def parse(self, src_sent, context=None, beam_size=5, debug=False):
"""Perform beam search to infer the target AST given a source utterance
Args:
src_sent: list of source utterance tokens
context: other context used for prediction
beam_size: beam size
Returns:
A list of `DecodeHypothesis`, each representing an AST
"""
args = self.args
primitive_vocab = self.vocab.primitive
T = torch.cuda if args.cuda else torch
src_sent_var = nn_utils.to_input_variable([src_sent],
self.vocab.source,
cuda=args.cuda,
training=False)
# Variable(1, src_sent_len, hidden_size * 2)
##########src_encodings, (last_state, last_cell) = self.encode(src_sent_var, [len(src_sent)])
src_encodings, last_state = self.encode(src_sent_var, [len(src_sent)])
# (1, src_sent_len, hidden_size)
src_encodings_att_linear = self.att_src_linear(src_encodings)
#######dec_init_vec = self.init_decoder_state(last_state, last_cell)
dec_init_vec = self.init_decoder_state(last_state) #####
if args.lstm == 'parent_feed':
h_tm1 = dec_init_vec[0], dec_init_vec[1], \
Variable(self.new_tensor(args.hidden_size).zero_()), \
Variable(self.new_tensor(args.hidden_size).zero_())
else:
h_tm1 = dec_init_vec
zero_action_embed = Variable(
self.new_tensor(args.action_embed_size).zero_())
with torch.no_grad():
hyp_scores = Variable(self.new_tensor([0.]))
# For computing copy probabilities, we marginalize over tokens with the same surface form
# `aggregated_primitive_tokens` stores the position of occurrence of each source token
aggregated_primitive_tokens = OrderedDict()
for token_pos, token in enumerate(src_sent):
aggregated_primitive_tokens.setdefault(token, []).append(token_pos)
t = 0
hypotheses = [DecodeHypothesis()]
hyp_states = [[]]
completed_hypotheses = []
while len(completed_hypotheses
) < beam_size and t < args.decode_max_time_step:
hyp_num = len(hypotheses)
# (hyp_num, src_sent_len, hidden_size * 2)
exp_src_encodings = src_encodings.expand(hyp_num,
src_encodings.size(1),
src_encodings.size(2))
# (hyp_num, src_sent_len, hidden_size)
exp_src_encodings_att_linear = src_encodings_att_linear.expand(
hyp_num, src_encodings_att_linear.size(1),
src_encodings_att_linear.size(2))
if t == 0:
with torch.no_grad():
x = Variable(
self.new_tensor(1,
self.decoder_lstm.input_size).zero_())
if args.no_parent_field_type_embed is False:
offset = args.action_embed_size # prev_action
offset += args.att_vec_size * (not args.no_input_feed)
offset += args.action_embed_size * (
not args.no_parent_production_embed)
offset += args.field_embed_size * (
not args.no_parent_field_embed)
x[0, offset: offset + args.type_embed_size] = \
self.type_embed.weight[self.grammar.type2id[self.grammar.root_type]]
else:
actions_tm1 = [hyp.actions[-1] for hyp in hypotheses]
a_tm1_embeds = []
for a_tm1 in actions_tm1:
if a_tm1:
if isinstance(a_tm1, ApplyRuleAction):
a_tm1_embed = self.production_embed.weight[
self.grammar.prod2id[a_tm1.production]]
elif isinstance(a_tm1, ReduceAction):
a_tm1_embed = self.production_embed.weight[len(
self.grammar)]
else:
a_tm1_embed = self.primitive_embed.weight[
self.vocab.primitive[a_tm1.token]]
a_tm1_embeds.append(a_tm1_embed)
else:
a_tm1_embeds.append(zero_action_embed)
a_tm1_embeds = torch.stack(a_tm1_embeds)
inputs = [a_tm1_embeds]
if args.no_input_feed is False:
inputs.append(att_tm1)
if args.no_parent_production_embed is False:
# frontier production
frontier_prods = [
hyp.frontier_node.production for hyp in hypotheses
]
frontier_prod_embeds = self.production_embed(
Variable(
self.new_long_tensor([
self.grammar.prod2id[prod]
for prod in frontier_prods
])))
inputs.append(frontier_prod_embeds)
if args.no_parent_field_embed is False:
# frontier field
frontier_fields = [
hyp.frontier_field.field for hyp in hypotheses
]
frontier_field_embeds = self.field_embed(
Variable(
self.new_long_tensor([
self.grammar.field2id[field]
for field in frontier_fields
])))
inputs.append(frontier_field_embeds)
if args.no_parent_field_type_embed is False:
# frontier field type
frontier_field_types = [
hyp.frontier_field.type for hyp in hypotheses
]
frontier_field_type_embeds = self.type_embed(
Variable(
self.new_long_tensor([
self.grammar.type2id[type]
for type in frontier_field_types
])))
inputs.append(frontier_field_type_embeds)
# parent states
if args.no_parent_state is False:
p_ts = [
hyp.frontier_node.created_time for hyp in hypotheses
]
parent_states = torch.stack([
hyp_states[hyp_id][p_t][0]
for hyp_id, p_t in enumerate(p_ts)
])
parent_cells = torch.stack([
hyp_states[hyp_id][p_t][1]
for hyp_id, p_t in enumerate(p_ts)
])
if args.lstm == 'parent_feed':
h_tm1 = (h_tm1[0], h_tm1[1], parent_states,
parent_cells)
else:
inputs.append(parent_states)
x = torch.cat(inputs, dim=-1)
(h_t, cell_t), att_t = self.step(x,
h_tm1,
exp_src_encodings,
exp_src_encodings_att_linear,
src_token_mask=None)
# Variable(batch_size, grammar_size)
# apply_rule_log_prob = torch.log(F.softmax(self.production_readout(att_t), dim=-1))
apply_rule_log_prob = F.log_softmax(self.production_readout(att_t),
dim=-1)
# Variable(batch_size, primitive_vocab_size)
gen_from_vocab_prob = F.softmax(self.tgt_token_readout(att_t),
dim=-1)
if args.no_copy:
primitive_prob = gen_from_vocab_prob
else:
# Variable(batch_size, src_sent_len)
primitive_copy_prob = self.src_pointer_net(
src_encodings, None, att_t.unsqueeze(0)).squeeze(0)
# Variable(batch_size, 2)
primitive_predictor_prob = F.softmax(
