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Rectification in MeasurableCheckAndRaise
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# 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. | ||
# | ||
# MIT License | ||
# | ||
# Copyright (c) 2021-2022 aesara-devs | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy | ||
# of this software and associated documentation files (the "Software"), to deal | ||
# in the Software without restriction, including without limitation the rights | ||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
# copies of the Software, and to permit persons to whom the Software is | ||
# furnished to do so, subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
# SOFTWARE. | ||
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from typing import List, Optional | ||
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import pytensor.tensor as pt | ||
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from pytensor.graph.rewriting.basic import node_rewriter | ||
from pytensor.raise_op import CheckAndRaise | ||
from pytensor.tensor.shape import SpecifyShape | ||
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from pymc.logprob.abstract import MeasurableVariable, _logprob, _logprob_helper | ||
from pymc.logprob.rewriting import PreserveRVMappings, measurable_ir_rewrites_db | ||
from pymc.logprob.utils import ignore_logprob | ||
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class MeasurableSpecifyShape(SpecifyShape): | ||
"""A placeholder used to specify a log-likelihood for a specify-shape sub-graph.""" | ||
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MeasurableVariable.register(MeasurableSpecifyShape) | ||
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@_logprob.register(MeasurableSpecifyShape) | ||
def logprob_specify_shape(op, values, inner_rv, *shapes, **kwargs): | ||
(value,) = values | ||
# transfer specify_shape from rv to value | ||
value = pt.specify_shape(value, shapes) | ||
return _logprob_helper(inner_rv, value) | ||
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@node_rewriter([SpecifyShape]) | ||
def find_measurable_specify_shapes(fgraph, node) -> Optional[List[MeasurableSpecifyShape]]: | ||
r"""Finds `SpecifyShapeOp`\s for which a `logprob` can be computed.""" | ||
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if isinstance(node.op, MeasurableSpecifyShape): | ||
return None # pragma: no cover | ||
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rv_map_feature: Optional[PreserveRVMappings] = getattr(fgraph, "preserve_rv_mappings", None) | ||
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if rv_map_feature is None: | ||
return None # pragma: no cover | ||
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rv = node.outputs[0] | ||
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base_rv, *shape = node.inputs | ||
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if not ( | ||
base_rv.owner | ||
and isinstance(base_rv.owner.op, MeasurableVariable) | ||
and base_rv not in rv_map_feature.rv_values | ||
): | ||
return None # pragma: no cover | ||
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new_op = MeasurableSpecifyShape() | ||
# Make base_var unmeasurable | ||
unmeasurable_base_rv = ignore_logprob(base_rv) | ||
new_rv = new_op.make_node(unmeasurable_base_rv, *shape).default_output() | ||
new_rv.name = rv.name | ||
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return [new_rv] | ||
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measurable_ir_rewrites_db.register( | ||
"find_measurable_specify_shapes", | ||
find_measurable_specify_shapes, | ||
"basic", | ||
"specify_shape", | ||
) | ||
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class MeasurableCheckAndRaise(CheckAndRaise): | ||
"""A placeholder used to specify a log-likelihood for an assert sub-graph.""" | ||
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MeasurableVariable.register(MeasurableCheckAndRaise) | ||
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@_logprob.register(MeasurableCheckAndRaise) | ||
def logprob_assert(op, values, inner_rv, *assertion, **kwargs): | ||
(value,) = values | ||
# transfer assertion from rv to value | ||
value = op(assertion, value) | ||
return _logprob_helper(inner_rv, value) | ||
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@node_rewriter([CheckAndRaise]) | ||
def find_measurable_asserts(fgraph, node) -> Optional[List[MeasurableCheckAndRaise]]: | ||
r"""Finds `AssertOp`\s for which a `logprob` can be computed.""" | ||
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if isinstance(node.op, MeasurableCheckAndRaise): | ||
return None # pragma: no cover | ||
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rv_map_feature: Optional[PreserveRVMappings] = getattr(fgraph, "preserve_rv_mappings", None) | ||
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if rv_map_feature is None: | ||
return None # pragma: no cover | ||
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rv = node.outputs[0] | ||
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base_rv, *conds = node.inputs | ||
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if not ( | ||
base_rv.owner | ||
and isinstance(base_rv.owner.op, MeasurableVariable) | ||
and base_rv not in rv_map_feature.rv_values | ||
): | ||
return None # pragma: no cover | ||
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new_op = MeasurableCheckAndRaise(exc_type=node.op.exc_type) | ||
# Make base_var unmeasurable | ||
unmeasurable_base_rv = ignore_logprob(base_rv) | ||
new_rv = new_op.make_node(unmeasurable_base_rv, *conds).default_output() | ||
new_rv.name = rv.name | ||
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return [new_rv] | ||
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measurable_ir_rewrites_db.register( | ||
"find_measurable_asserts", | ||
find_measurable_asserts, | ||
"basic", | ||
"assert", | ||
) |