def forward(self, words, labels=None):
multitask = labels is not None
if self.training:
words = self.word_vocab.unkify(words)
rnn = self.rnn_builder.initial_state()
word_ids = [self.word_vocab.index_or_unk(word)
for word in [START] + words + [STOP]]
prev_embeddings = [self.embeddings[word_id] for word_id in word_ids[:-1]]
lstm_outputs = rnn.transduce(prev_embeddings)
logits = self.out(dy.concatenate_to_batch(lstm_outputs))
nlls = dy.pickneglogsoftmax_batch(logits, word_ids[1:])
word_nll = dy.sum_batches(nlls)
if multitask:
label_ids = [self.label_vocab.index(label) for label in labels]
logits = self.f_label(dy.concatenate_to_batch(lstm_outputs[1:]))
nlls = dy.pickneglogsoftmax_batch(logits, label_ids)
label_nll = dy.sum_batches(nlls)
# easy proxy to track progress on this task
self.correct += np.sum(np.argmax(logits.npvalue(), axis=0) == label_ids)
self.predicted += len(label_ids)
nll = word_nll + label_nll
else:
nll = word_nll
return nll
def __evaluate(self, lstm_output):
length = len(lstm_output)
# (i, j) -> (i * length + j,)
# i = k / length, j = k % length
# 1 1 2 2 3 3 4 4 ..
heads = [
dn.transpose(self.activation(self.head_dense_layer(
lstm_output[i]))) for i in range(length)
]
mods = [
self.activation(self.dep_dense_layer(lstm_output[i]))
for i in range(length)
]
head_part = dn.concatenate_to_batch(
[heads[i // len(lstm_output)] for i in range(length * length)])
# 1 2 3 4 .. 1 2 3 4 ...
mod_part = dn.concatenate_to_batch([mods[i]
for i in range(length)] * length)
output = self.fusion_layer(head_part, mod_part)
exprs = [[
dn.pick_batch_elem(output, i * length + j) for j in range(length)
] for i in range(length)]
scores = output.npvalue()
scores = scores.reshape((len(lstm_output), len(lstm_output)))
return scores, exprs
def forward(self, words, spans=None):
multitask = spans is not None
if self.training:
words = self.word_vocab.unkify(words)
rnn = self.rnn_builder.initial_state()
word_ids = [self.word_vocab.index_or_unk(word)
for word in [START] + words + [STOP]]
prev_embeddings = [self.embeddings[word_id] for word_id in word_ids[:-1]]
lstm_outputs = rnn.transduce(prev_embeddings)
logits = self.out(dy.concatenate_to_batch(lstm_outputs))
nlls = dy.pickneglogsoftmax_batch(logits, word_ids[1:])
word_nll = dy.sum_batches(nlls)
if multitask:
# predict label for each possible span (null for nonexistent spans)
if self.predict_all_spans:
gold_spans = {(left, right): self.label_vocab.index(label)
for left, right, label in spans}
all_spans = [(left, left + length)
for length in range(1, len(words) + 1)
for left in range(0, len(words) + 1 - length)]
label_ids = [gold_spans.get((left, right), self.label_vocab.size) # last index is for null label
for left, right in all_spans]
# 'lstm minus' features, same as those of the crf parser
span_encodings = [lstm_outputs[right] - lstm_outputs[left]
for left, right in all_spans]
# only predict labels for existing spans
else:
label_ids = [self.label_vocab.index(label) for _, _, label in spans]
# 'lstm minus' features, same as those of the crf parser
span_encodings = [lstm_outputs[right] - lstm_outputs[left]
for left, right, label in spans]
logits = self.f_label(dy.concatenate_to_batch(span_encodings))
nlls = dy.pickneglogsoftmax_batch(logits, label_ids)
label_nll = dy.sum_batches(nlls)
# easy proxy to track progress on this task
self.correct += np.sum(np.argmax(logits.npvalue(), axis=0) == label_ids)
self.predicted += len(label_ids)
nll = word_nll + label_nll
else:
nll = word_nll
return nll
def __call__(self, encoder_output, hsz, beam_width=1):
h_i = self.get_state(encoder_output)
context = encoder_output.output
if beam_width > 1:
# To vectorize, we need to expand along the batch dimension, K times
context = [dy.concatenate_to_batch([c] * beam_width) for c in context]
h_i = [dy.concatenate_to_batch([h] * beam_width) for h in h_i]
_, batchsz = context[0].dim()
init_zeros = dy.zeros((hsz,), batch_size=batchsz)
return h_i, init_zeros, context
def transduce(self, inputs, masks, predict=False):
if not self.init:
print("No Initial state provided")
return
outputs = []
batch_size = inputs[0].dim()[1]
for idx, input_tensor in enumerate(inputs):
recur_s = []
cell_s = []
out = []
hidden = self.hidden_previous
cell = self.cell_previous
if not predict:
input_tensor = dy.cmult(input_tensor, self.input_drop_mask)