self.primitive_predictor(att_t), dim=-1)
# Variable(batch_size, primitive_vocab_size)
primitive_prob = primitive_predictor_prob[:, 0].unsqueeze(
1) * gen_from_vocab_prob
# if src_unk_pos_list:
# primitive_prob[:, primitive_vocab.unk_id] = 1.e-10
gentoken_prev_hyp_ids = []
gentoken_new_hyp_unks = []
applyrule_new_hyp_scores = []
applyrule_new_hyp_prod_ids = []
applyrule_prev_hyp_ids = []
for hyp_id, hyp in enumerate(hypotheses):
# generate new continuations
action_types = self.transition_system.get_valid_continuation_types(
hyp)
for action_type in action_types:
if action_type == ApplyRuleAction:
productions = self.transition_system.get_valid_continuating_productions(
hyp)
for production in productions:
prod_id = self.grammar.prod2id[production]
prod_score = apply_rule_log_prob[
hyp_id, prod_id].data.item()
new_hyp_score = hyp.score + prod_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(prod_id)
applyrule_prev_hyp_ids.append(hyp_id)
elif action_type == ReduceAction:
action_score = apply_rule_log_prob[
hyp_id, len(self.grammar)].data.item()
new_hyp_score = hyp.score + action_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(len(self.grammar))
applyrule_prev_hyp_ids.append(hyp_id)
else:
# GenToken action
gentoken_prev_hyp_ids.append(hyp_id)
hyp_copy_info = dict() # of (token_pos, copy_prob)
hyp_unk_copy_info = []
if args.no_copy is False:
for token, token_pos_list in aggregated_primitive_tokens.items(
):
sum_copy_prob = torch.gather(
primitive_copy_prob[hyp_id], 0,
Variable(
T.LongTensor(token_pos_list))).sum()
gated_copy_prob = primitive_predictor_prob[
hyp_id, 1] * sum_copy_prob
if token in primitive_vocab:
token_id = primitive_vocab[token]
primitive_prob[
hyp_id, token_id] = primitive_prob[
hyp_id, token_id] + gated_copy_prob
hyp_copy_info[token] = (
token_pos_list,
gated_copy_prob.data.item())
else:
hyp_unk_copy_info.append({
'token':
token,
'token_pos_list':
token_pos_list,
'copy_prob':
gated_copy_prob.data.item()
})
if args.no_copy is False and len(
hyp_unk_copy_info) > 0:
unk_i = np.array([
x['copy_prob'] for x in hyp_unk_copy_info
]).argmax()
token = hyp_unk_copy_info[unk_i]['token']
primitive_prob[
hyp_id, primitive_vocab.
unk_id] = hyp_unk_copy_info[unk_i]['copy_prob']
gentoken_new_hyp_unks.append(token)
hyp_copy_info[token] = (
hyp_unk_copy_info[unk_i]['token_pos_list'],
hyp_unk_copy_info[unk_i]['copy_prob'])
new_hyp_scores = None
if applyrule_new_hyp_scores:
new_hyp_scores = Variable(
self.new_tensor(applyrule_new_hyp_scores))
if gentoken_prev_hyp_ids:
primitive_log_prob = torch.log(primitive_prob)
gen_token_new_hyp_scores = (
hyp_scores[gentoken_prev_hyp_ids].unsqueeze(1) +
primitive_log_prob[gentoken_prev_hyp_ids, :]).view(-1)
if new_hyp_scores is None:
new_hyp_scores = gen_token_new_hyp_scores
else:
new_hyp_scores = torch.cat(
[new_hyp_scores, gen_token_new_hyp_scores])
top_new_hyp_scores, top_new_hyp_pos = torch.topk(
new_hyp_scores,
k=min(new_hyp_scores.size(0),
beam_size - len(completed_hypotheses)))
live_hyp_ids = []
new_hypotheses = []
for new_hyp_score, new_hyp_pos in zip(
top_new_hyp_scores.data.cpu(), top_new_hyp_pos.data.cpu()):
action_info = ActionInfo()
if new_hyp_pos < len(applyrule_new_hyp_scores):
# it's an ApplyRule or Reduce action
prev_hyp_id = applyrule_prev_hyp_ids[new_hyp_pos]
prev_hyp = hypotheses[prev_hyp_id]
prod_id = applyrule_new_hyp_prod_ids[new_hyp_pos]
# ApplyRule action
if prod_id < len(self.grammar):
production = self.grammar.id2prod[prod_id]
action = ApplyRuleAction(production)
# Reduce action
else:
action = ReduceAction()
else:
# it's a GenToken action
token_id = (new_hyp_pos - len(applyrule_new_hyp_scores)
) % primitive_prob.size(1)
k = (new_hyp_pos - len(applyrule_new_hyp_scores)
) // primitive_prob.size(1)
# try:
# copy_info = gentoken_copy_infos[k]
prev_hyp_id = gentoken_prev_hyp_ids[k]
prev_hyp = hypotheses[prev_hyp_id]
# except:
# print('k=%d' % k, file=sys.stderr)
# print('primitive_prob.size(1)=%d' % primitive_prob.size(1), file=sys.stderr)
# print('len copy_info=%d' % len(gentoken_copy_infos), file=sys.stderr)
# print('prev_hyp_id=%s' % ', '.join(str(i) for i in gentoken_prev_hyp_ids), file=sys.stderr)
# print('len applyrule_new_hyp_scores=%d' % len(applyrule_new_hyp_scores), file=sys.stderr)
# print('len gentoken_prev_hyp_ids=%d' % len(gentoken_prev_hyp_ids), file=sys.stderr)
# print('top_new_hyp_pos=%s' % top_new_hyp_pos, file=sys.stderr)
# print('applyrule_new_hyp_scores=%s' % applyrule_new_hyp_scores, file=sys.stderr)
# print('new_hyp_scores=%s' % new_hyp_scores, file=sys.stderr)
# print('top_new_hyp_scores=%s' % top_new_hyp_scores, file=sys.stderr)
#
# torch.save((applyrule_new_hyp_scores, primitive_prob), 'data.bin')
#
# # exit(-1)
# raise ValueError()
if token_id == primitive_vocab.unk_id:
if gentoken_new_hyp_unks:
token = gentoken_new_hyp_unks[k]
else:
token = primitive_vocab.id2word(
primitive_vocab.unk_id) ######
else:
token = primitive_vocab.id2word(token_id.item())
action = GenTokenAction(token)
if token in aggregated_primitive_tokens:
action_info.copy_from_src = True
action_info.src_token_position = aggregated_primitive_tokens[
token]
if debug:
action_info.gen_copy_switch = 'n/a' if args.no_copy else primitive_predictor_prob[
prev_hyp_id, :].log().cpu().data.numpy()
action_info.in_vocab = token in primitive_vocab
