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Jax.jit

As explained in Deep Learning with R, Third Edition

jax$jit() is a JAX transformation that compiles a stateless function for optimized execution, typically through XLA.

jax.jit() compiles a stateless JAX function into XLA-compiled code, typically making repeated execution considerably faster. section 3.5.3

JIT-compile a dense layer

Wrap the complete stateless function with jax.jit(): dense <- jax$jit(\(inputs, W, b) { jax$nn$relu(jnp$matmul(inputs, W) + b) }). Inputs, weights, and bias remain explicit arguments. section 3.5.3

Use jax.jit() with these constraints:

  • Decorate only stateless functions. section 3.5.3
  • Return any tensors updated by the function rather than mutating hidden state. section 3.5.3

How Deep Learning with R, Third Edition covers it

How deep it goes
Defines compilation behavior and state constraints, then stops short of explaining XLA internals.
Examples
Demonstrates JIT-compiling a ReLU dense layer using matrix multiplication, weights, bias, and explicit arguments.
What it assumes
Assumes familiarity with JAX functions and arrays, while emphasizing stateless function design.
What you can do after
Apply jax.jit() to accelerate a stateless JAX function while returning updated tensors explicitly.
How it connects
Connects stateless functions with XLA compilation and other JAX transformations such as value_and_grad().
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