def __call__(self, q_head, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat,
r_w_bias, r_r_bias, r_s_bias, attn_mask, **kwargs):
inputs = tf_utils.pack_inputs([
q_head, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat, r_w_bias,
r_r_bias, r_s_bias, attn_mask
])
return super(RelativeAttention, self).__call__(inputs, **kwargs)
def __call__(self, h, g, r, r_w_bias, r_r_bias, seg_mat, r_s_bias, seg_embed,
attn_mask_h, attn_mask_g, mems, target_mapping, **kwargs):
inputs = tf_utils.pack_inputs([
h, g, r, r_w_bias, r_r_bias, seg_mat, r_s_bias, seg_embed, attn_mask_h,
attn_mask_g, mems, target_mapping,
])
return super(RelativeMultiheadAttention, self).__call__(inputs, **kwargs)
def __call__(self,
input_word_ids,
input_mask=None,
input_type_ids=None,
**kwargs):
inputs = tf_utils.pack_inputs([input_word_ids, input_mask, input_type_ids])
return super(BertModel, self).__call__(inputs, **kwargs)
def __call__(self,
pooled_output,
sequence_output=None,
masked_lm_positions=None):
inputs = tf_utils.pack_inputs(
[pooled_output, sequence_output, masked_lm_positions])
return super(BertPretrainLayer, self).__call__(inputs)
def __call__(self,
lm_output,
sentence_output=None,
lm_label_ids=None,
lm_label_weights=None,
sentence_labels=None):
inputs = tf_utils.pack_inputs([
lm_output, sentence_output, lm_label_ids, lm_label_weights,
sentence_labels
])
return super(BertPretrainLossAndMetricLayer, self).__call__(inputs)
def __call__(self, input_tensor, attention_mask=None, **kwargs):
inputs = tf_utils.pack_inputs([input_tensor, attention_mask])
return super(TransformerBlock, self).__call__(inputs, **kwargs)
def __call__(self, from_tensor, to_tensor, attention_mask=None, **kwargs):
inputs = tf_utils.pack_inputs([from_tensor, to_tensor, attention_mask])
return super(Attention, self).__call__(inputs, **kwargs)
def __call__(self, word_embeddings, token_type_ids=None, **kwargs):
inputs = tf_utils.pack_inputs([word_embeddings, token_type_ids])
return super(EmbeddingPostprocessor, self).__call__(inputs, **kwargs)
def __call__(self, hidden, target, lookup_table, target_mask, **kwargs): inputs = tf_utils.pack_inputs([hidden, target, lookup_table, target_mask]) return super(LMLossLayer, self).__call__(inputs, **kwargs)
def __call__(self, hidden, labels, **kwargs): inputs = tf_utils.pack_inputs([hidden, labels]) return super(ClassificationLossLayer, self).__call__(inputs, **kwargs)
def __call__(self, word_embeddings, token_type_ids=None, **kwargs):
inputs = tf_utils.pack_inputs([word_embeddings, token_type_ids])
return super(EmbeddingPostprocessor, self).__call__(inputs, **kwargs) # pytype: disable=attribute-error # typed-keras