hidden = dy.cmult(hidden, self.recur_drop_mask)
gates = dy.affine_transform([
self.b.expr(),
self.WXH.expr(),
dy.concatenate([input_tensor, hidden])
])
iga = dy.pickrange(gates, 0, self.recur_size)
fga = dy.pickrange(gates, self.recur_size, 2 * self.recur_size)
oga = dy.pickrange(gates, 2 * self.recur_size, 3 * self.recur_size)
cga = dy.pickrange(gates, 3 * self.recur_size, 4 * self.recur_size)
ig = dy.logistic(iga)
fg = dy.logistic(fga) # +self.forget_bias
og = dy.logistic(oga)
c_tilda = dy.tanh(cga)
new_cell = dy.cmult(cell, fg) + dy.cmult(c_tilda, ig)
new_hidden = dy.cmult(dy.tanh(new_cell), og)
for jdx in range(batch_size):
if masks[idx][jdx] == 1:
h_t = dy.pick_batch_elem(new_hidden, jdx)
recur_s.append(h_t)
cell_s.append(dy.pick_batch_elem(new_cell, jdx))
out.append(h_t)
else:
recur_s.append(dy.pick_batch_elem(hidden, jdx))
cell_s.append(dy.pick_batch_elem(cell, jdx))
out.append(dy.zeros(self.recur_size))
new_cell = dy.concatenate_to_batch(cell_s)
new_hidden = dy.concatenate_to_batch(recur_s)
self.cell_previous = new_cell
self.hidden_previous = new_hidden
outputs.append(dy.concatenate_to_batch(out))
return outputs
def __call__(self, x, z=None, mask=None):
h = self.h
if z == None:
Q = self.W_Q(x)
K = self.W_K(x)
V = self.W_V(x)
else:
Q = self.W_Q(x)
K = self.W_K(z)
V = self.W_V(z)
(n_units, n_querys), batch = Q.dim()
(_, n_keys), _ = K.dim()
batch_Q = dy.concatenate_to_batch(self.split_rows(Q, h))
batch_K = dy.concatenate_to_batch(self.split_rows(K, h))
batch_V = dy.concatenate_to_batch(self.split_rows(V, h))
assert(batch_Q.dim() == (n_units // h, n_querys), batch * h)
assert(batch_K.dim() == (n_units // h, n_keys), batch * h)
assert(batch_V.dim() == (n_units // h, n_keys), batch * h)
mask = np.concatenate([mask] * h, axis=0)
mask = np.moveaxis(mask, [1, 0, 2], [0, 2, 1])
mask = dy.inputTensor(mask, batched=True)
batch_A = (dy.transpose(batch_Q) * batch_K) * self.scale_score
batch_A = dy.cmult(batch_A, mask) + (1 - mask)*MIN_VALUE
sent_len = batch_A.dim()[0][0]
if sent_len == 1:
batch_A = dy.softmax(batch_A)
else:
batch_A = dy.softmax(batch_A, d=1)
batch_A = dy.cmult(batch_A, mask)
assert (batch_A.dim() == ((n_querys, n_keys), batch * h))
if self.attn_dropout:
if self.dropout != 0.0:
batch_A = dy.dropout(batch_A, self.dropout)
batch_C = dy.transpose(batch_A * dy.transpose(batch_V))
assert (batch_C.dim() == ((n_units // h, n_querys), batch * h))
C = dy.concatenate(self.split_batch(batch_C, h), d=0)
assert (C.dim() == ((n_units, n_querys), batch))
C = self.finishing_linear_layer(C)
return C
def test_concatenate_to_batch(self):
dy.renew_cg()
x = dy.lookup_batch(self.p, [0, 1])
y = dy.pick_batch_elem(x, 0)
z = dy.pick_batch_elem(x, 1)
w = dy.concatenate_to_batch([y, z])
self.assertTrue(np.allclose(w.npvalue(), self.pval.T))
def test_concatenate_to_batch(self):
dy.renew_cg()
x = dy.lookup_batch(self.p, [0, 1])
y = dy.pick_batch_elem(x, 0)
z = dy.pick_batch_elem(x, 1)
w = dy.concatenate_to_batch([y, z])
self.assertTrue(np.allclose(w.npvalue(), self.pval.T))
def produce_parse_forest(self, sentence, required_probability_mass):
lstm_outputs = self._featurize_sentence(sentence, is_train=False)
encodings = []
spans = []
for start in range(0, len(sentence)):
for end in range(start + 1, len(sentence) + 1):
spans.append((start, end))
encodings.append(self._get_span_encoding(start, end, lstm_outputs))
label_scores = self.f_label(dy.concatenate_to_batch(encodings))
label_scores_reshaped = dy.reshape(label_scores,
(self.label_vocab.size, len(encodings)))
label_probabilities_np = dy.softmax(label_scores_reshaped).npvalue()
span_to_labels = {}
forest_prob_mass = 1
for index, span in enumerate(spans):
distribution = list(enumerate(label_probabilities_np[:, index]))
distribution.sort(key=lambda x: - x[1])
total_probability = 0
labels = []
while total_probability < required_probability_mass:
(label_index, probability) = distribution.pop()
labels.append(self.label_vocab.values[label_index])
total_probability += probability
forest_prob_mass *= total_probability
span_to_labels[span] = labels
return span_to_labels, forest_prob_mass
def embed(self, x):
if self.word_dropout > 0.0 and self.word_id_mask is None:
batch_size = len(x) if xnmt.batcher.is_batched(x) else 1
self.word_id_mask = [set(np.random.choice(self.vocab_size, int(self.vocab_size * self.word_dropout), replace=False)) for _ in range(batch_size)]
