# Copyright 2019 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. # ============================================================================== """Contains the loss scaling optimizer class.""" from tensorflow.python.distribute import collective_all_reduce_strategy from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.distribute import mirrored_strategy from tensorflow.python.distribute import one_device_strategy from tensorflow.python.distribute import tpu_strategy from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import indexed_slices from tensorflow.python.framework import ops from tensorflow.python.framework import smart_cond from tensorflow.python.keras import backend from tensorflow.python.keras import optimizers from tensorflow.python.keras.mixed_precision import loss_scale as keras_loss_scale_module from tensorflow.python.keras.optimizer_v2 import optimizer_v2 from tensorflow.python.keras.optimizer_v2 import utils as optimizer_utils from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging from tensorflow.python.trackable import base as trackable from tensorflow.python.trackable import base_delegate from tensorflow.python.training.experimental import loss_scale as loss_scale_module from tensorflow.python.training.experimental import mixed_precision from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export class _UnwrapPreventer(object): """Wrapper that DistributionStrategy will not unwrap. Typically, DistributionStrategy will unwrap values when going from a cross- replica context to a replica context via `call_for_each_replica`. This class is a wrapper that DistributionStrategy will not unwrap, so it can be used to prevent it from unwrapping a value. TODO(reedwm): Find/implement a better way of preventing values from being unwrapped by DistributionStrategy """ __slots__ = ['value'] def __init__(self, value): self.value = value def _is_all_finite(grads): """Returns a scalar boolean tensor indicating if all gradients are finite.""" is_finite_per_grad = [ math_ops.reduce_all(math_ops.is_finite(g)) for g in grads if g is not None ] return math_ops.reduce_all(is_finite_per_grad) def _op_in_graph_mode(tensor): """Returns the tensor's op in graph mode, or the tensor in eager mode. This is useful because sometimes an op is needed in graph mode instead of a tensor. In eager mode, there are no ops. Args: tensor: A tensor. Returns: The tensor's op in graph mode. The tensor in eager mode. """ if context.executing_eagerly(): return tensor return tensor.op def _assign_if_finite(var, value): """Assigns a value to a variable if the value is finite.""" return control_flow_ops.cond( math_ops.is_finite(value), lambda: _op_in_graph_mode(var.assign(value)), control_flow_ops.no_op) class _DynamicLossScaleState(trackable.Trackable): """The state of a dynamic loss scale.""" def __init__(self, initial_loss_scale, growth_steps, multiplier): """Creates the dynamic loss scale.""" super(_DynamicLossScaleState, self).__init__() self._initial_loss_scale = float(initial_loss_scale) self._growth_steps = int(growth_steps) self._multiplier = float(multiplier) self._weights = {} self._current_loss_scale = self._add_weight( name='current_loss_scale', dtype=dtypes.float32, initial_value=self._initial_loss_scale) # The number of consecutive steps with finite gradients since the last # nonfinite gradient or change in loss scale. The name is 'good_steps' for # backwards compatibility with older checkpoints. self._counter = self._add_weight( name='good_steps', dtype=dtypes.int64, initial_value=0) def _add_weight(self, name, initial_value, dtype=None): """Adds a weight to this loss scale. Args: name: Variable name. initial_value: The variable's initial value. dtype: The type of the variable. Returns: A variable. Raises: RuntimeError: If a weight with `name` has already been added. """ variable = variable_scope.variable( initial_value=initial_value, name=name, dtype=dtype, trainable=False, use_resource=True, synchronization=variables.VariableSynchronization.AUTO, # Set aggregation to NONE, as loss scaling variables should never be # aggregated. aggregation=variables.VariableAggregation.NONE) if context.executing_eagerly(): graph_key = None else: graph = ops.get_default_graph() graph_key = graph._graph_key # pylint: disable=protected-access key = (name, graph_key) self._weights[key] = variable self._handle_deferred_dependencies(name=name, trackable=variable) backend.track_variable(variable) return variable def _trackable_children(self, save_type=trackable.SaveType.CHECKPOINT, **kwargs): """From Trackable. Gather graph-specific weights to save.""" if context.executing_eagerly(): graph_key = None else: graph = ops.get_default_graph() graph_key = graph._graph_key # pylint: disable=protected-access weights = {} for (name, g), v in sorted(self._weights.items(), key=lambda i: i[0][0]): if g == graph_key: weights[name] = v weights.update( super(_DynamicLossScaleState, self)._trackable_children(save_type, **kwargs)) return weights def _lookup_dependency(self, name): """From Trackable. Find a weight in the current graph.""" unconditional = super(_DynamicLossScaleState, self)._lookup_dependency(name) if unconditional is