def test_update(self):
ones=np.ones((10, 10))
updated = np.ones((10, 10)) * 0.99
gradient = np.ones((10, 10)) * 0.01
dy.renew_cg()
pp1 = dy.parameter(self.p1)
pp2 = dy.parameter(self.p2)
a = pp1 * self.lp1[1]
b = pp2 * self.lp2[1]
l = dy.dot_product(a, b) / 100
self.assertEqual(l.scalar_value(),10,msg=str(l.scalar_value()))
l.backward()
self.assertTrue(np.allclose(self.p1.grad_as_array(), 0.1 * ones),msg=np.array_str(self.p1.grad_as_array()))
self.assertTrue(np.allclose(self.p2.grad_as_array(), 0.1 * ones),msg=np.array_str(self.p2.grad_as_array()))
self.assertTrue(np.allclose(self.lp1.grad_as_array()[1], ones[0]),msg=np.array_str(self.lp1.grad_as_array()))
self.assertTrue(np.allclose(self.lp2.grad_as_array()[1], ones[0]),msg=np.array_str(self.lp2.grad_as_array()))
self.trainer.update()
self.assertTrue(np.allclose(self.p1.as_array(), ones * 0.99),msg=np.array_str(self.p1.as_array()))
self.assertTrue(np.allclose(self.p2.as_array(), ones * 0.99),msg=np.array_str(self.p2.as_array()))
self.assertTrue(np.allclose(self.lp1.as_array()[1], ones[0] * 0.9),msg=np.array_str(self.lp1.as_array()[1]))
self.assertTrue(np.allclose(self.lp2.as_array()[1], ones[0] * 0.9),msg=np.array_str(self.lp2.as_array()))
def generate(input, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
def sample(probs):
rnd = random.random()
for i, p in enumerate(probs):
rnd -= p
if rnd <= 0: break
return i
embedded = embed_sentence(input)
encoded = encode_sentence(enc_fwd_lstm, enc_bwd_lstm, embedded)
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(dy.concatenate([dy.vecInput(STATE_SIZE * 2), last_output_embeddings]))
out = ''
count_EOS = 0
for i in range(len(input)*2):
if count_EOS == 2: break
vector = dy.concatenate([attend(encoded, s), last_output_embeddings])
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = dy.softmax(out_vector)
probs = probs.vec_value()
next_char = sample(probs)
last_output_embeddings = output_lookup[next_char]
if int2char[next_char] == EOS:
count_EOS += 1
continue
out += int2char[next_char]
return out
def expr_for_tree(self, tree):
if tree.isleaf():
return self.E[self.w2i.get(tree.label,0)]
if len(tree.children) == 1:
assert(tree.children[0].isleaf())
emb = self.expr_for_tree(tree.children[0])
Wi,Wo,Wu = [dy.parameter(w) for w in self.WS]
bi,bo,bu,_ = [dy.parameter(b) for b in self.BS]
i = dy.logistic(Wi*emb + bi)
o = dy.logistic(Wo*emb + bo)
u = dy.tanh( Wu*emb + bu)
c = dy.cmult(i,u)
expr = dy.cmult(o,dy.tanh(c))
return expr
assert(len(tree.children) == 2),tree.children[0]
e1 = self.expr_for_tree(tree.children[0])
e2 = self.expr_for_tree(tree.children[1])
Ui,Uo,Uu = [dy.parameter(u) for u in self.US]
Uf1,Uf2 = [dy.parameter(u) for u in self.UFS]
bi,bo,bu,bf = [dy.parameter(b) for b in self.BS]
e = dy.concatenate([e1,e2])
i = dy.logistic(Ui*e + bi)
o = dy.logistic(Uo*e + bo)
f1 = dy.logistic(Uf1*e1 + bf)
f2 = dy.logistic(Uf2*e2 + bf)
u = dy.tanh( Uu*e + bu)
c = dy.cmult(i,u) + dy.cmult(f1,e1) + dy.cmult(f2,e2)
h = dy.cmult(o,dy.tanh(c))
expr = h
return expr
def decode(dec_lstm, vectors, output):
output = [EOS] + list(output) + [EOS]
output = [char2int[c] for c in output]
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
input_mat = dy.concatenate_cols(vectors)
w1dt = None
last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(dy.concatenate([dy.vecInput(STATE_SIZE*2), last_output_embeddings]))
loss = []
for char in output:
# w1dt can be computed and cached once for the entire decoding phase
w1dt = w1dt or w1 * input_mat
vector = dy.concatenate([attend(input_mat, s, w1dt), last_output_embeddings])
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = dy.softmax(out_vector)
last_output_embeddings = output_lookup[char]
loss.append(-dy.log(dy.pick(probs, char)))
loss = dy.esum(loss)
return loss
def generate(in_seq, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
embedded = embed_sentence(in_seq)
encoded = encode_sentence(enc_fwd_lstm, enc_bwd_lstm, embedded)
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
w1 = dy.parameter(attention_w1)
input_mat = dy.concatenate_cols(encoded)
w1dt = None
last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(dy.concatenate([dy.vecInput(STATE_SIZE * 2), last_output_embeddings]))
out = ''
count_EOS = 0
for i in range(len(in_seq)*2):
if count_EOS == 2: break
# w1dt can be computed and cached once for the entire decoding phase
w1dt = w1dt or w1 * input_mat
vector = dy.concatenate([attend(input_mat, s, w1dt), last_output_embeddings])
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = dy.softmax(out_vector).vec_value()
next_char = probs.index(max(probs))
last_output_embeddings = output_lookup[next_char]
if int2char[next_char] == EOS:
count_EOS += 1
continue
out += int2char[next_char]
return out
def generate(sent):
dy.renew_cg()
src = sent
#initialize the LSTM
init_state_src = LSTM_SRC_BUILDER.initial_state()
#get the output of the first LSTM
src_output = init_state_src.add_inputs([LOOKUP_SRC[x] for x in src])[-1].output()
#generate until a eos tag or max is reached
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_word = sos_trg
trg_sent = []
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
for i in range(MAX_SENT_SIZE):
#feed the previous word into the lstm, calculate the most likely word, add it to the sentence
current_state = current_state.add_input(LOOKUP_TRG[prev_word])
output_embedding = current_state.output()
s = dy.affine_transform([b_sm, W_sm, output_embedding])
probs = (-dy.log_softmax(s)).value()
next_word = np.argmax(probs)
if next_word == eos_trg:
break
prev_word = next_word
trg_sent.append(i2w_trg[next_word])
return trg_sent
def calc_loss(sent):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src = sent[0]
trg = sent[1]
#initialize the LSTM
init_state_src = LSTM_SRC_BUILDER.initial_state()
#get the output of the first LSTM
src_output = init_state_src.add_inputs([LOOKUP_SRC[x] for x in src])[-1].output()
#now step through the output sentence
all_losses = []
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_word = trg[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
for next_word in trg[1:]:
#feed the current state into the
current_state = current_state.add_input(LOOKUP_TRG[prev_word])
output_embedding = current_state.output()
s = dy.affine_transform([b_sm, W_sm, output_embedding])
all_losses.append(dy.pickneglogsoftmax(s, next_word))
prev_word = next_word
return dy.esum(all_losses)
def calc_attention(src_output_matrix, tgt_output_embedding, fixed_attentional_component):
w1_att_src = dy.parameter(w1_att_src_p)
w1_att_tgt = dy.parameter(w1_att_tgt_p)
w2_att = dy.parameter(w2_att_p)
a_t = dy.transpose(dy.tanh(dy.colwise_add(fixed_attentional_component, w1_att_tgt * tgt_output_embedding))) * w2_att
alignment = dy.softmax(a_t)
att_output = src_output_matrix * alignment
return att_output, alignment
def __call__(self, obs, batched=False):
out = self.network(obs, batched)
W, b = dy.parameter(self.W), dy.parameter(self.b)
As = dy.affine_transform([b, W, out])
if self.dueling:
W_extra, b_extra = dy.parameter(self.W_extra), dy.parameter(self.b_extra)
V = dy.affine_transform([b_extra, W_extra, out])
return As, V
return As
def __call__(self, x):
assert(isinstance(x, dy.Expression))
self.W = dy.parameter(self.pW) # add parameters to graph as expressions # m2.add_parameters((8, len(inputs)))
self.b = dy.parameter(self.pb)
self.x = x
return self.W * self.x + self.b
def calc_loss(sents):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src_sents = [x[0] for x in sents]
tgt_sents = [x[1] for x in sents]
src_cws = []
src_len = [len(sent) for sent in src_sents]
max_src_len = np.max(src_len)
num_words = 0
for i in range(max_src_len):
src_cws.append([sent[i] for sent in src_sents])
#initialize the LSTM
init_state_src = LSTM_SRC_BUILDER.initial_state()
#get the output of the first LSTM
src_output = init_state_src.add_inputs([dy.lookup_batch(LOOKUP_SRC, cws) for cws in src_cws])[-1].output()
#now decode
all_losses = []
# Decoder
#need to mask padding at end of sentence
tgt_cws = []
tgt_len = [len(sent) for sent in sents]
max_tgt_len = np.max(tgt_len)
masks = []
for i in range(max_tgt_len):
tgt_cws.append([sent[i] if len(sent) > i else eos_trg for sent in tgt_sents])
mask = [(1 if len(sent) > i else 0) for sent in tgt_sents]
masks.append(mask)
num_words += sum(mask)
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_words = tgt_cws[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
for next_words, mask in zip(tgt_cws[1:], masks):
#feed the current state into the
current_state = current_state.add_input(dy.lookup_batch(LOOKUP_TRG, prev_words))
output_embedding = current_state.output()
s = dy.affine_transform([b_sm, W_sm, output_embedding])
loss = (dy.pickneglogsoftmax_batch(s, next_words))
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,),len(sents))
mask_loss = loss * mask_expr
all_losses.append(mask_loss)
prev_words = next_words
return dy.sum_batches(dy.esum(all_losses)), num_words
def renew_cg(self):
# renew the compute graph for every single instance
dy.renew_cg()
param_exprs = dict()
param_exprs['U'] = dy.parameter(self.params['word_score_U'])
param_exprs['pW'] = dy.parameter(self.params['predict_W'])
param_exprs['pb'] = dy.parameter(self.params['predict_b'])
param_exprs['<bos>'] = dy.parameter(self.params['<BoS>'])
self.param_exprs = param_exprs
def calc_scores(words):
dy.renew_cg()
# Transduce all batch elements with an LSTM
word_reps = LSTM.transduce([LOOKUP[x] for x in words])
# Softmax scores
W = dy.parameter(W_sm)
b = dy.parameter(b_sm)
scores = [dy.affine_transform([b, W, x]) for x in word_reps]
return scores
def __call__(self, obs, batched=False):
out = obs if isinstance(obs, dy.Expression) else dy.inputTensor(obs, batched=batched)
for i in range(self.n_layers):
b, W = dy.parameter(self.bs[i]), dy.parameter(self.Ws[i])
out = dy.affine_transform([b, W, out])
if self.layer_norm and i != self.n_layers - 1:
out = dy.layer_norm(out, self.ln_gs[i], self.ln_bs[i])
if self.specified_activation:
if self.activation[i] is not None:
out = self.activation[i](out)
else:
out = self.activation(out)
return out
def calc_scores_with_previous_tag(words, referent_tags=None):
"""
Calculate scores using previous tag as input. If the referent tags are provided, then we will sample from previous
referent tag or previous system prediction.