action_info.gen_token_prob = gen_from_vocab_prob[prev_hyp_id, token_id].log().cpu().data.item() \
if token in primitive_vocab else 'n/a'
action_info.copy_token_prob = torch.gather(primitive_copy_prob[prev_hyp_id],
0,
Variable(T.LongTensor(action_info.src_token_position))).sum().log().cpu().data.item() \
if args.no_copy is False and action_info.copy_from_src else 'n/a'
action_info.action = action
action_info.t = t
if t > 0:
action_info.parent_t = prev_hyp.frontier_node.created_time
action_info.frontier_prod = prev_hyp.frontier_node.production
action_info.frontier_field = prev_hyp.frontier_field.field
if debug:
action_info.action_prob = new_hyp_score - prev_hyp.score
new_hyp = prev_hyp.clone_and_apply_action_info(action_info)
new_hyp.score = new_hyp_score
if new_hyp.completed:
# add length normalization
new_hyp.score /= (t + 1)
completed_hypotheses.append(new_hyp)
else:
new_hypotheses.append(new_hyp)
live_hyp_ids.append(prev_hyp_id)
if live_hyp_ids:
hyp_states = [
hyp_states[i] + [(h_t[i], cell_t[i])] for i in live_hyp_ids
]
h_tm1 = (h_t[live_hyp_ids], cell_t[live_hyp_ids])
att_tm1 = att_t[live_hyp_ids]
hypotheses = new_hypotheses
hyp_scores = Variable(
self.new_tensor([hyp.score for hyp in hypotheses]))
t += 1
else:
break
completed_hypotheses.sort(key=lambda hyp: -hyp.score)
return completed_hypotheses
def parse_sample(self, src_sent):
# get samples of q_{\phi}(z|x) by sampling
args = self.args
sample_size = self.sample_size
primitive_vocab = self.vocab.primitive
src_sent_var = nn_utils.to_input_variable([src_sent],
self.vocab.source,
cuda=args.cuda,
training=False)
# Variable(1, src_sent_len, hidden_size * 2)
src_encodings, (last_state,
last_cell) = self.encode(src_sent_var, [len(src_sent)])
# (1, src_sent_len, hidden_size)
src_encodings_att_linear = self.att_src_linear(src_encodings)
h_tm1 = self.init_decoder_state(last_state, last_cell)
zero_action_embed = Variable(
self.new_tensor(args.action_embed_size).zero_())
hyp_scores = Variable(self.new_tensor([0.]), volatile=True)
src_token_vocab_ids = [primitive_vocab[token] for token in src_sent]
src_unk_pos_list = [
pos for pos, token_id in enumerate(src_token_vocab_ids)
if token_id == primitive_vocab.unk_id
]
# sometimes a word may appear multi-times in the source, in this case,
# we just copy its first appearing position. Therefore we mask the words
# appearing second and onwards to -1
token_set = set()
for i, tid in enumerate(src_token_vocab_ids):
if tid in token_set:
src_token_vocab_ids[i] = -1
else:
token_set.add(tid)
completed_hypotheses = []
while len(completed_hypotheses) < sample_size:
t = 0
hypotheses = [DecodeHypothesis()]
hyp_states = [[]]
while t < args.decode_max_time_step:
hyp_num = len(hypotheses)
# (hyp_num, src_sent_len, hidden_size * 2)
exp_src_encodings = src_encodings.expand(
hyp_num, src_encodings.size(1), src_encodings.size(2))
# (hyp_num, src_sent_len, hidden_size)
exp_src_encodings_att_linear = src_encodings_att_linear.expand(
hyp_num, src_encodings_att_linear.size(1),
src_encodings_att_linear.size(2))
if t == 0:
x = Variable(self.new_tensor(
1, self.decoder_lstm.input_size).zero_(),
volatile=True)
offset = args.action_embed_size * 2 + args.field_embed_size
x[0, offset:offset +
args.type_embed_size] = self.type_embed.weight[
self.grammar.type2id[self.grammar.root_type]]
else:
actions_tm1 = [hyp.actions[-1] for hyp in hypotheses]
a_tm1_embeds = []
for a_tm1 in actions_tm1:
if a_tm1:
if isinstance(a_tm1, ApplyRuleAction):
a_tm1_embed = self.production_embed.weight[
self.grammar.prod2id[a_tm1.production]]
elif isinstance(a_tm1, ReduceAction):
a_tm1_embed = self.production_embed.weight[len(
self.grammar)]
else:
a_tm1_embed = self.primitive_embed.weight[
self.vocab.primitive[a_tm1.token]]
a_tm1_embeds.append(a_tm1_embed)
else:
a_tm1_embeds.append(zero_action_embed)
a_tm1_embeds = torch.stack(a_tm1_embeds)
# frontier production
frontier_prods = [
hyp.frontier_node.production for hyp in hypotheses
]
frontier_prod_embeds = self.production_embed(
Variable(
self.new_long_tensor([
self.grammar.prod2id[prod]
for prod in frontier_prods
])))
# frontier field
frontier_fields = [
hyp.frontier_field.field for hyp in hypotheses
]
frontier_field_embeds = self.field_embed(
Variable(
self.new_long_tensor([
self.grammar.field2id[field]
for field in frontier_fields
])))
# frontier field type
frontier_field_types = [
hyp.frontier_field.type for hyp in hypotheses
]
frontier_field_type_embeds = self.type_embed(
Variable(
self.new_long_tensor([
self.grammar.type2id[type]
for type in frontier_field_types
])))
# parent states
p_ts = [
hyp.frontier_node.created_time for hyp in hypotheses
]
hist_states = torch.stack([
hyp_states[hyp_id][p_t]
for hyp_id, p_t in enumerate(p_ts)
])
x = torch.cat([
a_tm1_embeds, att_tm1, frontier_prod_embeds,
frontier_field_embeds, frontier_field_type_embeds,
hist_states
],
dim=-1)
if args.lstm == 'lstm_with_dropout':
self.decoder_lstm.set_dropout_masks(hyp_num)
(h_t, cell_t), att_t = self.step(x,
h_tm1,
exp_src_encodings,
exp_src_encodings_att_linear,
src_token_mask=None)
# Variable(batch_size, grammar_size)
apply_rule_log_prob = F.log_softmax(
self.production_readout(att_t), dim=-1)
# Variable(batch_size, src_sent_len)
primitive_copy_prob = self.src_pointer_net(
src_encodings, None, att_t.unsqueeze(0)).squeeze(0)
# Variable(batch_size, primitive_vocab_size)
gen_from_vocab_prob = F.softmax(self.tgt_token_readout(att_t),
