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# Copyright 2023 The PyMC Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import warnings
from functools import partial, reduce
import numpy as np
import pytensor
import pytensor.sparse
import pytensor.tensor as pt
import pytensor.tensor.slinalg # pylint: disable=unused-import
import scipy as sp
import scipy.sparse # pylint: disable=unused-import
from pytensor.graph.basic import Apply
from pytensor.graph.op import Op
# pylint: disable=unused-import
from pytensor.tensor import (
abs,
and_,
arccos,
arccosh,
arcsin,
arcsinh,
arctan,
arctanh,
broadcast_to,
ceil,
clip,
concatenate,
constant,
cos,
cosh,
cumprod,
cumsum,
dot,
eq,
erf,
erfc,
erfcinv,
erfinv,
exp,
flatten,
floor,
full,
full_like,
ge,
gt,
le,
log,
log1pexp,
logaddexp,
logsumexp,
lt,
matmul,
max,
maximum,
mean,
min,
minimum,
neq,
ones,
ones_like,
or_,
prod,
sgn,
sigmoid,
sin,
sinh,
sqr,
sqrt,
stack,
sum,
switch,
tan,
tanh,
where,
zeros,
zeros_like,
)
from pytensor.tensor.linalg import solve_triangular
from pytensor.tensor.nlinalg import matrix_inverse
from pytensor.tensor.special import log_softmax, softmax
from scipy.linalg import block_diag as scipy_block_diag
from pymc.pytensorf import floatX, ix_, largest_common_dtype
# pylint: enable=unused-import
__all__ = [
"abs",
"and_",
"arccos",
"arccosh",
"arcsin",
"arcsinh",
"arctan",
"arctanh",
"broadcast_to",
"ceil",
"clip",
"concatenate",
"constant",
"cos",
"cosh",
"cumprod",
"cumsum",
"dot",
"eq",
"erf",
"erfc",
"erfcinv",
"erfinv",
"exp",
"full",
"full_like",
"flatten",
"floor",
"ge",
"gt",
"le",
"log",
"log1pexp",
"logaddexp",
"logsumexp",
"lt",
"matmul",
"max",
"maximum",
"mean",
"min",
"minimum",
"neq",
"ones",
"ones_like",
"or_",
"prod",
"round",
"sgn",
"sigmoid",
"sin",
"sinh",
"sqr",
"sqrt",
"stack",
"sum",
"switch",
"tan",
"tanh",
"where",
"zeros",
"zeros_like",
"kronecker",
"cartesian",
"kron_dot",
"kron_solve_lower",
"kron_solve_upper",
"kron_diag",
"flat_outer",
"logdiffexp",
"invlogit",
"softmax",
"log_softmax",
"logbern",
"logit",
"log1mexp",
"flatten_list",
"logdet",
"probit",
"invprobit",
"expand_packed_triangular",
"batched_diag",
"block_diagonal",
]
def kronecker(*Ks):
r"""Return the Kronecker product of arguments:
:math:`K_1 \otimes K_2 \otimes ... \otimes K_D`
Parameters
----------
Ks : Iterable of 2D array-like
Arrays of which to take the product.
Returns
-------
np.ndarray :
Block matrix Kroncker product of the argument matrices.
"""
return reduce(pt.slinalg.kron, Ks)
def cartesian(*arrays):
"""Makes the Cartesian product of arrays.
Parameters
----------
arrays: N-D array-like
N-D arrays where earlier arrays loop more slowly than later ones
"""
N = len(arrays)
arrays_np = [np.asarray(x) for x in arrays]
arrays_2d = [x[:, None] if np.asarray(x).ndim == 1 else x for x in arrays_np]
arrays_integer = [np.arange(len(x)) for x in arrays_2d]
product_integers = np.stack(np.meshgrid(*arrays_integer, indexing="ij"), -1).reshape(-1, N)
return np.concatenate(
[array[product_integers[:, i]] for i, array in enumerate(arrays_2d)], axis=-1
)
def kron_matrix_op(krons, m, op):
r"""Apply op to krons and m in a way that reproduces ``op(kronecker(*krons), m)``
Parameters
----------
krons : list of square 2D array-like objects
D square matrices :math:`[A_1, A_2, ..., A_D]` to be Kronecker'ed
:math:`A = A_1 \otimes A_2 \otimes ... \otimes A_D`
Product of column dimensions must be :math:`N`
m : NxM array or 1D array (treated as Nx1)
Object that krons
10BC8
act upon
Returns
-------
numpy array
"""
def flat_matrix_op(flat_mat, mat):
Nmat = mat.shape[1]
flat_shape = flat_mat.shape
mat2 = flat_mat.reshape((Nmat, -1))
return op(mat, mat2).T.reshape(flat_shape)
def kron_vector_op(v):
return reduce(flat_matrix_op, krons, v)
if m.ndim == 1:
m = m[:, None] # Treat 1D array as Nx1 matrix
if m.ndim != 2: # Has not been tested otherwise
raise ValueError(f"m must have ndim <= 2, not {m.ndim}")
result = kron_vector_op(m)
result_shape = result.shape
return pt.reshape(result, (result_shape[1], result_shape[0])).T
# Define kronecker functions that work on 1D and 2D arrays
kron_dot = partial(kron_matrix_op, op=pt.dot)
kron_solve_lower = partial(kron_matrix_op, op=partial(solve_triangular, lower=True))
kron_solve_upper = partial(kron_matrix_op, op=partial(solve_triangular, lower=False))
def flat_outer(a, b):
return pt.outer(a, b).ravel()
def kron_diag(*diags):
"""Returns diagonal of a kronecker product.
