def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Implement forward computation.
Parameters
----------
inputs : NDArray
The training dataset.
begin_state : list
The initial hidden states.
Returns
-------
out: NDArray
The output of the model.
out_states: list
The list of output states of the model's encoder.
"""
encoded = self.embedding(inputs)
if not begin_state:
begin_state = self.begin_state(batch_size=inputs.shape[1])
out_states = []
for i, (e, s) in enumerate(zip(self.encoder, begin_state)):
encoded, state = e(encoded, s)
out_states.append(state)
if self._drop_h and i != len(self.encoder)-1:
encoded = nd.Dropout(encoded, p=self._drop_h, axes=(0,))
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0,))
with autograd.predict_mode():
out = self.decoder(encoded)
return out, out_states
def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Implement forward computation.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
initial recurrent state tensor with length equals to num_layers.
the initial state with shape `(1, batch_size, num_hidden)`
Returns
--------
out: NDArray
output tensor with shape `(sequence_length, batch_size, input_size)`
when `layout` is "TNC".
out_states: list
output recurrent state tensor with length equals to num_layers.
the state with shape `(1, batch_size, num_hidden)`
"""
encoded = self.embedding(inputs)
if not begin_state:
begin_state = self.begin_state(batch_size=inputs.shape[1])
out_states = []
for i, (e, s) in enumerate(zip(self.encoder, begin_state)):
encoded, state = e(encoded, s)
out_states.append(state)
if self._drop_h and i != len(self.encoder) - 1:
encoded = nd.Dropout(encoded, p=self._drop_h, axes=(0, ))
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0, ))
with autograd.predict_mode():
out = self.decoder(encoded)
return out, out_states
def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Implement the forward computation that the awd language model and cache model use.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
initial recurrent state tensor with length equals to num_layers.
the initial state with shape `(1, batch_size, num_hidden)`
Returns
--------
out: NDArray
output tensor with shape `(sequence_length, batch_size, input_size)`
when `layout` is "TNC".
out_states: list
output recurrent state tensor with length equals to num_layers.
the state with shape `(1, batch_size, num_hidden)`
encoded_raw: list
The list of outputs of the model's encoder with length equals to num_layers.
the shape of every encoder's output `(sequence_length, batch_size, num_hidden)`
encoded_dropped: list
The list of outputs with dropout of the model's encoder with length equals
to num_layers. The shape of every encoder's dropped output
`(sequence_length, batch_size, num_hidden)`
"""
encoded = self.embedding(inputs)
if not begin_state:
begin_state = self.begin_state(batch_size=inputs.shape[1])
out_states = []
encoded_raw = []
encoded_dropped = []
for i, (e, s) in enumerate(zip(self.encoder, begin_state)):
encoded, state = e(encoded, s)
encoded_raw.append(encoded)
out_states.append(state)
if self._drop_h and i != len(self.encoder)-1:
encoded = nd.Dropout(encoded, p=self._drop_h, axes=(0,))
encoded_dropped.append(encoded)
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0,))
encoded_dropped.append(encoded)
latent = nd.Dropout(self.latent(encoded), p=self._drop_l, axes=(0,))
logit = self.decoder(latent.reshape(-1, self._embed_size))
prior_logit = self.prior(encoded).reshape(-1, self._num_experts)
prior = nd.softmax(prior_logit)
prob = nd.softmax(logit.reshape(-1, self._vocab_size))
prob = prob.reshape(-1, self._num_experts, self._vocab_size)
prob = (prob * prior.expand_dims(2).broadcast_to(prob.shape)).sum(axis=1)
out = nd.log(nd.add(prob, 1e-8)).reshape(-1, inputs.shape[1], self._vocab_size)
return out, out_states, encoded_raw, encoded_dropped
def mlp(self, top_recur):
is_train = autograd.is_training()
if is_train:
top_recur = nd.Dropout(data=top_recur, axes=[0], p=self.dropout_mlp)
W_dep, b_dep = self.mlp_dep_W.data(), self.mlp_dep_b.data()
W_head, b_head = self.mlp_head_W.data(), self.mlp_head_b.data()
dep, head = leaky_relu(nd.dot(top_recur, W_dep.T) + b_dep), leaky_relu(nd.dot(top_recur, W_head.T) + b_head)
if is_train:
dep, head = nd.Dropout(data=dep, axes=[0], p=self.dropout_mlp), nd.Dropout(data=head, axes=[0],
p=self.dropout_mlp)
dep, head = nd.transpose(dep, axes=[2, 0, 1]), nd.transpose(head, axes=[2, 0, 1])
dep_arc, dep_rel = dep[:self.mlp_arc_size], dep[self.mlp_arc_size:]
head_arc, head_rel = head[:self.mlp_arc_size], head[self.mlp_arc_size:]
return dep_arc, dep_rel, head_arc, head_rel
def forward(self, inputs, begin_state=None):
"""Implement forward computation.
