from collections import defaultdict import paddle.v2.fluid.framework as framework from paddle.v2.fluid.framework import unique_name, Program from paddle.v2.fluid.backward import append_backward_ops from paddle.v2.fluid.initializer import ConstantInitializer from paddle.v2.fluid.regularizer import append_regularization_ops from paddle.v2.fluid.layer_helper import LayerHelper __all__ = [ 'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer', 'AdamaxOptimizer', 'DecayedAdagradOptimizer' ] class Optimizer(object): """Optimizer Base class. Define the common interface of an optimizer. User should not use this class directly, but need to use one of it's implementation. """ def __init__(self, global_step=None): self._global_step = global_step # Dictionary of accumulators. Some optimizer subclasses need to # allocate and manage extra variables associated with the parameters # to train. These variables are called accumulators. # {accum_name : { paramter_name : accumulator_for_parameter, ...}, ...} self._accumulators = defaultdict(lambda: dict()) self.helper = None def _append_optimize_op(self, block, param_and_grad): """ append optimize operator to block and return all the added optimize_op """ raise NotImplementedError() def _create_param_lr(self, param_and_grad): # create learning rate variable for every parameter param = param_and_grad[0] param_lr = param.optimize_attr['learning_rate'] param_lr_shape = [1] param_lr_var = self.helper.create_global_variable( name=unique_name("learning_rate"), dtype='float32', shape=param_lr_shape, lod_level=1, persistable=True) param_lr = param_lr * self._learning_rate self.helper.set_variable_initializer( var=param_lr_var, initializer=ConstantInitializer(param_lr)) return param_lr_var def _create_accumulators(self, block, parameters): """Create all accumulators needed by the parameters Args: block: the block in which the loss variable is present parameters: list of parameter variables for the optimizer """ pass def _finish_update(self, block): """Finish any custom updates needed before completing an optimization step Args: block: the block in which the loss variable is present parameters: list of parameter variables for the optimizer Returns: list of finish ops or None """ pass def _add_accumulator(self, name, param, dtype=None, fill_value=0.0): """Utility function to add an accumulator for a parameter Args: block: the block in which the loss variable is present name: name of the accumulator param: parameter variable for which accumulator is to be added dtype: data type of the accumulator variable fill_value: value to initialize the accumulator variable """ if (name in self._accumulators and param.name in self._accumulators[name]): raise Exception("Accumulator {} already exists for parameter {}". format(name, param.name)) assert isinstance(self.helper, LayerHelper) var = self.helper.create_global_variable( name=unique_name(name), persistable=True, dtype=dtype or param.data_type, type=param.type, shape=param.shape) self.helper.set_variable_initializer( var, initializer=ConstantInitializer(value=float(fill_value))) self._accumulators[name][param.name] = var def _get_accumulator(self, name, param): """Utility function to fetch an accumulator for a parameter Args: name: name of the accumulator param: parameter variable for which accumulator is to be fetched Returns: accumulator variable for the parameter """ if (name not in self._accumulators or param.name not in self._accumulators[name]): raise Exception("Accumulator {} does not exist for parameter {}". format(name, param.name)) return self._accumulators[name][param.name] def _increment_global_step(self, block): """Increment the global step by 1 after every iteration Args: block: the block in which the loss variable is present Returns: list with global_step increment op as its only element """ assert isinstance(block, framework.Block) assert self._global_step is not None # create the increment op increment_op = block.append_op( type="increment", inputs={"X": self._global_step}, outputs={"Out": self._global_step}, attrs={"step": 1.0}) return increment_op def create_optimization_pass(self, parameters_and_grads, loss, startup_program=None): """Add optimization operators to update gradients to variables. Args: loss: the target that this optimization is for. parameters_and_grads: a list of (variable, gradient) pair to update. Returns: return_op_list: a list of operators that will complete one step of optimization. This will include parameter update ops, global step update ops and any other custom ops required by subclasses to manage their internal state. :param startup_program: """ # This is a default implementation of create_optimization_pass that # can be shared by most optimizers. This implementation assumes that # the subclass will implement the _append_optimize_op method and the # _initialize_tensors method. The subclass can extend the # _create_accumulators method if it needs to create accumulators # for parameters and extend _finish_update method to add custom ops. # Create any accumulators program = loss.block.program self.helper = LayerHelper( self.