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LLMBoost is an MLIR-based compiler optimization solution that achieves 1.67x inference speedup on NVIDIA A30 clusters by automatically detecting and fusing the RMSNorm→Linear computation pattern in Transformers, eliminating one full HBM round trip.
Section 01
LLMBoost is an MLIR-based compiler optimization scheme targeting Transformer inference bottlenecks. Its core innovation is auto-detecting and fusing the RMSNorm→Linear pattern, eliminating one full HBM round trip. This achieves a 1.67x speedup on NVIDIA A30 clusters without model modifications, offering transparent gains for production deployments.
Section 02
In LLM inference, memory access often limits performance more than computational power. Transformer decoding layers execute RMSNorm followed by Linear—traditional implementations write RMSNorm results to HBM then read them immediately, creating a bottleneck for 4096-dimensional hidden layers.
Section 03
Key components:
llm.fused_rmsnorm_linear with TableGen shape validation.FuseRMSNormLinear.cpp detects exact RMSNorm→Linear patterns via iterator/block checks.__shfl_xor_sync and shared memory) to avoid global memory, plus cuBLAS HGEMM for Tensor Core use.Section 04
Setup: 4×NVIDIA A30 cluster (SM80, 24GB HBM2, CUDA12.3), input shape [512,4096] × [4096,4096] (fp16).
Latency: PyTorch (0.340ms,1x) vs LLMBoost (0.204ms,1.67x).
Correctness: Errors vs PyTorch fp32: max abs (1.07e-02), avg abs (9.27e-04), avg relative (1.48e-02) (all within fp16 tolerance).
Section 05
Why MLIR?
Section 06
Practical Benefits: Higher concurrency, better real-time experience, lower cloud costs. Future: Extend pattern matching to QKV projection fusion, linear+activation fusion as MLIR matures.
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