dim=-1)
# Variable(batch_size, 2)
primitive_predictor_prob = F.softmax(
self.primitive_predictor(att_t), dim=-1)
# Variable(batch_size, primitive_vocab_size)
primitive_prob = primitive_predictor_prob[:, 0].unsqueeze(
1) * gen_from_vocab_prob
if src_unk_pos_list:
primitive_prob[:, primitive_vocab.unk_id] = 1.e-10
gentoken_prev_hyp_ids = []
gentoken_new_hyp_unks = []
gentoken_copy_infos = []
applyrule_new_hyp_scores = []
applyrule_new_hyp_prod_ids = []
applyrule_prev_hyp_ids = []
for hyp_id, hyp in enumerate(hypotheses):
# generate new continuations
action_types = self.transition_system.get_valid_continuation_types(
hyp)
for action_type in action_types:
if action_type == ApplyRuleAction:
productions = self.transition_system.get_valid_continuating_productions(
hyp)
for production in productions:
prod_id = self.grammar.prod2id[production]
prod_score = apply_rule_log_prob[
hyp_id, prod_id].data[0]
new_hyp_score = hyp.score + prod_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(prod_id)
applyrule_prev_hyp_ids.append(hyp_id)
elif action_type == ReduceAction:
action_score = apply_rule_log_prob[
hyp_id, len(self.grammar)].data[0]
new_hyp_score = hyp.score + action_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(len(
self.grammar))
applyrule_prev_hyp_ids.append(hyp_id)
else:
# GenToken action
gentoken_prev_hyp_ids.append(hyp_id)
hyp_copy_info = dict() # of (token_pos, copy_prob)
# first, we compute copy probabilities for tokens in the source sentence
for token_pos, token_vocab_id in enumerate(
src_token_vocab_ids):
if token_vocab_id != -1 and token_vocab_id != primitive_vocab.unk_id:
p_copy = primitive_predictor_prob[
hyp_id,
1] * primitive_copy_prob[hyp_id,
token_pos]
primitive_prob[
hyp_id,
token_vocab_id] = primitive_prob[
hyp_id, token_vocab_id] + p_copy
token = src_sent[token_pos]
hyp_copy_info[token] = (token_pos,
p_copy.data[0])
# second, add the probability of copying the most probable unk word
if src_unk_pos_list:
unk_pos = primitive_copy_prob[
hyp_id][src_unk_pos_list].data.cpu().numpy(
).argmax()
unk_pos = src_unk_pos_list[unk_pos]
token = src_sent[unk_pos]
gentoken_new_hyp_unks.append(token)
unk_copy_score = primitive_predictor_prob[
hyp_id, 1] * primitive_copy_prob[hyp_id,
unk_pos]
primitive_prob[
hyp_id,
primitive_vocab.unk_id] = unk_copy_score
hyp_copy_info[token] = (unk_pos,
unk_copy_score.data[0])
gentoken_copy_infos.append(hyp_copy_info)
new_hyp_scores = None
if applyrule_new_hyp_scores:
new_hyp_scores = Variable(
self.new_tensor(applyrule_new_hyp_scores))
if gentoken_prev_hyp_ids:
primitive_log_prob = torch.log(primitive_prob)
gen_token_new_hyp_scores = (
hyp_scores[gentoken_prev_hyp_ids].unsqueeze(1) +
primitive_log_prob[gentoken_prev_hyp_ids, :]).view(-1)
if new_hyp_scores is None:
new_hyp_scores = gen_token_new_hyp_scores
else:
new_hyp_scores = torch.cat(
[new_hyp_scores, gen_token_new_hyp_scores])
# top_new_hyp_scores, top_new_hyp_pos = torch.topk(new_hyp_scores,
# k=min(new_hyp_scores.size(0), beam_size - len(completed_hypotheses)))
pro = F.softmax(new_hyp_scores, 0)
new_hyp_pos = torch.multinomial(pro, 1).data[0]
new_hyp_score = new_hyp_scores[new_hyp_pos].data[0]
live_hyp_ids = []
new_hypotheses = []
action_info = ActionInfo()
if new_hyp_pos < len(applyrule_new_hyp_scores):
# it's an ApplyRule or Reduce action
prev_hyp_id = applyrule_prev_hyp_ids[new_hyp_pos]
prev_hyp = hypotheses[prev_hyp_id]
prod_id = applyrule_new_hyp_prod_ids[new_hyp_pos]
# ApplyRule action
if prod_id < len(self.grammar):
production = self.grammar.id2prod[prod_id]
action = ApplyRuleAction(production)
# Reduce action
else:
action = ReduceAction()
else:
# it's a GenToken action
token_id = (new_hyp_pos - len(applyrule_new_hyp_scores)
) % primitive_prob.size(1)
k = (new_hyp_pos - len(applyrule_new_hyp_scores)
) // primitive_prob.size(1)
# try:
copy_info = gentoken_copy_infos[k]
prev_hyp_id = gentoken_prev_hyp_ids[k]
prev_hyp = hypotheses[prev_hyp_id]
if token_id == primitive_vocab.unk_id:
if gentoken_new_hyp_unks:
token = gentoken_new_hyp_unks[k]
else:
token = primitive_vocab.id2word[
primitive_vocab.unk_id]
else:
token = primitive_vocab.id2word[token_id]
action = GenTokenAction(token)
if token in copy_info:
action_info.copy_from_src = True
action_info.src_token_position = copy_info[token][0]
action_info.action = action
action_info.t = t
if t > 0:
action_info.parent_t = prev_hyp.frontier_node.created_time
action_info.frontier_prod = prev_hyp.frontier_node.production
action_info.frontier_field = prev_hyp.frontier_field.field
new_hyp = prev_hyp.clone_and_apply_action_info(action_info)
new_hyp.score = new_hyp_score
if new_hyp.completed:
completed_hypotheses.append(new_hyp)
else:
new_hypotheses.append(new_hyp)
live_hyp_ids.append(prev_hyp_id)
if live_hyp_ids:
hyp_states = [
hyp_states[i] + [h_t[i]] for i in live_hyp_ids
]
h_tm1 = (h_t[live_hyp_ids], cell_t[live_hyp_ids])
att_tm1 = att_t[live_hyp_ids]
hypotheses = new_hypotheses
hyp_scores = Variable(
self.new_tensor([hyp.score for hyp in hypotheses]))
t += 1
else:
break
return completed_hypotheses
def parse(self, src_sent, context=None, beam_size=5):
"""Perform beam search to infer the target AST given a source utterance
Args:
src_sent: list of source utterance tokens
context: other context used for prediction
beam_size: beam size
Returns:
A list of `DecodeHypothesis`, each representing an AST
"""
