https://github.com/GPflow/GPflow
Tip revision: a0c7c118f4a70a67ccc7c70e63a99fb3f9db93c5 authored by Vincent Dutordoir on 10 July 2020, 14:11:11 UTC
Update readme with new project using GPflow
Update readme with new project using GPflow
Tip revision: a0c7c11
base.py
import functools
from enum import Enum
from typing import Any, List, Optional, Sequence, TYPE_CHECKING, Tuple, Union
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.python.ops import array_ops
from typing_extensions import Final
from .config import default_float, default_summary_fmt
if TYPE_CHECKING:
from IPython.lib import pretty
DType = Union[np.dtype, tf.DType]
VariableData = Union[List, Tuple, np.ndarray, int, float] # deprecated
Transform = Union[tfp.bijectors.Bijector]
Prior = Union[tfp.distributions.Distribution]
TensorType = Union[tf.Tensor, tf.Variable, "Parameter"]
"""
Type alias for tensor-like types that are supported by most TensorFlow and GPflow operations.
NOTE: Union types like this do not work with the `register` method of `multipledispatch`'s
`Dispatcher` class. Instead use `TensorLike`.
"""
# We've left this as object until we've tested the performance consequences of using the full set
# (np.ndarray, tf.Tensor, tf.Variable, Parameter), see https://github.com/GPflow/GPflow/issues/1434
TensorLike: Final[Tuple[type, ...]] = (object,)
"""
:var TensorLike: Collection of tensor-like types for registering implementations with
`multipledispatch` dispatchers.
"""
_NativeScalar = Union[int, float]
_Array = Sequence[Any] # a nested array of int, float, bool etc. kept simple for readability
TensorData = Union[_NativeScalar, _Array, TensorType]
def _IS_PARAMETER(o: object) -> bool:
return isinstance(o, Parameter)
def _IS_TRAINABLE_PARAMETER(o: object) -> bool:
return isinstance(o, Parameter) and o.trainable
class Module(tf.Module):
@property
def parameters(self) -> Tuple["Parameter", ...]:
return tuple(self._flatten(predicate=_IS_PARAMETER))
@property
def trainable_parameters(self) -> Tuple["Parameter", ...]:
return tuple(self._flatten(predicate=_IS_TRAINABLE_PARAMETER))
def _representation_table(self, object_name: str, tablefmt: Optional[str]) -> str:
from .utilities import leaf_components, tabulate_module_summary
repr_components = [object_name]
if leaf_components(self):
repr_components.append(tabulate_module_summary(self, tablefmt=tablefmt))
return "\n".join(repr_components)
def _repr_html_(self) -> str:
""" Nice representation of GPflow objects in IPython/Jupyter notebooks """
from html import escape
return self._representation_table(escape(repr(self)), "html")
def _repr_pretty_(self, p: "pretty.RepresentationPrinter", cycle: bool) -> None:
""" Nice representation of GPflow objects in the IPython shell """
repr_str = self._representation_table(repr(self), default_summary_fmt())
p.text(repr_str)
class PriorOn(Enum):
CONSTRAINED = "constrained"
UNCONSTRAINED = "unconstrained"
class Parameter(tf.Module):
def __init__(
self,
value: TensorData,
*,
transform: Optional[Transform] = None,
prior: Optional[Prior] = None,
prior_on: Union[str, PriorOn] = PriorOn.CONSTRAINED,
trainable: bool = True,
dtype: Optional[DType] = None,
name: Optional[str] = None,
):
"""
A parameter retains both constrained and unconstrained
representations. If no transform is provided, these two values will be the same.
It is often challenging to operate with unconstrained parameters. For example, a variance cannot be negative,
therefore we need a positive constraint and it is natural to use constrained values.
A prior can be imposed either on the constrained version (default) or on the unconstrained version of the parameter.
"""
super().__init__()
self._transform = transform
self.prior = prior
self.prior_on = prior_on # type: ignore # see https://github.com/python/mypy/issues/3004
if isinstance(value, tf.Variable):
self._unconstrained = value
else:
unconstrained_value = self.validate_unconstrained_value(value, dtype)
self._unconstrained = tf.Variable(
unconstrained_value, dtype=dtype, name=name, trainable=trainable
)
def log_prior_density(self) -> tf.Tensor:
""" Log of the prior probability density of the constrained variable. """
if self.prior is None:
return tf.convert_to_tensor(0.0, dtype=self.dtype)
y = self.read_value()
if self.prior_on == PriorOn.CONSTRAINED:
# evaluation is in same space as prior
return tf.reduce_sum(self.prior.log_prob(y))
else:
# prior on unconstrained, but evaluating log-prior in constrained space
x = self._unconstrained
log_p = tf.reduce_sum(self.prior.log_prob(x))
if self.transform is not None:
# need to include log|Jacobian| to account for coordinate transform
log_det_jacobian = self.transform.inverse_log_det_jacobian(y, y.shape.ndims)
log_p += tf.reduce_sum(log_det_jacobian)
return log_p
@property
def prior_on(self) -> PriorOn:
return self._prior_on
@prior_on.setter
def prior_on(self, value: Union[str, PriorOn]) -> None:
self._prior_on = PriorOn(value)
def value(self) -> tf.Tensor:
return _to_constrained(self._unconstrained.value(), self.transform) # type: ignore # assumes _to_constrained returns a tf.Tensor
def read_value(self) -> tf.Tensor:
return _to_constrained(self._unconstrained.read_value(), self.transform) # type: ignore # assumes _to_constrained returns a tf.Tensor
def experimental_ref(self) -> "Parameter":
return self
def deref(self) -> "Parameter":
return self
@property
def unconstrained_variable(self) -> tf.Variable:
return self._unconstrained
@property
def transform(self) -> Optional[Transform]:
return self._transform
@transform.setter
def transform(self, new_transform: Optional[Transform]) -> None:
constrained_value = self.read_value()
self._transform = new_transform
self.assign(constrained_value)
@property
def trainable(self) -> bool:
"""
`True` if this instance is trainable, else `False`.
This attribute cannot be set directly. Use :func:`gpflow.set_trainable`.
"""
return self._unconstrained.trainable
@property
def initial_value(self) -> tf.Tensor:
return self._unconstrained.initial_value
def validate_unconstrained_value(self, value: TensorData, dtype: DType) -> tf.Tensor:
value = _cast_to_dtype(value, dtype)
unconstrained_value = _to_unconstrained(value, self.transform)
message = (
"gpflow.Parameter: the value to be assigned is incompatible with this parameter's "
"transform (the corresponding unconstrained value has NaN or Inf) and hence cannot be "
"assigned."
)
return tf.debugging.assert_all_finite(unconstrained_value, message=message)
def assign(
self,
value: TensorData,
use_locking: bool = False,
name: Optional[str] = None,
read_value: bool = True,
) -> tf.Tensor:
"""
Assigns constrained `value` to the unconstrained parameter's variable.
It passes constrained value through parameter's transform first.
Example:
```
a = Parameter(2.0, transform=tfp.bijectors.Softplus())
b = Parameter(3.0)
a.assign(4.0) # `a` parameter to `2.0` value.
a.assign(tf.constant(5.0)) # `a` parameter to `5.0` value.
a.assign(b) # `a` parameter to constrained value of `b`.
```
:param value: Constrained tensor-like value.
:param use_locking: If `True`, use locking during the assignment.
:param name: The name of the operation to be created.
:param read_value: if True, will return something which evaluates to the new
value of the variable; if False will return the assign op.
"""
unconstrained_value = self.validate_unconstrained_value(value, self.dtype)
return self._unconstrained.assign(
unconstrained_value, use_locking=use_locking, name=name, read_value=read_value
)
@property
def is_tensor_like(self) -> bool:
"""
This method means that TensorFlow's `tensor_util.is_tensor` function
will return `True`
"""
return True
@property
def name(self) -> str:
return self._unconstrained.name
@property
def initializer(self): # type unknown
return self._unconstrained.initializer
@property
def device(self) -> Optional[str]:
return self._unconstrained.device
@property
def dtype(self) -> tf.DType:
return self._unconstrained.dtype
@property
def op(self) -> tf.Operation:
return self._unconstrained.op
@property
def shape(self) -> tf.TensorShape:
if self.transform is not None:
return self.transform.forward_event_shape(self._unconstrained.shape)
return self._unconstrained.shape
def numpy(self) -> np.ndarray:
return self.read_value().numpy()
def get_shape(self) -> tf.TensorShape:
return self.shape
def _should_act_as_resource_variable(self): # type unknown
# needed so that Parameters are correctly identified by TensorFlow's
# is_resource_variable() in resource_variable_ops.py
pass # only checked by TensorFlow using hasattr()
@property
def handle(self): # type unknown
return self._unconstrained.handle
def __repr__(self) -> str:
unconstrained = self.unconstrained_variable
constrained = self.read_value()
if tf.executing_eagerly():
info = (
f"unconstrained-shape={unconstrained.shape} "
f"unconstrained-value={unconstrained.numpy()} "
f"constrained-shape={constrained.shape} "
f"constrained-value={constrained.numpy()}"
)
else:
if unconstrained.shape == constrained.shape:
info = f"shape={constrained.shape}"
else:
info = (
f"unconstrained-shape={unconstrained.shape} "
f"constrained-shape={constrained.shape}"
)
return f"<gpflow.Parameter {self.name!r} dtype={self.dtype.name} {info}>"
# Below
# TensorFlow copy-paste code to make variable-like object to work
@classmethod
def _OverloadAllOperators(cls): # pylint: disable=invalid-name
"""Register overloads for all operators."""
for operator in tf.Tensor.OVERLOADABLE_OPERATORS:
cls._OverloadOperator(operator)
# For slicing, bind getitem differently than a tensor (use SliceHelperVar
# instead)
# pylint: disable=protected-access
setattr(cls, "__getitem__", array_ops._SliceHelperVar)
@classmethod
def _OverloadOperator(cls, operator): # pylint: disable=invalid-name
"""Defer an operator overload to `ops.Tensor`.
We pull the operator out of ops.Tensor dynamically to avoid ordering issues.
Args:
operator: string. The operator name.
"""
tensor_oper = getattr(tf.Tensor, operator)
def _run_op(a, *args, **kwargs):
# pylint: disable=protected-access
return tensor_oper(a.read_value(), *args, **kwargs)
functools.update_wrapper(_run_op, tensor_oper)
setattr(cls, operator, _run_op)
# NOTE(mrry): This enables the Variable's overloaded "right" binary
# operators to run when the left operand is an ndarray, because it
# accords the Variable class higher priority than an ndarray, or a
# numpy matrix.
# TODO(mrry): Convert this to using numpy's __numpy_ufunc__
# mechanism, which allows more control over how Variables interact
# with ndarrays.
__array_priority__ = 100
Parameter._OverloadAllOperators()
tf.register_tensor_conversion_function(Parameter, lambda x, *args, **kwds: x.read_value())
def _cast_to_dtype(
value: TensorData, dtype: Optional[DType] = None
) -> Union[tf.Tensor, tf.Variable]:
if dtype is None:
dtype = default_float()
if tf.is_tensor(value):
# NOTE(awav) TF2.2 resolves issue with cast.
# From TF2.2, `tf.cast` can be used alone instead of this auxiliary function.
# workaround for https://github.com/tensorflow/tensorflow/issues/35938
return tf.cast(value, dtype)
else:
return tf.convert_to_tensor(value, dtype=dtype)
def _to_constrained(value: TensorType, transform: Optional[Transform]) -> TensorType:
if transform is not None:
return transform.forward(value)
return value
def _to_unconstrained(value: TensorType, transform: Optional[Transform]) -> TensorType:
if transform is not None:
return transform.inverse(value)
return value