:param words:
:param referent_tags:
:return:
"""
dy.renew_cg()
word_embs = [LOOKUP[x] for x in words]
# Transduce all batch elements for the backward LSTM, using the original word embeddings.
bwd_init = bwdLSTM.initial_state()
bwd_word_reps = bwd_init.transduce(reversed(word_embs))
# Softmax scores
W = dy.parameter(W_sm)
b = dy.parameter(b_sm)
scores = []
# Transduce one by one for the forward LSTM
fwd_init = fwdLSTM.initial_state()
s_fwd = fwd_init
prev_tag = start_tag
index = 0
for word, bwd_word_rep in zip(word_embs, reversed(bwd_word_reps)):
# Concatenate word and tag representation just as training.
fwd_input = dy.concatenate([word, TAG_LOOKUP[prev_tag]])
s_fwd = s_fwd.add_input(fwd_input)
combined_rep = dy.concatenate([s_fwd.output(), bwd_word_rep])
score = dy.affine_transform([b, W, combined_rep])
prediction = np.argmax(score.npvalue())
if referent_tags:
if sampler.sample_true():
prev_tag = referent_tags[index]
else:
prev_tag = prediction
index += 1
else:
prev_tag = prediction
scores.append(score)
return scores
def generate(sent):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src = sent
#get the output of the first LSTM
src_outputs = [dy.concatenate([x.output(), y.output()]) for x,y in LSTM_SRC.add_inputs([LOOKUP_SRC[word] for word in src])]
src_output = src_outputs[-1]
#gets the parameters for the attention
src_output_matrix = dy.concatenate_cols(src_outputs)
w1_att_src = dy.parameter(w1_att_src_p)
fixed_attentional_component = w1_att_src * src_output_matrix
#generate until a eos tag or max is reached
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_word = sos_trg
trg_sent = []
attention_matrix = []
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
W_m = dy.parameter(W_m_p)
b_m = dy.parameter(b_m_p)
for i in range(MAX_SENT_SIZE):
#feed the previous word into the lstm, calculate the most likely word, add it to the sentence
current_state = current_state.add_input(LOOKUP_TRG[prev_word])
output_embedding = current_state.output()
att_output, alignment = calc_attention(src_output_matrix, output_embedding, fixed_attentional_component)
attention_matrix.append(alignment)
middle_expr = dy.tanh(dy.affine_transform([b_m, W_m, dy.concatenate([output_embedding, att_output])]))
s = dy.affine_transform([b_sm, W_sm, middle_expr])
probs = (-dy.log_softmax(s)).value()
next_word = np.argmax(probs)
if next_word == eos_trg:
break
prev_word = next_word
trg_sent.append(i2w_trg[next_word])
return trg_sent, dy.concatenate_cols(attention_matrix).value()
def word_assoc_score(self, source_idx, target_idx, relation):
"""
NOTE THAT DROPOUT IS BEING APPLIED HERE
:param source_idx: embedding index of source atom
:param target_idx: embedding index of target atom
:param relation: relation type
:return: score
"""
# prepare
s = self.embeddings[source_idx]
if self.no_assoc:
A = dy.const_parameter(self.word_assoc_weights[relation])
else:
A = dy.parameter(self.word_assoc_weights[relation])
dy.dropout(A, self.dropout)
t = self.embeddings[target_idx]
# compute
if self.mode == BILINEAR_MODE:
return dy.transpose(s) * A * t
elif self.mode == DIAG_RANK1_MODE:
diag_A = dyagonalize(A[0])
rank1_BC = A[1] * dy.transpose(A[2])
ABC = diag_A + rank1_BC
return dy.transpose(s) * ABC * t
elif self.mode == TRANSLATIONAL_EMBED_MODE:
return -dy.l2_norm(s - t + A)
elif self.mode == DISTMULT:
return dy.sum_elems(dy.cmult(dy.cmult(s, A), t))
def attend(input_mat, state, w1dt):
global attention_w2
global attention_v
w2 = dy.parameter(attention_w2)
v = dy.parameter(attention_v)
# input_mat: (encoder_state x seqlen) => input vecs concatenated as cols
# w1dt: (attdim x seqlen)
# w2dt: (attdim x attdim)
w2dt = w2*dy.concatenate(list(state.s()))
# att_weights: (seqlen,) row vector
unnormalized = dy.transpose(v * dy.tanh(dy.colwise_add(w1dt, w2dt)))
att_weights = dy.softmax(unnormalized)
# context: (encoder_state)
context = input_mat * att_weights
return context
def attend(input_vectors, state):
global attention_w1
global attention_w2
global attention_v
w1 = dy.parameter(attention_w1)
w2 = dy.parameter(attention_w2)
v = dy.parameter(attention_v)
attention_weights = []
w2dt = w2*dy.concatenate(list(state.s()))
for input_vector in input_vectors:
attention_weight = v*dy.tanh(w1*input_vector + w2dt)
attention_weights.append(attention_weight)
attention_weights = dy.softmax(dy.concatenate(attention_weights))
output_vectors = dy.esum([vector*attention_weight for vector, attention_weight in zip(input_vectors, attention_weights)])
return output_vectors
def ergm_score(self):
"""
:return: ERGM score (dynet Expression) computed based on ERGM weights and features only
Does not populate any field
"""
W = dy.parameter(self.ergm_weights)
f = dy.transpose(dy.inputVector([self.feature_vals[k] for k in self.feature_set]))
return f * W
def calc_loss(sent):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src = sent[0]
trg = sent[1]
# initialize the LSTM
init_state_src = LSTM_SRC_BUILDER.initial_state()
# get the output of the first LSTM
src_output = init_state_src.add_inputs([LOOKUP_SRC[x] for x in src])[-1].output()
# Now compute mean and standard deviation of source hidden state.
W_mean = dy.parameter(W_mean_p)
V_mean = dy.parameter(V_mean_p)
b_mean = dy.parameter(b_mean_p)
W_var = dy.parameter(W_var_p)
V_var = dy.parameter(V_var_p)
b_var = dy.parameter(b_var_p)
# The mean vector from the encoder.
mu = mlp(src_output, W_mean, V_mean, b_mean)
# This is the diagonal vector of the log co-variance matrix from the encoder
# (regard this as log variance is easier for furture implementation)
log_var = mlp(src_output, W_var, V_var, b_var)
# Compute KL[N(u(x), sigma(x)) || N(0, I)]
# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
kl_loss = -0.5 * dy.sum_elems(1 + log_var - dy.pow(mu, dy.inputVector([2])) - dy.exp(log_var))
z = reparameterize(mu, log_var)
# now step through the output sentence
all_losses = []
current_state = LSTM_TRG_BUILDER.initial_state().set_s([z, dy.tanh(z)])
prev_word = trg[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
for next_word in trg[1:]:
# feed the current state into the
current_state = current_state.add_input(LOOKUP_TRG[prev_word])
output_embedding = current_state.output()
s = dy.affine_transform([b_sm, W_sm, output_embedding])
all_losses.append(dy.pickneglogsoftmax(s, next_word))
prev_word = next_word
softmax_loss = dy.esum(all_losses)
return kl_loss, softmax_loss
def calc_reinforce_loss(words, tags, delta):
dy.renew_cg()
# Transduce all batch elements with an LSTM
word_reps = LSTM.transduce([LOOKUP[x] for x in words])
# Softmax scores
W = dy.parameter(W_sm)
b = dy.parameter(b_sm)
#calculate the probability distribution
scores = [dy.affine_transform([b, W, x]) for x in word_reps]
losses = [dy.pickneglogsoftmax(score, tag) for score, tag in zip(scores, tags)]
probs = [-dy.exp(loss).as_array() for loss in losses]
#then take samples from the probability distribution
samples = [np.random.choice(range(len(x)), p=x) for x in probs]
#calculate accuracy=reward
correct = [sample == tag for sample, tag in zip(samples, tags)]
r_i = float(sum(correct))/len(correct)
r = dy.constant((1), r_i)
# Reward baseline for each word
W_bl = dy.parameter(W_bl_p)
b_bl = dy.parameter(b_bl_p)
r_b = [dy.affine_transform([b_bl, W_bl, dy.nobackprop(x)]) for x in word_reps]
#we need to take the value in order to break the computation graph
#as the reward portion is trained seperatley and not backpropogated through during the overall score
rewards_over_baseline = [(r - dy.nobackprop(x)) for x in r_b]
#the scores for training the baseline
baseline_scores = [dy.square(r - x) for x in r_b]
#then calculate the reinforce scores using reinforce
reinforce_scores = [r_s*score for r_s, score in zip(rewards_over_baseline, scores)]
#we want the first len(sent)-delta scores from xent then delta scores from reinforce
#for mixer
if len(scores) > delta:
mixer_scores = scores[:len(scores)-delta] + reinforce_scores[delta-1:]
else:
mixer_scores = reinforce_scores
return dy.esum(mixer_scores), dy.esum(baseline_scores)
def BuildLMGraph(self, sents):
dy.renew_cg()
# initialize the RNN
init_state = self.builder.initial_state()
# parameters -> expressions
R = dy.parameter(self.R)
bias = dy.parameter(self.bias)
S = vocab.w2i["<s>"]
# get the cids and masks for each step
tot_chars = 0
cids = []
masks = []
for i in range(len(sents[0])):
cids.append([(vocab.w2i[sent[i]] if len(sent) > i else S) for sent in sents])
mask = [(1 if len(sent)>i else 0) for sent in sents]
masks.append(mask)
tot_chars += sum(mask)
# start the rnn with "<s>"
init_ids = cids[0]
s = init_state.add_input(lookup_batch(self.lookup, init_ids))
losses = []
# feed char vectors into the RNN and predict the next char
for cid, mask in zip(cids[1:], masks[1:]):
score = dy.affine_transform([bias, R, s.output()])
loss = dy.pickneglogsoftmax_batch(score, cid)
# mask the loss if at least one sentence is shorter
if mask[-1] != 1:
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,), len(sents))
loss = loss * mask_expr
losses.append(loss)
# update the state of the RNN
cemb = dy.lookup_batch(self.lookup, cid)
s = s.add_input(cemb)
return dy.sum_batches(dy.esum(losses)), tot_chars
def build_tagging_graph(words):
dy.renew_cg()
# parameters -> expressions
H = dy.parameter(pH)
O = dy.parameter(pO)
# initialize the RNNs
f_init = fwdRNN.initial_state()
b_init = bwdRNN.initial_state()
cf_init = cFwdRNN.initial_state()
cb_init = cBwdRNN.initial_state()
# get the word vectors. word_rep(...) returns a 128-dim vector expression for each word.
wembs = [word_rep(w, cf_init, cb_init) for w in words]
wembs = [dy.noise(we,0.2) for we in wembs] # optional
# feed word vectors into biLSTM
fw_exps = f_init.transduce(wembs)
bw_exps = b_init.transduce(reversed(wembs))
# OR
# fw_exps = []
# s = f_init
# for we in wembs:
# s = s.add_input(we)
# fw_exps.append(s.output())
# bw_exps = []
# s = b_init
# for we in reversed(wembs):
# s = s.add_input(we)
# bw_exps.append(s.output())
# biLSTM states
bi_exps = [dy.concatenate([f,b]) for f,b in zip(fw_exps, reversed(bw_exps))]
# feed each biLSTM state to an MLP
exps = []
for x in bi_exps:
r_t = O*(dy.tanh(H * x))
exps.append(r_t)
return exps
def decode(dec_lstm, vectors, output):
output = [EOS] + list(output) + [EOS]
output = [char2int[c] for c in output]
w = dy.parameter(decoder_w)
b = dy.parameter(decoder_b)
last_output_embeddings = output_lookup[char2int[EOS]]
s = dec_lstm.initial_state().add_input(dy.concatenate([dy.vecInput(STATE_SIZE*2), last_output_embeddings]))
loss = []
for char in output:
vector = dy.concatenate([attend(vectors, s), last_output_embeddings])
s = s.add_input(vector)
out_vector = w * s.output() + b
probs = dy.softmax(out_vector)
last_output_embeddings = output_lookup[char]
loss.append(-dy.log(dy.pick(probs, char)))
loss = dy.esum(loss)
return loss
def create_network_return_best(inputs):
'''
inputs is a list of numbers
'''
dy.renew_cg()
W = dy.parameter(pW)
b = dy.parameter(pB)
if(len(inputs) > documentLength):
inputs = inputs[0:documentLength]
emb_vectors = [lookup[i] for i in inputs]
while(len(emb_vectors) < documentLength):
pad = dy.vecInput(embDimension)
pad.set(np.zeros(embDimension))
emb_vectors.append(pad)
net_input = dy.concatenate(emb_vectors)
net_output = dy.softmax( (W*net_input) + b)
return np.argmax(net_output.npvalue())
def word_repr(self, char_seq):
# obtain the word representation when given its character sequence
wlen = len(char_seq)
if 'rgW%d'%wlen not in self.param_exprs:
self.param_exprs['rgW%d'%wlen] = dy.parameter(self.params['reset_gate_W'][wlen-1])
self.param_exprs['rgb%d'%wlen] = dy.parameter(self.params['reset_gate_b'][wlen-1])
self.param_exprs['cW%d'%wlen] = dy.parameter(self.params['com_W'][wlen-1])
self.param_exprs['cb%d'%wlen] = dy.parameter(self.params['com_b'][wlen-1])
self.param_exprs['ugW%d'%wlen] = dy.parameter(self.params['update_gate_W'][wlen-1])
self.param_exprs['ugb%d'%wlen] = dy.parameter(self.params['update_gate_b'][wlen-1])
chars = dy.concatenate(char_seq)
reset_gate = dy.logistic(self.param_exprs['rgW%d'%wlen] * chars + self.param_exprs['rgb%d'%wlen])
comb = dy.concatenate([dy.tanh(self.param_exprs['cW%d'%wlen] * dy.cmult(reset_gate,chars) + self.param_exprs['cb%d'%wlen]),chars])
update_logits = self.param_exprs['ugW%d'%wlen] * comb + self.param_exprs['ugb%d'%wlen]
update_gate = dy.transpose(dy.concatenate_cols([dy.softmax(dy.pickrange(update_logits,i*(wlen+1),(i+1)*(wlen+1))) for i in xrange(self.options['ndims'])]))
