# @package optimizer # Module caffe2.python.optimizer import copy import logging from collections import defaultdict, namedtuple import numpy as np from caffe2.proto import caffe2_pb2 from caffe2.python import core, scope, utils, workspace from caffe2.python.modeling import parameter_info from past.builtins import basestring _LEARNING_RATE_INJECTION = "lr_injection" AuxOptimizerParams = namedtuple("AuxOptimizerParams", ["local", "shared"]) _optimizer_instance_count = defaultdict(int) FP16_ENGINES = ["SIMD_Q_FP16", "SIMD_Q_STOC_FP16", "SIMD_Q_STOC_MKL_FP16"] logger = logging.getLogger(__name__) def reset_optimizer_instance_count(): """ This function clears the _optimizer_instance_count. And keeps it empty. This functionality is needed in some situations where optimizer instance count might not reset even though the workplace is reset. """ _optimizer_instance_count.clear() class Optimizer(object): def __init__(self): self._aux_params = AuxOptimizerParams(local=[], shared=[]) self._instance_num = _optimizer_instance_count[self.__class__.__name__] _optimizer_instance_count[self.__class__.__name__] += 1 self._lr_multiplier = None self._local_lr_multiplier = None self._local_lr_multiplier_on_gpu = False """ Adds optimization operators to the net for given parameter and its gradient Parameter is specified by either 'param' being a ParameterInfo object. In this case param.grad has to be set Or by 'param' being a BlobReference and 'grad' being a BlobReference for its gradient. """ def __call__(self, net, param_init_net, param, grad=None): if grad is None: assert isinstance( param, parameter_info.ParameterInfo ), "Expected parameter to be of type ParameterInfo, got {}".format(param) assert param.grad is not None else: if isinstance(param, basestring): param = core.BlobReference(param) param = parameter_info.ParameterInfo(param_id=None, param=param, grad=grad) self._run(net, param_init_net, param) def _run(self, net, param_init_net, param_info): raise Exception("Not Implemented") def get_cpu_blob_name(self, base_str, node_name=""): classname = self.__class__.__name__ return "%s_%d_%s%s_cpu" % (classname, self._instance_num, base_str, node_name) def get_gpu_blob_name(self, base_str, gpu_id, node_name): classname = self.__class__.__name__ return "%s_%d_%s%s_gpu%d" % ( classname, self._instance_num, base_str, node_name, gpu_id, ) @property def attributes(self): # return a dict that contains attributes related to init args only attr = copy.deepcopy(self.__dict__) del attr["_instance_num"] return attr def make_unique_blob_name(self, base_str): """ Returns a blob name that will be unique to the current device and optimizer instance. """ current_scope = scope.CurrentDeviceScope() if current_scope is None: return self.get_cpu_blob_name(base_str) if core.IsGPUDeviceType(current_scope.device_type): return self.get_gpu_blob_name( base_str, current_scope.device_id, current_scope.node_name ) else: return self.get_cpu_blob_name(base_str, current_scope.node_name) def build_lr( self, net, param_init_net, base_learning_rate, learning_rate_blob=None, policy="fixed", iter_val=0, **kwargs ): if learning_rate_blob is None: learning_rate_blob = self.make_unique_blob_name("lr") iteration = utils.BuildUniqueMutexIter(param_init_net, net, iter_val=iter_val) if not net.BlobIsDefined(learning_rate_blob): # There is one interesting thing here: since we are minimizing, we are # doing "descent" so the learning rate is set to be negative. lr = net.LearningRate( [iteration], learning_rate_blob, base_lr=-base_learning_rate, policy=policy, **kwargs ) else: lr = net.GetBlobRef(learning_rate_blob) if self._lr_multiplier is not None: lr_multiplier = net.CopyFromCPUInput( self._lr_multiplier, self.make_unique_blob_name("lr_multiplier") ) lr = net.Mul( [lr, lr_multiplier], self.make_unique_blob_name("scaled_lr"), broadcast=1, ) if self._local_lr_multiplier is not None: current_scope = scope.CurrentDeviceScope() if ( current_scope is not None and core.IsGPUDeviceType(current_scope.device_type) and not self._local_lr_multiplier_on_gpu ): local_lr_multiplier = net.CopyFromCPUInput( self._local_lr_multiplier, self.make_unique_blob_name("local_lr_multiplier"), ) else: local_lr_multiplier = self._local_lr_multiplier lr = net.Mul( [lr, local_lr_multiplier], self.make_unique_blob_name("local_scaled_lr"), broadcast=1, ) return lr, iteration def add_lr_multiplier(self, lr_multiplier): """ Set the global learning rate multiplier. If a multiplier already existed, this will overwrite the existing multiplier. The multiplier is used for all future calls to _run(), unless it is overwritten. """ self._lr_multiplier = lr_multiplier def _add_local_lr_multiplier(self, local_lr_multiplier, is_gpu_blob=False): """ Set the local learning rate multiplier. This local multiplier is multiplied with the global learning rate multiplier if it exists. As with the global learning rate multiplier, this multiplier will be used for all future calls to _run(), so please call _clear_local_lr_multiplier() at the beginning of the optimizer's _run() before optionally calling this function. """ self._local_lr_multiplier = local_lr_multiplier self._local_lr_multiplier_on_gpu = is_gpu_blob def _clear_local_lr_multiplier(self): self._local_lr_multiplier = None self._local_lr_multiplier_on_gpu = False @staticmethod def dedup(net, sparse_dedup_aggregator, grad): assert isinstance( grad, core.GradientSlice ), "Dedup only works for sparse gradient, got {}".format(grad) if sparse_dedup_aggregator: return net.DeduplicateGradientSlices( grad, aggregator=sparse_dedup_aggregator ) else: return grad def get_auxiliary_parameters(self): """Returns a list of auxiliary parameters. Returns: aux_params: A namedtuple, AuxParams. aux_params.local stores a list of blobs. Each blob is a local auxiliary parameter. A local auxiliary parameter is a parameter in parallel to a learning rate parameter. Take adagrad as an example, the local auxiliary parameter is the squared sum parameter, because every learning rate has a squared sum associated with it. aux_params.shared also stores a list of blobs. Each blob is a shared auxiliary parameter. A shared auxiliary parameter is a parameter that is shared across all the learning rate parameters. Take adam as an example, the iteration parameter is a shared parameter, because all the learning rates share the same iteration parameter. """ return self._aux_params # TODO(xlwang): In transfer learning, parameter initialized from pretrained # model might require a different learning rate than otherwise initialized. # To this end, here we implement a python solution where # `base_learning_rate` is scaled by `scale`, by calling # `scale_learning_rate`; Alternatively, we can achieve same effect by # rewriting the LearningRate operator in C++ # Note that it is the responsibility of specific optimizer to decide what # logic should be used for `scale_learning_rate` def scale_learning_rate(self, *args, **kwargs): raise NotImplementedError( "Optimizer Need to Implement `scale_learning_rate` method." ) def create_lars_inputs(self, param_init_net, weight_decay, trust, lr_max): wd = param_init_net.ConstantFill( [], "weight_decay", shape=[1], value=weight_decay ) trust = param_init_net.ConstantFill([], "trust", shape=[1], value=trust) lr_max = param_init_net.ConstantFill([], "lr_max", shape=[1], value=lr_max) return wd, trust, lr_max class SgdOptimizer(Optimizer): def __init__( self, base_learning_rate=0.01, policy="fixed", momentum=0.0, nesterov=True, sparse_dedup_aggregator=None, lars=None, **kwargs ): super(SgdOptimizer, self).