### What this PR does / why we need it?
Qwen3.5 Moe supports enabling the dispatch_ffn_combine fusion operator.
Fix problem: In the w8a8 quantization scene, Qwen3.5 model's config.json
lacks the quantize field. The previous logic strictly relied on
quant_type == "w8a8_dynamic" to enable VLLM_ASCEND_ENABLE_FUSED_MC2.
This caused the dispatch_ffn_combine fusion operator to fail to activate
even when the environment variable was set.
Enable dispatch_ffn_combine fusion operator for BF16 scenarios.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: asunxiao <asunxiao@qq.com>
### What this PR does / why we need it?
Drop 0.16.0 support in main
- Fix eagle proposer break introduced by
https://github.com/vllm-project/vllm/pull/34552. Mainly change to use
the draft attention group to initialize the attention metadata builder.
- Fix the `ModelRunner` has no attribute `cudagraph_capture_sizes`
error, which is a bug in vLLM v0.17.0, and fixed by a later pr
https://github.com/vllm-project/vllm/pull/30515
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
# Feature: FlashLB algorithm
## Purpose
This Pull Request enhances the EPLB (Expert Parallelism Load Balancing)
system by introducing a novel load balancing algorithm: FlashLB.
1. The default algorithm adopts two separate sub-procedures to optimize
expert replication and placement independently:
a. **Expert Replica Allotment Sub-procedure** : Determines the number of
replicas for all experts. At each step, it greedily adds one more
replica to the expert with the highest per-replica load, aiming to
minimize load skew at the expert replica granularity (Min Max Replica,
MMR).
b. **Expert Replica Placement Sub-procedure** : Distributes all replicas
across devices. First, it sorts the generated replicas in descending
order of hotness, then iteratively places the currently hottest replica
onto the device with the lowest cumulative load and available slots.
However, this simplistic combination of two separate procedures lacks
synergy and often leads to sub-optimal load balancing. For example, in
the simple scenario illustrated below: Given 8 logical experts with
hotness values [600, 560, 120, 120, 20, 10, 10, 10], and 2 replicas
allocated per device across 8 devices, the default EPLB algorithm
results in a maximum per-device hotness of 232 (peak-average load ratio
1.28), while our proposed FlashLB algorithm reduces this value to 205
(peak-average load ratio 1.13).
<figure><img
src="https://github.com/user-attachments/assets/b9b10fab-651e-4524-9942-adbca8d044a4"
width="90%"</figure>
2. The default algorithm simply aggregates hotness measurements across
the entire profiling window. While this provides a coarse approximation
of the hotness distribution, it fails to capture the time-phased
variations and temporal correlations in expert hotness (both within and
between experts) across iterations—phenomena that have been observed in
real-world scenarios. Such single-point hotness estimation degrades the
solution quality of the load balancing algorithm.
3. The default algorithm regularly recalculates updated expert placement
results for all layers without discrimination. Considering that
excessive expert updates can impact Service Level Objectives (SLOs),
such full-scale redeployment leads to excessively high adjustment
overhead, which negatively affects end-to-end performance.
## FlashLB Algorithm Principle
### 1. Joint Optimization of Replica Allotment and Placement
FlashLB achieves joint optimization of replica allotment and placement
through a novel tree search approach, combined with carefully designed e
Fl fficient pruning and lightweight look-ahead estimation. We partition
all experts into several subsets, and for each subset, hierarchically
determine the optimal replica count and placement. Leveraging efficient
pruning and lightweight look-ahead estimation, the process consistently
aims to optimize the globally expected inter-device load balance degree
(considering both deployed and unexplored experts) while ensuring
sufficient computational efficiency. Additionally, precompilation
techniques are employed for acceleration, delivering load balancing that
is both high-quality and practically efficient.
### 2. Multi-Episode Enhancement
Instead of performing full-duration averaging like the default
algorithm, FlashLB partitions each profiling interval (e.g., 1024
iterations) into multiple consecutive smaller episodes (e.g., 16
iterations). This preserves hotness fluctuation and correlation
information. It then constructs a multi-objective optimization problem
to co-optimize these episodes simultaneously, enabling adaptability to
interleaved hotness patterns and improving statistical robustness.
