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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.models.roberta.hub_interface import RobertaHubInterface
import torch
import torch.nn.functional as F
class XMODHubInterface(RobertaHubInterface):
def extract_features(
self,
tokens: torch.LongTensor,
return_all_hiddens: bool = False,
lang_id=None,
) -> torch.Tensor:
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
if tokens.size(-1) > self.model.max_positions():
raise ValueError(
"tokens exceeds maximum length: {} > {}".format(
tokens.size(-1), self.model.max_positions()
)
)
features, extra = self.model(
tokens.to(device=self.device),
features_only=True,
return_all_hiddens=return_all_hiddens,
lang_id=lang_id,
)
if return_all_hiddens:
# convert from T x B x C -> B x T x C
inner_states = extra["inner_states"]
return [inner_state.transpose(0, 1) for inner_state in inner_states]
else:
return features # just the last layer's features
def predict(
self,
head: str,
tokens: torch.LongTensor,
return_logits: bool = False,
lang_id=None,
):
features = self.extract_features(tokens.to(device=self.device), lang_id=lang_id)
logits = self.model.classification_heads[head](features)
if return_logits:
return logits
return F.log_softmax(logits, dim=-1)
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