Parameters
----------
inputs : NDArray
The training dataset.
begin_state : list
The initial hidden states.
Returns
-------
out: NDArray
The output of the model.
out_states: list
The list of output states of the model's encoder.
"""
encoded = self.embedding(inputs)
if not begin_state:
begin_state = self.begin_state(batch_size=inputs.shape[1])
out_states = []
encoded_raw = []
encoded_dropped = []
for i, (e, s) in enumerate(zip(self.encoder, begin_state)):
encoded, state = e(encoded, s)
encoded_raw.append(encoded)
out_states.append(state)
if self._drop_h and i != len(self.encoder) - 1:
encoded = nd.Dropout(encoded, p=self._drop_h, axes=(0, ))
encoded_dropped.append(encoded)
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0, ))
states = out_states
encoded_dropped.append(encoded)
latent = nd.Dropout(self.latent(encoded), p=self._drop_l, axes=(0, ))
logit = self.decoder(latent.reshape(-1, self._embed_size))
prior_logit = self.prior(encoded).reshape(-1, self._num_experts)
prior = nd.softmax(prior_logit)
prob = nd.softmax(logit.reshape(-1, self._vocab_size))
prob = prob.reshape(-1, self._num_experts, self._vocab_size)
prob = (prob *
prior.expand_dims(2).broadcast_to(prob.shape)).sum(axis=1)
out = nd.log(nd.add(prob, 1e-8)).reshape(-1, inputs.shape[1],
self._vocab_size)
return out, out_states, encoded_raw, encoded_dropped
def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
initial recurrent state tensor with length equals to num_layers-1.
the initial state with shape `(num_layers, batch_size, num_hidden)`
Returns
--------
out: NDArray
output tensor with shape `(sequence_length, batch_size, input_size)`
when `layout` is "TNC".
out_states: list
output recurrent state tensor with length equals to num_layers-1.
the state with shape `(num_layers, batch_size, num_hidden)`
"""
encoded = self.embedding(inputs)
if not begin_state:
begin_state = self.begin_state(batch_size=inputs.shape[1])
encoded, state = self.encoder(encoded, begin_state)
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0, ))
out = self.decoder(encoded)
return out, state
def biLSTM(f_lstm, b_lstm, inputs, dropout_x=0.):
"""Feature extraction through BiLSTM
Parameters
----------
f_lstm : VariationalDropoutCell
Forward cell
b_lstm : VariationalDropoutCell
Backward cell
inputs : NDArray
seq_len x batch_size
dropout_x : float
Variational dropout on inputs
Returns
-------
outputs : NDArray
Outputs of BiLSTM layers, seq_len x 2 hidden_dims x batch_size
"""
for f, b in zip(f_lstm, b_lstm):
inputs = nd.Dropout(inputs, dropout_x, axes=[0]) # important for variational dropout
fo, _ = f.unroll(length=inputs.shape[0], inputs=inputs, layout='TNC', merge_outputs=True)
bo, _ = b.unroll(length=inputs.shape[0], inputs=inputs.flip(axis=0), layout='TNC',
merge_outputs=True)
f.reset()
b.reset()
inputs = nd.concat(fo, bo.flip(axis=0), dim=2)
return inputs
def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Implement the forward computation that the awd language model and cache model use.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
initial recurrent state tensor with length equals to num_layers.
the initial state with shape `(1, batch_size, num_hidden)`
Returns
--------
out: NDArray
output tensor with shape `(sequence_length, batch_size, input_size)`
when `layout` is "TNC".
out_states: list
output recurrent state tensor with length equals to num_layers.
the state with shape `(1, batch_size, num_hidden)`
encoded_raw: list
The list of outputs of the model's encoder with length equals to num_layers.