__class__.__name__, main_program=program, startup_program=startup_program) self._create_accumulators(loss.block, [p[0] for p in parameters_and_grads]) optimize_ops = [] for param_and_grad in parameters_and_grads: if param_and_grad[0].trainable is True and param_and_grad[ 1] is not None: optimize_op = self._append_optimize_op(loss.block, param_and_grad) optimize_ops.append(optimize_op) # Returned list of ops can include more ops in addition # to optimization ops return_ops = optimize_ops # Get custom finish ops for subclasses # FIXME: Need to fix this once we figure out how to handle dependencies finish_ops = self._finish_update(loss.block) if finish_ops is not None: return_ops += finish_ops if self._global_step is not None: return_ops.append(self._increment_global_step(loss.block)) return return_ops def minimize(self, loss, startup_program=None, parameter_list=None, no_grad_set=None): """Add operations to minimize `loss` by updating `parameter_list`. This method combines interface `append_backward_ops()` and `create_optimization_pass()` into one. """ params_grads = append_backward_ops(loss, parameter_list, no_grad_set or set()) # Add regularization if any params_grads = append_regularization_ops(params_grads) optimize_ops = self.create_optimization_pass(params_grads, loss, startup_program) return optimize_ops class SGDOptimizer(Optimizer): """ Simple SGD optimizer without any state. """ def __init__(self, learning_rate, global_step=None): assert learning_rate is not None super(SGDOptimizer, self).__init__(global_step) self.type = "sgd" self._learning_rate = learning_rate def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) # create the optimize op sgd_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "LearningRate": self._create_param_lr(param_and_grad) }, outputs={"ParamOut": param_and_grad[0]}) return sgd_op class MomentumOptimizer(Optimizer): """Simple Momentum optimizer with velocity state """ _velocity_acc_str = "velocity" def __init__(self, learning_rate, momentum, use_nesterov=False, global_step=None): assert learning_rate is not None assert momentum is not None super(MomentumOptimizer, self).__init__(global_step) self.type = "momentum" self._learning_rate = learning_rate self._momentum = momentum self._use_nesterov = bool(use_nesterov) def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) for p in parameters: self._add_accumulator(self._velocity_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) velocity_acc = self._get_accumulator(self._velocity_acc_str, param_and_grad[0]) # create the momentum optimize op momentum_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "Velocity": velocity_acc, "LearningRate": self._create_param_lr(param_and_grad) }, outputs={ "ParamOut": param_and_grad[0], "VelocityOut": velocity_acc }, attrs={"mu": self._momentum, "use_nesterov": self._use_nesterov}) return momentum_op class AdagradOptimizer(Optimizer): """Simple Adagrad optimizer with moment state """ _moment_acc_str = "moment" def __init__(self, learning_rate, epsilon=1.0e-6, global_step=None): assert learning_rate is not None assert epsilon is not None super(AdagradOptimizer, self).__init__(global_step) self.type = "adagrad" self._learning_rate = learning_rate self._epsilon = epsilon def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) for p in parameters: self._add_accumulator(self._moment_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) moment_acc = self._get_accumulator(self._moment_acc_str, param_and_grad[0]) # Create the adagrad optimizer op adagrad_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "Moment": moment_acc, "LearningRate": self._create_param_lr(param_and_grad) }, outputs={"ParamOut": param_and_grad[0], "MomentOut": moment_acc}, attrs={"epsilon": self._epsilon}) return adagrad_op class AdamOptimizer(Optimizer): """Implements the Adam Optimizer """ _moment1_acc_str = "moment1" _moment2_acc_str = "moment2" def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, global_step=None): assert learning_rate is not None assert beta1 is not None assert beta2 is not None assert epsilon is not None super(AdamOptimizer, self).