def test_cat(self):
a = torch.IntTensor([[1, 2, 3], [4, 5, 6]])
b = torch.IntTensor([[11, 12, 13], [14, 15, 16]])
ab = torch.IntTensor([[1, 2, 3, 11, 12, 13], [4, 5, 6, 14, 15, 16]])
npt.assert_array_equal(pytorch_utils.maybe_cat([a, b], dim=1), ab)
npt.assert_array_equal(
pytorch_utils.maybe_cat([a, None, b, None, None], dim=1), ab
)
npt.assert_array_equal(pytorch_utils.maybe_cat([None, None, a, None], dim=1), a)
def forward_unprojected(self,
input_tokens,
encoder_out,
incremental_state=None):
padded_tokens = F.pad(
input_tokens,
(self.history_len - 1, 0, 0, 0),
"constant",
self.dst_dict.eos(),
)
# We use incremental_state only to check whether we are decoding or not
# self.training is false even for the forward pass through validation
if incremental_state is not None:
padded_tokens = padded_tokens[:, -self.history_len:]
utils.set_incremental_state(self, incremental_state,
"incremental_marker", True)
bsz, seqlen = padded_tokens.size()
seqlen -= self.history_len - 1
# get outputs from encoder
(encoder_outs, final_hidden, _, src_lengths, _) = encoder_out
# padded_tokens has shape [batch_size, seq_len+history_len]
x = self.embed_tokens(padded_tokens)
x = F.dropout(x, p=self.dropout_in, training=self.training)
# Convolution needs shape [batch_size, channels, seq_len]
x = self.history_conv(x.transpose(1, 2)).transpose(1, 2)
x = F.dropout(x, p=self.dropout_out, training=self.training)
# x has shape [batch_size, seq_len, channels]
for i, layer in enumerate(self.layers):
prev_x = x
x = layer(x)
x = F.dropout(x, p=self.dropout_out, training=self.training)
if self.residual_level is not None and i >= self.residual_level:
x = x + prev_x
# Attention
attn_out, attn_scores = self.attention(
x.transpose(0, 1).contiguous().view(-1, self.hidden_dim),
encoder_outs.repeat(1, seqlen, 1),
src_lengths.repeat(seqlen),
)
if attn_out is not None:
attn_out = attn_out.view(seqlen, bsz, -1).transpose(1, 0)
attn_scores = attn_scores.view(-1, seqlen, bsz).transpose(0, 2)
x = maybe_cat((x, attn_out), dim=2)
# bottleneck layer
if hasattr(self, "additional_fc"):
x = self.additional_fc(x)
x = F.dropout(x, p=self.dropout_out, training=self.training)
return x, attn_scores
def forward_unprojected(self,
input_tokens,
encoder_out,
incremental_state=None):
if incremental_state is not None:
input_tokens = input_tokens[:, -1:]
bsz, seqlen = input_tokens.size()
# get outputs from encoder
(encoder_outs, final_hidden, final_cell, src_lengths,
src_tokens) = encoder_out
# embed tokens
x = self.embed_tokens(input_tokens)
x = F.dropout(x, p=self.dropout_in, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# initialize previous states (or get from cache during incremental generation)
cached_state = utils.get_incremental_state(self, incremental_state,
"cached_state")
input_feed = None
if cached_state is not None:
prev_hiddens, prev_cells, input_feed = cached_state
else:
# first time step, initialize previous states
prev_hiddens, prev_cells = self._init_prev_states(encoder_out)
if self.attention.context_dim:
input_feed = self.initial_attn_context.expand(
bsz, self.attention.context_dim)
attn_scores_per_step = []
outs = []
for j in range(seqlen):
# input feeding: concatenate context vector from previous time step
step_input = maybe_cat((x[j, :, :], input_feed), dim=1)
previous_layer_input = step_input
for i, rnn in enumerate(self.layers):
# recurrent cell
hidden, cell = rnn(step_input,
(prev_hiddens[i], prev_cells[i]))
# hidden state becomes the input to the next layer
layer_output = F.dropout(hidden,
p=self.dropout_out,
training=self.training)
if self.residual_level is not None and i >= self.residual_level:
# TODO add an assert related to sizes here
step_input = layer_output + previous_layer_input
else:
step_input = layer_output
previous_layer_input = step_input
# save state for next time step
prev_hiddens[i] = hidden
prev_cells[i] = cell
out, step_attn_scores = self.attention(hidden, encoder_outs,
src_lengths)
input_feed = out
attn_scores_per_step.append(step_attn_scores.unsqueeze(1))
attn_scores = torch.cat(attn_scores_per_step, dim=1)
# srclen x tgtlen x bsz -> bsz x tgtlen x srclen
attn_scores = attn_scores.transpose(0, 2)
combined_output_and_context = maybe_cat((hidden, out), dim=1)
# save final output
outs.append(combined_output_and_context)
# cache previous states (no-op except during incremental generation)
utils.set_incremental_state(
self,
incremental_state,
"cached_state",
(prev_hiddens, prev_cells, input_feed),
)
# collect outputs across time steps
x = torch.cat(outs, dim=0).view(seqlen, bsz,
self.combined_output_and_context_dim)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
# bottleneck layer
if hasattr(self, "additional_fc"):
x = self.additional_fc(x)
x = F.dropout(x, p=self.dropout_out, training=self.training)
return x, attn_scores
def test_nullable(self):
a = torch.IntTensor([[1, 2, 3], [4, 5, 6]])
pytorch_utils.maybe_cat([a, None], 1)
pytorch_utils.maybe_cat([a, None], 1, nullable=[True, True])
pytorch_utils.maybe_cat([a, None], 1, nullable=[False, True])
with self.assertRaises(RuntimeError):
pytorch_utils.maybe_cat([a, None], 1, nullable=[False, False])
with self.assertRaises(RuntimeError):
pytorch_utils.maybe_cat([None, None], 1)
with self.assertRaises(RuntimeError):
pytorch_utils.maybe_cat([], 1)
