FlyDSL
A Python-native, MLIR-based GPU kernel domain-specific language (DSL) introduced by AMD for generating optimized GPU kernels. It has been integrated into the TorchInductor compilation pipeline.
Timeline (last 90 days)
FlyDSL is a Python-native, MLIR-based GPU kernel DSL (Domain-Specific Language) introduced by AMD.
The AMD team integrated FlyDSL into TorchInductor as a new backend.
FlyDSL is connected to the core stages of Inductor, including lowering, compilation, caching, benchmarking, and the auto-tuning pipeline.
AMD has brought optimized kernels including GEMM, Group GEMM, and Flex Attention to PyTorch via FlyDSL.
On the AMD MI355, FlyDSL's FP16 GEMM achieved approximately 19% geometric mean speedup compared to Triton.
On the decode shapes of Minimax M3, FlyDSL Flex Attention achieved up to 5.8x speedup relative to Triton.
On the prefill sizes of DeepSeek V2 and V3, FlyDSL Flex Attention achieved close to a 2x improvement.
By being based on MLIR, FlyDSL can leverage MLIR's existing Pass infrastructure (such as loop transformations, vectorization, and memory hierarchy analysis) to generate code that more closely matches hardware characteristics, without needing to implement these optimization logics from scratch.
All Facts (8)
By being based on MLIR, FlyDSL can leverage MLIR's existing Pass infrastructure (such as loop transformations, vectorization, and memory hierarchy analysis) to generate code that more closely matches hardware characteristics, without needing to implement these optimization logics from scratch.
50%UnverifiedFlyDSL is a Python-native, MLIR-based GPU kernel DSL (Domain-Specific Language) introduced by AMD.
50%UnverifiedFlyDSL is connected to the core stages of Inductor, including lowering, compilation, caching, benchmarking, and the auto-tuning pipeline.
50%UnverifiedAMD has brought optimized kernels including GEMM, Group GEMM, and Flex Attention to PyTorch via FlyDSL.
50%UnverifiedOn the AMD MI355, FlyDSL's FP16 GEMM achieved approximately 19% geometric mean speedup compared to Triton.
50%UnverifiedOn the decode shapes of Minimax M3, FlyDSL Flex Attention achieved up to 5.8x speedup relative to Triton.
50%UnverifiedOn the prefill sizes of DeepSeek V2 and V3, FlyDSL Flex Attention achieved close to a 2x improvement.
50%UnverifiedThe AMD team integrated FlyDSL into TorchInductor as a new backend.
50%