# 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 warnings
from typing import (
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Sequence,
Set,
Tuple,
Union,
)
import numpy as np
import pandas as pd
import pytensor
import pytensor.tensor as pt
import scipy.sparse as sps
from pytensor import scalar
from pytensor.compile import Function, Mode, get_mode
from pytensor.gradient import grad
from pytensor.graph import node_rewriter, rewrite_graph
from pytensor.graph.basic import (
Apply,
Constant,
Variable,
clone_get_equiv,
graph_inputs,
walk,
)
from pytensor.graph.fg import FunctionGraph
from pytensor.graph.op import Op
from pytensor.scalar.basic import Cast
from pytensor.tensor.basic import _as_tensor_variable
from pytensor.tensor.elemwise import Elemwise
from pytensor.tensor.random.op import RandomVariable
from pytensor.tensor.random.type import RandomType
from pytensor.tensor.random.var import (
RandomGeneratorSharedVariable,
RandomStateSharedVariable,
)
from pytensor.tensor.rewriting.basic import topo_constant_folding
from pytensor.tensor.rewriting.shape import ShapeFeature
from pytensor.tensor.sharedvar import SharedVariable, TensorSharedVariable
from pytensor.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1
from pytensor.tensor.var import TensorConstant, TensorVariable
from pymc.exceptions import NotConstantValueError
from pymc.logprob.transforms import RVTransform
from pymc.logprob.utils import CheckParameterValue
from pymc.vartypes import continuous_types, isgenerator, typefilter
PotentialShapeType = Union[int, np.ndarray, Sequence[Union[int, Variable]], TensorVariable]
__all__ = [
"gradient",
"hessian",
"hessian_diag",
"inputvars",
"cont_inputs",
"floatX",
"intX",
"smartfloatX",
"jacobian",
"CallableTensor",
"join_nonshared_inputs",
"make_shared_replacements",
"generator",
"convert_observed_data",
"compile_pymc",
"constant_fold",
]
[docs]def convert_observed_data(data):
"""Convert user provided dataset to accepted formats."""
if hasattr(data, "to_numpy") and hasattr(data, "isnull"):
# typically, but not limited to pandas objects
vals = data.to_numpy()
null_data = data.isnull()
if hasattr(null_data, "to_numpy"):
# pandas Series
mask = null_data.to_numpy()
else:
# pandas Index
mask = null_data
if mask.any():
# there are missing values
ret = np.ma.MaskedArray(vals, mask)
else:
ret = vals
elif isinstance(data, np.ndarray):
if isinstance(data, np.ma.MaskedArray):
if not data.mask.any():
# empty mask
ret = data.filled()
else:
# already masked and rightly so
ret = data
else:
# already a ndarray, but not masked
mask = np.isnan(data)
if np.any(mask):
ret = np.ma.MaskedArray(data, mask)
else:
# no masking required
ret = data
elif isinstance(data, Variable):
ret = data
elif sps.issparse(data):
ret = data
elif isgenerator(data):
ret = generator(data)
else:
ret = np.asarray(data)
# type handling to enable index variables when data is int:
if hasattr(data, "dtype"):
if "int" in str(data.dtype):
return intX(ret)
# otherwise, assume float:
else:
return floatX(ret)
# needed for uses of this function other than with pm.Data:
else:
return floatX(ret)
@_as_tensor_variable.register(pd.Series)
@_as_tensor_variable.register(pd.DataFrame)
def dataframe_to_tensor_variable(df: pd.DataFrame, *args, **kwargs) -> TensorVariable:
return pt.as_tensor_variable(df.to_numpy(), *args, **kwargs)
def extract_obs_data(x: TensorVariable) -> np.ndarray:
"""Extract data from observed symbolic variables.