# The following implementation of Softmax fucntion is not safe, but faster...
#exp_update_logits = dy.exp(dy.reshape(update_logits,(self.options['ndims'],wlen+1)))
#update_gate = dy.cdiv(exp_update_logits, dy.concatenate_cols([dy.sum_cols(exp_update_logits)] *(wlen+1)))
#assert (not np.isnan(update_gate.npvalue()).any())
word = dy.sum_cols(dy.cmult(update_gate,dy.reshape(comb,(self.options['ndims'],wlen+1))))
return word
def expr_for_tree(self, tree):
if tree.isleaf():
return self.E[self.w2i.get(tree.label,0)]
if len(tree.children) == 1:
assert(tree.children[0].isleaf())
expr = self.expr_for_tree(tree.children[0])
return expr
assert(len(tree.children) == 2),tree.children[0]
e1 = self.expr_for_tree(tree.children[0])
e2 = self.expr_for_tree(tree.children[1])
W = dy.parameter(self.W)
expr = dy.tanh(W*dy.concatenate([e1,e2]))
return expr
def create_network_return_loss(inputs, expected_output):
'''
inputs is a list of numbers
'''
dy.renew_cg()
W = dy.parameter(pW) # from parameters to expressions
b = dy.parameter(pB)
if(len(inputs) > documentLength):
inputs = inputs[0:documentLength]
emb_vectors = [lookup[i] for i in inputs]
while(len(emb_vectors) < documentLength):
pad = dy.vecInput(embDimension)
pad.set(np.zeros(embDimension))
emb_vectors.append(pad)
net_input = dy.concatenate(emb_vectors)
net_output = dy.softmax( (W*net_input) + b)
loss = -dy.log(dy.pick(net_output, expected_output))
return loss
def sample(self, first=1, nchars=0, stop=-1):
res = [first]
dy.renew_cg()
state = self.builder.initial_state()
R = dy.parameter(self.R)
bias = dy.parameter(self.bias)
cw = first
while True:
x_t = dy.lookup(self.lookup, cw)
state = state.add_input(x_t)
y_t = state.output()
r_t = bias + (R * y_t)
ydist = dy.softmax(r_t)
dist = ydist.vec_value()
rnd = random.random()
for i,p in enumerate(dist):
rnd -= p
if rnd <= 0: break
res.append(i)
cw = i
if cw == stop: break
if nchars and len(res) > nchars: break
return res
WEMB_DIM = 128
RNN_HIDDEN_DIM = 64
HIDDEN_DIM = 32
pWembs = model.add_lookup_parameters((num_words, WEMB_DIM))
pH = model.add_parameters((HIDDEN_DIM, RNN_HIDDEN_DIM))
pHb = model.add_parameters(HIDDEN_DIM)
pO = model.add_parameters((num_tags, HIDDEN_DIM))
pOb = model.add_parameters(num_tags)
rnn_builder = dy.BiRNNBuilder(1, WEMB_DIM, RNN_HIDDEN_DIM, model,
dy.LSTMBuilder)
dy.renew_cg()
H = dy.parameter(pH)
Hb = dy.parameter(pHb)
O = dy.parameter(pO)
Ob = dy.parameter(pOb)
indexed_words, indexed_gold_tags = zip(*[(w2i[w], t2i[t])
for w, t in train_sentence])
wembs = [pWembs[wi] for wi in indexed_words]
noised_wembs = [dy.noise(we, 0.1) for we in wembs]
rnn_outputs = rnn_builder.transduce(noised_wembs)
errs = []
for rnn_output, gold_tag in zip(rnn_outputs, indexed_gold_tags):
hidden = dy.tanh(dy.affine_transform([Hb, H, rnn_output]))
def transduce(self, input, _true_output=None, feats=None):
# convert _true_output string to list of vocabulary indeces
if _true_output:
try:
true_output = [self.char_vocab.w2i[a] for a in _true_output]
except:
print a
print _true_output
true_output += [self.STOP]
true_output = list(reversed(true_output))
R = dy.parameter(self.R) # hidden to vocabulary
bias = dy.parameter(self.bias)
W_c = dy.parameter(self.W_c)
W__a = dy.parameter(self.W__a)
U__a = dy.parameter(self.U__a)
v__a = dy.parameter(self.v__a)
# biLSTM encoder of input string
input = [BEGIN_CHAR] + [c for c in input] + [STOP_CHAR]
input_emb = []
for char_ in reversed(input):
char_id = self.char_vocab.w2i.get(char_, self.UNK)
char_embedding = self.VOCAB_LOOKUP[char_id]
input_emb.append(char_embedding)
biencoder = self.bilstm_transduce(self.fbuffRNN, self.bbuffRNN,
input_emb)
losses = []
output = []
pred_history = [self.BEGIN] # <
s = self.decoder.initial_state()
while not len(pred_history) == MAX_PRED_SEQ_LEN:
# compute probability over vocabulary and choose a prediction
# either from the true prediction at train time or based on the model at test time
# decoder next state
prev_pred_id = pred_history[-1]
s = s.add_input(self.VOCAB_LOOKUP[prev_pred_id])
# soft attention vector
scores = [
v__a * dy.tanh(W__a * s.output() + U__a * h_input)
for h_input in biencoder
]
alphas = dy.softmax(dy.concatenate(scores))
c = dy.esum([
h_input * dy.pick(alphas, j)
for j, h_input in enumerate(biencoder)
])
# softmax over vocabulary
h_output = dy.tanh(W_c * dy.concatenate([s.output(), c]))
probs = dy.softmax(R * h_output + bias)
if _true_output is None:
pred_id = np.argmax(probs.npvalue())
else:
pred_id = true_output.pop()
losses.append(-dy.log(dy.pick(probs, pred_id)))
pred_history.append(pred_id)
if pred_id == self.STOP:
break
else:
pred_char = self.char_vocab.i2w.get(pred_id, UNK_CHAR)
output.append(pred_char)
output = u''.join(output)
return ((dy.average(losses) if losses else None), output)
def run_test(train_path,
test_path,
test_output_path,
hidden_layer,
embedding_size,
context_size,
saved_model_path,
activate_sub_word=False):
from time import gmtime, strftime
print strftime("%Y-%m-%d %H:%M:%S", gmtime())
TRAIN, VOCAB, LABELS = t1.read_data(train_path) # get training set
TEST, VOCAB_TEST = read_test_data(test_path) # get dev set
VOCAB["<BOS>"] = 2 # a word for representing the beginning of a sentence
VOCAB["<EOS>"] = 2 # a word for representing the end of a sentence
VOCAB["<UNK>"] = 1
# change TEST dataset by replacing words that did not appear in the vocab with <UNK>
TEST_ORIG = list(TEST)
TEST = []
for sentence in TEST_ORIG:
new_sentence = []
for word in sentence:
word = word.lower()
if word in VOCAB.keys():
new_sentence.append(word)
else:
new_sentence.append("<UNK>")
TEST.append(new_sentence)
# if the feature for taking into account sub words is activated
if activate_sub_word == True:
for word in VOCAB.keys():
if word not in ["<BOS>", "<EOS>", "<UNK>"]:
words_from_word = get_sub_words(word)
for part_word in words_from_word:
if VOCAB.has_key(part_word) == False:
VOCAB[part_word] = 1
m = dy.ParameterCollection() # create parameter collection
#define parameters
pW1 = m.add_parameters((hidden_layer, embedding_size * context_size))
pb1 = m.add_parameters(hidden_layer)
pW2 = m.add_parameters((len(LABELS), hidden_layer))
pb2 = m.add_parameters(len(LABELS))
params = [pW1, pb1, pW2, pb2]
e = m.add_lookup_parameters((len(VOCAB), embedding_size)) #
# load the parameters
print dy.parameter(pW1).value()
m.populate(saved_model_path)
print dy.parameter(pW1).value()
L2I = {l: i
for i, l in enumerate(list(sorted(LABELS.keys())))
} # enumerate the labels as 0,1,2,...
F2I = {f: i
for i, f in enumerate(list(sorted(VOCAB.keys())))
} # enumerate the vocabulary as 0,1,2,...
predictions = prediction(TEST, L2I, F2I, params, e, activate_sub_word)
# write predictions to file
file = open(test_output_path, "w")
for idx, sentence in enumerate(predictions):
word_idx = 0
for word, label in sentence:
orig_word = TEST_ORIG[idx][word_idx]
file.write(orig_word + " " + label + "\n")
word_idx += 1
file.write("\n")
def transduce(self, lemma, feats, oracle_actions=None, external_cg=True, sampling=False,
unk_avg=True, verbose=False):
"""
Transduce an encoded lemma and features.
Args:
lemma: The input lemma, a list of integer character codes.
feats: The features determining the morphological transformation. The most common
format is a list of integer codes, one code per feature-value pair.
oracle_actions: `None` means prediction.
List of action codes is a static oracle.
A dictionary of keys (explained below) is the config for a dynamic oracle.
* "target_word": List of action codes for the target word form.
* "loss": Which loss function to use (softmax-margin, NLL, MSE).
* "rollout_mixin_beta": How to mix reference and learned roll-outs
(1 is only reference, 0 is only model).
* "global_rollout": Whether to use one type of roll-out (expert or model)
at the sequence level.
* "optimal": Whether to use an optimal or noisy (=buggy) expert
* "bias_inserts": Whether to use a buggy roll-out for inserts
(which makes them as cheap as copies)
external_cg: Whether or not an external computation graph is defined.
sampling: Whether or not sampling should be used for decoding (e.g. for MRT) or
training (e.g. dynamic oracles with exploration / learned roll-ins).
dynamic: Whether or not `oracle_actions` is a static oracle (list of actions) or a confuguration
for a dynamic oracle.
unk_avg: Whether or not to average all char embeddings to produce UNK embedding
(see `self._build_lemma`).
verbose: Whether or not to report on processing steps.
"""
# Returns an expression of the loss for the sequence of actions.
# (that is, the oracle_actions if present or the predicted sequence otherwise)
def _valid_actions(encoder):
valid_actions = []
if len(encoder) > 1:
valid_actions += [COPY, DELETE]
else:
valid_actions += [END_WORD]
valid_actions += self.INSERTS
return valid_actions
if not external_cg:
dy.renew_cg()
dynamic = None # indicates prediction or static
if oracle_actions:
# if not, then prediction
if isinstance(oracle_actions, dict):
# dynamic oracle:
# @TODO NB target word is not wrapped in boundary tags
target_word = oracle_actions['target_word']
generation_errors = set()
dynamic = oracle_actions
else:
# static oracle:
# reverse to enable simple popping
oracle_actions = oracle_actions[::-1]
oracle_actions.pop() # COPY of BEGIN_WORD_CHAR
# vectorize lemma
lemma_enc = self._build_lemma(lemma, unk_avg, is_training=bool(oracle_actions))
# vectorize features
features = self._build_features(*feats)
# add encoder and decoder to computation graph
encoder = Encoder(self.fbuffRNN, self.bbuffRNN)
decoder = self.wordRNN.initial_state()
# add classifier to computation graph
if self.MLP_DIM:
# decoder output to hidden
W_s2h = dy.parameter(self.pW_s2h)
b_s2h = dy.parameter(self.pb_s2h)
# hidden to action
W_act = dy.parameter(self.pW_act)
b_act = dy.parameter(self.pb_act)
# encoder is a stack which pops lemma characters and their
# representations from the top. Thus, to get lemma characters
# in the right order, the lemma has to be reversed.
encoder.transduce(lemma_enc, lemma)
encoder.pop() # BEGIN_WORD_CHAR
action_history = [COPY]
word = []
losses = []
if verbose and not dynamic:
count = 0
print()
print(action2string(oracle_actions, self.vocab))
print(lemma2string(lemma, self.vocab))
if dynamic:
# use model rollout for the whole of this sequence
rollout_on = dynamic['global_rollout'] and np.random.rand() > dynamic['rollout_mixin_beta']
while len(action_history) <= MAX_ACTION_SEQ_LEN:
if verbose and not dynamic:
print('Action: ', count, self.vocab.act.i2w[action_history[-1]])
print('Encoder length, char: ', lemma, len(encoder), self.vocab.char.i2w[encoder.s[-1][-1]])
print('word: ', ''.join(word))
print(('Remaining actions: ', oracle_actions, action2string(oracle_actions, self.vocab)))
count += 1
# compute probability of each of the actions and choose an action
# either from the oracle or if there is no oracle, based on the model
valid_actions = _valid_actions(encoder)
encoder_embedding = encoder.embedding()
# decoder
decoder_input = dy.concatenate([encoder_embedding,
features,
self.ACT_LOOKUP[action_history[-1]]
])
decoder = decoder.add_input(decoder_input)
# classifier
if self.double_feats:
classifier_input = dy.concatenate([decoder.output(), features])
else:
classifier_input = decoder.output()
if self.MLP_DIM:
h = self.NONLIN(W_s2h * classifier_input + b_s2h)
else:
h = classifier_input
logits = W_act * h + b_act
# get action (argmax, sampling, or use oracle actions)
if oracle_actions is None:
# predicting by argmax or sampling
logits_cpu = logits # dy.to_device(logits, 'CPU')
log_probs = dy.log_softmax(logits, valid_actions)
log_probs_np = log_probs.npvalue()
if sampling:
action = sample(log_probs_np)
else:
action = np.argmax(log_probs_np)
losses.append(dy.pick(log_probs, action))
elif dynamic:
# training with dynamic oracle
if rollout_on or (not dynamic['global_rollout'] and np.random.rand() > dynamic['rollout_mixin_beta']):
# the second disjunct allows for model roll-out applied locally
logits_cpu = logits # dy.to_device(logits, 'CPU')
rollout = lambda action: self.rollout(action, dy.log_softmax(logits, valid_actions),
action_history, features, decoder, encoder, word,
W_act, b_act) # @TODO W_s2h ...
