def forward(self, state, x):
"""Forward neural networks."""
if state is None:
h = [
to_device(x, self.zero_state(x.size(0)))
for n in range(self.n_layers)
]
state = {"h": h}
if self.typ == "lstm":
c = [
to_device(x, self.zero_state(x.size(0)))
for n in range(self.n_layers)
]
state = {"c": c, "h": h}
h = [None] * self.n_layers
emb = self.embed(x)
if self.typ == "lstm":
c = [None] * self.n_layers
h[0], c[0] = self.rnn[0](self.dropout[0](emb),
(state["h"][0], state["c"][0]))
for n in range(1, self.n_layers):
h[n], c[n] = self.rnn[n](self.dropout[n](h[n - 1]),
(state["h"][n], state["c"][n]))
state = {"c": c, "h": h}
else:
h[0] = self.rnn[0](self.dropout[0](emb), state["h"][0])
for n in range(1, self.n_layers):
h[n] = self.rnn[n](self.dropout[n](h[n - 1]), state["h"][n])
state = {"h": h}
y = self.lo(self.dropout[-1](h[-1]))
return state, y
def init_state(self, init_tensor):
"""Initialize decoder states.
Args:
init_tensor (torch.Tensor): batch of input features
(B, emb_dim / dec_dim)
Returns:
(tuple): batch of decoder states
([L x (B, dec_dim)], [L x (B, dec_dim)])
"""
dtype = init_tensor.dtype
z_list = [
to_device(init_tensor, torch.zeros(init_tensor.size(0), self.dunits)).to(
dtype
)
for _ in range(self.dlayers)
]
c_list = [
to_device(init_tensor, torch.zeros(init_tensor.size(0), self.dunits)).to(
dtype
)
for _ in range(self.dlayers)
]
return (z_list, c_list)
def forward(self, state, x):
# update state with input label x
if state is None: # make initial states and log-prob vectors
self.var_word_eos = to_device(x, self.var_word_eos)
self.var_word_unk = to_device(x, self.var_word_eos)
wlm_state, z_wlm = self.wordlm(None, self.var_word_eos)
wlm_logprobs = F.log_softmax(z_wlm, dim=1)
clm_state, z_clm = self.subwordlm(None, x)
log_y = F.log_softmax(z_clm, dim=1) * self.subwordlm_weight
new_node = self.lexroot
clm_logprob = 0.0
xi = self.space
else:
clm_state, wlm_state, wlm_logprobs, node, log_y, clm_logprob = state
xi = int(x)
if xi == self.space: # inter-word transition
if node is not None and node[
1] >= 0: # check if the node is word end
w = to_device(x, torch.LongTensor([node[1]]))
else: # this node is not a word end, which means <unk>
w = self.var_word_unk
# update wordlm state and log-prob vector
wlm_state, z_wlm = self.wordlm(wlm_state, w)
wlm_logprobs = F.log_softmax(z_wlm, dim=1)
new_node = self.lexroot # move to the tree root
clm_logprob = 0.0
elif node is not None and xi in node[0]: # intra-word transition
new_node = node[0][xi]
clm_logprob += log_y[0, xi]
elif self.open_vocab: # if no path in the tree, enter open-vocabulary mode
new_node = None
clm_logprob += log_y[0, xi]
else: # if open_vocab flag is disabled, return 0 probabilities
log_y = to_device(
x, torch.full((1, self.subword_dict_size), self.logzero))
return (clm_state, wlm_state, wlm_logprobs, None, log_y,
0.0), log_y
clm_state, z_clm = self.subwordlm(clm_state, x)
log_y = F.log_softmax(z_clm, dim=1) * self.subwordlm_weight
# apply word-level probabilies for <space> and <eos> labels
if xi != self.space:
if new_node is not None and new_node[
1] >= 0: # if new node is word end
wlm_logprob = wlm_logprobs[:, new_node[1]] - clm_logprob
else:
wlm_logprob = wlm_logprobs[:, self.
word_unk] + self.log_oov_penalty
log_y[:, self.space] = wlm_logprob
log_y[:, self.eos] = wlm_logprob
else:
log_y[:, self.space] = self.logzero
log_y[:, self.eos] = self.logzero
return (
(clm_state, wlm_state, wlm_logprobs, new_node, log_y,
float(clm_logprob)),
log_y,
)
def score(self, hyp, cache, init_tensor=None):
"""Forward one step.
