Extending TorchInductor with FlyDSL: A New MLIR-Native Backend for High-Performance GEMMs

PyTorch Conference North America 2026

Oct 20, 2026 · 11:10–11:35 · Timezone not confirmed

TorchInductor is PyTorch's primary compiler backend, generating optimized kernels through graph lowering, autotuning, and backend-specific code generation. Recent efforts have expanded TorchInductor beyond Triton, demonstrating the value of a flexible multi-backend architecture. In this talk, we present the integration of FlyDSL, AMD's Python-native and MLIR-based GPU kernel DSL, into TorchInductor's GEMM compilation pipeline. We describe how FlyDSL connects to TorchInductor's autotuning framework, covering workload analysis, candidate generation, compilation, benchmarking, caching, and runtime backend selection. We discuss the design decisions that enable FlyDSL to coexist with existing TorchInductor backends while preserving the torch.compile user experience, along with challenges in kernel specialization, compilation latency, autotuning scalability, and backend fallback. Finally, we present performance results for transformer training and inference on AMD Instinct GPUs, comparing Triton and FlyDSL implementations. Attendees will gain practical insights into FlyDSL kernel design, how new compiler backends can be integrated into the PyTorch stack.

Session status: scheduled

Room: 210BF

Track: Kernel Engineering

Level: Any

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In person · San Jose Convention Center, San Jose, California, United States

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