the shape of every encoder's output `(sequence_length, batch_size, num_hidden)`
encoded_dropped: list
The list of outputs with dropout of the model's encoder with length equals
to num_layers. The shape of every encoder's dropped output
`(sequence_length, batch_size, num_hidden)`
"""
encoded = self.embedding(inputs)
if not begin_state:
begin_state = self.begin_state(batch_size=inputs.shape[1])
out_states = []
encoded_raw = []
encoded_dropped = []
for i, (e, s) in enumerate(zip(self.encoder, begin_state)):
encoded, state = e(encoded, s)
encoded_raw.append(encoded)
out_states.append(state)
if self._drop_h and i != len(self.encoder) - 1:
encoded = nd.Dropout(encoded, p=self._drop_h, axes=(0,))
encoded_dropped.append(encoded)
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0,))
encoded_dropped.append(encoded)
with autograd.predict_mode():
out = self.decoder(encoded)
return out, out_states, encoded_raw, encoded_dropped
def forward(self,
inputs,
char_inputs,
begin_state=None,
valid_length=None,
masks=None): # pylint: disable=arguments-differ
"""Implement the forward computation that the awd language model and cache model use.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
initial recurrent state tensor with length equals to num_layers.
the initial state with shape `(1, batch_size, num_hidden)`
Returns
--------
out: NDArray
output tensor with shape `(sequence_length, batch_size, input_size)`
when `layout` is "TNC".
out_states: list
output recurrent state tensor with length equals to num_layers.
the state with shape `(1, batch_size, num_hidden)`
encoded_raw: list
The list of outputs of the model's encoder with length equals to num_layers.
the shape of every encoder's output `(sequence_length, batch_size, num_hidden)`
encoded_dropped: list
The list of outputs with dropout of the model's encoder with length equals
to num_layers. The shape of every encoder's dropped output
`(sequence_length, batch_size, num_hidden)`
"""
if self._use_pretrained_embedding:
encoded = self.embedding(inputs, char_inputs)
else:
encoded = self.embedding(inputs)
encoded, _, encoded_raw, masks = self.encoder(
encoded, valid_length=valid_length, masks=masks)
if self._use_encoder_last_variational_dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(1, ))
else:
encoded = nd.Dropout(encoded, p=self._dropout)
encoded = encoded.swapaxes(dim1=0, dim2=1)
out = self.decoder(encoded)
return out, _, encoded_raw, _, masks
def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`."""
encoded = self.embedding(inputs)
if not begin_state:
begin_state = self.begin_state(batch_size=inputs.shape[1])
encoded, state = self.encoder(encoded, begin_state)
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0,))
out = self.decoder(encoded)
return out, state
def data(self, ctx=None):
"""Returns a copy of this parameter on one context. Must have been
initialized on this context before.
Parameters
----------
ctx : Context
Desired context.
Returns
-------
NDArray on ctx
"""
d = self._check_and_get(self._data, ctx)
if self._rate:
d = nd.Dropout(d, self._rate, self._mode, self._axes)
return d
def hybrid_forward(self, F, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
initial recurrent state tensor with length equals to num_layers-1.
the initial state with shape `(num_layers, batch_size, num_hidden)`
Returns
--------
out: NDArray
output tensor with shape `(sequence_length, batch_size, input_size)`
when `layout` is "TNC".
out_states: list
output recurrent state tensor with length equals to num_layers-1.
the state with shape `(num_layers, batch_size, num_hidden)`
"""
# XXX Temporary hack for hybridization as hybridblock does not support None inputs
if isinstance(begin_state, list) and len(begin_state) == 0:
begin_state = None
encoded = self.embedding(inputs)
if not begin_state:
if F == nd:
begin_state = self.begin_state(batch_size=inputs.shape[1])
else:
begin_state = self.begin_state(batch_size=0, func=sym.zeros)
encoded, state = self.encoder(encoded, begin_state)
if self._dropout:
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0,))
out = self.decoder(encoded)
return out, state
def dropout(x):
return nd.Dropout(x)
def forward(self,
word_inputs,
tag_inputs,
arc_targets=None,
rel_targets=None):
"""Run decoding
Parameters
----------
word_inputs : mxnet.ndarray.NDArray
word indices of seq_len x batch_size
tag_inputs : mxnet.ndarray.NDArray
tag indices of seq_len x batch_size
arc_targets : mxnet.ndarray.NDArray
gold arc indices of seq_len x batch_size
rel_targets : mxnet.ndarray.NDArray
gold rel indices of seq_len x batch_size
Returns
-------
tuple
(arc_accuracy, rel_accuracy, overall_accuracy, loss) when training, else if given gold target
then return arc_accuracy, rel_accuracy, overall_accuracy, outputs, otherwise return outputs, where outputs is a
list of (arcs, rels).
