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name: functional-transforms description: "Use Sonnet's functional transform API for stateless-style modules, gradients, jit/device helpers, and functional optimizers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Functional Transforms

Use this sub-skill for snt.functional.variables, transform, transform_with_state, without_state, grad, value_and_grad, jit, device_put, device_get, TensorVariable behavior, parameter/state dictionaries, and functional optimizers.

Start here

• references/api-reference.md: signatures, returned objects, TensorVariable semantics, and optimizer state.
• references/workflows.md: transform init/apply, state threading, gradients, and one-step functional optimizer recipes.
• references/troubleshooting.md: no-value TensorVariable errors, forgotten init, params/state confusion, and device surprises.
• scripts/sonnet_functional_transform_smoke.py: synthetic functional MLP + Adam smoke.

Boundaries

• Object-oriented modules: ../module-authoring/SKILL.md.
• Object-oriented optimizer loops: ../training-and-optimization/SKILL.md.
• Backend/device verification: ../serialization-and-distribution/SKILL.md.

Minimal protocol

Create modules inside fn.variables(), wrap a function with fn.transform or fn.transform_with_state, call init before apply, carry returned params and state explicitly, and use fn.value_and_grad plus a functional optimizer when the task wants stateless-style updates.