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optimizer_v2.py
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optimizer_v2.py
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# Copyright 2018 The TensorFlow Authors. 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.
# ==============================================================================
"""Version 2 of class Optimizer."""
import tensorflow.compat.v2 as tf
# pylint: disable=g-bad-name
import abc
import contextlib
import functools
import warnings
from keras import backend
from keras import initializers
from keras.engine import base_layer_utils
from keras.optimizer_v2 import learning_rate_schedule
from keras.optimizer_v2 import utils as optimizer_utils
from keras.utils import generic_utils
from keras.utils import layer_utils
from keras.utils import tf_inspect
from keras.utils import tf_utils
from tensorflow.python.util.tf_export import keras_export
keras_optimizers_gauge = tf.__internal__.monitoring.BoolGauge(
"/tensorflow/api/oss-keras/optimizers", "keras optimizer usage", "method")
_DEFAULT_VALID_DTYPES = frozenset([
tf.float16, tf.bfloat16, tf.float32, tf.float64,
tf.complex64, tf.complex128
])
def _deduplicate_indexed_slices(values, indices):
"""Sums `values` associated with any non-unique `indices`.
Args:
values: A `Tensor` with rank >= 1.
indices: A one-dimensional integer `Tensor`, indexing into the first
dimension of `values` (as in an IndexedSlices object).
Returns:
A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a
de-duplicated version of `indices` and `summed_values` contains the sum of
`values` slices associated with each unique index.
"""
unique_indices, new_index_positions = tf.unique(indices)
summed_values = tf.math.unsorted_segment_sum(
values, new_index_positions,
tf.compat.v1.shape(unique_indices)[0])
return (summed_values, unique_indices)
class NullContextmanager(object):
def __init__(self, *args, **kwargs):
pass
def __enter__(self):
pass
def __exit__(self, type_arg, value_arg, traceback_arg):
return False # False values do not suppress exceptions
def name_scope_only_in_function_or_graph(name):
"""Internal-only entry point for `name_scope*`.
Enters a compat.v1.name_scope only when in a function or graph,
not when running fully eagerly.
Args:
name: The name argument that is passed to the op function.
Returns:
`name_scope*` context manager.
"""
if not tf.executing_eagerly():
return tf.compat.v1.name_scope(name)
else:
return NullContextmanager()
@keras_export("keras.optimizers.Optimizer", metaclass=abc.ABCMeta)
class OptimizerV2(tf.__internal__.tracking.Trackable):
"""Base class for Keras optimizers.
You should not use this class directly, but instead instantiate one of its
subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`, etc.
### Usage
```python
# Create an optimizer with the desired parameters.
opt = tf.keras.optimizers.SGD(learning_rate=0.1)
# `loss` is a callable that takes no argument and returns the value
# to minimize.
loss = lambda: 3 * var1 * var1 + 2 * var2 * var2
# In graph mode, returns op that minimizes the loss by updating the listed
# variables.
opt_op = opt.minimize(loss, var_list=[var1, var2])
opt_op.run()
# In eager mode, simply call minimize to update the list of variables.
opt.minimize(loss, var_list=[var1, var2])
```
### Usage in custom training loops
In Keras models, sometimes variables are created when the model is first
called, instead of construction time. Examples include 1) sequential models
without input shape pre-defined, or 2) subclassed models. Pass var_list as
callable in these cases.
Example:
```python
opt = tf.keras.optimizers.SGD(learning_rate=0.1)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(num_hidden, activation='relu'))
model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid'))
loss_fn = lambda: tf.keras.losses.mse(model(input), output)
var_list_fn = lambda: model.trainable_weights
for input, output in data:
opt.minimize(loss_fn, var_list_fn)
```
### Processing gradients before applying them
Calling `minimize()` takes care of both computing the gradients and
applying them to the variables. If you want to process the gradients
before applying them you can instead use the optimizer in three steps:
