# Copyright 2024 - present 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.
"""
Created on Mar 7, 2011.
@author: johnsalvatier
"""
import warnings
from abc import ABC, abstractmethod
from collections.abc import Iterable, Mapping, Sequence
from dataclasses import field
from enum import IntEnum, unique
from typing import Any
import numpy as np
from pytensor.graph.basic import Variable
from pymc.blocking import PointType, StatDtype, StatsDict, StatShape, StatsType
from pymc.model import modelcontext
from pymc.step_methods.state import (
DataClassState,
RandomGeneratorState,
WithSamplingState,
dataclass_state,
)
from pymc.util import RandomGenerator, get_random_generator
__all__ = ("Competence", "CompoundStep")
@unique
class Competence(IntEnum):
"""Enum for characterizing competence classes of step methods.
Values include:
0: INCOMPATIBLE
1: COMPATIBLE
2: PREFERRED
3: IDEAL
"""
INCOMPATIBLE = 0
COMPATIBLE = 1
PREFERRED = 2
IDEAL = 3
def infer_warn_stats_info(
stats_dtypes: list[dict[str, StatDtype]],
sds: dict[str, tuple[StatDtype, StatShape]],
stepname: str,
) -> tuple[list[dict[str, StatDtype]], dict[str, tuple[StatDtype, StatShape]]]:
"""Get `stats_dtypes` and `stats_dtypes_shapes` from either of them."""
# Avoid side-effects on the original lists/dicts
stats_dtypes = [d.copy() for d in stats_dtypes]
sds = sds.copy()
# Disallow specification of both attributes
if stats_dtypes and sds:
raise TypeError(
"Only one of `stats_dtypes_shapes` or `stats_dtypes` must be specified."
f" `{stepname}.stats_dtypes` should be removed."
)
# Infer one from the other
if not sds and stats_dtypes:
warnings.warn(
f"`{stepname}.stats_dtypes` is deprecated."
" Please update it to specify `stats_dtypes_shapes` instead.",
DeprecationWarning,
)
if len(stats_dtypes) > 1:
raise TypeError(
f"`{stepname}.stats_dtypes` must be a list containing at most one dict."
)
for sd in stats_dtypes:
for sname, dtype in sd.items():
sds[sname] = (dtype, None)
elif sds:
stats_dtypes.append({sname: dtype for sname, (dtype, _) in sds.items()})
return stats_dtypes, sds
@dataclass_state
class StepMethodState(DataClassState):
var_names: list[str] = field(metadata={"tensor_name": True, "frozen": True})
rng: RandomGeneratorState
class BlockedStep(ABC, WithSamplingState):
stats_dtypes: list[dict[str, type]] = []
"""A list containing <=1 dictionary that maps stat names to dtypes.
This attribute is deprecated.
Use `stats_dtypes_shapes` instead.
"""
stats_dtypes_shapes: dict[str, tuple[StatDtype, StatShape]] = {}
"""Maps stat names to dtypes and shapes.
Shapes are interpreted in the following ways:
- `[]` is a scalar.
- `[3,]` is a length-3 vector.
- `[4, None]` is a matrix with 4 rows and a dynamic number of columns.
- `None` is a sparse stat (i.e. not always present) or a NumPy array with varying `ndim`.
"""
vars: list[Variable] = []
"""Variables that the step method is assigned to."""
def __new__(cls, *args, **kwargs):
blocked = kwargs.get("blocked")
if blocked is None:
# Try to look up default value from class
blocked = getattr(cls, "default_blocked", True)
kwargs["blocked"] = blocked
model = modelcontext(kwargs.get("model"))
kwargs.update({"model": model})
# vars can either be first arg or a kwarg
if "vars" not in kwargs and len(args) >= 1:
vars = args[0]
args = args[1:]
elif "vars" in kwargs:
vars = kwargs.pop("vars")
else: # Assume all model variables
vars = model.value_vars
if not isinstance(vars, tuple | list):
vars = [vars]
if len(vars) == 0:
raise ValueError("No free random variables to sample.")
# Auto-fill stats metadata attributes from whichever was given.
stats_dtypes, stats_dtypes_shapes = infer_warn_stats_info(
cls.stats_dtypes,
cls.stats_dtypes_shapes,
cls.__name__,
)
if not blocked and len(vars) > 1:
# In this case we create a separate sampler for each var
# and append them to a CompoundStep
steps = []
rngs = get_random_generator(kwargs.pop("rng", None)).spawn(len(vars))
for var, rng in zip(vars, rngs):
step = super().__new__(cls)
step.stats_dtypes = stats_dtypes
step.stats_dtypes_shapes = stats_dtypes_shapes
# If we don't return the instance we have to manually
# call __init__
_kwargs = kwargs.copy()
_kwargs["rng"] = rng
step.__init__([var], *args, **_kwargs)
# Hack for creating the class correctly when unpickling.
step.__newargs = ([var], *args), _kwargs
steps.append(step)
return CompoundStep(steps)
else:
step = super().__new__(cls)
step.stats_dtypes = stats_dtypes
step.stats_dtypes_shapes = stats_dtypes_shapes
# Hack for creating the class correctly when unpickling.
step.__newargs = (vars, *args), kwargs
return step
@staticmethod
def _progressbar_config(n_chains=1):
columns = []
stats = {}
return columns, stats
@staticmethod
def _make_update_stats_function():
def update_stats(stats, step_stats, chain_idx):
return stats
return update_stats
# Hack for creating the class correctly when unpickling.
def __getnewargs_ex__(self):
return self.__newargs
@abstractmethod
def step(self, point: PointType) -> tuple[PointType, StatsType]:
"""Perform a single step of the sampler."""
@staticmethod
def competence(var, has_grad):
return Competence.INCOMPATIBLE
@classmethod
def _competence(cls, vars, have_grad):
vars = np.atleast_1d(vars)
have_grad = np.atleast_1d(have_grad)
competences = []
for var, has_grad in zip(vars, have_grad):
try:
competences.append(cls.competence(var, has_grad))
except TypeError:
competences.append(cls.competence(var))
return competences
def stop_tuning(self):
if hasattr(self, "tune"):
self.tune = False
def set_rng(self, rng: RandomGenerator):
self.rng = get_random_generator(rng, copy=False)
def flat_statname(sampler_idx: int, sname: str) -> str:
"""Get the flat-stats name for a samplers stat."""
return f"sampler_{sampler_idx}__{sname}"
def get_stats_dtypes_shapes_from_steps(
steps: Iterable[BlockedStep],
) -> dict[str, tuple[StatDtype, StatShape]]:
"""Combine stats dtype shape dictionaries from multiple step methods.
In the resulting stats dict, each sampler stat is prefixed by `sampler_#__`.
"""
result = {}
for s, step in enumerate(steps):
for sname, (dtype, shape) in step.stats_dtypes_shapes.items():
result[flat_statname(s, sname)] = (dtype, shape)
return result
@dataclass_state
class CompoundStepState(DataClassState):
methods: list[StepMethodState]
def __init__(self, methods: list[StepMethodState]):
self.methods = methods
[docs]
class CompoundStep(WithSamplingState):
"""Step method composed of a list of several other step methods applied in sequence."""
_state_class = CompoundStepState
[docs]
def __init__(self, methods):
self.methods = list(methods)
self.stats_dtypes = []
for method in self.methods:
self.stats_dtypes.extend(method.stats_dtypes)
self.stats_dtypes_shapes = get_stats_dtypes_shapes_from_steps(methods)
self.name = (
f"Compound[{', '.join(getattr(m, 'name', 'UNNAMED_STEP') for m in self.methods)}]"
)
self.tune = True
[docs]
def step(self, point) -> tuple[PointType, StatsType]:
stats = []
for method in self.methods:
point, sts = method.step(point)
stats.extend(sts)
