Source code for pymc.aesaraf

#   Copyright 2020 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 aesara
import aesara.tensor as at
import numpy as np
import pandas as pd
import scipy.sparse as sps

from aeppl.logprob import CheckParameterValue
from aeppl.transforms import RVTransform
from aesara import scalar
from aesara.compile import Function, Mode, get_mode
from aesara.gradient import grad
from aesara.graph import node_rewriter, rewrite_graph
from aesara.graph.basic import (
    Apply,
    Constant,
    Variable,
    clone_get_equiv,
    graph_inputs,
    vars_between,
    walk,
)
from aesara.graph.fg import FunctionGraph
from aesara.graph.op import Op
from aesara.sandbox.rng_mrg import MRG_RandomStream as RandomStream
from aesara.scalar.basic import Cast
from aesara.tensor.basic import _as_tensor_variable
from aesara.tensor.elemwise import Elemwise
from aesara.tensor.random.op import RandomVariable
from aesara.tensor.random.var import (
    RandomGeneratorSharedVariable,
    RandomStateSharedVariable,
)
from aesara.tensor.rewriting.basic import topo_constant_folding
from aesara.tensor.rewriting.shape import ShapeFeature
from aesara.tensor.sharedvar import SharedVariable, TensorSharedVariable
from aesara.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1
from aesara.tensor.var import TensorConstant, TensorVariable

from pymc.exceptions import NotConstantValueError
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",
    "set_at_rng",
    "at_rng",
    "convert_observed_data",
    "compile_pymc",
    "constant_fold",
]


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 at.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: Dict[TensorVariable, RVTransform],
    **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
        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)
        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 []