not None: return unconditional if context.executing_eagerly(): graph_key = None else: graph = ops.get_default_graph() graph_key = graph._graph_key # pylint: disable=protected-access return self._weights.get((name, graph_key), None) @property def initial_loss_scale(self): return self._initial_loss_scale @property def growth_steps(self): return self._growth_steps @property def multiplier(self): return self._multiplier @property def current_loss_scale(self): """Returns the current loss scale as a float32 `tf.Variable`.""" return self._current_loss_scale @property def counter(self): """Returns the counter as a float32 `tf.Variable`.""" return self._counter def __call__(self): """Returns the current loss scale as a scalar `float32` tensor.""" return ops.convert_to_tensor_v2_with_dispatch(self._current_loss_scale) def update(self, grads): """Updates the value of the loss scale. Args: grads: A nested structure of unscaled gradients, each which is an all-reduced gradient of the loss with respect to a weight. Returns: update_op: In eager mode, None. In graph mode, an op to update the loss scale. should_apply_gradients: Either a bool or a scalar boolean tensor. If False, the caller should skip applying `grads` to the variables this step. """ grads = nest.flatten(grads) if distribution_strategy_context.has_strategy( ) and distribution_strategy_context.in_cross_replica_context(): distribution = distribution_strategy_context.get_strategy() is_finite_per_replica = distribution.extended.call_for_each_replica( _is_all_finite, args=(grads,)) # Each replica computed the same `is_finite` value, since `grads` is # all-reduced across replicas. Arbitrarily take `is_finite` from the first # replica. is_finite = ( distribution.experimental_local_results(is_finite_per_replica)[0]) else: is_finite = _is_all_finite(grads) def update_if_finite_grads(): """Update assuming the gradients are finite.""" def incr_loss_scale(): new_loss_scale = self.current_loss_scale * self.multiplier return control_flow_ops.group( _assign_if_finite(self.current_loss_scale, new_loss_scale), self.counter.assign(0)) return control_flow_ops.cond( self.counter + 1 >= self.growth_steps, incr_loss_scale, lambda: _op_in_graph_mode(self.counter.assign_add(1))) def update_if_not_finite_grads(): """Update assuming the gradients are nonfinite.""" new_loss_scale = math_ops.maximum( self.current_loss_scale / self.multiplier, 1) return control_flow_ops.group( self.counter.assign(0), self.current_loss_scale.assign(new_loss_scale)) update_op = control_flow_ops.cond(is_finite, update_if_finite_grads, update_if_not_finite_grads) should_apply_gradients = is_finite return update_op, should_apply_gradients # See LossScaleOptimizer docstring for why this is so big _DEFAULT_INITIAL_SCALE = 2 ** 15 _DEFAULT_GROWTH_STEPS = 2000 # pylint: disable=g-classes-have-attributes @keras_export('keras.mixed_precision.LossScaleOptimizer') class LossScaleOptimizer(base_delegate.DelegatingTrackableMixin, optimizer_v2.OptimizerV2): """An optimizer that applies loss scaling to prevent numeric underflow. Loss scaling is a technique to prevent numeric underflow in intermediate gradients when float16 is used. To prevent underflow, the loss is multiplied (or "scaled") by a certain factor called the "loss scale", which causes intermediate gradients to be scaled by the loss scale as well. The final gradients are divided (or "unscaled") by the loss scale to bring them back to their original value. `LossScaleOptimizer` wraps another optimizer and applies loss scaling to it. By default, the loss scale is dynamically updated over time so you do not have to choose the loss scale. The `minimize` method automatically scales the loss, unscales the gradients, and updates the loss scale so all you have to do is wrap your optimizer with a `LossScaleOptimizer` if you use `minimize`. For example: >>> opt = tf.keras.optimizers.SGD(0.25) >>> opt = tf.keras.mixed_precision.LossScaleOptimizer(opt) >>> var = tf.Variable(1.) >>> loss_fn = lambda: var ** 2 >>> # 'minimize' applies loss scaling and updates the loss sale. >>> opt.minimize(loss_fn, var_list=var) >>> var.numpy() 0.5 If a `tf.GradientTape` is used to compute gradients instead of `minimize`, you must scale the loss and gradients manually. This can be done with the `LossScaleOptimizer.get_scaled_loss` and `LossScaleOptimizer.get_unscaled_gradients` methods. For example: >>> with tf.GradientTape() as tape: ... loss = loss_fn() ... scaled_loss = opt.get_scaled_loss(loss) >>> scaled_grad = tape.gradient(scaled_loss, var) >>> (grad,) = opt.get_unscaled_gradients([scaled_grad]) >>> opt.apply_gradients([(grad, var)]) # Loss scale is updated here >>> var.numpy() 0.25 Warning: If you forget to call `get_scaled_loss` or `get_unscaled_gradients` (or both) when using a `tf.GradientTape`, the model will likely converge to a worse quality. Please make sure you call each function exactly once. When mixed precision with float16 is used, there is typically no risk of underflow affecting model quality if loss scaling is properly used. See [the mixed precision guide]( https://www.tensorflow.org/guide/keras/mixed_precision) for more information on how to use mixed