Raises
------
TypeError
"""
if isinstance(x, Constant):
return x.data
if isinstance(x, SharedVariable):
return x.get_value()
if x.owner and isinstance(x.owner.op, Elemwise) and isinstance(x.owner.op.scalar_op, Cast):
array_data = extract_obs_data(x.owner.inputs[0])
return array_data.astype(x.type.dtype)
if x.owner and isinstance(x.owner.op, (AdvancedIncSubtensor, AdvancedIncSubtensor1)):
array_data = extract_obs_data(x.owner.inputs[0])
mask_idx = tuple(extract_obs_data(i) for i in x.owner.inputs[2:])
mask = np.zeros_like(array_data)
mask[mask_idx] = 1
return np.ma.MaskedArray(array_data, mask)
raise TypeError(f"Data cannot be extracted from {x}")
def walk_model(
graphs: Iterable[TensorVariable],
stop_at_vars: Optional[Set[TensorVariable]] = None,
expand_fn: Callable[[TensorVariable], Iterable[TensorVariable]] = lambda var: [],
) -> Generator[TensorVariable, None, None]:
"""Walk model graphs and yield their nodes.
Parameters
----------
graphs
The graphs to walk.
stop_at_vars
A list of variables at which the walk will terminate.
expand_fn
A function that returns the next variable(s) to be traversed.
"""
if stop_at_vars is None:
stop_at_vars = set()
def expand(var):
new_vars = expand_fn(var)
if var.owner and var not in stop_at_vars:
new_vars.extend(reversed(var.owner.inputs))
return new_vars
yield from walk(graphs, expand, bfs=False)
def _replace_rvs_in_graphs(
graphs: Iterable[TensorVariable],
replacement_fn: Callable[[TensorVariable], Dict[TensorVariable, TensorVariable]],
**kwargs,
) -> Tuple[List[TensorVariable], Dict[TensorVariable, TensorVariable]]:
"""Replace random variables in graphs
This will *not* recompute test values.
Parameters
----------
graphs
The graphs in which random variables are to be replaced.
Returns
-------
Tuple containing the transformed graphs and a ``dict`` of the replacements
that were made.
"""
replacements = {}
def expand_replace(var):
new_nodes = []
if var.owner:
# Call replacement_fn to update replacements dict inplace and, optionally,
# specify new nodes that should also be walked for replacements. This
# includes `value` variables that are not simple input variables, and may
# contain other `random` variables in their graphs (e.g., IntervalTransform)
new_nodes.extend(replacement_fn(var, replacements))
return new_nodes
# This iteration populates the replacements
for var in walk_model(graphs, expand_fn=expand_replace, **kwargs):
pass
if replacements:
inputs = [i for i in graph_inputs(graphs) if not isinstance(i, Constant)]
equiv = {k: k for k in replacements.keys()}
equiv = clone_get_equiv(inputs, graphs, False, False, equiv)
fg = FunctionGraph(
[equiv[i] for i in inputs],
[equiv[o] for o in graphs],
clone=False,
)
# replacements have to be done in reverse topological order so that nested
# expressions get recursively replaced correctly
toposort = fg.toposort()
sorted_replacements = sorted(
tuple(replacements.items()),
key=lambda pair: toposort.index(pair[0].owner),
reverse=True,
)
fg.replace_all(sorted_replacements, import_missing=True)
graphs = list(fg.outputs)
return graphs, replacements
def rvs_to_value_vars(
graphs: Iterable[Variable],
apply_transforms: bool = True,
**kwargs,
) -> List[Variable]:
"""Clone and replace random variables in graphs with their value variables.
This will *not* recompute test values in the resulting graphs.
Parameters
----------
graphs
The graphs in which to perform the replacements.
apply_transforms
If ``True``, apply each value variable's transform.
