def forward(self, input, lengths=None, hidden=None):
""" See :obj:`EncoderBase.forward()`"""
self._check_args(input, lengths, hidden)
emb = self.embeddings(input)
s_len, n_batch, emb_dim = emb.size()
out = emb.transpose(0, 1).contiguous()
words = input[:, :, 0].transpose(0, 1)
# CHECKS
out_batch, out_len, _ = out.size()
w_batch, w_len = words.size()
aeq(out_batch, w_batch)
aeq(out_len, w_len)
# END CHECKS
# Make mask.
padding_idx = self.embeddings.word_padding_idx
mask = words.data.eq(padding_idx).unsqueeze(1) \
.expand(w_batch, w_len, w_len)
# Run the forward pass of every layer of the tranformer.
for i in range(self.num_layers):
out = self.transformer[i](out, mask)
out = self.layer_norm(out)
return Variable(emb.data), out.transpose(0, 1).contiguous()
def _run_forward_pass(self, tgt, memory_bank, state, memory_lengths=None):
"""
Private helper for running the specific RNN forward pass.
Must be overriden by all subclasses.
Args:
tgt (LongTensor): a sequence of input tokens tensors
[len x batch x nfeats].
memory_bank (FloatTensor): output(tensor sequence) from the encoder
RNN of size (src_len x batch x hidden_size).
state (FloatTensor): hidden state from the encoder RNN for
initializing the decoder.
memory_lengths (LongTensor): the source memory_bank lengths.
Returns:
decoder_final (Variable): final hidden state from the decoder.
decoder_outputs ([FloatTensor]): an array of output of every time
step from the decoder.
attns (dict of (str, [FloatTensor]): a dictionary of different
type of attention Tensor array of every time
step from the decoder.
"""
assert not self._copy # TODO, no support yet.
assert not self._coverage # TODO, no support yet.
# Initialize local and return variables.
attns = {}
emb = self.embeddings(tgt)
# Run the forward pass of the RNN.
if isinstance(self.rnn, nn.GRU):
rnn_output, decoder_final = self.rnn(emb, state.hidden[0])
else:
rnn_output, decoder_final = self.rnn(emb, state.hidden)
# Check
tgt_len, tgt_batch, _ = tgt.size()
output_len, output_batch, _ = rnn_output.size()
aeq(tgt_len, output_len)
aeq(tgt_batch, output_batch)
# END
# Calculate the attention.
decoder_outputs, p_attn = self.attn(
rnn_output.transpose(0, 1).contiguous(),
memory_bank.transpose(0, 1),
memory_lengths=memory_lengths
)
attns["std"] = p_attn
# Calculate the context gate.
if self.context_gate is not None:
decoder_outputs = self.context_gate(
emb.view(-1, emb.size(2)),
rnn_output.view(-1, rnn_output.size(2)),
decoder_outputs.view(-1, decoder_outputs.size(2))
)
decoder_outputs = \
decoder_outputs.view(tgt_len, tgt_batch, self.hidden_size)
decoder_outputs = self.dropout(decoder_outputs)
return decoder_final, decoder_outputs, attns
def _example_dict_iter(self, line, index):
line = line.split()
if self.line_truncate:
line = line[:self.line_truncate]
words, feats, n_feats = TextDataset.extract_text_features(line)
example_dict = {self.side: words, "indices": index}
if feats:
# All examples must have same number of features.
aeq(self.n_feats, n_feats)
prefix = self.side + "_feat_"
example_dict.update((prefix + str(j), f)
for j, f in enumerate(feats))
return example_dict
def _example_dict_iter(self, line, index):
line = line.split()
if self.line_truncate:
line = line[:self.line_truncate]
words, feats, n_feats = TextDataset.extract_text_features(line)
example_dict = {self.side: words, "indices": index}
if feats:
# All examples must have same number of features.
aeq(self.n_feats, n_feats)
prefix = self.side + "_feat_"
example_dict.update(
(prefix + str(j), f) for j, f in enumerate(feats))
return example_dict
def forward(self, input, context, state, context_lengths=None):
"""
Forward through the decoder.
Args:
input (LongTensor): a sequence of input tokens tensors
of size (len x batch x nfeats).
context (FloatTensor): output(tensor sequence) from the encoder
RNN of size (src_len x batch x hidden_size).
state (FloatTensor): hidden state from the encoder RNN for
initializing the decoder.
context_lengths (LongTensor): the source context lengths.
Returns:
outputs (FloatTensor): a Tensor sequence of output from the decoder
of shape (len x batch x hidden_size).
state (FloatTensor): final hidden state from the decoder.
attns (dict of (str, FloatTensor)): a dictionary of different
type of attention Tensor from the decoder
of shape (src_len x batch).
"""
# Args Check
assert isinstance(state, RNNDecoderState)
input_len, input_batch, _ = input.size()
contxt_len, contxt_batch, _ = context.size()
aeq(input_batch, contxt_batch)
# END Args Check
# Run the forward pass of the RNN.
hidden, outputs, attns, coverage, rnn_output, emb = \
self._run_forward_pass(input, context, state,
context_lengths=context_lengths)
# Update the state with the result.
final_output = outputs[-1]
state.update_state(hidden, final_output.unsqueeze(0),
coverage.unsqueeze(0)
if coverage is not None else None)
# Concatenates sequence of tensors along a new dimension.
outputs = torch.stack(outputs)
rnn_output = torch.stack(rnn_output)
for k in attns:
attns[k] = torch.stack(attns[k])
return (
outputs, state, attns,
# pointer_gen
rnn_output, emb
)
def _check_args_double_enc(self,
input,
lengths_src=None,
lengths_inter=None,
hidden_src=None,
hidden_inter=None):
#SRC
s_len, n_batch, n_feats = input[0].size()
if lengths_src is not None:
n_batch_, = lengths_src.size()
aeq(n_batch, n_batch_)
#INTER
s_len, n_batch, n_feats = input[1].size()
if lengths_inter is not None:
n_batch_, = lengths_inter.size()
aeq(n_batch, n_batch_)
def forward(self, tgt, memory_bank, state, memory_lengths=None):
"""
Args:
tgt (`LongTensor`): sequences of padded tokens
`[tgt_len x batch x nfeats]`.
memory_bank (`FloatTensor`): vectors from the encoder
`[src_len x batch x hidden]`.
state (:obj:`onmt.Models.DecoderState`):
decoder state object to initialize the decoder
memory_lengths (`LongTensor`): the padded source lengths
`[batch]`.
Returns:
(`FloatTensor`,:obj:`onmt.Models.DecoderState`,`FloatTensor`):
* decoder_outputs: output from the decoder (after attn)
`[tgt_len x batch x hidden]`.
* decoder_state: final hidden state from the decoder
* attns: distribution over src at each tgt
`[tgt_len x batch x src_len]`.