# print('!!!!!!')
args = self.args
primitive_vocab = self.vocab.primitive
src_sent_var = nn_utils.to_input_variable([src_sent], self.vocab.source, cuda=args.cuda, training=False)
# Variable(1, src_sent_len, hidden_size * 2)
src_encodings, (last_state, last_cell) = self.encode(src_sent_var, [len(src_sent)])
# (1, src_sent_len, hidden_size)
src_encodings_att_linear = self.att_src_linear(src_encodings)
dec_init_vec = self.init_decoder_state(last_state, last_cell)
if args.lstm == 'parent_feed':
h_tm1 = dec_init_vec[0], dec_init_vec[1], \
Variable(self.new_tensor(args.hidden_size).zero_()), \
Variable(self.new_tensor(args.hidden_size).zero_())
else:
h_tm1 = dec_init_vec
zero_action_embed = Variable(self.new_tensor(args.action_embed_size).zero_())
hyp_scores = Variable(self.new_tensor([0.]), volatile=True)
src_token_vocab_ids = [primitive_vocab[token] for token in src_sent]
src_unk_pos_list = [pos for pos, token_id in enumerate(src_token_vocab_ids) if token_id == primitive_vocab.unk_id]
# sometimes a word may appear multi-times in the source, in this case,
# we just copy its first appearing position. Therefore we mask the words
# appearing second and onwards to -1
token_set = set()
for i, tid in enumerate(src_token_vocab_ids):
if tid in token_set:
src_token_vocab_ids[i] = -1
else: token_set.add(tid)
t = 0
hypotheses = [DecodeHypothesis()]
hyp_states = [[]]
completed_hypotheses = []
while len(completed_hypotheses) < beam_size and t < args.decode_max_time_step:
hyp_num = len(hypotheses)
# (hyp_num, src_sent_len, hidden_size * 2)
exp_src_encodings = src_encodings.expand(hyp_num, src_encodings.size(1), src_encodings.size(2))
# (hyp_num, src_sent_len, hidden_size)
exp_src_encodings_att_linear = src_encodings_att_linear.expand(hyp_num, src_encodings_att_linear.size(1), src_encodings_att_linear.size(2))
if t == 0:
x = Variable(self.new_tensor(1, self.decoder_lstm.input_size).zero_(), volatile=True)
if args.no_parent_field_type_embed is False:
offset = args.action_embed_size # prev_action
offset += args.att_vec_size * (not args.no_input_feed)
offset += args.action_embed_size * (not args.no_parent_production_embed)
offset += args.field_embed_size * (not args.no_parent_field_embed)
x[0, offset: offset + args.type_embed_size] = \
self.type_embed.weight[self.grammar.type2id[self.grammar.root_type]]
else:
actions_tm1 = [hyp.actions[-1] for hyp in hypotheses]
a_tm1_embeds = []
for a_tm1 in actions_tm1:
if a_tm1:
if isinstance(a_tm1, ApplyRuleAction):
a_tm1_embed = self.production_embed.weight[self.grammar.prod2id[a_tm1.production]]
elif isinstance(a_tm1, ReduceAction):
a_tm1_embed = self.production_embed.weight[len(self.grammar)]
else:
a_tm1_embed = self.primitive_embed.weight[self.vocab.primitive[a_tm1.token]]
a_tm1_embeds.append(a_tm1_embed)
else:
a_tm1_embeds.append(zero_action_embed)
a_tm1_embeds = torch.stack(a_tm1_embeds)
inputs = [a_tm1_embeds]
if args.no_input_feed is False:
inputs.append(att_tm1)
if args.no_parent_production_embed is False:
# frontier production
frontier_prods = [hyp.frontier_node.production for hyp in hypotheses]
frontier_prod_embeds = self.production_embed(Variable(self.new_long_tensor(
[self.grammar.prod2id[prod] for prod in frontier_prods])))
inputs.append(frontier_prod_embeds)
if args.no_parent_field_embed is False:
# frontier field
frontier_fields = [hyp.frontier_field.field for hyp in hypotheses]
frontier_field_embeds = self.field_embed(Variable(self.new_long_tensor([
self.grammar.field2id[field] for field in frontier_fields])))
inputs.append(frontier_field_embeds)
if args.no_parent_field_type_embed is False:
# frontier field type
frontier_field_types = [hyp.frontier_field.type for hyp in hypotheses]
frontier_field_type_embeds = self.type_embed(Variable(self.new_long_tensor([
self.grammar.type2id[type] for type in frontier_field_types])))
inputs.append(frontier_field_type_embeds)
# parent states
if args.no_parent_state is False:
p_ts = [hyp.frontier_node.created_time for hyp in hypotheses]
parent_states = torch.stack([hyp_states[hyp_id][p_t][0] for hyp_id, p_t in enumerate(p_ts)])
parent_cells = torch.stack([hyp_states[hyp_id][p_t][1] for hyp_id, p_t in enumerate(p_ts)])
if args.lstm == 'parent_feed':
h_tm1 = (h_tm1[0], h_tm1[1], parent_states, parent_cells)
else:
inputs.append(parent_states)
x = torch.cat(inputs, dim=-1)
if args.lstm == 'lstm_with_dropout':
self.decoder_lstm.set_dropout_masks(hyp_num)
(h_t, cell_t), att_t = self.step(x, h_tm1, exp_src_encodings,
exp_src_encodings_att_linear,
src_token_mask=None)
# Variable(batch_size, grammar_size)
# apply_rule_log_prob = torch.log(F.softmax(self.production_readout(att_t), dim=-1))
apply_rule_log_prob = F.log_softmax(self.production_readout(att_t), dim=-1)
# Variable(batch_size, src_sent_len)
primitive_copy_prob = self.src_pointer_net(src_encodings, None, att_t.unsqueeze(0)).squeeze(0)
# Variable(batch_size, primitive_vocab_size)
gen_from_vocab_prob = F.softmax(self.tgt_token_readout(att_t), dim=-1)
# Variable(batch_size, 2)
primitive_predictor_prob = F.softmax(self.primitive_predictor(att_t), dim=-1)
# Variable(batch_size, primitive_vocab_size)
primitive_prob = primitive_predictor_prob[:, 0].unsqueeze(1) * gen_from_vocab_prob
if src_unk_pos_list:
primitive_prob[:, primitive_vocab.unk_id] = 1.e-10
gentoken_prev_hyp_ids = []