emb_e = dy.parameter(self.embeddings)
# single mode
if not xnmt.batcher.is_batched(x):
if self.train and self.word_id_mask and x in self.word_id_mask[0]:
ret = dy.zeros((self.emb_dim,))
else:
ret = dy.pick(emb_e, index=x)
if self.fix_norm != None:
ret = dy.cdiv(ret, dy.l2_norm(ret))
if self.fix_norm != 1:
ret *= self.fix_norm
# minibatch mode
else:
ret = dy.concatenate_to_batch([dy.pick(emb_e, index=xi) for xi in x])
if self.fix_norm != None:
ret = dy.cdiv(ret, dy.l2_norm(ret))
if self.fix_norm != 1:
ret *= self.fix_norm
if self.train and self.word_id_mask and any(x[i] in self.word_id_mask[i] for i in range(len(x))):
dropout_mask = dy.inputTensor(np.transpose([[0.0]*self.emb_dim if x[i] in self.word_id_mask[i] else [1.0]*self.emb_dim for i in range(len(x))]), batched=True)
ret = dy.cmult(ret, dropout_mask)
if self.train and self.weight_noise > 0.0:
ret = dy.noise(ret, self.weight_noise)
return ret
def beam_decode(self, encodings, input_len=10, beam_size=1): batch_size = 1 self.__dec.init_params(encodings, batch_size, self.__train_flag) context = dy.zeros((self.__enc.output_dim, )) beams = [Beam(self.__dec.dec_state, context, [self.__trg_sos], 0.0)] for i in xrange(int(min(self.__max_len, input_len * 1.5))): new_beams = [] p_list = [] for b in beams: if b.words[-1] == self.__trg_eos: p_list.append(dy.ones((self.__trg_vsize, ))) continue hidden, embs, b.state = self.__dec.next([b.words[-1]], b.context, self.__train_flag, b.state) b.context, _ = self.attend(encodings, hidden) score = self.__dec.score(hidden, b.context, embs, self.__train_flag) p_list.append(dy.softmax(score)) p_list = dy.concatenate_to_batch(p_list).npvalue().T.reshape(-1, self.__trg_vsize) for p, b in zip(p_list, beams): p = p.flatten() / p.sum() kbest = np.argsort(p) if b.words[-1] == self.__trg_eos: new_beams.append(Beam(b.state, b.context, b.words, b.log_prob)) else: for next_word in kbest[-beam_size:]: new_beams.append(Beam(b.state, b.context, b.words + [next_word], b.log_prob + np.log(p[next_word]))) beams = sorted(new_beams, key=lambda b: b.log_prob)[-beam_size:] if beams[-1].words[-1] == self.__trg_eos: break return beams[-1].words
def predict_sequence_batched(self,
inputs,
mask_array,
wlen,
predictFlag=False):
batch_size = inputs[0].dim()[1]
src_len = len(inputs)
if not predictFlag:
self.charlstm.set_dropouts(self.dropout, self.dropout)
self.charlstm.set_dropout_masks(batch_size)
char_fwd = self.charlstm.initial_state(batch_size)
recur_states, cells = char_fwd.add_inputs(inputs, mask_array,
predictFlag)
hidden_states = []
for idx in range(src_len):
mask = dy.inputVector(mask_array[idx])
mask_expr = dy.reshape(mask, (1, ), batch_size)
hidden_states.append(recur_states[idx] * mask_expr)
H = dy.concatenate_cols(hidden_states)
if (predictFlag):
a = dy.softmax(dy.transpose(self.W_atten.expr()) * H)
else:
#dropout attention connections(keep the same dim across the sequence)
a = dy.softmax(
dy.transpose(self.W_atten.expr()) *
dy.dropout_dim(H, 1, self.dropout))
cell_states = []
for idx in range(batch_size):
if (wlen[idx] > 0):
cell = dy.pick_batch_elem(cells[wlen[idx] - 1], idx)
else:
cell = dy.zeros(self.ldims)
cell_states.append(cell)
C = dy.concatenate_to_batch(cell_states)
H_atten = H * dy.transpose(a)
char_emb = dy.concatenate([H_atten, C])
if predictFlag:
proj_char_emb = dy.affine_transform(
[self.b_linear.expr(),
self.W_linear.expr(), char_emb])
else:
proj_char_emb = dy.affine_transform([
self.b_linear.expr(),
self.W_linear.expr(),
dy.dropout(char_emb, self.dropout)
])
return proj_char_emb
def disc_ll(self):
try:
return dy.concatenate_to_batch(self.ll_buffer)
finally:
# Make sure that the state is not used again after the log likelihood is requested
del self.ll_buffer
del self.batch_size
del self.counter
def _encodings_to_label_log_probabilities(self, encodings, lmbd=None):
label_scores = self.f_label(dy.concatenate_to_batch(encodings))
label_scores_reshaped = dy.reshape(label_scores, (self.label_vocab.size, len(encodings)))
if lmbd is not None:
label_scores_reshaped = dy.cmult(label_scores_reshaped, lmbd)
return dy.log_softmax(label_scores_reshaped)
def evaluate_all_states(self, states):
input_tensors = dn.concatenate_to_batch(
[state.get_input_tensor(self.k, self.empty) for state in states])
action_outputs = self.action_classifier(input_tensors)
