# 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.""" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import parameter_server_strategy from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.distribute import values as ds_values from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import generic_utils from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.training.tracking import tracking from tensorflow.python.util import nest from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import keras_export 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 = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export("keras.optimizers.Optimizer") class OptimizerV2(trackable.Trackable): """Updated base class for optimizers. This class defines the API to add Ops to train a model. You never use this class directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### 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]) ``` ### Custom training loop with Keras models 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 = vars = 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 Constraint 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 Slots. 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. ### Hyper parameters 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. Hyper parameters 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]) ``` ### Write a customized 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 dense) - resource_apply_sparse (update variable given gradient tensor is sparse) - 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, **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 in stateful and thread-compatible. Args: name: A non-empty string. The name to use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. """ allowed_kwargs = {"clipnorm", "clipvalue", "lr", "decay"} 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])) 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 # Set the gradient clipping properties self.clipnorm = kwargs.pop("clipnorm", None) self.clipvalue = kwargs.pop("clipvalue", None) if ((self.clipnorm is not None or self.clipvalue is not None) and distribute_ctx.has_strategy()): raise ValueError("Gradient clipping in the optimizer " "(by setting clipnorm or clipvalue) is currently " "unsupported when using a distribution strategy.") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=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: A callable taking no arguments which returns the value to minimize. 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 name for the returned operation. 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) return self.apply_gradients(grads_and_vars, name=name) def _clip_gradients(self, grads): """Clip gradients according to the clipnorm and clipvalue attributes.""" if self.clipnorm is not None: if distribute_ctx.has_strategy(): raise ValueError("Gradient clipping in the optimizer " "(by setting clipnorm or clipvalue) is currently " "unsupported when using a distribution strategy.") grads = [None if g is None else clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if self.clipvalue is not None: if distribute_ctx.has_strategy(): raise ValueError("Gradient clipping in the optimizer " "(by setting clipnorm or clipvalue) is currently " "unsupported when using a distribution strategy.") v = self.clipvalue grads = [ None if g is None else clip_ops.clip_by_value(g, -v, v) for g in grads ] return grads def _compute_gradients(self, loss, var_list, grad_loss=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: A callable taking no arguments which returns the value to minimize. 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`. 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(josh11b): Test that we handle weight decay in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) with backend.name_scope(self._name + "/gradients"): grads = tape.gradient(loss_value, var_list, grad_loss) grads = self._clip_gradients(grads) grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): """Returns gradients of `loss` with respect to `params`. Arguments: 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 = nest.flatten(params) with backend.get_graph().as_default(), backend.name_scope(self._name + "/gradients"): grads = gradients.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)) grads = self._clip_gradients(grads) return grads 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. """ grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] with backend.name_scope(self._name): # Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) if not grads_and_vars: # Distribution strategy does not support reducing an empty list of # gradients return control_flow_ops.no_op() if distribute_ctx.in_cross_replica_context(): raise RuntimeError( "`apply_gradients() cannot be called in cross-replica context. " "Use `tf.distribute.Strategy.experimental_run_v2` to enter replica " "context.") strategy = distribute_ctx.get_strategy() if (not experimental_aggregate_gradients and strategy and isinstance( strategy.extended, parameter_server_strategy.ParameterServerStrategyExtended)): raise NotImplementedError( "`experimental_aggregate_gradients=False is not supported for " "ParameterServerStrategy and CentralStorageStrategy") apply_state = self._prepare(var_list) if experimental_aggregate_gradients: reduced_grads = self._aggregate_gradients(grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = list(zip(reduced_grads, var_list)) return distribute_ctx.get_replica_context().merge_call( functools.partial(self._distributed_apply, apply_state=apply_state), args=(grads_and_vars,), kwargs={ "name": name, }) def _aggregate_gradients(self, grads_and_vars): """Returns all-reduced gradients. Args: grads_and_vars: List of (gradient, variable) pairs. Returns: A list of all-reduced gradients. """ grads_and_vars = list(grads_and_vars) filtered_grads_and_vars = _filter_grads(grads_and_vars) def all_reduce_fn(distribution, grads_and_vars): return distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) # We switch to a cross-replica context since there is a bug which causes # IndexedSlices to be converted to dense tensors when all-reduced in a # replica context. # TODO(b/150507409): Do not switch to a cross-replica context once the bug # is fixed. if filtered_grads_and_vars: reduced = distribute_ctx.get_replica_context().merge_call( all_reduce_fn, args=(filtered_grads_and_vars,)) else: reduced = [] # Copy 'reduced' but add None gradients back in reduced_with_nones = [] reduced_pos = 0 for g, _ in grads_and_vars: if g is None: reduced_with_nones.append(None) else: reduced_with_nones.append(reduced[reduced_pos]) reduced_pos += 1 assert reduced_pos == len(reduced), "Failed to add all gradients" return reduced_with_nones 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, ops.Tensor): raise NotImplementedError("Trying to update a Tensor ", var) apply_kwargs = {} if isinstance(grad, ops.