Parameters
----------
diags: 1D arrays
The diagonals of matrices that are to be Kroneckered
"""
return reduce(flat_outer, diags)
def round(*args, **kwargs):
"""
Temporary function to silence round warning in PyTensor. Please remove
when the warning disappears.
"""
kwargs["mode"] = "half_to_even"
return pt.round(*args, **kwargs)
def tround(*args, **kwargs):
warnings.warn("tround is deprecated. Use round instead.")
return round(*args, **kwargs)
def logdiffexp(a, b):
"""log(exp(a) - exp(b))"""
return a + pt.log1mexp(b - a)
def logdiffexp_numpy(a, b):
"""log(exp(a) - exp(b))"""
return a + log1mexp_numpy(b - a, negative_input=True)
invlogit = sigmoid
def logbern(log_p):
if np.isnan(log_p):
raise FloatingPointError("log_p can't be nan.")
return np.log(np.random.uniform()) < log_p
def logit(p):
return pt.log(p / (floatX(1) - p))
def log1mexp(x, *, negative_input=False):
r"""Return log(1 - exp(-x)).
This function is numerically more stable than the naive approach.
For details, see
https://cran.r-project.org/web/packages/Rmpfr/vignettes/log1mexp-note.pdf
References
----------
.. [Machler2012] Martin Mächler (2012).
"Accurately computing `\log(1-\exp(- \mid a \mid))` Assessed by the Rmpfr package"
"""
if not negative_input:
warnings.warn(
"pymc.math.log1mexp will expect a negative input in a future "
"version of PyMC.\n To suppress this warning set `negative_input=True`",
FutureWarning,
stacklevel=2,
)
x = -x
return pt.log1mexp(x)
def log1mexp_numpy(x, *, negative_input=False):
"""Return log(1 - exp(x)).
This function is numerically more stable than the naive approach.
For details, see
https://cran.r-project.org/web/packages/Rmpfr/vignettes/log1mexp-note.pdf
"""
x = np.asarray(x, dtype="float")
if not negative_input:
warnings.warn(
"pymc.math.log1mexp_numpy will expect a negative input in a future "
"version of PyMC.\n To suppress this warning set `negative_input=True`",
FutureWarning,
stacklevel=2,
)
x = -x
out = np.empty_like(x)
mask = x < -0.6931471805599453 # log(1/2)
out[mask] = np.log1p(-np.exp(x[mask]))
mask = ~mask
out[mask] = np.log(-np.expm1(x[mask]))
return out
def flatten_list(tensors):
return pt.concatenate([var.ravel() for var in tensors])
class LogDet(Op):
r"""Compute the logarithm of the absolute determinant of a square
matrix M, log(abs(det(M))) on the CPU. Avoids det(M) overflow/
underflow.
Notes
-----
Once PR #3959 (https://github.com/Theano/Theano/pull/3959/) by harpone is merged,
this must be removed.
"""
def make_node(self, x):
x = pytensor.tensor.as_tensor_variable(x)
o = pytensor.tensor.scalar(dtype=x.dtype)
return Apply(self, [x], [o])
def perform(self, node, inputs, outputs, params=None):
try:
(x,) = inputs
(z,) = outputs
s = np.linalg.svd(x, compute_uv=False)
log_det = np.sum(np.log(np.abs(s)))
z[0] = np.asarray(log_det, dtype=x.dtype)
except Exception:
print(f"Failed to compute logdet of {x}.", file=sys.stdout)
raise
def grad(self, inputs, g_outputs):
[gz] = g_outputs
[x] = inputs
return [gz * matrix_inverse(x).T]
def __str__(self):
return "LogDet"
logdet = LogDet()
def probit(p):
return -sqrt(2.0) * erfcinv(2.0 * p)
def invprobit(x):
return 0.5 * erfc(-x / sqrt(2.0))
def expand_packed_triangular(n, packed, lower=True, diagonal_only=False):
r"""Convert a packed triangular matrix into a two dimensional array.