gentoken_new_hyp_unks = []
gentoken_copy_infos = []
applyrule_new_hyp_scores = []
applyrule_new_hyp_prod_ids = []
applyrule_prev_hyp_ids = []
for hyp_id, hyp in enumerate(hypotheses):
# generate new continuations
action_types = self.transition_system.get_valid_continuation_types(hyp)
for action_type in action_types:
if action_type == ApplyRuleAction:
productions = self.transition_system.get_valid_continuating_productions(hyp)
for production in productions:
prod_id = self.grammar.prod2id[production]
prod_score = apply_rule_log_prob[hyp_id, prod_id].data[0]
# print(type(hyp.score),type(prod_score))
new_hyp_score = hyp.score + prod_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(prod_id)
applyrule_prev_hyp_ids.append(hyp_id)
elif action_type == ReduceAction:
action_score = apply_rule_log_prob[hyp_id, len(self.grammar)].data[0]
new_hyp_score = hyp.score + action_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(len(self.grammar))
applyrule_prev_hyp_ids.append(hyp_id)
else:
# GenToken action
gentoken_prev_hyp_ids.append(hyp_id)
hyp_copy_info = dict() # of (token_pos, copy_prob)
# first, we compute copy probabilities for tokens in the source sentence
for token_pos, token_vocab_id in enumerate(src_token_vocab_ids):
if args.no_copy is False and token_vocab_id != -1 and token_vocab_id != primitive_vocab.unk_id:
p_copy = primitive_predictor_prob[hyp_id, 1] * primitive_copy_prob[hyp_id, token_pos]
primitive_prob[hyp_id, token_vocab_id] = primitive_prob[hyp_id, token_vocab_id] + p_copy
token = src_sent[token_pos]
hyp_copy_info[token] = (token_pos, p_copy.data[0])
# second, add the probability of copying the most probable unk word
if args.no_copy is False and src_unk_pos_list:
unk_pos = primitive_copy_prob[hyp_id][src_unk_pos_list].data.cpu().numpy().argmax()
unk_pos = src_unk_pos_list[unk_pos]
token = src_sent[unk_pos]
gentoken_new_hyp_unks.append(token)
unk_copy_score = primitive_predictor_prob[hyp_id, 1] * primitive_copy_prob[hyp_id, unk_pos]
primitive_prob[hyp_id, primitive_vocab.unk_id] = unk_copy_score
hyp_copy_info[token] = (unk_pos, unk_copy_score.data[0])
gentoken_copy_infos.append(hyp_copy_info)
new_hyp_scores = None
if applyrule_new_hyp_scores:
new_hyp_scores = Variable(self.new_tensor(applyrule_new_hyp_scores))
if gentoken_prev_hyp_ids:
primitive_log_prob = torch.log(primitive_prob)
gen_token_new_hyp_scores = (hyp_scores[gentoken_prev_hyp_ids].unsqueeze(1) + primitive_log_prob[gentoken_prev_hyp_ids, :]).view(-1)
if new_hyp_scores is None: new_hyp_scores = gen_token_new_hyp_scores
else: new_hyp_scores = torch.cat([new_hyp_scores, gen_token_new_hyp_scores])
top_new_hyp_scores, top_new_hyp_pos = torch.topk(new_hyp_scores,
k=min(new_hyp_scores.size(0), beam_size - len(completed_hypotheses)))
live_hyp_ids = []
new_hypotheses = []
for new_hyp_score, new_hyp_pos in zip(top_new_hyp_scores.data.cpu(), top_new_hyp_pos.data.cpu()):
# for new_hyp_score, new_hyp_pos in zip(top_new_hyp_scores.data.cuda(), top_new_hyp_pos.data.cuda()):
action_info = ActionInfo()
if new_hyp_pos < len(applyrule_new_hyp_scores):
# it's an ApplyRule or Reduce action
prev_hyp_id = applyrule_prev_hyp_ids[new_hyp_pos]
prev_hyp = hypotheses[prev_hyp_id]
prod_id = applyrule_new_hyp_prod_ids[new_hyp_pos]
# ApplyRule action
if prod_id < len(self.grammar):
production = self.grammar.id2prod[prod_id]
action = ApplyRuleAction(production)
# Reduce action
else:
action = ReduceAction()
else:
# it's a GenToken action
token_id = (new_hyp_pos - len(applyrule_new_hyp_scores)) % primitive_prob.size(1)
k = (new_hyp_pos - len(applyrule_new_hyp_scores)) // primitive_prob.size(1)
# try:
copy_info = gentoken_copy_infos[k]
prev_hyp_id = gentoken_prev_hyp_ids[k]
prev_hyp = hypotheses[prev_hyp_id]
# except:
# print('k=%d' % k, file=sys.stderr)
# print('primitive_prob.size(1)=%d' % primitive_prob.size(1), file=sys.stderr)
# print('len copy_info=%d' % len(gentoken_copy_infos), file=sys.stderr)
# print('prev_hyp_id=%s' % ', '.join(str(i) for i in gentoken_prev_hyp_ids), file=sys.stderr)
# print('len applyrule_new_hyp_scores=%d' % len(applyrule_new_hyp_scores), file=sys.stderr)
# print('len gentoken_prev_hyp_ids=%d' % len(gentoken_prev_hyp_ids), file=sys.stderr)
# print('top_new_hyp_pos=%s' % top_new_hyp_pos, file=sys.stderr)
# print('applyrule_new_hyp_scores=%s' % applyrule_new_hyp_scores, file=sys.stderr)
# print('new_hyp_scores=%s' % new_hyp_scores, file=sys.stderr)
# print('top_new_hyp_scores=%s' % top_new_hyp_scores, file=sys.stderr)
#
# torch.save((applyrule_new_hyp_scores, primitive_prob), 'data.bin')
#
# # exit(-1)
# raise ValueError()
if token_id == primitive_vocab.unk_id:
if gentoken_new_hyp_unks:
token = gentoken_new_hyp_unks[k]
else:
token = primitive_vocab.id2word[primitive_vocab.unk_id]
else:
token = primitive_vocab.id2word[token_id]
action = GenTokenAction(token)
if token in copy_info:
action_info.copy_from_src = True
action_info.src_token_position = copy_info[token][0]
action_info.action = action
action_info.t = t
if t > 0:
action_info.parent_t = prev_hyp.frontier_node.created_time
action_info.frontier_prod = prev_hyp.frontier_node.production
action_info.frontier_field = prev_hyp.frontier_field.field
new_hyp = prev_hyp.clone_and_apply_action_info(action_info)
new_hyp.score = new_hyp_score
#advoid none new hyp
if new_hyp.completed:
# print('What happened!')
# print(new_hyp.actions)
# print(len(new_hyp.actions))
if len(new_hyp.actions) != 2:
completed_hypotheses.append(new_hyp)
else:
new_hypotheses.append(new_hyp)
live_hyp_ids.append(prev_hyp_id)
if live_hyp_ids:
hyp_states = [hyp_states[i] + [(h_t[i], cell_t[i])] for i in live_hyp_ids]
h_tm1 = (h_t[live_hyp_ids], cell_t[live_hyp_ids])
att_tm1 = att_t[live_hyp_ids]
hypotheses = new_hypotheses
hyp_scores = Variable(self.new_tensor([hyp.score for hyp in hypotheses]))
t += 1
else:
break
completed_hypotheses.sort(key=lambda hyp: -hyp.score)
return completed_hypotheses
def parse(self, src_sent, context=None, beam_size=5, debug=False):
"""Perform beam search to infer the target AST given a source utterance
Args:
src_sent: list of source utterance tokens
context: other context used for prediction
beam_size: beam size
Returns:
A list of `DecodeHypothesis`, each representing an AST
"""
with torch.no_grad():
args = self.args
primitive_vocab = self.vocab.primitive
src_sent_var = nn_utils.to_input_variable([src_sent],
self.vocab.source,
cuda=args.cuda,
training=False)
# Variable(1, src_sent_len, hidden_size)
src_encodings = self.encode(src_sent_var)
zero_action_embed = torch.zeros(args.action_embed_size)
hyp_scores = torch.tensor([0.0])
# For computing copy probabilities, we marginalize over tokens with the same surface form
# `aggregated_primitive_tokens` stores the position of occurrence of each source token
aggregated_primitive_tokens = OrderedDict()
for token_pos, token in enumerate(src_sent):
aggregated_primitive_tokens.setdefault(token,
[]).append(token_pos)
t = 0
hypotheses = [DecodeHypothesis()]
completed_hypotheses = []
while len(completed_hypotheses
) < beam_size and t < args.decode_max_time_step:
hyp_num = len(hypotheses)
# (hyp_num, src_sent_len, hidden_size)
exp_src_encodings = src_encodings.expand(
hyp_num, src_encodings.size(1), src_encodings.size(2))
if t == 0:
x = torch.zeros(1, self.d_model)
parent_ids = np.array([[0]])
if args.no_parent_field_type_embed is False:
offset = self.args.action_embed_size # prev_action
offset += self.args.action_embed_size * (
not self.args.no_parent_production_embed)
offset += self.args.field_embed_size * (
not self.args.no_parent_field_embed)
x[0, offset:offset +
self.type_embed_size] = self.type_embed(
torch.tensor(self.grammar.type2id[
self.grammar.root_type]))
x = x.unsqueeze(-2)
else:
actions_tm1 = [hyp.actions[-1] for hyp in hypotheses]
a_tm1_embeds = []
for a_tm1 in actions_tm1:
if a_tm1:
if isinstance(a_tm1, ApplyRuleAction):
a_tm1_embed = self.production_embed(
torch.tensor(self.grammar.prod2id[
a_tm1.production]))
elif isinstance(a_tm1, ReduceAction):
a_tm1_embed = self.production_embed(
torch.tensor(len(self.grammar)))
else:
a_tm1_embed = self.primitive_embed(
torch.tensor(
self.vocab.primitive[a_tm1.token]))
a_tm1_embeds.append(a_tm1_embed)
else:
a_tm1_embeds.append(zero_action_embed)
a_tm1_embeds = torch.stack(a_tm1_embeds)
inputs = [a_tm1_embeds]
if args.no_parent_production_embed is False:
# frontier production
frontier_prods = [
hyp.frontier_node.production for hyp in hypotheses
]
frontier_prod_embeds = self.production_embed(
torch.tensor([
self.grammar.prod2id[prod]
for prod in frontier_prods
],
dtype=torch.long))
inputs.append(frontier_prod_embeds)
if args.no_parent_field_embed is False:
# frontier field
frontier_fields = [
hyp.frontier_field.field for hyp in hypotheses
]
frontier_field_embeds = self.field_embed(
torch.tensor([
self.grammar.field2id[field]
for field in frontier_fields
],
dtype=torch.long))
inputs.append(frontier_field_embeds)
if args.no_parent_field_type_embed is False:
# frontier field type
frontier_field_types = [
hyp.frontier_field.type for hyp in hypotheses
]
frontier_field_type_embeds = self.type_embed(
torch.tensor([
self.grammar.type2id[type]
for type in frontier_field_types
],
dtype=torch.long))
inputs.append(frontier_field_type_embeds)
x = torch.cat(
[x, torch.cat(inputs, dim=-1).unsqueeze(-2)], dim=1)
recent_parents = np.array(
[[hyp.frontier_node.created_time]
if hyp.frontier_node else 0 for hyp in hypotheses])
parent_ids = np.hstack([parent_ids, recent_parents])
src_mask = torch.ones(
exp_src_encodings.shape[:-1],
dtype=torch.uint8,
device=exp_src_encodings.device).unsqueeze(-2)
tgt_mask = subsequent_mask(x.shape[-2])
att_t = self.decoder(x, exp_src_encodings, [parent_ids],
src_mask, tgt_mask)[:, -1]
# Variable(batch_size, grammar_size)
# apply_rule_log_prob = torch.log(F.softmax(self.production_readout(att_t), dim=-1))
apply_rule_log_prob = F.log_softmax(
self.production_readout(att_t), dim=-1)
# Variable(batch_size, primitive_vocab_size)
gen_from_vocab_prob = F.softmax(self.tgt_token_readout(att_t),
dim=-1)
if args.no_copy:
primitive_prob = gen_from_vocab_prob
else:
# Variable(batch_size, src_sent_len)
primitive_copy_prob = self.src_pointer_net(
src_encodings, None, att_t.unsqueeze(0)).squeeze(0)
# Variable(batch_size, 2)
primitive_predictor_prob = F.softmax(
self.primitive_predictor(att_t), dim=-1)
# Variable(batch_size, primitive_vocab_size)
primitive_prob = primitive_predictor_prob[:, 0].unsqueeze(
1) * gen_from_vocab_prob
# if src_unk_pos_list:
# primitive_prob[:, primitive_vocab.unk_id] = 1.e-10
gentoken_prev_hyp_ids = []
gentoken_new_hyp_unks = []
applyrule_new_hyp_scores = []
applyrule_new_hyp_prod_ids = []
applyrule_prev_hyp_ids = []
for hyp_id, hyp in enumerate(hypotheses):
# generate new continuations
action_types = self.transition_system.get_valid_continuation_types(
hyp)
for action_type in action_types:
if action_type == ApplyRuleAction:
productions = self.transition_system.get_valid_continuating_productions(
hyp)
for production in productions:
prod_id = self.grammar.prod2id[production]
prod_score = apply_rule_log_prob[
hyp_id, prod_id].item()
new_hyp_score = hyp.score + prod_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(prod_id)
applyrule_prev_hyp_ids.append(hyp_id)
elif action_type == ReduceAction:
action_score = apply_rule_log_prob[
hyp_id, len(self.grammar)].item()
new_hyp_score = hyp.score + action_score
applyrule_new_hyp_scores.append(new_hyp_score)
applyrule_new_hyp_prod_ids.append(len(
self.grammar))
applyrule_prev_hyp_ids.append(hyp_id)
else:
# GenToken action
gentoken_prev_hyp_ids.append(hyp_id)
hyp_copy_info = dict() # of (token_pos, copy_prob)
hyp_unk_copy_info = []
if args.no_copy is False:
for (token, token_pos_list
) in aggregated_primitive_tokens.items():
sum_copy_prob = torch.gather(
primitive_copy_prob[hyp_id], 0,
torch.tensor(token_pos_list,
dtype=torch.long)).sum()
gated_copy_prob = primitive_predictor_prob[
hyp_id, 1] * sum_copy_prob
if token in primitive_vocab:
token_id = primitive_vocab[token]
primitive_prob[hyp_id, token_id] = (
primitive_prob[hyp_id, token_id] +
gated_copy_prob)
hyp_copy_info[token] = (
token_pos_list,
gated_copy_prob.item())
else:
hyp_unk_copy_info.append({
"token":
token,
"token_pos_list":
token_pos_list,
"copy_prob":
gated_copy_prob.item(),
})
if args.no_copy is False and len(
hyp_unk_copy_info) > 0:
unk_i = np.array([
x["copy_prob"] for x in hyp_unk_copy_info
]).argmax()
token = hyp_unk_copy_info[unk_i]["token"]
primitive_prob[hyp_id, primitive_vocab.