relation_outputs = None
if self.relation:
relation_outputs = self.relation_classifier(input_tensors)
return action_outputs, relation_outputs
def aggressive_annotation(self,
sentence,
sentence_number,
span_to_gold_label,
low_conf_cutoff,
seen):
if len(span_to_gold_label) == 0:
return [] # , []
lstm_outputs = self._featurize_sentence(sentence, is_train=False)
encodings = []
spans = span_to_gold_label.keys()
for (start, end) in spans:
encodings.append(self._get_span_encoding(start, end, lstm_outputs))
label_scores = self.f_label(dy.concatenate_to_batch(encodings))
label_scores_reshaped = dy.reshape(label_scores,
(self.label_vocab.size, len(encodings)))
label_probabilities_np = dy.softmax(label_scores_reshaped).npvalue()
low_confidence_labels = []
# high_confidence_labels = []
on_labels = []
for index, (start, end) in list(enumerate(spans)):
distribution = label_probabilities_np[:, index]
entropy = stats.entropy(distribution)
oracle_label = span_to_gold_label[(start, end)]
annotation_request = dict(
sentence_number=sentence_number,
left=start,
right=end,
entropy=entropy,
non_constituent_probability=distribution[0],
label=oracle_label
)
if (start, end) in seen:
del span_to_gold_label[(start, end)]
continue
if low_conf_cutoff < entropy and distribution[self.empty_label_index] < 0.5:
# annotation_request['label'] = oracle_label
low_confidence_labels.append(annotation_request)
elif entropy < 10 ** -5 and distribution[self.empty_label_index] > 0.99:
del span_to_gold_label[(start, end)]
# if entropy > 10 ** -7:
# high_confidence_labels.append(annotation_request)
if np.max(distribution) > distribution[self.empty_label_index]:
on_labels.append(annotation_request)
for index, label_a in enumerate(on_labels):
span_a = (label_a['left'], label_a['right'])
for label_b in on_labels[index + 1:]:
span_b = (label_b['left'], label_b['right'])
if check_overlap(span_a, span_b):
label_a['entropy'] = 10
low_confidence_labels.append(label_a)
label_b['entropy'] = 10
low_confidence_labels.append(label_b)
return low_confidence_labels # , high_confidence_labels
def _encodings_to_label_log_probabilities(self, encodings, lmbd=None, alpha=None):
label_scores = self.f_label(dy.concatenate_to_batch(encodings))
label_scores_reshaped = dy.reshape(label_scores, (self.label_vocab.size, len(encodings)))
# if alpha is not None:
# temp = dy.abs(dy.reshape(alpha[0], (1, 1)))
# label_scores_reshaped = dy.cmult(dy.logistic(dy.cmult(label_scores_reshaped, temp) + alpha[1]), lmbd) + alpha[2]
# 990.51641846]] [ 0.03124614 4.00097179 -9.43100834
# label_scores_reshaped = dy.logistic(label_scores_reshaped * 0.03124614 + 4.00097179) * 990.51641846 - 9.43100834
return dy.log_softmax(label_scores_reshaped)
def cal_scores(self, src_encodings, masks, train):
src_len = len(src_encodings)
batch_size = src_encodings[0].dim()[1]
heads_LRlayer = []
mods_LRlayer = []
for encoding in src_encodings:
heads_LRlayer.append(
self.leaky_ReLu(self.b_head.expr() +
self.W_head.expr() * encoding))
mods_LRlayer.append(
self.leaky_ReLu(self.b_mod.expr() +
self.W_mod.expr() * encoding))
heads_labels = []
heads = []
labels = []
neg_inf = dy.constant(1, -float("inf"))
for row in range(
1, src_len
): #exclude root @ index=0 since roots do not have heads
scores_idx = []
for col in range(src_len):
dist = col - row
mdist = self.dist_max
dist_i = (min(dist, mdist - 1) + mdist if dist >= 0 else int(
min(-1.0 * dist, mdist - 1)))
dist_vec = dy.lookup_batch(self.dlookup, [dist_i] * batch_size)
if train:
input_vec = dy.concatenate([
dy.esum([
dy.dropout(heads_LRlayer[col], self.dropout),
dy.dropout(mods_LRlayer[row], self.dropout)
]), dist_vec
])
else:
input_vec = dy.concatenate([
dy.esum([heads_LRlayer[col], mods_LRlayer[row]]),
dist_vec
])
score = self.scoreHeadModLabel(input_vec, train)
mask = masks[row] and masks[col]
join_scores = []
for bdx in range(batch_size):
if (mask[bdx] == 1):
join_scores.append(dy.pick_batch_elem(score, bdx))
else:
join_scores.append(
dy.concatenate([neg_inf] * self.n_labels))
scores_idx.append(dy.concatenate_to_batch(join_scores))
heads_labels.append(dy.concatenate(scores_idx))
return heads_labels
def transduce(self,
embed_sent: ExpressionSequence) -> List[ExpressionSequence]:
batch_size = embed_sent[0].dim()[1]
actions = self.sample_segmentation(embed_sent, batch_size)
sample_size = len(actions)
