this pull
request adds the ability to upload logs for other nodes.
Next plan: Output structured directory logs, including logs from each
node and the polog.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
Currently, the vllm pr: https://github.com/vllm-project/vllm/pull/24252
is causing operator fusion to fail, which can be mitigated by patching
the backend. Once the problem is completely resolved, I will submit a
new pull request to remove the patch.
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
Description:
This PR updates the implementation of the Triton operator for deployment
on NPU devices, focusing on optimizing grid size and memory handling
based on NPU limitations.
Design Plan:
Grid Calculation: The grid size is now dynamically calculated by batch
and dim to ensure that the number of programs executed does not exceed
the NPU's vector core capacity. This ensures optimal parallelism without
overloading the hardware.
Data Block Handling: Due to the limited on-chip memory (UB) on Ascend
NPUs, this implementation splits large data into smaller chunks of 32k
or less per block. The kernel performs a for-loop to process the data in
these smaller chunks, minimizing memory usage and avoiding potential
overflows.
Changes Compared to GPU Implementation:
Grid and Block Sizing:
For GPU, the grid and block size were determined based on available
thread counts and memory size. In contrast, the NPU version dynamically
adjusts these parameters using B_TILE and BLOCK_N to optimize for NPU’s
architecture.
Memory Chunking:
The original GPU implementation did not require chunking due to the
higher available memory and processing capacity. For the NPU, data is
divided into smaller chunks (32k or smaller) to comply with memory
constraints on the device. The kernel has been modified to handle this
chunking mechanism inside a loop.
Optimized Thread Usage:
The NPU implementation takes into account the hardware-specific thread
limit (24 threads per vector core), ensuring that the number of active
programs is aligned with the NPU's vector core count, avoiding
over-subscription that would lead to serial processing.
This PR ensures that the operator functions efficiently on Ascend NPU,
considering hardware limitations while maintaining the same
functionality and input parameters as the GPU implementation.
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
Signed-off-by: maoxx241 <maomaoyu870@gmail.com>
### What this PR does / why we need it?
fix xlite decode-only e2e test, xlite decode-only mode utilizes
aclgraph's prefill and will be affected by aclgraph, so shortened test
length.
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
Signed-off-by: changdawei1 <changdawei3@huawei.com>
Co-authored-by: changdawei1 <changdawei3@huawei.com>
### What this PR does / why we need it?
Fixed the error in the CI process for
vllm-ascend/tests/e2e/nightly/ops/triton/test_rejection_sampler.py
Error: test_rejection_sampler_block_verify_triton_kernel: duplicate
parametrization of 'vocab_size'.
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
Signed-off-by: chenaoxuan <cax1165@163.com>
`VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE` is only used together with
`VLLM_ASCEND_ENABLE_PREFETCH_MLP` which is useless totally. This PR
remove 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?
When matmul_and_reduce is enabled, the prefix attribute is required.
However, in some models, the prefix is not passed correctly, causing
errors when starting the service.
The issue of incorrect prefix passing will be fixed in vLLM in the
future.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Wang Kunpeng <1289706727@qq.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?
add xlite e2e test
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
Signed-off-by: DaweiChang <405739598@qq.com>
### What this PR does / why we need it?
1. MagicMTP (paper: "Block Verification Accelerates Speculative
Decoding") was introduced to consider the influence among multiple draft
tokens, improving the acceptance rate without compromising accuracy.
2. The rejection sampling logic in rejection_sampler.py was restructured
using Triton-Ascend, enabling it to operate under high concurrency, thus
resolving CPU and NPU operator bottlenecks and enhancing throughput.
### Does this PR introduce _any_ user-facing change?
MagicMTP will automatically take effect when the parameter
"num_speculative_tokens" >= 3.
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
Signed-off-by: chenaoxuan <cax1165@163.com>
### What this PR does / why we need it?
This pull request introduces an L2 normalization kernel implemented in
Triton, specifically optimized for Ascend NPUs.
### Does this PR introduce _any_ user-facing change?
No, this PR does not introduce any user-facing changes.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: Ascendyh <hw7osiris@outlook.com>
Co-authored-by: Mengqing Cao <cmq0113@163.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>
`VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP` is not used anywhere, let's
remove 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?
1. Use optimized apply_top_k_top_p for NPU platfrom in rejection
sampler; (avoid scatter elements which can reduce ~26ms TPOT with bs=24
per DP)
2. <del>Avoid D2H Synchronization before calling npu_top_k_top_p
introduced by parameter validation which improves inference speed with
`async_scheduling` enabled;</del> In order to elminate the D2H
synchronization introduced by parameter validation before calling
`npu_top_k_top_p`, we directly drop this fused operator since the
performance improvement is not significant compared to async_scheduling
and may bring potential accuracy problem.
3. Refactor the implementation of AscendTopKTopPSampler to align that of
vLLM.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E serving test with combinations of `k=500` and `p=0.95` with
async_scheduling in single node and wide-EP scenarios.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Co-authored-by: realliujiaxu <realliujiaxu@163.com>
### What this PR does / why we need it?
[E2E] Optimize e2e test.
