Source code for pymc.model.fgraph

#   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.
from copy import copy, deepcopy

import pytensor

from pytensor import Variable
from pytensor.compile import SharedVariable
from pytensor.graph import Apply, FunctionGraph, Op, node_rewriter
from pytensor.graph.rewriting.basic import out2in
from pytensor.scalar import Identity
from pytensor.tensor.elemwise import Elemwise

from pymc.logprob.transforms import Transform
from pymc.model.core import Model
from pymc.pytensorf import StringType, find_rng_nodes, toposort_replace

class ModelVar(Op):
    """A dummy Op that describes the purpose of a Model variable and contains
    meta-information as additional inputs (value and dims).

    def make_node(self, rv, *dims):
        assert isinstance(rv, Variable)
        dims = self._parse_dims(rv, *dims)
        return Apply(self, [rv, *dims], [rv.type(])

    def _parse_dims(self, rv, *dims):
        if dims:
            dims = [pytensor.as_symbolic(dim) for dim in dims]
            assert all(isinstance(dim.type, StringType) for dim in dims)
            assert len(dims) == rv.type.ndim
        return dims

    def infer_shape(self, fgraph, node, inputs_shape):
        return [inputs_shape[0]]

    def do_constant_folding(self, fgraph, node):
        return False

    def perform(self, *args, **kwargs):
        raise RuntimeError("ModelVars should never be in a final graph!")

class ModelValuedVar(ModelVar):
    __props__ = ("transform",)

    def __init__(self, transform: Transform | None = None):
        if transform is not None and not isinstance(transform, Transform):
            raise TypeError(f"transform must be None or RVTransform type, got {type(transform)}")
        self.transform = transform

    def make_node(self, rv, value, *dims):
        assert isinstance(rv, Variable)
        dims = self._parse_dims(rv, *dims)
        if value is not None:
            assert isinstance(value, Variable)
            assert rv.type.dtype == value.type.dtype
            return Apply(self, [rv, value, *dims], [rv.type(])

class ModelFreeRV(ModelValuedVar):

class ModelObservedRV(ModelValuedVar):

class ModelPotential(ModelVar):

class ModelDeterministic(ModelVar):

class ModelNamed(ModelVar):

def model_free_rv(rv, value, transform, *dims):
    return ModelFreeRV(transform=transform)(rv, value, *dims)

model_observed_rv = ModelObservedRV()
model_potential = ModelPotential()
model_deterministic = ModelDeterministic()
model_named = ModelNamed()

def local_remove_identity(fgraph, node):
    if isinstance(node.op.scalar_op, Identity):
        return [node.inputs[0]]

remove_identity_rewrite = out2in(local_remove_identity)

def deepcopy_shared_variable(var: SharedVariable) -> SharedVariable:
    # Shared variables don't have a deepcopy method (SharedVariable.clone reuses the old container and contents).
    # We recreate Shared Variables manually after deepcopying their container.
    new_var = type(var)(
    assert new_var.type == var.type
    new_var.tag = copy(var.tag)
    return new_var