"""
# Check
assert isinstance(state, RNNDecoderState)
tgt_len, tgt_batch, _ = tgt.size()
_, memory_batch, _ = memory_bank.size()
aeq(tgt_batch, memory_batch)
# END
# Run the forward pass of the RNN.
decoder_final, decoder_outputs, attns = self._run_forward_pass(
tgt, memory_bank, state, memory_lengths=memory_lengths)
# Update the state with the result.
final_output = decoder_outputs[-1]
coverage = None
if "coverage" in attns:
coverage = attns["coverage"][-1].unsqueeze(0)
state.update_state(decoder_final, final_output.unsqueeze(0), coverage)
# Concatenates sequence of tensors along a new dimension.
# Change for torch0.4
if type(decoder_outputs
) is not torch.Tensor: # If input feeding is being used
decoder_outputs = torch.stack(decoder_outputs)
for k in attns:
if type(attns[k]) is not torch.Tensor:
attns[k] = torch.stack(attns[k])
return decoder_outputs, state, attns
def forward(self, input, context, state,
context_lengths=None, **kwargs):
"""
Args:
input (`LongTensor`): sequences of padded tokens
`[tgt_len x batch x nfeats]`.
context (`FloatTensor`): vectors from the encoder
`[src_len x batch x hidden]`.
state (:obj:`onmt.Models.DecoderState`):
decoder state object to initialize the decoder
context_lengths (`LongTensor`): the padded source lengths
`[batch]`.
Returns:
(`FloatTensor`,:obj:`onmt.Models.DecoderState`,`FloatTensor`):
* outputs: output from the decoder
`[tgt_len x batch x hidden]`.
* state: final hidden state from the decoder
* attns: distribution over src at each tgt
`[tgt_len x batch x src_len]`.
"""
# Args Check
assert isinstance(state, RNNDecoderState)
input_len, input_batch, _ = input.size()
contxt_len, contxt_batch, _ = context.size()
aeq(input_batch, contxt_batch)
# END Args Check
# Run the forward pass of the RNN.
# All the latent variables and additional inputs are found in kwargs
hidden, outputs, attns, coverage = self._run_forward_pass(
input, context, state,
context_lengths=context_lengths,
**kwargs)
# Update the state with the result.
final_output = outputs[-1]
state.update_state(hidden, final_output.unsqueeze(0),
coverage.unsqueeze(0)
if coverage is not None else None)
# Concatenates sequence of tensors along a new dimension.
outputs = torch.stack(outputs)
for k in attns:
if not k in ["q_latent", "p_latent"]:
attns[k] = torch.stack(attns[k])
return outputs, state, attns
def forward_mm(self, input, img_proj, lengths=None, hidden=None):
"""
Args:
input (:obj:`LongTensor`):
padded sequences of sparse indices `[src_len x batch x nfeat]`
lengths (:obj:`LongTensor`): length of each sequence `[batch]`
hidden (class specific):
initial hidden state.
Returns:k
(tuple of :obj:`FloatTensor`, :obj:`FloatTensor`):
* final encoder state, used to initialize decoder
`[layers x batch x hidden]`
* contexts for attention, `[src_len x batch x hidden]`
"""
self._check_args(input, lengths, hidden)
emb = self.embeddings(input)
s_len, n_batch, emb_dim = emb.size()
emb = emb.transpose(0, 1).contiguous() # (batch, src_len, nfeat)
input_mm = torch.cat([emb, img_proj], dim=1)
out = input_mm
# words = input[:, :, 0].transpose(0, 1) # (batch, src_len)
words = emb[:, :, 0]
# CHECKS
out_batch, out_len, _ = out.size()
w_batch, w_len = words.size()
aeq(out_batch, w_batch)
aeq(s_len, w_len)
# END CHECKS
# Make mask. the mask here is no use
padding_idx = self.embeddings.word_padding_idx
mask = words.data.eq(padding_idx).unsqueeze(1) \
.expand(w_batch, out_len, s_len)
# assert not words.data.eq(padding_idx).max(), "there are some mask items eqaul to 1"
# Run the forward pass of every layer of the tranformer.
for i in range(self.num_layers):
out, attn = self.transformer[i](emb, out, mask) # attn 1x49x10
out = self.layer_norm(out)
return Variable(input_mm.data), out.transpose(0, 1).contiguous(), attn
def forward(self, hidden, attn, src_map, align=None, ptrs=None):
"""
Compute a distribution over the target dictionary
extended by the dynamic dictionary implied by compying
source words.
Args:
hidden (`FloatTensor`): hidden outputs `[batch*tlen, input_size]`
attn (`FloatTensor`): attn for each `[batch*tlen, input_size]`
src_map (`FloatTensor`):
A sparse indicator matrix mapping each source word to
its index in the "extended" vocab containing.
`[src_len, batch, extra_words]`
"""
# CHECKS
batch_by_tlen, _ = hidden.size()
batch_by_tlen_, slen = attn.size()
slen_, batch, cvocab = src_map.size()
aeq(batch_by_tlen, batch_by_tlen_)
aeq(slen, slen_)
# Original probabilities.
logits = self.linear(hidden)
logits[:, self.tgt_dict.stoi[onmt.io.PAD_WORD]] = -float('inf')
prob = F.softmax(logits)
# Probability of copying p(z=1) batch.
p_copy = F.sigmoid(self.linear_copy(hidden))
# Probibility of not copying: p_{word}(w) * (1 - p(z))
if self.training:
align_unk = align.eq(0).float().view(-1, 1)
align_not_unk = align.ne(0).float().view(-1, 1)
out_prob = torch.mul(prob, align_unk.expand_as(prob))
mul_attn = torch.mul(attn, align_not_unk.expand_as(attn))
mul_attn = torch.mul(mul_attn, ptrs.view(-1, slen_).float())
else:
out_prob = torch.mul(prob, 1 - p_copy.expand_as(prob))
mul_attn = torch.mul(attn, p_copy.expand_as(attn))
copy_prob = torch.bmm(
mul_attn.view(-1, batch, slen).transpose(0, 1),
src_map.transpose(0, 1)).transpose(0, 1)
copy_prob = copy_prob.contiguous().view(-1, cvocab)
return torch.cat([out_prob, copy_prob], 1), p_copy
def score(self, h_t, h_s, entity_attn=False):
"""
Args:
h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]`
h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]`
Returns:
:obj:`FloatTensor`:
raw attention scores (unnormalized) for each src index
`[batch x tgt_len x src_len]`
"""
# Check input sizes
src_batch, src_len, src_dim = h_s.size()
tgt_batch, tgt_len, tgt_dim = h_t.size()
aeq(src_batch, tgt_batch)
aeq(self.dim, tgt_dim)
if self.attn_type in ["general", "dot"]:
if self.attn_type == "general":
h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim)
if entity_attn:
h_t_ = self.linear_in_entity(h_t_)
else:
h_t_ = self.linear_in(h_t_)
h_t = h_t_.view(tgt_batch, tgt_len, src_dim)
h_s_ = h_s.transpose(1, 2)
# (batch, t_len, d) x (batch, d, s_len) --> (batch, t_len, s_len)
return torch.bmm(h_t, h_s_)
else:
dim = self.dim
wq = self.linear_query(h_t.view(-1, dim))
wq = wq.view(tgt_batch, tgt_len, 1, dim)
wq = wq.expand(tgt_batch, tgt_len, src_len, dim)
uh = self.linear_context(h_s.contiguous().view(-1, dim))
uh = uh.view(src_batch, 1, src_len, dim)
uh = uh.expand(src_batch, tgt_len, src_len, dim)
# (batch, t_len, s_len, d)
wquh = self.tanh(wq + uh)
return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)
def forward(self, tgt, memory_bank, state, memory_lengths=None):
"""
Args:
tgt (`LongTensor`): sequences of padded tokens
`[tgt_len x batch x nfeats]`.
memory_bank (`FloatTensor`): vectors from the encoder
`[src_len x batch x hidden]`.
state (:obj:`onmt.Models.DecoderState`):
decoder state object to initialize the decoder
memory_lengths (`LongTensor`): the padded source lengths
`[batch]`.
