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cutejax

cutedsl jax wrapper that adds some useful features. Mostly written by Fable, so caveat claudor. But it seems to work.

What it adds over cutlass.jax.cutlass_call

  • Pytree arguments that can contain code. cutlass_call only takes jax arrays. cutejax.call takes any pytree: arrays become kernel tensors, everything else (callables, config objects, eqx.Modules) becomes part of the cache key and runs as codegen inside the trace. This is how FlashAttention-4 flex-attention masks work in examples/fa4_attention.py.
  • Per-dimension static/dynamic choice. cutlass_call has one all-or-nothing use_static_tensors flag. ArraySpec(static_dims=...) lets you bake some dims in and leave others symbolic — e.g. static heads/head_dim, dynamic batch/seqlen.
  • Tied dimensions. ArraySpec(dim_names=...) makes two dims share one symbolic variable, so the compiler knows they're equal and the runtime checks it.
  • Small HLO. cutlass_call embeds the compiled object file in the HLO at every call site; cutejax registers a named FFI target, so the HLO is just a custom_call. Dispatch speed is the same either way.
  • A compile watchdog. Turns a runaway trace into an error instead of letting it eat all your RAM.
  • Convenience. cutejax.Module registers with both jax's and cute's pytree registries; cutejax.cond gives you data-dependent branches in undecorated code.
  • Works on older jax. Tested on 0.8.2 through 0.11.0.

When to use the built-in instead

  • You only pass arrays in and get arrays out — then cutejax buys you nothing.
  • You need non-row-major layouts. cutlass_call's TensorSpec(mode=...) can remap dimensions; cutejax can't, because the XLA FFI boundary doesn't carry strides, and rejects those layouts up front rather than reading them wrong.
  • You'd rather depend only on things NVIDIA ships and supports. cutejax needs jax-tvm-ffi, and is one person's side project.

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