- Remove the test_basic_camem testcase.
- Change Qwen2.5-0.5B-Instruct-W8A8 to Qwen3-0.6B-W8A8
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: menogrey <1299267905@qq.com>
### What this PR does / why we need it?
Some E2E testcases are not in our CI workflow, this PR add them back.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: menogrey <1299267905@qq.com>
RFC: https://github.com/vllm-project/vllm-ascend/issues/4629
Reason：
The functions related to Cp differ significantly from those of normal
MLA-Attention, but the coupling is quite severe.
Steps：
1)Extract common code AscendMLAMetadataBuilder.build to 4 functions:
build_prefill_metadata, build_decode_metadata,build_cp_metadata,
build_chunked_metadata
todo：
1)refactor function _compute_prefill_context;
2)refactor function _mla_preprocess,_mla_decode_preprocess
3）Extract public data and processing functions from the attention_cp.py
and mla_cp.py files to the common_cp file.
vLLM version: 0.13.0rc3
vLLM main:
ad32e3e19c
- vLLM version: 0.13.0rc3
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wujinyuan1 <wjy9595@qq.com>
Signed-off-by: wujinyuan1 <wujinyuan1@huawei.com>
Co-authored-by: wujinyuan1 <wjy9595@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
Fix vllm break:
1. [Enable cuda graph for deepepHT, 5.3% throughput improvement, 4.4%
TTFT improvement] (https://github.com/vllm-project/vllm/pull/29558)
Fix Solution: Add the now-necessary `all2all_backend` parameter. The
impact of this parameter on the original `set_splitting_ops_for_v1`
implementation is only that graph mode is disabled in `vllm` if
`deepep_high_throughput` is enabled; it has no effect on the
`vllm-ascend` logic.
2.[Migrate legacy ViT MultiHeadAttention to new MMEncoderAttention
interface ] (https://github.com/vllm-project/vllm/pull/30684)
Fix Solution: The reason why the GPU does not need to convert qkv to 3D
is that the GPU's flash_attention operator is compatible with 3D and 4D
(b s h d and s b ( h d)), but the NPU's flash_attention_unpad operator
only supports 3D (s b ( h d)). Therefore, we need to introduce the
reshape_qkv_to_3d operation.
4.Skip Tencent-Hunyuan/HunyuanOCR test case, as it has following issue
in upgrade vllm code:
https://github.com/vllm-project/vllm-ascend/issues/5297
### How was this patch tested?
Co-authored-by: zxwang <1476209578@qq.com>
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
Signed-off-by: zxwang <1476209578@qq.com>
Co-authored-by: zxwang <1476209578@qq.com>
### What this PR does / why we need it?
Move all_reduce logic to AscendFusedMoE.forward, reuse vLLM's logic.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: weichen <calvin_zhu0210@outlook.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
efect e2e ci test：
1. tests/e2e/singlecard/pooling/test_embedding.py: remove the eager
parameter and rename test case
2. tests/e2e/singlecard/pooling/test_scoring.py: Rename test cases
3. tests/e2e/singlecard/pooling/test_classification.py: Rename test case
4. tests/e2e/singlecard/test_quantization.py: remove the eager parameter
and chage model to vllm-ascend/Qwen2.5-0.6B-W8A8 and Rename test case
5. tests/e2e/multicard/test_shared_expert_dp.py: Rename test cases
6. tests/e2e/singlecard/test_sampler.py: Rename test cases
7. tests/e2e/singlecard/test_aclgraph_accuracy.py: Rename test cases
8. tests/e2e/multicard/test_offline_inference_distributed.py: Rename
test cases and remove the eager parameter
9. tests/e2e/multicard/long_sequence/test_accuracy.py: Rename test cases
and remove the eager parameter
10. tests/e2e/multicard/long_sequence/test_basic.py: Rename test cases
and remove the eager parameter
11.tests/e2e/multicard/test_expert_parallel.py:remove the eager
parameter
12.tests/e2e/multicard/test_full_graph_mode.py:remove the eager
parameter
13.tests/e2e/multicard/test_ilama_lora_tp2.py:remove the eager parameter
14.tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py:remove
the eager parameter
15.tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py:remove the
eager parameter
16.tests/e2e/singlecard/test_aclgraph_accuracy.py:remove the eager
parameter
17.tests/e2e/singlecard/test_camem.py:remove the eager parameter
18.tests/e2e/singlecard/test_ilama_lora.py:remove the eager parameter
19.tests/e2e/singlecard/test_multistream_overlap_shared_expert.py:remove
the eager parameter
20.tests/e2e/singlecard/test_vlm.py:remove the eager parameter
21.tests/e2e/singlecard/test_xli:remove the eager parameter
### 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: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
Using `spawn` in continuous testing scenarios
### 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: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
- Standardize test case naming in `vllm-ascend/tests/e2e/multicard/` to
follow the `<model>_<feature>_<distributed>` convention.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain>
Co-authored-by: root <root@LAPTOP-VQKDDVMG.localdomain>
### What this PR does / why we need it?
Add dynamic EPLB CI for qwen3-moe-30B-W8A8
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: shenchuxiaofugui <1311027364@qq.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>
### What this PR does / why we need it?
In code files such as`mooncake_connector.py`,
`vllm_config.model_config.hf_config` is used to get the LLM configs.