"""
warnings.warn(
"rvs_to_value_vars is deprecated. Use model.replace_rvs_by_values instead",
FutureWarning,
)
def populate_replacements(
random_var: TensorVariable, replacements: Dict[TensorVariable, TensorVariable]
) -> List[TensorVariable]:
# Populate replacements dict with {rv: value} pairs indicating which graph
# RVs should be replaced by what value variables.
value_var = getattr(
random_var.tag, "observations", getattr(random_var.tag, "value_var", None)
)
# No value variable to replace RV with
if value_var is None:
return []
transform = getattr(value_var.tag, "transform", None)
if transform is not None and apply_transforms:
# We want to replace uses of the RV by the back-transformation of its value
value_var = transform.backward(value_var, *random_var.owner.inputs)
replacements[random_var] = value_var
# Also walk the graph of the value variable to make any additional replacements
# if that is not a simple input variable
return [value_var]
# Clone original graphs
inputs = [i for i in graph_inputs(graphs) if not isinstance(i, Constant)]
equiv = clone_get_equiv(inputs, graphs, False, False, {})
graphs = [equiv[n] for n in graphs]
graphs, _ = _replace_rvs_in_graphs(
graphs,
replacement_fn=populate_replacements,
**kwargs,
)
return graphs
def replace_rvs_by_values(
graphs: Sequence[TensorVariable],
*,
rvs_to_values: Dict[TensorVariable, TensorVariable],
rvs_to_transforms: Optional[Dict[TensorVariable, RVTransform]] = None,
**kwargs,
) -> List[TensorVariable]:
"""Clone and replace random variables in graphs with their value variables.
This will *not* recompute test values in the resulting graphs.
Parameters
----------
graphs
The graphs in which to perform the replacements.
rvs_to_values
Mapping between the original graph RVs and respective value variables
rvs_to_transforms, optional
Mapping between the original graph RVs and respective value transforms
"""
# Clone original graphs so that we don't modify variables in place
inputs = [i for i in graph_inputs(graphs) if not isinstance(i, Constant)]
equiv = clone_get_equiv(inputs, graphs, False, False, {})
graphs = [equiv[n] for n in graphs]
# Get needed mappings for equivalent cloned variables
equiv_rvs_to_values = {}
equiv_rvs_to_transforms = {}
for rv, value in rvs_to_values.items():
equiv_rv = equiv.get(rv, rv)
equiv_rvs_to_values[equiv_rv] = equiv.get(value, value)
if rvs_to_transforms is not None:
equiv_rvs_to_transforms[equiv_rv] = rvs_to_transforms[rv]
def poulate_replacements(rv, replacements):
# Populate replacements dict with {rv: value} pairs indicating which graph
# RVs should be replaced by what value variables.
# No value variable to replace RV with
value = equiv_rvs_to_values.get(rv, None)
if value is None:
return []
if rvs_to_transforms is not None:
transform = equiv_rvs_to_transforms.get(rv, None)
if transform is not None:
# We want to replace uses of the RV by the back-transformation of its value
value = transform.backward(value, *rv.owner.inputs)
# The value may have a less precise type than the rv. In this case
# filter_variable will add a SpecifyShape to ensure they are consistent
value = rv.type.filter_variable(value, allow_convert=True)
value.name = rv.name
replacements[rv] = value
# Also walk the graph of the value variable to make any additional
# replacements if that is not a simple input variable
return [value]
graphs, _ = _replace_rvs_in_graphs(
graphs,
replacement_fn=poulate_replacements,
**kwargs,
)
return graphs
[docs]def floatX(X):
"""
Convert an PyTensor tensor or numpy array to pytensor.config.floatX type.
"""
try:
return X.astype(pytensor.config.floatX)
except AttributeError:
# Scalar passed
return np.asarray(X, dtype=pytensor.config.floatX)
_conversion_map = {"float64": "int32", "float32": "int16", "float16": "int8", "float8": "int8"}
[docs]def intX(X):
"""
Convert a pytensor tensor or numpy array to pytensor.tensor.int32 type.
"""
intX = _conversion_map[pytensor.config.floatX]
try:
return X.astype(intX)
except AttributeError:
# Scalar passed
return np.asarray(X, dtype=intX)
[docs]def smartfloatX(x):
"""
Converts numpy float values to floatX and leaves values of other types unchanged.