embeddings = dy.concatenate(embed_sent.expr_list, d=1)
embeddings.value()
#
composed_words = []
for i in range(batch_size):
sequence = dy.pick_batch_elem(embeddings, i)
# For each sampled segmentations
for j, sample in enumerate(actions):
lower_bound = 0
# Read every 'segment' decision
for k, upper_bound in enumerate(sample[i]):
char_sequence = dy.pick_range(sequence, lower_bound,
upper_bound + 1, 1)
composed_words.append(
(dy.pick_range(sequence, lower_bound, upper_bound + 1,
1), j, i, k, lower_bound,
upper_bound + 1))
#self.segment_composer.set_word_boundary(lower_bound, upper_bound, self.src_sent[i])
#composed = self.segment_composer.transduce(char_sequence)
#outputs[j][i].append(composed)
lower_bound = upper_bound + 1
outputs = self.segment_composer.compose(composed_words, sample_size,
batch_size)
# Padding + return
try:
if self.length_prior:
seg_size_unpadded = [[
len(outputs[i][j]) for j in range(batch_size)
] for i in range(sample_size)]
enc_outputs = []
for batched_sampled_sentence in outputs:
sampled_sentence, segment_mask = self.pad(
batched_sampled_sentence)
expr_seq = ExpressionSequence(
expr_tensor=dy.concatenate_to_batch(sampled_sentence),
mask=segment_mask)
sent_context = self.final_transducer.transduce(expr_seq)
self.final_states.append(
self.final_transducer.get_final_states())
enc_outputs.append(sent_context)
return CompoundSeqExpression(enc_outputs)
finally:
if self.length_prior:
self.seg_size_unpadded = seg_size_unpadded
self.compose_output = outputs
self.segment_actions = actions
if not self.train and self.compute_report:
self.add_sent_for_report({"segment_actions": actions})
def fit_partial(self, instances):
random.shuffle(instances)
self.iter += 1
losses = []
dy.renew_cg()
total_loss, total_size = 0., 0
prog = tqdm(desc="Epoch {}".format(self.iter), ncols=80, total=len(instances) + 1)
for i, ins in enumerate(instances, 1):
losses.extend(list(self.model.loss(*ins)))
if i % self.batch_size == 0:
loss = dy.sum_batches(dy.concatenate_to_batch(losses))
total_loss += loss.value()
total_size += len(losses)
prog.set_postfix(loss=loss.value()/len(losses))
loss.backward()
self.opt.update()
dy.renew_cg()
losses = []
prog.update()
if losses:
loss = dy.sum_batches(dy.concatenate_to_batch(losses))
total_loss += loss.value()
total_size += len(losses)
self.loss = total_loss / total_size
prog.set_postfix(loss=self.loss)
loss.backward()
self.opt.update()
dy.renew_cg()
prog.update()
self.opt.learning_rate *= self.lr_decay
prog.close()
def get_complete_raw_exprs(self, lstm_output):
length = len(lstm_output)
lstm_output_as_batch = dn.concatenate_to_batch(lstm_output)
headfov = self.bilinear_layer.w1.expr() * lstm_output_as_batch
modfov = self.bilinear_layer.w2.expr() * lstm_output_as_batch
# (i, j) -> (i * length + j,)
# i = k / length, j = k % length
# 1 1 2 2 3 3 4 4 ..
heads = [dn.pick_batch_elem(headfov, i) for i in range(length)]
mods = [dn.pick_batch_elem(modfov, i) for i in range(length)]
head_part = dn.concatenate_to_batch([heads[i // len(lstm_output)] for i in range(length * length)])
# 1 2 3 4 .. 1 2 3 4 ...
mod_part = dn.concatenate_to_batch([mods[i] for i in range(length)] * length)
hidden = self.activation(head_part + mod_part + self.bilinear_layer.bias.expr())
struct_dropout = getattr(self.options, "struct_dropout", 0.0)
if self.options.is_train and struct_dropout > 0:
hidden = dn.dropout(hidden, struct_dropout)
output = self.dense_layer(hidden)
return output
def get_complete_raw_exprs(self, lstm_output):
length = len(lstm_output)
lstm_output_as_batch = dn.concatenate_to_batch(lstm_output)
headfov = self.bilinear_layer.w1.expr() * lstm_output_as_batch
modfov = self.bilinear_layer.w2.expr() * lstm_output_as_batch
# (i, j) -> (i * length + j,)
# i = k / length, j = k % length
# 1 1 2 2 3 3 4 4 ..
heads = [dn.pick_batch_elem(headfov, i) for i in range(length)]
mods = [dn.pick_batch_elem(modfov, i) for i in range(length)]
head_part = dn.concatenate_to_batch(
[heads[i // len(lstm_output)] for i in range(length * length)])
# 1 2 3 4 .. 1 2 3 4 ...
mod_part = dn.concatenate_to_batch([mods[i]
for i in range(length)] * length)
output = self.dense_layer(
self.activation(head_part + mod_part +
self.bilinear_layer.bias.expr()))
return output
def rnn_encode(rnn, input_, lengths):
"""Return the final output for each batch based on lengths.