precision. Args: inner_optimizer: The `tf.keras.optimizers.Optimizer` instance to wrap. dynamic: Bool indicating whether dynamic loss scaling is used. Defaults to True. If True, the loss scale will be dynamically updated over time using an algorithm that keeps the loss scale at approximately its optimal value. If False, a single fixed loss scale is used and `initial_scale` must be specified, which is used as the loss scale. Recommended to keep as True, as choosing a fixed loss scale can be tricky. Currently, there is a small performance overhead to dynamic loss scaling compared to fixed loss scaling. initial_scale: The initial loss scale. If `dynamic` is True, this defaults to `2 ** 15`. If `dynamic` is False, this must be specified and acts as the sole loss scale, as the loss scale does not change over time. When dynamic loss scaling is used, is better for this to be a very high number, because a loss scale that is too high gets lowered far more quickly than a loss scale that is too low gets raised. dynamic_growth_steps: With dynamic loss scaling, every `dynamic_growth_steps` steps with finite gradients, the loss scale is doubled. Defaults to 2000. If a nonfinite gradient is encountered, the count is reset back to zero, gradients are skipped that step, and the loss scale is halved. The count can be queried with `LossScaleOptimizer.dynamic_counter`. This argument can only be specified if `dynamic` is True. `LossScaleOptimizer` will occasionally skip applying gradients to the variables, in which case the trainable variables will not change that step. This is done because the dynamic loss scale will sometimes be raised too high, causing overflow in the gradients. Typically, the first 2 to 15 steps of the model are skipped as the initial loss scale is very high, but afterwards steps will only be skipped on average 0.05% of the time (the fraction of steps skipped is `1 / dynamic_growth_steps`). `LossScaleOptimizer` delegates all public `Optimizer` methods to the inner optimizer. Additionally, in methods `minimize` and `get_gradients`, it scales the loss and unscales the gradients. In methods `minimize` and `apply_gradients`, it additionally updates the loss scale and skips applying gradients if any gradient has a nonfinite value. ### Hyperparameters Hyperparameters can be accessed and set on the LossScaleOptimizer, which will be delegated to the wrapped optimizer. >>> opt = tf.keras.optimizers.Adam(beta_1=0.8, epsilon=1e-5) >>> opt = tf.keras.mixed_precision.LossScaleOptimizer(opt) >>> opt.beta_1 # Equivalent to `opt.inner_optimizer.beta_1` 0.8 >>> opt.beta_1 = 0.7 # Equivalent to `opt.inner_optimizer.beta_1 = 0.7` >>> opt.beta_1 0.7 >>> opt.inner_optimizer.beta_1 0.7 However, accessing or setting non-hyperparameters is not delegated to the LossScaleOptimizer. In an Adam optimizer, `beta_1` is a hyperparameter but `epsilon` is not, as the Adam optimizer only calls `Optimizer._set_hyper` on `beta_1`. >>> opt.inner_optimizer.epsilon 1e-5 >>> opt.epsilon Traceback (most recent call last): ... AttributeError: 'LossScaleOptimizer' object has no attribute 'epsilon' >>> opt.epsilon = 1e-4 # This does NOT set epsilon on `opt.inner_optimizer` >>> opt.inner_optimizer.epsilon >>> 1e-5 In the above example, despite epsilon being set on the LossScaleOptimizer, the old epsilon value will still be used when training as epsilon was not set on the inner optimizer. """ _HAS_AGGREGATE_GRAD = True def __init__(self, inner_optimizer, dynamic=True, initial_scale=None, dynamic_growth_steps=None): if not isinstance(inner_optimizer, optimizer_v2.OptimizerV2): raise TypeError('"inner_optimizer" must be an instance of OptimizerV2, ' 'but got: %s' % inner_optimizer) if not isinstance(dynamic, bool): # Catch errors if a user incorrectly passes a string or float to the # second argument argument, as this is commonly done for # LossScaleOptimizerV1. raise TypeError('"dynamic" argument to LossScaleOptimizer.__init__ must ' 'be a bool, but got: %r' % (dynamic,)) if isinstance(inner_optimizer, LossScaleOptimizer): raise TypeError('LossScaleOptimizer cannot wrap another ' 'LossScaleOptimizer, but got: %s' % (inner_optimizer,)) self._raise_if_strategy_unsupported() if getattr(inner_optimizer, '_is_wrapped_by_loss_scale_optimizer', False): # TODO(reedwm): Maybe support this. The difficulty is that LSO has the # same checkpoint format as the inner optimizer, so multiple LSOs wrapping # the same optimizer causes the checkpointing logic to become confused. raise ValueError('"inner_optimizer" is already wrapped by a ' 'LossScaleOptimizer. An optimizer can only be wrapped ' 'by a single LossScaleOptimizer') self._optimizer = inner_optimizer self._optimizer._is_wrapped_by_loss_scale_optimizer = True # We don't call super().__init__, since we do not want to call OptimizerV2's # constructor. base_delegate.DelegatingTrackableMixin.