Source code for pymc.pytensorf

#   Copyright 2024 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
#   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 import Callable, Generator, Iterable, Sequence

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 import OpFromGraph
from pytensor.gradient import grad
from pytensor.graph import Type, rewrite_graph
from pytensor.graph.basic import (
from pytensor.graph.fg import FunctionGraph
from pytensor.graph.op import Op
from pytensor.scalar.basic import Cast
from pytensor.scan.op import Scan
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 (
from pytensor.tensor.rewriting.shape import ShapeFeature
from pytensor.tensor.sharedvar import SharedVariable, TensorSharedVariable
from pytensor.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1
from pytensor.tensor.variable import TensorVariable

from pymc.exceptions import NotConstantValueError
from pymc.util import makeiter
from pymc.vartypes import continuous_types, isgenerator, typefilter

PotentialShapeType = int | np.ndarray | Sequence[int | Variable] | TensorVariable

__all__ = [

[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 =, mask) else: ret = vals elif isinstance(data, np.ndarray): if isinstance(data, 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 =, 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 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, mask) raise TypeError(f"Data cannot be extracted from {x}") def walk_model( graphs: Iterable[TensorVariable], stop_at_vars: set[TensorVariable] | None = 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. """ warnings.warn("walk_model will be removed in a future relase of PyMC", FutureWarning) 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_vars_in_graphs( graphs: Iterable[Variable], replacements: dict[Variable, Variable], ) -> list[Variable]: """Replace variables in graphs. Graphs are cloned and not modified in place, unless the replacement expressions include variables from the original graphs. """ # Clone graphs and get equivalences 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, ) # Filter replacement keys that are actually present in the graph vars = fg.variables final_replacements = tuple((k, v) for k, v in replacements.items() if k in vars) # Replacements have to be done in reverse topological order so that nested # expressions get recursively replaced correctly toposort_replace(fg, final_replacements, reverse=True) return list(fg.outputs)
[docs] def inputvars(a): """ Get the inputs into PyTensor variables Parameters ---------- a: PyTensor variable Returns ------- r: list of tensor variables that are inputs """ return [ v for v in graph_inputs(makeiter(a)) if isinstance(v, Variable) and not isinstance(v, Constant | SharedVariable) ]
[docs] def cont_inputs(a): """ Get the continuous inputs into PyTensor variables Parameters ---------- a: PyTensor variable Returns ------- r: list of tensor variables that are continuous inputs """ return typefilter(inputvars(a), continuous_types)
[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, 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, negate_output=True): res = jacobian(gradient(f, vars), vars) if negate_output: warnings.warn( "hessian will stop negating the output in a future version of PyMC.\n" "To suppress this warning set `negate_output=False`", FutureWarning, stacklevel=2, ) res = -res return res
@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, idx)[0]
[docs] @pytensor.config.change_flags(compute_test_value="ignore") def hessian_diag(f, vars=None, negate_output=True): if vars is None: vars = cont_inputs(f) if vars: res = pt.concatenate([hessian_diag1(f, v) for v in vars], axis=0) if negate_output: warnings.warn( "hessian_diag will stop negating the output in a future version of PyMC.\n" "To suppress this warning set `negate_output=False`", FutureWarning, stacklevel=2, ) res = -res return res else: return empty_gradient
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[], + "_shared", shape=var.type.shape) for var in othervars }
