[ROCm] Use AITER fused_ar_rms API and refine use_1stage heuristic

[rbrugaro-amd]

Summary

• Migrate from the deprecated custom_fused_ar_rms to the newer
  fused_ar_rms keyword-based API in AITER.
• Add hidden_dim=7168 to the supported dimensions for the 1-stage
  allreduce+RMSNorm kernel.
• Refine use_1stage size thresholds based on profiling data
  (256 KB for TP ≤ 4, 128 KB for TP ≤ 8).
• Pass registered=is_capturing so AITER manages IPC buffers during
  CUDA-graph capture.

Motivation — use_1stage heuristic

Benchmarking fused allreduce+RMSNorm on TP 4 (hidden_dim=7168, bf16)
shows that the 1-stage kernel is faster up to concurrency 16, after
which the 2-stage kernel wins:

Concurrency	1-stage (µs)	2-stage (µs)	2-stage / 1-stage
4	5.3	8.4	1.58
8	6.0	8.7	1.45
16	7.6	9.3	1.22
32	11.6	11.1	0.96
64	19.5	15.3	0.78

The crossover falls between concurrency 16 and 32. The byte threshold
that gates 1-stage for TP ≤ 4 is derived from:

total_bytes = token_num × hidden_dim × element_size
           = 16 × 7168 × 2 = 224 KB < 256 KB  ✓

so size_ok = total_bytes < 256 KB ensures 1-stage is used up to
conc16 for the largest supported hidden_dim.

For TP ≤ 8 a more conservative threshold of 128 KB is applied, since
allreduce cost increases with world_size.

vllm & aiter version

aiter: 0.1.12.post2.dev29+gb633fba1c
aiter commit: b633fba1c
vllm: 0.19.1rc1.dev83+g83d09d36b.d20260413.rocm700
vllm commit: 83d09d3

Accuracy

No fusion:

With fusion:

Performance

Kimi-K2-Thinking-MXFP4 TP=4

CONC	ISL/OSL	baseline	allreduce	speedup
4	1k1k	197	203	1.03
8	1k1k	349	358	1.03
16	1k1k	573	592	1.03
32	1k1k	927	929	1.00
64	1k1k	1386	1397	1.01

For higher concurrencies we observe fewer calls meet the 1 stage fused condition and we see less uplift

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[tjtanaa]

@rbrugaro-amd which AITER version does this rely on? Currently upstream is still at v0.1.10.post3

[tjtanaa]

@rbrugaro-amd we will have to evaluate end to end performance and accuracy once you provide us the aiter version.

[tjtanaa]

Thanks for the getting the e2e accuracy and performance data. Will this PR also work for aiter version v0.1.10.post3 because for upstream we are at v0.1.10.post3

[rbrugaro-amd]

@tjtanaa I made one more commit that skips the fusion if the hidden dimension is outside the range of supported shapes by the fusion in the v1.10.post3 but will still get the benefit from our patch on later aiter versions. Please check

[ProExpertProg]

@tjtanaa what's the AITER versioning plan here? it would be good to know when we'll be able to remove the fallback. Also I think the comment is very verbose; the context is helpful, but I think this would be better served as a quick description and a link to a vLLM or even better AITER repo GH issue.

[tjtanaa]

We are planning for v0.1.10.post3 which is currently the active version on vLLM.

Greg is currently validating vLLM on v0.1.12.post1, if things go smoothly we will upgrade it by this week.

[ProExpertProg]

This lgtm overall btw

[rbrugaro-amd]

@ProExpertProg the issue is fixed in AITER ≥ 0.1.12 so there's nothing to file upstream. I've trimmed the comments and kept the permalink to the old DISPATCH_AR_FUSION_KERNEL macro. Happy to also open a vLLM tracking issue to remove this fallback once we bump the minimum AITER version — @tjtanaa, do you have a timeline for that bump?

[vllmellm]

@rbrugaro-amd there seem to be issue on our branch when enabling "fuse_allreduce_rms": true the accuracy of Qwen/Qwen3-30B-A3B-FP8 is 0. during debug mode it shows 2 pattern replaced for this fusion pass but there is an accuracy degrade. BTW the Kimi and deepseek models are skipped during compile range check since these models are large. That is why the accuracy report you have shared shows fine eval scores the fusion patterns are not actually replaced.
set VLLM_LOGGING_LEVEL=DEBUG when running fusion passes to see if the patterns are actually replaced.
we are debugging to find out the issue for accuracy drop. will update here as soon as we have more information.

