### 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>
### 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>
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?
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?
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?
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>
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?
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?
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>
### What this PR does / why we need it?
vLLM community has integrated their MooncakeConnector. The original
scripts will now find this MooncakeConnector instead of the one from
vLLM-Ascend. All scripts that involve using the MooncakeConnector need
to be modified to another name.
### Does this PR introduce _any_ user-facing change?
Yes, users need to use a new name to load vLLM-Ascend MooncakeConnector.
### How was this patch tested?
By CI.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
### What this PR does / why we need it?
1. In addition to
[#4168](https://github.com/vllm-project/vllm-ascend/pull/4168),
[#5011](https://github.com/vllm-project/vllm-ascend/pull/5011)， this PR
adds two more pattern for AddRmsnormQuant with SP enabled. The key
difference is to insert an additional `maybe_all_gather_and_maybe_unpad`
between `addrmsnorm` and `quantize`.
2. This PR also introduce another api `torch.ops.vllm.quantize`, so that
we pass `input_scale` and `input_scale_reciprocal` at the same time.
This is because `npu_add_rms_norm_quant` and `npu_quantize` requires
different `div_mode`. To avoid introducing additional reciprocal
calculation in runtime, we have to pass both of them to quantize api.
3. Removes redundant `AscendQuantRmsnorm`.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Angazenn <supperccell@163.com>
#4257 This PR implements the dense_ffn TP of the first three layers of
the deepseek model, I have refactored this PR and used very little code
to support the implementation of this feature.
This PR adds a function `is_moe_layer` to mlp_tp, which supports MLP TP
in models with both mlp and moe, such as deepseek or chat GLM.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: 子潜 <ziqian@U-DMKXH32D-2015.local>
Co-authored-by: chenxiao <Jaychou1620@Gmail.com>
Co-authored-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
This PR aim to implement model runner v2 basic framework in vllm-ascend,
the e2e function is not guaranteed by this pr.
### Does this PR introduce _any_ user-facing change?
use envs.VLLM_USE_V2_MODEL_RUNNER to decide if choose model_runenr_v2.
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
We will expose the enabling switch for npugraph_ex to better facilitate
subsequent optimization.
### Does this PR introduce _any_ user-facing change?
Previously, the enable_npugraph_ex switch would trigger an error; now we
have removed the error reporting mechanism to better facilitate
subsequent optimization efforts.
Basic functionalities are available in CANN and torch_npu for Q3, while
advanced optimizations will depend on the Q4 release.
### How was this patch tested?
llm =LLM(
model=model,
enforce_eager=False ,
additional_config={
"enable_npugraph_ex": True
},
compilation_config={
"cudagraph_mode": "FULL_DECODE_ONLY",
"cudagraph_capture_sizes": [16],
},
}
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@huawei.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
delete sekf.in_profile_run in model_runner to make EPLB works as expect
### 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: zhenwenqi2024 <zhenwenqi_2022@qq.com>
Signed-off-by: Mengqing Cao <cmq0113@163.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
RFC: https://github.com/vllm-project/vllm-ascend/issues/4629
Reason:
We distinguish the branches based on the applicable scenarios of
pagedAttention and fusedInferAttention, making the code more clear.
At the same time, it is convenient for the subsequent iterations of
sliding_window and sinks and removePA ops after FIA is ready.
Todo:
remove PA ops after FIA is ready
add slidingwindow and ops for gpt_oss
replace FIA with FIA_v2
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>
### What this PR does / why we need it?
The mooncake_connector.py file was importing the wrong arguments to the
file, which could cause errors when use PCP; this issue has been
corrected.
### 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: daishixun <dsxsteven@sina.com>
### What this PR does / why we need it?
PanguProMoEV1 is no longer supported in vllm-ascend, remove 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>
### What this PR does / why we need it?
Rename `_910B` to `A2`;
Rename `_910_93` to `A3`;
Rename `_910_95` to `A5`;
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: zzzzwwjj <1183291235@qq.com>
