Source code for espnet2.asr.state_spaces.ff

# This code is derived from

"""Implementation of FFN block in the style of Transformers."""

from functools import partial

from torch import nn

from espnet2.asr.state_spaces.base import SequenceModule
from espnet2.asr.state_spaces.components import DropoutNd, LinearActivation

[docs]class FF(SequenceModule): def __init__( self, d_input, expand=2, d_output=None, transposed=False, activation="gelu", initializer=None, dropout=0.0, tie_dropout=False, ): super().__init__() self.d_output = d_input if d_output is None else d_output self.transposed = transposed d_inner = expand * d_input linear1 = LinearActivation( d_input, d_inner, transposed=transposed, activation=activation, initializer=initializer, activate=True, ) dropout_cls = ( partial(DropoutNd, transposed=self.transposed) if tie_dropout else nn.Dropout ) # dropout_cls = nn.Dropout2d if self.transposed else nn.Dropout drop = dropout_cls(dropout) if dropout > 0.0 else nn.Identity() linear2 = LinearActivation( d_inner, self.d_output, transposed=transposed, activation=None, initializer=initializer, activate=False, ) self.ff = nn.Sequential( linear1, drop, linear2, )
[docs] def forward(self, x, *args, **kwargs): return self.ff(x), None
[docs] def step(self, x, state, **kwargs): # x: [batch, d_input] if self.transposed: # expects: [batch, d_input, seq_len] return self.ff(x.unsqueeze(-1)).squeeze(-1), state else: return self.ff(x), state