### What this PR does / why we need it?
This PR focuses on performance optimization for the DispatchFFNCombine
operator. The key optimizations include:
1. Improving communication efficiency by merging the transmission of
tokens and scales;
2. Decoupling multi-core dependencies and reducing waiting bubbles in
the combine process through tile-granularity communication;
3. Optimizing the full-card synchronization overhead before the
umpermute operation.
These optimizations aim to reduce the overall execution latency of the
DispatchFFNCombine operator and enhance the runtime performance of the
model inference process on Ascend devices.
### Does this PR introduce _any_ user-facing change?
No. This PR only involves internal performance optimization of the
DispatchFFNCombine operator and does not introduce any changes to
user-facing APIs, interfaces, or behaviors.
### How was this patch tested?
1. Enable the DispatchFFNCombine operator by setting the environment
variable:
```
export VLLM_ASCEND_ENABLE_FUSED_MC2=1
```
2. Run the standard model inference test suite with the above
environment variable enabled;
4. Verify the correctness of model outputs (ensuring no functional
regression) and measure the performance improvement of the
DispatchFFNCombine operator (reduced latency and improved throughput).
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: xulei_ict <xulei292@huawei.com>
Co-authored-by: xulei_ict <xulei292@huawei.com>
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
Some synchronization logic of the fusion operator copies EP *
expertPerRank int32 values. This part of data contains synchronization
signals and data.
The 512B DataBlock of Ascend A3 writes all data in the same block
atomically to the HBM.
For the DeepSeek model, when expertPerRank per device is 16, the 512B
alignment is met in both 16-device single-node and 32-device two-node
scenarios. Therefore, we check the first position of each 512B data. If
the value is not 0, it indicates that the current 512B data has been
sent.
However, for other cases where expertPerRank per device is not 16, EP *
expertPerRank does not meet the 512B alignment. If the above logic is
used for checking, there will be problems.
Therefore, here we will pad the EP * expertPerRank data length to the
length aligned to 512B.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: lhchg <lhao_cheng@163.com>
Co-authored-by: lihaocheng <lihaosheng1@h-partners.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 introduces the Ascend implementation of the
`dispatch_ffn_combine` kernel and wires it into the vLLM-Ascend runtime,
together with follow‑up fixes to ensure the kernel builds and runs
correctly in CI.
- Add full host and device implementation of the `dispatch_ffn_combine`
kernel under `csrc/dispatch_ffn_combine`, including tiling logic, MOE
routing helpers, and kernel utilities for quantized FFN dispatch.
- Integrate the new kernel with the PyTorch binding
(csrc/torch_binding.cpp, csrc/torch_binding_meta.cpp) and the Ascend
runtime (vllm_ascend/ascend_forward_context.py,
vllm_ascend/worker/model_runner_v1.py).
- Extend fused MoE communication and token dispatch support in
`vllm_ascend/ops/fused_moe`, adding methods/utilities needed by the new
dispatch path.
- Update quantization logic in vllm_ascend/quantization/w8a8_dynamic.py
to support the new FFN dispatch flow.
- Fix kernel build issues by adjusting `csrc/build_aclnn.sh`, CMake
configuration, and include/namespace usage in the new kernel files.
- Add an end‑to‑end nightly test
`tests/e2e/nightly/ops/test_dispatch_ffn_combine.py` and helper
utilities in `vllm_ascend/utils.py` to validate the new kernel.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.12.0
---------
Signed-off-by: mojave2 <chenchen145@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