else:
rollout = None
optim_actions, costs = oracle_with_rollout(word, target_word, encoder.get_extra(),
valid_actions, rollout, self.vocab,
optimal=dynamic['optimal'],
bias_inserts=dynamic['bias_inserts'],
errors=generation_errors,
verbose=verbose)
logits_cpu = logits # dy.to_device(logits, 'CPU')
log_probs = dy.log_softmax(logits, valid_actions)
log_probs_np = log_probs.npvalue()
if sampling == 1. or np.random.rand() <= sampling:
# action is picked by sampling
action = sample(log_probs_np)
# @TODO IL learned roll-ins are done with policy i.e. greedy / beam search decoding
if verbose: print('Rolling in with model: ', action, self.vocab.act.i2w[action])
else:
# action is picked from optim_actions
action = optim_actions[np.argmax([log_probs_np[a] for a in optim_actions])]
#print [log_probs_np[a] for a in optim_actions]
# loss is over all optimal actions.
if dynamic['loss'] == 'softmax-margin':
loss = log_sum_softmax_margin_loss(optim_actions, logits, self.NUM_ACTS,
costs=costs, valid_actions=None, verbose=verbose)
elif dynamic['loss'] == 'nll':
loss = log_sum_softmax_loss(optim_actions, logits, self.NUM_ACTS,
valid_actions=valid_actions, verbose=verbose)
elif dynamic['loss'] == 'mse':
loss = cost_sensitive_reg_loss(optim_actions, logits, self.NUM_ACTS,
# NB expects both costs and valid actions!
costs=costs, valid_actions=valid_actions, verbose=verbose)
################
else:
raise NotImplementedError
losses.append(loss)
#print 'Action'
#print action
#print self.vocab.act.i2w[action]
else:
# training with static oracle
action = oracle_actions.pop()
log_probs = dy.log_softmax(logits, valid_actions)
losses.append(dy.pick(log_probs, action))
action_history.append(action)
#print 'action, log_probs: ', action, self.vocab.act.i2w[action], losses[-1].scalar_value(), log_probs.npvalue()
# execute the action to update the transducer state
if action == COPY:
# 1. Increment attention index
try:
char_ = encoder.pop()
except IndexError as e:
print(np.exp(log_probs.npvalue()))
print('COPY: ', action)
# 2. Append copied character to the output word
word.append(self.vocab.char.i2w[char_])
elif action == DELETE:
# 1. Increment attention index
try:
encoder.pop()
except IndexError as e:
print(np.exp(log_probs.npvalue()))
print('DELETE: ', action)
elif action == END_WORD:
# 1. Finish transduction
break
else:
# one of the INSERT actions
assert action in self.INSERTS
# 1. Append inserted character to the output word
char_ = self.vocab.act.i2w[action]
word.append(char_)
word = ''.join(word)
return losses, word, action_history
regression_hidden_size = 300
#pretrained embeddings
embedding_dim = emb_matrix_pretrained.shape[1]
embedding_parameters = RNN_model.lookup_parameters_from_numpy(emb_matrix_pretrained)
#add RNN unit
fw_RNN_unit = dy.LSTMBuilder(num_layers, embedding_dim, hidden_size, RNN_model)
bw_RNN_unit = dy.LSTMBuilder(num_layers, embedding_dim, hidden_size, RNN_model)
second_fw_RNN_unit = dy.LSTMBuilder(num_layers, 2*hidden_size, hidden_size, RNN_model)
second_bw_RNN_unit = dy.LSTMBuilder(num_layers, 2*hidden_size, hidden_size, RNN_model)
pv1 = RNN_model.add_parameters(
(regression_hidden_size, 2*hidden_size))
dy.parameter(pv1).npvalue().shape
pb1 = RNN_model.add_parameters(
(regression_hidden_size)
)
dy.parameter(pb1).npvalue().shape
pv2 = RNN_model.add_parameters(
(regression_hidden_size))
dy.parameter(pv2).npvalue().shape
pb2 = RNN_model.add_parameters(
def transduce(self,
lemma,
feats,
oracle_actions=None,
external_cg=True,
sampling=False,
unk_avg=True,
debug_mode=False):
def _valid_actions(encoder):
valid_actions = list(self.INSERTS)
if len(encoder) > 1:
valid_actions += [STEP]
else:
valid_actions += [END_WORD]
return valid_actions
if not external_cg:
dy.renew_cg()
if oracle_actions:
# reverse to enable simple popping
oracle_actions = oracle_actions[::-1]
oracle_actions.pop() # Deterministic insertion of BEGIN_WORD
# vectorize lemma
lemma_enc = self._build_lemma(lemma,
unk_avg,
is_training=bool(oracle_actions))
# vectorize features
features = self._build_features(*feats)
# add encoder and decoder to computation graph
encoder = Encoder(self.fbuffRNN, self.bbuffRNN)
decoder = self.wordRNN.initial_state()
# add classifier to computation graph
if self.MLP_DIM:
# decoder output to hidden
W_s2h = dy.parameter(self.pW_s2h)
b_s2h = dy.parameter(self.pb_s2h)
# hidden to action
W_act = dy.parameter(self.pW_act)
b_act = dy.parameter(self.pb_act)
# encoder is a stack which pops lemma characters
# and their representations from the top
encoder.transduce(lemma_enc, lemma)
action_history = [BEGIN_WORD]
word = []
losses = []
count = 0
if debug_mode:
print
if oracle_actions: print action2string(oracle_actions, self.vocab)
print lemma2string(lemma, self.vocab)
while len(action_history) <= MAX_ACTION_SEQ_LEN:
# what is at the top of encoder?
encoder_embedding, char_enc = encoder.embedding(extra=True)
if debug_mode:
print 'Action history: ', action_history, action2string(
action_history, self.vocab)
print 'Encoder length: ', len(encoder)
print 'Current char: ', char_enc, lemma2string([char_enc],
self.vocab)
print 'Word so far: ', u''.join(word)
# decoder
decoder_input = dy.concatenate([
encoder_embedding, features,
self.ACT_LOOKUP[action_history[-1]]
])
decoder = decoder.add_input(decoder_input)
decoder_output = decoder.output()
# classifier
if self.MLP_DIM:
h = self.NONLIN(W_s2h * decoder_output + b_s2h)
else:
h = decoder_output
valid_actions = _valid_actions(encoder)
log_probs = dy.log_softmax(W_act * h + b_act, valid_actions)
if oracle_actions is None:
if sampling:
dist = np.exp(log_probs.npvalue())
# sample according to softmax
rand = np.random.rand()
for action, p in enumerate(dist):
rand -= p
if rand <= 0: break
else:
action = np.argmax(log_probs.npvalue())
else:
action = oracle_actions.pop()
losses.append(dy.pick(log_probs, action))
action_history.append(action)
if action == STEP:
# Delete action
encoder.pop()
elif action == END_WORD:
# Finish transduction
break
else:
# Insert action
assert action in self.INSERTS, (char_,
action2string([char_],
self.vocab),
self.INSERTS)
char_ = self.vocab.act.i2w[action]
word.append(char_)
word = u''.join(word)
return losses, word, action_history
def transform(self, input_expr):
W1 = dy.parameter(self.embeddings)
b1 = dy.parameter(self.bias)
return dy.affine_transform([b1, W1, input_expr])
def transduce(
self, expr_seq: expression_seqs.ExpressionSequence
) -> expression_seqs.ExpressionSequence:
"""
transduce the sequence
Args:
expr_seq: expression sequence or list of expression sequences (where each inner list will be concatenated)
Returns:
expression sequence
"""
Wq, Wk, Wv, Wo = [
dy.parameter(x) for x in (self.pWq, self.pWk, self.pWv, self.pWo)
]
bq, bk, bv, bo = [
dy.parameter(x) for x in (self.pbq, self.pbk, self.pbv, self.pbo)
]
# Start with a [(length, model_size) x batch] tensor
x = expr_seq.as_transposed_tensor()
x_len = x.dim()[0][0]
x_batch = x.dim()[1]
# Get the query key and value vectors
# TODO: do we need bias broadcasting in DyNet?
# q = dy.affine_transform([bq, x, Wq])
# k = dy.affine_transform([bk, x, Wk])
# v = dy.affine_transform([bv, x, Wv])
q = bq + x * Wq
k = bk + x * Wk
v = bv + x * Wv
# Split to batches [(length, head_dim) x batch * num_heads] tensor
q, k, v = [
dy.reshape(x, (x_len, self.head_dim),
batch_size=x_batch * self.num_heads) for x in (q, k, v)
]
# Do scaled dot product [(length, length) x batch * num_heads], rows are queries, columns are keys
attn_score = q * dy.transpose(k) / sqrt(self.head_dim)
if expr_seq.mask is not None:
mask = dy.inputTensor(np.repeat(
expr_seq.mask.np_arr, self.num_heads, axis=0).transpose(),
batched=True) * -1e10
attn_score = attn_score + mask
attn_prob = dy.softmax(attn_score, d=1)
if self.train and self.dropout > 0.0:
attn_prob = dy.dropout(attn_prob, self.dropout)
# Reduce using attention and resize to match [(length, model_size) x batch]
o = dy.reshape(attn_prob * v, (x_len, self.input_dim),
batch_size=x_batch)
# Final transformation
# o = dy.affine_transform([bo, attn_prob * v, Wo])
o = bo + o * Wo
expr_seq = expression_seqs.ExpressionSequence(expr_transposed_tensor=o,
mask=expr_seq.mask)
self._final_states = [
transducers.FinalTransducerState(expr_seq[-1], None)
]
return expr_seq
def __step_batch(self, batch):
dy.renew_cg()
W_s = dy.parameter(self.W_s)
b_s = dy.parameter(self.b_s)
W_y = dy.parameter(self.W_y)
b_y = dy.parameter(self.b_y)
W_m = dy.parameter(self.W_m)
b_m = dy.parameter(self.b_m)
W1_att_f = dy.parameter(self.W1_att_f)
w2_att = dy.parameter(self.w2_att)
src_batch = [x[0] for x in batch]
tgt_batch = [x[1] for x in batch]
batch_size = len(src_batch)
attended_batch = []
for src_sent in src_batch:
attended = []
c_t_sense = dy.vecInput(self.embed_size)
sense_start = dy.concatenate([
self.lookup_frozen(self.src_lookup,
self.src_token_to_id['<S>'][0]),
dy.tanh(c_t_sense)
])
sense_state = self.sense_builder.initial_state().add_input(
sense_start)
for cw in src_sent:
cw_sense_ids = self.src_token_to_id[cw]
cw_senses = [
self.lookup_frozen(self.src_lookup, sense_id)
for sense_id in cw_sense_ids
]
h_senses = dy.concatenate_cols(cw_senses)
h_m = sense_state.output()
c_t_sense = self.__sense_attention_mlp(h_senses, h_m)
sense_state = sense_state.add_input(
dy.concatenate([c_t_sense, dy.tanh(c_t_sense)]))
attended.append(c_t_sense)
attended_batch.append(attended)
attended_batch_rev = [list(reversed(sent)) for sent in attended_batch]
# Encoder
src_cws_l2r = []
src_cws_r2l = []
src_len = [len(sent) for sent in attended_batch]
max_src_len = np.max(src_len)
for i in range(max_src_len):
src_cws_l2r.append([sent[i] for sent in attended_batch])
src_cws_r2l.append([sent[i] for sent in attended_batch_rev])
l2r_state = self.l2r_builder.initial_state()
r2l_state = self.r2l_builder.initial_state()
l2r_contexts = []
r2l_contexts = []
for i, (cws_l2r, cws_r2l) in enumerate(zip(src_cws_l2r, src_cws_r2l)):
l2r_batch = dy.reshape(dy.concatenate_cols(cws_l2r),
(self.embed_size, ),
batch_size=batch_size)
l2r_state = l2r_state.add_input(l2r_batch)
r2l_batch = dy.reshape(dy.concatenate_cols(cws_r2l),
(self.embed_size, ),
batch_size=batch_size)
r2l_state = r2l_state.add_input(r2l_batch)
l2r_contexts.append(l2r_state.output())
r2l_contexts.append(r2l_state.output())
r2l_contexts.reverse()
h_fs = []
for (l2r_i, r2l_i) in zip(l2r_contexts, r2l_contexts):
h_fs.append(dy.concatenate([l2r_i, r2l_i]))
h_fs_matrix = dy.concatenate_cols(h_fs)
fixed_attentional_component = W1_att_f * h_fs_matrix
losses = []
num_words = 0