1. Compute the gradients with `tf.GradientTape`.
2. Process the gradients as you wish.
3. Apply the processed gradients with `apply_gradients()`.
Example:
```python
# Create an optimizer.
opt = tf.keras.optimizers.SGD(learning_rate=0.1)
# Compute the gradients for a list of variables.
with tf.GradientTape() as tape:
loss = <call_loss_function>
vars = <list_of_variables>
grads = tape.gradient(loss, vars)
# Process the gradients, for example cap them, etc.
# capped_grads = [MyCapper(g) for g in grads]
processed_grads = [process_gradient(g) for g in grads]
# Ask the optimizer to apply the processed gradients.
opt.apply_gradients(zip(processed_grads, var_list))
```
### Use with `tf.distribute.Strategy`
This optimizer class is `tf.distribute.Strategy` aware, which means it
automatically sums gradients across all replicas. To average gradients,
you divide your loss by the global batch size, which is done
automatically if you use `tf.keras` built-in training or evaluation loops.
See the `reduction` argument of your loss which should be set to
`tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or
`tf.keras.losses.Reduction.SUM` for not.
To aggregate gradients yourself, call `apply_gradients` with
`experimental_aggregate_gradients` set to False. This is useful if you need to
process aggregated gradients.
If you are not using these and you want to average gradients, you should use
`tf.math.reduce_sum` to add up your per-example losses and then divide by the
global batch size. Note that when using `tf.distribute.Strategy`, the first
component of a tensor's shape is the *replica-local* batch size, which is off
by a factor equal to the number of replicas being used to compute a single
step. As a result, using `tf.math.reduce_mean` will give the wrong answer,
resulting in gradients that can be many times too big.
### Variable Constraints
All Keras optimizers respect variable constraints. If constraint function is
passed to any variable, the constraint will be applied to the variable after
the gradient has been applied to the variable.
Important: If gradient is sparse tensor, variable constraint is not supported.
### Thread Compatibility
The entire optimizer is currently thread compatible, not thread-safe. The user
needs to perform synchronization if necessary.
### Slots
Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage
additional variables associated with the variables to train. These are called
<i>Slots</i>. Slots have names and you can ask the optimizer for the names of
the slots that it uses. Once you have a slot name you can ask the optimizer
for the variable it created to hold the slot value.
This can be useful if you want to log debug a training algorithm, report stats
about the slots, etc.
### Hyperparameters
These are arguments passed to the optimizer subclass constructor
(the `__init__` method), and then passed to `self._set_hyper()`.
They can be either regular Python values (like 1.0), tensors, or
callables. If they are callable, the callable will be called during
`apply_gradients()` to get the value for the hyper parameter.
Hyperparameters can be overwritten through user code:
Example:
```python
# Create an optimizer with the desired parameters.
opt = tf.keras.optimizers.SGD(learning_rate=0.1)
# `loss` is a callable that takes no argument and returns the value
# to minimize.
loss = lambda: 3 * var1 + 2 * var2
# In eager mode, simply call minimize to update the list of variables.
opt.minimize(loss, var_list=[var1, var2])
# update learning rate
opt.learning_rate = 0.05
opt.minimize(loss, var_list=[var1, var2])
```
### Callable learning rate
Optimizer accepts a callable learning rate in two ways. The first way is
through built-in or customized
`tf.keras.optimizers.schedules.LearningRateSchedule`. The schedule will be
called on each iteration with `schedule(iteration)`, a `tf.Variable`
owned by the optimizer.
Example:
>>> var = tf.Variable(np.random.random(size=(1,)))
>>> learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(
... initial_learning_rate=.01, decay_steps=20, decay_rate=.1)
>>> opt = tf.keras.optimizers.SGD(learning_rate=learning_rate)
>>> loss = lambda: 3 * var
>>> opt.minimize(loss, var_list=[var])
<tf.Variable...
The second way is through a callable function that
does not accept any arguments.
Example:
>>> var = tf.Variable(np.random.random(size=(1,)))
>>> def lr_callable():
... return .1
>>> opt = tf.keras.optimizers.SGD(learning_rate=lr_callable)
>>> loss = lambda: 3 * var
>>> opt.minimize(loss, var_list=[var])
<tf.Variable...