Returns:
(`FloatTensor`,:obj:`onmt.Models.DecoderState`,`FloatTensor`):
* decoder_outputs: output from the decoder (after attn)
`[tgt_len x batch x hidden]`.
* decoder_state: final hidden state from the decoder
* attns: distribution over src at each tgt
`[tgt_len x batch x src_len]`.
"""
# Check
assert isinstance(state, RNNDecoderState)
tgt_len, tgt_batch, _ = tgt.size()
_, memory_batch, _ = memory_bank.size()
aeq(tgt_batch, memory_batch)
# END
# Run the forward pass of the RNN.
decoder_final, decoder_outputs, attns = self._run_forward_pass(
tgt, memory_bank, state, memory_lengths=memory_lengths)
# Update the state with the result.
final_output = decoder_outputs[-1]
coverage = None
if "coverage" in attns:
coverage = attns["coverage"][-1].unsqueeze(0)
state.update_state(decoder_final, final_output.unsqueeze(0), coverage)
# Concatenates sequence of tensors along a new dimension.
decoder_outputs = torch.stack(decoder_outputs)
for k in attns:
attns[k] = torch.stack(attns[k])
return decoder_outputs, state, attns
def forward(self, input):
"""
Computes the partly linked embeddings for words.
Args:
input (`LongTensor`): index tensor `[len x batch x 1]`
Return:
`FloatTensor`: word embeddings `[len x batch x embedding_size]`
"""
in_length, in_batch, nfeat = input.size()
aeq(nfeat, 1)
flat_input = input.view(-1)
cluster_indices = self.cluster_mapping.index_select(0, flat_input)
concat = torch.cat([input, cluster_indices.view(input.shape)], dim=-1)
emb = self.make_embedding(concat)
return emb
def _get_word_context(self, query, context, index, mask_word):
""" Verify sizes """
b_size, t_size, d_size = query.size()
b_size_, s_size, d_size_ = context.size()
aeq(d_size, d_size_)
b_size__, c_size = index.size()
aeq(b_size, b_size__)
b_size__, t_size_, s_size_ = mask_word.size()
aeq(b_size_, b_size__)
aeq(s_size, s_size_)
aeq(t_size, t_size_)
""" Padding index of previous invalid sentences index (<0) to 0, and saving mask for sentences """
mask_sent = index < 0
index_ = copy.deepcopy(index)
index_[mask_sent] = 0
""" Select context with index vector """
context_ = context.view(b_size_, -1).expand(b_size, b_size_,
s_size * d_size)
index__ = index_.unsqueeze(2).expand(b_size, c_size, s_size * d_size)
context_word = torch.gather(context_, 1,
Variable(index__,
requires_grad=False)).view(
b_size * c_size, s_size,
d_size)
""" Create complete mask for context: word padding + sentence padding """
mask_ = mask_word.contiguous().view(b_size_,
-1).expand(b_size, b_size_,
t_size_ * s_size)
index__ = index_.unsqueeze(2).expand(b_size, c_size, t_size_ * s_size)
context_pad_mask = torch.gather(mask_, 1,
index__).view(b_size * c_size, t_size_,
s_size)
mask_sent_ = mask_sent.unsqueeze(2).expand(
b_size, c_size,
t_size_ * s_size).contiguous().view(b_size * c_size, t_size_,
s_size)
context_pad_mask[mask_sent_] = self.padding_idx
""" Reshape query for future operations """
query_ = query.unsqueeze(1).expand(b_size, c_size, t_size,
d_size).contiguous().view(
b_size * c_size, t_size, d_size)
return query_, context_word, context_pad_mask
def forward(self,
input,
lengths=None,
hidden=None,
contexts=None,
neg=None,
tau=0.5,
scale=0.5):
""" See :obj:`EncoderBase.forward()`"""
self._check_args(input, lengths, hidden)
emb = self.embeddings(input,
contexts=contexts,
neg=neg,
tau=tau,
scale=scale)
if neg is not None:
sense_loss = emb[1]
emb = emb[0]
s_len, n_batch, emb_dim = emb.size()
out = emb.transpose(0, 1).contiguous()
words = input[:, :, 0].transpose(0, 1)
# CHECKS
out_batch, out_len, _ = out.size()
w_batch, w_len = words.size()
aeq(out_batch, w_batch)
aeq(out_len, w_len)
# END CHECKS
# Make mask.
padding_idx = self.embeddings.word_padding_idx
mask = words.data.eq(padding_idx).unsqueeze(1) \
.expand(w_batch, w_len, w_len)
# Run the forward pass of every layer of the tranformer.
for i in range(self.num_layers):
out = self.transformer[i](out, mask)
out = self.layer_norm(out)
if neg is None:
return Variable(emb.data), out.transpose(0, 1).contiguous()
else:
return Variable(emb.data), out.transpose(
0, 1).contiguous(), sense_loss
def forward(self,
tgt,
memory_bank,
state,
memory_lengths=None,
q_scores=None,
tgt_emb=None):
# Check
assert isinstance(state, RNNDecoderState)
tgt_len, tgt_batch, _ = tgt.size()
_, memory_batch, _ = memory_bank.size()
aeq(tgt_batch, memory_batch)
# END
# Run the forward pass of the RNN.
decoder_final, decoder_outputs, input_feed, attns, dist_info, decoder_outputs_baseline = self._run_forward_pass(
tgt,
memory_bank,
state,
memory_lengths=memory_lengths,
q_scores=q_scores,
tgt_emb=tgt_emb)
# Update the state with the result.
final_output = decoder_outputs[-1]
coverage = None
if "coverage" in attns:
coverage = attns["coverage"][-1].unsqueeze(0)
state.update_state(decoder_final, input_feed.unsqueeze(0), coverage)
# Concatenates sequence of tensors along a new dimension.
# T x K x N x H
decoder_outputs = torch.stack(decoder_outputs, dim=0)
if len(decoder_outputs_baseline) > 0:
decoder_outputs_baseline = torch.stack(decoder_outputs_baseline,
dim=0)
else:
decoder_outputs_baseline = None
for k in attns:
attns[k] = torch.stack(attns[k])
return decoder_outputs, state, attns, dist_info, decoder_outputs_baseline
def coalesce_datasets(datasets):
"""Coalesce all dataset instances. """
final = datasets[0]
for d in datasets[1:]:
# `src_vocabs` is a list of `torchtext.vocab.Vocab`.
# Each sentence transforms into on Vocab.
# Coalesce them into one big list.
final.src_vocabs += d.src_vocabs
# All datasets have same number of features.
aeq(final.n_src_feats, d.n_src_feats)
aeq(final.n_tgt_feats, d.n_tgt_feats)
# `examples` is a list of `torchtext.data.Example`.
# Coalesce them into one big list.
final.examples += d.examples
# All datasets have same fields, no need to update.
return final
def forward(self, input):
in_length, in_batch, nfeat = input.size()
aeq(nfeat, len(self.emb_luts))
emb = self.make_embedding(input)
out_length, out_batch, emb_size = emb.size()
aeq(in_length, out_length)
aeq(in_batch, out_batch)
aeq(emb_size, self.embedding_size)
return emb
def forward(self, src, lengths=None, encoder_state=None, entities_list=None, entities_len=None):
"See :obj:`EncoderBase.forward()`"
self._check_args(src, lengths, encoder_state)
emb = self.dropout(self.embeddings(src))
s_len, batch, emb_dim = emb.size()
mean = emb.mean(0).expand(self.num_layers, batch, emb_dim)
memory_bank = emb
encoder_final = (mean, mean)
s_len_batch, batch_ent, num_entities = entities_list.size()
s_len_entities, batch_len_ent = entities_len.size()
aeq(batch_len_ent, batch_ent)
aeq(num_entities, s_len_entities)
ent_emb = emb.unsqueeze(1).expand(-1, num_entities, -1, -1)
s_len, batch, emb_dim = emb.size()
ent_dim = entities_list.transpose(1,2).unsqueeze(3).expand(-1, -1, -1, emb_dim)
ent_len_dim = entities_len.unsqueeze(2).expand(-1, -1, emb_dim)
ent_emb = (ent_emb[:s_len_batch,:,:,:]*ent_dim).sum(0)
ent_emb = ent_emb/ent_len_dim
return encoder_final, memory_bank, ent_emb
def get_normal_scores(self, h_s, h_t):
""" h_s: [batch x src_length x rnn_size]
h_t: [batch x tgt_length x rnn_size]
"""
src_batch, src_len, src_dim = h_s.size()
tgt_batch, tgt_len, tgt_dim = h_t.size()
aeq(src_batch, tgt_batch)
aeq(src_dim, tgt_dim)
aeq(self.rnn_size, src_dim)
#import pdb; pdb.set_trace()
h_t_expand = h_t.unsqueeze(2).expand(-1, -1, src_len, -1)
h_s_expand = h_s.unsqueeze(1).expand(-1, tgt_len, -1, -1)
# [batch, tgt_len, src_len, src_dim]
h_expand = torch.cat((h_t_expand, h_s_expand), dim=3)
h_fold = h_expand.contiguous().view(-1, src_dim + tgt_dim)
h_enc = self.softplus(self.linear_1(h_fold))
h_enc = self.softplus(self.linear_2(h_enc))
h_mean = self.softplus(self.mean_out(h_enc))
h_var = self.softplus(self.var_out(h_enc))
h_mean = h_mean.view(tgt_batch, tgt_len, src_len)
h_var = h_var.view(tgt_batch, tgt_len, src_len)
return [h_mean, h_var]
def score(self, h_t, h_s):
# Check input sizes
src_batch, src_len, src_dim = h_s.size()
tgt_batch, tgt_len, tgt_dim = h_t.size()
aeq(src_batch, tgt_batch)
aeq(src_dim, tgt_dim)
aeq(self.dim, src_dim)
if self.attn_type in ["general", "dot"]:
if self.attn_type == "general":
h_t_ = h_t.view(tgt_batch * tgt_len, tgt_dim)
h_t_ = self.linear_in(h_t_)
h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim)
h_s_ = h_s.transpose(1, 2)
# (batch, t_len, d) x (batch, d, s_len) --> (batch, t_len, s_len)
return torch.bmm(h_t, h_s_)
else:
dim = self.dim
wq = self.linear_query(h_t.view(-1, dim))
wq = wq.view(tgt_batch, tgt_len, 1, dim)
wq = wq.expand(tgt_batch, tgt_len, src_len, dim)
uh = self.linear_context(h_s.contiguous().view(-1, dim))
uh = uh.view(src_batch, 1, src_len, dim)
uh = uh.expand(src_batch, tgt_len, src_len, dim)
# (batch, t_len, s_len, d)
wquh = self.tanh(wq + uh)
return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)
def forward(self, hidden, attn, src_map, rnn_output, src_emb):
"""
Computes p(w) = p(z=1) p_{copy}(w|z=0) + p(z=0) * p_{softmax}(w|z=0)
"""
# CHECKS
batch_by_tlen, _ = hidden.size()
batch_by_tlen_, slen = attn.size()
slen_, batch, cvocab = src_map.size()
aeq(batch_by_tlen, batch_by_tlen_)
aeq(slen, slen_)
# Original probabilities.
logits = self.linear(hidden)
logits[:, self.tgt_dict.stoi[onmt.IO.PAD_WORD]] = -float('inf')
prob = F.softmax(logits)
# Probability of copying p(z=1) batch.
if self.pointer_gen:
"""
p_gen = sigm(w1*hidden + w2*decoder_state + w3*decoder_input)
hidden = post-attention hidden_state
decoder_state = pre-attention hidden_state
"""
copy = F.sigmoid(
self.linear_hidden(hidden) +
self.linear_decoder_state(rnn_output) +
self.linear_decoder_input(src_emb))
else:
copy = F.sigmoid(self.linear_copy(hidden))
# Probability of not copying: p_{word}(w) * (1 - p(z))
out_prob = torch.mul(prob,
1 - copy.expand_as(prob.unsqueeze(0))).squeeze(0)
mul_attn = torch.mul(attn, copy.expand_as(attn.unsqueeze(0)))
copy_prob = torch.bmm(
mul_attn.view(-1, batch, slen).transpose(0, 1),
src_map.transpose(0, 1)).transpose(0, 1)
copy_prob = copy_prob.contiguous().view(-1, cvocab)
return torch.cat([out_prob, copy_prob], 1)
def _run_forward_pass(self, tgt, memory_bank, state, memory_lengths=None):
assert not self._copy # TODO, no support yet.
assert not self._coverage # TODO, no support yet.
