def forward(self,
input_ids: torch.LongTensor,
attention_mask=None,
token_type_ids=None,
token_span=None,
**kwargs):
if self.word_dropout:
input_ids = self.word_dropout(input_ids)
x = transformer_encode(
self.transformer,
input_ids,
attention_mask,
token_type_ids,
token_span,
layer_range=self.scalar_mix.mixture_range
if self.scalar_mix else 0,
max_sequence_length=self.max_sequence_length,
average_subwords=self.average_subwords,
ret_raw_hidden_states=self.ret_raw_hidden_states)
if self.ret_raw_hidden_states:
x, raw_hidden_states = x
if self.scalar_mix:
x = self.scalar_mix(x)
if self.ret_raw_hidden_states:
# noinspection PyUnboundLocalVariable
return x, raw_hidden_states
return x
def run_transformer(self, input_ids, token_span):
return transformer_encode(
self.transformer,
input_ids,
None,
None,
token_span,
average_subwords=self.config.average_subwords)