### 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?
1. Currently, eplb registers different attributes for different models,
but these attributes are not actually used. Now, these attributes are
directly deleted.
2. Add some log about eplb.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
#### Deepseek v3.1 chat
Of course! Here is a comprehensive explanation of deep learning, broken
down for clarity.\n\n### The Simple Analogy: A Child Learning to
Recognize a Cat\n\nImagine teaching a child what a cat is. You don't
give them a rulebook with instructions like \"has pointy ears, whiskers,
and a tail.\" Instead, you show them many pictures, saying \"this is a
cat\" or \"this is not a cat.\" The child's brain gradually learns to
identify the complex patterns—the combination of shapes, colors, and
textures—that define \"cat-ness.\"\n\n**Deep learning is essentially
this, but for computers.** It's a method for teaching computers to learn
from examples and recognize patterns directly from data (like images,
sound, or text) without being explicitly programmed with rigid
rules.\n\n---\n\n### The Technical Definition\n\n**Deep Learning is a
subfield of machine learning, which itself is a subfield of artificial
intelligence (AI).** It uses artificial **neural networks** with many
layers (\"deep\" networks) to model and understand complex patterns in
data.\n\nHere are the key concepts in that definition:\n\n1.
**Artificial Intelligence (AI):** The broad science of making machines
smart and capable of performing tasks that typically require human
intelligence.\n2. **Machine Learning (ML):** A subset of AI that gives
computers the ability to learn from data *without* being explicitly
programmed for every single rule.\n3. **Deep Learning (DL):** A
specific, powerful
- 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?
[Feature] Adapt DispathGmmCombineDecode opertor to align with weight
scale dtype of small operators.
- **Before**: weight scale must be float32
- **After**: weight scale can be float32/float16 when x is float16,
float32/bfloat16 when x is float32/bfloat16. And w1 scale can use
different dtype with w2 scale.
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?
### How was this patch tested?
#### Perf
> When scale is of type fp16 or bf16, it will be cast to fp32 internally
within the operator, while the subsequent computations remain unchanged.
Therefore, this PR will introduce an additional cast operation but halve
the memory copy operations for scale . Furthermore, since the scale data
is only a few KB in size and participates in relatively few
computations, its impact is almost negligible compared to major
operations like matrix multiplication. Thus, the theoretical performance
change should be minimal.
test single operator cases from qwen3-235b,
- single A3 node(ep16), 64 moe experts, 4 experts / die (like qwen3-235b
ep32)
- batch=18/32, token_hidden_size 4096, moe_intermediate_size 1536
The test was conducted for 100 rounds, and the average of the last 95
rounds was taken.
| | bs18(us)| bs32(us)|
| -----| -----| -----|
|Without this PR|96.28|108.83|
|With this PR|96.06|107.90|
Note: Single-operator benchmarks represent an ideal scenario. They are
usually only useful for referencing relative changes and may not fully
align with performance data observed within the full model.
#### 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 | 83.33 |
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wangqiankun <wangqiankun13@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?
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
### What this PR does / why we need it?
The expert mapping table and weights of the dynamic EPLB were not
updated, causing the accuracy to be correct but not effective. This bug
has now been fixed.
- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
---------
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
When using dynamic eplb,it will be blocking by nz tensor.We fix these
prolems by clone src tensor and recv tensor.
### Does this PR introduce any user-facing change?
### How was this patch tested?
Qwen3_moe in A3.
- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0
---------
Signed-off-by: offline0806 <3337230449@qq.com>
Co-authored-by: offline0806 <3337230449@qq.com>
### Motivation
Currently dynamically experts balancing would stop-the-world.
Asynchronously expert load balancing would be better without flowing
problems:
Host-bound latency:
There are many cpu operations during EPLB such as
eplb-algorithm、creating p2p ops、and log2phy expert converting would
spend long cpu time, as ~1s.
Communication latency: The transfer time would cost much in the
situation without nvlink. As the weight of an expert maybe transfer to
multiple new positions, thus N times send/recv for one expert, with
result long latency. We had tested that batch_isend_irecv cost more
100ms for 16 experts weight transmission in A2 server of ascend.
SwiftBalancer would not stop-the-world anymore, in out test on NPU 1~2ms
cost for each layer while benefit 5ms-8ms decode latency with ep_size =
64.
The following updates have been made:
1、expert distribution recording with lower cost.
2、async cpu computing for eplb algo and other python operator.
3、new eplb algo with less expert rebalancing while almost the same
effect.
### Proposed Change
We will gradually migrate the EPLB logic to the VLLM community and
implement a generalized design. Relevant RFC:
https://github.com/vllm-project/vllm/issues/22246
The overall workflow involves:
<img width="801" height="302"
alt="474430541-23b06f58-23bc-44a3-a1be-00f268aeb15c"
src="https://github.com/user-attachments/assets/1d73a459-1b23-4b0a-812a-bf0a75debfed"
/>
1. Record experts distribution during forward. We using expert_token_num
after disptach instead of topk_ids, thus we got much smaller tensor
shape to reduce cost of hbm recording and add-operator.
2. Do all-gather for experts distribution. Using all-gather instead of
all-reduce as less traffic volume.
3. Wake up eplb worker process with experts distribution when
num_iterations comes. Run eplb algorithm in eplb worker.
4. Generate p2p send/recv ops and other operator such as log2phy would
cost long cpu time.
5. Lanch ibatch_send_recv in async_stream before forward.
6. After forward, wait for the ibatch_send_recv finish, then do uapte
expert map and expert weights.
### Co-author
Co-authored-by: raindaywhu raindaywhu@raindaywhu@ 163.con
Co-authored-by: njuyuan yuanjl19@smail.nju.edu.cn
Co-authored-by: qmkakaxi wjh1594260677@qq.com
Co-authored-by: Skywalker-EP 173723846@qq.com
- vLLM version: v0.10.2
- vLLM main:
567939953b
---------
Signed-off-by: offline0806 <z00858301@china.huawei.com>
Co-authored-by: offline0806 <z00858301@china.huawei.com>