[docs] def join_nonshared_inputs( point: dict[str, np.ndarray], outputs: list[TensorVariable], inputs: list[TensorVariable], shared_inputs: dict[TensorVariable, TensorSharedVariable] | None = None, make_inputs_shared: bool = False, ) -> tuple[list[TensorVariable], TensorVariable]: """ Create new outputs and input TensorVariables where the non-shared inputs are joined in a single raveled vector input. Parameters ---------- point : dict of {str : array_like} Dictionary that maps each input variable name to a numerical variable. The values are used to extract the shape of each input variable to establish a correct mapping between joined and original inputs. The shape of each variable is assumed to be fixed. outputs : list of TensorVariable List of output TensorVariables whose non-shared inputs will be replaced by a joined vector input. inputs : list of TensorVariable List of input TensorVariables which will be replaced by a joined vector input. shared_inputs : dict of {TensorVariable : TensorSharedVariable}, optional Dict of TensorVariable and their associated TensorSharedVariable in subgraph replacement. make_inputs_shared : bool, default False Whether to make the joined vector input a shared variable. Returns ------- new_outputs : list of TensorVariable List of new outputs `outputs` TensorVariables that depend on `joined_inputs` and new shared variables as inputs. joined_inputs : TensorVariable Joined input vector TensorVariable for the `new_outputs` Examples -------- Join the inputs of a simple PyTensor graph. .. code-block:: python import pytensor.tensor as pt import numpy as np from pymc.pytensorf import join_nonshared_inputs # Original non-shared inputs x = pt.scalar("x") y = pt.vector("y") # Original output out = x + y print(out.eval({x: np.array(1), y: np.array([1, 2, 3])})) # [2, 3, 4] # New output and inputs [new_out], joined_inputs = join_nonshared_inputs( point={ # Only shapes matter "x": np.zeros(()), "y": np.zeros(3), }, outputs=[out], inputs=[x, y], ) print(new_out.eval({ joined_inputs: np.array([1, 1, 2, 3]), })) # [2, 3, 4] Join the input value variables of a model logp. .. code-block:: python import pymc as pm with pm.Model() as model: mu_pop = pm.Normal("mu_pop") sigma_pop = pm.HalfNormal("sigma_pop") mu = pm.Normal("mu", mu_pop, sigma_pop, shape=(3, )) y = pm.Normal("y", mu, 1.0, observed=[0, 1, 2]) print(model.compile_logp()({ "mu_pop": 0, "sigma_pop_log__": 1, "mu": [0, 1, 2], })) # -12.691227342634292 initial_point = model.initial_point() inputs = model.value_vars [logp], joined_inputs = join_nonshared_inputs( point=initial_point, outputs=[model.logp()], inputs=inputs, ) print(logp.eval({ joined_inputs: [0, 1, 0, 1, 2], })) # -12.691227342634292 Same as above but with the `mu_pop` value variable being shared. .. code-block:: python from pytensor import shared mu_pop_input, *other_inputs = inputs shared_mu_pop_input = shared(0.0) [logp], other_joined_inputs = join_nonshared_inputs( point=initial_point, outputs=[model.logp()], inputs=other_inputs, shared_inputs={ mu_pop_input: shared_mu_pop_input }, ) print(logp.eval({ other_joined_inputs: [1, 0, 1, 2], })) # -12.691227342634292 """ if not inputs: raise ValueError("Empty list of input variables.") raveled_inputs = pt.concatenate([var.ravel() for var in inputs]) if not make_inputs_shared: tensor_type = raveled_inputs.type joined_inputs = tensor_type("joined_inputs") else: joined_values = np.concatenate([point[].ravel() for var in inputs]) joined_inputs = pytensor.shared(joined_values, "joined_inputs") if pytensor.config.compute_test_value != "off": joined_inputs.tag.test_value = raveled_inputs.tag.test_value replace: dict[TensorVariable, TensorVariable] = {} last_idx = 0 for var in inputs: shape = point[].shape arr_len =, dtype=int) replace[var] = joined_inputs[last_idx : last_idx + arr_len].reshape(shape).astype(var.dtype) last_idx += arr_len if shared_inputs is not None: replace.update(shared_inputs) new_outputs = [ pytensor.clone_replace(output, replace, rebuild_strict=False) for output in outputs ] return new_outputs, joined_inputs
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 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 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 def find_rng_nodes( variables: Iterable[Variable], ) -> list[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[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 = None | 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: 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_inner_fgraph(node: Node) -> dict[Variable, Variable]: """Collect default updates from node with inner fgraph.""" op = node.op inner_updates = collect_default_updates( inputs=op.inner_inputs, outputs=op.inner_outputs, must_be_shared=False ) # Map inner updates to outer inputs/outputs updates = {} for rng, update in inner_updates.items(): inp_idx = op.inner_inputs.index(rng) out_idx = op.inner_outputs.index(update) updates[node.inputs[inp_idx]] = node.outputs[out_idx] return