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import torch
import torch.nn as nn
# Adopted from allennlp (https://github.com/allenai/allennlp/blob/master/allennlp/nn/util.py)
def masked_log_softmax(vector: torch.Tensor, mask: torch.Tensor, dim: int = -1) -> torch.Tensor:
if mask is not None:
mask = mask.float()
while mask.dim() < vector.dim():
mask = mask.unsqueeze(1)
# vector + mask.log() is an easy way to zero out masked elements in logspace, but it
# results in nans when the whole vector is masked. We need a very small value instead of a
# zero in the mask for these cases. log(1 + 1e-45) is still basically 0, so we can safely
# just add 1e-45 before calling mask.log(). We use 1e-45 because 1e-46 is so small it
# becomes 0 - this is just the smallest value we can actually use.
vector = vector + (mask + 1e-45).log()
return torch.nn.functional.log_softmax(vector, dim=dim)
# Adopted from allennlp (https://github.com/allenai/allennlp/blob/master/allennlp/nn/util.py)
def masked_max(vector: torch.Tensor,
mask: torch.Tensor,
dim: int,
keepdim: bool = False,
min_val: float = -1e7) -> (torch.Tensor, torch.Tensor):
one_minus_mask = (1.0 - mask).byte()
replaced_vector = vector.masked_fill(one_minus_mask, min_val)
max_value, max_index = replaced_vector.max(dim=dim, keepdim=keepdim)
return max_value, max_index
class Encoder(nn.Module):
def __init__(self, embedding_dim, hidden_size, num_layers=1, batch_first=True, bidirectional=True):
super(Encoder, self).__init__()
self.batch_first = batch_first
self.rnn1 = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_size, num_layers=num_layers,
batch_first=batch_first, bidirectional=bidirectional)
self.rnn2 = nn.LSTM(input_size=embedding_dim*4, hidden_size=hidden_size, num_layers=num_layers,
batch_first=batch_first, bidirectional=bidirectional)
# self.scale = torch.sqrt(torch.FloatTensor([hidden_size])).to(device)
def forward(self, embedded_inputs, input_lengths):
# Pack padded batch of sequences for RNN module
packed = nn.utils.rnn.pack_padded_sequence(embedded_inputs, input_lengths, batch_first=self.batch_first)
# Forward pass through RNN
outputs1, hidden1 = self.rnn1(packed)
outputs, hidden = self.rnn2(outputs1)
# Unpack padding
outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=self.batch_first)
# Return output and final hidden state
# self.dropout(self)
return outputs, hidden
class Attention(nn.Module):
def __init__(self, hidden_size):
super(Attention, self).__init__()
self.hidden_size = hidden_size
self.W1 = nn.Linear(hidden_size, hidden_size, bias=False)
self.W2 = nn.Linear(hidden_size, hidden_size, bias=False)
self.vt = nn.Linear(hidden_size, 1, bias=False)
def forward(self, decoder_state, encoder_outputs, mask):
# (batch_size, max_seq_len, hidden_size)
encoder_transform = self.W1(encoder_outputs)
# (batch_size, 1 (unsqueezed), hidden_size)
decoder_transform = self.W2(decoder_state).unsqueeze(1)
# 1st line of Eq.(3) in the paper
# (batch_size, max_seq_len, 1) => (batch_size, max_seq_len)
u_i = self.vt(torch.tanh(encoder_transform + decoder_transform)).squeeze(-1)
# softmax with only valid inputs, excluding zero padded parts
# log-softmax for a better numerical stability
log_score = masked_log_softmax(u_i, mask, dim=-1)
return log_score
class PointerNet(nn.Module):
def __init__(self, input_dim, embedding_dim, hidden_size, bidirectional=True, batch_first=True):
super(PointerNet, self).__init__()
