JAX FP8 matmul fusion tutorial. #133
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In this tutorial notebook, we investigate how the ML stack JAX + XLA handles the specificities of FP8 matmuls, while still generating an optimal fused kernel call including: * FP8 inputs scaling; * FP8 output scaling & clamping; * Non-linearity & bias fusing; * Abs-max output capture; Note: some open questions remain on bias or gelu fusing.
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In this tutorial notebook, we investigate how the ML stack JAX + XLA handles the specificities of FP8 matmuls,
while still generating an optimal fused kernel call including:
Note: some open questions remain on bias or gelu fusing.