"""
is_train = autograd.is_training()
def flatten_numpy(ndarray):
"""Flatten nd-array to 1-d column vector
Parameters
----------
ndarray : numpy.ndarray
input tensor
Returns
-------
numpy.ndarray
A column vector
"""
return np.reshape(ndarray, (-1, ), 'F')
batch_size = word_inputs.shape[1]
seq_len = word_inputs.shape[0]
mask = np.greater(word_inputs, self._vocab.ROOT).astype(np.float32)
num_tokens = int(np.sum(mask)) # non padding, non root token number
if is_train or arc_targets is not None:
mask_1D = flatten_numpy(mask)
mask_1D_tensor = nd.array(mask_1D)
unked_words = np.where(word_inputs < self._vocab.words_in_train,
word_inputs, self._vocab.UNK)
word_embs = self.word_embs(nd.array(unked_words, dtype='int'))
if self.pret_word_embs:
word_embs = word_embs + self.pret_word_embs(nd.array(word_inputs))
tag_embs = self.tag_embs(nd.array(tag_inputs))
# Dropout
emb_inputs = nd.concat(word_embs, tag_embs,
dim=2) # seq_len x batch_size
top_recur = biLSTM(
self.f_lstm,
self.b_lstm,
emb_inputs,
batch_size,
dropout_x=self.dropout_lstm_input if is_train else 0)
top_recur = nd.Dropout(data=top_recur, axes=[0], p=self.dropout_mlp)
W_dep, b_dep = self.mlp_dep_W.data(), self.mlp_dep_b.data()
W_head, b_head = self.mlp_head_W.data(), self.mlp_head_b.data()
dep, head = leaky_relu(nd.dot(top_recur, W_dep.T) + b_dep), leaky_relu(
nd.dot(top_recur, W_head.T) + b_head)
dep, head = nd.Dropout(data=dep, axes=[0],
p=self.dropout_mlp), nd.Dropout(
data=head, axes=[0], p=self.dropout_mlp)
dep, head = nd.transpose(dep, axes=[2, 0,
1]), nd.transpose(head,
axes=[2, 0, 1])
dep_arc, dep_rel = dep[:self.mlp_arc_size], dep[self.mlp_arc_size:]
head_arc, head_rel = head[:self.mlp_arc_size], head[self.mlp_arc_size:]
W_arc = self.arc_W.data()
arc_logits = bilinear(dep_arc,
W_arc,
head_arc,
self.mlp_arc_size,
seq_len,
batch_size,
num_outputs=1,
bias_x=True,
bias_y=False)
# (#head x #dep) x batch_size
flat_arc_logits = reshape_fortran(arc_logits,
(seq_len, seq_len * batch_size))
# (#head ) x (#dep x batch_size)
arc_preds = arc_logits.argmax(0)
# seq_len x batch_size
if is_train or arc_targets is not None:
correct = np.equal(arc_preds.asnumpy(), arc_targets)
arc_correct = correct.astype(np.float32) * mask
arc_accuracy = np.sum(arc_correct) / num_tokens
targets_1D = flatten_numpy(arc_targets)
losses = self.softmax_loss(flat_arc_logits, nd.array(targets_1D))
arc_loss = nd.sum(losses * mask_1D_tensor) / num_tokens
if not is_train:
arc_probs = np.transpose(
np.reshape(
nd.softmax(flat_arc_logits, axis=0).asnumpy(),
(seq_len, seq_len, batch_size), 'F'))
# #batch_size x #dep x #head
W_rel = self.rel_W.data()
rel_logits = bilinear(dep_rel,
W_rel,
head_rel,
self.mlp_rel_size,
seq_len,
batch_size,
num_outputs=self._vocab.rel_size,
bias_x=True,
bias_y=True)
# (#head x rel_size x #dep) x batch_size
flat_rel_logits = reshape_fortran(
rel_logits, (seq_len, self._vocab.rel_size, seq_len * batch_size))
# (#head x rel_size) x (#dep x batch_size)
_target_vec = nd.array(targets_1D if is_train else flatten_numpy(
arc_preds.asnumpy())).reshape(seq_len * batch_size, 1)
_target_mat = _target_vec * nd.ones((1, self._vocab.rel_size))
partial_rel_logits = nd.pick(flat_rel_logits, _target_mat.T, axis=0)
# (rel_size) x (#dep x batch_size)
if is_train or arc_targets is not None:
rel_preds = partial_rel_logits.argmax(0)
targets_1D = flatten_numpy(rel_targets)
rel_correct = np.equal(rel_preds.asnumpy(), targets_1D).astype(
np.float32) * mask_1D
rel_accuracy = np.sum(rel_correct) / num_tokens
losses = self.softmax_loss(partial_rel_logits,
nd.array(targets_1D))
rel_loss = nd.sum(losses * mask_1D_tensor) / num_tokens
if not is_train:
rel_probs = np.transpose(
np.reshape(
nd.softmax(flat_rel_logits.transpose([1, 0, 2]),
axis=0).asnumpy(),
(self._vocab.rel_size, seq_len, seq_len, batch_size), 'F'))
# batch_size x #dep x #head x #nclasses
if is_train or arc_targets is not None:
loss = arc_loss + rel_loss
correct = rel_correct * flatten_numpy(arc_correct)
overall_accuracy = np.sum(correct) / num_tokens
if is_train:
return arc_accuracy, rel_accuracy, overall_accuracy, loss
outputs = []
for msk, arc_prob, rel_prob in zip(np.transpose(mask), arc_probs,
rel_probs):
# parse sentences one by one
msk[0] = 1.