Args:
hyp (dataclass): hypothesis
cache (dict): states cache
Returns:
y (torch.Tensor): decoder outputs (1, dec_dim)
state (tuple): decoder states
([L x (1, dec_dim)], [L x (1, dec_dim)]),
(torch.Tensor): token id for LM (1)
"""
vy = to_device(self, torch.full((1, 1), hyp.yseq[-1], dtype=torch.long))
str_yseq = "".join([str(x) for x in hyp.yseq])
if str_yseq in cache:
y, state = cache[str_yseq]
else:
ey = self.embed(vy)
y, state = self.rnn_forward(ey[0], hyp.dec_state)
cache[str_yseq] = (y, state)
return y, state, vy[0]
def final(self, state):
wlm_state, cumsum_probs, node = state
if node is not None and node[1] >= 0: # check if the node is word end
w = to_device(cumsum_probs, torch.LongTensor([node[1]]))
else: # this node is not a word end, which means <unk>
w = self.var_word_unk
wlm_state, z_wlm = self.wordlm(wlm_state, w)
return float(F.log_softmax(z_wlm, dim=1)[:, self.word_eos])
def score(self, hyp, cache, init_tensor=None):
"""Forward one step.
Args:
hyp (dataclass): hypothesis
cache (dict): states cache
Returns:
y (torch.Tensor): decoder outputs (1, dec_dim)
(list): decoder and attention states
[L x (1, max_len, dec_dim)]
lm_tokens (torch.Tensor): token id for LM (1)
"""
tgt = to_device(self, torch.tensor(hyp.yseq).unsqueeze(0))
lm_tokens = tgt[:, -1]
str_yseq = "".join([str(x) for x in hyp.yseq])
if str_yseq in cache:
y, new_state = cache[str_yseq]
else:
tgt_mask = to_device(self, subsequent_mask(len(hyp.yseq)).unsqueeze(0))
state = check_state(hyp.dec_state, (tgt.size(1) - 1), self.blank)
tgt = self.embed(tgt)
new_state = []
for s, decoder in zip(state, self.decoders):
tgt, tgt_mask = decoder(tgt, tgt_mask, cache=s)
new_state.append(tgt)
y = self.after_norm(tgt[:, -1])
cache[str_yseq] = (y, new_state)
return y, new_state, lm_tokens
def forward(self, xs_pad, ilens, prev_states=None):
"""Encoder forward
:param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D)
:param torch.Tensor ilens: batch of lengths of input sequences (B)
:param torch.Tensor prev_state: batch of previous encoder hidden states (?, ...)
:return: batch of hidden state sequences (B, Tmax, eprojs)
:rtype: torch.Tensor
"""
if prev_states is None:
prev_states = [None] * len(self.enc)
assert len(prev_states) == len(self.enc)
current_states = []
for module, prev_state in zip(self.enc, prev_states):
xs_pad, ilens, states = module(xs_pad,
ilens,
prev_state=prev_state)
current_states.append(states)
# make mask to remove bias value in padded part
mask = to_device(xs_pad, make_pad_mask(ilens).unsqueeze(-1))
return xs_pad.masked_fill(mask, 0.0), ilens, current_states
def batch_score(self, hyps, batch_states, cache, init_tensor=None):
"""Forward batch one step.
Args:
hyps (list): batch of hypotheses
batch_states (list): decoder states
[L x (B, max_len, dec_dim)]
cache (dict): states cache
Returns:
batch_y (torch.Tensor): decoder output (B, dec_dim)
batch_states (list): decoder states
[L x (B, max_len, dec_dim)]
lm_tokens (torch.Tensor): batch of token ids for LM (B)
"""
final_batch = len(hyps)
tokens = []
process = []
done = [None for _ in range(final_batch)]
for i, hyp in enumerate(hyps):
str_yseq = "".join([str(x) for x in hyp.yseq])
if str_yseq in cache:
done[i] = (*cache[str_yseq], hyp.yseq)
else:
tokens.append(hyp.yseq)
process.append((str_yseq, hyp.dec_state, hyp.yseq))
if process:
batch = len(tokens)
tokens = pad_sequence(tokens, self.blank)
b_tokens = to_device(self, torch.LongTensor(tokens).view(batch, -1))
tgt_mask = to_device(
self,
subsequent_mask(b_tokens.size(-1)).unsqueeze(0).expand(batch, -1, -1),
)
dec_state = self.init_state()
dec_state = self.create_batch_states(
dec_state,
[p[1] for p in process],
tokens,
)
tgt = self.embed(b_tokens)
next_state = []
for s, decoder in zip(dec_state, self.decoders):
tgt, tgt_mask = decoder(tgt, tgt_mask, cache=s)
next_state.append(tgt)
tgt = self.after_norm(tgt[:, -1])
j = 0
for i in range(final_batch):
if done[i] is None:
new_state = self.select_state(next_state, j)
done[i] = (tgt[j], new_state, process[j][2])
cache[process[j][0]] = (tgt[j], new_state)
j += 1
batch_states = self.create_batch_states(
batch_states, [d[1] for d in done], [d[2] for d in done]
)
batch_y = torch.stack([d[0] for d in done])
