[Hardware][AMD][Kernel] mori all2all backend integration

[whitememory]

Purpose

This PR is to integrate mori(https://github.com/ROCm/mori) kernel as all2all backend of vLLM to boost up EP performance at rocm. This is a work by a team from Moreh Inc.
Note that only low latency mode is available on mori. (internal static buffer size)
to activate mori -> install mori using from https://github.com/ROCm/mori and add export VLLM_ALL2ALL_BACKEND="mori" at server booting script

Test Plan

server

#!/bin/bash
export VLLM_ROCM_USE_AITER=1
export VLLM_RANDOMIZE_DP_DUMMY_INPUTS=1
export VLLM_ALL2ALL_BACKEND="mori"
export VLLM_MOE_DP_CHUNK_SIZE=1024
vllm serve "/app/model/DeepSeek-R1"
--served-model-name "deepseek-ai/DeepSeek-R1"
--port 8001
--trust-remote-code
--tensor-parallel-size 1
--data_parallel_size 8
--no-enable-prefix-caching
--enable-expert-parallel
--max-model-len 8192
--max-num-seqs 2048
--max_num_batched_token 32768
--gpu-memory-utilization 0.92
--block-size 1 --enforce-eager

curl test

curl http://localhost:8001/v1/chat/completions -H "Content-Type:
application/json" -d '{
"model": "deepseek-ai/DeepSeek-R1",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who won the world series in 2020?"}
],
"max_tokens": 200
}'

benchmark script

#!/bin/bash
model_name="deepseek-ai/DeepSeek-R1"
CON=2000

curl -X POST http://localhost:8001/reset_prefix_cache
date
vllm bench serve
--backend vllm
--model $model_name
--metric-percentiles "25,50,75"
--percentile-metrics "itl,tps,ttft,e2el"
--port 8001
--num-prompts $CON
--max-concurrency $CON
--ignore-eos
--dataset-name random
--random-input-len 2000
--random-output-len 100

Test Result

1. Introduction
   Since mori supports only low-latency, mori backend must use chunked forward (repeated moe)
Prefill performance can be worse than a2a backends which do not use chunked forward. (We set VLLM_MOE_DP_CHUNK_SIZE=1024, but test ISL was 2000 per each request)
   Thus, performance is measured using ITL metric.
   Cuda graph capture seems to have a problem at main 1405f0c, so we gave --enforce-eager to server argument.

2. Performance result

mori

---------------Inter-token Latency----------------
Mean ITL (ms): 1146.22
Median ITL (ms): 189.88
P25 ITL (ms): 175.00
P50 ITL (ms): 189.88
P75 ITL (ms): 199.09

default(allgather_reducescatter)

---------------Inter-token Latency----------------
Mean ITL (ms): 1087.56
Median ITL (ms): 193.03
P25 ITL (ms): 175.06
P50 ITL (ms): 193.03
P75 ITL (ms): 203.93

naive

---------------Inter-token Latency----------------
Mean ITL (ms): 1198.89
Median ITL (ms): 259.74
P25 ITL (ms): 244.77
P50 ITL (ms): 259.74
P75 ITL (ms): 274.06

3. Environment

test machine : mi300x
test model : DeepSeek-R1
base docker image : rocm/vllm-dev:official_0909_rc3_20250906
mori version : main 0a6f908
vllm version : 0.11.0rc1 and main 1405f0c

4. Etc tests
   4-1. test at 0.11.0rc1
   curl test passed at all a2a backends tested on rocm. (naive, allgather_reducescatter, mori)
   cuda graph capture passed at all a2a backends tested on rocm. (allgather_reducescatter, mori) - naive backend is supposed to fail
   with this fix from mori cuda graph replay is also fine.
   4-2. test at main 1405f0c -> main at commit 1405f0c seems to have problem.
   curl test DID NOT PASS using all a2a backends tested on rocm. (naive, allgather_reducescatter, mori)
   cuda graph capture DID NOT PASS using all a2a backends tested on rocm. (naive, allgather_reducescatter, mori) - naive backend is supposed to fail
   4-2-1.
   Result of curl test seems like, at main 1405f0c, server responded to “You are a helpful assistant.” not "Who won the world series in 2020?”.
   server at 0.11.0rc1 responded to "Who won the world series in 2020?” correctly.

5. Future works
   5-1. Even at 0.11.0rc1, cuda graph replay seems to have a problem. cuda graph capture is done, but it hangs at replay. We will investigate on this.
   5-2 integrate mori with DBO
   5-3. when mori HT mode releases, apply the mode to enhance prefill performance

---

Essential Elements of an Effective PR Description Checklist

• [O] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• [O] The test plan, such as providing test command.
• [O] The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[github-actions]

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[gemini-code-assist]

Code Review

This pull request integrates the mori all2all backend to enhance performance on ROCm platforms. The changes are extensive, touching communication, model execution layers, and configuration. My review focuses on the correctness and robustness of the new integration. I've identified a few critical issues related to state management, incorrect caching, and potential runtime errors that should be addressed to ensure the stability and correctness of this new feature.

[whitememory]

At my environment, the pre-commit hanged...

I will apply pre-commit result, and respond to any reviews or code-assists tomorrow.

[whitememory]

NOTE : we made a progress in cuda graph problem. (capture ok, but replay hang)
-> if some rank runs cuda graph, and other ranks do not run cuda graph (like dummy run in some versions of vllm). mori hangs (mori depends on a cpu variable, which is updated in eager mode, but not updated in cuda graph mode)
We believe it is mori's bug which can occur at some versions of vllm.
We will test more and my colleague will register PR to mori repo.
-> we found similar fix at mori branch, and it's merged. we are "fixing that fix" in this PR.

Test Result is updated.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @whitememory.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[ihbang]

I added "json_config" to EpDispatchCombineConfig and dispatch/combine methods of EpDispatchCombineOp to change its configs easily.
You can manage warp_num_per_block and block_num values with json file like below.
(VLLM_MORI_CONFIG_PATH should be set to the path of your json file)

{
  "global": {
    "warp_num_per_block": 8,
    "block_num": 80
  },
  "dispatch": {
    "warp_num_per_block": 16,
    "block_num": 80
  }
}

[HAIAI]

@whitememory may you fix the conflict? Thanks.

[whitememory]

@whitememory may you fix the conflict? Thanks.

@HAIAI
we fixed the conflict, thank you

[whitememory]

@HAIAI
Is it possible to continue reviewing before more conflicts come in?

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @whitememory.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[github-actions]

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[github-actions]

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