Adding JAX, Numba and Pytorch support for Ops#
PyTensor is able to convert its graphs into JAX, Numba and Pytorch compiled functions. In order to do
this, each Op in an PyTensor graph must have an equivalent JAX/Numba/Pytorch implementation function.
This tutorial will explain how JAX, Numba and Pytorch implementations are created for an Op.
Step 1: Identify the PyTensor Op you’d like to implement#
Find the source for the PyTensor Op you’d like to be supported 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, you want to extend support for CumsumOp:
class CumsumOp(Op):
__props__ = ("axis",)
def __new__(typ, *args, **kwargs):
obj = object.__new__(CumOp, *args, **kwargs)
obj.mode = "add"
return obj
CumsumOp turns out to be a variant of CumOpOp
which currently has an Op.make_node() as follows:
def make_node(self, x):
x = ptb.as_tensor_variable(x)
out_type = x.type()
if self.axis is None:
out_type = vector(dtype=x.dtype) # Flatten
elif self.axis >= x.ndim or self.axis < -x.ndim:
raise ValueError(f"axis(={self.axis}) out of bounds")
return Apply(self, [x], [out_type])
The Apply instance that’s returned specifies the exact types of inputs that
our 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/shapes.
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 there is one input that is
automatically converted to PyTensor variables via as_tensor_variable().
There is another parameter, axis, that is used to determine the direction
of the operation, hence shape of the output. The check that follows imply that
axis must refer to a dimension in the input tensor. The input’s elements
could also have any data type (e.g. floats, ints), so our 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 x.type() i.e., the data type of the original tensor.
This implies that the result is necessarily a matrix.
Some class may have a more complex behavior. For example, the CumOpOp
also has another variant CumprodOpOp with the exact signature
as CumsumOpOp. The difference lies in that the mode attribute in
CumOp definition:
class CumOp(COp):
# See function cumsum/cumprod for docstring
__props__ = ("axis", "mode")
check_input = False
params_type = ParamsType(
c_axis=int_t, mode=EnumList(("MODE_ADD", "add"), ("MODE_MUL", "mul"))
)
def __init__(self, axis: int | None = None, mode="add"):
if mode not in ("add", "mul"):
raise ValueError(f'{type(self).__name__}: Unknown mode "{mode}"')
self.axis = axis
self.mode = mode
c_axis = property(lambda self: np.MAXDIMS if self.axis is None else self.axis)
__props__ is used to parametrize the general behavior of the Op. One need to
pay attention to this to decide whether the implementation should support all variants
or raise an explicit NotImplementedError for cases that are not supported e.g., when
CumsumOp of CumOp("add") is supported but not CumprodOp of
CumOp("mul").
Next, we 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.
Step 2: Find the relevant method in JAX/Numba/Pytorch (or something close)#
With a precise idea of what the PyTensor Op does we need to figure out how
to implement it in JAX, Numba or Pytorch. In the best case scenario, there is 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()
and a Pytorch equivalent: torch.eye().
If we wanted to implement an Op like DimShuffle, 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 DimShuffle:
def dimshuffle(x, op):
res = jnp.transpose(x, op.transposition)
shape = list(res.shape[: len(op.shuffle)])
for augm in op.augment:
shape.insert(augm, 1)
res = jnp.reshape(res, shape)
if not op.inplace:
res = jnp.copy(res)
return res
def numba_funcify_DimShuffle(op, node, **kwargs):
shuffle = tuple(op.shuffle)
transposition = tuple(op.transposition)
augment = tuple(op.augment)
inplace = op.inplace
ndim_new_shape = len(shuffle) + len(augment)
no_transpose = all(i == j for i, j in enumerate(transposition))
if no_transpose:
@numba_basic.numba_njit
def transpose(x):
return x
else:
@numba_basic.numba_njit
def transpose(x):
return np.transpose(x, transposition)