# Initialize local and return variables.
attns = {}
emb = self.embeddings(tgt)
# Run the forward pass of the RNN.
if isinstance(self.rnn, nn.GRU):
rnn_output, decoder_final = self.rnn(emb, state.hidden[0])
else:
rnn_output, decoder_final = self.rnn(emb, state.hidden)
# Check
tgt_len, tgt_batch, _ = tgt.size()
output_len, output_batch, _ = rnn_output.size()
aeq(tgt_len, output_len)
aeq(tgt_batch, output_batch)
# END
# Calculate the attention.
decoder_outputs, p_attn = self.attn(rnn_output.transpose(
0, 1).contiguous(),
memory_bank.transpose(0, 1),
memory_lengths=memory_lengths)
attns["std"] = p_attn
# Calculate the context gate.
if self.context_gate is not None:
decoder_outputs = self.context_gate(
emb.view(-1, emb.size(2)),
rnn_output.view(-1, rnn_output.size(2)),
decoder_outputs.view(-1, decoder_outputs.size(2)))
decoder_outputs = \
decoder_outputs.view(tgt_len, tgt_batch, self.hidden_size)
decoder_outputs = self.dropout(decoder_outputs)
return decoder_final, decoder_outputs, attns
def score(self, h_t, h_s):
"""
Args:
h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]`
h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]`
Returns:
:obj:`FloatTensor`:
raw attention scores (unnormalized) for each src index
`[batch x tgt_len x src_len]`
"""
# Check input sizes
src_batch, src_len, src_dim = h_s.size()
tgt_batch, tgt_len, tgt_dim = h_t.size()
aeq(src_batch, tgt_batch)
aeq(src_dim, tgt_dim)
aeq(self.dim, src_dim)
if self.attn_type in ["general", "dot"]:
if self.attn_type == "general":
h_t_ = h_t.view(tgt_batch*tgt_len, tgt_dim)
h_t_ = self.linear_in(h_t_)
h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim)
h_s_ = h_s.transpose(1, 2)
# (batch, t_len, d) x (batch, d, s_len) --> (batch, t_len, s_len)
return torch.bmm(h_t, h_s_)
def _get_word_context(self, query, context, index, mask_word):
b_size, t_size, d_size = query.size()
b_size_, s_size, d_size_ = context.size()
aeq(d_size, d_size_)
b_size__, c_size = index.size()
aeq(b_size, b_size__)
b_size__, t_size_, s_size_ = mask_word.size()
aeq(b_size_, b_size__)
aeq(s_size, s_size_)
aeq(t_size, t_size_)
# Create padding mask for previous sentences
mask_sent = index < 0
index_ = copy.deepcopy(index)
index_[mask_sent] = 0
# Get context
context_ = context.view(b_size_, -1).expand(b_size, b_size_, s_size * d_size)
index__ = index_.unsqueeze(2).expand(b_size, c_size, s_size * d_size)
context_word = torch.gather(context_, 1, Variable(index__,
requires_grad=False)).view(b_size * c_size, s_size, d_size)
# Get mask for context
mask_ = mask_word.contiguous().view(b_size_, -1).expand(b_size, b_size_, t_size_ * s_size)
index__ = index_.unsqueeze(2).expand(b_size, c_size, t_size_ * s_size)
context_pad_mask = torch.gather(mask_, 1, index__).view(b_size * c_size, t_size_, s_size)
# Mask previous sentences
mask_sent_ = mask_sent.unsqueeze(2).expand(b_size,
c_size, t_size_ * s_size).contiguous().view(b_size * c_size, t_size_,
s_size)
context_pad_mask[mask_sent_] = self.padding_idx
# Expand query for each context sentence
query_ = query.unsqueeze(1).expand(b_size, c_size,
t_size, d_size).contiguous().view(b_size * c_size, t_size, d_size)
return query_, context_word, context_pad_mask,
def forward(self, input, contexts=None, neg=None, tau=0.5, scale=0.5):
"""
Computes the embeddings for words and features.
Args:
input (`LongTensor`): index tensor `[len x batch x nfeat]`
Return:
`FloatTensor`: word embeddings `[len x batch x embedding_size]`
"""
in_length, in_batch, nfeat = input.size()
word_emb, sense_loss = None, None
if self.SenseModule is not None:
aeq(nfeat - 1, len(self.emb_luts))
assert contexts is not None
if neg is None:
word_emb = self.SenseModule(input[:, :, 0],
contexts,
tau=tau,
scale=scale)
else:
word_emb, sense_loss = self.SenseModule(input[:, :, 0],
contexts,
neg=neg,
tau=tau,
scale=scale)
else:
aeq(nfeat, len(self.emb_luts))
emb = self.make_embedding((input, word_emb))
out_length, out_batch, emb_size = emb.size()
aeq(in_length, out_length)
aeq(in_batch, out_batch)
aeq(emb_size, self.embedding_size)
if neg is None:
return emb
else:
return emb, sense_loss
def forward(self,
input,
context,
state,
context_lengths=None,
r_input=None):
# Args Check
assert isinstance(state, RNNDecoderState)
input_len, input_batch, _ = input.size()
contxt_len, contxt_batch, _ = context.size()
aeq(input_batch, contxt_batch)
# END Args Check
# Run the forward pass of the RNN.
context = self.context_mlp(context)
if self.training:
bk_att_output, bk_rnn_output = self._run_backward_pass(
r_input, context, state)
self.bk_rnn_output = bk_rnn_output.detach()
# self.bk_rnn_output = bk_rnn_output
hidden, outputs, attns, coverage = self._run_forward_pass(
input, context, state, context_lengths=context_lengths)
# Update the state with the result.
final_output = outputs[-1]
state.update_state(
hidden, final_output.unsqueeze(0),
coverage.unsqueeze(0) if coverage is not None else None)
# Concatenates sequence of tensors along a new dimension.
outputs = torch.stack(outputs)
for k in attns:
attns[k] = torch.stack(attns[k])
if self.training:
return outputs, bk_att_output, state, attns
else:
return outputs, state, attns
def _example_dict_iter(self, line, index):
if self.symbol_representation == "char":
line = list(line.strip())
elif self.symbol_representation == "word":
line = line.split()
if self.line_truncate:
line = line[:self.line_truncate]
words, feats, n_feats = TextDataset.extract_text_features(line)
if self.revert:
words = tuple(reversed(words))
feats = tuple(reversed(feats))
example_dict = {self.side: words, "indices": index}
if feats:
# All examples must have same number of features.
aeq(self.n_feats, n_feats)
prefix = self.side + "_feat_"
example_dict.update(
(prefix + str(j), f) for j, f in enumerate(feats))
return example_dict
def _example_dict_iter(self, line, index):
line = line.split()
if self.line_truncate:
line = line[:self.line_truncate]
words, feats, n_feats = TextDataset.extract_text_features(line)
example_dict = {self.side: words, "indices": index}
if self.side == 'tgt1':
example_dict = {
self.side: words,
'tgt1_planning': [int(word) for word in words],
'player_row_indices': [int(word) for word in words],
'team_row_indices': [int(word) for word in words],
"indices": index
}
if feats:
# All examples must have same number of features.
aeq(self.n_feats, n_feats)
prefix = self.side + "_feat_"
example_dict.update(
(prefix + str(j), f) for j, f in enumerate(feats))
return example_dict
def _get_sent_context(self, query, context_word, context_index, attn_word):
b_size, t_size, d_size = query.size()
_, c_size = context_index.size()
# Sequence size now context_word is context size
context_sent = context_word.view(b_size, c_size, t_size, d_size).transpose(1, 2).contiguous().view(
b_size * t_size, c_size, d_size)
# Creating the mask for padding by word and sentence
mask_sent = context_index < 0
context_pad_mask = mask_sent.unsqueeze(1).expand(b_size,
t_size, c_size).contiguous().view(b_size * t_size, -1)
context_pad_mask = context_pad_mask.unsqueeze(1).contiguous()
# Re-arrange the query
query_ = query.view(b_size * t_size, 1, d_size)
_, h, t, s = attn_word.size()
aeq(t, t_size)
attn_word = attn_word.view(b_size, c_size, h, t, s)
return query_, context_sent, context_pad_mask, attn_word
def forward(self, base_target_emb, input, encoder_out_top,
encoder_out_combine):
"""
It's like Luong Attetion.