__init__() self.base_learning_rate = base_learning_rate self.policy = policy self.momentum = momentum self.nesterov = nesterov self.sparse_dedup_aggregator = sparse_dedup_aggregator self.lars = lars self.init_kwargs = kwargs def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.base_learning_rate == 0: return assert ( self.base_learning_rate > 0 ), "Expect positive base learning rate, got {}".format(self.base_learning_rate) self._clear_local_lr_multiplier() # TODO(zqq): support LARS for sparse parameters if self.lars is not None and not isinstance(grad, core.GradientSlice): assert self.lars >= 0, "Lars offset must be nonnegative, got {}".format( self.lars ) wd, trust, lr_max = self.create_lars_inputs( param_init_net, 0.0, 1.0, np.finfo(np.float32).max ) lr_lars_multiplier = net.Lars( [param, grad, wd, trust, lr_max], self.make_unique_blob_name(str(param) + "_lars"), offset=self.lars, lr_min=0.0, ) current_scope = scope.CurrentDeviceScope() self._add_local_lr_multiplier( lr_lars_multiplier, is_gpu_blob=( current_scope is not None and core.IsGPUDeviceType(current_scope.device_type) ), ) # We need negative sign for LR when used directly with WeightedSum # below. lr_sign = -1 if self.momentum else 1 lr, _ = self.build_lr( net, param_init_net, base_learning_rate=self.base_learning_rate * lr_sign, policy=self.policy, **(self.init_kwargs) ) dev = scope.CurrentDeviceScope() if dev is None: dev = core.DeviceOption(caffe2_pb2.CPU) # Each GPU/CPU must have its own ONE blob, thus modify the name # to include device information. ONE = param_init_net.ConstantFill( [], "ONE_{}_{}{}".format(dev.device_type, dev.device_id, dev.node_name), shape=[1], value=1.0, ) self._aux_params.shared.append(ONE) if self.momentum > 0: momentum_data = param_init_net.ConstantFill( param, str(param) + "_momentum", value=0.0 ) self._aux_params.local.append(momentum_data) if isinstance(grad, core.GradientSlice): grad = self.dedup(net, self.sparse_dedup_aggregator, grad) if self.momentum > 0.0: net.SparseMomentumSGDUpdate( [grad.values, momentum_data, lr, param, grad.indices], [grad.values, momentum_data, param], momentum=self.momentum, nesterov=self.nesterov, ) else: net.ScatterWeightedSum( [param, ONE, grad.indices, grad.values, lr], param ) else: if self.momentum > 0.0: net.MomentumSGDUpdate( [grad, momentum_data, lr, param], [grad, momentum_data, param], momentum=self.momentum, nesterov=self.nesterov, ) else: coeff = lr net.WeightedSum([param, ONE, grad, coeff], param) def scale_learning_rate(self, scale): self.base_learning_rate *= scale return class MultiPrecisionSgdOptimizer(SgdOptimizer): def __init__( self, base_learning_rate=0.1, momentum=0.0, policy="fixed", nesterov=True, sparse_dedup_aggregator=None, **kwargs ): super(MultiPrecisionSgdOptimizer, self).__init__( base_learning_rate=base_learning_rate, policy=policy, momentum=momentum, nesterov=nesterov, sparse_dedup_aggregator=sparse_dedup_aggregator, **kwargs ) def _run(self, net, param_init_net, param_info): param = param_info.blob param_fp32 = ( param_info.blob_copy[core.DataType.FLOAT] if param_info.blob_copy is not None else None ) # If we have a straight fp32 parameter, run the base class if param_fp32 is None: return SgdOptimizer._run(self, net, param_init_net, param_info) grad = param_info.grad if self.base_learning_rate == 0: return assert ( self.base_learning_rate > 0 ), "Expect positive base learning rate, got {}".format(self.base_learning_rate) lr, _ = self.build_lr( net, param_init_net, base_learning_rate=-self.base_learning_rate, policy=self.policy, **(self.init_kwargs) ) momentum_data = param_init_net.ConstantFill( param_fp32, str(param) + "_momentum", value=0.0 ) self._aux_params.local.append(momentum_data) assert not isinstance( grad, core.GradientSlice ), "MultiPrecisionSgd does not support sparse gradients" # Copy gradient to fp32 grad_fp32 = net.HalfToFloat(grad, grad + "_fp32") # update (fused) in fp32 net.MomentumSGDUpdate( [grad_fp32, momentum_data, lr, param_fp32], [grad_fp32, momentum_data, param_fp32], momentum=self.momentum, nesterov=self.nesterov, ) # Copy updated param back to fp16 net.FloatToHalf(param_fp32, param) class FP16SgdOptimizer(SgdOptimizer): def __init__( self, base_learning_rate=0.1, momentum=0.0, policy="fixed", nesterov=True, weight_decay=0.0001, sparse_dedup_aggregator=None, **kwargs ): super(FP16SgdOptimizer, self).__init__( base_learning_rate=base_learning_rate, policy=policy, momentum=momentum, nesterov=nesterov, sparse_dedup_aggregator=sparse_dedup_aggregator, **kwargs ) self.weight_decay = weight_decay def _run(self, net, param_init_net, param_info, fp32_update=False): fp32_update_flag = 0 param_name = str(param_info.blob) # should only be triggered in FP16 training by SpatialBN, which # requires FP32 params in CuDNN. if param_name.find("spatbn") != -1: fp32_update = True if fp32_update: # doing a 32bit update # Have to assume param_info.blob is FP32 as there is no way # (that i currently know of) to query a blob's type in python fp32_update_flag = 1 param = param_info.blob param_fp32 = param_info.blob else: if param_info.blob_copy is None: # doing a 32bit update # Have to assume param_info.blob is FP32 as there is no way # (that i currently know of) to query a blob's type in python fp32_update_flag = 1 param = param_info.blob param_fp32 = param_info.blob else: if core.DataType.FLOAT in param_info.blob_copy: param = param_info.blob param_fp32 = param_info.blob_copy[core.DataType.FLOAT] elif core.DataType.FLOAT16 in param_info.blob_copy: param = param_info.blob_copy[core.DataType.FLOAT16] param_fp32 = param_info.blob else: AssertionError( "Unrecognized parameter format to be updated " "by FP16 Optimizer. Parameter: {}".format(param_info.name) ) grad = param_info.grad if self.base_learning_rate == 0: return assert ( self.base_learning_rate > 0 ), "Expect positive base learning rate, got {}".format(self.base_learning_rate) lr, _ = self.build_lr( net, param_init_net, base_learning_rate=-self.base_learning_rate, policy=self.policy, **(self.init_kwargs) ) momentum_data_fp32 = param_init_net.ConstantFill( param_fp32, str(param) + "_momentum_fp32", value=0.0 ) momentum_data = param_init_net.FloatToHalf( momentum_data_fp32, str(param) + "_momentum" ) self._aux_params.local.append(momentum_data) assert not isinstance( grad, core.GradientSlice ), "FP16Sgd does not