updates def collect_default_updates( outputs: Variable | Sequence[Variable], *, inputs: Sequence[Variable] | None = None, must_be_shared: bool = True, ) -> dict[Variable, Variable]: """Collect default update expression for shared-variable RNGs used by RVs between inputs and outputs. Parameters ---------- outputs: list of PyTensor variables List of variables in which graphs default updates will be collected. inputs: list of PyTensor variables, optional Input nodes above which default updates should not be collected. When not provided, search will include top level inputs (roots). must_be_shared: bool, default True Used internally by PyMC. Whether updates should be collected for non-shared RNG input variables. This is used to collect update expressions for inner graphs. Examples -------- .. code:: python import pymc as pm from pytensor.scan import scan from pymc.pytensorf import collect_default_updates def scan_step(xtm1): x = xtm1 + pm.Normal.dist() x_update = collect_default_updates([x]) return x, x_update x0 = pm.Normal.dist() xs, updates = scan( fn=scan_step, outputs_info=[x0], n_steps=10, ) # PyMC makes use of the updates to seed xs properly. # Without updates, it would raise an error. xs_draws = pm.draw(xs, draws=10) """ # Avoid circular import from pymc.distributions.distribution import SymbolicRandomVariable def find_default_update(clients, rng: Variable) -> 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 found for at least one RNG used in SymbolicRandomVariable Op {client.op}" ) elif isinstance(client.op, Scan): # Check if any shared output corresponds to the RNG rng_idx = client.inputs.index(rng) io_map = client.op.get_oinp_iinp_iout_oout_mappings()["outer_out_from_outer_inp"] out_idx = io_map.get(rng_idx, -1) if out_idx != -1: next_rng = client.outputs[out_idx] else: # No break raise ValueError( f"No update found for at least one RNG used in Scan Op {client.op}.\n" "You can use `pytensorf.collect_default_updates` inside the Scan function to return updates automatically." ) elif isinstance(client.op, OpFromGraph): try: next_rng = collect_default_updates_inner_fgraph(client)[rng] except (ValueError, KeyError): raise ValueError( f"No update found for at least one RNG used in OpFromGraph Op {client.op}.\n" "You can use `pytensorf.collect_default_updates` and include those updates as outputs." ) else: # We don't know how this RNG should be updated. The user should provide an update manually return None # Recurse until we find final update for RNG return find_default_update(clients, next_rng) if inputs is None: inputs = [] 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=inputs, outputs=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) # By default, rewrite_graph includes canonicalize which includes constant-folding as the final rewrite folded_xs = rewrite_graph(fg).outputs if raise_not_constant and not all(isinstance(folded_x, Constant) for folded_x in folded_xs): raise NotConstantValueError return tuple( if isinstance(folded_x, Constant) else folded_x for folded_x in folded_xs )
def rewrite_pregrad(graph): """Apply simplifying or stabilizing rewrites to graph that are safe to use pre-grad. """ return rewrite_graph(graph, include=("canonicalize", "stabilize")) class StringType(Type[str]): def clone(self, **kwargs): return type(self)() def filter(self, x, strict=False, allow_downcast=None): if isinstance(x, str): return x else: raise TypeError("Expected a string!") def __str__(self): return "string" @staticmethod def may_share_memory(a, b): return isinstance(a, str) and a is b stringtype = StringType() class StringConstant(Constant): pass @pytensor._as_symbolic.register(str) def as_symbolic_string(x, **kwargs): return StringConstant(stringtype, x) def toposort_replace( fgraph: FunctionGraph, replacements: Sequence[tuple[Variable, Variable]], reverse: bool = False ) -> None: """Replace multiple variables in place in topological order.""" toposort = fgraph.toposort() sorted_replacements = sorted( replacements, key=lambda pair: toposort.index(pair[0].owner) if pair[0].owner else -1, reverse=reverse, ) fgraph.replace_all(sorted_replacements, import_missing=True) def normalize_rng_param(rng: None | Variable) -> Variable: """Validate rng is a valid type or create a new one if None""" if rng is None: rng = pytensor.shared(np.random.default_rng()) elif not isinstance(rng.type, RandomType): raise TypeError( "The type of rng should be an instance of either RandomGeneratorType or RandomStateType" ) return rng