lm_tokens = to_device(
self, torch.LongTensor([h.yseq[-1] for h in hyps]).view(final_batch)
)
return batch_y, batch_states, lm_tokens
def forward(self, state, x):
# update state with input label x
if state is None: # make initial states and cumlative probability vector
self.var_word_eos = to_device(x, self.var_word_eos)
self.var_word_unk = to_device(x, self.var_word_eos)
self.zero_tensor = to_device(x, self.zero_tensor)
wlm_state, z_wlm = self.wordlm(None, self.var_word_eos)
cumsum_probs = torch.cumsum(F.softmax(z_wlm, dim=1), dim=1)
new_node = self.lexroot
xi = self.space
else:
wlm_state, cumsum_probs, node = state
xi = int(x)
if xi == self.space: # inter-word transition
if node is not None and node[
1] >= 0: # check if the node is word end
w = to_device(x, torch.LongTensor([node[1]]))
else: # this node is not a word end, which means <unk>
w = self.var_word_unk
# update wordlm state and cumlative probability vector
wlm_state, z_wlm = self.wordlm(wlm_state, w)
cumsum_probs = torch.cumsum(F.softmax(z_wlm, dim=1), dim=1)
new_node = self.lexroot # move to the tree root
elif node is not None and xi in node[0]: # intra-word transition
new_node = node[0][xi]
elif self.open_vocab: # if no path in the tree, enter open-vocabulary mode
new_node = None
else: # if open_vocab flag is disabled, return 0 probabilities
log_y = to_device(
x, torch.full((1, self.subword_dict_size), self.logzero))
return (wlm_state, None, None), log_y
if new_node is not None:
succ, wid, wids = new_node
# compute parent node probability
sum_prob = ((cumsum_probs[:, wids[1]] - cumsum_probs[:, wids[0]])
if wids is not None else 1.0)
if sum_prob < self.zero:
log_y = to_device(
x, torch.full((1, self.subword_dict_size), self.logzero))
return (wlm_state, cumsum_probs, new_node), log_y
# set <unk> probability as a default value
unk_prob = (cumsum_probs[:, self.word_unk] -
cumsum_probs[:, self.word_unk - 1])
y = to_device(
x,
torch.full((1, self.subword_dict_size),
float(unk_prob) * self.oov_penalty),
)
# compute transition probabilities to child nodes
for cid, nd in succ.items():
y[:, cid] = (cumsum_probs[:, nd[2][1]] -
cumsum_probs[:, nd[2][0]]) / sum_prob
# apply word-level probabilies for <space> and <eos> labels
if wid >= 0:
wlm_prob = (cumsum_probs[:, wid] -
cumsum_probs[:, wid - 1]) / sum_prob
y[:, self.space] = wlm_prob
y[:, self.eos] = wlm_prob
elif xi == self.space:
y[:, self.space] = self.zero
y[:, self.eos] = self.zero
log_y = torch.log(torch.max(
y, self.zero_tensor)) # clip to avoid log(0)
else: # if no path in the tree, transition probability is one
log_y = to_device(x, torch.zeros(1, self.subword_dict_size))
return (wlm_state, cumsum_probs, new_node), log_y
def batch_score(self, hyps, batch_states, cache, init_tensor=None):
"""Forward batch one step.
Args:
hyps (list): batch of hypotheses
batch_states (tuple): batch of decoder states
([L x (B, dec_dim)], [L x (B, dec_dim)])
cache (dict): states cache
Returns:
batch_y (torch.Tensor): decoder output (B, dec_dim)
batch_states (tuple): batch of decoder states
([L x (B, dec_dim)], [L x (B, dec_dim)])
lm_tokens (torch.Tensor): batch of token ids for LM (B)
"""
final_batch = len(hyps)
tokens = []
process = []
done = [None for _ in range(final_batch)]
for i, hyp in enumerate(hyps):
str_yseq = "".join([str(x) for x in hyp.yseq])
if str_yseq in cache:
done[i] = cache[str_yseq]
else:
tokens.append(hyp.yseq[-1])
process.append((str_yseq, hyp.dec_state))
if process:
batch = len(process)
tokens = to_device(self, torch.LongTensor(tokens).view(batch))
dec_state = self.init_state(torch.zeros((batch, self.dunits)))
dec_state = self.create_batch_states(dec_state, [p[1] for p in process])
ey = self.embed(tokens)
y, dec_state = self.rnn_forward(ey, dec_state)
j = 0
for i in range(final_batch):
if done[i] is None:
new_state = self.select_state(dec_state, j)
done[i] = (y[j], new_state)
cache[process[j][0]] = (y[j], new_state)
j += 1
batch_states = self.create_batch_states(batch_states, [d[1] for d in done])
batch_y = torch.stack([d[0] for d in done])
lm_tokens = to_device(
self, torch.LongTensor([h.yseq[-1] for h in hyps]).view(final_batch)
)
return batch_y, batch_states, lm_tokens