__init__(self, self._optimizer) if dynamic: if initial_scale is None: initial_scale = _DEFAULT_INITIAL_SCALE if dynamic_growth_steps is None: dynamic_growth_steps = _DEFAULT_GROWTH_STEPS self._loss_scale = _DynamicLossScaleState( initial_scale, dynamic_growth_steps, multiplier=2) self._track_trackable(self._loss_scale, 'loss_scale') else: if initial_scale is None: raise ValueError('"initial_scale" must be specified if "dynamic" is ' 'False') self._loss_scale = float(initial_scale) if dynamic_growth_steps is not None: raise ValueError('"dynamic_growth_steps" must be None if "dynamic" ' 'is False, but got: %s' % (dynamic_growth_steps,)) # To support restoring TensorFlow 2.2 checkpoints. self._track_trackable(FakeOptimizerForRestoration(self._optimizer), 'base_optimizer') @property def dynamic(self): """Bool indicating whether dynamic loss scaling is used.""" return isinstance(self._loss_scale, _DynamicLossScaleState) @property def loss_scale(self): """The current loss scale as a float32 scalar tensor.""" if isinstance(self._loss_scale, _DynamicLossScaleState): return ops.convert_to_tensor_v2_with_dispatch( self._loss_scale.current_loss_scale) else: return ops.convert_to_tensor_v2_with_dispatch(self._loss_scale) @property def dynamic_counter(self): """The number of steps since the loss scale was last increased or decreased. This is None if `LossScaleOptimizer.dynamic` is False. The counter is incremented every step. Once it reaches `LossScaleOptimizer.dynamic_growth_steps`, the loss scale will be doubled and the counter will be reset back to zero. If nonfinite gradients are encountered, the loss scale will be halved and the counter will be reset back to zero. """ if isinstance(self._loss_scale, _DynamicLossScaleState): return self._loss_scale.counter else: return None @property def initial_scale(self): """The initial loss scale. If `LossScaleOptimizer.dynamic` is False, this is the same number as `LossScaleOptimizer.loss_scale`, as the loss scale never changes. """ if isinstance(self._loss_scale, _DynamicLossScaleState): return self._loss_scale.initial_loss_scale else: return self._loss_scale @property def dynamic_growth_steps(self): """The number of steps it takes to increase the loss scale. This is None if `LossScaleOptimizer.dynamic` is False. Every `dynamic_growth_steps` consecutive steps with finite gradients, the loss scale is increased. """ if isinstance(self._loss_scale, _DynamicLossScaleState): return self._loss_scale.growth_steps else: return None @property def inner_optimizer(self): """The optimizer that this LossScaleOptimizer is wrapping.""" return self._optimizer def get_scaled_loss(self, loss): """Scales the loss by the loss scale. This method is only needed if you compute gradients manually, e.g. with `tf.GradientTape`. In that case, call this method to scale the loss before passing the loss to `tf.GradientTape`. If you use `LossScaleOptimizer.minimize` or `LossScaleOptimizer.get_gradients`, loss scaling is automatically applied and this method is unneeded. If this method is called, `get_unscaled_gradients` should also be called. See the `tf.keras.mixed_precision.LossScaleOptimizer` doc for an example. Args: loss: The loss, which will be multiplied by the loss scale. Can either be a tensor or a callable returning a tensor. Returns: `loss` multiplied by `LossScaleOptimizer.loss_scale`. """ if callable(loss): def new_loss(): loss_val = loss() return loss_val * math_ops.cast(self.loss_scale, loss_val.dtype) return new_loss else: return loss * math_ops.cast(self.loss_scale, loss.dtype) def get_unscaled_gradients(self, grads): """Unscales the gradients by the loss scale. This method is only needed if you compute gradients manually, e.g. with `tf.GradientTape`. In that case, call this method to unscale the gradients after computing them with `tf.GradientTape`. If you use `LossScaleOptimizer.minimize` or `LossScaleOptimizer.get_gradients`, loss scaling is automatically applied and this method is unneeded. If this method is called, `get_scaled_loss` should also be called. See the `tf.keras.mixed_precision.LossScaleOptimizer` doc for an example. Args: grads: A list of tensors, each which will be divided by the loss scale. Can have None values, which are ignored. Returns: A new list the same size as `grads`, where every non-None value in `grads` is divided by `LossScaleOptimizer.loss_scale`. """ loss_scale_reciprocal = 1. / self.loss_scale return [ _multiply_gradient(g, loss_scale_reciprocal) if g is not None else None for g in grads ] def _compute_gradients(self, loss, var_list, grad_loss=None, tape=None): tape = backprop.GradientTape() if tape is None else tape with tape: loss = self.get_scaled_loss(loss) grads_and_vars = self._optimizer._compute_gradients( # pylint: disable=protected-access loss, var_list, grad_loss, tape=tape) grads = [g for g, _ in grads_and_vars] weights = [v for _, v in grads_and_vars] unscaled_grads = self.get_unscaled_gradients(grads) return list(zip(unscaled_grads, weights)) def get_gradients(self, loss, params): loss = self.get_scaled_loss(loss) grads = self._optimizer.get_gradients(loss, params) return self.get_unscaled_gradients(grads) def _create_all_weights(self, var_list): self._optimizer._create_all_weights(var_list) # pylint: disable=protected-access def apply_gradients(self, grads_and_vars, name=None, experimental_aggregate_gradients=True): if distribution_strategy_context.in_cross_replica_context(): raise ValueError('apply_gradients() must be called in a replica context.') # We check for