### Creating a custom optimizer
If you intend to create your own optimization algorithm, simply inherit from
this class and override the following methods:
- `_resource_apply_dense` (update variable given gradient tensor is a dense
`tf.Tensor`)
- `_resource_apply_sparse` (update variable given gradient tensor is a
sparse `tf.IndexedSlices`. The most common way for this to happen
is if you are taking the gradient through a `tf.gather`.)
- `_create_slots`
(if your optimizer algorithm requires additional variables)
- `get_config`
(serialization of the optimizer, include all hyper parameters)
"""
# Subclasses should set this to True unless they override `apply_gradients`
# with a version that does not have the `experimental_aggregate_gradients`
# argument. Older versions of Keras did not have this argument so custom
# optimizers may have overridden `apply_gradients` without the
# `experimental_aggregate_gradients` argument. Keras only passes
# `experimental_aggregate_gradients` if this attribute is True.
# Note: This attribute will likely be removed in an upcoming release.
_HAS_AGGREGATE_GRAD = False
def __init__(self,
name,
gradient_aggregator=None,
gradient_transformers=None,
**kwargs):
"""Create a new Optimizer.
This must be called by the constructors of subclasses.
Note that Optimizer instances should not bind to a single graph,
and so shouldn't keep Tensors as member variables. Generally
you should be able to use the _set_hyper()/state.get_hyper()
facility instead.
This class is stateful and thread-compatible.
Example of custom gradient transformations:
```python
def my_gradient_transformer(grads_and_vars):
# Simple example, double the gradients.
return [(2. * g, v) for g, v in grads_and_vars]
optimizer = tf.keras.optimizers.SGD(
1e-3, gradient_transformers=[my_gradient_transformer])
```
Args:
name: String. The name to use for momentum accumulator weights created
by the optimizer.
gradient_aggregator: The function to use to aggregate gradients across
devices (when using `tf.distribute.Strategy`). If `None`, defaults to
summing the gradients across devices. The function should accept and
return a list of `(gradient, variable)` tuples.
gradient_transformers: Optional. List of functions to use to transform
gradients before applying updates to Variables. The functions are
applied after `gradient_aggregator`. The functions should accept and
return a list of `(gradient, variable)` tuples.
**kwargs: keyword arguments. Allowed arguments are `clipvalue`,
`clipnorm`, `global_clipnorm`.
If `clipvalue` (float) is set, the gradient of each weight
is clipped to be no higher than this value.
If `clipnorm` (float) is set, the gradient of each weight
is individually clipped so that its norm is no higher than this value.
If `global_clipnorm` (float) is set the gradient of all weights is
clipped so that their global norm is no higher than this value.
Raises:
ValueError: in case of any invalid argument.
"""
# Instrument optimizer usages
keras_optimizers_gauge.get_cell(self.__class__.__name__).set(True)
allowed_kwargs = {"clipnorm", "clipvalue", "lr", "decay", "global_clipnorm"}
for k in kwargs:
if k not in allowed_kwargs:
raise TypeError("Unexpected keyword argument "
"passed to optimizer: " + str(k))
# checks that all keyword arguments are non-negative.
if kwargs[k] is not None and kwargs[k] < 0:
raise ValueError("Expected {} >= 0, received: {}".format(k, kwargs[k]))
if k == "lr":
warnings.warn(
"The `lr` argument is deprecated, use `learning_rate` instead.")
self._use_locking = True
self._init_set_name(name)
self._hyper = {}
# dict: {variable name : {slot name : variable}}
self._slots = {}
self._slot_names = []
self._weights = []
self._iterations = None
# For implementing Trackable. Stores information about how to restore
# slot variables which have not yet been created
# (trackable._CheckpointPosition objects).
# {slot_name :
# {_var_key(variable_to_train): [checkpoint_position, ... ], ... },
# ... }
self._deferred_slot_restorations = {}
decay = kwargs.pop("decay", 0.0)
if decay < 0.:
raise ValueError("decay cannot be less than 0: {}".format(decay))
self._initial_decay = decay
self._hypers_created = False
# Store the distribution strategy object if the optimizer is created inside
# strategy scope, so it could be used to create variables later.
if tf.distribute.has_strategy():
self._distribution_strategy = tf.distribute.get_strategy()
else:
self._distribution_strategy = None
# Configure gradient transformations.
if gradient_aggregator is None:
gradient_aggregator = optimizer_utils.all_reduce_sum_gradients
self.gradient_aggregator = gradient_aggregator
if gradient_transformers is None:
gradient_transformers = []
self.gradient_transformers = gradient_transformers
self.clipnorm = kwargs.pop("clipnorm", None)
self.global_clipnorm = kwargs.pop("global_clipnorm", None)
if self.clipnorm is not None and self.global_clipnorm is not None:
raise ValueError("Cannot accept both `clipnorm` and `global_clipnorm`, "
"passed `clipnorm` {}, `global_clipnorm` {}".format(
self.clipnorm, self.global_clipnorm))
self.clipvalue = kwargs.pop("clipvalue", None)
@property
def clipnorm(self):
"""`float` or `None`. If set, clips gradients to a maximum norm."""
return self._clipnorm
@property
def global_clipnorm(self):
"""`float` or `None`. If set, clips gradients to a maximum norm."""
return self._global_clipnorm
@clipnorm.setter
def clipnorm(self, val):
if val is not None and self.gradient_transformers:
raise ValueError("`clipnorm` cannot be set when `gradient_transformers` "
"is set. Instead, use the `gradient_transformers` to "
"specify clipping and other transformations.")
self._clipnorm = val
self._clipnorm_fn = optimizer_utils.make_gradient_clipnorm_fn(
self._clipnorm)
@global_clipnorm.setter
def global_clipnorm(self, val):
if val is not None and self.gradient_transformers:
raise ValueError("`clipnorm` cannot be set when `gradient_transformers` "
"is set. Instead, use the `gradient_transformers` to "
"specify clipping and other transformations.")
self._global_clipnorm = val
self._global_clipnorm_fn = optimizer_utils.make_global_gradient_clipnorm_fn(
self._global_clipnorm)
@property
def clipvalue(self):
"""`float` or `None`. If set, clips gradients to a maximum value."""
return self._clipvalue
@clipvalue.setter
def clipvalue(self, val):
if val is not None and self.gradient_transformers:
raise ValueError("`clipvalue` cannot be set when `gradient_transformers` "
"is set. Instead, use the `gradient_transformers` to "
"specify clipping and other transformations.")
self._clipvalue = val
self._clipvalue_fn = optimizer_utils.make_gradient_clipvalue_fn(
self._clipvalue)
def _transform_loss(self, loss):
"""Called in `.minimize` to transform loss before computing gradients."""
return loss
def _get_gradients(self, tape, loss, var_list, grad_loss=None):
"""Called in `minimize` to compute gradients from loss."""
grads = tape.gradient(loss, var_list, grad_loss)
return list(zip(grads, var_list))
def _transform_unaggregated_gradients(self, grads_and_vars):
"""Called in `apply_gradients` before gradient aggregation."""
return grads_and_vars
def _aggregate_gradients(self, grads_and_vars):
"""Called in `apply_gradients` to aggregate gradients across devices.
Note that user subclasses may override this, so the interface should not be
changed.
Args:
grads_and_vars: List of (gradient, variable) pairs.
Returns:
A list of (aggregrated_gradient, variable) pairs. By default, this calls
`self.gradient_aggregator`.
"""
return self.gradient_aggregator(grads_and_vars)
def _transform_gradients(self, grads_and_vars):
"""Called in `apply_gradients` after aggregation."""
if self._clipvalue is not None:
grads_and_vars = self._clipvalue_fn(grads_and_vars)
if self._clipnorm is not None:
grads_and_vars = self._clipnorm_fn(grads_and_vars)
if self._global_clipnorm is not None:
grads_and_vars = self._global_clipnorm_fn(grads_and_vars)
for fn in self.gradient_transformers:
grads_and_vars = fn(grads_and_vars)
return grads_and_vars
def minimize(self, loss, var_list, grad_loss=None, name=None, tape=None):
"""Minimize `loss` by updating `var_list`.
This method simply computes gradient using `tf.GradientTape` and calls
`apply_gradients()`. If you want to process the gradient before applying
then call `tf.GradientTape` and `apply_gradients()` explicitly instead
of using this function.