# Embedding dimension
self.embedding_dim1 = embedding_dim
self.embedding_dim2 = embedding_dim
# (Decoder) hidden size
self.hidden_size = hidden_size
# Bidirectional Encoder
self.bidirectional = bidirectional
self.num_directions = 2 if bidirectional else 1
self.num_layers = 2
self.batch_first = batch_first
# We use an embedding layer for more complicate application usages later, e.g., word sequences.
self.embedding1 = nn.Linear(in_features=input_dim, out_features=embedding_dim, bias=True)
# self.ln1 = nn.LayerNorm(512)
self.embedding2 = nn.Linear(in_features=embedding_dim, out_features=512, bias=True)
# self.ln2 = nn.LayerNorm(512)
self.embedding3 = nn.Linear(in_features=512, out_features=int(embedding_dim/2), bias=True)
self.encoder = Encoder(embedding_dim=int(embedding_dim/2), hidden_size=hidden_size, num_layers=self.num_layers,
bidirectional=bidirectional, batch_first=batch_first)
self.decoding_rnn1 = nn.LSTMCell(input_size=hidden_size, hidden_size=hidden_size)
self.decoding_rnn2 = nn.LSTMCell(input_size=hidden_size, hidden_size=hidden_size)
self.attn = Attention(hidden_size=hidden_size)
for m in self.modules():
if isinstance(m, nn.Linear):
if m.bias is not None:
torch.nn.init.zeros_(m.bias)
def forward(self, input_seq, input_lengths):
if self.batch_first:
batch_size = input_seq.size(0)
max_seq_len = input_seq.size(1)
else:
batch_size = input_seq.size(1)
max_seq_len = input_seq.size(0)
# Embedding
embedded1 = self.embedding1(input_seq)
# ln1 = self.ln1(embedded1)
embedded2 = self.embedding2(embedded1)
# ln2 = self.ln2(embedded2)
embedded3 = self.embedding3(embedded2)
# (batch_size, max_seq_len, embedding_dim)
# encoder_output => (batch_size, max_seq_len, hidden_size) if batch_first else (max_seq_len, batch_size, hidden_size)
# hidden_size is usually set same as embedding size
# encoder_hidden => (num_layers * num_directions, batch_size, hidden_size) for each of h_n and c_n
encoder_outputs, encoder_hidden = self.encoder(embedded3, input_lengths)
if self.bidirectional:
# Optionally, Sum bidirectional RNN outputs
encoder_outputs = encoder_outputs[:, :, :self.hidden_size] + encoder_outputs[:, :, self.hidden_size:]
encoder_h_n, encoder_c_n = encoder_hidden
encoder_h_n = encoder_h_n.view(self.num_layers, self.num_directions, batch_size, self.hidden_size)
encoder_c_n = encoder_c_n.view(self.num_layers, self.num_directions, batch_size, self.hidden_size)
# Lets use zeros as an intial input for sorting example
decoder_input = encoder_outputs.new_zeros(torch.Size((batch_size, self.hidden_size)))
decoder_hidden = (encoder_h_n[-1, 0, :, :].squeeze(), encoder_c_n[-1, 0, :, :].squeeze())
range_tensor = torch.arange(max_seq_len, device=input_lengths.device, dtype=input_lengths.dtype).expand(batch_size, max_seq_len, max_seq_len)
each_len_tensor = input_lengths.view(-1, 1, 1).expand(batch_size, max_seq_len, max_seq_len)
row_mask_tensor = (range_tensor < each_len_tensor)
col_mask_tensor = row_mask_tensor.transpose(1, 2)
mask_tensor = row_mask_tensor * col_mask_tensor
pointer_log_scores = []
pointer_argmaxs = []
for i in range(max_seq_len):
# We will simply mask out when calculating attention or max (and loss later)
# not all input and hiddens, just for simplicity
sub_mask = mask_tensor[:, i, :].float()
# h, c: (batch_size, hidden_size)
h_i1, c_i1 = self.decoding_rnn1(decoder_input, decoder_hidden)
h_i, c_i = self.decoding_rnn2(h_i1, decoder_hidden)
# next hidden
decoder_hidden = (h_i, c_i)
# Get a pointer distribution over the encoder outputs using attention
# (batch_size, max_seq_len)
log_pointer_score = self.attn(h_i, encoder_outputs, sub_mask)
pointer_log_scores.append(log_pointer_score)
# Get the indices of maximum pointer
_, masked_argmax = masked_max(log_pointer_score, sub_mask, dim=1, keepdim=True)
pointer_argmaxs.append(masked_argmax)
index_tensor = masked_argmax.unsqueeze(-1).expand(batch_size, 1, self.hidden_size)
# (batch_size, hidden_size)
decoder_input = torch.gather(encoder_outputs, dim=1, index=index_tensor).squeeze(1)
pointer_log_scores = torch.stack(pointer_log_scores, 1)
pointer_argmaxs = torch.cat(pointer_argmaxs, 1)
return pointer_log_scores, pointer_argmaxs, mask_tensor