# Model logp can only be the logp of the _last_ stats,
# if there is one. Pop all others.
for sts in stats[:-1]:
sts.pop("model_logp", None)
return point, stats
[docs]
def stop_tuning(self):
for method in self.methods:
method.stop_tuning()
[docs]
def reset_tuning(self):
for method in self.methods:
if hasattr(method, "reset_tuning"):
method.reset_tuning()
@property
def sampling_state(self) -> DataClassState:
return CompoundStepState(methods=[method.sampling_state for method in self.methods])
@sampling_state.setter
def sampling_state(self, state: DataClassState):
assert isinstance(state, self._state_class), (
f"Invalid sampling state class {type(state)}. Expected {self._state_class}"
)
for method, state_method in zip(self.methods, state.methods):
method.sampling_state = state_method
@property
def vars(self) -> list[Variable]:
return [var for method in self.methods for var in method.vars]
[docs]
def set_rng(self, rng: RandomGenerator):
_rngs = get_random_generator(rng, copy=False).spawn(len(self.methods))
for method, _rng in zip(self.methods, _rngs):
method.set_rng(_rng)
def _progressbar_config(self, n_chains=1):
from functools import reduce
column_lists, stat_dict_list = zip(
*[method._progressbar_config(n_chains) for method in self.methods]
)
flat_list = reduce(lambda left_list, right_list: left_list + right_list, column_lists)
columns = []
headers = []
for col in flat_list:
name = col.get_table_column().header
if name not in headers:
headers.append(name)
columns.append(col)
stats = reduce(lambda left_dict, right_dict: left_dict | right_dict, stat_dict_list)
return columns, stats
def _make_update_stats_function(self):
update_fns = [method._make_update_stats_function() for method in self.methods]
def update_stats(stats, step_stats, chain_idx):
for step_stat, update_fn in zip(step_stats, update_fns):
stats = update_fn(stats, step_stat, chain_idx)
return stats
return update_stats
def flatten_steps(step: BlockedStep | CompoundStep) -> list[BlockedStep]:
"""Flatten a hierarchy of step methods to a list."""
if isinstance(step, BlockedStep):
return [step]
steps = []
if not isinstance(step, CompoundStep):
raise ValueError(f"Unexpected type of step method: {step}")
for sm in step.methods:
steps += flatten_steps(sm)
return steps
def check_step_emits_tune(step: CompoundStep | BlockedStep):
if isinstance(step, BlockedStep) and "tune" not in step.stats_dtypes_shapes:
raise TypeError(f"{type(step)} does not emit the required 'tune' stat.")
elif isinstance(step, CompoundStep):
for sstep in step.methods:
if "tune" not in sstep.stats_dtypes_shapes:
raise TypeError(f"{type(sstep)} does not emit the required 'tune' stat.")
return
class StatsBijection:
"""Map between a `list` of stats to `dict` of stats."""
def __init__(self, sampler_stats_dtypes: Sequence[Mapping[str, type]]) -> None:
# Keep a list of flat vs. original stat names
stat_groups = []
for s, names_dtypes in enumerate(sampler_stats_dtypes):
group = []
for statname, dtype in names_dtypes.items():
flatname = flat_statname(s, statname)
is_obj = np.dtype(dtype) == np.dtype(object)
group.append((flatname, statname, is_obj))
stat_groups.append(group)
self._stat_groups: list[list[tuple[str, str, bool]]] = stat_groups
self.object_stats = {
fname: (s, sname)
for s, group in enumerate(self._stat_groups)
for fname, sname, is_obj in group
if is_obj
}
@property
def n_samplers(self) -> int:
return len(self._stat_groups)
def map(self, stats_list: Sequence[Mapping[str, Any]]) -> StatsDict:
"""Combine stats dicts of multiple samplers into one dict."""
stats_dict = {}
for s, sts in enumerate(stats_list):
for fname, sname, is_obj in self._stat_groups[s]:
if sname not in sts:
continue
stats_dict[fname] = sts[sname]
return stats_dict
def rmap(self, stats_dict: Mapping[str, Any]) -> StatsType:
"""Split a global stats dict into a list of sampler-wise stats dicts.
The ``stats_dict`` can be a subset of all sampler stats.
"""
stats_list = []
for group in self._stat_groups:
d = {}
for fname, sname, is_obj in group:
if fname not in stats_dict:
continue
d[sname] = stats_dict[fname]
stats_list.append(d)
return stats_list