# Decoder
tgt_cws = []
tgt_len = [len(sent) for sent in tgt_batch]
max_tgt_len = np.max(tgt_len)
masks = []
for i in range(max_tgt_len):
tgt_cws.append([
self.tgt_token_to_id[sent[i]]
if len(sent) > i else self.tgt_token_to_id['</S>']
for sent in tgt_batch
])
mask = [(1 if len(sent) > i else 0) for sent in tgt_batch]
masks.append(mask)
num_words += sum(mask)
c_t = dy.vecInput(self.hidden_size * 2)
start_state = dy.affine_transform([b_s, W_s, h_fs[-1]])
dec_state = self.word_dec_builder.initial_state().set_s(
[start_state, dy.tanh(start_state)])
for i, (cws, nws, mask) in enumerate(zip(tgt_cws, tgt_cws[1:], masks)):
embed_t = dy.lookup_batch(self.tgt_lookup, cws)
x_t = dy.concatenate([embed_t, c_t])
dec_state = dec_state.add_input(x_t)
h_e = dec_state.output()
c_t = self.__word_attention_mlp(h_fs_matrix, h_e,
fixed_attentional_component)
m_t = dy.tanh(
dy.affine_transform([b_m, W_m,
dy.concatenate([h_e, c_t])]))
y_star = dy.affine_transform([b_y, W_y, m_t])
loss = dy.pickneglogsoftmax_batch(y_star, nws)
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1, ), len(batch))
mask_loss = loss * mask_expr
losses.append(mask_loss)
return dy.sum_batches(dy.esum(losses)), num_words
def translate_sentence(self, sent):
dy.renew_cg()
W_s = dy.parameter(self.W_s)
b_s = dy.parameter(self.b_s)
W_y = dy.parameter(self.W_y)
b_y = dy.parameter(self.b_y)
W_m = dy.parameter(self.W_m)
b_m = dy.parameter(self.b_m)
W1_att_f = dy.parameter(self.W1_att_f)
w2_att = dy.parameter(self.w2_att)
# Sense-level attention
attended = []
c_t_sense = dy.vecInput(self.embed_size)
sense_start = dy.concatenate([
self.lookup_frozen(self.src_lookup,
self.src_token_to_id['<S>'][0]), c_t_sense
])
sense_state = self.sense_builder.initial_state().add_input(sense_start)
for cw in sent:
cw_sense_ids = self.src_token_to_id[cw]
cw_senses = [
self.lookup_frozen(self.src_lookup, sense_id)
for sense_id in cw_sense_ids
]
h_senses = dy.concatenate_cols(cw_senses)
h_m = sense_state.output()
c_t_sense = self.__sense_attention_mlp(h_senses, h_m)
sense_state = sense_state.add_input(
dy.concatenate([c_t_sense, dy.tanh(c_t_sense)]))
attended.append(c_t_sense)
attended_rev = list(reversed(attended))
# Bidirectional representations
l2r_state = self.l2r_builder.initial_state()
r2l_state = self.r2l_builder.initial_state()
l2r_contexts = []
r2l_contexts = []
for (cw_l2r, cw_r2l) in zip(attended, attended_rev):
l2r_state = l2r_state.add_input(cw_l2r)
r2l_state = r2l_state.add_input(cw_r2l)
l2r_contexts.append(l2r_state.output())
r2l_contexts.append(r2l_state.output())
r2l_contexts.reverse()
h_fs = []
for (l2r_i, r2l_i) in zip(l2r_contexts, r2l_contexts):
h_fs.append(dy.concatenate([l2r_i, r2l_i]))
h_fs_matrix = dy.concatenate_cols(h_fs)
fixed_attentional_component = W1_att_f * h_fs_matrix
# Decoder
trans_sentence = ['<S>']
cw = trans_sentence[-1]
c_t = dy.vecInput(self.hidden_size * 2)
start_state = dy.affine_transform([b_s, W_s, h_fs[-1]])
dec_state = self.word_dec_builder.initial_state().set_s(
[start_state, dy.tanh(start_state)])
while len(trans_sentence) < self.max_len:
embed_t = dy.lookup(self.tgt_lookup, self.tgt_token_to_id[cw])
x_t = dy.concatenate([embed_t, c_t])
dec_state = dec_state.add_input(x_t)
h_e = dec_state.output()
c_t = self.__word_attention_mlp(h_fs_matrix, h_e,
fixed_attentional_component)
m_t = dy.tanh(
dy.affine_transform([b_m, W_m,
dy.concatenate([h_e, c_t])]))
y_star = dy.affine_transform([b_y, W_y, m_t])
p = dy.softmax(y_star)
cw = self.tgt_id_to_token[np.argmax(p.vec_value())]
if cw == '</S>':
break
trans_sentence.append(cw)
return ' '.join(trans_sentence[1:])
def __step(self, instance):
dy.renew_cg()
W_s = dy.parameter(self.W_s)
b_s = dy.parameter(self.b_s)
W_y = dy.parameter(self.W_y)
b_y = dy.parameter(self.b_y)
W_m = dy.parameter(self.W_m)
b_m = dy.parameter(self.b_m)
W1_att_f = dy.parameter(self.W1_att_f)
W1_att_e = dy.parameter(self.W1_att_e)
w2_att = dy.parameter(self.w2_att)
src_sent, tgt_sent = instance
# Sense-level attention
attended = []
c_t_sense = dy.vecInput(self.embed_size)
sense_start = dy.concatenate([
self.lookup_frozen(self.src_lookup,
self.src_token_to_id['<S>'][0]),
dy.tanh(c_t_sense)
])
sense_state = self.sense_builder.initial_state().add_input(sense_start)
for cw in src_sent:
cw_sense_ids = self.src_token_to_id[cw]
cw_senses = [
self.lookup_frozen(self.src_lookup, sense_id)
for sense_id in cw_sense_ids
]
h_senses = dy.concatenate_cols(cw_senses)
h_m = sense_state.output()
c_t_sense = self.__sense_attention_mlp(h_senses, h_m)
sense_state = sense_state.add_input(
dy.concatenate([c_t_sense, dy.tanh(c_t_sense)]))
attended.append(c_t_sense)
attended_rev = list(reversed(attended))
# Bidirectional representations
l2r_state = self.l2r_builder.initial_state()
r2l_state = self.r2l_builder.initial_state()
l2r_contexts = []
r2l_contexts = []
for (cw_l2r, cw_r2l) in zip(attended, attended_rev):
l2r_state = l2r_state.add_input(cw_l2r)
r2l_state = r2l_state.add_input(cw_r2l)
l2r_contexts.append(l2r_state.output())
r2l_contexts.append(r2l_state.output())
r2l_contexts.reverse()
h_fs = []
for (l2r_i, r2l_i) in zip(l2r_contexts, r2l_contexts):
h_fs.append(dy.concatenate([l2r_i, r2l_i]))
h_fs_matrix = dy.concatenate_cols(h_fs)
fixed_attentional_component = W1_att_f * h_fs_matrix
losses = []
num_words = 0
# Decoder
c_t = dy.vecInput(self.hidden_size * 2)
start_state = dy.affine_transform([b_s, W_s, h_fs[-1]])
dec_state = self.word_dec_builder.initial_state().set_s(
[start_state, dy.tanh(start_state)])
for (cw, nw) in zip(tgt_sent, tgt_sent[1:]):
embed_t = dy.lookup(self.tgt_lookup, self.tgt_token_to_id[cw])
x_t = dy.concatenate([embed_t, c_t])
dec_state = dec_state.add_input(x_t)
h_e = dec_state.output()
c_t = self.__word_attention_mlp(h_fs_matrix, h_e,
fixed_attentional_component)
m_t = dy.tanh(
dy.affine_transform([b_m, W_m,
dy.concatenate([h_e, c_t])]))
y_star = dy.affine_transform([b_y, W_y, m_t])
loss = dy.pickneglogsoftmax(y_star, self.tgt_token_to_id[nw])
losses.append(loss)
num_words += 1
return dy.esum(losses), num_words
def generate(self, s_sentence, max_len=150):
dy.renew_cg()
global beam_size
W_y = dy.parameter(self.params["W_y"])
b_y = dy.parameter(self.params["b_y"])
s_lookup = self.params["s_lookup"]
t_lookup = self.params["t_lookup"]
s_sentence = [self.s_vocab[EOS]] + s_sentence + [self.s_vocab[EOS]]
s_sentence_rev = list(reversed(s_sentence))
l2r_state = self.l2r_builder.initial_state()
r2l_state = self.r2l_builder.initial_state()
l2r_contexts = []
r2l_contexts = []
for cw_l2r in s_sentence:
l2r_state = l2r_state.add_input(s_lookup[cw_l2r])
l2r_contexts.append(l2r_state.output())
for cw_r2l in s_sentence_rev:
r2l_state = r2l_state.add_input(s_lookup[cw_r2l])
r2l_contexts.append(r2l_state.output())
r2l_contexts.reverse()
H_f = []
H_f = [dy.concatenate(list(p)) for p in zip(l2r_contexts, r2l_contexts)]
H_f_mat = dy.concatenate_cols(H_f)
W1_att = dy.parameter(self.params["W1_att"])
w1dt = W1_att * H_f_mat
c_t_init = dy.vecInput(2*self.HIDDEN_DIM)
# c_t = dy.concatenate([l2r_contexts[-1], r2l_contexts[-1]])
dec_state_init = self.dec_builder.initial_state()
possible_list = {("<EOS>", dec_state_init, c_t_init): 0.0}
for i in range(len(s_sentence)*2):
t_list = {}
count_eos = 0
for (poss, dec_state, c_t), prob in possible_list.iteritems():
spl_poss = poss.split(' ')
if i > 1 and spl_poss[-1] == "<EOS>":
count_eos += 1
t_list[(poss, dec_state, c_t)] = prob
continue
embedding = t_lookup[self.t_vocab[spl_poss[-1]]]
x_t = dy.concatenate([c_t, embedding])
dec_state = dec_state.add_input(x_t)
c_t = self.attend(H_f_mat, dec_state, w1dt, len(s_sentence), 1)
probs = dy.softmax(W_y*dy.concatenate([c_t, dec_state.output()]) + b_y).vec_value()
inds = self.list_nlargest(probs, beam_size)
for ind in inds:
sent = poss + " " + self.t_id_lookup[ind]
sent_prob = prob + math.log(probs[ind])
# lp = (5 + len(sent.split()))/(5+1)
# sent_prob = sent_prob/pow(lp, alpha)
t_list[(sent, dec_state, c_t)] = sent_prob
if count_eos == beam_size:
break
possible_list = {}
for tup in self.dict_nlargest(t_list, beam_size):
possible_list[tup] = t_list[tup]
final_sent = self.dict_nlargest(possible_list, 1)[0][0]
return " ".join(final_sent.replace("<EOS>", "").strip().split())
def transduce(
self, expr_seq: expression_seqs.ExpressionSequence
) -> expression_seqs.ExpressionSequence:
if expr_seq.dim()[1] > 1:
raise ValueError(
f"LatticeLSTMTransducer requires batch size 1, got {expr_seq.dim()[1]}"
)
lattice = self.cur_src[0]
Wx_iog = dy.parameter(self.p_Wx_iog)
Wh_iog = dy.parameter(self.p_Wh_iog)
b_iog = dy.parameter(self.p_b_iog)
Wx_f = dy.parameter(self.p_Wx_f)
Wh_f = dy.parameter(self.p_Wh_f)
b_f = dy.parameter(self.p_b_f)
h = {}
c = {}
h_list = []
batch_size = expr_seq.dim()[1]
if self.dropout_rate > 0.0 and self.train:
self.set_dropout_masks(batch_size=batch_size)
for i, cur_node_id in enumerate(lattice.graph.topo_sort()):
prev_node = lattice.graph.predecessors(cur_node_id)
val = expr_seq[i]
if self.dropout_rate > 0.0 and self.train:
val = dy.cmult(val, self.dropout_mask_x)
i_ft_list = []
if len(prev_node) == 0:
tmp_iog = dy.affine_transform([b_iog, Wx_iog, val])
else:
h_tilde = sum(h[pred] for pred in prev_node)
tmp_iog = dy.affine_transform(
[b_iog, Wx_iog, val, Wh_iog, h_tilde])
for pred in prev_node:
i_ft_list.append(
dy.logistic(
dy.affine_transform(
[b_f, Wx_f, val, Wh_f, h[pred]])))
i_ait = dy.pick_range(tmp_iog, 0, self.hidden_dim)
i_aot = dy.pick_range(tmp_iog, self.hidden_dim,
self.hidden_dim * 2)
i_agt = dy.pick_range(tmp_iog, self.hidden_dim * 2,
self.hidden_dim * 3)
i_it = dy.logistic(i_ait)
i_ot = dy.logistic(i_aot)
i_gt = dy.tanh(i_agt)
if len(prev_node) == 0:
c[cur_node_id] = dy.cmult(i_it, i_gt)
else:
fc = dy.cmult(i_ft_list[0], c[prev_node[0]])
for i in range(1, len(prev_node)):
fc += dy.cmult(i_ft_list[i], c[prev_node[i]])
c[cur_node_id] = fc + dy.cmult(i_it, i_gt)
h_t = dy.cmult(i_ot, dy.tanh(c[cur_node_id]))
if self.dropout_rate > 0.0 and self.train:
h_t = dy.cmult(h_t, self.dropout_mask_h)
h[cur_node_id] = h_t
h_list.append(h_t)
self._final_states = [
transducers.FinalTransducerState(h_list[-1], h_list[-1])
]
return expression_seqs.ExpressionSequence(expr_list=h_list)
def _decoder_input_embedding(self,
rnn_state,
previous_triple,
encoded_string,
enc_state,
encoded_history,
training=False,
initial_state=None):
attention_vecs = {}
# Compute attention over encodded string.
utterance_attn, utterance_dist = attend(encoded_string,
rnn_state.h()[-1],
dy.parameter(self._utterance_attention_w),
self._dropout if training else 0.)