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 ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op eagerly_outside_functions = ops.executing_eagerly_outside_functions() update_ops = [] with ops.name_scope(name or self._name, skip_on_eager=True): for grad, var in grads_and_vars: # TODO(crccw): It's not allowed to assign PerReplica value to # MirroredVariable. Remove this after we relax this restriction. def _assume_mirrored(grad): if isinstance(grad, ds_values.PerReplica): return ds_values.Mirrored(grad.values) return grad grad = nest.map_structure(_assume_mirrored, grad) # Colocate the update with variables to avoid unnecessary communication # delays. See b/136304694. with distribution.extended.colocate_vars_with(var): with ops.name_scope("update" if eagerly_outside_functions else "update_" + var.op.name, skip_on_eager=True): update_ops.extend(distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.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 ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) 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 != dtypes.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, trackable.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, (ops.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 math_ops.cast(value, dtype) else: return value 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 __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"): """Add a new slot variable for `var`.""" 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, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.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_variables.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, ds_values.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 ops.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 = array_ops.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 # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance( value, (ops.Tensor, tf_variables.Variable)) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.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: self._iterations = self.add_weight( "iter", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.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 = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper("decay", var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): """Returns the config of the optimizer. An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration. Returns: Python dictionary. """ config = {"name": self._name} if self.clipnorm is not None: config["clipnorm"] = self.clipnorm if self.clipvalue is not None: config["clipvalue"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): """Creates an optimizer from its config. This method is the reverse of `get_config`, capable of instantiating the same optimizer from the config dictionary. Arguments: config: A Python dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter. Returns: An optimizer instance. """ if "lr" in config: config["learning_rate"] = config.pop("lr") if "learning_rate" in config: if isinstance(config["learning_rate"], dict): config["learning_rate"] = learning_rate_schedule.deserialize( config["learning_rate"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): """Serialize a hyperparameter that can be a float, callable, or Tensor.""" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): """Returns variables of this Optimizer based on the order created.""" return self._weights @property def weights(self): """Returns variables of this Optimizer based on the order created.""" return self._weights def get_weights(self): """Returns the current weights of the optimizer. The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers. For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer: >>> opt = tf.keras.optimizers.RMSprop() >>> m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) >>> m.compile(opt, loss='mse') >>> data = np.arange(100).reshape(5, 20) >>> labels = np.zeros(5) >>> print('Training'); results = m.fit(data, labels) Training ... >>> len(opt.get_weights()) 3 Returns: Weights values as a list of numpy arrays. """ params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): """Set the weights of the optimizer. The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer. For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer: >>> opt = tf.keras.optimizers.RMSprop() >>> m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)]) >>> m.compile(opt, loss='mse') >>> data = np.arange(100).reshape(5, 20) >>> labels = np.zeros(5) >>> print('Training'); results = m.fit(data, labels) Training ... >>> new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])] >>> opt.set_weights(new_weights) >>> opt.iterations Arguments: weights: weight values as a list of numpy arrays. """ params = self.weights if len(params) != len(weights): raise ValueError( "You called `set_weights(weights)` on optimizer " + self._name + " with a weight list of length " + str(len(weights)) + ", but the optimizer was expecting " + str(len(params)) + " weights. Provided weights: " + str(weights)[:50] + "...") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError("Optimizer weight shape " + str(pv.shape) + " not compatible with " "provided weight shape " + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer="zeros", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( "Synchronization value can be set to " "VariableSynchronization.ON_READ only for non-trainable variables. " "You have specified trainable=True and " "synchronization=VariableSynchronization.ON_READ.") else: # Set trainable to be false when variable is to be synced on read. trainable = False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _init_set_name(self, name, zero_based=True): if not name: self._name = backend.unique_object_name( generic_utils.to_snake_case(self.__class__.