[docs] def fgraph_from_model( model: Model, inlined_views=False ) -> tuple[FunctionGraph, dict[Variable, Variable]]: """Convert Model to FunctionGraph. See: model_from_fgraph Parameters ---------- model: PyMC model inlined_views: bool, default False Whether "view" variables (Deterministics and Data) should be inlined among RVs in the fgraph, or show up as separate branches. Returns ------- fgraph: FunctionGraph FunctionGraph that includes a copy of model variables, wrapped in dummy `ModelVar` Ops. It should be possible to reconstruct a valid PyMC model using `model_from_fgraph`. memo: Dict A dictionary mapping original model variables to the equivalent nodes in the fgraph. """ if any(v is not None for v in model.rvs_to_initial_values.values()): raise NotImplementedError("Cannot convert models with non-default initial_values") if model.parent is not None: raise ValueError( "Nested sub-models cannot be converted to fgraph. Convert the parent model instead" ) # Collect PyTensor variables rvs_to_values = model.rvs_to_values rvs = list(rvs_to_values.keys()) free_rvs = model.free_RVs observed_rvs = model.observed_RVs potentials = model.potentials named_vars = model.named_vars.values() # We copy Deterministics (Identity Op) so that they don't show in between "main" variables # We later remove these Identity Ops when we have a Deterministic ModelVar Op as a separator old_deterministics = model.deterministics deterministics = [det if inlined_views else det.copy( for det in old_deterministics] # Value variables (we also have to decide whether to inline named ones) old_value_vars = list(rvs_to_values.values()) unnamed_value_vars = [val for val in old_value_vars if val not in named_vars] named_value_vars = [ val if inlined_views else val.copy( for val in old_value_vars if val in named_vars ] value_vars = old_value_vars.copy() if inlined_views: # In this case we want to use the named_value_vars as the value_vars in RVs for named_val in named_value_vars: idx = value_vars.index(named_val) value_vars[idx] = named_val # Other variables that are in named_vars but are not any of the categories above (e.g., Data) # We use the same trick as deterministics! accounted_for = set(free_rvs + observed_rvs + potentials + old_deterministics + old_value_vars) other_named_vars = [ var if inlined_views else var.copy( for var in named_vars if var not in accounted_for ] model_vars = ( rvs + potentials + deterministics + other_named_vars + named_value_vars + unnamed_value_vars ) memo = {} # Replace the following shared variables in the model: # 1. RNGs # 2. Data (could increase memory usage significantly) # 3. Symbolic coords dim lengths shared_vars_to_copy = find_rng_nodes(model_vars) shared_vars_to_copy += [v for v in model.dim_lengths.values() if isinstance(v, SharedVariable)] shared_vars_to_copy += [v for v in model.named_vars.values() if isinstance(v, SharedVariable)] for var in shared_vars_to_copy: new_var = deepcopy_shared_variable(var) # We can replace input variables by placing them in the memo memo[var] = new_var fgraph = FunctionGraph( outputs=model_vars, clone=True, memo=memo, copy_orphans=True, copy_inputs=True, ) # Copy model meta-info to fgraph fgraph._coords = model._coords.copy() fgraph._dim_lengths = {k: memo.get(v, v) for k, v in model._dim_lengths.items()} rvs_to_transforms = model.rvs_to_transforms named_vars_to_dims = model.named_vars_to_dims # Introduce dummy `ModelVar` Ops free_rvs_to_transforms = {memo[k]: tr for k, tr in rvs_to_transforms.items()} free_rvs_to_values = {memo[k]: memo[v] for k, v in zip(rvs, value_vars) if k in free_rvs} observed_rvs_to_values = { memo[k]: memo[v] for k, v in zip(rvs, value_vars) if k in observed_rvs } potentials = [memo[k] for k in potentials] deterministics = [memo[k] for k in deterministics] named_vars = [memo[k] for k in other_named_vars + named_value_vars] vars = fgraph.outputs new_vars = [] for var in vars: dims = named_vars_to_dims.get(, ()) if var in free_rvs_to_values: new_var = model_free_rv( var, free_rvs_to_values[var], free_rvs_to_transforms[var], *dims ) elif var in observed_rvs_to_values: new_var = model_observed_rv(var, observed_rvs_to_values[var], *dims) elif var in potentials: new_var = model_potential(var, *dims) elif