jax.pure_callback(callback, result_shape_dtypes, *args, sharding=None, vectorized=False, **kwargs)[source]#

Applies a functionally pure Python callable. Works under jit()/pmap()/etc.

pure_callback enables calling a Python function in JIT-ed JAX functions. The input callback will be passed NumPy arrays in place of JAX arrays and should also return NumPy arrays. Execution takes place on CPU, like any Python+NumPy function.

The callback is treated as functionally pure, meaning it has no side-effects and its output value depends only on its argument values. As a consequence, it is safe to be called multiple times (e.g. when transformed by vmap() or pmap()), or not to be called at all when e.g. the output of a jit-decorated function has no data dependence on its value. Pure callbacks may also be reordered if data-dependence allows.

When pmap()-ed, the pure callback will be called several times (one on each axis of the map). When vmap-ed the behavior will depend on the value of the vectorized keyword argument. When vectorized is True, the callback is assumed to obey jax.vmap(callback)(xs) == callback(xs) == jnp.stack([callback(x) for x in xs]). Therefore, the callback will be called directly on batched inputs (where the batch axes are the leading dimensions). Additionally, the callbacks should return outputs that have corresponding leading batch axes. If not vectorized callback will be mapped sequentially across the batched axis. For example, if callback = lambda x, y: np.matmul(x, y), then we are free to set vectorized=True because the np.matmul function handles arbitrary leading batch dimensions.

  • callback (Callable[..., Any]) – A Python callable. The callable will be passed PyTrees of NumPy arrays as arguments, and should return a PyTree of NumPy arrays that matches result_shape_dtypes.

  • result_shape_dtypes (Any) – A PyTree with leaves that are objects with shape and dtype attributes which represent to the shapes and dtypes of the value of callback applied to args and kwargs.

  • *args (Any) – The positional arguments to the callback. Must be PyTrees of JAX types.

  • sharding (SingleDeviceSharding | None) – optional sharding that specifies the device from which the callback should be invoked.

  • vectorized (bool) – A boolean that indicates whether or not callback is vectorized, meaning it can handle arrays with additional leading dimensions. If vectorized is True, when the callback is mapped via jax.vmap, it will be called directly on inputs with leading batch dimensions instead of executing callback on each mapped input individually. The callback should also return outputs batched across the leading axis. By default, vectorized is False.

  • **kwargs (Any) – The keyword arguments to the callback. Must be PyTrees of JAX types.


The value of callback(*args, **kwargs).