adding more tests on TF and pytorch serialization - updating configuration for better serialization

transformers/modeling_tf_bert.py

@@ -30,7 +30,6 @@ import tensorflow as tf
 from .configuration_bert import BertConfig
 from .modeling_tf_utils import TFPreTrainedModel, get_initializer
 from .file_utils import add_start_docstrings
-from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
 
 logger = logging.getLogger(__name__)
 
@@ -52,14 +51,6 @@ TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
 }
 
 
-def load_bert_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
-    # build the network
-    inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
-    tf_inputs = tf.constant(inputs_list)
-    tfo = tf_model(tf_inputs, training=False)
-    return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=tf_inputs)
-
-
 def gelu(x):
     """ Gaussian Error Linear Unit.
     Original Implementation of the gelu activation function in Google Bert repo when initially created.
@@ -545,7 +536,6 @@ class TFBertPreTrainedModel(TFPreTrainedModel):
     """
     config_class = BertConfig
     pretrained_model_archive_map = TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
-    load_pt_weights = load_bert_pt_weights_in_tf2
     base_model_prefix = "bert"