shape_template = (1,) * ndim_new_shape
# When `len(shuffle) == 0`, the `shuffle_shape[j]` expression below
# is typed as `getitem(Tuple(), int)`, which has no implementation
# (since getting an item from an empty sequence doesn't make sense).
# To avoid this compile-time error, we omit the expression altogether.
if len(shuffle) > 0:
# Use the statically known shape if available
if all(length is not None for length in node.outputs[0].type.shape):
shape = node.outputs[0].type.shape
@numba_basic.numba_njit
def find_shape(array_shape):
return shape
else:
@numba_basic.numba_njit
def find_shape(array_shape):
shape = shape_template
j = 0
for i in range(ndim_new_shape):
if i not in augment:
length = array_shape[j]
shape = numba_basic.tuple_setitem(shape, i, length)
j = j + 1
return shape
else:
@numba_basic.numba_njit
def find_shape(array_shape):
return shape_template
if ndim_new_shape > 0:
@numba_basic.numba_njit
def dimshuffle_inner(x, shuffle):
x = transpose(x)
shuffle_shape = x.shape[: len(shuffle)]
new_shape = find_shape(shuffle_shape)
# FIXME: Numba's `array.reshape` only accepts C arrays.
res_reshape = np.reshape(np.ascontiguousarray(x), new_shape)
if not inplace:
return res_reshape.copy()
else:
return res_reshape
else:
@numba_basic.numba_njit
def dimshuffle_inner(x, shuffle):
return np.reshape(np.ascontiguousarray(x), ())
# Without the following wrapper function we would see this error:
# E No implementation of function Function(<built-in function getitem>) found for signature:
# E
# E >>> getitem(UniTuple(int64 x 2), slice<a:b>)
# E
# E There are 22 candidate implementations:
# E - Of which 22 did not match due to:
# E Overload of function 'getitem': File: <numerous>: Line N/A.
# E With argument(s): '(UniTuple(int64 x 2), slice<a:b>)':
# E No match.
# ...(on this line)...
# E shuffle_shape = res.shape[: len(shuffle)]
@numba_basic.numba_njit(inline="always")
def dimshuffle(x):
return dimshuffle_inner(np.asarray(x), shuffle)
return dimshuffle
def dimshuffle(x, op):
res = torch.permute(x, op.transposition)
shape = list(res.shape[: len(op.shuffle)])
for augm in op.augment:
shape.insert(augm, 1)
res = torch.reshape(res, shape)
if not op.inplace:
res = res.clone()
return res
In this case, CumOp is implemented with NumPy’s numpy.cumsum()
and numpy.cumprod(), which have JAX equivalents: jax.numpy.cumsum()
and jax.numpy.cumprod(). The Pytorch equivalents are torch.cumsum()
and torch.cumprod()
def perform(self, node, inputs, output_storage):
x = inputs[0]
z = output_storage[0]
if self.mode == "add":
z[0] = np.cumsum(x, axis=self.axis)
else:
z[0] = np.cumprod(x, axis=self.axis)
Step 3: Register the function with the respective dispatcher#
With the PyTensor Op replicated, we’ll need to register the
function with the backends 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(),
pytensor.link.jax.dispatch.jax_funcify() and pytensor.link.pytorch.dispatch.pytorch_funcify().
Here’s an example for the CumOpOp:
import jax.numpy as jnp
from pytensor.tensor.extra_ops import CumOp
from pytensor.link.jax.dispatch import jax_funcify
@jax_funcify.register(CumOp)
def jax_funcify_CumOp(op, **kwargs):
axis = op.axis
mode = op.mode
def cumop(x, axis=axis, mode=mode):
if mode == "add":
return jnp.cumsum(x, axis=axis)
else:
return jnp.cumprod(x, axis=axis)
return cumop
Suppose jnp.cumprod does not exist, we will need to register the function as follows:
import jax.numpy as jnp
from pytensor.tensor.extra_ops import CumOp
from pytensor.link.jax.dispatch import jax_funcify
@jax_funcify.register(CumOp)
def jax_funcify_CumOp(op, **kwargs):
axis = op.axis
mode = op.mode
def cumop(x, axis=axis, mode=mode):
if mode == "add":
return jnp.cumsum(x, axis=axis)
else:
raise NotImplementedError("JAX does not support cumprod function at the moment.")
