Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Simplify implementation of tile #1187

Merged
merged 1 commit into from
Feb 4, 2025
Merged

Simplify implementation of tile #1187

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
181 changes: 113 additions & 68 deletions pytensor/tensor/basic.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
from collections.abc import Sequence
from functools import partial
from numbers import Number
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, Union
from typing import cast as type_cast

import numpy as np
Expand All @@ -33,7 +33,7 @@
from pytensor.link.c.op import COp
from pytensor.link.c.params_type import ParamsType
from pytensor.printing import Printer, min_informative_str, pprint, set_precedence
from pytensor.raise_op import CheckAndRaise, assert_op
from pytensor.raise_op import CheckAndRaise
from pytensor.scalar import int32
from pytensor.scalar.basic import ScalarConstant, ScalarType, ScalarVariable
from pytensor.tensor import (
Expand Down Expand Up @@ -3084,87 +3084,132 @@ def flatten(x, ndim=1):
return x_reshaped


def tile(x, reps, ndim=None):
def tile(
A: "TensorLike", reps: Union[Sequence[Union[int, "TensorLike"]], "TensorLike"]
) -> TensorVariable:
"""
Tile input array `x` according to `reps`.
Tile input tensor `A` according to `reps`.

See the docstring of `numpy.tile` for details.

'reps' can be constant integer (e.g. 3), constant vector(e.g. [2 3]),
symbolic scalar (e.g. tensor.iscalar()), symbolic vector (e.g. tensor.ivector())
or a list of symbolic scalar (e.g. [tensor.iscalar(), tensor.iscalar()]).
If `reps` is a PyTensor vector, its length must be statically known.
You can use `specify_shape` to set the length.
ricardoV94 marked this conversation as resolved.
Show resolved Hide resolved

Examples
--------

.. testcode::

import pytensor.tensor as pt

A = pt.matrix("A", dtype=int)
A_tiled = pt.tile(A, 2)
print(A_tiled.eval({A: [[1, 2], [3, 4]]}))

.. testoutput::

[[1 2 1 2]
[3 4 3 4]]

Reps can be a sequence of constants and/ or symbolic integer variables

.. testcode::

rep0 = pt.scalar("rep0", dtype=int)
A_tiled = pt.tile(A, (rep0, 1))
print(A_tiled.eval({A: [[1, 2], [3, 4]], rep0: 2}))

.. testoutput::

[[1 2]
[3 4]
[1 2]
[3 4]]

Reps can be a single integer vector, in which case its length must be statically known.
Either of the following is a valid way to specify the length:

.. testcode::

reps = pt.vector("reps", dtype=int, shape=(2,))
A_tiled = pt.tile(A, reps)
print(A_tiled.eval({A: [[1, 2], [3, 4]], reps: [1, 2]}))

.. testoutput::

[[1 2 1 2]
[3 4 3 4]]

.. testcode::

ndim is the number of the dimensions of the output, if it is provided, ndim
should be equal or larger than x.ndim and len(reps), otherwise, we will use
max(x.ndim, len(reps)) as ndim. If reps is symbolic vector, the ndim has to
be provided.
reps = pt.vector("reps", dtype=int)
reps = pt.specify_shape(reps, (2,))
A_tiled = pt.tile(A, reps)
print(A_tiled.eval({A: [[1, 2], [3, 4]], reps: [2, 2]}))

.. testoutput::

[[1 2 1 2]
[3 4 3 4]
[1 2 1 2]
[3 4 3 4]]

"""
from pytensor.tensor.math import ge

_x = as_tensor_variable(x)
if ndim is not None and ndim < _x.ndim:
raise ValueError("ndim should be equal or larger than _x.ndim")
A = as_tensor_variable(A)

# If reps is a scalar, integer or vector, we convert it to a list.
# Convert symbolic reps to a tuple
if not isinstance(reps, list | tuple):
reps_astensor = as_tensor_variable(reps)
ndim_check = reps_astensor.ndim
if reps_astensor.dtype not in discrete_dtypes:
raise ValueError("elements of reps must be integer dtype")