sent_len = int(np.sum(msk))
arc_pred = arc_mst(arc_prob, sent_len, msk)
rel_prob = rel_prob[np.arange(len(arc_pred)), arc_pred]
rel_pred = rel_argmax(rel_prob, sent_len)
outputs.append((arc_pred[1:sent_len], rel_pred[1:sent_len]))
if arc_targets is not None:
return arc_accuracy, rel_accuracy, overall_accuracy, outputs
return outputs
def forward(self,
inputs,
begin_state=None,
token_types=None,
valid_length=None,
masked_positions=None): # pylint: disable=arguments-differ
"""Implement the forward computation that the awd language model and cache model use.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
initial recurrent state tensor with length equals to num_layers.
the initial state with shape `(1, batch_size, num_hidden)`
token_types: NDArray
input token type tensor, shape (batch_size, seq_length).
If the inputs contain two sequences, then the token type of the first
sequence differs from that of the second one.
valid_length: NDArray
optional tensor of input sequence valid lengths, shape (batch_size,)
masked_positions: optional tensor of position of tokens for masked LM decoding,
shape (batch_size, num_masked_positions).
Returns
--------
out: NDArray
output tensor with shape `(sequence_length, batch_size, input_size)`
when `layout` is "TNC".
out_states: list
output recurrent state tensor with length equals to num_layers.
the state with shape `(1, batch_size, num_hidden)`
encoded_raw: list
The list of outputs of the model's encoder with length equals to num_layers.
the shape of every encoder's output `(sequence_length, batch_size, num_hidden)`
encoded_dropped: list
The list of outputs with dropout of the model's encoder with length equals
to num_layers. The shape of every encoder's dropped output
`(sequence_length, batch_size, num_hidden)`
"""
batch_size = inputs.shape[1]
inputs = nd.transpose(inputs, axes=(1, 0))
if token_types is None:
token_types = nd.zeros_like(inputs)
encoded = self.embedding(inputs,
token_types=token_types,
valid_length=valid_length,
masked_positions=masked_positions)
encoded = nd.transpose(encoded, axes=(1, 0, 2))
encoded = nd.Dropout(encoded, p=self._drop_i, axes=(0, ))
if not begin_state:
begin_state = self.begin_state(batch_size=batch_size)
out_states = []
encoded_raw = []
encoded_dropped = []
for i, (e, s) in enumerate(zip(self.encoder, begin_state)):
encoded, state = e(encoded, s)
encoded_raw.append(encoded)
out_states.append(state)
if i != len(self.encoder) - 1:
encoded = nd.Dropout(encoded, p=self._drop_h, axes=(0, ))
encoded_dropped.append(encoded)
encoded = nd.Dropout(encoded, p=self._dropout, axes=(0, ))
encoded_dropped.append(encoded)
#use mos
latent = nd.Dropout(self.latent(encoded), p=self._drop_l, axes=(0, ))
logit = self.decoder(latent.reshape(-1, self._embed_size))
prior_logit = self.prior(encoded).reshape(-1, self._num_experts)
prior = nd.softmax(prior_logit, axis=-1)
prob = nd.softmax(logit.reshape(-1, self._vocab_size), axis=-1)
prob = prob.reshape(-1, self._num_experts, self._vocab_size)
prob = (prob *
prior.expand_dims(2).broadcast_to(prob.shape)).sum(axis=1)
out = nd.log(nd.add(prob, 1e-8)).reshape(-1, batch_size,
self._vocab_size)
return out, out_states, encoded_raw, encoded_dropped
def dropout2(X, drop_rate):
autograd.set_training(True)
Z = nd.zeros_like(X)
nd.Dropout(X, p=drop_rate, out=Z)
return Z