Args:
loss: `Tensor` or callable. If a callable, `loss` should take no arguments
and return the value to minimize. If a `Tensor`, the `tape` argument
must be passed.
var_list: list or tuple of `Variable` objects to update to minimize
`loss`, or a callable returning the list or tuple of `Variable` objects.
Use callable when the variable list would otherwise be incomplete before
`minimize` since the variables are created at the first time `loss` is
called.
grad_loss: (Optional). A `Tensor` holding the gradient computed for
`loss`.
name: (Optional) str. Name for the returned operation.
tape: (Optional) `tf.GradientTape`. If `loss` is provided as a `Tensor`,
the tape that computed the `loss` must be provided.
Returns:
An `Operation` that updates the variables in `var_list`. The `iterations`
will be automatically increased by 1.
Raises:
ValueError: If some of the variables are not `Variable` objects.
"""
grads_and_vars = self._compute_gradients(
loss, var_list=var_list, grad_loss=grad_loss, tape=tape)
return self.apply_gradients(grads_and_vars, name=name)
def _compute_gradients(self, loss, var_list, grad_loss=None, tape=None):
"""Compute gradients of `loss` for the variables in `var_list`.
This is the first part of `minimize()`. It returns a list
of (gradient, variable) pairs where "gradient" is the gradient
for "variable". Note that "gradient" can be a `Tensor`, an
`IndexedSlices`, or `None` if there is no gradient for the
given variable.
Args:
loss: `Tensor` or callable. If a callable, `loss` should take no
arguments and return the value to minimize. If a `Tensor`, the `tape`
argument must be passed.
var_list: list or tuple of `Variable` objects to update to minimize
`loss`, or a callable returning the list or tuple of `Variable` objects.
Use callable when the variable list would otherwise be incomplete before
`minimize` and the variables are created at the first time when `loss`
is called.
grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.
tape: (Optional) `tf.GradientTape`. If `loss` is provided as a `Tensor`,
the tape that computed the `loss` must be provided.
Returns:
A list of (gradient, variable) pairs. Variable is always present, but
gradient can be `None`.
Raises:
TypeError: If `var_list` contains anything else than `Variable` objects.
ValueError: If some arguments are invalid, or var_list is None.
"""
# TODO(joshl): Test that we handle weight decay in a reasonable way.
if not callable(loss) and tape is None:
raise ValueError("`tape` is required when a `Tensor` loss is passed.")
tape = tape if tape is not None else tf.GradientTape()
if callable(loss):
with tape:
if not callable(var_list):
tape.watch(var_list)
loss = loss()
if callable(var_list):
var_list = var_list()
with tape:
loss = self._transform_loss(loss)
var_list = tf.nest.flatten(var_list)
with tf.name_scope(self._name + "/gradients"):
grads_and_vars = self._get_gradients(tape, loss, var_list, grad_loss)
self._assert_valid_dtypes([
v for g, v in grads_and_vars
if g is not None and v.dtype != tf.resource
])
return grads_and_vars
def apply_gradients(self,
grads_and_vars,
name=None,
experimental_aggregate_gradients=True):
"""Apply gradients to variables.
This is the second part of `minimize()`. It returns an `Operation` that
applies gradients.
The method sums gradients from all replicas in the presence of
`tf.distribute.Strategy` by default. You can aggregate gradients yourself by
passing `experimental_aggregate_gradients=False`.
Example:
```python
grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars),
experimental_aggregate_gradients=False)
```
Args:
grads_and_vars: List of (gradient, variable) pairs.
name: Optional name for the returned operation. Default to the name passed
to the `Optimizer` constructor.
experimental_aggregate_gradients: Whether to sum gradients from different
replicas in the presense of `tf.distribute.Strategy`. If False, it's
user responsibility to aggregate the gradients. Default to True.
Returns:
An `Operation` that applies the specified gradients. The `iterations`
will be automatically increased by 1.
Raises:
TypeError: If `grads_and_vars` is malformed.
ValueError: If none of the variables have gradients.
RuntimeError: If called in a cross-replica context.