# The scalar/integer case
if ndim_check == 0:
reps = [reps]

# The vector case
elif ndim_check == 1:
if ndim is None:
reps = as_tensor_variable(reps)
if reps.type.ndim == 0:
reps = (reps,)
elif reps.type.ndim == 1:
try:
reps = tuple(reps)
except ValueError:
raise ValueError(
"if reps is tensor.vector, you should specify the ndim"
"Length of repetitions tensor cannot be determined. Use specify_shape to set the length."
)
else:
offset = ndim - reps.shape[0]

# assert that reps.shape[0] does not exceed ndim
offset = assert_op(offset, ge(offset, 0))
else:
raise ValueError(
f"Repetitions tensor must be a scalar or a vector, got ndim={reps.type.ndim}"
)

# if reps.ndim is less than _x.ndim, we pad the reps with
# "1" so that reps will have the same ndim as _x.
reps_ = [switch(i < offset, 1, reps[i - offset]) for i in range(ndim)]
reps = reps_
reps = [as_tensor_variable(rep) for rep in reps]
if not all(
rep.type.ndim == 0 and rep.type.dtype in discrete_dtypes for rep in reps
):
raise ValueError(
f"All reps entries shoud be scalar integers, got {reps} of type {[rep.type for rep in reps]}"
)

# For others, raise an error
else:
raise ValueError("the dimension of reps should not exceed 1")
else:
if ndim is not None and len(reps) > ndim:
raise ValueError("len(reps) should be equal or less than ndim")
if not all(
isinstance(r, int)
or (isinstance(r, TensorVariable) and r.dtype in discrete_dtypes)
for r in reps
):
raise ValueError("elements of reps must be scalars of integer dtype")
len_reps = len(reps)
out_ndim = builtins.max(len_reps, A.type.ndim)

# Pad reps on the left (if needed)
if len_reps < out_ndim:
reps = (*((1,) * (out_ndim - len_reps)), *reps)

# Pad A's shape on the left (if needed)
elif A.type.ndim < out_ndim:
A = shape_padleft(A, out_ndim - A.type.ndim)

# Expand every other dim of A and expand n-reps via Alloc
# A_replicated = alloc(A[None, :, ..., None, :], reps[0], A.shape[0], ..., reps[-1], A.shape[-1])
A_shape = A.shape
interleaved_reps_shape = [
d for pair in zip(reps, A_shape, strict=True) for d in pair
]
every_other_axis = tuple(range(0, out_ndim * 2, 2))
A_replicated = alloc(
expand_dims(A, every_other_axis),
*interleaved_reps_shape,
)

# If reps.ndim is less than _x.ndim, we pad the reps with
# "1" so that reps will have the same ndim as _x
reps = list(reps)
if ndim is None:
ndim = builtins.max(len(reps), _x.ndim)
if len(reps) < ndim:
reps = [1] * (ndim - len(reps)) + reps

_shape = [1] * (ndim - _x.ndim) + [_x.shape[i] for i in range(_x.ndim)]
alloc_shape = reps + _shape
y = alloc(_x, *alloc_shape)
shuffle_ind = np.arange(ndim * 2).reshape(2, ndim)
shuffle_ind = shuffle_ind.transpose().flatten()
y = y.dimshuffle(*shuffle_ind)
new_shapes = [sh * reps[i] for i, sh in enumerate(_shape)]
y = y.reshape(new_shapes)

return y
# Combine replicate and original dimensions via reshape
# A_tiled = A_replicated.reshape(reps[0] * A.shape[0], ..., reps[-1] * A.shape[-1])
tiled_shape = tuple(rep * A_dim for rep, A_dim in zip(reps, A_shape, strict=True))
return A_replicated.reshape(tiled_shape)


class ARange(Op):
Expand Down
Loading