"""
if str(x.dtype).startswith("float"):
x = floatX(x)
return x
"""
PyTensor derivative functions
"""
def gradient1(f, v):
"""flat gradient of f wrt v"""
return pt.flatten(grad(f, v, disconnected_inputs="warn"))
empty_gradient = pt.zeros(0, dtype="float32")
[docs]def gradient(f, vars=None):
if vars is None:
vars = cont_inputs(f)
if vars:
return pt.concatenate([gradient1(f, v) for v in vars], axis=0)
else:
return empty_gradient
def jacobian1(f, v):
"""jacobian of f wrt v"""
f = pt.flatten(f)
idx = pt.arange(f.shape[0], dtype="int32")
def grad_i(i):
return gradient1(f[i], v)
return pytensor.map(grad_i, idx)[0]
[docs]def jacobian(f, vars=None):
if vars is None:
vars = cont_inputs(f)
if vars:
return pt.concatenate([jacobian1(f, v) for v in vars], axis=1)
else:
return empty_gradient
def jacobian_diag(f, x):
idx = pt.arange(f.shape[0], dtype="int32")
def grad_ii(i, f, x):
return grad(f[i], x)[i]
return pytensor.scan(
grad_ii, sequences=[idx], n_steps=f.shape[0], non_sequences=[f, x], name="jacobian_diag"
)[0]
[docs]@pytensor.config.change_flags(compute_test_value="ignore")
def hessian(f, vars=None):
return -jacobian(gradient(f, vars), vars)
@pytensor.config.change_flags(compute_test_value="ignore")
def hessian_diag1(f, v):
g = gradient1(f, v)
idx = pt.arange(g.shape[0], dtype="int32")
def hess_ii(i):
return gradient1(g[i], v)[i]
return pytensor.map(hess_ii, idx)[0]
[docs]@pytensor.config.change_flags(compute_test_value="ignore")
def hessian_diag(f, vars=None):
if vars is None:
vars = cont_inputs(f)
if vars:
return -pt.concatenate([hessian_diag1(f, v) for v in vars], axis=0)
else:
return empty_gradient
def makeiter(a):
if isinstance(a, (tuple, list)):
return a
else:
return [a]
class IdentityOp(scalar.UnaryScalarOp):
@staticmethod
def st_impl(x):
return x
def impl(self, x):
return x
def grad(self, inp, grads):
return grads
def c_code(self, node, name, inp, out, sub):
return f"{out[0]} = {inp[0]};"
def __eq__(self, other):
return isinstance(self, type(other))
def __hash__(self):
return hash(type(self))
scalar_identity = IdentityOp(scalar.upgrade_to_float, name="scalar_identity")
identity = Elemwise(scalar_identity, name="identity")
[docs]def make_shared_replacements(point, vars, model):
"""
Makes shared replacements for all *other* variables than the ones passed.
This way functions can be called many times without setting unchanging variables. Allows us
to use func.trust_input by removing the need for DictToArrayBijection and kwargs.
Parameters
----------
point: dictionary mapping variable names to sample values
vars: list of variables not to make shared
model: model
Returns
-------
Dict of variable -> new shared variable
"""
othervars = set(model.value_vars) - set(vars)
return {
var: pytensor.shared(point[var.name], var.name + "_shared", shape=var.type.shape)
for var in othervars
}
class PointFunc:
"""Wraps so a function so it takes a dict of arguments instead of arguments."""
def __init__(self, f):
self.f = f
def __call__(self, state):
return self.f(**state)
[docs]class CallableTensor:
"""Turns a symbolic variable with one input into a function that returns symbolic arguments
with the one variable replaced with the input.
"""
[docs] def __init__(self, tensor):
self.tensor = tensor
def __call__(self, input):
"""Replaces the single input of symbolic variable to be the passed argument.
Parameters
----------
input: TensorVariable
"""
(oldinput,) = inputvars(self.tensor)
return pytensor.clone_replace(self.tensor, {oldinput: input}, rebuild_strict=False)
class GeneratorOp(Op):
"""
Generator Op is designed for storing python generators inside pytensor graph.