attention_vecs['utterance'] = utterance_dist
# Key for state and history attention.
attn_key = dy.concatenate([utterance_attn, rnn_state.h()[-1]])
if training:
attn_key = dy.dropout(attn_key, self._dropout)
# Attend on history using current state and utterance attention.
history_attn, history_dist = attend(encoded_history,
attn_key,
dy.parameter(self._history_attention_w),
self._dropout if training else 0.)
attention_vecs['history'] = history_dist
# Attend on state.
state_attn, state_dist = attend(enc_state,
attn_key,
dy.parameter(self._state_attention_w),
self._dropout if training else 0.)
state_attn2, state_dist2 = attend(enc_state,
attn_key,
dy.parameter(self._state_attention_w2),
self._dropout if training else 0.)
attention_vecs['state_1'] = state_dist
attention_vecs['state_2'] = state_dist2
# Compute previous embedding
prev_emb = self._embed_predicted_triple(previous_triple)
# Concatenate with history and state, and mix with a feed-forward
# layer.
situated_embedding = dy.concatenate([utterance_attn,
history_attn,
state_attn,
state_attn2,
prev_emb])
# Attend on initial state (if provided)
if self.args.feed_updated_state and self.args.always_initial_state:
if not initial_state:
raise ValueError("Encoding the initial state but it was not provided.")
initial_attn, initial_dist = attend(initial_state,
attn_key,
dy.parameter(self._state_attention_winitial),
self._dropout if training else 0.)
initial_attn2, initial_dist2 = attend(initial_state,
attn_key,
dy.parameter(self._state_attention_winitial2),
self._dropout if training else 0.)
attention_vecs['initial_1'] = initial_dist
attention_vecs['initial_2'] = initial_dist2
situated_embedding = dy.concatenate([situated_embedding,
initial_attn,
initial_attn2])
# Situated embedding mixing parameters.
weights = dy.parameter(self._situated_w)
biases = dy.parameter(self._situated_b)
situated_embedding = dy.tanh(weights * situated_embedding + biases)
return situated_embedding, attention_vecs
def calc_loss(sents):
dy.renew_cg()
# Transduce all batch elements with an LSTM
src_sents = [x[0] for x in sents]
tgt_sents = [x[1] for x in sents]
src_cws = []
src_len = [len(sent) for sent in src_sents]
max_src_len = np.max(src_len)
num_words = 0
for i in range(max_src_len):
src_cws.append([sent[i] for sent in src_sents])
tgt_cws = []
tgt_len = [len(sent) for sent in sents]
max_tgt_len = np.max(tgt_len)
masks = []
for i in range(max_tgt_len):
tgt_cws.append([sent[i] if len(sent) > i else eos_trg for sent in tgt_sents])
mask = [(1 if len(sent) > i else 0) for sent in tgt_sents]
masks.append(mask)
num_words += sum(mask)
# get the outputs of the first LSTM
src_outputs = [dy.concatenate([x.output(), y.output()]) for x, y in
LSTM_SRC.add_inputs([dy.lookup_batch(LOOKUP_SRC, cws) for cws in src_cws])]
src_output = src_outputs[-1]
# gets the parameters for the attention
src_output_matrix = dy.concatenate_cols(src_outputs)
w1_att_src = dy.parameter(w1_att_src_p)
fixed_attentional_component = w1_att_src * src_output_matrix
# now decode
all_losses = []
# Decoder
# need to mask padding at end of sentence
current_state = LSTM_TRG_BUILDER.initial_state().set_s([src_output, dy.tanh(src_output)])
prev_words = tgt_cws[0]
W_sm = dy.parameter(W_sm_p)
b_sm = dy.parameter(b_sm_p)
W_m = dy.parameter(W_m_p)
b_m = dy.parameter(b_m_p)
for next_words, mask in zip(tgt_cws[1:], masks):
# feed the current state into the
current_state = current_state.add_input(dy.lookup_batch(LOOKUP_TRG, prev_words))
output_embedding = current_state.output()
att_output, _ = calc_attention(src_output_matrix, output_embedding, fixed_attentional_component)
middle_expr = dy.tanh(dy.affine_transform([b_m, W_m, dy.concatenate([output_embedding, att_output])]))
s = dy.affine_transform([b_sm, W_sm, middle_expr])
loss = (dy.pickneglogsoftmax_batch(s, next_words))
mask_expr = dy.inputVector(mask)
mask_expr = dy.reshape(mask_expr, (1,), len(sents))
mask_loss = loss * mask_expr
all_losses.append(mask_loss)
prev_words = next_words
return dy.sum_batches(dy.esum(all_losses)), num_words
def embed_sent(self, sent_len):
embeddings = dy.strided_select(dy.parameter(self.embeddings), [1, 1],
[0, 0], [self.emb_dim, sent_len])
return expression_seqs.ExpressionSequence(expr_tensor=embeddings,
mask=None)
def one_word_loss(model, char_lookup, feat_lookup, R, bias, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats, word, alphabet_index, aligned_pair,
feat_index, feature_types):
pc.renew_cg()
# read the parameters
# char_lookup = model["char_lookup"]
# feat_lookup = model["feat_lookup"]
# R = pc.parameter(model["R"])
# bias = pc.parameter(model["bias"])
R = pc.parameter(R)
bias = pc.parameter(bias)
padded_lemma = BEGIN_WORD + lemma + END_WORD
# convert characters to matching embeddings
lemma_char_vecs = encode_lemma(alphabet_index, char_lookup, padded_lemma)
# convert features to matching embeddings, if UNK handle properly
feat_vecs = encode_feats(feat_index, feat_lookup, feats, feature_types)
feats_input = pc.concatenate(feat_vecs)
blstm_outputs = bilstm_transduce(encoder_frnn, encoder_rrnn, lemma_char_vecs)
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
loss = []
# i is input index, j is output index
i = 0
j = 0
# go through alignments, progress j when new output is introduced, progress i when new char is seen on lemma (no ~)
aligned_lemma, aligned_word = aligned_pair
aligned_lemma += END_WORD
aligned_word += END_WORD
# run through the alignments
for align_index, (input_char, output_char) in enumerate(zip(aligned_lemma, aligned_word)):
possible_outputs = []
# feedback, blstm[i], feats
decoder_input = pc.concatenate([prev_output_vec, blstm_outputs[i], feats_input])
# if reached the end word symbol
if output_char == END_WORD:
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
# compute local loss
loss.append(-pc.log(pc.pick(probs, alphabet_index[END_WORD])))
continue
# initially, if there is no prefix in the output (shouldn't delay on current input), step forward
# TODO: check if can remove this condition entirely by adding '<' to both the aligned lemma/word
if padded_lemma[i] == BEGIN_WORD and aligned_lemma[align_index] != ALIGN_SYMBOL:
# perform rnn step
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
# compute local loss
loss.append(-pc.log(pc.pick(probs, alphabet_index[STEP])))
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[STEP]]
prev_char_vec = char_lookup[alphabet_index[EPSILON]]
i += 1
# if 0-to-1 or 1-to-1 alignment, compute loss for predicting the output symbol
if aligned_word[align_index] != ALIGN_SYMBOL:
decoder_input = pc.concatenate([prev_output_vec, blstm_outputs[i], feats_input])
# feed new input to decoder
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
if aligned_word[align_index] in alphabet_index:
current_loss = -pc.log(pc.pick(probs, alphabet_index[aligned_word[align_index]]))
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[aligned_word[align_index]]]
else:
current_loss = -pc.log(pc.pick(probs, alphabet_index[UNK]))
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[UNK]]
loss.append(current_loss)
j += 1
# if the input's not done and the next is not a 0-to-1 alignment, perform step
if i < len(padded_lemma) - 1 and aligned_lemma[align_index + 1] != ALIGN_SYMBOL:
# perform rnn step
# feedback, blstm[i], feats
decoder_input = pc.concatenate([prev_output_vec, blstm_outputs[i], feats_input])
s = s.add_input(decoder_input)
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
# compute local loss for the step action
loss.append(-pc.log(pc.pick(probs, alphabet_index[STEP])))
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[alphabet_index[STEP]]
i += 1
# loss = esum(loss)
loss = pc.average(loss)
return loss
def calc_scores(words):
dy.renew_cg()
b_sm_exp = dy.parameter(b_sm)
score = dy.esum([dy.lookup(W_sm, x) for x in words])
return score + b_sm_exp
def get_output(self, input, h_start=None, c_start=None):
'''
apply the LSTM to a matrix or list of vectors
input - a list of vectors of dimension d
h_start - optional start state for continuation (default is to start at the beginning with h_0)
c_start - optional start cell for continuation (default is to start at the beginning with c_0)
'''
W_f = parameter(self.W_f)
U_f = parameter(self.U_f)
b_f = parameter(self.b_f)
W_i = parameter(self.W_i)
U_i = parameter(self.U_i)
b_i = parameter(self.b_i)
W_o = parameter(self.W_o)
U_o = parameter(self.U_o)
b_o = parameter(self.b_o)
W_c = parameter(self.W_c)
U_c = parameter(self.U_c)
b_c = parameter(self.b_c)
if h_start is None:
h_0 = parameter(self.h_0)
else:
h_0 = h_start
if c_start is None:
c_0 = parameter(self.c_0)
else:
c_0 = c_start
#IMPLEMENT YOUR LSTM CODE HERE
result = []
hidden = []
cell = []
h_t = h_0
c_t = c_0
if not self.reverse:
for i in range(0, len(input), 1):
f_t = logistic(W_f * input[i] + U_f * h_t + b_f)
i_t = logistic(W_i * input[i] + U_i * h_t + b_i)
o_t = logistic(W_o * input[i] + U_o * h_t + b_o)
c_t = cmult(f_t, c_t) + cmult(
i_t, tanh(W_c * input[i] + U_c * h_t + b_c))
h_t = cmult(o_t, tanh(c_t))
hidden.append(h_t)
cell.append(c_t)
else:
for i in range(len(input) - 1, -1, -1):
f_t = logistic(W_f * input[i] + U_f * h_t + b_f)
i_t = logistic(W_i * input[i] + U_i * h_t + b_i)
o_t = logistic(W_o * input[i] + U_o * h_t + b_o)
c_t = cmult(f_t, c_t) + cmult(
i_t, tanh(W_c * input[i] + U_c * h_t + b_c))
h_t = cmult(o_t, tanh(c_t))
hidden.append(h_t)
cell.append(c_t)
result.append(hidden)
result.append(cell)
return result
def cal_scores(self, src_encodings, predict=False):
src_len = len(src_encodings)
src_encodings = dy.concatenate_cols(
src_encodings) # src_ctx_dim, src_len, batch_size
batch_size = src_encodings.dim()[1]
W_arc_hidden_to_head = dy.parameter(self.W_arc_hidden_to_head)
b_arc_hidden_to_head = dy.parameter(self.b_arc_hidden_to_head)
W_arc_hidden_to_dep = dy.parameter(self.W_arc_hidden_to_dep)
b_arc_hidden_to_dep = dy.parameter(self.b_arc_hidden_to_dep)
W_label_hidden_to_head = dy.parameter(self.W_label_hidden_to_head)
b_label_hidden_to_head = dy.parameter(self.b_label_hidden_to_head)
W_label_hidden_to_dep = dy.parameter(self.W_label_hidden_to_dep)
b_label_hidden_to_dep = dy.parameter(self.b_label_hidden_to_dep)
U_arc_1 = dy.parameter(self.U_arc_1)
u_arc_2 = dy.parameter(self.u_arc_2)
U_label_1 = [dy.parameter(x) for x in self.U_label_1]
u_label_2_1 = [dy.parameter(x) for x in self.u_label_2_1]
u_label_2_2 = [dy.parameter(x) for x in self.u_label_2_2]
b_label = [dy.parameter(x) for x in self.b_label]