### 3. Layer-wise Cherry-Picking Redeployment
To reduce the overhead of frequent expert redeployment, FlashLB
introduces a cherry-picking redeployment scheme. During each algorithmic
decision cycle, it real-time tracks load balance degree of all layers
and triggers expert placement updates only for those layers whose
peak-average ratio exceeds a predefined threshold. This avoids
unnecessary redeployment for stable layers, significantly reducing
adjustment overhead and thereby improving end-to-end performance gains.
## Co-author:
Co-authored-by: Skywalker-EP 173723846@qq.com
This PR mainly introduces two key optimizations for load balancing
scheduling:
1. **Add per-step heat collection function**:
Support real-time collection of per-step heat information during model
inference. This enables more fine-grained load balancing decisions by
taking per-step heat as the optimization target, improving scheduling
accuracy for dynamic and fluctuating workloads.
2. **Update FlashLB algorithm**:
Upgrade the FlashLB scheduling logic to better adapt to multi-stage heat
distribution scenarios. The improved algorithm can comprehensively
perceive and utilize multi-stage heat characteristics, achieving more
stable and efficient load balancing under complex expert deployment and
dynamic traffic patterns.
---------
Signed-off-by: Mercykid-bash <ruanche0218@gmail.com>
Signed-off-by: xuzewei28 <xuzewei2@h-partners.com>
Co-authored-by: xuzewei28 <xuzewei2@h-partners.com>
### What this PR does / why we need it?
If expert_map is on the device, there may be occasional repeated answers
in long output scenarios.
dsv3.2-exp-w8a8
No garbled characters are displayed in the output.
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2025 | ef2f4f | accuracy | gen | 60.00 |
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
This pull request addresses a bug related to the fused mc2 functionality
within the EPLB (Expert Parallelism Load Balancing) system, specifically
impacting quantization and MoE communication.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
Signed-off-by: Spicy-Stick <873805887@qq.com>
Signed-off-by: root <root@localhost.localdomain>
### What this PR does / why we need it?
Fix the moe_forward error when setting enable_static_kernel to true.
When static kernels are enabled, the forward pass runs twice
(compilation + capture), causing moe_layer_index to overflow. Wrap the
index to prevent out-of-bounds errors.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
CI passed with new added test
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
Signed-off-by: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@huawei.com>
### What this PR does / why we need it?
`mxfp_compat` only provides dtype/symbol compatibility helpers for
different `torch_npu` versions, but it was placed under
`vllm_ascend.quantization`. Importing it from device/ops paths could
trigger `quantization/__init__.py` and pull in heavy quantization method
dependencies, increasing startup coupling and causing import-cycle risk
(especially on 310P paths).
### Does this PR introduce _any_ user-facing change?
No functional behavior change intended.
### How was this patch tested?
CI passed.
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
1. Fix a vec error caused by unaligned UB accesss in the
DispatchFFNCombine;
2. Fix expert_token_nums tensor defined on host instead of NPU in
moe_comm_method.py
3. Fix multi-core copy issue of expert_token_nums in dispatchffnCombine
op (single aiv copy is sufficient)
### Does this PR introduce _any_ user-facing change?
No, this PR does not introduce any user-facing changes. The fix only
addresses internal memory access logic and does not modify any public
APIs, interfaces, or user-visible behaviors.
### How was this patch tested?
`export VLLM_ASCEND_ENABLE_FUSED_MC2=1`
vLLM version: v0.15.0
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: xulei_ict <xulei292@huawei.com>
Co-authored-by: xulei_ict <xulei292@huawei.com>
### What this PR does / why we need it?
To prevent confusion between different QuantType classes, we remove**
QuantType in prepare_finalize.py
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
This pull request integrates comprehensive support for Mixture of
Experts (MoE) models on the Ascend 310P device within the vllm-ascend
framework. It achieves this by introducing specialized modules for
expert selection, fused MoE layers, and optimized all-gather
communication. The changes also refine existing NPU operations, making
them more consistent and efficient for 310P, ultimately enhancing the
performance and compatibility of MoE models on this hardware.