        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)
            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 inputvars(a): """ Get the inputs into Aesara variables Parameters ---------- a: Aesara variable Returns ------- r: list of tensor variables that are inputs """ return [ v for v in graph_inputs(makeiter(a)) if isinstance(v, TensorVariable) and not isinstance(v, TensorConstant) ]
[docs]def cont_inputs(a): """ Get the continuous inputs into Aesara variables Parameters ---------- a: Aesara variable Returns ------- r: list of tensor variables that are continuous inputs """ return typefilter(inputvars(a), continuous_types)
[docs]def floatX(X): """ Convert an Aesara tensor or numpy array to aesara.config.floatX type. """ try: return X.astype(aesara.config.floatX) except AttributeError: # Scalar passed return np.asarray(X, dtype=aesara.config.floatX)
_conversion_map = {"float64": "int32", "float32": "int16", "float16": "int8", "float8": "int8"}
[docs]def intX(X): """ Convert a aesara tensor or numpy array to aesara.tensor.int32 type. """ intX = _conversion_map[aesara.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
""" Aesara derivative functions """ def gradient1(f, v): """flat gradient of f wrt v""" return at.flatten(grad(f, v, disconnected_inputs="warn")) empty_gradient = at.zeros(0, dtype="float32")
[docs]def gradient(f, vars=None): if vars is None: vars = cont_inputs(f) if vars: return at.concatenate([gradient1(f, v) for v in vars], axis=0) else: return empty_gradient
def jacobian1(f, v): """jacobian of f wrt v""" f = at.flatten(f) idx = at.arange(f.shape[0], dtype="int32") def grad_i(i): return gradient1(f[i], v) return aesara.map(grad_i, idx)[0]
[docs]def jacobian(f, vars=None): if vars is None: vars = cont_inputs(f) if vars: return at.concatenate([jacobian1(f, v) for v in vars], axis=1) else: return empty_gradient
def jacobian_diag(f, x): idx = at.arange(f.shape[0], dtype="int32") def grad_ii(i, f, x): return grad(f[i], x)[i] return aesara.scan( grad_ii, sequences=[idx], n_steps=f.shape[0], non_sequences=[f, x], name="jacobian_diag" )[0]
[docs]@aesara.config.change_flags(compute_test_value="ignore") def hessian(f, vars=None): return -jacobian(gradient(f, vars), vars)
@aesara.config.change_flags(compute_test_value="ignore") def hessian_diag1(f, v): g = gradient1(f, v) idx = at.arange(g.shape[0], dtype="int32") def hess_ii(i): return gradient1(g[i], v)[i] return aesara.map(hess_ii, idx)[0]
[docs]@aesara.config.change_flags(compute_test_value="ignore") def hessian_diag(f, vars=None): if vars is None: vars = cont_inputs(f) if vars: return -at.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: aesara.shared(point[var.name], var.name + "_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: Optional[Dict[TensorVariable, TensorSharedVariable]] = 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 Aesara graph. .. code-block:: python import aesara.tensor as at import numpy as np from pymc.aesaraf import join_nonshared_inputs # Original non-shared inputs x = at.scalar("x") y = at.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 aesara 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 = at.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[var.name].ravel() for var in inputs]) joined_inputs = aesara.shared(joined_values, "joined_inputs") if aesara.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[var.name].shape arr_len = np.prod(shape, 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 = [ aesara.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 aesara.clone_replace(self.tensor, {oldinput: input}, rebuild_strict=False)
class GeneratorOp(Op): """ Generator Op is designed for storing python generators inside aesara 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__ = aesara.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)()
_at_rng = RandomStream()
[docs]def at_rng(random_seed=None): """ Get the package-level random number generator or new with specified seed. Parameters ---------- random_seed: int If not None returns *new* aesara random generator without replacing package global one Returns ------- `aesara.tensor.random.utils.RandomStream` instance `aesara.tensor.random.utils.RandomStream` instance passed to the most recent call of `set_at_rng` """ if random_seed is None: return _at_rng else: ret = RandomStream(random_seed) return ret
[docs]def set_at_rng(new_rng): """ Set the package-level random number generator. Parameters ---------- new_rng: `aesara.tensor.random.utils.RandomStream` instance The random number generator to use. """ # pylint: disable=global-statement global _at_rng # pylint: enable=global-statement if isinstance(new_rng, int): new_rng = RandomStream(new_rng) _at_rng = new_rng
def floatX_array(x): return floatX(np.array(x)) def ix_(*args): """ Aesara 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 = at.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 Aeppl's 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 isinstance(node.op, CheckParameterValue): logp_expr, *logp_conds = node.inputs if len(logp_conds) > 1: logp_cond = at.all(logp_conds) else: (logp_cond,) = logp_conds out = at.switch(logp_cond, logp_expr, -np.inf) out.name = node.op.msg if out.dtype != node.outputs[0].dtype: out = at.cast(out, node.outputs[0].dtype) return [out] aesara.compile.optdb["canonicalize"].register( "local_remove_check_parameter", local_remove_check_parameter, use_db_name_as_tag=False, ) aesara.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, at.random.var.RandomStateSharedVariable): rng_cls = np.random.RandomState else: rng_cls = np.random.Generator new_rng_nodes.append(aesara.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, at.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)
[docs]def compile_pymc( inputs, outputs, random_seed: SeedSequenceSeed = None, mode=None, **kwargs, ) -> Function: """Use ``aesara.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 Aesara function outputs: list of TensorVariables, optional Outputs of the compiled Aesara 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 Aesara 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 Aeppl's CheckParameterValue assertions is logp expressions with Switches that return -inf in place of the assert. Optional rewrites ----------------- local_remove_check_parameter Replaces Aeppl's 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. """ # Avoid circular import from pymc.distributions.distribution import SymbolicRandomVariable # Create an update mapping of RandomVariable's RNG so that it is automatically # updated after every function call rng_updates = {} output_to_list = outputs if isinstance(outputs, (list, tuple)) else [outputs] for random_var in ( var for var in vars_between(inputs, output_to_list) if var.owner and isinstance(var.owner.op, (RandomVariable, SymbolicRandomVariable)) and var not in inputs ): # All nodes in `vars_between(inputs, outputs)` have owners. # But mypy doesn't know, so we just assert it: assert random_var.owner.op is not None if isinstance(random_var.owner.op, RandomVariable): rng = random_var.owner.inputs[0] if hasattr(rng, "default_update"): update_map = {rng: rng.default_update} else: update_map = {rng: random_var.owner.outputs[0]} else: update_map = random_var.owner.op.update(random_var.owner) # Check that we are not setting different update expressions for the same variables for rng, update in update_map.items(): if rng not in rng_updates: rng_updates[rng] = update # When a variable has multiple outputs, it will be called twice with the same # update expression. We don't want to raise in that case, only if the update # expression in different from the one already registered elif rng_updates[rng] is not update: raise ValueError(f"Multiple update expressions found for the variable {rng}") # 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) aesara_function = aesara.function( inputs, outputs, updates={**rng_updates, **kwargs.pop("updates", {})}, mode=mode, **kwargs, ) return aesara_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 )