# -*- encoding: utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. import logging import os import re from abc import abstractmethod import six import tensorflow as tf from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from easy_rec.python.compat import regularizers from easy_rec.python.layers import input_layer from easy_rec.python.layers.backbone import Backbone from easy_rec.python.utils import constant from easy_rec.python.utils import estimator_utils from easy_rec.python.utils import restore_filter from easy_rec.python.utils.load_class import get_register_class_meta try: import horovod.tensorflow as hvd from sparse_operation_kit.experiment import raw_ops as dynamic_variable_ops from sparse_operation_kit import experiment as sok except Exception: dynamic_variable_ops = None sok = None try: from tensorflow.python.framework.load_library import load_op_library import easy_rec load_embed_lib_path = os.path.join(easy_rec.ops_dir, 'libload_embed.so') load_embed_lib = load_op_library(load_embed_lib_path) except Exception as ex: logging.warning('load libload_embed.so failed: %s' % str(ex)) load_embed_lib = None if tf.__version__ >= '2.0': tf = tf.compat.v1 _EASY_REC_MODEL_CLASS_MAP = {} _meta_type = get_register_class_meta( _EASY_REC_MODEL_CLASS_MAP, have_abstract_class=True) class EasyRecModel(six.with_metaclass(_meta_type, object)): def __init__(self, model_config, feature_configs, features, labels=None, is_training=False): self._base_model_config = model_config self._model_config = model_config self._is_training = is_training self._is_predicting = labels is None self._feature_dict = features # embedding variable parameters self._global_ev_params = None if model_config.HasField('ev_params'): self._global_ev_params = model_config.ev_params if self.embedding_regularization > 0: self._emb_reg = regularizers.l2_regularizer(self.embedding_regularization) else: self._emb_reg = None if self.l2_regularization > 0: self._l2_reg = regularizers.l2_regularizer(self.l2_regularization) else: self._l2_reg = None # only used by model with wide feature groups, e.g. WideAndDeep self._wide_output_dim = -1 if self.has_backbone: wide_dim = Backbone.wide_embed_dim(model_config.backbone) if wide_dim: self._wide_output_dim = wide_dim logging.info('set `wide_output_dim` to %d' % wide_dim) self._feature_configs = feature_configs self.build_input_layer(model_config, feature_configs) self._labels = labels self._prediction_dict = {} self._loss_dict = {} self._metric_dict = {} # add sample weight from inputs self._sample_weight = 1.0 if constant.SAMPLE_WEIGHT in features: self._sample_weight = features[constant.SAMPLE_WEIGHT] self._backbone_output = None self._backbone_net = self.build_backbone_network() def build_backbone_network(self): if self.has_backbone: return Backbone( self._base_model_config.backbone, self._feature_dict, input_layer=self._input_layer, l2_reg=self._l2_reg) return None @property def has_backbone(self): return self._base_model_config.HasField('backbone') @property def backbone(self): if self._backbone_output: return self._backbone_output if self._backbone_net: kwargs = { 'loss_dict': self._loss_dict, 'metric_dict': self._metric_dict, 'prediction_dict': self._prediction_dict, 'labels': self._labels, constant.SAMPLE_WEIGHT: self._sample_weight } return self._backbone_net(self._is_training, **kwargs) return None @property def embedding_regularization(self): return self._base_model_config.embedding_regularization @property def kd(self): return self._base_model_config.kd @property def feature_groups(self): return self._base_model_config.feature_groups @property def l2_regularization(self): model_config = getattr(self._base_model_config, self._base_model_config.WhichOneof('model')) l2_regularization = 0.0 if hasattr(model_config, 'dense_regularization') and \ model_config.HasField('dense_regularization'): # backward compatibility logging.warn( 'dense_regularization is deprecated, please use l2_regularization') l2_regularization = model_config.dense_regularization elif hasattr(model_config, 'l2_regularization'): l2_regularization = model_config.l2_regularization return l2_regularization def build_input_layer(self, model_config, feature_configs): self._input_layer = input_layer.InputLayer( feature_configs, model_config.feature_groups, wide_output_dim=self._wide_output_dim, ev_params=self._global_ev_params, embedding_regularizer=self._emb_reg, kernel_regularizer=self._l2_reg, variational_dropout_config=model_config.variational_dropout if model_config.HasField('variational_dropout') else None, is_training=self._is_training, is_predicting=self._is_predicting) @abstractmethod def build_predict_graph(self): pass @abstractmethod def build_loss_graph(self): pass def build_metric_graph(self, eval_config): return self._metric_dict @abstractmethod def get_outputs(self): pass def build_output_dict(self): """For exporting: get standard output nodes.""" outputs = {} for name in self.get_outputs(): if name not in self._prediction_dict: raise KeyError( 'output node {} not in prediction_dict, can not be exported'.format( name)) outputs[name] = self._prediction_dict[name] return outputs def build_feature_output_dict(self): """For exporting: get output feature nodes.""" outputs = {} for feature_name in self._feature_dict: out_name = 'feature_' + feature_name feature_value = self._feature_dict[feature_name] if isinstance(feature_value, tf.SparseTensor): sparse_values = feature_value.values if sparse_values.dtype != tf.string: sparse_values = tf.as_string(sparse_values) feature_value = tf.sparse_to_dense(feature_value.indices, feature_value.dense_shape, sparse_values, '') elif feature_value.dtype != tf.string: feature_value = tf.as_string(feature_value) feature_value = tf.reduce_join(feature_value, axis=-1, separator=',') outputs[out_name] = feature_value return outputs def build_rtp_output_dict(self): """For exporting: get output nodes for RTP infering.""" return {} def restore(self, ckpt_path, include_global_step=False, ckpt_var_map_path='', force_restore_shape_compatible=False): """Restore variables from ckpt_path. steps: 1. list the variables in graph that need to be restored 2. inspect checkpoint and find the variables that could restore from checkpoint substitute scope names in case necessary 3. call tf.train.init_from_checkpoint to restore the variables Args: ckpt_path: checkpoint path to restore from include_global_step: whether to restore global_step variable ckpt_var_map_path: variable map from graph variables to variables in a checkpoint each line consists of: variable name in graph variable name in ckpt force_restore_shape_compatible: if variable shape is incompatible, clip or pad variables in checkpoint, and then restore Returns: IncompatibleShapeRestoreHook if force_shape_compatible else None """ name2var_map = self._get_restore_vars(ckpt_var_map_path) logging.info('start to restore from %s' % ckpt_path) ckpt_reader = tf.train.NewCheckpointReader(ckpt_path) ckpt_var2shape_map = ckpt_reader.get_variable_to_shape_map() if not include_global_step: ckpt_var2shape_map.pop(tf.GraphKeys.GLOBAL_STEP, None) vars_in_ckpt = {} incompatible_shape_var_map = {} fail_restore_vars = [] for variable_name, variable in sorted(name2var_map.items()): if variable_name in ckpt_var2shape_map: print('restore %s' % variable_name) ckpt_var_shape = ckpt_var2shape_map[variable_name] if type(variable) == list: shape_arr = [x.get_shape() for x in variable] var_shape = list(shape_arr[0]) for x in shape_arr[1:]: var_shape[0] += x[0] var_shape = tensor_shape.TensorShape(var_shape) variable = variables.PartitionedVariable( variable_name, var_shape, variable[0].dtype, variable, partitions=[len(variable)] + [1] * (len(var_shape) - 1)) else: var_shape = variable.shape.as_list() if ckpt_var_shape == var_shape: vars_in_ckpt[variable_name] = list(variable) if isinstance( variable, variables.PartitionedVariable) else variable elif len(ckpt_var_shape) == len(var_shape): if force_restore_shape_compatible: # create a variable compatible with checkpoint to restore dtype = variable[0].dtype if isinstance(variable, list) else variable.dtype with tf.variable_scope('incompatible_shape_restore'): tmp_var = tf.get_variable( name=variable_name + '_T_E_M_P', shape=ckpt_var_shape, trainable=False, # add to a special collection for easy reference # by tf.get_collection('T_E_M_P_RESTROE') collections=['T_E_M_P_RESTROE'], dtype=dtype) vars_in_ckpt[variable_name] = tmp_var incompatible_shape_var_map[variable] = tmp_var print('incompatible restore %s[%s, %s]' % (variable_name, str(var_shape), str(ckpt_var_shape))) else: logging.warning( 'Variable [%s] is available in checkpoint, but ' 'incompatible shape with model variable.', variable_name) else: logging.warning( 'Variable [%s] is available in checkpoint, but ' 'incompatible shape dims with model variable.', variable_name) elif 'EmbeddingVariable' in str(type(variable)): if '%s-keys' % variable_name not in ckpt_var2shape_map: continue print('restore embedding_variable %s' % variable_name) from tensorflow.python.training import saver names_to_saveables = saver.BaseSaverBuilder.OpListToDict([variable]) saveable_objects = [] for name, op in names_to_saveables.items(): for s in saver.BaseSaverBuilder.SaveableObjectsForOp(op, name): saveable_objects.append(s) init_op = saveable_objects[0].restore([ckpt_path], None) variable._initializer_op = init_op elif type(variable) == list and 'EmbeddingVariable' in str( type(variable[0])): if '%s/part_0-keys' % variable_name not in ckpt_var2shape_map: continue print('restore partitioned embedding_variable %s' % variable_name) from tensorflow.python.training import saver for part_var in variable: names_to_saveables = saver.BaseSaverBuilder.OpListToDict([part_var]) saveable_objects = [] for name, op in names_to_saveables.items(): for s in saver.BaseSaverBuilder.SaveableObjectsForOp(op, name): saveable_objects.append(s) init_op = saveable_objects[0].restore([ckpt_path], None) part_var._initializer_op = init_op elif sok is not None and isinstance(variable, sok.DynamicVariable): print('restore dynamic_variable %s' % variable_name) keys, vals = load_embed_lib.load_kv_embed( task_index=hvd.rank(), task_num=hvd.size(), embed_dim=variable._dimension, var_name='embed-' + variable.name.replace('/', '__'), ckpt_path=ckpt_path) with ops.control_dependencies([variable._initializer_op]): variable._initializer_op = dynamic_variable_ops.dummy_var_assign( variable.handle, keys, vals) else: fail_restore_vars.append(variable_name) for variable_name in fail_restore_vars: if 'Momentum' not in variable_name: logging.warning('Variable [%s] is not available in checkpoint', variable_name) tf.train.init_from_checkpoint(ckpt_path, vars_in_ckpt) if force_restore_shape_compatible: return estimator_utils.IncompatibleShapeRestoreHook( incompatible_shape_var_map) else: return None def _get_restore_vars(self, ckpt_var_map_path): """Restore by specify variable map between graph variables and ckpt variables. Args: ckpt_var_map_path: variable map from graph variables to variables in a checkpoint each line consists of: variable name in graph variable name in ckpt Returns: the list of variables which need to restore from checkpoint """ # here must use global_variables, because variables such as moving_mean # and moving_variance is usually not trainable in detection models all_vars = tf.global_variables() PARTITION_PATTERN = '/part_[0-9]+' VAR_SUFIX_PATTERN = ':[0-9]$' name2var = {} for one_var in all_vars: var_name = re.sub(VAR_SUFIX_PATTERN, '', one_var.name) if re.search(PARTITION_PATTERN, var_name) and one_var._save_slice_info is not None: var_name = re.sub(PARTITION_PATTERN, '', var_name) is_part = True else: is_part = False if var_name in name2var: assert is_part, 'multiple vars: %s' % var_name name2var[var_name].append(one_var) else: name2var[var_name] = [one_var] if is_part else one_var if ckpt_var_map_path != '': if not gfile.Exists(ckpt_var_map_path): logging.warning('%s not exist' % ckpt_var_map_path) return name2var # load var map name_map = {} with gfile.GFile(ckpt_var_map_path, 'r') as fin: for one_line in fin: one_line = one_line.strip() line_tok = [x for x in one_line.split() if x != ''] if len(line_tok) != 2: logging.warning('Failed to process: %s' % one_line) continue name_map[line_tok[0]] = line_tok[1] update_map = {} old_keys = [] for var_name in name2var: if var_name in name_map: in_ckpt_name = name_map[var_name] update_map[in_ckpt_name] = name2var[var_name] old_keys.append(var_name) for tmp_key in old_keys: del name2var[tmp_key] name2var.update(update_map) return name2var else: var_filter, scope_update = self.get_restore_filter() if var_filter is not None: name2var = { var_name: name2var[var_name] for var in name2var if var_filter.keep(var.name) } # drop scope prefix if necessary if scope_update is not None: name2var = { scope_update(var_name): name2var[var_name] for var_name in name2var } return name2var def get_restore_filter(self): """Get restore variable filter. Return: filter: type of Filter in restore_filter.py scope_drop: type of ScopeDrop in restore_filter.py """ if len(self._base_model_config.restore_filters) == 0: return None, None for x in self._base_model_config.restore_filters: logging.info('restore will filter out pattern %s' % x) all_filters = [ restore_filter.KeywordFilter(x, True) for x in self._base_model_config.restore_filters ] return restore_filter.CombineFilter(all_filters, restore_filter.Logical.AND), None def get_grouped_vars(self, opt_num): """Group the vars into different optimization groups. Each group will be optimized by a separate optimizer. Args: opt_num: number of optimizers from easyrec config. Return: list of list of variables. """ assert opt_num == 2, 'could only support 2 optimizers, one for embedding, one for the other layers' embedding_vars = [] deep_vars = [] for tmp_var in variables.trainable_variables(): if tmp_var.name.startswith( 'input_layer') or '/embedding_weights' in tmp_var.name: embedding_vars.append(tmp_var) else: deep_vars.append(tmp_var) return [embedding_vars, deep_vars]