Source code for easy_rec.python.model.easy_rec_model

# -*- encoding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.

import logging
import re
from abc import abstractmethod

import six
import tensorflow as tf
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops.variables import PartitionedVariable

from easy_rec.python.compat import regularizers
from easy_rec.python.layers import input_layer
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

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)


[docs]class EasyRecModel(six.with_metaclass(_meta_type, object)):
[docs] 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._feature_dict = features self._emb_reg = regularizers.l2_regularizer(self.embedding_regularization) self._l2_reg = regularizers.l2_regularizer(self.l2_regularization) # only used by model with wide feature groups, e.g. WideAndDeep self._wide_output_dim = -1 self._feature_configs = feature_configs self.build_input_layer(model_config, feature_configs) self._labels = labels self._prediction_dict = {} self._loss_dict = {} # add sample weight from inputs self._sample_weight = 1.0 if constant.SAMPLE_WEIGHT in features: self._sample_weight = features[constant.SAMPLE_WEIGHT]
@property def embedding_regularization(self): return self._base_model_config.embedding_regularization @property def kd(self): return self._base_model_config.kd @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 tf.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
[docs] 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, use_embedding_variable=model_config.use_embedding_variable, 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)
[docs] @abstractmethod def build_predict_graph(self): pass
[docs] @abstractmethod def build_loss_graph(self): pass
[docs] @abstractmethod def build_metric_graph(self, eval_config): pass
[docs] @abstractmethod def get_outputs(self): pass
[docs] 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
[docs] 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
[docs] def build_rtp_output_dict(self): """For exporting: get output nodes for RTP infering.""" return {}
[docs] 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) if ckpt_path.endswith('/') or tf.gfile.IsDirectory(ckpt_path + '/'): ckpt_path = estimator_utils.latest_checkpoint(ckpt_path) print('ckpt_path is model_dir, will use the latest checkpoint: %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 = 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, 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) 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 (not var_name.endswith('/AdamAsync_2') and not var_name.endswith('/AdamAsync_3')): 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 tf.gfile.Exists(ckpt_var_map_path): logging.warning('%s not exist' % ckpt_var_map_path) return name2var # load var map name_map = {} with open(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] var_map = {} for var_name in name2var: if var_name in name_map: in_ckpt_name = name_map[var_name] var_map[in_ckpt_name] = name2var[var_name] else: logging.warning('Failed to find in var_map_file(%s): %s' % (ckpt_var_map_path, var_name)) 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
[docs] 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
[docs] def get_grouped_vars(self): """Get grouped variables, each group will be optimized by a separate optimizer. Return: grouped_vars: list of list of variables """ raise NotImplementedError()