Highlights
310P MoE Support: Introduces dedicated implementations for Mixture of
Experts (MoE) models on Ascend 310P devices, including new modules for
expert selection, fused MoE layers, and communication.
All-Gather Communication: Enforces the use of ALLGATHER communication
for MoE operations on 310P, optimizing data transfer and leveraging
NPU-specific token dispatching.
Simplified NPU Operations: Removes conditional type casting for
npu_swiglu and enables custom rotary embedding kernels unconditionally,
suggesting improved native support for 310P.
New MoE Classes Registered: Registers AscendFusedMoE310 and
AscendSharedFusedMoE310 to integrate 310P-specific MoE layers into the
system's custom operation registry.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
offline test and server test, with qwen3-30b-a3b,tp/ep 4 on 310p
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
### What this PR does / why we need it?
Add New Output for Expert Token Count
An additional output tensor expert_token_nums is added to both operators
to meet the requirement of tracking token distribution among experts:
Tensor Name: expert_token_nums
Dimension: 1D tensor
Shape: (local_expert_num,)
Data Type: int32
Semantics: Represents the number of tokens actually received by each
expert on the current card.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: guanguan0308 <1546542263@qq.com>
Signed-off-by: guanguan0308 <162653673+guanguan0308@users.noreply.github.com>
### What this PR does / why we need it?
This PR upgrades the vLLM dependency from `v0.14.1` to `v0.15.0`. This
involves:
- Updating the `VLLM_TAG` in all `Dockerfile`.
- Updating the vLLM version in `docs/source/conf.py`.
- Removing conditional code paths specific to `v0.14.1` across the
codebase, which simplifies maintenance.
- Fix `TypeError: MMEncoderAttention.__init__() got an unexpected
keyword argument 'multimodal_config'` due to
https://github.com/vllm-project/vllm/pull/31972.
- Fix `_shared_experts: 'NoneType' object is not callable` due to
https://github.com/vllm-project/vllm/pull/32082 by
https://github.com/vllm-project/vllm-ascend/pull/6335.
- Fix `ReshapeAndCacheOperation setup failed!` due to
https://github.com/vllm-project/vllm/pull/25954 by overriding attention
metadata slots.
This upgrade is necessary to keep the project aligned with the latest
features, bug fixes, and API changes in the vLLM project.
### Does this PR introduce _any_ user-facing change?
No, this is an internal dependency update and does not introduce any
user-facing changes.
### How was this patch tested?
CI is expected to pass with these changes, ensuring that all existing
tests are successful with the new vLLM version.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
co-authored-by: shen-shanshan <467638484@qq.com>
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
PR https://github.com/vllm-project/vllm/pull/32082 in vLLM makes
Qwen3-Moe models also go into `SharedFusedMoE`, while current
implementation of our `AscendSharedFusedMoE` assumes shared_experts
always exist. This PR adds checking to
`multistream_overlap_shared_expert` and `multistream_overlap_gate` in
order to only enable these features when shared experts exist.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
All ci passed
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: whx-sjtu <2952154980@qq.com>
This PR fixes the numerical error in moe_load accumulation under ACL
graph mode on NPU: using += for NPU tensors in graph mode does not throw
errors but leads to incorrect values, so we replace it with the in-place
add_() method to ensure accurate calculation.
Signed-off-by: Mercykid-bash <ruanche0218@gmail.com>
### What this PR does / why we need it?
Drop vLLM 0.13.0 support, upgrade to 0.14.0
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
In [PR 5040](https://github.com/vllm-project/vllm-ascend/pull/5040), the
`dispatch_gmm_combine_decode` operator was configured with an incorrect
global_bs parameter. This PR is to fix the bug.
The global_bs provided as input should have the same meaning as in the
`moe_distributed_dispatch` operator, specifically: (the maximum batch
size across all cards) * (expert parallel world size).
However, the implementation incorrectly used the variable
max_num_tokens, which does not account for tensor parallelism. This
error likely resulted in an unnecessarily large (overestimated) value.