support sparse gradients" if fp32_update_flag == 0: net.FP16MomentumSGDUpdate( [grad, momentum_data, lr, param], [grad, momentum_data, param], momentum=self.momentum, nesterov=self.nesterov, weight_decay=self.weight_decay, ) else: # flag set to 1, therefore doing FP32 update net.FP32MomentumSGDUpdate( [grad, momentum_data_fp32, lr, param], [grad, momentum_data_fp32, param], momentum=self.momentum, nesterov=self.nesterov, weight_decay=self.weight_decay, ) class WeightDecayBuilder(Optimizer): def __init__(self, weight_decay): self.weight_decay = weight_decay def _run(self, net, param_init_net, param_info): dev = scope.CurrentDeviceScope() if dev is None: dev = core.DeviceOption(caffe2_pb2.CPU) ONE = param_init_net.ConstantFill( [], "ONE_{}_{}".format(dev.device_type, dev.device_id), shape=[1], value=1.0 ) WD = param_init_net.ConstantFill( [], "wd_{}_{}".format(dev.device_type, dev.device_id), shape=[1], value=self.weight_decay, ) if isinstance(param_info.grad, core.GradientSlice): raise ValueError("Weight decay does not yet support sparse gradients") else: net.WeightedSum( [param_info.grad, ONE, param_info.blob, WD], param_info.grad ) class AdagradOptimizer(Optimizer): def __init__( self, alpha=0.01, epsilon=1e-4, decay=1, weight_decay=0.0, policy="fixed", sparse_dedup_aggregator=None, rowWise=False, engine="", lars=None, output_effective_lr=False, output_effective_lr_and_update=False, pruning_options=None, swa_options=None, ema_options=None, weight_scale=None, counter_halflife=-1, **kwargs ): super(AdagradOptimizer, self).__init__() self.alpha = alpha self.epsilon = epsilon self.decay = decay self.weight_decay = float(weight_decay) self.policy = policy self.sparse_dedup_aggregator = sparse_dedup_aggregator self.rowWise = rowWise self.engine = engine self.lars = lars self.output_effective_lr = output_effective_lr self.output_effective_lr_and_update = output_effective_lr_and_update self.counter_halflife = counter_halflife self.init_kwargs = kwargs self.weight_scale = weight_scale self._process_pruning_options(pruning_options) self._process_swa_options(swa_options) self._process_ema_options(ema_options) def _process_swa_options(self, swa_options): self.swa_enabled = True if swa_options else False if self.swa_enabled: self.swa_avg_start_it = swa_options.get("swa_avg_start_it", None) self.swa_avg_end_it = swa_options.get("swa_avg_end_it", None) self.swa_feedback_start_it = swa_options.get("swa_feedback_start_it", None) self.swa_feedback_step = swa_options.get("swa_feedback_step", None) self.swa_feedback_end_it = swa_options.get("swa_feedback_end_it", None) def _process_ema_options(self, ema_options): self.ema_enabled = True if ema_options else False if self.ema_enabled: self.ema_start = ema_options.get("ema_start", None) self.ema_end = ema_options.get("ema_end", None) self.ema_step = ema_options.get("ema_step", None) self.ema_alpha = ema_options.get("ema_alpha", None) def _process_pruning_options(self, pruning_options): self.use_mask = False if pruning_options is None: pruning_options = {} else: assert isinstance(pruning_options, dict), ( "pruning_options can only " "be provided as a dictionary, currently: {}".format(pruning_options) ) self.mask_tensor = pruning_options.get("mask_tensor", None) self.mask_db_path = pruning_options.get("mask_db_path", None) self.mask_db_type = pruning_options.get("mask_db_type", None) self.mask_blob_name = pruning_options.get("mask_blob_name", None) self.prune_delays = pruning_options.get("prune_delays", []) self.prune_ratios = pruning_options.get("prune_ratios", []) self.prune_block_size = pruning_options.get("prune_block_size", 1) if self.mask_tensor is not None: assert ( type(self.mask_tensor) is np.ndarray ), "mask_tensor must be a numpy array!" assert self.mask_db_path is None, ( "mask can be provided through either a numpy array " "or a db path, not both" ) assert self.mask_db_type is None, ( "mask can be provided through either a numpy array " "or a db path, not both" ) assert self.mask_blob_name is None, ( "mask can be provided through either a numpy array " "or a db path, not both" ) self.use_mask = True if self.mask_db_path is not None or self.mask_db_type is not None: assert self.mask_db_path is not None, ( "when mask is provided through db, " "db path, db type, and blob name are all needed" ) assert self.mask_db_type is not None, ( "when mask is provided through db, " "db path, db type, and blob name are all needed" ) assert self.mask_tensor is None, ( "mask can be provided through either a numpy array " "or a db path, not both" ) self.use_mask = True if self.prune_delays: assert self.prune_ratios is not None and len(self.prune_delays) == len( self.prune_ratios ), "Prune Delays and prune ratios should be of the same length" assert ( self.mask_tensor is None ), "Mask Tensor should be None with prune ratios" assert ( self.mask_db_path is None ), "Mask DB Path should be None with prune ratios" self.use_mask = True def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.alpha <= 0: return self._clear_local_lr_multiplier() if self.lars is not None and not isinstance(grad, core.GradientSlice): assert ( self.weight_decay == 0 ), "weight decay is not implemented for LARS yet" assert self.lars >= 0, "Lars offset must be nonnegative, got {}".format( self.lars ) wd, trust, lr_max = self.create_lars_inputs( param_init_net, 0.0, 1.0, np.finfo(np.float32).max ) lr_lars_multiplier = net.Lars( [param, grad, wd, trust, lr_max], self.make_unique_blob_name(str(param) + "_lars"), offset=self.lars, lr_min=0.0, ) current_scope = scope.CurrentDeviceScope() self._add_local_lr_multiplier( lr_lars_multiplier, is_gpu_blob=( current_scope is not None and core.IsGPUDeviceType(current_scope.device_type) ), ) lr, lr_iteration = self.build_lr( net, param_init_net, base_learning_rate=self.alpha, policy=self.policy, **(self.init_kwargs) ) iteration = lr_iteration if self.counter_halflife > 0: self._aux_params.shared.append(iteration) if self.rowWise: logger.debug( "Using engine {} for rowWise Adagrad to train param {}".format( self.engine, param ) ) shapes, types = workspace.InferShapesAndTypes([param_init_net]) if str(param) not in shapes: # Type/shape inference is not available for this param, fallback # on Shape/Slice logic shape = param_init_net.Shape(param, str(param) + "_shape") num_rows = param_init_net.Slice( [shape], str(shape) + "_numrows", starts=[0], ends=[1] ) param_squared_sum = param_init_net.ConstantFill( num_rows, str(param) + "_avg_squared_sum", input_as_shape=1, value=0.0, ) else: param_squared_sum = param_init_net.ConstantFill( [], str(param) + "_avg_squared_sum", shape=[shapes[str(param)][0]], value=0.0, ) else: logger.debug( "Using engine {} for regular Adagrad to train param {}".format( self.engine, param ) ) if self.engine in FP16_ENGINES: assert ( self.weight_decay == 0 ), "weight decay is not tested for engine: {}".format(self.engine) shapes, types = workspace.InferShapesAndTypes([param_init_net]) assert