Conv attention takes a key matrix, a value matrix and a query vector.
Attention weight is calculated by key matrix with the query vector
and sum on the value matrix. And the same operation is applied
in each decode conv layer.
Args:
base_target_emb: target emb tensor
input: output of decode conv
encoder_out_t: the key matrix for calculation of attetion weight,
which is the top output of encode conv
encoder_out_c: the value matrix for the attention-weighted sum,
which is the combination of base emb and top output of encode
"""
# checks
batch, channel, height, width = base_target_emb.size()
batch_, channel_, height_, width_ = input.size()
aeq(batch, batch_)
aeq(height, height_)
enc_batch, enc_channel, enc_height = encoder_out_top.size()
enc_batch_, enc_channel_, enc_height_ = encoder_out_combine.size()
aeq(enc_batch, enc_batch_)
aeq(enc_height, enc_height_)
preatt = seq_linear(self.linear_in, input)
target = (base_target_emb + preatt) * SCALE_WEIGHT
target = torch.squeeze(target, 3)
target = torch.transpose(target, 1, 2)
pre_attn = torch.bmm(target, encoder_out_top)
if self.mask is not None:
pre_attn.data.masked_fill_(self.mask, -float('inf'))
pre_attn = pre_attn.transpose(0, 2)
attn = F.softmax(pre_attn)
attn = attn.transpose(0, 2).contiguous()
context_output = torch.bmm(
attn, torch.transpose(encoder_out_combine, 1, 2))
context_output = torch.transpose(
torch.unsqueeze(context_output, 3), 1, 2)
return context_output, attn
def forward(self, hidden, attn, src_map):
"""
Compute a distribution over the target dictionary
extended by the dynamic dictionary implied by compying
source words.
Args:
hidden (`FloatTensor`): hidden outputs `[batch*tlen, input_size]`
attn (`FloatTensor`): attn for each `[batch*tlen, input_size]`
src_map (`FloatTensor`):
A sparse indicator matrix mapping each source word to
its index in the "extended" vocab containing.
`[src_len, batch, extra_words]`
"""
# CHECKS
batch_by_tlen, _ = hidden.size()
batch_by_tlen_, slen = attn.size()
slen_, batch, cvocab = src_map.size()
aeq(batch_by_tlen, batch_by_tlen_)
aeq(slen, slen_)
# Original probabilities.
logits = self.linear(hidden)
logits[:, self.tgt_dict.stoi[onmt.io.PAD_WORD]] = -float('inf')
prob = F.softmax(logits)
# Probability of copying p(z=1) batch.
p_copy = F.sigmoid(self.linear_copy(hidden))
# Probibility of not copying: p_{word}(w) * (1 - p(z))
out_prob = torch.mul(prob, 1 - p_copy.expand_as(prob))
mul_attn = torch.mul(attn, p_copy.expand_as(attn))
copy_prob = torch.bmm(mul_attn.view(-1, batch, slen)
.transpose(0, 1),
src_map.transpose(0, 1)).transpose(0, 1)
copy_prob = copy_prob.contiguous().view(-1, cvocab)
return torch.cat([out_prob, copy_prob], 1)
def forward(self, hidden, attn, src_map):
# CHECKS
batch_by_tlen, _ = hidden.size()
batch_by_tlen_, slen = attn.size()
slen_, batch, cvocab = src_map.size()
aeq(batch_by_tlen, batch_by_tlen_)
aeq(slen, slen_)
# Original probabilities.
logits = self.linear(hidden)
logits[:, self.tgt_dict.stoi[onmt.io.PAD_WORD]] = -float('inf')
prob = F.softmax(logits)
# Probability of copying p(z=1) batch.
p_copy = F.sigmoid(self.linear_copy(hidden))
# Probibility of not copying: p_{word}(w) * (1 - p(z))
out_prob = torch.mul(prob, 1 - p_copy.expand_as(prob))
mul_attn = torch.mul(attn, p_copy.expand_as(attn))
copy_prob = torch.bmm(
mul_attn.view(-1, batch, slen).transpose(0, 1),
src_map.transpose(0, 1)).transpose(0, 1)
copy_prob = copy_prob.contiguous().view(-1, cvocab)
return torch.cat([out_prob, copy_prob], 1)
def forward(self, input):
"""
Return the embeddings for words, and features if there are any.
Args:
input (LongTensor): len x batch x nfeat
Return:
emb (FloatTensor): len x batch x self.embedding_size
"""
in_length, in_batch, nfeat = input.size()
aeq(nfeat, len(self.emb_luts))
emb = self.make_embedding(input)
out_length, out_batch, emb_size = emb.size()
aeq(in_length, out_length)
aeq(in_batch, out_batch)
aeq(emb_size, self.embedding_size)
return emb
def forward(self, input):
"""
Computes the embeddings for words and features.
Args:
input (`LongTensor`): index tensor `[len x batch x nfeat]`
Return:
`FloatTensor`: word embeddings `[len x batch x embedding_size]`
"""
in_length, in_batch, nfeat = input.size()
aeq(nfeat, len(self.emb_luts))
emb = self.make_embedding(input)
out_length, out_batch, emb_size = emb.size()
aeq(in_length, out_length)
aeq(in_batch, out_batch)
aeq(emb_size, self.embedding_size)
return emb
def forward(self, base_target_emb, input, encoder_out_top,
encoder_out_combine):
"""
Args:
base_target_emb: target emb tensor
input: output of decode conv
encoder_out_t: the key matrix for calculation of attetion weight,
which is the top output of encode conv
encoder_out_combine:
the value matrix for the attention-weighted sum,
which is the combination of base emb and top output of encode
"""
# checks
batch, channel, height, width = base_target_emb.size()
batch_, channel_, height_, width_ = input.size()
aeq(batch, batch_)
aeq(height, height_)
enc_batch, enc_channel, enc_height = encoder_out_top.size()
enc_batch_, enc_channel_, enc_height_ = encoder_out_combine.size()
aeq(enc_batch, enc_batch_)
aeq(enc_height, enc_height_)
preatt = seq_linear(self.linear_in, input)
target = (base_target_emb + preatt) * SCALE_WEIGHT
target = torch.squeeze(target, 3)
target = torch.transpose(target, 1, 2)
pre_attn = torch.bmm(target, encoder_out_top)
if self.mask is not None:
pre_attn.data.masked_fill_(self.mask, -float('inf'))
pre_attn = pre_attn.transpose(0, 2)
attn = F.softmax(pre_attn)
attn = attn.transpose(0, 2).contiguous()
context_output = torch.bmm(
attn, torch.transpose(encoder_out_combine, 1, 2))
context_output = torch.transpose(
torch.unsqueeze(context_output, 3), 1, 2)
return context_output, attn
def forward(self, input):
"""
Computes the embeddings for words and features.