More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Acc
test qwen3-235b eplb on a single A3 node(ep16),
with dispatch_gmm_combine_decode
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 80.00 |
- vLLM version: v0.13.0
- vLLM main:
11b6af5280
Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
### What this PR does / why we need it?
Set a additional config parameter to control whether the gmmswigluequant
fuseion operator is enabled; it is enabled by True. / When enabled with
a small number of GPUs, the gmmswigluquant fused operator can cause some
performance degradation.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
#### Perf
test model: GLM 4.6(w8a8)
- single A3 node(ep16, tp16), async-scheduling, mtp, FULL_DECODE_ONLY
- bs=1, input_lens=32000, ouput_lens=1024
Without this PR: TPOT 32.22.ms
With this PR: TPOT 30.23ms
---------
Signed-off-by: zjks98 <zhangjiakang4@huawei.com>
Co-authored-by: zjks98 <zhangjiakang4@huawei.com>
### What this PR does / why we need it?
1. If the model has dense layers, the current code will attempt to
obtain the routing experts of the dense layers, which will cause an
error. This should be fixed by modifying the code to skip the dense
layers when obtaining the routing experts.
2. The global_expert_map that the function directly outputs a affects
the performance of dsv3.2.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
DeepSeek V3.1 conversation is normal.
#### aime precision test (dsv3.1)
baseline without eplb
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 66.67 |
eplb
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 70.00 |
- vLLM version: v0.13.0
- vLLM main:
11b6af5280
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
1. Rename num_iterations_eplb_update to expert_heat_collection_interval.
2. Rename num_wait_worker_iterations to algorithm_execution_interval.
3. Rename init_redundancy_expert to num_redundant_experts because the
variable with the same meaning in vLLM is named this way.
4. Delete gate_eplb because we don't need this feature.
5. Move eplb config into a dict in additional config.
6. Depend on pr5817
### Does this PR introduce _any_ user-facing change?
before this pr：
`--additional-config '{"dynamic_eplb":true,
"num_iterations_eplb_update": 4000, "num_wait_worker_iterations": 150,
"init_redundancy_expert": 16, "expert_map_path": "xxx.json"}'`
after this pr:
`--additional-config
'{"eplb_config":{"dynamic_eplb":true,"expert_heat_collection_interval":4000,
"algorithm_execution_interval":150,"num_redundant_experts": 16,
"expert_map_path": "xxx.json"}}'`
### How was this patch tested?
#### test qwen3-235b eplb num_redundant_experts=16
without pr5817
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 83.33 |
with pr5817
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
Fix acc bug when enbale dispatch_gmm_combine_decode and eplb.
After eplb, expert table may change, so mapping is needed, while
fused_mc2 miss the mapping.
More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
without this pr, qwen3-235b eplb with dispatch_gmm_combine_decode get
acc 3.33% on aime2024.
with this pr,
test qwen3-235b eplb on a single A3 node(ep16)
without dispatch_gmm_combine_decode
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
with dispatch_gmm_combine_decode
| dataset | version | metric | mode | vllm-api-stream-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
### What this PR does / why we need it?
To support tensorList for dispatch_ffn_combine, to adjust eplb
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
Single Operator Testing
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: lhchg <lhao_cheng@163.com>
Co-authored-by: lihaocheng <lihaosheng1@h-partners.com>
### What this PR does / why we need it?
This PR enables custom op `aclnnMoeInitRoutingCustom` introduced in PR
#5251
### 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: QianChenxi <chenxi.qian.cq@outlook.com>
Signed-off-by: zzzzwwjj <1183291235@qq.com>
Co-authored-by: zzzzwwjj <1183291235@qq.com>
### What this PR does / why we need it?
1.replace moe_gating_top_k from torch_npu with custom op
2.enable the renorm function of moe_gating_top_k in softmax scenerio
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No need test
- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: ZCG12345 <2097562023@qq.com>
#### What this PR does / why we need it?
This PR adapt DispatchGmmCombineDecode operator to eplb tensor list and
expert token numbers.