return cumop
import numpy as np
from pytensor import config
from pytensor.graph import Apply
from pytensor.link.numba.dispatch import basic as numba_basic
from pytensor.tensor import TensorVariable
from pytensor.tensor.extra_ops import CumOp,
def numba_funcify_CumOp(op: CumOp, node: Apply, **kwargs):
axis = op.axis
mode = op.mode
ndim = cast(TensorVariable, node.outputs[0]).ndim
if axis is not None:
if axis < 0:
axis = ndim + axis
if axis < 0 or axis >= ndim:
raise ValueError(f"Invalid axis {axis} for array with ndim {ndim}")
reaxis_first = (axis, *(i for i in range(ndim) if i != axis))
reaxis_first_inv = tuple(np.argsort(reaxis_first))
if mode == "add":
if axis is None or ndim == 1:
@numba_basic.numba_njit()
def cumop(x):
return np.cumsum(x)
else:
@numba_basic.numba_njit(boundscheck=False)
def cumop(x):
out_dtype = x.dtype
if x.shape[axis] < 2:
return x.astype(out_dtype)
x_axis_first = x.transpose(reaxis_first)
res = np.empty(x_axis_first.shape, dtype=out_dtype)
res[0] = x_axis_first[0]
for m in range(1, x.shape[axis]):
res[m] = res[m - 1] + x_axis_first[m]
return res.transpose(reaxis_first_inv)
else:
if axis is None or ndim == 1:
@numba_basic.numba_njit()
def cumop(x):
return np.cumprod(x)
else:
@numba_basic.numba_njit(boundscheck=False)
def cumop(x):
out_dtype = x.dtype
if x.shape[axis] < 2:
return x.astype(out_dtype)
x_axis_first = x.transpose(reaxis_first)
res = np.empty(x_axis_first.shape, dtype=out_dtype)
res[0] = x_axis_first[0]
for m in range(1, x.shape[axis]):
res[m] = res[m - 1] * x_axis_first[m]
return res.transpose(reaxis_first)
return cumop
import torch
from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
from pytensor.tensor.extra_ops import CumOp
@pytorch_funcify.register(CumOp)
def pytorch_funcify_Cumop(op, **kwargs):
axis = op.axis
mode = op.mode
def cumop(x,):
if axis is None:
x = x.reshape(-1)
dim = 0
else:
dim=axis
if mode == "add":
return torch.cumsum(x, dim=dim)
else:
return torch.cumprod(x, dim=dim)
return cumop
Suppose torch.cumprod does not exist, we will need to register the function as follows:
import torch
from pytensor.tensor.extra_ops import CumOp
from pytensor.link.pytorch.dispatch import pytorch_funcify
@pytorch_funcify.register(CumOp)
def pytorch_funcify_Cumop(op, **kwargs):
axis = op.axis
mode = op.mode
def cumop(x, axis=axis, mode=mode):
if mode == "add":
return torch.cumsum(x, axis=axis)
else:
raise NotImplementedError("Pytorch does not support cumprod function at the moment.")
return cumop
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.jax).
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 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 CumOp above:
import numpy as np
import pytensor.tensor as pt
from pytensor.configdefaults import config
from tests.link.jax.test_basic import compare_jax_and_py
from pytensor.graph import FunctionGraph
from pytensor.graph.op import get_test_value
def test_jax_CumOp():
"""Test JAX conversion of the `CumOp` `Op`."""