str(param) in shapes, shapes shape = shapes[str(param)] param_squared_sum = param_init_net.Float16ConstantFill( [], str(param) + "_squared_sum", value=0.0, shape=shape ) else: param_squared_sum = param_init_net.ConstantFill( [param], str(param) + "_squared_sum", value=0.0 ) if self.use_mask is True: assert ( self.weight_decay == 0 ), "weight decay is not implemented for use_mask yet" if self.mask_tensor is not None: if not isinstance(grad, core.GradientSlice): mask_blob = param_init_net.GivenTensorFill( [], [str(param) + "_mask"], values=self.mask_tensor, shape=self.mask_tensor.shape, ) else: self.mask_tensor = self.mask_tensor.astype(np.uint8) mask_blob = param_init_net.GivenTensorBoolFill( [], [str(param) + "_mask"], values=self.mask_tensor, shape=self.mask_tensor.shape, ) mask_blob = param_init_net.Cast(mask_blob, to=core.DataType.UINT8) mask_changed_blob = param_init_net.ConstantFill( [], [str(param) + "_mask_changed_blob"], value=False, dtype=core.DataType.BOOL, shape=[1], ) elif ( self.mask_db_path is not None or self.mask_db_type is not None ): # mask is provided through a db file # if mask_blob_name is not given use the param name to derive mask name self.mask_blob_name = self.mask_blob_name or str(param) + "_mask" mask_blob = param_init_net.Load( [], self.mask_blob_name, db=self.mask_db_path, db_type=self.mask_db_type, absolute_path=True, ) if isinstance(grad, core.GradientSlice): mask_changed_blob = param_init_net.ConstantFill( [], [str(param) + "_mask_changed_blob"], value=False, dtype=core.DataType.BOOL, shape=[1], ) elif self.prune_delays: last_mask_updated_iter = param_init_net.ConstantFill( [], [str(param) + "_last_mask_updated_iter"], value=-1, dtype=core.DataType.INT64, shape=[1], ) if isinstance(grad, core.GradientSlice): AssertionError( "Prune Delays and Prune Ratios are currently not supported" "for sparse operators" ) else: mask_blob = param_init_net.GivenTensorFill( [], [str(param) + "_empty_mask"], values=[], dtype=core.DataType.FLOAT, shape=[0], ) else: raise NotImplementedError( "If mask is used, it needs a numpy array or a db file or" "a delay iter needs to be provided" ) self._aux_params.local.append(param_squared_sum) if self.counter_halflife > 0: shapes, types = workspace.InferShapesAndTypes([param_init_net]) if str(param) not in shapes: shape = param_init_net.Shape(param, str(param) + "_shape") num_rows = param_init_net.Slice( [shape], str(shape) + "_numrows", starts=[0], ends=[1] ) update_counter = param_init_net.ConstantFill( num_rows, str(param) + "_update_counter", input_as_shape=1, value=0.0, dtype=core.DataType.DOUBLE, ) prev_update_iter = param_init_net.ConstantFill( num_rows, str(param) + "_prev_update_iter", input_as_shape=1, value=0, dtype=core.DataType.INT64, ) else: update_counter = param_init_net.ConstantFill( [], str(param) + "_update_counter", shape=[shapes[str(param)][0]], value=0.0, dtype=core.DataType.DOUBLE, ) prev_update_iter = param_init_net.ConstantFill( [], str(param) + "_prev_update_iter", shape=[shapes[str(param)][0]], value=0, dtype=core.DataType.INT64, ) self._aux_params.local.append(update_counter) self._aux_params.local.append(prev_update_iter) if self.rowWise: assert isinstance(grad, core.GradientSlice), ( "If SparseAdagrad with rowWise=True, gradient must be " "a gradientslice. PLease ensure that rowWise is not enabled " "for the dense Adagrad optimizer, as it is not supported." ) shapes, _ = workspace.InferShapesAndTypes([param_init_net]) param_shape = shapes[str(param)] weight_decay = 0.0 if isinstance(grad, core.GradientSlice): if len(param_shape) == 1: weight_decay = 0.0 logger.warn( "SKIPPING weight decay on 1d sparse param: {}.shape is {}".format( str(param), param_shape ) ) else: weight_decay = self.weight_decay else: # Skip weight decay for 1d parameters if len(param_shape) == 1: weight_decay = 0.0 logger.warning( "SKIPPING weight decay on 1d dense param: {}.shape is {}".format( str(param), param_shape ) ) else: weight_decay = self.weight_decay logger.debug( "weight_decay for {} (shape:{}): {}".format( str(param), param_shape, weight_decay ) ) if isinstance(grad, core.GradientSlice): assert ( self.decay == 1.0 ), "Decay is not implemented for SparseAdagrad and must be set to 1" grad = self.dedup(net, self.sparse_dedup_aggregator, grad) input_args = [param, param_squared_sum, grad.indices, grad.values, lr] output_args = [param, param_squared_sum] if self.rowWise: if self.use_mask is True: op = "MaskedRowWiseSparseAdagrad" assert ( weight_decay == 0 ), "weight decay is not implemented for {} yet".format(op) input_args += [mask_blob, mask_changed_blob] else: if self.counter_halflife > 0: input_args += [update_counter] op = "RowWiseSparseAdagrad" else: if self.use_mask is True: op = "MaskedSparseAdagrad" assert ( weight_decay == 0 ), "weight decay is not implemented for {} yet".format(op) input_args += [mask_blob, mask_changed_blob] else: op = "SparseAdagrad" logger.debug("using {} for {}".format(op, str(param))) if self.prune_delays: input_args += [lr_iteration, last_mask_updated_iter] output_args += [mask_blob, last_mask_updated_iter] if weight_decay > 0 and self.counter_halflife == -1: net.__getattr__(op)( input_args, output_args, epsilon=self.epsilon, weight_decay=weight_decay, engine=self.engine, ) elif weight_decay > 0 and self.counter_halflife != -1: net.__getattr__(op)( input_args, output_args, epsilon=self.epsilon, weight_decay=weight_decay, engine=self.engine, counter_halflife=self.counter_halflife, ) else: net.__getattr__(op)( input_args, output_args, epsilon=self.epsilon, engine=self.engine ) if self.counter_halflife > 0: net.RowWiseCounter( [prev_update_iter, update_counter, grad.indices, iteration], [prev_update_iter, update_counter], counter_halflife=self.counter_halflife, ) else: input_args = [param, param_squared_sum, grad, lr] output_args = [param, param_squared_sum] if self.output_effective_lr_and_update: assert ( self.use_mask is False ), "MaskedAdagrad doesn't support outputting effective_lr_and_update" output_args.append(str(param) + "_effective_lr") output_args.append(str(param) + "_update") elif self.output_effective_lr: assert ( self.use_mask is False ), "MaskedAdagrad doesn't support outputting effective_lr" output_args.append(str(param) + "_effective_lr") if self.use_mask is True: input_args += [mask_blob] if self.prune_delays: input_args += [lr_iteration, last_mask_updated_iter] output_args += [mask_blob, last_mask_updated_iter] if self.use_mask: assert ( weight_decay == 0 ), "weight decay is not implemented for use_mask yet" net.MaskedAdagrad( input_args, output_args, epsilon=self.epsilon, decay=float(self.decay), block_size=self.prune_block_size, delays=self.prune_delays, prune_ratios=self.prune_ratios, engine=self.engine, ) else: if weight_decay > 0: net.Adagrad( input_args, output_args, epsilon=self.epsilon, decay=float(self.decay), weight_decay=weight_decay, engine=self.engine, ) else: net.Adagrad( input_args, output_args, epsilon=self.epsilon, decay=float(self.decay), engine=self.engine, ) if self.swa_enabled: param_swa = str(param) + "_swa" if not param_init_net.BlobIsDefined(param_swa): param_init_net.ConstantFill([param], param_swa, value=0.0) self._aux_params.local.append(param_swa) net.SWA( [param, param_swa, lr_iteration], [param, param_swa], avg_start=self.swa_avg_start_it, avg_end=self.swa_avg_end_it, feedback_start=self.swa_feedback_start_it, feedback_step=self.swa_feedback_step, feedback_end=self.swa_feedback_end_it, ) if self.ema_enabled: param_ema = str(param) + "_ema" if not param_init_net.BlobIsDefined(param_ema): param_init_net.ConstantFill([param], param_ema, value=0.0) self._aux_params.local.append(param_ema) net.EMA( [param, param_ema, lr_iteration], [param, param_ema], ema_start=self.ema_start, ema_end=self.ema_end, ema_step=self.ema_step, ema_alpha=self.ema_alpha, ) if self.weight_scale: net.WeightScale( [param, lr_iteration], [param], stepsize=self.weight_scale.stepsize, upper_bound_iter=self.weight_scale.upper_bound_iter, scale=float(self.weight_scale.scale), ) if self.weight_scale.to_aux: net.WeightScale( [param_squared_sum, lr_iteration], [param_squared_sum], stepsize=self.weight_scale.stepsize, upper_bound_iter=self.weight_scale.upper_bound_iter, scale=float(self.weight_scale.scale), ) def scale_learning_rate(self, scale): self.alpha *= scale return class WngradOptimizer(Optimizer): def __init__( self, alpha=1.0, epsilon=1e-9, policy="fixed", sparse_dedup_aggregator=None, engine="", moment_init=100.0, lars=None, output_effective_lr=False, output_effective_lr_and_update=False, **kwargs ): super(WngradOptimizer, self).__init__() self.alpha = alpha self.epsilon = epsilon self.policy = policy self.sparse_dedup_aggregator = sparse_dedup_aggregator self.engine = engine self.moment_init = moment_init self.lars = lars self.output_effective_lr = output_effective_lr self.output_effective_lr_and_update = output_effective_lr_and_update self.init_kwargs = kwargs def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.alpha <= 0: return self._clear_local_lr_multiplier() if self.lars is not None and not isinstance(grad, core.GradientSlice): assert self.lars >= 0, "Lars offset must be nonnegative, got {}".format( self.lars ) wd, trust, lr_max = self.create_lars_inputs( param_init_net, 0.0, 1.0, np.finfo(np.float32).max ) lr_lars_multiplier = net.Lars( [param, grad, wd, trust, lr_max], self.make_unique_blob_name(str(param) + "_lars"), offset=self.lars, lr_min=0.0, ) current_scope = scope.CurrentDeviceScope() self._add_local_lr_multiplier( lr_lars_multiplier, is_gpu_blob=( current_scope is not None and core.IsGPUDeviceType(current_scope.device_type) ), ) lr, _ = self.build_lr( net, param_init_net, base_learning_rate=self.alpha, policy=self.policy, **(self.init_kwargs) ) moment = param_init_net.ConstantFill( [], str(param) + "_moment", shape=[1], value=self.moment_init ) self._aux_params.local.append(moment) if isinstance(grad, core.GradientSlice): grad = self.dedup(net, self.sparse_dedup_aggregator, grad) net.SparseWngrad( [param, moment, grad.indices, grad.values, lr], [param, moment], epsilon=self.epsilon, engine=self.engine, ) else: output_args = [param, moment] if self.output_effective_lr_and_update: output_args.append(str(param) + "_effective_lr") output_args.append(str(param) + "_update") elif self.output_effective_lr: output_args.append(str(param) + "_effective_lr") net.Wngrad( [param, moment, grad, lr], output_args, epsilon=self.epsilon, engine=self.engine, ) def scale_learning_rate(self, scale): self.alpha *= scale return class StormOptimizer(Optimizer): def __init__( self, lr=0.1, momentum=10.0, beta=0.1, grad_sq_init=0.01, policy="fixed", sparse_dedup_aggregator=None, lars=None, **kwargs ): """Constructor function to add STORM Optimizer Args: lr: learning rate scaling (called k in the original paper) momentum: momentum scaling (called c in the original paper) beta: initial value of denominator in adaptive learning rate ( called c in the original paper) grad_sq_init: initial value of gradient squared accumulator. policy: specifies how learning rate should be applied, options are 'fixed', 'step', 'exp', etc. sparse_dedup_aggregator: specifies deduplication strategy for gradient slices. Works while using sparse gradients. Options include 'mean' and 'sum'. lars: lars offset. """ super(StormOptimizer, self).__init__() self.lr = lr self.momentum = momentum self.beta = beta self.grad_sq_init = grad_sq_init self.policy = policy self.sparse_dedup_aggregator = sparse_dedup_aggregator self.lars = lars self.init_kwargs = kwargs def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.lr <= 0: return self._clear_local_lr_multiplier() if self.lars is not None and not isinstance(grad, core.GradientSlice): assert self.lars >= 0, "Lars offset must be nonnegative, got {}".format( self.lars ) wd, trust, lr_max = self.create_lars_inputs( param_init_net, 0.0, 1.0, np.finfo(np.float32).max ) lr_lars_multiplier = net.Lars( [param, grad, wd, trust, lr_max], self.make_unique_blob_name(str(param) + "_lars"), offset=self.lars, lr_min=0.0, ) current_scope = scope.CurrentDeviceScope() self._add_local_lr_multiplier( lr_lars_multiplier, is_gpu_blob=( current_scope is not None and core.IsGPUDeviceType(current_scope.device_type) ), ) lr, _ = self.build_lr( net, param_init_net, base_learning_rate=self.lr, policy=self.policy, **(self.init_kwargs) ) moment = param_init_net.ConstantFill(param, str(param) + "_moment", value=0.0) self._aux_params.local.append(moment) grad_sq_sum = param_init_net.ConstantFill( [], str(param) + "_grad_sq_sum", shape=[1], value=self.grad_sq_init ) self._aux_params.local.append(grad_sq_sum) if isinstance(grad, core.GradientSlice): grad = self.dedup(net, self.sparse_dedup_aggregator, grad) net.SparseStorm( [param, moment, grad_sq_sum, grad.values, grad.indices, lr], [param, moment, grad_sq_sum], momentum=self.momentum, beta=self.beta, ) else: net.Storm( [param, moment, grad_sq_sum, grad, lr], [param, moment, grad_sq_sum], momentum=self.momentum, beta=self.beta, ) def scale_learning_rate(self, scale): self.lr *= scale class AdadeltaOptimizer(Optimizer): def __init__( self, alpha=0.01, epsilon=1e-4, decay=0.95, policy="fixed", sparse_dedup_aggregator=None, engine="", **kwargs ): """Constructor function to add Adadelta Optimizer Args: alpha: learning rate epsilon: attribute of Adadelta to avoid numerical issues decay: attribute of Adadelta to decay the squared gradient sum policy: specifies how learning rate should be applied, options are "fixed", "step", "exp", etc. sparse_dedup_aggregator: specifies deduplication strategy for gradient slices. Works while using sparse gradients. Options include "mean" and "sum". engine: the engine used, options include "", "CUDNN", etc. """ super(AdadeltaOptimizer, self).