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[Docs] Reduce custom syntax used in docs (#27009)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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Harry Mellor
2025-10-17 04:05:34 +01:00
committed by GitHub
parent 965c5f4914
commit 4ffd6e8942
65 changed files with 381 additions and 402 deletions
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docs/design/fused_moe_modular_kernel.md
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@@ -2,7 +2,7 @@
## Introduction
FusedMoEModularKernel is implemented [here](gh-file:/vllm/model_executor/layers/fused_moe/modular_kernel.py)
FusedMoEModularKernel is implemented [here](../..//vllm/model_executor/layers/fused_moe/modular_kernel.py)
Based on the format of the input activations, FusedMoE implementations are broadly classified into 2 types.
@@ -44,7 +44,7 @@ FusedMoEModularKernel splits the FusedMoE operation into 3 parts,
The TopK Weight Application and Reduction components happen right after the Unpermute operation and before the All2All Combine. Note that the `FusedMoEPermuteExpertsUnpermute` is responsible for the Unpermute and `FusedMoEPrepareAndFinalize` is responsible for the All2All Combine. There is value in doing the TopK Weight Application and Reduction in the `FusedMoEPermuteExpertsUnpermute`. But some implementations choose to do it `FusedMoEPrepareAndFinalize`. In order to enable this flexibility, we have a TopKWeightAndReduce abstract class.
Please find the implementations of TopKWeightAndReduce [here](gh-file:vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py).
Please find the implementations of TopKWeightAndReduce [here](../../vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py).
`FusedMoEPrepareAndFinalize::finalize()` method accepts a `TopKWeightAndReduce` argument that is invoked inside the method.
The `FusedMoEModularKernel` acts as a bridge between the `FusedMoEPermuteExpertsUnpermute` and `FusedMoEPerpareAndFinalize` implementations to determine where the TopK Weight Application and Reduction happens.
@@ -138,7 +138,7 @@ Typically a FusedMoEPrepareAndFinalize type is backed by an All2All Dispatch & C
#### Step 1: Add an All2All manager
The purpose of the All2All Manager is to set up the All2All kernel implementations. The `FusedMoEPrepareAndFinalize` implementations typically fetch a kernel-implementation "handle" from the All2All Manager to invoke the Dispatch and Combine functions. Please look at the All2All Manager implementations [here](gh-file:vllm/distributed/device_communicators/all2all.py).
The purpose of the All2All Manager is to set up the All2All kernel implementations. The `FusedMoEPrepareAndFinalize` implementations typically fetch a kernel-implementation "handle" from the All2All Manager to invoke the Dispatch and Combine functions. Please look at the All2All Manager implementations [here](../../vllm/distributed/device_communicators/all2all.py).
#### Step 2: Add a FusedMoEPrepareAndFinalize Type
@@ -213,29 +213,29 @@ Please take a look at [init_prepare_finalize](https://github.com/vllm-project/vl
### How To Unit Test
We have `FusedMoEModularKernel` unit tests at [test_modular_kernel_combinations.py](gh-file:tests/kernels/moe/test_modular_kernel_combinations.py).
We have `FusedMoEModularKernel` unit tests at [test_modular_kernel_combinations.py](../../tests/kernels/moe/test_modular_kernel_combinations.py).
The unit test iterates through all combinations of `FusedMoEPrepareAndFinalize` and `FusedMoEPremuteExpertsUnpermute` types and if they are
compatible, runs some correctness tests.
If you are adding some `FusedMoEPrepareAndFinalize` / `FusedMoEPermuteExpertsUnpermute` implementations,
1. Add the implementation type to `MK_ALL_PREPARE_FINALIZE_TYPES` and `MK_FUSED_EXPERT_TYPES` in [mk_objects.py](gh-file:tests/kernels/moe/modular_kernel_tools/mk_objects.py) respectively.
1. Add the implementation type to `MK_ALL_PREPARE_FINALIZE_TYPES` and `MK_FUSED_EXPERT_TYPES` in [mk_objects.py](../../tests/kernels/moe/modular_kernel_tools/mk_objects.py) respectively.
2. Update `Config::is_batched_prepare_finalize()`, `Config::is_batched_fused_experts()`, `Config::is_standard_fused_experts()`,
`Config::is_fe_16bit_supported()`, `Config::is_fe_fp8_supported()`, `Config::is_fe_block_fp8_supported()`,
`Config::is_fe_supports_chunking()` methods in [/tests/kernels/moe/modular_kernel_tools/common.py](gh-file:tests/kernels/moe/modular_kernel_tools/common.py)
`Config::is_fe_supports_chunking()` methods in [/tests/kernels/moe/modular_kernel_tools/common.py](../../tests/kernels/moe/modular_kernel_tools/common.py)
Doing this will add the new implementation to the test suite.
### How To Check `FusedMoEPrepareAndFinalize` & `FusedMoEPermuteExpertsUnpermute` Compatibility
The unit test file [test_modular_kernel_combinations.py](gh-file:tests/kernels/moe/test_modular_kernel_combinations.py) can also be executed as a standalone script.
The unit test file [test_modular_kernel_combinations.py](../../tests/kernels/moe/test_modular_kernel_combinations.py) can also be executed as a standalone script.
Example: `python3 -m tests.kernels.moe.test_modular_kernel_combinations --pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts`
As a side effect, this script can be used to test `FusedMoEPrepareAndFinalize` & `FusedMoEPermuteExpertsUnpermute` compatibility. When invoked
with incompatible types, the script will error.
### How To Profile
Please take a look at [profile_modular_kernel.py](gh-file:tests/kernels/moe/modular_kernel_tools/profile_modular_kernel.py)
Please take a look at [profile_modular_kernel.py](../../tests/kernels/moe/modular_kernel_tools/profile_modular_kernel.py)
The script can be used to generate Torch traces for a single `FusedMoEModularKernel::forward()` call for any compatible
`FusedMoEPrepareAndFinalize` and `FusedMoEPermuteExpertsUnpermute` types.
Example: `python3 -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel --pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts`