utilities.py
# Copyright 2017-2020 The GPflow Contributors. All Rights Reserved.
#
# 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.
import copy
import re
from functools import lru_cache
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from packaging.version import Version
from tabulate import tabulate
from ..base import Parameter
from ..config import default_float, default_int, default_summary_fmt
from .ops import cast
__all__ = [
"set_trainable",
"multiple_assign",
"training_loop",
"print_summary",
"tabulate_module_summary",
"deepcopy",
"freeze",
"leaf_components",
"parameter_dict",
"read_values",
"to_default_float",
"to_default_int",
"reset_cache_bijectors",
"select_dict_parameters_with_prior",
]
TraverseInput = TypeVar("TraverseInput", tf.Variable, tf.Module, Parameter)
State = Any
Path = str
Accumulator = Tuple[Path, State]
TraverseUpdateCallable = Callable[[TraverseInput, Path, State], State]
def to_default_int(x):
return cast(x, dtype=default_int())
def to_default_float(x):
return cast(x, dtype=default_float())
def set_trainable(model: Union[tf.Module, Iterable[tf.Module]], flag: bool) -> None:
"""
Set trainable flag for all `tf.Variable`s and `gpflow.Parameter`s in a `tf.Module` or collection
of `tf.Module`s.
"""
modules = [model] if isinstance(model, tf.Module) else model
for mod in modules:
for variable in mod.variables:
variable._trainable = flag
def multiple_assign(module: tf.Module, parameters: Dict[str, tf.Tensor]):
"""
Multiple assign takes a dictionary with new values. Dictionary keys are paths to the
`tf.Variable`s or `gpflow.Parameter` of the input module.
:param module: `tf.Module`.
:param parameters: a dictionary with keys of the form ".module.path.to.variable" and new value tensors.
"""
reference_var_dict = parameter_dict(module)
for path, value in parameters.items():
reference_var_dict[path].assign(value)
def read_values(module: tf.Module) -> Dict[str, np.ndarray]:
"""Returns a dictionary of numpy values of the module parameters (variables)."""
return {k: v.numpy() for k, v in parameter_dict(module).items()}
def parameter_dict(module: tf.Module) -> Dict[str, Union[Parameter, tf.Variable]]:
"""
Returns a dictionary of parameters (variables) for the `tf.Module` component.
Dictionary keys are relative paths to the attributes to which parameters (variables) assigned to.
class SubModule(tf.Module):
def __init__(self):
self.parameter = gpflow.Parameter(1.0)
self.variable = tf.Variable(1.0)
class Module(tf.Module):
def __init__(self):
self.submodule = SubModule()
m = Module()
params = parameter_dict(m)
# {
# ".submodule.parameter": <parameter object>,
# ".submodule.variable": <variable object>
# }
"""
param_dict = leaf_components(module)
return {f".{key.split('.', 1)[-1]}": value for key, value in param_dict.items()}
def training_loop(
closure: Callable[[], tf.Tensor],
optimizer: Optional[tf.optimizers.Optimizer] = None,
var_list: List[tf.Variable] = None,
maxiter=1e3,
compile=False,
):
"""
Simple generic training loop. At each iteration uses a GradientTape to compute
the gradients of a loss function with respect to a set of variables.
:param closure: Callable that constructs a loss function based on data and model being trained
:param optimizer: tf.optimizers or tf.keras.optimizers that updates variables by applying the
corresponding loss gradients. Adam is a default optimizer with default settings.
:param var_list: List of model variables to be learnt during training
:param maxiter: Maximum number of
:return:
"""
optimizer = tf.optimizers.Adam() if optimizer is None else optimizer
def optimization_step():
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(var_list)
loss = closure()
grads = tape.gradient(loss, var_list)
optimizer.apply_gradients(zip(grads, var_list))
if compile:
optimization_step = tf.function(optimization_step)
for _ in range(int(maxiter)):
optimization_step()
def print_summary(module: tf.Module, fmt: str = None):
"""
Prints a summary of the parameters and variables contained in a tf.Module.
"""
fmt = fmt if fmt is not None else default_summary_fmt()
if fmt == "notebook":
from IPython.core.display import HTML, display
tab = tabulate_module_summary(module, "html")
display(HTML(tab))
else:
print(tabulate_module_summary(module, fmt))
def tabulate_module_summary(module: tf.Module, tablefmt: Optional[str] = None) -> str:
def get_transform(path, var):
if hasattr(var, "transform") and var.transform is not None:
if isinstance(var.transform, tfp.bijectors.Chain):
return " + ".join(b.__class__.__name__ for b in var.transform.bijectors[::-1])
return var.transform.__class__.__name__
return None
def get_prior(path, var):
if hasattr(var, "prior") and var.prior is not None:
return var.prior.name
return None
# list of (column_name: str, column_getter: Callable[[tf.Variable], str]) tuples:
column_definition = [
("name", lambda path, var: path),
("class", lambda path, var: var.__class__.__name__),
("transform", get_transform),
("prior", get_prior),
("trainable", lambda path, var: var.trainable),
("shape", lambda path, var: var.shape),
("dtype", lambda path, var: var.dtype.name),
("value", lambda path, var: _str_tensor_value(var.numpy())),
]
column_names, column_getters = zip(*column_definition)
merged_leaf_components = _merge_leaf_components(leaf_components(module))
column_values = [
[getter(path, variable) for getter in column_getters]
for path, variable in merged_leaf_components.items()
]
return tabulate(column_values, headers=column_names, tablefmt=tablefmt)
def leaf_components(input: tf.Module):
return _get_leaf_components(input)
def _merge_leaf_components(
input: Dict[str, Union[tf.Variable, tf.Tensor, Parameter]]
) -> Dict[str, Union[tf.Variable, tf.Tensor, Parameter]]:
ref_fn = lambda x: (x if isinstance(x, Parameter) else x.ref())
deref_fn = lambda x: (x if isinstance(x, Parameter) else x.deref())
input_values = set([ref_fn(value) for value in input.values()])
if len(input_values) == len(input):
return input
tmp_dict = dict() # Type: Dict[ref, str]
for key, value in input.items():
ref = ref_fn(value)
if ref in tmp_dict:
tmp_dict[ref] = f"{tmp_dict[ref]}\n{key}"
else:
tmp_dict[ref] = key
return {key: deref_fn(ref) for ref, key in tmp_dict.items()}
def _get_leaf_components(input_module: tf.Module):
"""
Returns a list of tuples each corresponding to a gpflow.Parameter or tf.Variable in the each
submodules of a given tf.Module. Each tuple consists of an specific Parameter (or Variable) and
its relative path inside the module, which is constructed recursively by adding a prefix with
the path to the current module. Designed to be used as a helper for the method 'print_summary'.
:param input_module: tf.Module including keras.Model, keras.layers.Layer and gpflow.Module.
:return:
"""
target_types = (Parameter, tf.Variable)
input_name, state = input_module.__class__.__name__, dict()
accumulator = (input_name, state)
def update_state(parameter_or_variable, path, state):
state[path] = parameter_or_variable
return state
state = traverse_module(input_module, accumulator, update_state, target_types)
return state
def reset_cache_bijectors(input_module: tf.Module) -> tf.Module:
"""
Recursively finds tfp.bijectors.Bijector-s inside the components of the tf.Module using `traverse_component`.
Resets the caches stored inside each tfp.bijectors.Bijector.
:param input_module: tf.Module including keras.Model, keras.layers.Layer and gpflow.Module.
:return:
"""
if Version(tfp.__version__) >= Version("0.11.0"):
if hasattr(tfp.bijectors.Identity()._cache, "clear"):
# implementation in `master` branch (checked 29 Sep 2020) provides clear():
def _clear_bijector_cache(bijector: tfp.bijectors.Bijector):
bijector._cache.clear()
else:
# previous versions (including the versions 0.11.0 and 0.11.1 released as of 29 Sep 2020) provide reset(), but its implementation is broken
def _clear_bijector_cache(bijector: tfp.bijectors.Bijector):
# workaround for broken implementation of bijector._cache.reset():
cache = bijector._cache
cache_type = type(cache.forward)
assert type(cache.inverse) == cache_type
cache.__init__(cache.forward._func, cache.inverse._func, cache_type)
else:
# fallback for backwards-compatibility with tensorflow_probability < 0.11.0
def _clear_bijector_cache(bijector: tfp.bijectors.Bijector):
# `_from_x` and `_from_y` are cache dictionaries for forward and inverse transformations
bijector._from_x.clear()
bijector._from_y.clear()
target_types = (tfp.bijectors.Bijector,)
accumulator = ("", None)
def clear_bijector(bijector, _, state):
if not isinstance(bijector, tfp.bijectors.Bijector):
return # skip submodules that are not bijectors
_clear_bijector_cache(bijector)
if isinstance(bijector, tfp.bijectors.Chain):
# recursively clear caches of sub-bijectors
for m in bijector.submodules:
if isinstance(m, tfp.bijectors.Bijector):
_clear_bijector_cache(m)
return state
_ = traverse_module(input_module, accumulator, clear_bijector, target_types)
return input_module
M = TypeVar("M", bound=tf.Module)
def deepcopy(input_module: M, memo: Optional[Dict[int, Any]] = None) -> M:
"""
Returns a deepcopy of the input tf.Module. To do that first resets the caches stored inside each
tfp.bijectors.Bijector to allow the deepcopy of the tf.Module.
:param input_module: tf.Module including keras.Model, keras.layers.Layer and gpflow.Module.
:param memo: passed through to func:`copy.deepcopy` (see https://docs.python.org/3/library/copy.html).
:return: Returns a deepcopy of an input object.
"""
return copy.deepcopy(reset_cache_bijectors(input_module), memo)
def freeze(input_module: M) -> M:
"""
Returns a deepcopy of the input tf.Module with constants instead of variables and parameters.
:param input_module: tf.Module or gpflow.Module.
:return: Returns a frozen deepcopy of an input object.
"""
objects_to_freeze = _get_leaf_components(input_module)
memo_tensors = {id(v): tf.convert_to_tensor(v) for v in objects_to_freeze.values()}
module_copy = deepcopy(input_module, memo_tensors)
return module_copy
def traverse_module(
m: TraverseInput, acc: Accumulator, update_cb: TraverseUpdateCallable, target_types: tuple
) -> Accumulator:
"""
Recursively traverses `m`, accumulating in `acc` a path and a state until it finds an object of type
in `target_types` to apply `update_cb` to update the accumulator `acc` and/or the object.
:param m: tf.Module, tf.Variable or gpflow.Parameter
:param acc: Tuple of path and state
:param update_cb: Callable
:param target_types: target class types
:return:
"""
path, state = acc
new_state = state
if isinstance(m, target_types):
return update_cb(m, path, state)
if isinstance(m, (list, tuple)):
for term_idx, subterm in enumerate(m):
new_acc = (f"{path}[{term_idx}]", new_state)
new_state = traverse_module(subterm, new_acc, update_cb, target_types)
elif isinstance(m, dict):
for term_idx, subterm in m.items():
new_acc = (f"{path}['{term_idx}']", new_state)
new_state = traverse_module(subterm, new_acc, update_cb, target_types)
elif isinstance(m, tf.Module):
for name, submodule in vars(m).items():
ignored_attributes = m._TF_MODULE_IGNORED_PROPERTIES
# NOTE(awav): since tfp version 0.10.0, tfp.bijectors.Bijector instances have
# `_parameters` dictionary with "self" references that cause
# infinite recursive loop.
if isinstance(m, tfp.bijectors.Bijector):
ignored_attributes = ignored_attributes.union({"_parameters"})
if name in ignored_attributes:
continue
new_acc = (f"{path}.{name}", new_state)
new_state = traverse_module(submodule, new_acc, update_cb, target_types)
return new_state
@lru_cache()
def _first_three_elements_regexp():
num_re = r"[+\-]?(?:0|[1-9]\d*)(?:\.\d*)?(?:[eE][+\-]?\d+)?"
pat_re = rf"^(?:(\[+)\s*)?({num_re})(?:\s+({num_re})(?:\s+({num_re}))?)?.*?"
return re.compile(pat_re)
def _str_tensor_value(value: np.ndarray):
value_str = str(value)
if value.size <= 3:
return value_str
max_chars = 500
value_str = value_str[:max_chars]
regexp = _first_three_elements_regexp()
match = regexp.match(value_str)
assert match is not None
brackets, elem1, elem2, elem3 = match.groups()
out = f"{elem1}"
if elem2 is not None:
out = f"{out}{f', {elem2}'}"
if elem3 is not None:
out = f"{out}{f', {elem3}'}"
if brackets is not None:
out = f"{brackets}{out}..."
return out
def select_dict_parameters_with_prior(model: tf.Module) -> Dict[str, Parameter]:
"""Collects parameters with prior into a dictionary."""
return {
k: p
for k, p in parameter_dict(model).items()
if hasattr(p, "prior") and p.prior is not None
}