Triangular matrices can be stored with better space efficiency by
storing the non-zero values in a one-dimensional array. We number
the elements by row like this (for lower or upper triangular matrices):
[[0 - - -] [[0 1 2 3]
[1 2 - -] [- 4 5 6]
[3 4 5 -] [- - 7 8]
[6 7 8 9]] [- - - 9]
Parameters
----------
n: int
The number of rows of the triangular matrix.
packed: pytensor.vector
The matrix in packed format.
lower: bool, default=True
If true, assume that the matrix is lower triangular.
diagonal_only: bool
If true, return only the diagonal of the matrix.
"""
if packed.ndim != 1:
raise ValueError("Packed triangular is not one dimensional.")
if not isinstance(n, int):
raise TypeError("n must be an integer")
if diagonal_only and lower:
diag_idxs = np.arange(1, n + 1).cumsum() - 1
return packed[diag_idxs]
elif diagonal_only and not lower:
diag_idxs = np.arange(2, n + 2)[::-1].cumsum() - n - 1
return packed[diag_idxs]
elif lower:
out = pt.zeros((n, n), dtype=pytensor.config.floatX)
idxs = np.tril_indices(n)
# tag as lower triangular to enable pytensor rewrites
out = pt.set_subtensor(out[idxs], packed)
out.tag.lower_triangular = True
return out
elif not lower:
out = pt.zeros((n, n), dtype=pytensor.config.floatX)
idxs = np.triu_indices(n)
# tag as upper triangular to enable pytensor rewrites
out = pt.set_subtensor(out[idxs], packed)
out.tag.upper_triangular = True
return out
class BatchedDiag(Op):
"""
Fast BatchedDiag allocation
"""
__props__ = ()
def make_node(self, diag):
diag = pt.as_tensor_variable(diag)
if diag.type.ndim != 2:
raise TypeError("data argument must be a matrix", diag.type)
return Apply(self, [diag], [pt.tensor3(dtype=diag.dtype)])
def perform(self, node, ins, outs, params=None):
(C,) = ins
(z,) = outs
bc = C.shape[0]
dim = C.shape[-1]
Cd = np.zeros((bc, dim, dim), C.dtype)
bidx = np.repeat(np.arange(bc), dim)
didx = np.tile(np.arange(dim), bc)
Cd[bidx, didx, didx] = C.flatten()
z[0] = Cd
def grad(self, inputs, gout):
(gz,) = gout
idx = pt.arange(gz.shape[-1])
return [gz[..., idx, idx]]
def infer_shape(self, fgraph, nodes, shapes):
return [(shapes[0][0],) + (shapes[0][1],) * 2]
def batched_diag(C):
C = pt.as_tensor(C)
dim = C.shape[-1]
<
7A69
/div> if C.ndim == 2:
# diag -> matrices
return BatchedDiag()(C)
elif C.ndim == 3:
# matrices -> diag
idx = pt.arange(dim)
return C[..., idx, idx]
else:
raise ValueError("Input should be 2 or 3 dimensional")
class BlockDiagonalMatrix(Op):
__props__ = ("sparse", "format")
def __init__(self, sparse=False, format="csr"):
if format not in ("csr", "csc"):
raise ValueError(f"format must be one of: 'csr', 'csc', got {format}")
self.sparse = sparse
self.format = format
def make_node(self, *matrices):
if not matrices:
raise ValueError("no matrices to allocate")
matrices = list(map(pt.as_tensor, matrices))
if any(mat.type.ndim != 2 for mat in matrices):
raise TypeError("all data arguments must be matrices")
if self.sparse:
out_type = pytensor.sparse.matrix(self.format, dtype=largest_common_dtype(matrices))
else:
out_type = pytensor.tensor.matrix(dtype=largest_common_dtype(matrices))
return Apply(self, matrices, [out_type])
def perform(self, node, inputs, output_storage, params=None):
dtype = largest_common_dtype(inputs)
if self.sparse:
output_storage[0][0] = sp.sparse.block_diag(inputs, self.format, dtype)
else:
output_storage[0][0] = scipy_block_diag(*inputs).astype(dtype)
def grad(self, inputs, gout):
shapes = pt.stack([i.shape for i in inputs])
index_end = shapes.cumsum(0)
index_begin = index_end - shapes
slices = [
ix_(
pt.arange(index_begin[i, 0], index_end[i, 0]),
pt.arange(index_begin[i, 1], index_end[i, 1]),
)
for i in range(len(inputs))
]
return [gout[0][slc] for slc in slices]
def infer_shape(self, fgraph, nodes, shapes):
first, second = zip(*shapes)
return [(pt.add(*first), pt.add(*second))]
def block_diagonal(matrices, sparse=False, format="csr"):
r"""See scipy.sparse.block_diag or
scipy.linalg.block_diag for reference
Parameters
----------
matrices: tensors
format: str (default 'csr')
must be one of: 'csr', 'csc'
sparse: bool (default False)
if True return sparse format
Returns
-------
matrix
"""
if len(matrices) == 1: # graph optimization
return matrices[0]
return BlockDiagonalMatrix(sparse=sparse, format=format)(*matrices)