the strategy here despite already checking in the constructor # as frequently the optimizer is created outside the strategy's scope. self._raise_if_strategy_unsupported() grads_and_vars = optimizer_utils.filter_empty_gradients(grads_and_vars) if experimental_aggregate_gradients: # We must aggregate the gradients here instead of in # self.optimizer.apply_gradients, so that any NaN or Inf gradients are # propogated to each replica. If any replica has a NaN or Inf gradient, # they must all have a NaN or Inf gradient so that they all skip the step. # pylint: disable=protected-access grads_and_vars = self._optimizer._transform_unaggregated_gradients( grads_and_vars) grads_and_vars = self._optimizer._aggregate_gradients(grads_and_vars) # pylint: enable=protected-access grads_and_vars = tuple(grads_and_vars) grads = [g for g, _ in grads_and_vars] # We do not want DistributionStrategy to unwrap any MirroredVariables in # grads_and_vars, because even in a replica context, the wrapped # optimizer expects mirrored variables. So we wrap the variables with an # _UnwrapPreventer, preventing DistributionStrategy from unwrapping the # MirroredVariables. wrapped_vars = _UnwrapPreventer([v for _, v in grads_and_vars]) def do_not_apply_fn(): # Normally self._optimizer.iterations is incremented in # self._optimizer.apply_gradients(). Since that is not called in this # branch, we increment it here instead. return self._optimizer.iterations.assign_add(1, read_value=False) def _if_should_apply_grads(grads): if isinstance(self._loss_scale, _DynamicLossScaleState): return self._loss_scale.update(grads) else: return (control_flow_ops.no_op(), True) if optimizer_utils.strategy_supports_no_merge_call(): loss_scale_update_op, should_apply_grads = _if_should_apply_grads(grads) def apply_fn(): return self._apply_gradients(grads, wrapped_vars, name) maybe_apply_op = smart_cond.smart_cond(should_apply_grads, apply_fn, do_not_apply_fn) return control_flow_ops.group(maybe_apply_op, loss_scale_update_op) else: def _apply_gradients_cross_replica(distribution, grads, wrapped_vars, name): loss_scale_update_op, should_apply_grads = _if_should_apply_grads(grads) def apply_fn(): return distribution.extended.call_for_each_replica( self._apply_gradients, args=(grads, wrapped_vars, name)) # Note: We must call this cond() in a cross-replica context. # DistributionStrategy does not support having a cond in a replica # context with a branch that calls `merge_call`, and # self._optimizer.apply_gradients calls `merge_call`. maybe_apply_op = smart_cond.smart_cond(should_apply_grads, apply_fn, do_not_apply_fn) return control_flow_ops.group(maybe_apply_op, loss_scale_update_op) return distribution_strategy_context.get_replica_context().merge_call( _apply_gradients_cross_replica, args=(grads, wrapped_vars, name)) def _apply_gradients(self, grads, wrapped_vars, name): # Pass experimental_aggregate_gradients=False since LossScaleOptimizer # already aggregated the gradients. # TODO(reedwm): This will raise a fairly cryptic error message if # self._optimizer.apply_gradients does not take # experimental_aggregate_gradients. return self._optimizer.apply_gradients( list(zip(grads, wrapped_vars.value)), name, experimental_aggregate_gradients=False) def get_config(self): serialized_optimizer = optimizers.serialize(self._optimizer) return { 'inner_optimizer': serialized_optimizer, 'dynamic': self.dynamic, 'initial_scale': self.initial_scale, 'dynamic_growth_steps': self.dynamic_growth_steps, } @classmethod def from_config(cls, config, custom_objects=None): config = config.copy() # Make a copy, since we mutate config if 'loss_scale' in config: # If loss_scale is in config, we assume we are deserializing a # LossScaleOptimizer from TF 2.3 or below. We convert the config so it # can be deserialized in the current LossScaleOptimizer. loss_scale = keras_loss_scale_module.deserialize( config.pop('loss_scale')) if isinstance(loss_scale, loss_scale_module.FixedLossScale): config['dynamic'] = False config['initial_scale'] = loss_scale._loss_scale_value # pylint: disable=protected-access elif isinstance(loss_scale, loss_scale_module.DynamicLossScale): config['dynamic'] = True config['initial_scale'] = loss_scale.initial_loss_scale config['dynamic_growth_steps'] = loss_scale.increment_period if loss_scale.multiplier != 2: raise ValueError('Cannot deserialize LossScaleOptimizer with a ' 'DynamicLossScale whose multiplier is not 2. Got ' 'DynamicLossScale: %s' % (loss_scale,)) else: raise ValueError( 'Serialized LossScaleOptimizers with a LossScale that is neither a ' 'FixedLossScale nor a DynamicLossScale can no longer be ' 'deserialized') config['inner_optimizer'] = config.pop('optimizer') config['inner_optimizer'] = optimizers.deserialize( config['inner_optimizer'], custom_objects=custom_objects) return cls(**config) def _raise_if_strategy_unsupported(self): if not strategy_supports_loss_scaling(): strategy = distribution_strategy_context.get_strategy() if isinstance(strategy, (tpu_strategy.TPUStrategy, tpu_strategy.TPUStrategyV1, tpu_strategy.TPUStrategyV2)): raise ValueError( 'Loss scaling is not supported with TPUStrategy. Loss scaling is ' 'unnecessary with TPUs, since they support bfloat16 instead of ' 'float16 and bfloat16 does not require loss scaling. You should ' 'remove the use of the LossScaleOptimizer when TPUs are used.') else: raise ValueError('Loss scaling is not supported with the ' 'tf.distribute.Strategy: %s. Try using a different ' 'Strategy, e.g. a MirroredStrategy' % strategy.