__name__), zero_based=zero_based) else: self._name = name def _assert_valid_dtypes(self, tensors): """Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor is not a valid type. """ valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError("Invalid type %r for %s, expected: %s." % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): """Valid types for loss, variables and gradients. Subclasses should override to allow other float types. Returns: Valid types for loss, variables and gradients. """ return set([ dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128 ]) def _call_if_callable(self, param): """Call the function if param is callable.""" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle, apply_state): """Add ops to apply dense gradients to the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which points to the variable to be updated. apply_state: A dict which is used across multiple apply calls. Returns: An `Operation` which updates the value of the variable. """ raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices, **kwargs): """Add ops to apply sparse gradients to `handle`, with repeated indices. Optimizers which override this method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique indices and their associated gradients, is enforced by first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override this method to avoid the overhead of summing. Args: grad: a `Tensor` representing the gradient for the affected indices. handle: a `Tensor` of dtype `resource` which points to the variable to be updated. indices: a `Tensor` of integral type representing the indices for which the gradient is nonzero. Indices may be repeated. **kwargs: May optionally contain `apply_state` Returns: An `Operation` which updates the value of the variable. """ summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices, **kwargs) def _resource_apply_sparse(self, grad, handle, indices, apply_state): """Add ops to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the gradient for the affected indices. handle: a `Tensor` of dtype `resource` which points to the variable to be updated. indices: a `Tensor` of integral type representing the indices for which the gradient is nonzero. Indices are unique. apply_state: A dict which is used across multiple apply calls. Returns: An `Operation` which updates the value of the variable. """ raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() @property @tracking.cached_per_instance def _dense_apply_args(self): return tf_inspect.getfullargspec(self._resource_apply_dense).args @property @tracking.cached_per_instance def _sparse_apply_args(self): return tf_inspect.getfullargspec(self._resource_apply_sparse).args # --------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): """Restore a newly created slot variable's value.""" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): """Restore a slot variable's value, possibly creating it. Called when a variable which has an associated slot variable is created or restored. When executing eagerly, we create the slot variable with a restoring initializer. No new variables are created when graph building. Instead, _restore_slot_variable catches these after normal creation and adds restore ops to the graph. This method is nonetheless important when graph building for the case when a slot variable has already been created but `variable` has just been added to a dependency graph (causing us to realize that the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored. slot_name: The name of this `Optimizer`'s slot to restore into. variable: The variable object this slot is being created for. """ variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active variable creator # scope. Generally we'd like to eagerly create/restore slot variables # when possible, but this may mean that scopes intended to catch # `variable` also catch its eagerly created slot variable # unintentionally (specifically make_template would add a dependency on # a slot variable if not for this case). Deferring is mostly harmless # (aside from double initialization), and makes variable creator scopes # behave the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one object (because we don't want to # save the slot variable if the optimizer is saved without the non-slot # variable, or if the non-slot variable is saved without the optimizer; # it's a dependency hypergraph with edges of the form (optimizer, non-slot # variable, variable)). So we don't _track_ slot variables anywhere, and # instead special-case this dependency and otherwise pretend it's a normal # graph. if slot_variable is not None: # If we've either made this slot variable, or if we've pulled out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring until it gets created # normally. We keep a list rather than the one with the highest restore # UID in case slot variables have their own dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): """Filter out iterable with grad equal to None.""" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError("No gradients provided for any variable: %s." % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( ("Gradients do not exist for variables %s when minimizing the loss."), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): """Key for representing a primary variable, for looking up slots. In graph mode the name is derived from the var shared name. In eager mode the name is derived from the var unique id. If distribution strategy exists, get the primary variable first. Args: var: the variable. Returns: the unique name of the variable. """ # pylint: disable=protected-access # Get the distributed variable if it exists. if hasattr(var, "_distributed_container"): var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): """Get the slot key for the variable: var_name/slot_name.""" name = _var_key(var) return name + "/" + slot_name class RestoredOptimizer(OptimizerV2): """A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is restored from a SavedModel. These variables may be referenced in functions along with ops created by the original optimizer, but currently we do not support using the optimizer object iself (e.g. through `apply_gradients`). """ # TODO(allenl): Make the restored optimizer functional by tracing its apply # methods. def __init__(self): super(RestoredOptimizer, self).__init__("RestoredOptimizer") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( "Restoring functional Optimizers from SavedModels is not currently " "supported. Please file a feature request if this limitation bothers " "you.") revived_types.register_revived_type( "optimizer", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=RestoredOptimizer._set_hyper # pylint: disable=protected-access )])