# Create a symbolic input for the first input of `CumOp`
a = pt.matrix("a")
# Create test value tag for a
a.tag.test_value = np.arange(9, dtype=config.floatX).reshape((3, 3))
# Create the output variable
out = pt.cumsum(a, axis=0)
# Create a PyTensor `FunctionGraph`
fgraph = FunctionGraph([a], [out])
# Pass the graph and inputs to the testing function
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
# For the second mode of CumOp
out = pt.cumprod(a, axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
If the variant CumprodOp is not implemented, we can add a test for it as follows:
import pytest
def test_jax_CumOp():
"""Test JAX conversion of the `CumOp` `Op`."""
a = pt.matrix("a")
a.tag.test_value = np.arange(9, dtype=config.floatX).reshape((3, 3))
with pytest.raises(NotImplementedError):
out = pt.cumprod(a, axis=1)
fgraph = FunctionGraph([a], [out])
compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
Test that your registered Op is working correctly by adding tests to the
appropriate test suites in PyTensor (e.g. in tests.link.numba).
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 Numba implementation and its Op.
For example, the compare_numba_and_py() function streamlines the steps
involved in making comparisons with Op.perform.
Here’s a small example of a test for CumOp above:
from tests.link.numba.test_basic import compare_numba_and_py
from pytensor.graph import FunctionGraph
from pytensor.compile.sharedvalue import SharedVariable
from pytensor.graph.basic import Constant
from pytensor.tensor import extra_ops
def test_CumOp(val, axis, mode):
g = extra_ops.CumOp(axis=axis, mode=mode)(val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, SharedVariable | Constant)
],
)
Test that your registered Op is working correctly by adding tests to the
appropriate test suites in PyTensor (tests.link.pytorch). 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 Pytorch implementation and its Op.
For example, the compare_pytorch_and_py() function streamlines the steps
involved in making comparisons with Op.perform.
Here’s a small example of a test for CumOp above:
import numpy as np
import pytest
import pytensor.tensor as pt
from pytensor.configdefaults import config
from tests.link.pytorch.test_basic import compare_pytorch_and_py
from pytensor.graph import FunctionGraph
@pytest.mark.parametrize(
"dtype",
["float64", "int64"],
)
@pytest.mark.parametrize(
"axis",
[None, 1, (0,)],
)
def test_pytorch_CumOp(axis, dtype):
"""Test PyTorch conversion of the `CumOp` `Op`."""
# Create a symbolic input for the first input of `CumOp`
a = pt.matrix("a", dtype=dtype)
# Create test value
test_value = np.arange(9, dtype=dtype).reshape((3, 3))
# Create the output variable
if isinstance(axis, tuple):
with pytest.raises(TypeError, match="axis must be an integer or None."):
out = pt.cumsum(a, axis=axis)
with pytest.raises(TypeError, match="axis must be an integer or None."):
out = pt.cumprod(a, axis=axis)
else:
out = pt.cumsum(a, axis=axis)
# Create a PyTensor `FunctionGraph`
fgraph = FunctionGraph([a], [out])
# Pass the graph and inputs to the testing function
compare_pytorch_and_py(fgraph, [test_value])
# For the second mode of CumOp
out = pt.cumprod(a, axis=axis)
fgraph = FunctionGraph([a], [out])
compare_pytorch_and_py(fgraph, [test_value])
Note#
In out previous example of extending JAX, EyeOp was used with the test function as follows:
def test_jax_Eye():
"""Test JAX conversion of the `Eye` `Op`."""
# Create a symbolic input for `Eye`
x_at = pt.scalar()
# Create a variable that is the output of an `Eye` `Op`
eye_var = pt.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])
This one nowadays leads to a test failure due to new restrictions in JAX + JIT,
as reported in issue #654.
All jitted functions now must have constant shape, which means a graph like the
one of Eye can never be translated to JAX, since it’s fundamentally a
function with dynamic shapes. In other words, only PyTensor graphs with static shapes
can be translated to JAX at the moment.