Args:
input (`LongTensor`): index tensor `[len x batch x nfeat]`
Return:
`FloatTensor`: word embeddings `[len x batch x embedding_size]`
"""
in_length, in_batch, nfeat = input.size()
aeq(nfeat, len(self.emb_luts))
emb = self.make_embedding(input)
out_length, out_batch, emb_size = emb.size()
aeq(in_length, out_length)
aeq(in_batch, out_batch)
aeq(emb_size, self.embedding_size)
return emb
def score(self, h_t, h_s):
"""
Args:
h_t (`FloatTensor`): sequence of queries `[batch x tgt_len x dim]`
h_s (`FloatTensor`): sequence of sources `[batch x src_len x dim]`
Returns:
:obj:`FloatTensor`:
raw attention scores (unnormalized) for each src index
`[batch x tgt_len x src_len]`
"""
# Check input sizes
src_batch, src_len, src_dim = h_s.size()
tgt_batch, tgt_len, tgt_dim = h_t.size()
aeq(src_batch, tgt_batch)
aeq(src_dim, tgt_dim)
aeq(self.dim, src_dim)
if self.attn_type in ["general", "dot"]:
if self.attn_type == "general":
h_t_ = h_t.view(tgt_batch*tgt_len, tgt_dim)
h_t_ = self.linear_in(h_t_)
h_t = h_t_.view(tgt_batch, tgt_len, tgt_dim)
h_s_ = h_s.transpose(1, 2)
# (batch, t_len, d) x (batch, d, s_len) --> (batch, t_len, s_len)
return torch.bmm(h_t, h_s_)
else:
dim = self.dim
wq = self.linear_query(h_t.view(-1, dim))
wq = wq.view(tgt_batch, tgt_len, 1, dim)
wq = wq.expand(tgt_batch, tgt_len, src_len, dim)
uh = self.linear_context(h_s.contiguous().view(-1, dim))
uh = uh.view(src_batch, 1, src_len, dim)
uh = uh.expand(src_batch, tgt_len, src_len, dim)
# (batch, t_len, s_len, d)
wquh = self.tanh(wq + uh)
return self.v(wquh.view(-1, dim)).view(tgt_batch, tgt_len, src_len)
def _run_forward_pass(self, tgt, memory_bank, state, memory_lengths=None):
"""
See StdRNNDecoder._run_forward_pass() for description
of arguments and return values.
"""
# Additional args check.
input_feed = state.input_feed.squeeze(0)
input_feed_batch, _ = input_feed.size()
tgt_len, tgt_batch, _ = tgt.size()
aeq(tgt_batch, input_feed_batch)
# END Additional args check.
# Initialize local and return variables.
decoder_outputs = []
attns = {"std": []}
if self._copy:
attns["copy"] = []
if self._coverage:
attns["coverage"] = []
emb = self.embeddings(tgt)
assert emb.dim() == 3 # len x batch x embedding_dim
hidden = state.hidden
coverage = state.coverage.squeeze(0) \
if state.coverage is not None else None
# Input feed concatenates hidden state with
# input at every time step.
for i, emb_t in enumerate(emb.split(1)):
emb_t = emb_t.squeeze(0)
decoder_input = torch.cat([emb_t, input_feed], 1)
rnn_output, hidden = self.rnn(decoder_input, hidden)
decoder_output, p_attn = self.attn(
rnn_output,
memory_bank.transpose(0, 1),
memory_lengths=memory_lengths)
if self.context_gate is not None:
# TODO: context gate should be employed
# instead of second RNN transform.
decoder_output = self.context_gate(
decoder_input, rnn_output, decoder_output
)
decoder_output = self.dropout(decoder_output)
input_feed = decoder_output
decoder_outputs += [decoder_output]
attns["std"] += [p_attn]
# Update the coverage attention.
if self._coverage:
coverage = coverage + p_attn \
if coverage is not None else p_attn
attns["coverage"] += [coverage]
# Run the forward pass of the copy attention layer.
if self._copy and not self._reuse_copy_attn:
_, copy_attn = self.copy_attn(decoder_output,
memory_bank.transpose(0, 1))
attns["copy"] += [copy_attn]
elif self._copy:
attns["copy"] = attns["std"]
# Return result.
return hidden, decoder_outputs, attns
def forward(self, input, memory_bank, memory_lengths=None, coverage=None):
"""
Args:
input (`FloatTensor`): query vectors `[batch x tgt_len x dim]`
memory_bank (`FloatTensor`): source vectors `[batch x src_len x dim]`
memory_lengths (`LongTensor`): the source context lengths `[batch]`
coverage (`FloatTensor`): None (not supported yet)
Returns:
(`FloatTensor`, `FloatTensor`):
* Computed vector `[tgt_len x batch x dim]`
* Attention distribtutions for each query
`[tgt_len x batch x src_len]`
"""
# one step input
if input.dim() == 2:
one_step = True
input = input.unsqueeze(1)
else:
one_step = False
batch, sourceL, dim = memory_bank.size()
batch_, targetL, dim_ = input.size()
aeq(batch, batch_)
aeq(dim, dim_)
aeq(self.dim, dim)
if coverage is not None:
batch_, sourceL_ = coverage.size()
aeq(batch, batch_)
aeq(sourceL, sourceL_)
if coverage is not None:
cover = coverage.view(-1).unsqueeze(1)
memory_bank += self.linear_cover(cover).view_as(memory_bank)
memory_bank = self.tanh(memory_bank)
# compute attention scores, as in Luong et al.
align = self.score(input, memory_bank)
if memory_lengths is not None:
mask = sequence_mask(memory_lengths)
mask = mask.unsqueeze(1) # Make it broadcastable.
align.data.masked_fill_(1 - mask, -float('inf'))
# Softmax to normalize attention weights
align_vectors = self.sm(align.view(batch*targetL, sourceL))
align_vectors = align_vectors.view(batch, targetL, sourceL)
# each context vector c_t is the weighted average
# over all the source hidden states
c = torch.bmm(align_vectors, memory_bank)
# concatenate
concat_c = torch.cat([c, input], 2).view(batch*targetL, dim*2)
attn_h = self.linear_out(concat_c).view(batch, targetL, dim)
if self.attn_type in ["general", "dot"]:
attn_h = self.tanh(attn_h)
if one_step:
attn_h = attn_h.squeeze(1)
align_vectors = align_vectors.squeeze(1)
# Check output sizes
batch_, dim_ = attn_h.size()
aeq(batch, batch_)
aeq(dim, dim_)
batch_, sourceL_ = align_vectors.size()
aeq(batch, batch_)
aeq(sourceL, sourceL_)
else:
attn_h = attn_h.transpose(0, 1).contiguous()
align_vectors = align_vectors.transpose(0, 1).contiguous()
# Check output sizes
targetL_, batch_, dim_ = attn_h.size()
aeq(targetL, targetL_)
aeq(batch, batch_)
aeq(dim, dim_)
targetL_, batch_, sourceL_ = align_vectors.size()
aeq(targetL, targetL_)
aeq(batch, batch_)
aeq(sourceL, sourceL_)
return attn_h, align_vectors
def _check_args(self, input, lengths=None, hidden=None):
s_len, n_batch, n_feats = input.size()
if lengths is not None:
n_batch_, = lengths.size()
aeq(n_batch, n_batch_)
def forward(self, key, value, query, mask=None):
"""
Compute the context vector and the attention vectors.