"""
grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars)
var_list = [v for (_, v) in grads_and_vars]
with tf.name_scope(self._name):
# Create iteration if necessary.
with tf.init_scope():
self._create_all_weights(var_list)
if not grads_and_vars:
# Distribution strategy does not support reducing an empty list of
# gradients
return tf.no_op()
if tf.distribute.in_cross_replica_context():
raise RuntimeError(
"`apply_gradients() cannot be called in cross-replica context. "
"Use `tf.distribute.Strategy.run` to enter replica "
"context.")
strategy = tf.distribute.get_strategy()
if (not experimental_aggregate_gradients and strategy and
isinstance(strategy,
(tf.compat.v1.distribute.experimental.ParameterServerStrategy,
tf.distribute.experimental.ParameterServerStrategy,
tf.distribute.experimental.CentralStorageStrategy,
tf.compat.v1.distribute.experimental.CentralStorageStrategy))):
raise NotImplementedError(
"`experimental_aggregate_gradients=False is not supported for "
"ParameterServerStrategy and CentralStorageStrategy")
apply_state = self._prepare(var_list)
if experimental_aggregate_gradients:
grads_and_vars = self._transform_unaggregated_gradients(grads_and_vars)
grads_and_vars = self._aggregate_gradients(grads_and_vars)
grads_and_vars = self._transform_gradients(grads_and_vars)
if optimizer_utils.strategy_supports_no_merge_call():
return self._distributed_apply(strategy, grads_and_vars, name,
apply_state)
else:
return tf.distribute.get_replica_context().merge_call(
functools.partial(self._distributed_apply, apply_state=apply_state),
args=(grads_and_vars,),
kwargs={
"name": name,
})
def _distributed_apply(self, distribution, grads_and_vars, name, apply_state):
"""`apply_gradients` using a `DistributionStrategy`."""
def apply_grad_to_update_var(var, grad):
"""Apply gradient to variable."""
if isinstance(var, tf.Tensor):
raise NotImplementedError("Trying to update a Tensor ", var)
apply_kwargs = {}
if isinstance(grad, tf.IndexedSlices):
if var.constraint is not None:
raise RuntimeError(
"Cannot use a constraint function on a sparse variable.")
if "apply_state" in self._sparse_apply_args:
apply_kwargs["apply_state"] = apply_state
return self._resource_apply_sparse_duplicate_indices(
grad.values, var, grad.indices, **apply_kwargs)
if "apply_state" in self._dense_apply_args:
apply_kwargs["apply_state"] = apply_state
update_op = self._resource_apply_dense(grad, var, **apply_kwargs)
if var.constraint is not None:
with tf.control_dependencies([update_op]):
return var.assign(var.constraint(var))
else:
return update_op
eagerly_outside_functions = tf.compat.v1.executing_eagerly_outside_functions()
update_ops = []
with name_scope_only_in_function_or_graph(name or self._name):
for grad, var in grads_and_vars:
# Colocate the update with variables to avoid unnecessary communication
# delays. See b/136304694.
with distribution.extended.colocate_vars_with(var):
with name_scope_only_in_function_or_graph(
"update" if eagerly_outside_functions else "update_" +
var.op.name):
update_op = distribution.extended.update(
var, apply_grad_to_update_var, args=(grad,), group=False)
if tf.distribute.in_cross_replica_context():
# In cross-replica context, extended.update returns a list of
# update ops from all replicas (group=False).
update_ops.extend(update_op)
else:
# In replica context, extended.update return the single update op
# of current replica.
update_ops.append(update_op)
any_symbolic = any(isinstance(i, tf.Operation) or
tf_utils.is_symbolic_tensor(i) for i in update_ops)
if not tf.executing_eagerly() or any_symbolic:
# If the current context is graph mode or any of the update ops are
# symbolic then the step update should be carried out under a graph
# context. (eager updates execute immediately)
with backend._current_graph(update_ops).as_default(): # pylint: disable=protected-access
with tf.control_dependencies([tf.group(update_ops)]):
return self._iterations.assign_add(1, read_value=False)
return self._iterations.assign_add(1)
def get_gradients(self, loss, params):
"""Returns gradients of `loss` with respect to `params`.
Should be used only in legacy v1 graph mode.
Args:
loss: Loss tensor.
params: List of variables.