:param rnn: dy.RNNBuilder or dy.BiRNNBuilder
:param input_: List[dy.Expression]
:param lengths: List[int]
Returns:
dy.Expression
"""
states = rnn_forward(rnn, input_)
final_states = [dy.pick_batch_elem(states[l - 1], i) for i, l in enumerate(lengths)]
return dy.concatenate_to_batch(final_states)
def rnn_encode(rnn, input_, lengths):
"""Return the final output for each batch based on lengths.
:param rnn: dy.RNNBuilder or dy.BiRNNBuilder
:param input_: List[dy.Expression]
:param lengths: List[int]
Returns:
dy.Expression
"""
states = rnn_forward(rnn, input_)
final_states = [dy.pick_batch_elem(states[l - 1], i) for i, l in enumerate(lengths)]
return dy.concatenate_to_batch(final_states)
def return_spans_and_uncertainties(self,
sentence,
sentence_number,
gold,
use_oracle,
low_conf_cutoff,
pseudo_label_cutoff,
seen):
spans = [span for span in get_all_spans(gold).keys() if
(span, sentence_number) not in seen]
if len(spans) == 0:
return []
lstm_outputs = self._featurize_sentence(sentence, is_train=False)
encodings = []
for (start, end) in spans:
encodings.append(self._get_span_encoding(start, end, lstm_outputs))
label_scores = self.f_label(dy.concatenate_to_batch(encodings))
label_scores_reshaped = dy.reshape(label_scores,
(self.label_vocab.size, len(encodings)))
label_probabilities_np = dy.softmax(label_scores_reshaped).npvalue()
low_confidence_labels = []
high_confidence_labels = []
for index, (start, end) in enumerate(spans):
distribution = label_probabilities_np[:, index]
entropy = stats.entropy(distribution)
oracle_label = gold.oracle_label(start, end)
predicted_label_index = distribution.argmax()
predicted_label = self.label_vocab.value(predicted_label_index)
annotation_request = dict(
sentence_number=sentence_number,
left=start,
right=end,
entropy=entropy,
non_constituent_probability=distribution[0]
)
if use_oracle:
oracle_label_index = self.label_vocab.index(oracle_label)
if oracle_label_index != predicted_label_index and distribution[
oracle_label_index] > 0.01:
annotation_request['label'] = oracle_label
low_confidence_labels.append(annotation_request)
elif max(distribution) > pseudo_label_cutoff and (
distribution[
self.empty_label_index] < 0.001 or random.random() < 0.001):
annotation_request['label'] = predicted_label
high_confidence_labels.append(annotation_request)
elif low_conf_cutoff < entropy:
annotation_request['label'] = oracle_label
low_confidence_labels.append(annotation_request)
return low_confidence_labels, high_confidence_labels
def rnn_forward_with_state(rnn,
input_,
lengths=None,
state=None,
batched=True,
backward=False):
"""Return the output of the final layers and the final state of the RNN.
:param rnn: dy.RNNBuilder
:param input_: List[dy.Expression]
:param lengths: List[int]
:param state: List[np.ndarray] The previous state (used in TBPTT)
:param batched: bool Is the state batched?
:param backward: bool Is this a backward rnn in a bRNN?
Returns:
List[dy.Expression] (Seq_len): The outputs
List[dy.Expression] (2 * layers if lstm): The state
"""
if state is not None:
state = [dy.inputTensor(s, batched) for s in state]
lstm_state = rnn.initial_state(state)
if backward:
states = lstm_state.add_inputs(reversed(input_))
outputs = list(reversed([s.h()[-1] for s in states]))
# When going backwards (we pad right) the final state of the rnn
# is always the last one.
final_state = states[-1].s()
return outputs, final_state
states = lstm_state.add_inputs(input_)
outputs = [s.h()[-1] for s in states]
if lengths is None:
if backward:
outputs = list(reversed(outputs))
return outputs, states[-1].s()
final_states = [states[l - 1].s() for l in lengths]
final_state_by_batch = []
for i, state in enumerate(final_states):
batch_state = [dy.pick_batch_elem(s, i) for s in state]
final_state_by_batch.append(batch_state)
final_state = []
for i in range(len(final_state_by_batch[0])):
col = dy.concatenate_to_batch([
final_state_by_batch[j][i]
for j in range(len(final_state_by_batch))
])
final_state.append(col)
if backward:
outputs = list(reversed(outputs))
return outputs, final_state
def forward(self, words):
if self.training:
words = self.word_vocab.unkify(words)
rnn = self.rnn_builder.initial_state()
word_ids = [self.word_vocab.index_or_unk(word) for word in [START] + words + [STOP]]
prev_embeddings = [self.embeddings[word_id] for word_id in word_ids[:-1]]
lstm_outputs = rnn.transduce(prev_embeddings)
logits = self.out(dy.concatenate_to_batch(lstm_outputs))
nlls = dy.pickneglogsoftmax_batch(logits, word_ids[1:])
return dy.sum_batches(nlls)