__call__ creates TensorVariable
It has 2 new methods
- var.set_gen(gen): sets new generator
- var.set_default(value): sets new default value (None erases default value)
If generator is exhausted, variable will produce default value if it is not None,
else raises `StopIteration` exception that can be caught on runtime.
Parameters
----------
gen: generator that implements __next__ (py3) or next (py2) method
and yields np.arrays with same types
default: np.array with the same type as generator produces
"""
__props__ = ("generator",)
def __init__(self, gen, default=None):
from pymc.data import GeneratorAdapter
super().__init__()
if not isinstance(gen, GeneratorAdapter):
gen = GeneratorAdapter(gen)
self.generator = gen
self.set_default(default)
def make_node(self, *inputs):
gen_var = self.generator.make_variable(self)
return Apply(self, [], [gen_var])
def perform(self, node, inputs, output_storage, params=None):
if self.default is not None:
output_storage[0][0] = next(self.generator, self.default)
else:
output_storage[0][0] = next(self.generator)
def do_constant_folding(self, fgraph, node):
return False
__call__ = pytensor.config.change_flags(compute_test_value="off")(Op.__call__)
def set_gen(self, gen):
from pymc.data import GeneratorAdapter
if not isinstance(gen, GeneratorAdapter):
gen = GeneratorAdapter(gen)
if not gen.tensortype == self.generator.tensortype:
raise ValueError("New generator should yield the same type")
self.generator = gen
def set_default(self, value):
if value is None:
self.default = None
else:
value = np.asarray(value, self.generator.tensortype.dtype)
t1 = (False,) * value.ndim
t2 = self.generator.tensortype.broadcastable
if not t1 == t2:
raise ValueError("Default value should have the same type as generator")
self.default = value
[docs]def generator(gen, default=None):
"""
Generator variable with possibility to set default value and new generator.
If generator is exhausted variable will produce default value if it is not None,
else raises `StopIteration` exception that can be caught on runtime.
Parameters
----------
gen: generator that implements __next__ (py3) or next (py2) method
and yields np.arrays with same types
default: np.array with the same type as generator produces
Returns
-------
TensorVariable
It has 2 new methods
- var.set_gen(gen): sets new generator
- var.set_default(value): sets new default value (None erases default value)
"""
return GeneratorOp(gen, default)()
def floatX_array(x):
return floatX(np.array(x))
def ix_(*args):
"""
PyTensor np.ix_ analog
See numpy.lib.index_tricks.ix_ for reference
"""
out = []
nd = len(args)
for k, new in enumerate(args):
if new is None:
out.append(slice(None))
new = pt.as_tensor(new)
if new.ndim != 1:
raise ValueError("Cross index must be 1 dimensional")
new = new.reshape((1,) * k + (new.size,) + (1,) * (nd - k - 1))
out.append(new)
return tuple(out)
def largest_common_dtype(tensors):
dtypes = {
str(t.dtype) if hasattr(t, "dtype") else smartfloatX(np.asarray(t)).dtype for t in tensors
}
return np.stack([np.ones((), dtype=dtype) for dtype in dtypes]).dtype
@node_rewriter(tracks=[CheckParameterValue])
def local_remove_check_parameter(fgraph, node):
"""Rewrite that removes CheckParameterValue
This is used when compile_rv_inplace
"""
if isinstance(node.op, CheckParameterValue):
return [node.inputs[0]]
@node_rewriter(tracks=[CheckParameterValue])
def local_check_parameter_to_ninf_switch(fgraph, node):
if not node.op.can_be_replaced_by_ninf:
return None
logp_expr, *logp_conds = node.inputs
if len(logp_conds) > 1:
logp_cond = pt.all(logp_conds)
else:
(logp_cond,) = logp_conds
out = pt.switch(logp_cond, logp_expr, -np.inf)
out.name = node.op.msg
if out.dtype != node.outputs[0].dtype:
out = pt.cast(out, node.outputs[0].dtype)
return [out]
pytensor.compile.optdb["canonicalize"].register(
"local_remove_check_parameter",
local_remove_check_parameter,
use_db_name_as_tag=False,
)
pytensor.compile.optdb["canonicalize"].register(
"local_check_parameter_to_ninf_switch",
local_check_parameter_to_ninf_switch,
use_db_name_as_tag=False,
)
def find_rng_nodes(
variables: Iterable[Variable],
) -> List[Union[RandomStateSharedVariable, RandomGeneratorSharedVariable]]:
"""Return RNG variables in a graph"""
return [
node
for node in graph_inputs(variables)
if isinstance(node, (RandomStateSharedVariable, RandomGeneratorSharedVariable))
]
def replace_rng_nodes(outputs: Sequence[TensorVariable]) -> Sequence[TensorVariable]:
"""Replace any RNG nodes upstream of outputs by new RNGs of the same type
This can be used when combining a pre-existing graph with a cloned one, to ensure
RNGs are unique across the two graphs.
