### What this PR does / why we need it?
Delete redundant codes related to communication
### Does this PR introduce _any_ user-facing change?
not involve
### How was this patch tested?
not involve
- vLLM version: v0.10.1.1
- vLLM main:
6c7af8110a
---------
Signed-off-by: 刘哲续 <liuzhexu1@huawei.com>
Co-authored-by: 刘哲续 <liuzhexu1@huawei.com>
### What this PR does / why we need it?
* **Unify execution paths:** Consolidates the quantized and
non-quantized execution paths into a single `fused_experts` function,
removing duplicated logic and making the control flow clearer and easier
to maintain.
* **W8A8 dynamic quantization:** Adds support for W8A8 dynamic
quantization inside the unified MoE kernel. Communication routines are
updated to correctly handle dynamic quantization scales for activations.
* **Weight pre-processing:** Prae-transpose the `w13` and `w2` weight
matrices (as implemented in PR #2025) so that quantized and
non-quantized models follow the same code path for the MoE gating,
up-projection, and down-projection operations.
* **All-to-all communication:** Adds an `all-to-all` collective
communication pattern. For large token counts on modern hardware,
`all-to-all` is more efficient than the previous `all-gather` strategy.
However, `all-to-all` is not really captured and replayed due to
multiple D2H operations which will trigger synchronization, and thus
raise error when capture graphs. We only use `all-to-all` when fallback
to `compiled_graph_for_general_shape`.
* **Dynamic communication selection:** The model runner now selects the
optimal MoE communication method (`mc2`, `allgather`, or `alltoall`) at
runtime based on token count and the Ascend SoC version.
* **Limitation:** `all-gather` is not yet supported for quantized
models, which means there is still something left to do on A2.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
No further test cases needed.
- vLLM version: v0.10.1.1
- vLLM main:
d660c98c1b
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
The determination of attention state, padding, and other forward
metadata has been moved to an earlier stage within the input preparation
process. This change enables us to utilize a single all-reduce
operation, maximizing synchronization efficiency as early as possible.
The logic for synchronizing metadata—such as the number of tokens,
prefill status, and DBO status—across data parallel (DP) ranks has now
been unified and simplified.
For performance improvements, the all-reduce operation has been switched
from the `gloo` backend to the `npu` backend, which results in an
reduction of several milliseconds per step (**approximately 10%
performance gain for TPOT!**).
Additionally, the multi-DP server hang issue has been resolved, ensuring
no more hangs occur when `num_requests < dp_size`. Alas, a relief.
Finally, the miscalculated memory usage issue has been addressed by
removing the unnecessary `DummyCommImpl`, allowing the system to use the
real communication method when determining available memory.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Maybe we should add an test case for multi-DP online server?
@MengqingCao
- vLLM version: v0.10.1.1
- vLLM main:
c5d004aaaf
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
This method replaces the previous all-gather approach for small numbers
of tokens.
The key changes include:
- A new `AscendFusedMoE` layer that handles token splitting, local
computation, and final aggregation via all-gather.
- Logic in the model runner to dynamically select between the new MC2
method and the existing all-gather method based on the number of input
tokens.
- Sharding the MoE communication mask across tensor-parallel ranks.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Test case fixed.
- vLLM version: v0.10.1.1
- vLLM main:
b00e69f8ca
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
This PR refactors the MoE (Mixture of Experts) communication logic by
introducing a strategy pattern. It defines an abstract base class,
`MoECommMethod`, which encapsulates different communication strategies
for MoE layers. By decoupling the MoE implementation from any single
communication method, this change makes it simpler to add, replace, or
optimize communication strategies in the future.
Plan / Roadmap
1. Introduce `MoECommMethod`, implement `AllGatherImpl`, and adapt ACL
Graph handling to cover all scenarios (this PR).
2. Implement `MC2CommImpl` and `AllToAllCommImpl` to optimize
performance in specific scenarios.
3. Enable W8A8 / Int8 models to use `unified_fused_experts`.
Other notes
* Data-parallel (DP) communication currently does not work with vLLM's
dispatch/combine mechanisms; an alternative approach is required to
resolve this incompatibility.
- vLLM version: v0.10.0
- vLLM main:
f7ad6a1eb3
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