### What this PR does / why we need it?
add the UT of pcp and dcp in the attention_cp file
### 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: pichangping <1337510399@qq.com>
### What this PR does / why we need it?
We refactored the eagle_proposer.py to adapt the framework of eagle.py
in vllm-v0.12.0, to support the logit of padded drafter batch and
async-scheduler.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
Co-authored-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
(1)refactor npu_model_runner for profile_run
(2) move _select_moe_comm_method to ascend_forward_context
(3) delete _init_model_kwargs in npu_model_runner
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Na
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
Signed-off-by: zhenwenqi2024 <155598497+zhenwenqi2024@users.noreply.github.com>
The `attn_metadata` is not used by any draft proposer, so we can remove
it.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
Adding UT for DCP/PCP
-vLLM version: v0.12.0
-vLLM main:
ad32e3e19c
Signed-off-by: zengran <zengran2@huawei.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：
Isolate PCP and DCP
(1) create a new python file: mla_cp.py
(2) add classes AscendMlaCPImpl and
AscendMlaCPMetadataBuilder，Inheritance AscendMLAImpl and
AscendMLAMetadataBuilder
(3) Remove PCP and DCP-related methods from mla_v1.py to mla_cp.py
vLLM version: v0.12.0
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wujinyuan1 <wjy9595@qq.com>
Co-authored-by: wujinyuan1 <wjy9595@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
add ut for model runner
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: LookAround <lixushi@huawei.com>
### What this PR does / why we need it?
moe multistream overlap to improve the performance.
### How was this patch tested?
--additional-config '{"multistream_overlap_gate": true}'
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: AlvisGong <gwly0401@163.com>
Signed-off-by: chenxiao <Jaychou1620@Gmail.com>
Co-authored-by: clrs97 <524936896@qq.com>
Co-authored-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: chenxiao <Jaychou1620@Gmail.com>
pick from https://github.com/vllm-project/vllm-ascend/pull/4736 to fix
the merge conflict
### What this PR does / why we need it?
Currently, the all_reduce operation in _sync_metadata_across_dp is
performed with gloo backend which is extremely time-consuming when
DPEngineCores are in different nodes. This operation cannot be ignored
by async scheduling in multi-node-scenarios with speculative decoding
(e.g., EAGLE, mtp).
This pr eliminates the all_reduce operation for D Nodes and change the
input parameter of MoEDispatch & MoeCombine operators to make MC2EP
support different num_tokens across all ranks.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Tested with PD disaggregation (2P: DP2TP8EP16 1D: DP8TP4EP32) scenarios
while enabling async scheduling. This pr can remove cross-node
all_reduce with gloo backend and further reduce latency with correct
accuracy.
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Co-authored-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
Add mtp_proposer ut
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: chenmenglong <chenmenglong1@huawei.com>
### What this PR does / why we need it?
refactor npu_modelrunner， we should be close to gpu_modelrunner
### Does this PR introduce _any_ user-facing change?
NO
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
Signed-off-by: zhenwenqi2024 <155598497+zhenwenqi2024@users.noreply.github.com>
### What this PR does / why we need it?
This PR fixes a bug in the moe_mlp module by correcting the arguments
passed to the torch_npu.npu_dequant_swiglu_quant function.It properly
converts group_list from a cumulative sum to counts for the group_index
parameter.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: tanqingshan (A) <50050625@china.huawei.com>
Co-authored-by: tanqingshan (A) <50050625@china.huawei.com>
Similar to #2309 , this PR introduces Embedding tensor model parallel to
achieve decreasing of memory consumption. It support both eager mode and
graph mode.
And this PR refactor module tensor parallel configurations supported in
#2309, #2167, #2120, merge all config into `finegrained_tp_config` in
`additional_config`, including:
`lmhead_tensor_parallel_size`
`oproj_tensor_parallel_size`
`embedding_tensor_parallel_size`
`mlp_tensor_parallel_size`
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: zzhx1 <zzh_201018@outlook.com>
Signed-off-by: zzhxx <zhangzihang23@mails.ucas.ac.cn>
Co-authored-by: zzhx1 <zzh_201018@outlook.com>
Co-authored-by: chenxiao <Jaychou1620@Gmail.com>
Co-authored-by: zzhxx <zhangzihang23@mails.ucas.ac.cn>
Co-authored-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
Currently, the initialization and fundamental functions of
RecomputeScheduler are broken with `vLLM v0.12.0`. This PR fixes the
conflicts of `RecomputeScheduler` and refactor its implementations by
inheriting original `Scheduler` of vLLM. Meanwhile, this PR also
supports async cheduling with recompute scheduler by implementing
`AsyncRecomputeScheduler` which is simply inherited `AsncyScheduler` of
vLLM and `RecomputeScheduler` of vLLM-Ascend with python MRO.
### Does this PR introduce _any_ user-facing change?
No. The switch naming is the same as v0.11.0 :
`recompute_scheduler_enable`
### How was this patch tested?
E2E serving with 2P1D dsv3.1 passed. The performance was the same as
original vllm scheduler with `async_scheduling` and preempted requests
in D Nodes are successfully transfered to Proxy and further to P Node.
This significantly improves the performance and robustness of PD
disaggregation deployments.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
mindie_turbo is out of data for long time. This PR remove the related register method.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
