[ROCm] Disable AITER allreduce fusion for HIP graph replay#41816
[ROCm] Disable AITER allreduce fusion for HIP graph replay#41816akii96 wants to merge 1 commit intovllm-project:mainfrom
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Code Review
This pull request modifies the compilation backends and pass manager to ensure compatibility with ROCm AITER by disabling certain optimization passes, such as inplace functionalization and clone elimination, which can cause HIP graph replay corruption. Additionally, it simplifies the graph capture logic in the distributed module by removing AITER-specific context handling and reverts to using the standard all-reduce fusion pass. I have no feedback to provide as there were no review comments.
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ROCm AITER allreduce fusion and graph-capture integration can corrupt HIP graph replay, causing decode-time accuracy failures. This splits the draft vLLM PR vllm-project#41760 by Frida to address the accuracy issues alone while also scoping the graph-pass changes to ROCm AITER so other backends keep their existing compile pipeline. Co-authored-by: frida-andersson <fanderss@amd.com> Signed-off-by: Aakif Nawaz <aakif.nawaz@amd.com>
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Should the clone_elimination pass be disabled unconditionally for any model when aiter is on?
Or does it only affect specific models or backends?
Carry forward the DeepSeek-specific TP4 fixes from Frida's original ROCm HIP graph draft while keeping the shared graph replay fixes split out in vllm-project#41816. Co-authored-by: Frida Andersson <fanderss@amd.com> Signed-off-by: Aakif Nawaz <aakif.nawaz@amd.com>
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This PR is a slimmer derivative of #41760 where almost (95%) the same changes you see here helped DS3.2 accuracy issues as well. That PR was trying to push multiple feats in the same PR so we split them up and made this PR in a way that does not affect other backends Apart from that internal tests by others of the same #41760 PR also shows that the accuracy recovers for Kimi-K-2.5 I cannot promise to run it right away but, in an hour/ hour and half I can run a Kimi/DS test with this PR too |
What impact would removing these other passes (it's only the reduce+rms fusion that breaks accuracy almost across the board) have on the performance of LLama/Qwen models? |
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Closed in favor of the now merged solution #41972 |
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Purpose
This PR is a minimal split from draft PR #41760, which originally reported DeepSeek accuracy/output corruption under ROCm AITER HIP graph replay.
The same shared failure mode also affects non-DeepSeek ROCm AITER serving. In MiniMax/Kimi-K2.5-style workloads, HIP graph replay produces decode-time output corruption and severe accuracy loss. The common issue is not DeepSeek MLA logic itself, but the ROCm AITER allreduce fusion / graph capture / compiler pass interaction.
This PR keeps only the shared ROCm AITER graph replay fixes:
AllReduceFusionPassinstead ofRocmAiterAllReduceFusionPasswhen ROCm AITER is enabled.aiter_ar.capture().UnsafeCloneEliminationPassonly when ROCm AITER is enabled.VllmIRInplaceFunctionalizationPassonly when ROCm AITER is enabled.The DeepSeek-specific fixes from draft PR #41760 are intentionally excluded. Those changes involve model-specific MLA head support and DeepSeek MoE bias dtype handling. Keeping them separate lets this PR focus on the shared ROCm AITER HIP graph replay corruption path, while DeepSeek-specific behavior can be validated and reviewed independently.
Care was taken to avoid changing CUDA and non-AITER behavior. The broad compile-pass changes from the draft PR are scoped here to ROCm AITER only, so other backends keep their existing compilation pipeline.
Test Plan
Test Result
Validated on MiniMax serving with ROCm AITER enabled, using TP=2.
Serving configuration:
MiniMaxAI/MiniMax-M2.5VLLM_ROCM_USE_AITER=1,VLLM_ROCM_USE_AITER_MHA=0,VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION=1,VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=INT4--tensor-parallel-size 2,--attention-backend ROCM_AITER_UNIFIED_ATTN,--max-model-len 12288,--block-size 64,--max-num-seqs 512,--max-num-batched-tokens 32768,--gpu-memory-utilization 0.95,--performance-mode balanced,--async-scheduling,--no-enable-prefix-caching,--kv-cache-dtype auto,--compilation-config '{"mode":3}'Accuracy was measured with
lm-evalon GSM8K 5-shot usinglocal-completions,num_concurrent=32,max_gen_toks=1024,max_length=12288,--apply_chat_template, andtemperature=0.Accuracy
Serving Benchmark
Serving performance was measured with
vllm bench serveusing random prompts, ISL=1000, OSL=100, 512 requests,request-rate=inf, and 0 failed requests.Overall, this PR restores MiniMax ROCm AITER accuracy from near-zero GSM8K exact match to expected accuracy levels. The measured serving cost is small at concurrency 8, with output throughput down 1.6%, but more visible at concurrency 64, with output throughput down 19.0% and median TPOT up 38.8%.
Co-authored-by: frida-andersson fanderss@amd.com