The demonstration relies on an optimizer named einfold, which automatically produces the FlashAttention execution plan from standard relational queries. Inputs are formatted as tables of tokens, features, and values derived from xarray Datasets to compute attention using joins and sums. Rather than hardcoding transformer mechanics, the engine applies a generalized fusion rule for softmax-weighted averages that also optimizes non-attention operations like the Nadaraya–Watson estimator. The system evaluates causal attention across engines such as DuckDB and DataFusion, verifying forward passes and ddx-generated gradient calculations directly against JAX.
Key points
Relational query optimizers can reproduce specialized machine learning algorithms without…: Relational query optimizers can reproduce specialized machine learning algorithms without requiring attention-specific logic.
Input data structures leverage xarray Datasets to represent attention mechanics as sums c…: Input data structures leverage xarray Datasets to represent attention mechanics as sums calculated across standard relational joins.
Forward outputs and gradients produced in a single SQL statement match results from Googl…: Forward outputs and gradients produced in a single SQL statement match results from Google's JAX library.
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