__class__.__name__) # Delegations: We delegate most OptimizerV2 methods to the wrapped optimizer # below. @property def iterations(self): return self._optimizer.iterations @iterations.setter def iterations(self, variable): self._optimizer.iterations = variable def get_slot_names(self): return self._optimizer.get_slot_names() def variables(self): return self._optimizer.variables() @property def weights(self): return self._optimizer.weights def get_weights(self): return self._optimizer.get_weights() def set_weights(self, weights): return self._optimizer.set_weights(weights) @property def clipnorm(self): return self._optimizer.clipnorm @clipnorm.setter def clipnorm(self, val): self._optimizer.clipnorm = val @property def global_clipnorm(self): return self._optimizer.global_clipnorm @global_clipnorm.setter def global_clipnorm(self, val): self._optimizer.global_clipnorm = val @property def clipvalue(self): return self._optimizer.clipvalue @clipvalue.setter def clipvalue(self, val): self._optimizer.clipvalue = val def _aggregate_gradients(self, grads_and_vars): return self._optimizer._aggregate_gradients(grads_and_vars) # pylint: disable=protected-access def _restore_slot_variable(self, slot_name, variable, slot_variable): return self._optimizer._restore_slot_variable(slot_name, variable, # pylint: disable=protected-access slot_variable) def _create_or_restore_slot_variable(self, slot_variable_position, slot_name, variable): return self._optimizer._create_or_restore_slot_variable( # pylint: disable=protected-access slot_variable_position, slot_name, variable) def get_slot(self, var, slot_name): return self._optimizer.get_slot(var, slot_name) def add_slot(self, var, slot_name, initializer='zeros'): return self._optimizer.add_slot(var, slot_name, initializer) def __getattribute__(self, name): try: return object.__getattribute__(self, name) except AttributeError as e: if name == '_optimizer' or name == '_hyper': # Avoid infinite recursion raise e # Delegate hyperparameter accesses to inner optimizer. if name == 'lr': name = 'learning_rate' if name in self._optimizer._hyper: return self._optimizer._get_hyper(name) raise e def __dir__(self): result = set(super(LossScaleOptimizer, self).__dir__()) if '_optimizer' in result: result |= self._optimizer._hyper.keys() if 'learning_rate' in self._optimizer._hyper.keys(): result.add('lr') return list(result) def __setattr__(self, name, value): if name == 'lr': name = 'learning_rate' # Delegate setting hyperparameter to inner optimizer if the attribute does # not exist on the LossScaleOptimizer try: # We cannot check for the 'iterations' attribute as it cannot be set after # it is accessed. if name != 'iterations': object.__getattribute__(self, name) has_attribute = True except AttributeError: has_attribute = False if (name != '_optimizer' and name in self._optimizer._hyper and not has_attribute): self._optimizer._set_hyper(name, value) else: super(LossScaleOptimizer, self).__setattr__(name, value) # Explicitly delegate learning_rate. Normally hyperparameters are delegated in # __getattribute__, but if a hyperparameter is not in self._optimizer._hyper # (e.g. because self._optimizer itself wraps another optimizer), then it won't # be delegated. Since learning_rate is a very commonly accessed # hyperparameter, we delegate it here. @property def learning_rate(self): return self._optimizer.learning_rate @learning_rate.setter def learning_rate(self, value): self._optimizer.learning_rate = value @property def lr(self): return self._optimizer.learning_rate @lr.setter def lr(self, value): self._optimizer.lr = value # We do not override some OptimizerV2 methods. For each, we describe why we do # not delegate them to self._optimizer: # * get_updates: get_updates() calls get_gradients(). Since we override # get_gradients(), we cannot delegate get_updates() to self._optimizer, # otherwise the overridden get_gradients() method would not be called. # Luckily, get_updates() does not access any OptimizerV2 fields, so # inheriting the OptimizerV2 version works fine. # * minimize: We don't delegate for a similar as get_updates(): it calls # both self._compute_gradients() and self.apply_gradients(), and both need # to have the LossScaleOptimizer version called. # TODO(reedwm): Maybe throw an error if mixed precision is used without this # optimizer being used. @keras_export('keras.mixed_precision.experimental.LossScaleOptimizer') class LossScaleOptimizerV1(LossScaleOptimizer): """An deprecated optimizer that applies loss scaling. Warning: This class is deprecated and will be removed in a future version of TensorFlow. Please use the non-experimental class `tf.keras.mixed_precision.LossScaleOptimizer` instead. This class is identical to the non-experimental `keras.mixed_precision.LossScaleOptimizer` except its constructor takes different arguments. For this class (the