__init__(global_step) self.type = "adam" self._learning_rate = learning_rate self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) main_block = block.program.global_block() # Create beta1 and beta2 power tensors beta_shape = [1] self._beta1_pow_acc = self.helper.create_global_variable( name=unique_name('beta1_pow_acc'), dtype='float32', shape=beta_shape, lod_level=0, persistable=True) self.helper.set_variable_initializer( self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1)) self._beta2_pow_acc = self.helper.create_global_variable( name=unique_name('beta2_pow_acc'), dtype='float32', shape=beta_shape, lod_level=0, persistable=True) self.helper.set_variable_initializer( self._beta2_pow_acc, initializer=ConstantInitializer(self._beta2)) # Create accumulator tensors for first and second moments for p in parameters: self._add_accumulator(self._moment1_acc_str, p) self._add_accumulator(self._moment2_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) moment1 = self._get_accumulator(self._moment1_acc_str, param_and_grad[0]) moment2 = self._get_accumulator(self._moment2_acc_str, param_and_grad[0]) # create the adam optimize op adam_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "LearningRate": self._create_param_lr(param_and_grad), "Moment1": moment1, "Moment2": moment2, "Beta1Pow": self._beta1_pow_acc, "Beta2Pow": self._beta2_pow_acc }, outputs={ "ParamOut": param_and_grad[0], "Moment1Out": moment1, "Moment2Out": moment2 }, attrs={ "beta1": self._beta1, "beta2": self._beta2, "epsilon": self._epsilon }) return adam_op def _finish_update(self, block): """Update Beta1 and Beta2 Power accumulators """ assert isinstance(block, framework.Block) main_block = block.program.global_block() scale_beta1 = main_block.append_op( type="scale", inputs={"X": self._beta1_pow_acc}, outputs={"Out": self._beta1_pow_acc}, attrs={"scale": self._beta1}) scale_beta2 = main_block.append_op( type="scale", inputs={"X": self._beta2_pow_acc}, outputs={"Out": self._beta2_pow_acc}, attrs={"scale": self._beta2}) return [scale_beta1, scale_beta2] class AdamaxOptimizer(Optimizer): """Implements the Adamax Optimizer """ _moment_acc_str = "moment" _inf_norm_acc_str = "inf_norm" def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, global_step=None): assert learning_rate is not None assert beta1 is not None assert beta2 is not None assert epsilon is not None super(AdamaxOptimizer, self).__init__() self.type = "adamax" self._learning_rate = learning_rate self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon def _create_accumulators(self, block, parameters): # Create beta1 power accumulator tensor beta_shape = [1] self._beta1_pow_acc = self.helper.create_global_variable( name=unique_name('beta1_pow_acc'), dtype='float32', shape=beta_shape, lod_level=0, persistable=True) self.helper.set_variable_initializer( self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1)) # Create accumulator tensors for first moment and infinity norm for p in parameters: self._add_accumulator(self._moment_acc_str, p) self._add_accumulator(self._inf_norm_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) moment = self._get_accumulator(self._moment_acc_str, param_and_grad[0]) inf_norm = self._get_accumulator(self._inf_norm_acc_str, param_and_grad[0]) # create the adamax optimize op adamax_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "LearningRate": self._create_param_lr(param_and_grad), "Moment": moment, "InfNorm": inf_norm, "Beta1Pow": self._beta1_pow_acc }, outputs={ "ParamOut": param_and_grad[0], "MomentOut": moment, "InfNormOut": inf_norm }, attrs={ "beta1": self._beta1, "beta2": self._beta2, "epsilon": self._epsilon }) return adamax_op def _finish_update(self, block): """Update Beta1 Power accumulator """ assert isinstance(block, framework.Block) main_block = block.program.global_block() scale_beta1 = main_block.append_op( type="scale", inputs={"X": self._beta1_pow_acc}, outputs={"Out": self._beta1_pow_acc}, attrs={"scale": self._beta1}) return [scale_beta1] class DecayedAdagradOptimizer(Optimizer): """Simple Decayed Adagrad optimizer with moment state """ _moment_acc_str = "moment" def __init__(self, learning_rate, decay=0.95, epsilon=1.0e-6, global_step=None): assert learning_rate is not None assert decay is not None assert epsilon is not None super(DecayedAdagradOptimizer, self).__init__(global_step) self.type = "decayed_adagrad" self._learning_rate = learning_rate self._decay = decay self._epsilon = epsilon def _create_accumulators(self, block, parameters): assert isinstance(block, framework.Block) for p in parameters: self._add_accumulator(self._moment_acc_str, p) def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, framework.Block) moment_acc = self._get_accumulator(self._moment_acc_str, param_and_grad[0]) # Create the decayed adagrad optimizer op decayed_adagrad_op = block.append_op( type=self.type, inputs={ "Param": param_and_grad[0], "Grad": param_and_grad[1], "Moment": moment_acc, "LearningRate": self._create_param_lr(param_and_grad) }, outputs={"ParamOut": param_and_grad[0], "MomentOut": moment_acc}, attrs={"epsilon": self._epsilon}) return decayed_adagrad_op