This operator support gmm1, gmm2, gmm1Scale and gmm2Scale in format of
list.
This operator support couting how many token each local expert recieves
by expertTokensNum .
- vLLM version: v0.13.0
- vLLM main:
7157596103
More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476
#### Overview
This PR fixes a shape mismatch bug between `expert_placement_map` and
`log2phy_expert_map` when **redundant experts** are enabled in the
vLLM-Ascend platform. The issue occurred during the initialization of
expert maps and their updates via EPLB (Expert Load Balancer)
adjustment, leading to potential tensor shape errors and incorrect
expert routing in distributed MoE deployments.
#### Key Changes
1. **Unify expert map shape calculation logic**
- Ensure the shape of `expert_placement_map` and `log2phy_expert_map`
strictly aligns with the total number of experts (including redundant
experts) during initialization.
- Update the shape adjustment logic in EPLB dynamic update process to
match the initial expert map dimensions.
2. **Add shape consistency checks**
- Add assertion statements to verify the shape consistency of the two
maps after initialization and EPLB adjustment, preventing silent shape
mismatches in subsequent operations.
#### Impact
- Resolves tensor shape errors when using redundant experts with EPLB on
Ascend platform.
- Ensures correct expert routing and load balancing for MoE models with
redundant expert configurations.
- No breaking changes to existing functionality; compatible with
non-redundant expert deployments.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Che Ruan <cr623@ic.ac.uk>
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Co-authored-by: Che Ruan <cr623@ic.ac.uk>
Co-authored-by: shenchuxiaofugui <1311027364@qq.com>
This reverts commit fb9fdcdbe4.
### What this PR does / why we need it?
this pr breaks the smoke test because of that leads the error of
aclnnNeScalar:Kernel Run failed. opType: 25, NotEqual
launch failed for NotEqual, errno:361001
<img width="1149" height="166"
alt="A6C9453D-4F0B-4256-DD80-A9C181DAB2D9"
src="https://github.com/user-attachments/assets/cab9c4b8-3fd1-4c6b-b424-474b46042726"
/>
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: zxwang <1476209578@qq.com>
### What this PR does / why we need it?
Previously, it was necessary to set the environment variables
HCCL_INTRA_PCIE_ENABLE=1 and HCCL_INTRA_ROCE_ENABLE=0. This PR enables
hierarchical MC2 operations on A2 by default.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: hwhaokun <haokun0405@163.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>
Currently in the Fused MoE module, functions of classes like
MoECommMethod and MoETokenDispatcher output data in dictionary or tuple
format, which hampers code maintainability, readability, and
extensibility. This PR introduces dataclasses for these key output types
to address these issues.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
The float kernel of MOE_init_routing_v2 in the dispatch allgather
operation does not support tensor format for active_expert_range; it
only supports int.
PR5311 To unify the variables `local_num_experts` and
`self.local_num_experts`, `self.local_num_experts` was used
consistently, which led to the subsequent integer type parameter being
converted to a tensor type.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
gsm8k | exact_match,strict-match: ground_truth=0.89 | measured=0.8939 |
success=✅
gsm8k | exact_match,flexible-extract: ground_truth=0.85 | measured=0.856
| success=✅
ceval-valid | acc,none: ground_truth=0.84 | measured=0.8373 | success=✅
Model Parameters:
{'pretrained': 'Qwen/Qwen3-30B-A3B', 'tensor_parallel_size': 2, 'dtype':
'auto', 'trust_remote_code': False, 'max_model_len': 4096,
'gpu_memory_utilization': 0.6, 'enable_expert_parallel': True}
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
1. What this PR does / why we need it?
This PR supports the moe_gating_top_k operator, which enables
post-positioned renormalization (renorm) on the basis of softmax.
2. Does this PR introduce any user-facing change?
No user-facing changes are required.
3. How was this patch tested?
This patch was tested with the test_npu_moe_gating_top_k test case.
vLLM version: release/v0.13.0
vLLM main:
ad32e3e19c
---------
Signed-off-by: ZCG12345 <2097562023@qq.com>
Signed-off-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
Co-authored-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
