Adding JAX and Numba support for Ops

PyTensor is able to convert its graphs into JAX and Numba compiled functions. In order to do this, each Op in an PyTensor graph must have an equivalent JAX/Numba implementation function.

This tutorial will explain how JAX and Numba implementations are created for an Op. It will focus specifically on the JAX case, but the same mechanisms are used for Numba as well.

Step 1: Identify the PyTensor Op you’d like to implement in JAX

Find the source for the PyTensor Op you’d like to be supported in JAX, and identify the function signature and return values. These can be determined by looking at the Op.make_node() implementation. In general, one needs to be familiar with PyTensor Ops in order to provide a conversion implementation, so first read Creating a new Op: Python implementation if you are not familiar.

For example, the EyeOp current has an Op.make_node() as follows:

def make_node(self, n, m, k):
    n = as_tensor_variable(n)
    m = as_tensor_variable(m)
    k = as_tensor_variable(k)
    assert n.ndim == 0
    assert m.ndim == 0
    assert k.ndim == 0
    return Apply(
        self,
        [n, m, k],
        [TensorType(dtype=self.dtype, shape=(None, None))()],
    )

The Apply instance that’s returned specifies the exact types of inputs that our JAX implementation will receive and the exact types of outputs it’s expected to return–both in terms of their data types and number of dimensions. The actual inputs our implementation will receive are necessarily numeric values or NumPy ndarrays; all that Op.make_node() tells us is the general signature of the underlying computation.

More specifically, the Apply implies that the inputs come from values that are automatically converted to PyTensor variables via as_tensor_variable(), and the asserts that follow imply that they must be scalars. According to this logic, the inputs could have any data type (e.g. floats, ints), so our JAX implementation must be able to handle all the possible data types.

It also tells us that there’s only one return value, that it has a data type determined by Eye.dtype, and that it has two non-broadcastable dimensions. The latter implies that the result is necessarily a matrix. The former implies that our JAX implementation will need to access the dtype attribute of the PyTensor EyeOp it’s converting.

Next, we can look at the Op.perform() implementation to see exactly how the inputs and outputs are used to compute the outputs for an Op in Python. This method is effectively what needs to be implemented in JAX.

Step 2: Find the relevant JAX method (or something close)

With a precise idea of what the PyTensor Op does we need to figure out how to implement it in JAX. In the best case scenario, JAX has a similarly named function that performs exactly the same computations as the Op. For example, the Eye operator has a JAX equivalent: jax.numpy.eye() (see the documentation).

If we wanted to implement an Op like IfElse, we might need to recreate the functionality with some custom logic. In many cases, at least some custom logic is needed to reformat the inputs and outputs so that they exactly match the Op’s.

Here’s an example for IfElse:

def ifelse(cond, *args, n_outs=n_outs):
    res = jax.lax.cond(
        cond, lambda _: args[:n_outs], lambda _: args[n_outs:], operand=None
    )
    return res if n_outs > 1 else res[0]

Step 3: Register the function with the jax_funcify dispatcher

With the PyTensor Op replicated in JAX, we’ll need to register the function with the PyTensor JAX Linker. This is done through the use of singledispatch. If you don’t know how singledispatch works, see the Python documentation.

The relevant dispatch functions created by singledispatch are pytensor.link.numba.dispatch.numba_funcify() and pytensor.link.jax.dispatch.jax_funcify().

Here’s an example for the EyeOp:

import jax.numpy as jnp

from pytensor.tensor.basic import Eye
from pytensor.link.jax.dispatch import jax_funcify


@jax_funcify.register(Eye)
def jax_funcify_Eye(op):

    # Obtain necessary "static" attributes from the Op being converted
    dtype = op.dtype

    # Create a JAX jit-able function that implements the Op
    def eye(N, M, k):
        return jnp.eye(N, M, k, dtype=dtype)

    return eye

Step 4: Write tests

Test that your registered Op is working correctly by adding tests to the appropriate test suites in PyTensor (e.g. in tests.link.test_jax and one of the modules in tests.link.numba.dispatch). The tests should ensure that your implementation can handle the appropriate types of inputs and produce outputs equivalent to Op.perform. Check the existing tests for the general outline of these kinds of tests. In most cases, a helper function can be used to easily verify the correspondence between a JAX/Numba implementation and its Op.

For example, the compare_jax_and_py() function streamlines the steps involved in making comparisons with Op.perform.

Here’s a small example of a test for Eye:

import pytensor.tensor as at

def test_jax_Eye():
    """Test JAX conversion of the `Eye` `Op`."""

    # Create a symbolic input for `Eye`
    x_at = at.scalar()

    # Create a variable that is the output of an `Eye` `Op`
    eye_var = at.eye(x_at)

    # Create an PyTensor `FunctionGraph`
    out_fg = FunctionGraph(outputs=[eye_var])

    # Pass the graph and any inputs to the testing function
    compare_jax_and_py(out_fg, [3])