var in deterministics: new_var = model_deterministic(var, *dims) elif var in named_vars: new_var = model_named(var, *dims) else: # Unnamed value variables new_var = var new_vars.append(new_var) replacements = tuple(zip(vars, new_vars)) toposort_replace(fgraph, replacements, reverse=True) # Reference model vars in memo inverse_memo = {v: k for k, v in memo.items()} for var, model_var in replacements: if not inlined_views and ( model_var.owner and isinstance(model_var.owner.op, ModelDeterministic | ModelNamed) ): # Ignore extra identity that will be removed at the end var = var.owner.inputs[0] original_var = inverse_memo[var] memo[original_var] = model_var # Remove the last outputs corresponding to unnamed value variables, now that they are graph inputs first_idx_to_remove = len(fgraph.outputs) - len(unnamed_value_vars) for _ in unnamed_value_vars: fgraph.remove_output(first_idx_to_remove) # Now that we have Deterministic dummy Ops, we remove the noisy `Identity`s from the graph remove_identity_rewrite.apply(fgraph) return fgraph, memo
[docs] def model_from_fgraph(fgraph: FunctionGraph) -> Model: """Convert FunctionGraph to PyMC model. This requires nodes to be properly tagged with `ModelVar` dummy Ops. See: fgraph_from_model """ def first_non_model_var(var): if var.owner and isinstance(var.owner.op, ModelVar): new_var = var.owner.inputs[0] return first_non_model_var(new_var) else: return var model = Model() if model.parent is not None: raise RuntimeError("model_to_fgraph cannot be called inside a PyMC model context") model._coords = getattr(fgraph, "_coords", {}) model._dim_lengths = getattr(fgraph, "_dim_lengths", {}) # Replace dummy `ModelVar` Ops by the underlying variables, fgraph = fgraph.clone() model_dummy_vars = [ model_node.outputs[0] for model_node in fgraph.toposort() if isinstance(model_node.op, ModelVar) ] model_dummy_vars_to_vars = { # Deterministics could refer to other model variables directly, # We make sure to replace them by the first non-model variable dummy_var: first_non_model_var(dummy_var.owner.inputs[0]) for dummy_var in model_dummy_vars } toposort_replace(fgraph, tuple(model_dummy_vars_to_vars.items())) # Populate new PyMC model mappings for model_var in model_dummy_vars: if isinstance(model_var.owner.op, ModelFreeRV): var, value, *dims = model_var.owner.inputs transform = model_var.owner.op.transform model.free_RVs.append(var) model.create_value_var(var, transform=transform, value_var=value) model.set_initval(var, initval=None) elif isinstance(model_var.owner.op, ModelObservedRV): var, value, *dims = model_var.owner.inputs model.observed_RVs.append(var) model.create_value_var(var, transform=None, value_var=value) elif isinstance(model_var.owner.op, ModelPotential): var, *dims = model_var.owner.inputs model.potentials.append(var) elif isinstance(model_var.owner.op, ModelDeterministic): var, *dims = model_var.owner.inputs # If a Deterministic is a direct view on an RV, copy it if var in model.basic_RVs: var = var.copy() model.deterministics.append(var) elif isinstance(model_var.owner.op, ModelNamed): var, *dims = model_var.owner.inputs else: raise TypeError(f"Unexpected ModelVar type {type(model_var)}") = dims = [ for dim in dims] if dims else None model.add_named_variable(var, dims=dims) return model
[docs] def clone_model(model: Model) -> Model: """Clone a PyMC model. Recreates a PyMC model with clones of the original variables. Shared variables will point to the same container but be otherwise different objects. Constants are not cloned. Examples -------- .. code-block:: python import pymc as pm from pymc.model.fgraph import clone_model with pm.Model() as m: p = pm.Beta("p", 1, 1) x = pm.Bernoulli("x", p=p, shape=(3,)) with clone_model(m) as clone_m: # Access cloned variables by name clone_x = clone_m["x"] # z will be part of clone_m but not m z = pm.Deterministic("z", clone_x + 1) """ return model_from_fgraph(fgraph_from_model(model)[0])
def extract_dims(var) -> tuple: dims = () node = var.owner if node and isinstance(node.op, ModelVar): if isinstance(node.op, ModelValuedVar): dims = node.inputs[2:] else: dims = node.inputs[1:] return dims __all__ = ( "fgraph_from_model", "model_from_fgraph", "clone_model", )