Building Spyre as a Native PyTorch Device

IBM's torch-spyre team details how the Spyre dataflow AI accelerator was integrated into PyTorch as a native device using the PrivateUse1 mechanism. The post covers four mappings: execution pipelines to streams, device memory to the allocator, ahead-of-time compiled programs to launch-time patching, and independent pipelines to concurrent streams. It explains why an earlier runtime-graph-based approach was replaced by typed, ordered operation queues, and shows measured performance gains on Granite 3.3 8B (1.7x faster prefill, 2.4x faster decode) from removing graph construction overhead at launch time.

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