[rbrugaro-amd]

@rbrugaro-amd there seem to be issue on our branch when enabling "fuse_allreduce_rms": true the accuracy of Qwen/Qwen3-30B-A3B-FP8 is 0. during debug mode it shows 2 pattern replaced for this fusion pass but there is an accuracy degrade. BTW the Kimi and deepseek models are skipped during compile range check since these models are large. That is why the accuracy report you have shared shows fine eval scores the fusion patterns are not actually replaced. set VLLM_LOGGING_LEVEL=DEBUG when running fusion passes to see if the patterns are actually replaced. we are debugging to find out the issue for accuracy drop. will update here as soon as we have more information.

@vllmellm was your run with this PR applied or just the original PR? we did see accuracy issues before but with the two PR's merged we see passing accuracy on Kimi-k2 and confirmed fusion is active. I will try to reproduce the issue on Qwen/Qwen3-30B-A3B-FP8 but I wanted to clarify what branch you tested with.

[vllmellm]

@rbrugaro-amd there seem to be issue on our branch when enabling "fuse_allreduce_rms": true the accuracy of Qwen/Qwen3-30B-A3B-FP8 is 0. during debug mode it shows 2 pattern replaced for this fusion pass but there is an accuracy degrade. BTW the Kimi and deepseek models are skipped during compile range check since these models are large. That is why the accuracy report you have shared shows fine eval scores the fusion patterns are not actually replaced. set VLLM_LOGGING_LEVEL=DEBUG when running fusion passes to see if the patterns are actually replaced. we are debugging to find out the issue for accuracy drop. will update here as soon as we have more information.

@vllmellm was your run with this PR applied or just the original PR? we did see accuracy issues before but with the two PR's merged we see passing accuracy on Kimi-k2 and confirmed fusion is active. I will try to reproduce the issue on Qwen/Qwen3-30B-A3B-FP8 but I wanted to clarify what branch you tested with.

Thank you for sharing this. I tested both our PR branch EmbeddedLLM:aiter-all-reduce-fused-rmsnorm and your PR branch rbugaro-amd:rbugaro/add_to37646
could you please share your commands as well with us and your docker image env?
we I am using rocm/vllm-dev:nightly but reinstalling aiter v0.1.10.post3 to match the upstream version.
using th following command
VLLM_LOGGING_LEVEL=DEBUG \ VLLM_ROCM_USE_AITER=1 vllm serve Qwen/Qwen3-30B-A3B-FP8 -tp 2 --port 9090 \ --compilation-config '{"pass_config": {"fuse_norm_quant": false, "fuse_allreduce_rms": true}}'

let me know if you could reproduce the error.

[rbrugaro-amd]

@vllmellm why that image? shouldn't we be testing with upstream that already has the pinned aiter version?

Reproducing the fusion on vllm/vllm-openai-rocm:nightly (amd-aiter 0.1.10.post3):

# 1. Clone the PR40773 branch (combines 2 PRs + reviewer feedback)
git clone --branch allreduce_rms_comb_37646_81 https://github.com/rbrugaro-amd/vllm.git
cd vllm

# 2. Generate a patch (applies only our PR changes, preserves upstream code)
MERGE_BASE=$(git merge-base HEAD origin/main)
git diff "$MERGE_BASE" HEAD -- \
    vllm/_aiter_ops.py \
    vllm/compilation/passes/fusion/allreduce_rms_fusion.py \
    vllm/compilation/passes/fusion/act_quant_fusion.py \
    vllm/compilation/passes/pass_manager.py \
    vllm/compilation/passes/vllm_inductor_pass.py \
    vllm/config/vllm.py \
    vllm/distributed/parallel_state.py \
    > /tmp/pr_changes.patch

# 3. Run on nightly image (amd-aiter 0.1.10.post3, TP=2)
docker run --rm \
  --ipc=host --shm-size=16g --network=host --privileged \
  --cap-add=CAP_SYS_ADMIN --device=/dev/kfd --device=/dev/dri --device=/dev/mem \
  --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
  -v /tmp/pr_changes.patch:/workspace/pr_patch.patch:ro \
  --entrypoint /bin/bash \
  vllm/vllm-openai-rocm:nightly \
  -c '
VLLM_SITE=$(python3 -c "import vllm, pathlib; print(pathlib.Path(vllm.__file__).parent)")
cd "$(dirname "$VLLM_SITE")"
patch -p1 --forward < /workspace/pr_patch.patch

export VLLM_LOGGING_LEVEL=DEBUG VLLM_ROCM_USE_AITER=1
vllm serve Qwen/Qwen3-30B-A3B-FP8 \
  -tp 2 --port 9090 --host 0.0.0.0 \
  --compilation-config "{\"splitting_ops\": [], \"pass_config\": {\"fuse_allreduce_rms\": true, \"fuse_act_quant\": false, \"fuse_norm_quant\": false}, \"custom_ops\": [\"none\", \"+rms_norm\"], \"compile_ranges_endpoints\": [64], \"cudagraph_mode\": \"full_and_piecewise\"}"
'

The nightly defaults splitting_ops to attention ops, which splits the graph at layer boundaries. This puts allreduce at the end of one subgraph and rms_norm at the start of the next, breaking adjacency for the fusion pattern. Setting it to [] produces a monolithic graph where all 97 allreduce→rms_norm pairs are fusible. The fusion still works with default splitting_ops (a few matches), but [] gets the full benefit.