__init__() self.alpha = alpha self.epsilon = epsilon self.decay = decay self.policy = policy self.sparse_dedup_aggregator = sparse_dedup_aggregator self.engine = engine self.init_kwargs = kwargs def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.alpha <= 0: return lr, _ = self.build_lr( net, param_init_net, base_learning_rate=self.alpha, policy=self.policy, **(self.init_kwargs) ) moment = param_init_net.ConstantFill( [param], str(param) + "_squared_moment", value=0.0 ) moment_update = param_init_net.ConstantFill( [param], str(param) + "_squared_moment_update", value=0.0 ) self._aux_params.local.append(moment) self._aux_params.local.append(moment_update) if isinstance(grad, core.GradientSlice): grad = self.dedup(net, self.sparse_dedup_aggregator, grad) net.SparseAdadelta( [param, moment, moment_update, grad.indices, grad.values, lr], [param, moment, moment_update], epsilon=self.epsilon, decay=self.decay, engine=self.engine, ) else: net.Adadelta( [param, moment, moment_update, grad, lr], [param, moment, moment_update], epsilon=self.epsilon, decay=self.decay, engine=self.engine, ) def scale_learning_rate(self, scale): self.alpha *= scale return class FtrlOptimizer(Optimizer): def __init__( self, alpha=0.01, beta=1e-4, lambda1=0, lambda2=0, sparse_dedup_aggregator=None, engine="", ): super(FtrlOptimizer, self).__init__() self.alpha = alpha self.beta = beta self.lambda1 = lambda1 self.lambda2 = lambda2 self.sparse_dedup_aggregator = sparse_dedup_aggregator self.engine = engine def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.alpha <= 0: return nz = param_init_net.ConstantFill( [param], str(param) + "_ftrl_nz", extra_shape=[2], value=0.0 ) self._aux_params.local.append(nz) if isinstance(grad, core.GradientSlice): grad = self.dedup(net, self.sparse_dedup_aggregator, grad) net.SparseFtrl( [param, nz, grad.indices, grad.values], [param, nz], engine=self.engine, alpha=self.alpha, beta=self.beta, lambda1=self.lambda1, lambda2=self.lambda2, ) else: net.Ftrl( [param, nz, grad], [param, nz], engine=self.engine, alpha=self.alpha, beta=self.beta, lambda1=self.lambda1, lambda2=self.lambda2, ) def scale_learning_rate(self, scale): self.alpha *= scale return class GFtrlOptimizer(Optimizer): """Group Lasso FTRL Optimizer.""" def __init__( self, alpha=0.01, beta=1e-4, lambda1=0, lambda2=0, sparse_dedup_aggregator=None, engine="", ): super(GFtrlOptimizer, self).__init__() self.alpha = alpha self.beta = beta self.lambda1 = lambda1 self.lambda2 = lambda2 self.sparse_dedup_aggregator = sparse_dedup_aggregator self.engine = engine def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.alpha <= 0: return nz = param_init_net.ConstantFill( [param], str(param) + "_gftrl_nz", extra_shape=[2], value=0.0 ) self._aux_params.local.append(nz) net.GFtrl( [param, nz, grad], [param, nz], engine=self.engine, alpha=self.alpha, beta=self.beta, lambda1=self.lambda1, lambda2=self.lambda2, ) def scale_learning_rate(self, scale): self.alpha *= scale return class AdamOptimizer(Optimizer): def __init__( self, alpha=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, policy="fixed", use_lr_adaption=False, lr_alpha=0.01, normalized_lr_adaption=True, sparse_dedup_aggregator=None, rowWise=False, engine="", enableRAdam=False, use_smart_decay=False, # See https://fburl.com/2jdiwrhy for context. **kwargs ): super(AdamOptimizer, self).__init__() self.alpha = alpha self.beta1 = beta1 self.beta2 = beta2 self.epsilon = epsilon self.policy = policy self.use_lr_adaption = use_lr_adaption self.lr_alpha = lr_alpha self.normalized_lr_adaption = normalized_lr_adaption self.sparse_dedup_aggregator = sparse_dedup_aggregator self.rowWise = rowWise self.engine = engine self.enableRAdam = enableRAdam if use_smart_decay: if rowWise: raise NotImplementedError(('Smart decay is not implemented for rowWise Adam. ' 'Set rowWise or use_smart_decay to False.')) if enableRAdam: raise NotImplementedError(('Smart decay is not implemented for RAdam. ' 'Set enableRAdam or use_smart_decay to False.')) if use_lr_adaption: raise NotImplementedError(('Smart decay is not implemented with lr_adaption. ' 'Set use_lr_adaption or use_smart_decay to False.')) self.use_smart_decay = use_smart_decay self.init_kwargs = kwargs def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.alpha <= 0: return lr, iteration = self.build_lr( net, param_init_net, base_learning_rate=self.alpha, policy=self.policy, **(self.init_kwargs) ) m1 = param_init_net.ConstantFill([param], param + "_first_moment", value=0.0) if self.rowWise: shapes, types = workspace.InferShapesAndTypes([param_init_net]) m2 = param_init_net.ConstantFill( [], param + "_avg_second_moment", shape=[shapes[param][0]], value=0.0 ) else: m2 = param_init_net.ConstantFill( [param], param + "_second_moment", value=0.0 ) # Initialize "minibatch in which this parameter was last seen" for smart decay. if self.use_smart_decay: shapes, _ = workspace.InferShapesAndTypes([param_init_net]) last_seen = param_init_net.ConstantFill( [], param + "_last_seen", shape=[shapes[param][0]], value=0, dtype=core.DataType.INT64 ) self._aux_params.local.append(last_seen) self._aux_params.shared.append(iteration) self._aux_params.local.append(m1) self._aux_params.local.append(m2) if self.rowWise: assert isinstance(grad, core.GradientSlice), ( "If SparseAdam with rowWise=True, gradient must be " "a gradientslice. PLease ensure that rowWise is not enabled " "for the dense Adam optimizer, as it is not supported." ) output_blobs = [param, m1, m2] if self.use_smart_decay: output_blobs.append(last_seen) if self.use_lr_adaption: effective_grad = str(param) + "_effective_grad" output_blobs.append(effective_grad) if isinstance(grad, core.GradientSlice): grad = self.dedup(net, self.sparse_dedup_aggregator, grad) if self.rowWise: op = "RowWiseSparseAdam" elif self.use_smart_decay: op = "SmartDecaySparseAdam" else: op = "SparseAdam" # Currently, only SparseAdam support RAdam, other Adam Ops will support later if op == "SparseAdam": net.__getattr__(op)( [param, m1, m2, grad.indices, grad.values, lr, iteration], output_blobs, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon, enableRAdam=self.enableRAdam, ) elif op == "SmartDecaySparseAdam": net.__getattr__(op)( [param, m1, m2, last_seen, grad.indices, grad.values, lr, iteration], output_blobs, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon, ) else: assert ( not self.enableRAdam ), "Currently, RowWiseSparseAdam is not supported by RAdam!" net.__getattr__(op)( [param, m1, m2, grad.indices, grad.values, lr, iteration], output_blobs, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon, ) if self.use_lr_adaption: net.LearningRateAdaption( [lr, grad.values, effective_grad], [lr], lr_alpha=self.lr_alpha, normalized_lr_adaption=self.normalized_lr_adaption, ) else: net.Adam( [param, m1, m2, grad, lr, iteration], output_blobs, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon, ) if self.use_lr_adaption: net.LearningRateAdaption( [lr, grad, effective_grad], [lr], lr_alpha=self.lr_alpha, normalized_lr_adaption=self.normalized_lr_adaption, ) def scale_learning_rate(self, scale): self.alpha *= scale return class DecayAdagradOptimizer(Optimizer): def __init__( self, alpha=0.01, beta1=0.0, beta2=0.999, epsilon=0.1, weight_decay=0.0, ema_options=None, bias_correction_first=True, policy="fixed", engine="", **kwargs ): super(DecayAdagradOptimizer, self).__init__() self.alpha = alpha self.beta1 = beta1 self.beta2 = beta2 self.epsilon = epsilon self.weight_decay = weight_decay self.bias_correction_first = bias_correction_first self.policy = policy self.engine = engine self.init_kwargs = kwargs self._process_ema_options(ema_options) def _process_ema_options(self, ema_options): self.ema_enabled = True if ema_options else False if self.ema_enabled: self.ema_start = ema_options.get("ema_start", None) self.ema_end = ema_options.get("ema_end", None) self.ema_step = ema_options.get("ema_step", None) self.ema_alpha = ema_options.get("ema_alpha", None) def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad if self.alpha <= 0: return lr, iteration = self.build_lr( net, param_init_net, base_learning_rate=self.alpha, policy=self.policy, **(self.init_kwargs) ) if isinstance(grad, core.GradientSlice): # hack for position weighted. param_squared_sum = param_init_net.ConstantFill([param], param + "_squared_sum", value=0.0) self._aux_params.local.append(param_squared_sum) output_blobs = [param, param_squared_sum] net.SparseAdagrad( [param, param_squared_sum, grad.indices, grad.values, lr], output_blobs, epsilon=self.epsilon, ) else: m1 = param_init_net.ConstantFill([param], param + "_first_mo1ment", value=0.0) m2 = param_init_net.ConstantFill([param], param + "_second_moment", value=0.0) self._aux_params.shared.append(iteration) self._aux_params.local.append(m1) self._aux_params.local.append(m2) output_blobs = [param, m1, m2] net.DecayAdagrad( [param, m1, m2, grad, lr, iteration], output_blobs, beta1=self.beta1, beta2=self.beta2, epsilon=self.epsilon, weight_decay=self.weight_decay, bias_correction_first=self.bias_correction_first, ) if self.ema_enabled: param_ema = str(param) + "_ema" if not param_init_net.BlobIsDefined(param_ema): param_init_net.ConstantFill([param], param_ema, value=0.0) self._aux_params.local.append(param_ema) net.EMA( [param, param_ema, iteration], [param, param_ema], ema_start=self.ema_start, ema_end=self.ema_end, ema_step=self.ema_step, ema_alpha=self.ema_alpha, ) def scale_learning_rate(self, scale): self.alpha *= scale return class YellowFinOptimizer(Optimizer): """YellowFin: An automatic tuner for momentum SGD See https://arxiv.org/abs/1706.03471 for more details. This implementation has separate learning rate and momentum per each parameter.""" def __init__( self, alpha=0.1, mu=0.0, beta=0.999, curv_win_width=20, zero_debias=True, epsilon=0.1 ** 6, policy="fixed", sparse_dedup_aggregator=None, **kwargs ): super(YellowFinOptimizer, self).__init__() self.alpha = alpha self.mu = mu self.beta = beta self.curv_win_width = curv_win_width self.zero_debias = zero_debias self.epsilon = epsilon self.policy = policy self.sparse_dedup_aggregator = sparse_dedup_aggregator self.init_kwargs = kwargs def _run(self, net, param_init_net, param_info): # Note: This is number of persistent scalars in YellowFin optimizer. # It should always be the number of scalars being used. The same # number should be used in class for the operation. SCALARS_MEMORY_SIZE = 5 param = param_info.blob grad = param_info.grad moment = param_init_net.ConstantFill([param], param + "_moment", value=0.0) curv_win = param_init_net.ConstantFill( [], param + "_curv_win", shape=[self.curv_win_width], value=0.0 ) g_avg = param_init_net.ConstantFill([param], param + "_g_avg", value=0.0) g2_avg = param_init_net.ConstantFill([param], param + "_g2_avg", value=0.0) lr_avg = param_init_net.ConstantFill( [], param + "_lr_avg", shape=[1], value=self.alpha ) mu_avg = param_init_net.ConstantFill( [], param + "_mu_avg", shape=[1], value=self.mu ) scalars_memory = param_init_net.ConstantFill( [], param + "_scalars_memory", shape=[SCALARS_MEMORY_SIZE], value=0.0 ) assert self.alpha > 0 assert not isinstance( grad, core.GradientSlice ), "YellowFin does not support sparse gradients" iteration = utils.BuildUniqueMutexIter(param_init_net, net, iter_val=0) self._aux_params.shared.append(iteration) self._aux_params.local.append(moment) self._aux_params.local.append(lr_avg) self._aux_params.local.append(mu_avg) self._aux_params.local.append(curv_win) self._aux_params.local.append(g_avg) self._aux_params.local.append(g2_avg) self._aux_params.local.append(scalars_memory) yf_in_out_args = [ param, moment, lr_avg, mu_avg, curv_win, g_avg, g2_avg, scalars_memory, ] net.YellowFin( yf_in_out_args + [grad, iteration], yf_in_out_args, beta=self.beta, epsilon=self.epsilon, curv_win_width=self.curv_win_width, zero_debias=self.zero_debias, ) def scale_learning_rate(self, scale): self.alpha *= scale return class RmsPropOptimizer(Optimizer): def __init__( self, alpha=0.01, decay=0.9, momentum=0.0, epsilon=1e-5, policy="fixed", engine="", **kwargs ): super(RmsPropOptimizer, self).__init__() self.alpha = alpha self.decay = decay self.momentum = momentum self.epsilon = epsilon self.policy = policy self.engine = engine self.init_kwargs = kwargs def _run(self, net, param_init_net, param_info): param = param_info.blob grad = param_info.grad assert self.alpha > 0 assert not isinstance( grad, core.GradientSlice ), "RmsPropOptimizer doesn't support sparse gradients" dev = scope.CurrentDeviceScope() if dev is None: dev = core.DeviceOption(caffe2_pb2.CPU) ONE = param_init_net.ConstantFill( [], "ONE_{}_{}".format(dev.device_type, dev.device_id), shape=[1], value=1.0 ) lr, _ = self.build_lr( net, param_init_net, base_learning_rate=-self.alpha, policy=self.policy, **(self.init_kwargs) ) grad_o = param_init_net.ConstantFill( [param], str(param) + "_grad_o", values=0.0 ) ms = param_init_net.ConstantFill( [param], str(param) + "_mean_squares", values=0.0 ) mom = param_init_net.ConstantFill([param], str(param) + "_momentum", values=0.0) self._aux_params.local.append(ms) self._aux_params.local.append(mom) net.RmsProp( [grad, ms, mom, ONE], [grad_o, ms, mom], decay=self.decay, momentum=self.momentum, epsilon=self.epsilon, engine=self.engine, ) net.MomentumSGDUpdate([grad_o, mom, lr, param], [grad_o, mom, param]) def scale_learning_rate(self, scale): self.alpha *= scale return def _get_param_to_device(model): # Infer blob devices by going through the net and param_init_net # ops and observing the device used to create or use the blob. param_to_device = core.InferBlobDevices(model.net) param_to_device.update(core.InferBlobDevices(model.param_init_net)) return param_to_device def get_param_device(param_name, grad, param_to_device=None, default_device=None): device = default_device param_to_device = param_to_device or {} # We first check if parameter's device has been inferred. If not, # we check the gradient. This can happen if parameter is not output # by any blob but created by a FetchBlob. if param_name in param_to_device: device = param_to_device[param_name] else: if isinstance(grad, core.GradientSlice): grad = grad if str(grad.values) in param_to_device: device = param_to_device[str(grad.values)] elif str(grad.indices) in param_to_device: device = param_to_device[str(grad.indices)] else: grad_name = str(grad) if grad_name in param_to_device: device = param_to_device[grad_name] assert device is not None, "Cannot infer device for {}: no op creates it".format( param_name ) return device def get_lr_injection(): """ Gets current value for lr_injection, a multiplier for all base learning rates. Must set allow_lr_injection=True when building optimizer, as it relies on synchronization over CPU. """ return workspace.FetchBlob(_LEARNING_RATE_INJECTION) def set_lr_injection(lr_injection_value): """ Sets lr_injection, a multiplier for all base learning rates. Must set allow_lr_injection=True when building optimizer, as it relies on synchronization over CPU. """ workspace.FeedBlob( _LEARNING_RATE_INJECTION, np.array([float(lr_injection_value)], dtype=np.float32), ) def _calc_norm_ratio(model, params, name_scope, param_to_device, max_gradient_norm): with core.NameScope(name_scope): grad_squared_sums = [] for i, param in enumerate(params): device = get_param_device(str(param.blob), param.grad, param_to_device) with core.DeviceScope(device): grad = ( param.grad if not isinstance(param.grad, core.GradientSlice) else param.grad.values ) grad_squared_sum_name = "grad_{}_squared_sum".format(i) grad_squared_sum = model.net.SumSqrElements(grad, grad_squared_sum_name) grad_squared_sum_cpu = model.net.EnsureCPUOutput(grad_squared_sum) grad_squared_sums.append(grad_squared_sum_cpu) with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU)): grad_squared_full_sum = model.net.Sum( grad_squared_sums, "grad_squared_full_sum" ) global_norm = model.net.Pow( grad_squared_full_sum, "global_norm", exponent=0.5 ) clip_norm = model.param_init_net.ConstantFill( [], "clip_norm", shape=[], value=float(max_gradient_norm) ) max_norm = model.net.Max([global_norm, clip_norm], "max_norm") norm_ratio = model.net.Div([clip_norm, max_norm], "norm_ratio") return norm_ratio def _build( model, optimizer, weights_only=False, use_param_info_optim=True, max_gradient_norm=None, allow_lr_injection=False, ): param_to_device = _get_param_to_device(model) # Validate there are no duplicate params model.Validate() params = [] for param_info in model.GetOptimizationParamInfo(): if weights_only and param_info.blob not in model.weights: continue params.append(param_info) lr_multiplier = None if max_gradient_norm is not None: lr_multiplier = _calc_norm_ratio( model, params, "norm_clipped_grad_update", param_to_device, max_gradient_norm, ) if allow_lr_injection: if not model.net.BlobIsDefined(_LEARNING_RATE_INJECTION): lr_injection = model.param_init_net.ConstantFill( [], _LEARNING_RATE_INJECTION, shape=[1], value=1.0 ) else: lr_injection = _LEARNING_RATE_INJECTION if lr_multiplier is None: lr_multiplier = lr_injection else: lr_multiplier = model.net.Mul( [lr_multiplier, lr_injection], "lr_multiplier", broadcast=1 ) optimizer.add_lr_multiplier(lr_multiplier) for param_info in params: param_name = str(param_info.blob) device = get_param_device(param_name, param_info.grad, param_to_device) with core.DeviceScope(device): if param_info.optimizer and use_param_info_optim: param_info.optimizer(model.net, model.param_init_net, param_info) else: optimizer(model.net, model.param_init_net, param_info) return optimizer def add_weight_decay(model, weight_decay): """Adds a decay to weights in the model. This is a form of L2 regularization. Args: weight_decay: strength of the regularization """ _build( model, WeightDecayBuilder(weight_decay=weight_decay), weights_only=True, use_param_info_optim=False, ) def build_sgd( model, base_learning_rate, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): sgd_optimizer = SgdOptimizer(base_learning_rate, **kwargs) return _build( model, sgd_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_multi_precision_sgd( model, base_learning_rate, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): multi_prec_sgd_optimizer = MultiPrecisionSgdOptimizer(base_learning_rate, **kwargs) return _build( model, multi_prec_sgd_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_fp16_sgd(model, base_learning_rate, **kwargs): fp16_sgd_optimizer = FP16SgdOptimizer(base_learning_rate, **kwargs) return _build(model, fp16_sgd_optimizer) def build_ftrl(model, engine="SIMD", **kwargs): if engine == "SIMD": assert core.IsOperator("Ftrl_ENGINE_SIMD") assert core.IsOperator("SparseFtrl_ENGINE_SIMD") ftrl_optimizer = FtrlOptimizer(engine=engine, **kwargs) return _build(model, ftrl_optimizer) def build_gftrl(model, engine="", **kwargs): if engine == "SIMD": assert core.IsOperator("GFtrl_ENGINE_SIMD") gftrl_optimizer = GFtrlOptimizer(engine=engine, **kwargs) return _build(model, gftrl_optimizer) def build_adagrad( model, base_learning_rate, parameters=None, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): adagrad_optimizer = AdagradOptimizer(alpha=base_learning_rate, **kwargs) return _build( model, adagrad_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_wngrad( model, base_learning_rate, parameters=None, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): wngrad_optimizer = WngradOptimizer(alpha=base_learning_rate, **kwargs) return _build( model, wngrad_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_storm( model, base_learning_rate, parameters=None, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): storm_optimizer = StormOptimizer(lr=base_learning_rate, **kwargs) return _build( model, storm_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_adadelta( model, base_learning_rate, parameters=None, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): adadelta_optimizer = AdadeltaOptimizer(alpha=base_learning_rate, **kwargs) return _build( model, adadelta_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_adam( model, base_learning_rate, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): adam_optimizer = AdamOptimizer(alpha=base_learning_rate, **kwargs) return _build( model, adam_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_decay_adagrad( model, base_learning_rate, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): decay_adagrad_optimizer = DecayAdagradOptimizer(alpha=base_learning_rate, **kwargs) return _build( model, decay_adagrad_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, ) def build_yellowfin(model, base_learning_rate=0.1, **kwargs): yellowfin_optimizer = YellowFinOptimizer(alpha=base_learning_rate, **kwargs) return _build(model, yellowfin_optimizer) def build_rms_prop( model, base_learning_rate, max_gradient_norm=None, allow_lr_injection=False, **kwargs ): rms_prop_optimizer = RmsPropOptimizer(alpha=base_learning_rate, **kwargs) return _build( model, rms_prop_optimizer, max_gradient_norm=max_gradient_norm, allow_lr_injection=allow_lr_injection, )