Returns:
List of gradient tensors.
Raises:
ValueError: In case any gradient cannot be computed (e.g. if gradient
function not implemented).
"""
params = tf.nest.flatten(params)
with backend.get_graph().as_default(), backend.name_scope(self._name +
"/gradients"):
grads = tf.compat.v1.gradients(loss, params)
for grad, param in zip(grads, params):
if grad is None:
raise ValueError("Variable {} has `None` for gradient. "
"Please make sure that all of your ops have a "
"gradient defined (i.e. are differentiable). "
"Common ops without gradient: "
"K.argmax, K.round, K.eval.".format(param))
return grads
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
grads_and_vars = list(zip(grads, params))
self._assert_valid_dtypes([
v for g, v in grads_and_vars
if g is not None and v.dtype != tf.resource
])
return [self.apply_gradients(grads_and_vars)]
def _set_hyper(self, name, value):
"""set hyper `name` to value. value can be callable, tensor, numeric."""
if isinstance(value, tf.__internal__.tracking.Trackable):
self._track_trackable(value, name, overwrite=True)
if name not in self._hyper:
self._hyper[name] = value
else:
prev_value = self._hyper[name]
if (callable(prev_value)
or isinstance(prev_value,
(tf.Tensor, int, float,
learning_rate_schedule.LearningRateSchedule))
or isinstance(value, learning_rate_schedule.LearningRateSchedule)):
self._hyper[name] = value
else:
backend.set_value(self._hyper[name], value)
def _get_hyper(self, name, dtype=None):
if not self._hypers_created:
self._create_hypers()
value = self._hyper[name]
if isinstance(value, learning_rate_schedule.LearningRateSchedule):
return value
if callable(value):
value = value()
if dtype:
return tf.cast(value, dtype)
else:
return value
def _create_slots(self, var_list):
pass
def _create_all_weights(self, var_list):
"""Creates all weights, including iterations, hyperparameters and slot vars.
This will add newly created variables to `optimizer.weights`.
New variables are only created when this method is called the first time, or
when called with different variables in the var_list.
Args:
var_list: list or tuple of `Variable` objects that will be minimized
using this optimizer.
"""
_ = self.iterations
self._create_hypers()
self._create_slots(var_list)
def __getattribute__(self, name):
"""Overridden to support hyperparameter access."""
try:
return super(OptimizerV2, self).__getattribute__(name)
except AttributeError as e:
# Needed to avoid infinite recursion with __setattr__.
if name == "_hyper":
raise e
# Backwards compatibility with Keras optimizers.
if name == "lr":
name = "learning_rate"
if name in self._hyper:
return self._get_hyper(name)
raise e
def __dir__(self):
result = set(super(OptimizerV2, self).__dir__())
if "_hyper" in result:
result |= self._hyper.keys()
if "learning_rate" in self._hyper.keys():
result.add("lr")
return list(result)
def __setattr__(self, name, value):
"""Override setattr to support dynamic hyperparameter setting."""
# Backwards compatibility with Keras optimizers.
if name == "lr":
name = "learning_rate"
if hasattr(self, "_hyper") and name in self._hyper:
self._set_hyper(name, value)
else:
super(OptimizerV2, self).__setattr__(name, value)
def get_slot_names(self):
"""A list of names for this optimizer's slots."""
return self._slot_names
def add_slot(self, var, slot_name, initializer="zeros", shape=None):
"""Add a new slot variable for `var`.
A slot variable is an additional variable associated with `var` to train.
It is allocated and managed by optimizers, e.g. `Adam`.
Args:
var: a `Variable` object.
slot_name: name of the slot variable.
initializer: initializer of the slot variable
shape: (Optional) shape of the slot variable. If not set, it will default
to the shape of `var`.
Returns:
A slot variable.