Args:
key (`FloatTensor`): set of `key_len`
key vectors `[batch, key_len, dim]`
value (`FloatTensor`): set of `key_len`
value vectors `[batch, key_len, dim]`
query (`FloatTensor`): set of `query_len`
query vectors `[batch, query_len, dim]`
mask: binary mask indicating which keys have
non-zero attention `[batch, query_len, key_len]`
Returns:
(`FloatTensor`, `FloatTensor`) :
* output context vectors `[batch, query_len, dim]`
* one of the attention vectors `[batch, query_len, key_len]`
"""
# CHECKS
batch, k_len, d = key.size()
batch_, k_len_, d_ = value.size()
aeq(batch, batch_)
aeq(k_len, k_len_)
aeq(d, d_)
batch_, q_len, d_ = query.size()
aeq(batch, batch_)
aeq(d, d_)
aeq(self.model_dim % 8, 0)
if mask is not None:
batch_, q_len_, k_len_ = mask.size()
aeq(batch_, batch)
aeq(k_len_, k_len)
aeq(q_len_ == q_len)
# END CHECKS
batch_size = key.size(0)
dim_per_head = self.dim_per_head
head_count = self.head_count
key_len = key.size(1)
query_len = query.size(1)
def shape(x):
return x.view(batch_size, -1, head_count, dim_per_head) \
.transpose(1, 2)
def unshape(x):
return x.transpose(1, 2).contiguous() \
.view(batch_size, -1, head_count * dim_per_head)
# 1) Project key, value, and query.
key_up = shape(self.linear_keys(key))
value_up = shape(self.linear_values(value))
query_up = shape(self.linear_query(query))
# 2) Calculate and scale scores.
query_up = query_up / math.sqrt(dim_per_head)
scores = torch.matmul(query_up, key_up.transpose(2, 3))
if mask is not None:
mask = mask.unsqueeze(1).expand_as(scores)
scores = scores.masked_fill(Variable(mask), -1e18)
# 3) Apply attention dropout and compute context vectors.
attn = self.sm(scores)
drop_attn = self.dropout(attn)
context = unshape(torch.matmul(drop_attn, value_up))
output = self.final_linear(context)
# CHECK
batch_, q_len_, d_ = output.size()
aeq(q_len, q_len_)
aeq(batch, batch_)
aeq(d, d_)
# Return one attn
top_attn = attn \
.view(batch_size, head_count,
query_len, key_len)[:, 0, :, :] \
.contiguous()
# END CHECK
return output, top_attn
def forward(self, inputs, memory_bank, src_pad_mask, tgt_pad_mask,
previous_input=None):
# Args Checks
input_batch, input_len, _ = inputs.size()
if previous_input is not None:
pi_batch, _, _ = previous_input.size()
aeq(pi_batch, input_batch)
contxt_batch, contxt_len, _ = memory_bank.size()
aeq(input_batch, contxt_batch)
src_batch, t_len, s_len = src_pad_mask.size()
tgt_batch, t_len_, t_len__ = tgt_pad_mask.size()
aeq(input_batch, contxt_batch, src_batch, tgt_batch)
# aeq(t_len, t_len_, t_len__, input_len)
aeq(s_len, contxt_len)
# END Args Checks
dec_mask = torch.gt(tgt_pad_mask +
self.mask[:, :tgt_pad_mask.size(1),
:tgt_pad_mask.size(1)], 0)
input_norm = self.layer_norm_1(inputs)
all_input = input_norm
if previous_input is not None:
all_input = torch.cat((previous_input, input_norm), dim=1)
dec_mask = None
query, attn = self.self_attn(all_input, all_input, input_norm,
mask=dec_mask)
query = self.drop(query) + inputs
query_norm = self.layer_norm_2(query)
mid, attn = self.context_attn(memory_bank, memory_bank, query_norm,
mask=src_pad_mask)
output = self.feed_forward(self.drop(mid) + query)
# CHECKS
output_batch, output_len, _ = output.size()
aeq(input_len, output_len)
aeq(contxt_batch, output_batch)
n_batch_, t_len_, s_len_ = attn.size()
aeq(input_batch, n_batch_)
aeq(contxt_len, s_len_)
aeq(input_len, t_len_)
# END CHECKS
return output, attn, all_input
def forward(self, tgt, memory_bank, state, memory_lengths=None):
"""
See :obj:`onmt.modules.RNNDecoderBase.forward()`
"""
# CHECKS
assert isinstance(state, TransformerDecoderState)
tgt_len, tgt_batch, _ = tgt.size()
memory_len, memory_batch, _ = memory_bank.size()
aeq(tgt_batch, memory_batch)
src = state.src
src_words = src[:, :, 0].transpose(0, 1)
tgt_words = tgt[:, :, 0].transpose(0, 1)
src_batch, src_len = src_words.size()
tgt_batch, tgt_len = tgt_words.size()
aeq(tgt_batch, memory_batch, src_batch, tgt_batch)
aeq(memory_len, src_len)
if state.previous_input is not None:
tgt = torch.cat([state.previous_input, tgt], 0)
# END CHECKS
# Initialize return variables.
outputs = []
attns = {"std": []}
if self._copy:
attns["copy"] = []
# Run the forward pass of the TransformerDecoder.
emb = self.embeddings(tgt)
if state.previous_input is not None:
emb = emb[state.previous_input.size(0):, ]
assert emb.dim() == 3 # len x batch x embedding_dim
output = emb.transpose(0, 1).contiguous()
src_memory_bank = memory_bank.transpose(0, 1).contiguous()
padding_idx = self.embeddings.word_padding_idx
src_pad_mask = src_words.data.eq(padding_idx).unsqueeze(1) \
.expand(src_batch, tgt_len, src_len)
tgt_pad_mask = tgt_words.data.eq(padding_idx).unsqueeze(1) \
.expand(tgt_batch, tgt_len, tgt_len)
saved_inputs = []
for i in range(self.num_layers):
prev_layer_input = None
if state.previous_input is not None:
prev_layer_input = state.previous_layer_inputs[i]
output, attn, all_input \
= self.transformer_layers[i](output, src_memory_bank,
src_pad_mask, tgt_pad_mask,
previous_input=prev_layer_input)
saved_inputs.append(all_input)
saved_inputs = torch.stack(saved_inputs)
output = self.layer_norm(output)
# Process the result and update the attentions.
outputs = output.transpose(0, 1).contiguous()
attn = attn.transpose(0, 1).contiguous()
attns["std"] = attn
if self._copy:
attns["copy"] = attn
# Update the state.
state = state.update_state(tgt, saved_inputs)
return outputs, state, attns