This approach works for LLMs, but not for multi-modal models. For
multi-modal models, `vllm_config.model_config.hf_text_config` must be
used instead to get the LLM configs.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing UT
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: ApsarasX <apsarax@outlook.com>
### What this PR does / why we need it?
This PR updates the mm param --mm-processor-cache-gb, we need it to run
the case
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
### What this PR does / why we need it?
Currently, `torch_npu.npu_grouped_matmul_swiglu_quant` can only support
weight nz, so we need to trans w13_weight, w2_weight to nz forcely.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
### What this PR does / why we need it?
This PR deletes `cudagraph_batch_sizes` in `MtpProposer` and reuses the
one in `NPUModelRunner`.
During our deployment of DeepSeek-V3.2 with MTP across machines 2P2D and
conducting AISBench stress testing, an error occurred (see below). After
investigation, we found that
`compilation_config.cudagraph_capture_sizes` is modified by
`adjust_cudagraph_sizes_for_spec_decode` in `NPUModelRunner`. This
modification only updates `cudagraph_batch_sizes` in `NPUModelRunner`
but is not synchronized to `MtpProposer`. After discussion (CC @yiz-liu)
, we believe it is unnecessary to maintain `cudagraph_batch_sizes` in
`MtpProposer`; it should directly use the variable from
`NPUModelRunner`.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
Add a control to enable the exponential distribution operator
overlapping with model executing (default is OFF due to this feature
might not perform well on MOE models, i.e. For Qwen3-30B).
Enable async exponential overlapping will provides performance
improvement.
Also, overlapping the exponential operator with module execution can
cover the performance drop introduced by AICPU-version's exponential
operator.
**UPDATE**: (12/12)
Now our overlap will use the same stream that introduced in this pr:
#4908 .
We move the `do_async_exponential` from `model_runner_v1.py` to
`sampler.py`.
Now we are using `additional_config` to enable async exponential:
Add `"enable_async_exponential": 1` in `addition_config`.
Now we **ONLY** support default exponential/AI-CPU exponential, the old
`"enable_async_exponential": 2` option has been aborted to keep
consistency.
### Does this PR introduce _any_ user-facing change?
**YES**, added a new `additional_config` : `"enable_async_exponential":
1`.
When `enable_async_exponential` is set to 1, we enable the async
exponential and overlap with model runner.
When `enable_async_exponential` is set to 0 (default is 0), we disable
the async exponential, but exponential will still running on a different
stream using stream introduced in #4908.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: YuhanBai <yuhan.bai0830@gmail.com>
Signed-off-by: YuhanBai yuhan.bai0830@gmail.com
### What this PR does / why we need it?
add pcp accuracy e2e test case
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
unblock CI on suffix spec decoding
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
We decided to release v0.13.0 soon. So no need to support 0.12.0 now.
Let's drop it.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
For single node test, the lack of a retry mechanism for accessing
ModelScope resulted in an HTTP 400 error sometimes. I recommend using a
local offline cache instead.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
qwen3_next add fused_sigmoid_gating_delta_rule_update op which fused
fused_gdn_gating+fused_recurrent_gated_delta_rule
- vLLM version: v0.12.0
- 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?
In this PR, DispatchGmmCombineDecode add an optional input
x_active_mask, with which
only token masked True will be dispatched and handle.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
### What this PR does / why we need it?
Now `VLLM_ASCEND_ENABLE_NZ` will have three options:
0: disable nz;
1: only quant case enable nz;
2: enable nz as long as possible;
And `VLLM_ASCEND_ENABLE_NZ`=1 by default.
All cases are shown in the table below:
| | W4A4 | W4A8 | W8A8 | fp16/bf16 | fp32 |
|---|---|---|---|---|---|
| trans nz | can't support nz | trans nz by default | trans nz by
default | trans nz when VLLM_ASCEND_ENABLE_NZ is 2 | can't support nz |
| transpose | only support not transpose case | only support transpose
case | only support transpose case | linear: only support not transpose
case<br>gmm: only support transpose case | same to fp16/bf16 |
Some exceptional cases:
1. MLAPO op need to do some additional processing on the weights,
including trans nz. If use MLAPO op, some weight will be transformed to
nz forcely;
2. MLA/SFA's weight `W_UV` will be used by op
`torch.ops._C_ascend.batch_matmul_transpose`, and this op can't support
nz currently;
### Does this PR introduce _any_ user-facing change?
Now fp16/bf16 weight will not trans nz by default.
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
### What this PR does / why we need it?
support basic long_seq feature st
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: LookAround <lixushi@huawei.com>
### What this PR does / why we need it?
Remove Pangu Related Code
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
e2e & ut
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: weichen <calvin_zhu0210@outlook.com>
