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"""
control_unit.py: Implementation of the Control Unit for the MAC network. Cf https://arxiv.org/abs/1803.03067 for the \
reference paper.
"""
__author__ = "Vincent Marois"
import torch
from torch.nn import Module
from miprometheus.models.mac.utils_mac import linear
[docs]class ControlUnit(Module):
"""
Implementation of the ``ControlUnit`` of the MAC network.
"""
[docs] def __init__(self, dim, max_step):
"""
Constructor for the control unit.
:param dim: global 'd' hidden dimension
:type dim: int
:param max_step: maximum number of steps -> number of MAC cells in the network.
:type max_step: int
"""
# call base constructor
super(ControlUnit, self).__init__()
# define the linear layers (one per step) used to make the questions
# encoding
self.pos_aware_layers = torch.nn.ModuleList()
for _ in range(max_step):
self.pos_aware_layers.append(linear(2 * dim, dim, bias=True))
# define the linear layer used to create the cqi values
self.ctrl_question = linear(2 * dim, dim, bias=True)
# define the linear layer used to create the attention weights. Should
# be one scalar weight per contextual word
self.attn = linear(dim, 1, bias=True)
self.step = 0
[docs] def forward(self, step, contextual_words, question_encoding, ctrl_state):
"""
Forward pass of the ``ControlUnit``.
:param step: index of the current MAC cell.
:type step: int
:param contextual_words: tensor of shape [batch_size x maxQuestionLength x dim] containing the words \
encodings ('representation of each word in the context of the question').
:type contextual_words: torch.tensor
:param question_encoding: question representation, of shape [batch_size x 2*dim].
:type question_encoding: torch.tensor
:param ctrl_state: previous control state, of shape [batch_size x dim]
:type ctrl_state: torch.tensor
:return: new control state, [batch_size x dim]
"""
self.step = step
# select current 'position aware' linear layer & pass questions through
# it
pos_aware_question_encoding = self.pos_aware_layers[step](
question_encoding)
cqi = torch.cat([ctrl_state, pos_aware_question_encoding], dim=-1)
cqi = self.ctrl_question(cqi) # [batch_size x dim]
# compute element-wise product between cqi & contextual words
# [batch_size x maxQuestionLength x dim]
context_ctrl = cqi.unsqueeze(1) * contextual_words
# compute attention weights
cai = self.attn(context_ctrl) # [batch_size x maxQuestionLength x 1]
self.cvi = torch.nn.functional.softmax(cai, dim=1)
# compute next control state
# [batch_size x dim]
next_ctrl_state = (self.cvi * contextual_words).sum(1)
return next_ctrl_state