class ElmoTokenEmbedderWrapper(TokenEmbedder):
"""
Wraps the Elmo call so that the parameters are saved correctly
Forwards all calls to Elmo
"""
def __init__(self,
options_file: str,
weight_file: str,
do_layer_norm: bool = False,
dropout: float = 0.5,
requires_grad: bool = False,
projection_dim: int = None,
num_output_representations: int = 1) -> None:
super(ElmoTokenEmbedderWrapper, self).__init__()
# other arguments can be passed in when needed
self._elmo = Elmo(options_file=options_file,
weight_file=weight_file,
num_output_representations=num_output_representations,
dropout=dropout)
def get_output_dim(self):
return self._elmo.get_output_dim()
def forward(self, inputs: torch.Tensor) -> Dict[str, torch.Tensor]: # pylint: disable=arguments-differ
return self._elmo(inputs)
# this is also deferred to elmo
@classmethod
def from_params(cls, params: Params):
self._elmo = Elmo.from_params(params)
return self
class ELMoEmbedding(nn.Module):
r"""
Contextual word embeddings using word-level bidirectional LM :cite:`peters-etal-2018-deep`.
Args:
model (str):
The name of the pretrained ELMo registered in `OPTION` and `WEIGHT`. Default: ``'original_5b'``.
bos_eos (tuple[bool]):
A tuple of two boolean values indicating whether to keep start/end boundaries of sentence outputs.
Default: ``(True, True)``.
n_out (int):
The requested size of the embeddings. If 0, uses the default size of ELMo outputs. Default: 0.
dropout (float):
The dropout ratio for the ELMo layer. Default: 0.
requires_grad (bool):
If ``True``, the model parameters will be updated together with the downstream task. Default: ``False``.
"""
OPTION = {
'small': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x1024_128_2048cnn_1xhighway/elmo_2x1024_128_2048cnn_1xhighway_options.json', # noqa
'medium': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x2048_256_2048cnn_1xhighway/elmo_2x2048_256_2048cnn_1xhighway_options.json', # noqa
'original': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_options.json', # noqa
'original_5b': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_options.json', # noqa
}
WEIGHT = {
'small': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x1024_128_2048cnn_1xhighway/elmo_2x1024_128_2048cnn_1xhighway_weights.hdf5', # noqa
'medium': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x2048_256_2048cnn_1xhighway/elmo_2x2048_256_2048cnn_1xhighway_weights.hdf5', # noqa
'original': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5', # noqa
'original_5b': 'https://s3-us-west-2.amazonaws.com/allennlp/models/elmo/2x4096_512_2048cnn_2xhighway_5.5B/elmo_2x4096_512_2048cnn_2xhighway_5.5B_weights.hdf5', # noqa
}
def __init__(self, model='original_5b', bos_eos=(True, True), n_out=0, dropout=0.5, requires_grad=False):
super().__init__()
from allennlp.modules import Elmo
self.elmo = Elmo(options_file=self.OPTION[model],
weight_file=self.WEIGHT[model],
num_output_representations=1,
dropout=dropout,
requires_grad=requires_grad,
keep_sentence_boundaries=True)
self.model = model
self.bos_eos = bos_eos
self.hidden_size = self.elmo.get_output_dim()
self.n_out = n_out or self.hidden_size
self.dropout = dropout
self.requires_grad = requires_grad
self.projection = nn.Linear(self.hidden_size, self.n_out, False) if self.hidden_size != n_out else nn.Identity()
def __repr__(self):
s = f"{self.model}, n_out={self.n_out}"
if self.dropout > 0:
s += f", dropout={self.dropout}"
if self.requires_grad:
s += f", requires_grad={self.requires_grad}"
return f"{self.__class__.__name__}({s})"
def forward(self, chars):
r"""
Args:
chars (~torch.Tensor): ``[batch_size, seq_len, fix_len]``.
Returns:
~torch.Tensor:
ELMo embeddings of shape ``[batch_size, seq_len, n_out]``.
"""
x = self.projection(self.elmo(chars)['elmo_representations'][0])
if not self.bos_eos[0]:
x = x[:, 1:]
if not self.bos_eos[1]:
x = x[:, :-1]
return x