我试图使用拥抱面变压器层来构建模型。但是，我一直遇到AttributeError: Tensor.op is meaningless when eager execution is enabled.
我不想禁用急切的执行，因为我听说它会干扰keras的其他功能。 （如果这是错误的，请随时进行纠正）。
这是一次尝试的删节代码（完整代码在https://colab.research.google.com/drive/1pnFDEQB4EuxNM1pSgbWJNKD2208dIIN0?usp=sharing）
from transformers.modeling_tf_bert import TFBertLayer
class TFBertEncoderAlter(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.output_hidden_states = config.output_hidden_states
self.layer = [TFBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
def call(self, inputs, training=False):
hidden_states, attention_mask, output_attentions = inputs
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
[hidden_states, attention_mask, output_attentions], training=training
)
hidden_states = layer_outputs[0]
if cast_bool_to_primitive(output_attentions) is True:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if cast_bool_to_primitive(output_attentions) is True:
outputs = outputs + (all_attentions,)
return outputs # outputs, (hidden states), (attentions)
P_trans11 = TFBertEncoderAlter(config, name='Encoder')
inputHiddenVals = tf.keras.Input(shape=[None, None], dtype=tf.float32, name='input_Q',
batch_size=None)
P_outputs = P_trans11((outt, None, None))
modelNew = tf.keras.Model(inputHiddenVals,P_outputs)
这是输出
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-35-8cd9393cb573> in <module>()
5
6 P_outputs = P_trans11((outt, None, None))
----> 7 modelNew = tf.keras.Model(inputHiddenVals,P_outputs)
6 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in __init__(self, *args, **kwargs)
165
166 def __init__(self, *args, **kwargs):
--> 167 super(Model, self).__init__(*args, **kwargs)
168 _keras_api_gauge.get_cell('model').set(True)
169 # Model must be created under scope of DistStrat it will be trained with.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in __init__(self, *args, **kwargs)
171 'inputs' in kwargs and 'outputs' in kwargs):
172 # Graph network
--> 173 self._init_graph_network(*args, **kwargs)
174 else:
175 # Subclassed network
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
454 self._self_setattr_tracking = False # pylint: disable=protected-access
455 try:
--> 456 result = method(self, *args, **kwargs)
457 finally:
458 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
252
253 if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
--> 254 base_layer_utils.create_keras_history(self._nested_outputs)
255
256 self._base_init(name=name, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py in create_keras_history(tensors)
184 keras_tensors: The Tensors found that came from a Keras Layer.
185 """
--> 186 _, created_layers = _create_keras_history_helper(tensors, set(), [])
187 return created_layers
188
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
210 if getattr(tensor, '_keras_history', None) is not None:
211 continue
--> 212 op = tensor.op # The Op that created this Tensor.
213 if op not in processed_ops:
214 if op.type.startswith('Sparse'):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in op(self)
1111 def op(self):
1112 raise AttributeError(
-> 1113 "Tensor.op is meaningless when eager execution is enabled.")
1114
1115 @property
AttributeError: Tensor.op is meaningless when eager execution is enabled.
这是另一种方法（完整的代码在https://colab.research.google.com/drive/1bieigPh98l9POzT3Tdz8DWDN_nh18YG1?usp=sharing）
from transformers.modeling_tf_bert import TFBertEncoder, TFBertMainLayer, TFBertLayer
from transformers.modeling_tf_utils import (
get_initializer,
keras_serializable,
shape_list,
)
from transformers.configuration_bert import BertConfig
class TFBertEncoder0(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = [TFBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module([hidden_states, attention_mask, head_mask], training=training)
hidden_states = layer_outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
return outputs # outputs, (hidden states), (attentions)
@keras_serializable
class TFBertMainLayerAlter4(tf.keras.layers.Layer):
config_class = BertConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.initializer_range = config.initializer_range
self.output_attentions = config.output_attentions
self.encoder = TFBertEncoder0(config, name="encoder")
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
raise NotImplementedError
def call(
self,
inputs,
training=False,
):
encoder_outputs = self.encoder(
[inputs, None, None], training=training
)
sequence_output = encoder_outputs[0]
outputs = (sequence_output,) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions)
P_trans11 = TFBertMainLayerAlter4(config3, name="roberta")
inputHiddenVals = tf.keras.Input(shape=[None, None], dtype=tf.float32, name='input_Q',
batch_size=None)
P_outputs = P_trans11(outt)
modelNew = tf.keras.Model(inputHiddenVals,P_outputs)
再次，相同的结果
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-43-79b3b73e5f5f> in <module>()
5
6 P_outputs = P_trans11(outt)
----> 7 modelNew = tf.keras.Model(inputHiddenVals,P_outputs)
6 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in __init__(self, *args, **kwargs)
165
166 def __init__(self, *args, **kwargs):
--> 167 super(Model, self).__init__(*args, **kwargs)
168 _keras_api_gauge.get_cell('model').set(True)
169 # Model must be created under scope of DistStrat it will be trained with.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in __init__(self, *args, **kwargs)
171 'inputs' in kwargs and 'outputs' in kwargs):
172 # Graph network
--> 173 self._init_graph_network(*args, **kwargs)
174 else:
175 # Subclassed network
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
454 self._self_setattr_tracking = False # pylint: disable=protected-access
455 try:
--> 456 result = method(self, *args, **kwargs)
457 finally:
458 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name, **kwargs)
252
253 if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
--> 254 base_layer_utils.create_keras_history(self._nested_outputs)
255
256 self._base_init(name=name, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py in create_keras_history(tensors)
184 keras_tensors: The Tensors found that came from a Keras Layer.
185 """
--> 186 _, created_layers = _create_keras_history_helper(tensors, set(), [])
187 return created_layers
188
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors, processed_ops, created_layers)
210 if getattr(tensor, '_keras_history', None) is not None:
211 continue
--> 212 op = tensor.op # The Op that created this Tensor.
213 if op not in processed_ops:
214 if op.type.startswith('Sparse'):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in op(self)
1111 def op(self):
1112 raise AttributeError(
-> 1113 "Tensor.op is meaningless when eager execution is enabled.")
1114
1115 @property
AttributeError: Tensor.op is meaningless when eager execution is enabled.
这是另一种尝试（此处的完整代码为https://colab.research.google.com/drive/1UVJ7XSx0vXpgApe6E7ECVb9LNSJN_D9-?usp=sharing）
from transformers.modeling_tf_bert import TFBertLayer
l1 = TFBertLayer(config)
l2 = TFBertLayer(config)
inputHiddenVals = tf.keras.Input(shape=[None, None], dtype=tf.float32, name='input_Q',
batch_size=None)
P_outputs = l1((outt, None, None))[0]
P_outputs2 = l2((outt, None, None))[0]
modelNew = tf.keras.Model(inputHiddenVals,P_outputs2)
同样，结果相同。