experimental version), the constructor takes a `loss_scale` argument. For the non-experimental class, the constructor encodes the loss scaling information in multiple arguments. Note that unlike this class, the non-experimental class does not accept a `tf.compat.v1.mixed_precision.LossScale`, which is deprecated. If you currently use this class, you should switch to the non-experimental `tf.keras.mixed_precision.LossScaleOptimizer` instead. We show several examples of converting the use of the experimental class to the equivalent non-experimental class. >>> # In all of the examples below, `opt1` and `opt2` are identical >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer( ... tf.keras.optimizers.SGD(), loss_scale='dynamic') >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer( ... tf.keras.optimizers.SGD()) >>> assert opt1.get_config() == opt2.get_config() >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer( ... tf.keras.optimizers.SGD(), loss_scale=123) >>> # dynamic=False indicates to use fixed loss scaling. initial_scale=123 >>> # refers to the initial loss scale, which is the single fixed loss scale >>> # when dynamic=False. >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer( ... tf.keras.optimizers.SGD(), dynamic=False, initial_scale=123) >>> assert opt1.get_config() == opt2.get_config() >>> loss_scale = tf.compat.v1.mixed_precision.experimental.DynamicLossScale( ... initial_loss_scale=2048, increment_period=500) >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer( ... tf.keras.optimizers.SGD(), loss_scale=loss_scale) >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer( ... tf.keras.optimizers.SGD(), initial_scale=2048, ... dynamic_growth_steps=500) >>> assert opt1.get_config() == opt2.get_config() Make sure to also switch from this class to the non-experimental class in isinstance checks, if you have any. If you do not do this, your model may run into hard-to-debug issues, as the experimental `LossScaleOptimizer` subclasses the non-experimental `LossScaleOptimizer`, but not vice versa. It is safe to switch isinstance checks to the non-experimental `LossScaleOptimizer` even before using the non-experimental `LossScaleOptimizer`. >>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer( ... tf.keras.optimizers.SGD(), loss_scale='dynamic') >>> # The experimental class subclasses the non-experimental class >>> isinstance(opt1, tf.keras.mixed_precision.LossScaleOptimizer) True >>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer( ... tf.keras.optimizers.SGD()) >>> # The non-experimental class does NOT subclass the experimental class. >>> isinstance(opt2, tf.keras.mixed_precision.experimental.LossScaleOptimizer) False Args: optimizer: The Optimizer instance to wrap. loss_scale: The loss scale to scale the loss and gradients. This can either be an int/float to use a fixed loss scale, the string "dynamic" to use dynamic loss scaling, or an instance of a LossScale. The string "dynamic" equivalent to passing `DynamicLossScale()`, and passing an int/float is equivalent to passing a FixedLossScale with the given loss scale. If a DynamicLossScale is passed, DynamicLossScale.multiplier must be 2 (the default). """ def __init__(self, optimizer, loss_scale): warn_msg_prefix = ( 'tf.keras.mixed_precision.experimental.LossScaleOptimizer is ' 'deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer ' 'instead. ') if isinstance(loss_scale, dict): loss_scale = keras_loss_scale_module.deserialize(loss_scale) if isinstance(loss_scale, (int, float)): tf_logging.warning( warn_msg_prefix + 'For example:\n' ' opt = tf.keras.mixed_precision.LossScaleOptimizer(' 'opt, dynamic=False, initial_scale={})'.format(loss_scale)) super(LossScaleOptimizerV1, self).__init__(optimizer, dynamic=False, initial_scale=loss_scale) elif isinstance(loss_scale, loss_scale_module.FixedLossScale): ls_val = loss_scale._loss_scale_value # pylint: disable=protected-access tf_logging.warning( warn_msg_prefix + 'For example:\n' ' opt = tf.keras.mixed_precision.LossScaleOptimizer(' 'opt, dynamic=False, initial_scale={})'.format(ls_val)) super(LossScaleOptimizerV1, self).__init__(optimizer, dynamic=False, initial_scale=ls_val) elif loss_scale == 'dynamic': tf_logging.warning( warn_msg_prefix + 'For example:\n' ' opt = tf.keras.mixed_precision.LossScaleOptimizer(' 'opt)') super(LossScaleOptimizerV1, self).__init__(optimizer) elif isinstance(loss_scale, loss_scale_module.DynamicLossScale): kwargs = {} extra_arguments = '' if loss_scale.initial_loss_scale != _DEFAULT_INITIAL_SCALE: kwargs['initial_scale'] = loss_scale.initial_loss_scale extra_arguments += (', initial_scale=%s' % loss_scale.initial_loss_scale) if loss_scale.increment_period != _DEFAULT_GROWTH_STEPS: kwargs['dynamic_growth_steps'] = loss_scale.increment_period extra_arguments += (', dynamic_growth_steps=%s' % loss_scale.increment_period) if loss_scale.multiplier != 2: raise ValueError('When passing a DynamicLossScale to "loss_scale", ' 'DynamicLossScale.multiplier must be 2. Got: %s' % (loss_scale,)) tf_logging.warning( warn_msg_prefix + 'Note that the non-experimental LossScaleOptimizer does not take a ' 'DynamicLossScale but instead takes the dynamic configuration ' 'directly in the constructor. For example:\n' ' opt = tf.keras.mixed_precision.LossScaleOptimizer(' 'opt{})\n'.format(extra_arguments)) super(LossScaleOptimizerV1, self).