# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) # # See ../../../../LICENSE for clarification regarding multiple authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn as nn import torch.nn.functional as F from scaling import ScaledLinear class Joiner(nn.Module): def __init__(self, encoder_dim: int, decoder_dim: int, joiner_dim: int, vocab_size: int): super().__init__() # We don't bother giving the 'initial_speed' arg to the decoder # submodules, because it does not affect the initial convergence of the # system (only the simple joiner is involved in that). self.encoder_proj = ScaledLinear(encoder_dim, joiner_dim) self.decoder_proj = ScaledLinear(decoder_dim, joiner_dim) self.output_linear = ScaledLinear(joiner_dim, vocab_size) def forward( self, encoder_out: torch.Tensor, decoder_out: torch.Tensor ) -> torch.Tensor: """ Args: encoder_out: Output from the encoder. Its shape is (N, T, s_range, C). decoder_out: Output from the decoder. Its shape is (N, T, s_range, C). Returns: Return a tensor of shape (N, T, s_range, C). """ assert encoder_out.ndim == decoder_out.ndim == 4 assert encoder_out.shape == decoder_out.shape logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out) logit = self.output_linear(torch.tanh(logit)) return logit