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| 1 | +# Copyright 2025 - present The PyMC Developers |
| 2 | +# |
| 3 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +# you may not use this file except in compliance with the License. |
| 5 | +# You may obtain a copy of the License at |
| 6 | +# |
| 7 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +# See the License for the specific language governing permissions and |
| 13 | +# limitations under the License. |
| 14 | +from collections.abc import Callable, Sequence |
| 15 | +from itertools import chain |
| 16 | + |
| 17 | +from pytensor.graph.basic import Variable |
| 18 | +from pytensor.tensor.elemwise import DimShuffle |
| 19 | +from pytensor.xtensor import as_xtensor |
| 20 | +from pytensor.xtensor.type import XTensorVariable |
| 21 | + |
| 22 | +from pymc import modelcontext |
| 23 | +from pymc.dims.model import with_dims |
| 24 | +from pymc.distributions import transforms |
| 25 | +from pymc.distributions.distribution import _support_point, support_point |
| 26 | +from pymc.distributions.shape_utils import DimsWithEllipsis, convert_dims_with_ellipsis |
| 27 | +from pymc.logprob.transforms import Transform |
| 28 | +from pymc.util import UNSET |
| 29 | + |
| 30 | + |
| 31 | +@_support_point.register(DimShuffle) |
| 32 | +def dimshuffle_support_point(ds_op, _, rv): |
| 33 | + # We implement support point for DimShuffle because |
| 34 | + # DimDistribution can register a transposed version of a variable. |
| 35 | + |
| 36 | + return ds_op(support_point(rv)) |
| 37 | + |
| 38 | + |
| 39 | +class DimDistribution: |
| 40 | + """Base class for PyMC distribution that wrap pytensor.xtensor.random operations, and follow xarray-like semantics.""" |
| 41 | + |
| 42 | + xrv_op: Callable |
| 43 | + default_transform: Transform | None = None |
| 44 | + |
| 45 | + @staticmethod |
| 46 | + def _as_xtensor(x): |
| 47 | + try: |
| 48 | + return as_xtensor(x) |
| 49 | + except TypeError: |
| 50 | + try: |
| 51 | + return with_dims(x) |
| 52 | + except ValueError: |
| 53 | + raise ValueError( |
| 54 | + f"Variable {x} must have dims associated with it.\n" |
| 55 | + "To avoid subtle bugs, PyMC does not make any assumptions about the dims of parameters.\n" |
| 56 | + "Use `as_xtensor` with the `dims` keyword argument to specify the dims explicitly." |
| 57 | + ) |
| 58 | + |
| 59 | + def __new__( |
| 60 | + cls, |
| 61 | + name: str, |
| 62 | + *dist_params, |
| 63 | + dims: DimsWithEllipsis | None = None, |
| 64 | + initval=None, |
| 65 | + observed=None, |
| 66 | + total_size=None, |
| 67 | + transform=UNSET, |
| 68 | + default_transform=UNSET, |
| 69 | + model=None, |
| 70 | + **kwargs, |
| 71 | + ): |
| 72 | + try: |
| 73 | + model = modelcontext(model) |
| 74 | + except TypeError: |
| 75 | + raise TypeError( |
| 76 | + "No model on context stack, which is needed to instantiate distributions. " |
| 77 | + "Add variable inside a 'with model:' block, or use the '.dist' syntax for a standalone distribution." |
| 78 | + ) |
| 79 | + |
| 80 | + if not isinstance(name, str): |
| 81 | + raise TypeError(f"Name needs to be a string but got: {name}") |
| 82 | + |
| 83 | + dims = convert_dims_with_ellipsis(dims) |
| 84 | + if dims is None: |
| 85 | + dim_lengths = {} |
| 86 | + else: |
| 87 | + try: |
| 88 | + dim_lengths = {dim: model.dim_lengths[dim] for dim in dims if dim is not Ellipsis} |
| 89 | + except KeyError: |
| 90 | + raise ValueError( |
| 91 | + f"Not all dims {dims} are part of the model coords. " |
| 92 | + f"Add them at initialization time or use `model.add_coord` before defining the distribution." |
| 93 | + ) |
| 94 | + |
| 95 | + if observed is not None: |
| 96 | + observed = cls._as_xtensor(observed) |
| 97 | + |
| 98 | + # Propagate observed dims to dim_lengths |
| 99 | + for observed_dim in observed.type.dims: |