def pad_embedding(embeddings) -> expression_seqs.ExpressionSequence:
max_col = max(len(xs) for xs in embeddings)
p0 = dy.zeros(embeddings[0][0].dim()[0][0])
masks = np.zeros((len(embeddings), max_col), dtype=int)
modified = False
ret = []
for xs, mask in zip(embeddings, masks):
deficit = max_col - len(xs)
if deficit > 0:
xs = xs + ([p0] * deficit)
mask[-deficit:] = 1
modified = True
ret.append(dy.concatenate_cols(xs))
mask = Mask(masks) if modified else None
return expression_seqs.ExpressionSequence(
expr_tensor=dy.concatenate_to_batch(ret), mask=mask)
def pad(self, outputs):
# Padding
max_col = max(len(xs) for xs in outputs)
P0 = dy.vecInput(outputs[0][0].dim()[0][0])
masks = numpy.zeros((len(outputs), max_col), dtype=int)
ret = []
modified = False
for xs, mask in zip(outputs, masks):
deficit = max_col - len(xs)
if deficit > 0:
xs.extend([P0 for _ in range(deficit)])
mask[-deficit:] = 1
modified = True
ret.append(dy.concatenate_cols(xs))
mask = Mask(masks) if modified else None
return dy.concatenate_to_batch(ret), mask
def calc_output(self, sents, train_mode):
cache = {}
cf_init, cb_init = [b.initial_state() for b in self.char_lstms]
wf_init, wb_init = [b.initial_state() for b in self.word_lstms]
#get input/output for T1
#get list of tokens
xs = [['<SOS>'] + x.split() + ['<EOS>'] for (x, _) in sents]
#fill the word embedding cache
for x in xs:
for w in x:
if w not in cache:
t = [dy.lookup(self.char_lookup, c) for c in w.encode()]
fw = [x.output() for x in cf_init.add_inputs(t)]
bw = [x.output() for x in cb_init.add_inputs(reversed(t))]
wid = 0
if w in self.word_to_idx:
wid = self.word_to_idx[w]
if self.level == Level.HYBRID:
cache[w] = dy.lookup(self.word_lookup,
wid) + fw[-1] + bw[-1]
if self.level == Level.CHAR:
cache[w] = fw[-1] + bw[-1]
if self.level == Level.WORD:
cache[w] = dy.lookup(self.word_lookup, wid)
src_len = [len(x) for x in xs]
max_src_len = np.max(src_len)
num_words = 0
#build the batch. Be careful!
src_cws = []
for i in range(max_src_len):
src_cws.append(
dy.concatenate_to_batch([
dy.dropout(cache[x[i]], self.dropout_rate)
if train_mode else cache[x[i]] for x in xs
]))
fw = [x.output() for x in wf_init.add_inputs(src_cws)]
bw = [x.output() for x in wb_init.add_inputs(reversed(src_cws))]
return (fw, bw)
def rnn_forward_with_state(rnn, input_, lengths=None, state=None, batched=True, backward=False):
"""Return the output of the final layers and the final state of the RNN.
:param rnn: dy.RNNBuilder
:param input_: List[dy.Expression]
:param lengths: List[int]
:param state: List[np.ndarray] The previous state (used in TBPTT)
:param batched: bool Is the state batched?
:param backward: bool Is this a backward rnn in a bRNN?
Returns:
List[dy.Expression] (Seq_len): The outputs
List[dy.Expression] (2 * layers if lstm): The state
"""
if state is not None:
state = [dy.inputTensor(s, batched) for s in state]
lstm_state = rnn.initial_state(state)
if backward:
states = lstm_state.add_inputs(reversed(input_))
outputs = list(reversed([s.h()[-1] for s in states]))
# When going backwards (we pad right) the final state of the rnn
# is always the last one.
final_state = states[-1].s()
return outputs, final_state
states = lstm_state.add_inputs(input_)
outputs = [s.h()[-1] for s in states]
if lengths is None:
if backward:
outputs = list(reversed(outputs))
return outputs, states[-1].s()
final_states = [states[l - 1].s() for l in lengths]
final_state_by_batch = []
for i, state in enumerate(final_states):
batch_state = [dy.pick_batch_elem(s, i) for s in state]
final_state_by_batch.append(batch_state)
final_state = []
for i in range(len(final_state_by_batch[0])):
col = dy.concatenate_to_batch([final_state_by_batch[j][i] for j in range(len(final_state_by_batch))])
final_state.append(col)
if backward:
outputs = list(reversed(outputs))
return outputs, final_state
def transduce(self,
embed_sent: ExpressionSequence) -> List[ExpressionSequence]:
batch_size = embed_sent[0].dim()[1]
actions = self.sample_segmentation(embed_sent, batch_size)
embeddings = dy.concatenate(embed_sent.expr_list, d=1)
embeddings.value()
#
composed_words = []
for i in range(batch_size):
sequence = dy.pick_batch_elem(embeddings, i)
# For each sampled segmentations
lower_bound = 0
for j, upper_bound in enumerate(actions[i]):
if self.no_char_embed:
char_sequence = []
else:
char_sequence = dy.pick_range(sequence, lower_bound,
upper_bound + 1, 1)
composed_words.append(
(char_sequence, i, j, lower_bound, upper_bound + 1))
lower_bound = upper_bound + 1
outputs = self.segment_composer.compose(composed_words, batch_size)