if predict:
h_arc_head = self.leaky_ReLu(
dy.affine_transform([
b_arc_hidden_to_head, W_arc_hidden_to_head, src_encodings
])) # n_arc_ml_units, src_len, bs
h_arc_dep = self.leaky_ReLu(
dy.affine_transform(
[b_arc_hidden_to_dep, W_arc_hidden_to_dep, src_encodings]))
h_label_head = self.leaky_ReLu(
dy.affine_transform([
b_label_hidden_to_head, W_label_hidden_to_head,
src_encodings
]))
h_label_dep = self.leaky_ReLu(
dy.affine_transform([
b_label_hidden_to_dep, W_label_hidden_to_dep, src_encodings
]))
else:
src_encodings = dy.dropout_dim(src_encodings, 1,
self.arc_mlp_dropout)
h_arc_head = dy.dropout_dim(
self.leaky_ReLu(
dy.affine_transform([
b_arc_hidden_to_head, W_arc_hidden_to_head,
src_encodings
])), 1,
self.arc_mlp_dropout) # n_arc_ml_units, src_len, bs
h_arc_dep = dy.dropout_dim(
self.leaky_ReLu(
dy.affine_transform([
b_arc_hidden_to_dep, W_arc_hidden_to_dep, src_encodings
])), 1, self.arc_mlp_dropout)
h_label_head = dy.dropout_dim(
self.leaky_ReLu(
dy.affine_transform([
b_label_hidden_to_head, W_label_hidden_to_head,
src_encodings
])), 1, self.label_mlp_dropout)
h_label_dep = dy.dropout_dim(
self.leaky_ReLu(
dy.affine_transform([
b_label_hidden_to_dep, W_label_hidden_to_dep,
src_encodings
])), 1, self.label_mlp_dropout)
h_arc_head_transpose = dy.transpose(h_arc_head)
h_label_head_transpose = dy.transpose(h_label_head)
s_arc = h_arc_head_transpose * dy.colwise_add(U_arc_1 * h_arc_dep,
u_arc_2)
s_label = []
for U_1, u_2_1, u_2_2, b in zip(U_label_1, u_label_2_1, u_label_2_2,
b_label):
e1 = h_label_head_transpose * U_1 * h_label_dep
e2 = h_label_head_transpose * u_2_1 * dy.ones((1, src_len))
e3 = dy.ones((src_len, 1)) * u_2_2 * h_label_dep
s_label.append(e1 + e2 + e3 + b)
return s_arc, s_label
def build_tagging_graph(self, words, ltags):
# parameters -> expressions
self.w1 = dy.parameter(self.W1)
self.b1 = dy.parameter(self.B1)
self.w2 = dy.parameter(self.W2)
self.b2 = dy.parameter(self.B2)
self.xw1 = dy.parameter(self.xW1)
self.xb1 = dy.parameter(self.xB1)
self.xw2 = dy.parameter(self.xW2)
self.xb2 = dy.parameter(self.xB2)
# apply dropout
if self.eval:
self.disable_dropout()
else:
self.enable_dropout()
# initialize the RNNs
f_init = self.fwdRNN.initial_state()
b_init = self.bwdRNN.initial_state()
f2_init = self.fwdRNN2.initial_state()
b2_init = self.bwdRNN2.initial_state()
self.hcf_init = self.hcfwdRNN.initial_state()
self.hcb_init = self.hcbwdRNN.initial_state()
self.ecf_init = self.ecfwdRNN.initial_state()
self.ecb_init = self.ecbwdRNN.initial_state()
xf_init = self.xfwdRNN.initial_state()
xb_init = self.xbwdRNN.initial_state()
xf2_init = self.xfwdRNN2.initial_state()
xb2_init = self.xbwdRNN2.initial_state()
self.xhcf_init = self.xhcfwdRNN.initial_state()
self.xhcb_init = self.xhcbwdRNN.initial_state()
self.xecf_init = self.xecfwdRNN.initial_state()
self.xecb_init = self.xecbwdRNN.initial_state()
# get the word vectors. word_rep(...) returns a 128-dim vector expression for each word.
wembs = [self.word_rep(w, l) for w, l in zip(words, ltags)]
cembs = [
self.char_rep(w, self.hcf_init, self.hcb_init, self.ecf_init,
self.ecb_init, l) for w, l in zip(words, ltags)
]
xembs = [dy.concatenate([w, c]) for w, c in zip(wembs, cembs)]
# feed word vectors into biLSTM
fw_exps = f_init.transduce(xembs)
bw_exps = b_init.transduce(reversed(xembs))
# biLSTM states
bi_exps = [
dy.concatenate([f, b]) for f, b in zip(fw_exps, reversed(bw_exps))
]
# feed word vectors into biLSTM
fw_exps = f2_init.transduce(bi_exps)
bw_exps = b2_init.transduce(reversed(bi_exps))
# biLSTM states
bi_exps = [
dy.concatenate([f, b]) for f, b in zip(fw_exps, reversed(bw_exps))
]
# feed each biLSTM state to an MLP
exps = []
pos_hidden = []
for xi in bi_exps:
xh = self.w1 * xi
#xh = self.meta.activation(xh) + self.b1
pos_hidden.append(xh)
cembs = [
self.char_rep(w, self.xhcf_init, self.xhcb_init, self.xecf_init,
self.xecb_init, l) for w, l in zip(words, ltags)
]
xembs = [
dy.concatenate([w, c, p])
for w, c, p in zip(wembs, cembs, pos_hidden)
]
xfw_exps = xf_init.transduce(xembs)
xbw_exps = xb_init.transduce(reversed(xembs))
# biLSTM states
bi_exps = [
dy.concatenate([f, b])
for f, b in zip(xfw_exps, reversed(xbw_exps))
]
# feed word vectors into biLSTM
fw_exps = xf2_init.transduce(bi_exps)
bw_exps = xb2_init.transduce(reversed(bi_exps))
# biLSTM states
bi_exps = [
dy.concatenate([f, b]) for f, b in zip(fw_exps, reversed(bw_exps))
]
exps = []
for xi in bi_exps:
xh = self.xw1 * xi
xh = self.meta.activation(xh) + self.xb1
xo = self.xw2 * xh + self.xb2
exps.append(xo)
return exps
def predict_output_sequence(model, char_lookup, feat_lookup, R, bias, encoder_frnn, encoder_rrnn, decoder_rnn, lemma, feats, alphabet_index,
inverse_alphabet_index, feat_index, feature_types):
pc.renew_cg()
# read the parameters
# char_lookup = model["char_lookup"]
# feat_lookup = model["feat_lookup"]
# R = pc.parameter(model["R"])
# bias = pc.parameter(model["bias"])
R = pc.parameter(R)
bias = pc.parameter(bias)
# convert characters to matching embeddings, if UNK handle properly
padded_lemma = BEGIN_WORD + lemma + END_WORD
lemma_char_vecs = encode_lemma(alphabet_index, char_lookup, padded_lemma)
# convert features to matching embeddings, if UNK handle properly
feat_vecs = encode_feats(feat_index, feat_lookup, feats, feature_types)
feats_input = pc.concatenate(feat_vecs)
blstm_outputs = bilstm_transduce(encoder_frnn, encoder_rrnn, lemma_char_vecs)
# initialize the decoder rnn
s_0 = decoder_rnn.initial_state()
s = s_0
# set prev_output_vec for first lstm step as BEGIN_WORD
prev_output_vec = char_lookup[alphabet_index[BEGIN_WORD]]
# i is input index, j is output index
i = 0
num_outputs = 0
predicted_output_sequence = []
# run the decoder through the sequence and predict characters, twice max prediction as step outputs are added
while num_outputs < MAX_PREDICTION_LEN * 3:
# prepare input vector and perform LSTM step
decoder_input = pc.concatenate([prev_output_vec,
blstm_outputs[i],
feats_input])
s = s.add_input(decoder_input)
# compute softmax probs vector and predict with argmax
decoder_rnn_output = s.output()
probs = pc.softmax(R * decoder_rnn_output + bias)
probs = probs.vec_value()
predicted_output_index = common.argmax(probs)
predicted_output = inverse_alphabet_index[predicted_output_index]
predicted_output_sequence.append(predicted_output)
# check if step or char output to promote i.
if predicted_output == STEP:
if i < len(padded_lemma) - 1:
i += 1
num_outputs += 1
# check if reached end of word
if predicted_output_sequence[-1] == END_WORD:
break
# prepare for the next iteration - "feedback"
prev_output_vec = char_lookup[predicted_output_index]
# remove the end word symbol
return u''.join(predicted_output_sequence[0:-1])
def initialize_graph_nodes(self):
# convert parameters to expressions
self.pad = dy.parameter(self.PAD)
self.ps_W1 = dy.parameter(self.ps_pW1)
self.ps_b1 = dy.parameter(self.ps_pb1)
self.ps_W2 = dy.parameter(self.ps_pW2)
self.ps_b2 = dy.parameter(self.ps_pb2)
self.pr_W1 = dy.parameter(self.pr_pW1)
self.pr_b1 = dy.parameter(self.pr_pb1)
self.pr_W2 = dy.parameter(self.pr_pW2)
self.pr_b2 = dy.parameter(self.pr_pb2)
#######################################
self.xpad = dy.parameter(self.XPAD)
self.xps_W1 = dy.parameter(self.xps_pW1)
self.xps_b1 = dy.parameter(self.xps_pb1)
self.xps_W2 = dy.parameter(self.xps_pW2)
self.xps_b2 = dy.parameter(self.xps_pb2)
self.xpr_W1 = dy.parameter(self.xpr_pW1)
self.xpr_b1 = dy.parameter(self.xpr_pb1)
self.xpr_W2 = dy.parameter(self.xpr_pW2)
self.xpr_b2 = dy.parameter(self.xpr_pb2)
# apply dropout
if self.eval:
self.disable_dropout()
else:
self.enable_dropout()
# initialize the RNNs
self.f_init = self.fwdRNN.initial_state()
self.b_init = self.bwdRNN.initial_state()
self.cf_init_eng = self.ecfwdRNN.initial_state()
self.cb_init_eng = self.ecbwdRNN.initial_state()
self.cf_init_bh = self.bhcfwdRNN.initial_state()
self.cb_init_bh = self.bhcbwdRNN.initial_state()
################################################
self.xcf_init_eng = self.xecfwdRNN.initial_state()
self.xcb_init_eng = self.xecbwdRNN.initial_state()
self.xcf_init_bh = self.xbhcfwdRNN.initial_state()
self.xcb_init_bh = self.xbhcbwdRNN.initial_state()
self.ps_f_init = self.ps_fwdRNN.initial_state()
self.ps_b_init = self.ps_bwdRNN.initial_state()
###############################################
self.xps_f_init = self.xps_fwdRNN.initial_state()
self.xps_b_init = self.xps_bwdRNN.initial_state()
self.pr_f_init = self.pr_fwdRNN.initial_state()
self.pr_b_init = self.pr_bwdRNN.initial_state()
###############################################
self.xpr_f_init = self.xpr_fwdRNN.initial_state()
self.xpr_b_init = self.xpr_bwdRNN.initial_state()
def translate_sentence_beam(self, sent):
dy.renew_cg()
W_y = dy.parameter(self.W_y)
b_y = dy.parameter(self.b_y)
sent_rev = list(reversed(sent))
# Bidirectional representations
l2r_state = self.l2r_builder.initial_state()
r2l_state = self.r2l_builder.initial_state()
l2r_contexts = []
r2l_contexts = []
for (cw_l2r, cw_r2l) in zip(sent, sent_rev):
l2r_state = l2r_state.add_input(dy.lookup(self.src_lookup, self.src_token_to_id[cw_l2r]))
r2l_state = r2l_state.add_input(dy.lookup(self.src_lookup, self.src_token_to_id[cw_r2l]))
l2r_contexts.append(l2r_state.output())
r2l_contexts.append(r2l_state.output())
r2l_contexts.reverse()
h_fs = []
for (l2r_i, r2l_i) in zip(l2r_contexts, r2l_contexts):
h_fs.append(dy.concatenate([l2r_i, r2l_i]))
h_fs_matrix = dy.concatenate_cols(h_fs)
# Decoder
cws = []
c_t = dy.vecInput(self.hidden_size * 2)
start = dy.concatenate([dy.lookup(self.tgt_lookup, self.tgt_token_to_id['<S>']), c_t])
dec_state = self.dec_builder.initial_state().add_input(start)
sentence_dict = defaultdict(lambda : defaultdict(list))
end_sign = self.tgt_token_to_id['</S>']
#first_iter
h_e = dec_state.output()
c_t = self.__attention_mlp(h_fs_matrix, h_e)
embed_t = dy.lookup(self.tgt_lookup, self.tgt_token_to_id['<S>'])
x_t = dy.concatenate([embed_t, c_t])
dec_state = dec_state.add_input(x_t)
y_star = (b_y + W_y * dec_state.output())
p = dy.log(dy.softmax(y_star)).npvalue()
cws = np.argpartition(-p,self.beam_size)[:self.beam_size]
#print p
# print 'one',np.argmax(p),p[np.argmax(p)],self.tgt_id_to_token[np.argmax(p)]
# print 'many',cws,p[cws],self.debug(cws)
history_path = p[cws]
# print 'history_path',history_path
trans_iter = 0
for i in range(self.beam_size):
sentence_dict[trans_iter][i] = [self.tgt_id_to_token[cws[i]]]
try:
index = np.where(cws==end_sign)[0][0]