"""
rng_nodes = find_rng_nodes(outputs)
# Nothing to do here
if not rng_nodes:
return outputs
graph = FunctionGraph(outputs=outputs, clone=False)
new_rng_nodes: List[Union[np.random.RandomState, np.random.Generator]] = []
for rng_node in rng_nodes:
rng_cls: type
if isinstance(rng_node, pt.random.var.RandomStateSharedVariable):
rng_cls = np.random.RandomState
else:
rng_cls = np.random.Generator
new_rng_nodes.append(pytensor.shared(rng_cls(np.random.PCG64())))
graph.replace_all(zip(rng_nodes, new_rng_nodes), import_missing=True)
return graph.outputs
SeedSequenceSeed = Optional[Union[int, Sequence[int], np.ndarray, np.random.SeedSequence]]
def reseed_rngs(
rngs: Sequence[SharedVariable],
seed: SeedSequenceSeed,
) -> None:
"""Create a new set of RandomState/Generator for each rng based on a seed"""
bit_generators = [
np.random.PCG64(sub_seed) for sub_seed in np.random.SeedSequence(seed).spawn(len(rngs))
]
for rng, bit_generator in zip(rngs, bit_generators):
new_rng: Union[np.random.RandomState, np.random.Generator]
if isinstance(rng, pt.random.var.RandomStateSharedVariable):
new_rng = np.random.RandomState(bit_generator)
else:
new_rng = np.random.Generator(bit_generator)
rng.set_value(new_rng, borrow=True)
def collect_default_updates(
inputs: Sequence[Variable],
outputs: Sequence[Variable],
must_be_shared: bool = True,
) -> Dict[Variable, Variable]:
"""Collect default update expression for shared-variable RNGs used by RVs between inputs and outputs.
If `must_be_shared` is False, update expressions will also be returned for non-shared input RNGs.
This can be useful to obtain the symbolic update expressions from inner graphs.
"""
# Avoid circular import
from pymc.distributions.distribution import SymbolicRandomVariable
def find_default_update(clients, rng: Variable) -> Union[None, Variable]:
rng_clients = clients.get(rng, None)
# Root case, RNG is not used elsewhere
if not rng_clients:
return rng
if len(rng_clients) > 1:
warnings.warn(
f"RNG Variable {rng} has multiple clients. This is likely an inconsistent random graph.",
UserWarning,
)
return None
[client, _] = rng_clients[0]
# RNG is an output of the function, this is not a problem
if client == "output":
return rng
# RNG is used by another operator, which should output an update for the RNG
if isinstance(client.op, RandomVariable):
# RandomVariable first output is always the update of the input RNG
next_rng = client.outputs[0]
elif isinstance(client.op, SymbolicRandomVariable):
# SymbolicRandomVariable have an explicit method that returns an
# update mapping for their RNG(s)
next_rng = client.op.update(client).get(rng)
if next_rng is None:
raise ValueError(
f"No update mapping found for RNG used in SymbolicRandomVariable Op {client.op}"
)
else:
# We don't know how this RNG should be updated (e.g., Scan).
# The user should provide an update manually
return None
# Recurse until we find final update for RNG
return find_default_update(clients, next_rng)
outputs = makeiter(outputs)
fg = FunctionGraph(outputs=outputs, clone=False)
clients = fg.clients
rng_updates = {}
# Iterate over input RNGs. Only consider shared RNGs if `must_be_shared==True`
for input_rng in (
inp
for inp in graph_inputs(outputs, blockers=inputs)
if (
(not must_be_shared or isinstance(inp, SharedVariable))
and isinstance(inp.type, RandomType)
)
):
# Even if an explicit default update is provided, we call it to
# issue any warnings about invalid random graphs.
default_update = find_default_update(clients, input_rng)
# Respect default update if provided
if getattr(input_rng, "default_update", None):
rng_updates[input_rng] = input_rng.default_update
else:
if default_update is not None:
rng_updates[input_rng] = default_update
return rng_updates
[docs]def compile_pymc(
inputs,
outputs,
random_seed: SeedSequenceSeed = None,
mode=None,
**kwargs,
) -> Function:
"""Use ``pytensor.function`` with specialized pymc rewrites always enabled.
This function also ensures shared RandomState/Generator used by RandomVariables
in the graph are updated across calls, to ensure independent draws.
Parameters
----------
inputs: list of TensorVariables, optional
Inputs of the compiled PyTensor function
outputs: list of TensorVariables, optional
Outputs of the compiled PyTensor function
random_seed: int, array-like of int or SeedSequence, optional
Seed used to override any RandomState/Generator shared variables in the graph.
If not specified, the value of original shared variables will still be overwritten.
mode: optional
PyTensor mode used to compile the function
Included rewrites
-----------------
random_make_inplace
Ensures that compiled functions containing random variables will produce new
samples on each call.
local_check_parameter_to_ninf_switch
Replaces CheckParameterValue assertions is logp expressions with Switches
that return -inf in place of the assert.
Optional rewrites
-----------------
local_remove_check_parameter
Replaces CheckParameterValue assertions is logp expressions. This is used
as an alteranative to the default local_check_parameter_to_ninf_switch whenenver
this function is called within a model context and the model `check_bounds` flag
is set to False.
"""
# Create an update mapping of RandomVariable's RNG so that it is automatically
# updated after every function call
rng_updates = collect_default_updates(inputs, outputs)
# We always reseed random variables as this provides RNGs with no chances of collision
if rng_updates:
reseed_rngs(rng_updates.keys(), random_seed)
# If called inside a model context, see if check_bounds flag is set to False
try:
from pymc.model import modelcontext
model = modelcontext(None)
check_bounds = model.check_bounds
except TypeError:
check_bounds = True
check_parameter_opt = (
"local_check_parameter_to_ninf_switch" if check_bounds else "local_remove_check_parameter"
)
mode = get_mode(mode)
opt_qry = mode.provided_optimizer.including("random_make_inplace", check_parameter_opt)
mode = Mode(linker=mode.linker, optimizer=opt_qry)
pytensor_function = pytensor.function(
inputs,
outputs,
updates={**rng_updates, **kwargs.pop("updates", {})},
mode=mode,
**kwargs,
)
return pytensor_function
[docs]def constant_fold(
xs: Sequence[TensorVariable], raise_not_constant: bool = True
) -> Tuple[np.ndarray, ...]:
"""Use constant folding to get constant values of a graph.
Parameters
----------
xs: Sequence of TensorVariable
The variables that are to be constant folded
raise_not_constant: bool, default True
Raises NotConstantValueError if any of the variables cannot be constant folded.
This should only be disabled with care, as the graphs are cloned before
attempting constant folding, and any old non-shared inputs will not work with
the returned outputs
"""
fg = FunctionGraph(outputs=xs, features=[ShapeFeature()], clone=True)
folded_xs = rewrite_graph(fg, custom_rewrite=topo_constant_folding).outputs
if raise_not_constant and not all(isinstance(folded_x, Constant) for folded_x in folded_xs):
raise NotConstantValueError
return tuple(
folded_x.data if isinstance(folded_x, Constant) else folded_x for folded_x in folded_xs
)