__init__(optimizer, **kwargs) elif isinstance(loss_scale, loss_scale_module.LossScale): raise TypeError('Passing a LossScale that is not a FixedLossScale or a ' 'DynamicLossScale is no longer supported. Got: {}' .format(loss_scale)) else: raise ValueError('Invalid value passed to loss_scale. loss_scale ' 'must be the string "dynamic" (recommended), an int, ' 'a float, a FixedLossScale, or a DynamicLossScale. Got ' 'value: {}'.format(loss_scale)) @classmethod def from_config(cls, config, custom_objects=None): config = config.copy() # Make a copy, since we mutate config # If loss_scale is in config, we assume we are deserializing a # LossScaleOptimizer from TF 2.3 or below. Otherwise, we assume we are # deserializing a LossScaleOptimizer from TF 2.4 or above. if 'loss_scale' in config: config['loss_scale'] = keras_loss_scale_module.deserialize( config['loss_scale']) if (isinstance(config['loss_scale'], loss_scale_module.DynamicLossScale) and config['loss_scale'].multiplier != 2): raise ValueError('Cannot deserialize LossScaleOptimizer with a ' 'DynamicLossScale whose multiplier is not 2. Got ' 'DynamicLossScale: %s' % (config['loss_scale'],)) config['optimizer'] = optimizers.deserialize( config['optimizer'], custom_objects=custom_objects) return cls(**config) # We convert the config, as generated by LossScaleOptimizer.get_config, to a # version that can be passed to LossScaleOptimizerV1.__init__ if config['dynamic']: config['loss_scale'] = loss_scale_module.DynamicLossScale( config['initial_scale'], config['dynamic_growth_steps'], multiplier=2) else: config['loss_scale'] = loss_scale_module.FixedLossScale( config['initial_scale']) del config['dynamic'] del config['initial_scale'] del config['dynamic_growth_steps'] config['optimizer'] = optimizers.deserialize( config.pop('inner_optimizer'), custom_objects=custom_objects) return cls(**config) class FakeOptimizerForRestoration(trackable.Trackable): """A fake optimizer used to support restoring TensorFlow 2.2 checkpoints. The checkpoint format for LossScaleOptimizers changed after TF 2.2. This class exists to support restoring TF 2.2 checkpoints in newer version of TensorFlow. In TF 2.2, LossScaleOptimizer would track the wrapped optimizer by calling the following in LossScaleOptimizer.__init__ ``` self._track_trackable(self._optimizer, 'base_optimizer') ``` This means a dependency from the LossScaleOptimizer to the wrapped optimizer would be stored in the checkpoint. However now, the checkpoint format with a LossScaleOptimizer is the same as the format without a LossScaleOptimizer, except the loss scale is also stored. This means there is no dependency from the LossScaleOptimizer to the wrapped optimizer. Instead, the LossScaleOptimizer acts as if it is the wrapped optimizer, from a checkpoint's perspective, by overriding all Trackable methods and delegating them to the wrapped optimizer. To allow restoring TF 2.2. checkpoints, LossScaleOptimizer adds a dependency on this class instead of the inner optimizer. When restored, this class will instead restore the slot variables of the inner optimizer. Since this class has no variables, it does not affect the checkpoint when saved. """ def __init__(self, optimizer): self._optimizer = optimizer def get_slot_names(self): return self._optimizer.get_slot_names() def _create_or_restore_slot_variable(self, slot_variable_position, slot_name, variable): return self._optimizer._create_or_restore_slot_variable( # pylint: disable=protected-access slot_variable_position, slot_name, variable) mixed_precision.register_loss_scale_wrapper(optimizer_v2.OptimizerV2, LossScaleOptimizerV1) def _multiply_gradient(gradient, scale): """Multiply a (possibly sparse) gradient by the given scale factor.""" scale = math_ops.cast(scale, gradient.dtype) if isinstance(gradient, indexed_slices.IndexedSlices): return indexed_slices.IndexedSlices( gradient.values * scale, gradient.indices, dense_shape=gradient.dense_shape) else: return gradient * scale def strategy_supports_loss_scaling(): """Returns True if the current Strategy supports loss scaling.""" if not distribution_strategy_context.has_strategy(): return True strategy = distribution_strategy_context.get_strategy() # Strategies are supported if either there is only one replica or if variables # are replicated per device. Otherwise, the current model.fit() implementation # and most custom training loops incorrectly unscale the gradients. Currently, # gradients are unscaled once per compute replica, but they should be unscaled # once per variable replica. When there is one variable replica for each # compute replica, this works fine, but otherwise issues will occur. # TODO(reedwm): Support all strategies. return isinstance(strategy, ( collective_all_reduce_strategy.CollectiveAllReduceStrategy, collective_all_reduce_strategy.CollectiveAllReduceStrategyV1, one_device_strategy.OneDeviceStrategy, one_device_strategy.OneDeviceStrategyV1, mirrored_strategy.MirroredStrategy, mirrored_strategy.MirroredStrategyV1, ))