unk_id] = hyp_unk_copy_info[
unk_i]["copy_prob"]
gentoken_new_hyp_unks.append(token)
hyp_copy_info[token] = (
hyp_unk_copy_info[unk_i]["token_pos_list"],
hyp_unk_copy_info[unk_i]["copy_prob"],
)
new_hyp_scores = None
if applyrule_new_hyp_scores:
new_hyp_scores = torch.tensor(applyrule_new_hyp_scores)
if gentoken_prev_hyp_ids:
primitive_log_prob = torch.log(primitive_prob)
gen_token_new_hyp_scores = (
hyp_scores[gentoken_prev_hyp_ids].unsqueeze(1) +
primitive_log_prob[gentoken_prev_hyp_ids, :]).view(-1)
if new_hyp_scores is None:
new_hyp_scores = gen_token_new_hyp_scores
else:
new_hyp_scores = torch.cat(
[new_hyp_scores, gen_token_new_hyp_scores])
top_new_hyp_scores, top_new_hyp_pos = torch.topk(
new_hyp_scores,
k=min(new_hyp_scores.size(0),
beam_size - len(completed_hypotheses)))
live_hyp_ids = []
new_hypotheses = []
for new_hyp_score, new_hyp_pos in zip(
top_new_hyp_scores.data.cpu(),
top_new_hyp_pos.data.cpu()):
action_info = ActionInfo()
if new_hyp_pos < len(applyrule_new_hyp_scores):
# it's an ApplyRule or Reduce action
prev_hyp_id = applyrule_prev_hyp_ids[new_hyp_pos]
prev_hyp = hypotheses[prev_hyp_id]
prod_id = applyrule_new_hyp_prod_ids[new_hyp_pos]
# ApplyRule action
if prod_id < len(self.grammar):
production = self.grammar.id2prod[prod_id]
action = ApplyRuleAction(production)
# Reduce action
else:
action = ReduceAction()
else:
# it's a GenToken action
token_id = int(
(new_hyp_pos - len(applyrule_new_hyp_scores)) %
primitive_prob.size(1))
k = (new_hyp_pos - len(applyrule_new_hyp_scores)
) // primitive_prob.size(1)
# try:
# copy_info = gentoken_copy_infos[k]
prev_hyp_id = gentoken_prev_hyp_ids[k]
prev_hyp = hypotheses[prev_hyp_id]
# except:
# print('k=%d' % k, file=sys.stderr)
# print('primitive_prob.size(1)=%d' % primitive_prob.size(1), file=sys.stderr)
# print('len copy_info=%d' % len(gentoken_copy_infos), file=sys.stderr)
# print('prev_hyp_id=%s' % ', '.join(str(i) for i in gentoken_prev_hyp_ids), file=sys.stderr)
# print('len applyrule_new_hyp_scores=%d' % len(applyrule_new_hyp_scores), file=sys.stderr)
# print('len gentoken_prev_hyp_ids=%d' % len(gentoken_prev_hyp_ids), file=sys.stderr)
# print('top_new_hyp_pos=%s' % top_new_hyp_pos, file=sys.stderr)
# print('applyrule_new_hyp_scores=%s' % applyrule_new_hyp_scores, file=sys.stderr)
# print('new_hyp_scores=%s' % new_hyp_scores, file=sys.stderr)
# print('top_new_hyp_scores=%s' % top_new_hyp_scores, file=sys.stderr)
#
# torch.save((applyrule_new_hyp_scores, primitive_prob), 'data.bin')
#
# # exit(-1)
# raise ValueError()
if token_id == int(primitive_vocab.unk_id):
if gentoken_new_hyp_unks:
token = gentoken_new_hyp_unks[k]
else:
token = primitive_vocab.id2word[
primitive_vocab.unk_id]
else:
token = primitive_vocab.id2word[token_id]
action = GenTokenAction(token)
if token in aggregated_primitive_tokens:
action_info.copy_from_src = True
action_info.src_token_position = aggregated_primitive_tokens[
token]
if debug:
action_info.gen_copy_switch = (
"n/a"
if args.no_copy else primitive_predictor_prob[
prev_hyp_id, :].log().cpu().data.numpy())
action_info.in_vocab = token in primitive_vocab
action_info.gen_token_prob = (
gen_from_vocab_prob[prev_hyp_id,
token_id].log().cpu().
item() if token in primitive_vocab else "n/a")
action_info.copy_token_prob = (
torch.gather(
primitive_copy_prob[prev_hyp_id],
0,
torch.tensor(
action_info.src_token_position,
dtype=torch.long,
device=self.device),
).sum().log().cpu().item()
if args.no_copy is False
and action_info.copy_from_src else "n/a")
action_info.action = action
action_info.t = t
if t > 0:
action_info.parent_t = prev_hyp.frontier_node.created_time
action_info.frontier_prod = prev_hyp.frontier_node.production
action_info.frontier_field = prev_hyp.frontier_field.field
if debug:
action_info.action_prob = new_hyp_score - prev_hyp.score
new_hyp = prev_hyp.clone_and_apply_action_info(action_info)
new_hyp.score = new_hyp_score
if new_hyp.completed:
completed_hypotheses.append(new_hyp)
else:
new_hypotheses.append(new_hyp)
live_hyp_ids.append(prev_hyp_id)
if live_hyp_ids:
x = x[live_hyp_ids]
parent_ids = parent_ids[live_hyp_ids]
hypotheses = new_hypotheses
hyp_scores = torch.tensor(
[hyp.score for hyp in hypotheses])
t += 1
else:
break
completed_hypotheses.sort(key=lambda hyp: -hyp.score)
return completed_hypotheses