"""
if slot_name not in self._slot_names:
self._slot_names.append(slot_name)
var_key = _var_key(var)
slot_dict = self._slots.setdefault(var_key, {})
weight = slot_dict.get(slot_name, None)
if weight is None:
if isinstance(initializer, str) or callable(initializer):
initializer = initializers.get(initializer)
if isinstance(
initializer,
tf.__internal__.tracking.CheckpointInitialValueCallable) or (shape is not None):
slot_shape = shape
else:
slot_shape = var.shape
initial_value = functools.partial(
initializer, shape=slot_shape, dtype=var.dtype)
else:
initial_value = initializer
with self._distribution_strategy_scope():
strategy = tf.distribute.get_strategy()
if not strategy.extended.variable_created_in_scope(var):
raise ValueError(
"Trying to create optimizer slot variable under the scope for "
"tf.distribute.Strategy ({}), which is different from the scope "
"used for the original variable ({}). Make sure the slot "
"variables are created under the same strategy scope. This may "
"happen if you're restoring from a checkpoint outside the scope"
.format(strategy, var))
with strategy.extended.colocate_vars_with(var):
weight = tf.Variable(
name="%s/%s" % (var._shared_name, slot_name), # pylint: disable=protected-access
dtype=var.dtype,
trainable=False,
initial_value=initial_value)
backend.track_variable(weight)
slot_dict[slot_name] = weight
self._restore_slot_variable(
slot_name=slot_name, variable=var,
slot_variable=weight)
self._weights.append(weight)
return weight
def get_slot(self, var, slot_name):
var_key = _var_key(var)
slot_dict = self._slots[var_key]
return slot_dict[slot_name]
def _prepare(self, var_list):
keys = set()
for var in var_list:
if isinstance(var, tf.distribute.DistributedValues):
var_devices = var._devices # pylint: disable=protected-access
else:
var_devices = [var.device]
var_dtype = var.dtype.base_dtype
for var_device in var_devices:
keys.add((var_device, var_dtype))
apply_state = {}
for var_device, var_dtype in keys:
apply_state[(var_device, var_dtype)] = {}
with tf.compat.v1.device(var_device):
self._prepare_local(var_device, var_dtype, apply_state)
return apply_state
def _prepare_local(self, var_device, var_dtype, apply_state):
if "learning_rate" in self._hyper:
lr_t = tf.identity(self._decayed_lr(var_dtype))
apply_state[(var_device, var_dtype)]["lr_t"] = lr_t
def _fallback_apply_state(self, var_device, var_dtype):
"""Compatibility for subclasses that don't pass apply_state through."""
apply_state = {(var_device, var_dtype): {}}
self._prepare_local(var_device, var_dtype, apply_state)
return apply_state[(var_device, var_dtype)]
def _create_hypers(self):
if self._hypers_created:
return
with self._distribution_strategy_scope():
# Iterate hyper values deterministically.
for name, value in sorted(self._hyper.items()):
if isinstance(value,
(tf.Tensor, tf.Variable)) or callable(value):
# The check for `callable` covers the usage when `value` is a
# `LearningRateSchedule`, in which case it does not need to create a
# variable.
continue
else:
self._hyper[name] = self.add_weight(
name,
shape=[],
trainable=False,
initializer=value,
aggregation=tf.compat.v1.VariableAggregation.ONLY_FIRST_REPLICA)
self._hypers_created = True
@property
def iterations(self):
"""Variable. The number of training steps this Optimizer has run."""
if self._iterations is None:
with self._distribution_strategy_scope():
self._iterations = self.add_weight(
"iter",
shape=[],
dtype=tf.int64,
trainable=False,
aggregation=tf.compat.v1.VariableAggregation.ONLY_FIRST_REPLICA)
self._weights.append(self._iterations)
return self._iterations
@iterations.setter
def iterations(self, variable):
if self._iterations is not None:
raise RuntimeError("Cannot set `iterations` to a new Variable after "
"the Optimizer weights have been created")
self._iterations = variable
self._weights.append(self._iterations)
def _decayed_lr(self, var_dtype):
"""Get decayed learning rate as a Tensor with dtype=var_dtype."""
lr_t = self._get_hyper("learning_rate", var_dtype)
if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule):
local_step = tf.cast(self.iterations, var_dtype)
lr_t = tf.cast(lr_t(local_step), var_dtype)
if self._initial_decay > 0.:
local_step = tf.cast(self.iterations, var_dtype)
decay_t = tf.cast(self._initial_decay, var_dtype)