# Padding + return
try:
if self.length_prior:
seg_size_unpadded = [
len(outputs[i]) for i in range(batch_size)
]
sampled_sentence, segment_mask = self.pad(outputs)
expr_seq = ExpressionSequence(
expr_tensor=dy.concatenate_to_batch(sampled_sentence),
mask=segment_mask)
return self.final_transducer.transduce(expr_seq)
finally:
if self.length_prior:
self.seg_size_unpadded = seg_size_unpadded
self.compose_output = outputs
self.segment_actions = actions
if not self.train and self.is_reporting():
if len(actions) == 1: # Support only AccuracyEvalTask
self.report_sent_info({"segment_actions": actions})
def calc_loss(self, rewards):
loss = FactoredLossExpr()
## Z-Normalization
if self.z_normalization:
reward_batches = dy.concatenate_to_batch(rewards)
mean_batches = dy.mean_batches(reward_batches)
std_batches = dy.std_batches(reward_batches)
rewards = [
dy.cdiv(reward - mean_batches, std_batches)
for reward in rewards
]
## Calculate baseline
if self.baseline is not None:
pred_reward, baseline_loss = self.calc_baseline_loss(rewards)
loss.add_loss("rl_baseline", baseline_loss)
## Calculate Confidence Penalty
if self.confidence_penalty:
loss.add_loss("rl_confpen",
self.confidence_penalty.calc_loss(self.policy_lls))
## Calculate Reinforce Loss
reinf_loss = []
# Loop through all action in one sequence
for i, (policy,
action_sample) in enumerate(zip(self.policy_lls,
self.actions)):
# Discount the reward if we use baseline
if self.baseline is not None:
rewards = [reward - pred_reward[i] for reward in rewards]
# Main Reinforce calculation
sample_loss = []
for action, reward in zip(action_sample, rewards):
ll = dy.pick_batch(policy, action)
if self.valid_pos is not None:
ll = dy.pick_batch_elems(ll, self.valid_pos[i])
reward = dy.pick_batch_elems(reward, self.valid_pos[i])
sample_loss.append(dy.sum_batches(ll * reward))
# Take the average of the losses accross multiple samples
reinf_loss.append(dy.esum(sample_loss) / len(sample_loss))
loss.add_loss("rl_reinf", self.weight * -dy.esum(reinf_loss))
## the composed losses
return loss
def predict_one(self, src, encoder_outputs, **kwargs):
K = int(kwargs.get('beam', 5))
mxlen = int(kwargs.get('mxlen', 100))
paths = [[Offsets.GO] for _ in range(K)]
# Which beams are done?
done = np.array([False] * K)
scores = np.array([0.0]*K)
hidden, output_i, context = self.arc_policy(encoder_outputs, self.hsz, beam_width=K)
num_states = len(hidden)
rnn_state = self.decoder_rnn.initial_state(hidden)
self.attn_cache(context)
src_mask = encoder_outputs.src_mask
for i in range(mxlen):
dst_last = np.array([path[-1] for path in paths]).reshape(1, K)
embed_i = self.tgt_embeddings.encode(dst_last)[-1]
embed_i = self.input_i(embed_i, output_i)
rnn_state = rnn_state.add_input(embed_i)
rnn_output_i = rnn_state.output()
output_i = self.attn(rnn_output_i, src_mask)
wll = self.prediction([output_i])[-1].npvalue() # (V,) K
V = wll.shape[0]
if i > 0:
# expanded_history = np.expand_dims(scores, -1)
# done_mask = np.expand_dims((done == False).astype(np.uint8), -1)
# sll = np.multiply(wll.T, done_mask) + expanded_history
wll = wll.T
expanded_history = np.expand_dims(scores, -1)
done_mask = np.expand_dims((done == False).astype(np.uint8), -1)
done_mask_inv = (done_mask != 1).astype(np.uint8)
eos_mask = np.zeros((1, V)).astype(np.uint8)
mask = ((done_mask & eos_mask) != 1).astype(np.uint8)
masked_wll = np.multiply(done_mask, wll)
negged_wll = masked_wll + (done_mask_inv * -1e4)
removed_eos = np.multiply(mask, negged_wll)
sll = removed_eos + expanded_history
else:
sll = wll.T
flat_sll = sll.reshape(-1)
bests = topk(K, flat_sll)
best_idx_flat = np.array(list(bests.keys()))
best_beams = best_idx_flat // V
best_idx = best_idx_flat % V
new_paths = []
new_done = []
hidden = rnn_state.s()
new_hidden = [[] for _ in range(num_states)]
for j, best_flat in enumerate(best_idx_flat):
beam_id = best_beams[j]
best_word = best_idx[j]
if done[j]:
new_paths.append(paths[beam_id] + [Offsets.EOS])
else:
new_paths.append(paths[beam_id] + [best_word])
if best_word == Offsets.EOS:
done[j] = True
new_done.append(done[beam_id])
scores[j] = bests[best_flat]
# For each path, we need to pick that out and add it to the hiddens
# This will be (c1, c2, ..., h1, h2, ...)
for h_i, h in enumerate(hidden):
new_hidden[h_i].append(dy.pick_batch_elem(h, beam_id))
done = np.array(new_done)
new_hidden = [dy.concatenate_to_batch(new_h) for new_h in new_hidden]
paths = new_paths
rnn_state = self.decoder_rnn.initial_state(new_hidden)
paths = np.stack([p[1:] for p in paths])
return paths, scores
