# -*- 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 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)
[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._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()
[docs] 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,
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
[docs] @abstractmethod
def build_predict_graph(self):
pass
[docs] @abstractmethod
def build_loss_graph(self):
pass
[docs] def build_metric_graph(self, eval_config):
return self._metric_dict
[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)
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)):
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])):
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 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, 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]