| 100 | + if observed_dim not in dim_lengths: |
| 101 | + dim_lengths[observed_dim] = model.dim_lengths[observed_dim] |
| 102 | + |
| 103 | + rv = cls.dist(*dist_params, dim_lengths=dim_lengths, **kwargs) |
| 104 | + |
| 105 | + # User provided dims must specify all dims or use ellipsis |
| 106 | + if dims is not None: |
| 107 | + if (... not in dims) and (set(dims) != set(rv.type.dims)): |
| 108 | + raise ValueError( |
| 109 | + f"Provided dims {dims} do not match the distribution's output dims {rv.type.dims}. " |
| 110 | + "Use ellipsis to specify all other dimensions." |
| 111 | + ) |
| 112 | + # Use provided dims to transpose the output to the desired order |
| 113 | + rv = rv.transpose(*dims) |
| 114 | + |
| 115 | + rv_dims = rv.type.dims |
| 116 | + if observed is None: |
| 117 | + if default_transform is UNSET: |
| 118 | + default_transform = cls.default_transform |
| 119 | + else: |
| 120 | + # Align observed dims with those of the RV |
| 121 | + # TODO: If this fails give a more informative error message |
| 122 | + observed = observed.transpose(*rv_dims).values |
| 123 | + |
| 124 | + rv = model.register_rv( |
| 125 | + rv.values, |
| 126 | + name=name, |
| 127 | + observed=observed, |
| 128 | + total_size=total_size, |
| 129 | + dims=rv_dims, |
| 130 | + transform=transform, |
| 131 | + default_transform=default_transform, |
| 132 | + initval=initval, |
| 133 | + ) |
| 134 | + |
| 135 | + return as_xtensor(rv, dims=rv_dims) |
| 136 | + |
| 137 | + @classmethod |
| 138 | + def dist( |
| 139 | + cls, |
| 140 | + dist_params, |
| 141 | + *, |
| 142 | + dim_lengths: dict[str, Variable | int] | None = None, |
| 143 | + core_dims: str | Sequence[str] | None = None, |
| 144 | + **kwargs, |
| 145 | + ) -> XTensorVariable: |
| 146 | + for invalid_kwarg in ("size", "shape", "dims"): |
| 147 | + if invalid_kwarg in kwargs: |
| 148 | + raise TypeError(f"DimDistribution does not accept {invalid_kwarg} argument.") |
| 149 | + |
| 150 | + # XRV requires only extra_dims, not dims |
| 151 | + dist_params = [cls._as_xtensor(param) for param in dist_params] |
| 152 | + |
| 153 | + if dim_lengths is None: |
| 154 | + extra_dims = None |
| 155 | + else: |
| 156 | + # Exclude dims that are implied by the parameters or core_dims |
| 157 | + implied_dims = set(chain.from_iterable(param.type.dims for param in dist_params)) |
| 158 | + if core_dims is not None: |
| 159 | + if isinstance(core_dims, str): |
| 160 | + implied_dims.add(core_dims) |
| 161 | + else: |
| 162 | + implied_dims.update(core_dims) |
| 163 | + |
| 164 | + extra_dims = { |
| 165 | + dim: length for dim, length in dim_lengths.items() if dim not in implied_dims |
| 166 | + } |
| 167 | + return cls.xrv_op(*dist_params, extra_dims=extra_dims, core_dims=core_dims, **kwargs) |
| 168 | + |
| 169 | + |
| 170 | +class VectorDimDistribution(DimDistribution): |
| 171 | + @classmethod |
| 172 | + def dist(self, *args, core_dims: str | Sequence[str] | None = None, **kwargs): |
| 173 | + # Add a helpful error message if core_dims is not provided |
| 174 | + if core_dims is None: |
| 175 | + raise ValueError( |
| 176 | + f"{self.__name__} requires core_dims to be specified, as it involves non-scalar inputs or outputs." |
| 177 | + "Check the documentation of the distribution for details." |
| 178 | + ) |
| 179 | + return super().dist(*args, core_dims=core_dims, **kwargs) |
| 180 | + |
| 181 | + |
| 182 | +class PositiveDimDistribution(DimDistribution): |
| 183 | + """Base class for positive continuous distributions.""" |
| 184 | + |
| 185 | + default_transform = transforms.log |
| 186 | + |
| 187 | + |
| 188 | +class UnitDimDistribution(DimDistribution): |
| 189 | + """Base class for unit-valued distributions.""" |
| 190 | + |
| 191 | + default_transform = transforms.logodds |
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