# print index,'sentence ends normaly.'
return ''
except :
#print 'no </S>'
pass
#print 'first dict',sentence_dict
while trans_iter < self.max_len:
h_e = dec_state.output()
c_t = self.__attention_mlp(h_fs_matrix, h_e)
y_stars = np.zeros([self.tgt_vocab_size,self.beam_size])
#print 'before y_stars.shape',y_stars.shape
embed_t = dy.lookup_batch(self.tgt_lookup, cws)
x_t = dy.concatenate([embed_t, c_t])
dec_state = dec_state.add_input(x_t)
#print 'element',(b_y + W_y * dec_state.output()).npvalue().shape
y_stars = dy.log(dy.softmax(b_y + W_y * dec_state.output())).npvalue()
#print 'y_stars.shape',y_stars.shape
#print 'history_path',history_path.shape
#print 'beam',self.beam_size,self.tgt_vocab_size
#print 'y_stars_before',y_stars.shape,y_stars
#print 'history_path',history_path.shape, history_path
y_stars += history_path
#print 'y_stars_after',y_stars.shape,y_stars
y_star_all = np.reshape(y_stars.T,(self.beam_size * self.tgt_vocab_size,))
#print 'y_star_all',y_star_all.shape
#print 'y_star_all_sort',np.sort(y_star_all)
beam_indexes = np.argpartition(-y_star_all,self.beam_size)[:self.beam_size]
beam_sentence_index = list(map( (lambda x: x/self.tgt_vocab_size),beam_indexes))
#end_sign = np.array([self.tgt_token_to_id['</S>'],self.tgt_token_to_id['</S>']+self.tgt_vocab_size,self.tgt_token_to_id['</S>']+2*self.tgt_vocab_size,self.tgt_token_to_id['</S>']+3*self.tgt_vocab_size])
cws = [index % self.tgt_vocab_size for index in beam_indexes]
history_path = y_star_all[beam_indexes]
# print 'beam_indexes ',beam_indexes, y_star_all[beam_indexes] #y_star_all[end_sign]
# print 'beam_sentence_index',beam_sentence_index
# print 'history_path',history_path
# print 'cws words',cws,end_sign
trans_iter += 1
#print trans_iter -1 ,'dict',sentence_dict
max_score = -np.inf
find_end_sign = False
for i,cw in enumerate(cws):
if cw == end_sign:
find_end_sign = True
if max_score < history_path[i]:
max_score = history_path[i]
max_index = i
if find_end_sign:
#print 'return',trans_iter-1,max_index, beam_sentence_index[max_index]
return ' '.join(sentence_dict[trans_iter-1][beam_sentence_index[max_index]])
for i in range(self.beam_size):
sentence_dict[trans_iter][i] = sentence_dict[trans_iter - 1][beam_sentence_index[i]] + [self.tgt_id_to_token[cws[i]]]
#print trans_iter,'dict',sentence_dict
#index = cws.index(self.tgt_token_to_id['</S>'])
#print 'sentence too long'
return ' '.join(sentence_dict[trans_iter][np.argmax(history_path)])
def _get_probs(self, rnn_output):
output_w = dy.parameter(self.output_w)
output_b = dy.parameter(self.output_b)
probs = dy.softmax(output_w * rnn_output + output_b)
return probs
# regular lookup
a = lp[1].npvalue()
b = lp[2].npvalue()
c = lp[3].npvalue()
# batch lookup instead of single elements.
# two ways of doing this.
abc1 = dy.lookup_batch(lp, [1,2,3])
print(abc1.npvalue())
abc2 = lp.batch([1,2,3])
print(abc2.npvalue())
print(np.hstack([a,b,c]))
# use pick and pickneglogsoftmax in batch mode
# (must be used in conjunction with lookup_batch):
print("\nPick")
W = dy.parameter( m.add_parameters((5, 10)) )
h = W * lp.batch([1,2,3])
print(h.npvalue())
print(dy.pick_batch(h,[1,2,3]).npvalue())
print(dy.pick(W*lp[1],1).value(), dy.pick(W*lp[2],2).value(), dy.pick(W*lp[3],3).value())
# using pickneglogsoftmax_batch
print("\nPick neg log softmax")
print((-dy.log(dy.softmax(h))).npvalue())
print(dy.pickneglogsoftmax_batch(h,[1,2,3]).npvalue())
# number of layers in `RNN`
num_layers = 1
#pretrained embeddings
embedding_dim = emb_matrix_pretrained.shape[1]
embedding_parameters = RNN_model.lookup_parameters_from_numpy(
emb_matrix_pretrained)
#add RNN unit
RNN_unit = dy.VanillaLSTMBuilder(num_layers, embedding_dim, hidden_size,
RNN_model)
#add projection layer
# W (hidden x num_labels)
pW = RNN_model.add_parameters((hidden_size, len(list(l2i.keys()))))
dy.parameter(pW).npvalue().shape
# b (1 x num_labels)
pb = RNN_model.add_parameters((len(list(l2i.keys()))))
# note: we are just giving one dimension (ignoring the "1" dimension)
# this makes manipulating the shapes in forward_pass() easier
dy.parameter(pb).npvalue().shape
RNN_model.populate("trained.model")
batch_size = 256
num_batches_testing = int(np.ceil(len(test_tokens) / batch_size))
predictions = test()
overall_accuracy, unknown_accuracy = evaluate(predictions, test_labels,
unknown_index)
endtime = datetime.datetime.now()
trainer = dy.AdamTrainer(model)
trainer.set_clip_threshold(-1.0)
trainer.set_sparse_updates(True if args.SPARSE == 1 else False)
print("startup time: %r" % (time.time() - start))
sents = 0
all_time = 0
for ITER in range(100):
random.shuffle(train)
closs = 0.0
cwords = 0
start = time.time()
batch = []
for i, tree in enumerate(train, 1):
sents += 1
W = dy.parameter(W_)
h, c = builder.expr_for_tree(tree, True)
nodes = tree.nonterms()
losses = [dy.pickneglogsoftmax(W * nt._e, l2i[nt.label]) for nt in nodes]
loss = dy.esum(losses)
batch.append(loss)
if len(batch) == 50:
loss = dy.esum(batch)
closs += loss.value()
cwords += len(nodes)
loss.backward()
trainer.update()
batch = []
dy.renew_cg()
if sents % 1000 == 0:
trainer.status()
def beam_search_decode(self, lemma, feats, external_cg=True, unk_avg=True, beam_width=4):
# Returns an expression of the loss for the sequence of actions.
# (that is, the oracle_actions if present or the predicted sequence otherwise)
def _valid_actions(encoder):
valid_actions = []
if len(encoder) > 1:
valid_actions += [COPY, DELETE]
else:
valid_actions += [END_WORD]
valid_actions += self.INSERTS
return valid_actions
if not external_cg:
dy.renew_cg()
# vectorize lemma
lemma_enc = self._build_lemma(lemma, unk_avg, is_training=False)
# vectorize features
features = self._build_features(*feats)
# add encoder and decoder to computation graph
encoder = Encoder(self.fbuffRNN, self.bbuffRNN)
decoder = self.wordRNN.initial_state()
# encoder is a stack which pops lemma characters and their
# representations from the top.
encoder.transduce(lemma_enc, lemma)
# add classifier to computation graph
if self.MLP_DIM:
# decoder output to hidden
W_s2h = dy.parameter(self.pW_s2h)
b_s2h = dy.parameter(self.pb_s2h)
# hidden to action
W_act = dy.parameter(self.pW_act)
b_act = dy.parameter(self.pb_act)
encoder.pop() # BEGIN_WORD_CHAR
# a list of tuples:
# (decoder state, encoder state, list of previous actions,
# log prob of previous actions, log prob of previous actions as dynet object,
# word generated so far)
beam = [(decoder, encoder, [COPY], 0., 0., [])]
beam_length = 0
complete_hypotheses = []
while beam_length <= MAX_ACTION_SEQ_LEN:
if not beam or beam_width == 0:
break
#if show_oracle_actions:
# print 'Action: ', count, self.vocab.act.i2w[action_history[-1]]
# print 'Encoder length, char: ', lemma, len(encoder), self.vocab.char.i2w[encoder.s[-1][-1]]
# print 'word: ', u''.join(word)
# print 'Remaining actions: ', oracle_actions, u''.join([self.vocab.act.i2w[a] for a in oracle_actions])
# count += 1
#elif action_history[-1] >= self.NUM_ACTS:
# print 'Will be adding unseen act embedding: ', self.vocab.act.i2w[action_history[-1]]
# compute probability of each of the actions and choose an action
# either from the oracle or if there is no oracle, based on the model
expansion = []
#print 'Beam length: ', beam_length
for decoder, encoder, prev_actions, log_p, log_p_expr, word in beam:
#print 'Expansion: ', action2string(prev_actions, self.vocab), log_p, ''.join(word)
valid_actions = _valid_actions(encoder)
# decoder
decoder_input = dy.concatenate([encoder.embedding(),
features,
self.ACT_LOOKUP[prev_actions[-1]]
])
decoder = decoder.add_input(decoder_input)
# classifier
if self.double_feats:
classifier_input = dy.concatenate([decoder.output(), features])
else:
classifier_input = decoder.output()
if self.MLP_DIM:
h = self.NONLIN(W_s2h * classifier_input + b_s2h)
else:
h = classifier_input
logits = W_act * h + b_act
# logits_cpu = logits # dy.to_device(logits, 'CPU')
log_probs_expr = dy.log_softmax(logits, valid_actions)
log_probs = log_probs_expr.npvalue()
top_actions = np.argsort(log_probs)[-beam_width:]
#print 'top_actions: ', top_actions, action2string(top_actions, self.vocab)
#print 'log_probs: ', log_probs
#print
prev_actions_int = list(int(x) for x in prev_actions)
top_actions_int = list(int(x) for x in top_actions)
expansion.extend((
(decoder, encoder.copy(),
list(prev_actions_int), a, log_p + log_probs[a],
log_p_expr + log_probs_expr[a], list(word)) for a in list(top_actions_int)))
#print 'Overall, {} expansions'.format(len(expansion))
beam = []
expansion.sort(key=lambda e: e[4])
for e in expansion[-beam_width:]:
decoder, encoder, prev_actions, action, log_p, log_p_expr, word = e
prev_actions.append(action)
# execute the action to update the transducer state
if action == END_WORD:
# 1. Finish transduction:
# * beam width should be decremented
# * expansion should be taken off the beam and
# stored to final hypotheses set
beam_width -= 1
complete_hypotheses.append((log_p, log_p_expr, ''.join(word), prev_actions))
else:
if action == COPY:
# 1. Increment attention index
char_ = encoder.pop()
# 2. Append copied character to the output word
word.append(self.vocab.char.i2w[char_])
elif action == DELETE:
# 1. Increment attention index
encoder.pop()
else:
# one of the INSERT actions
assert action in self.INSERTS
# 1. Append inserted character to the output word
char_ = self.vocab.act.i2w[action]
word.append(char_)
beam.append((decoder, encoder, prev_actions, log_p, log_p_expr, word))
beam_length += 1
if not complete_hypotheses:
# nothing found because the model is so crappy
complete_hypotheses = [(log_p, log_p_expr, ''.join(word), prev_actions)
for _, _, prev_actions, log_p, log_p_expr, word in beam]
complete_hypotheses.sort(key=lambda h: h[0], reverse=True)
#print u'Complete hypotheses:'
#for log_p, _, word, actions in complete_hypotheses:
# print u'Actions {}, word {}, log p {:.3f}'.format(action2string(actions, self.vocab), word, log_p)
return complete_hypotheses
network_nodes[n] = s
return network_nodes, nodes[-1]
m = dy.ParameterCollection()
initial_values = [0.2, 0.8]
p = {}
for idx, val in enumerate(initial_values):
p[idx] = m.add_parameters((1), init=dy.ConstInitializer(val))
trainer = dy.AdamTrainer(m, alpha=0.01)
for i in range(1, 4):
print("\nSTART of Epoch", i, "\n")
counts = {}
for idx in p:
print("rule", idx, "prob:", dy.parameter(p[idx]).value())
counts[idx] = 0.0
print()
for elem in corpus:
hypergraph = build_hypergraph_rec(elem, {})
print("hypergraph for", elem, ":", hypergraph)
network, output = build_network(p, hypergraph)
loss = 1 * network[output]
loss.backward()
for n in range(1, output + 1):
print("node", n, "value", network[n].value(), "gradient",
network[n].gradient())
for r in p:
rv = dy.parameter(p[r])
count = (rv.gradient() * rv.value() / loss.value())[0]