Look for fusion pass matches: {'rocm_aiter_allreduce_rmsnorm_fusion_pass': 97} in the logs — 97 matches across both compile ranges on each worker.

Accuracy check (GSM8K, from another terminal once the server is healthy):

pip install "lm-eval[api]" datasets
export OPENAI_API_KEY=dummy HF_HUB_ENABLE_HF_TRANSFER=0

lm_eval --model openai-completions \
  --model_args "model=Qwen/Qwen3-30B-A3B-FP8,base_url=http://0.0.0.0:9090/v1/completions,trust_remote_code=True,add_bos_token=true,enforce_eager=true,num_concurrent=64,max_retries=10,max_gen_toks=1024,tokenizer_backend=huggingface" \
  --tasks gsm8k --num_fewshot 5 --batch_size 64 --limit 250

[vllmellm]

@vllmellm why that image? shouldn't we be testing with upstream that already has the pinned aiter version?

Reproducing the fusion on vllm/vllm-openai-rocm:nightly (amd-aiter 0.1.10.post3):

# 1. Clone the PR40773 branch (combines 2 PRs + reviewer feedback)
git clone --branch allreduce_rms_comb_37646_81 https://github.com/rbrugaro-amd/vllm.git
cd vllm

# 2. Generate a patch (applies only our PR changes, preserves upstream code)
MERGE_BASE=$(git merge-base HEAD origin/main)
git diff "$MERGE_BASE" HEAD -- \
    vllm/_aiter_ops.py \
    vllm/compilation/passes/fusion/allreduce_rms_fusion.py \
    vllm/compilation/passes/fusion/act_quant_fusion.py \
    vllm/compilation/passes/pass_manager.py \
    vllm/compilation/passes/vllm_inductor_pass.py \
    vllm/config/vllm.py \
    vllm/distributed/parallel_state.py \
    > /tmp/pr_changes.patch

# 3. Run on nightly image (amd-aiter 0.1.10.post3, TP=2)
docker run --rm \
  --ipc=host --shm-size=16g --network=host --privileged \
  --cap-add=CAP_SYS_ADMIN --device=/dev/kfd --device=/dev/dri --device=/dev/mem \
  --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
  -v /tmp/pr_changes.patch:/workspace/pr_patch.patch:ro \
  --entrypoint /bin/bash \
  vllm/vllm-openai-rocm:nightly \
  -c '
VLLM_SITE=$(python3 -c "import vllm, pathlib; print(pathlib.Path(vllm.__file__).parent)")
cd "$(dirname "$VLLM_SITE")"
patch -p1 --forward < /workspace/pr_patch.patch

export VLLM_LOGGING_LEVEL=DEBUG VLLM_ROCM_USE_AITER=1
vllm serve Qwen/Qwen3-30B-A3B-FP8 \
  -tp 2 --port 9090 --host 0.0.0.0 \
  --compilation-config "{\"splitting_ops\": [], \"pass_config\": {\"fuse_allreduce_rms\": true, \"fuse_act_quant\": false, \"fuse_norm_quant\": false}, \"custom_ops\": [\"none\", \"+rms_norm\"], \"compile_ranges_endpoints\": [64], \"cudagraph_mode\": \"full_and_piecewise\"}"
'

The nightly defaults splitting_ops to attention ops, which splits the graph at layer boundaries. This puts allreduce at the end of one subgraph and rms_norm at the start of the next, breaking adjacency for the fusion pattern. Setting it to [] produces a monolithic graph where all 97 allreduce→rms_norm pairs are fusible. The fusion still works with default splitting_ops (a few matches), but [] gets the full benefit.

Look for fusion pass matches: {'rocm_aiter_allreduce_rmsnorm_fusion_pass': 97} in the logs — 97 matches across both compile ranges on each worker.

Accuracy check (GSM8K, from another terminal once the server is healthy):

pip install "lm-eval[api]" datasets
export OPENAI_API_KEY=dummy HF_HUB_ENABLE_HF_TRANSFER=0

lm_eval --model openai-completions \
  --model_args "model=Qwen/Qwen3-30B-A3B-FP8,base_url=http://0.0.0.0:9090/v1/completions,trust_remote_code=True,add_bos_token=true,enforce_eager=true,num_concurrent=64,max_retries=10,max_gen_toks=1024,tokenizer_backend=huggingface" \
  --tasks gsm8k --num_fewshot 5 --batch_size 64 --limit 250

Thank you. it is working just fine. i will merge this first then resolve the merge conflict with upstream.