def subgraph_make_adj(subgraphs, cuda=True):
subgraph_triples, subgraph_node_lists = subgraphs
adjs = []
for triples, node_lists in zip(subgraph_triples, subgraph_node_lists):
if len(node_lists) != 0:
adj = make_adj(triples,
len(node_lists),
len(node_lists),
cuda=cuda)
else:
adj = []
adjs.append(adj)
return adjs
def batchify_fn_graph_rl(pad,
start,
end,
data,
cuda=True,
adj_type='concat_triple',
docgraph=True,
reward_data_dir=None):
if reward_data_dir is not None:
batch, ext_word2id, raw_articles, raw_targets, questions = data
else:
batch, ext_word2id, raw_articles, raw_targets = data
questions = []
if docgraph:
sources, ext_srcs, nodes, word_freq_feat, nodefreq, relations, triples = tuple(
map(list, unzip(batch)))
if adj_type == 'concat_triple':
adjs = [
make_adj_triple(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
elif adj_type == 'edge_as_node':
adjs = [
make_adj_edge_in(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
else:
adjs = [
make_adj(triple, len(node), len(node), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
else:
sources, ext_srcs, nodes, word_freq_feat, nodefreq, relations, triples, node_lists = tuple(
map(list, unzip(batch)))
if adj_type == 'edge_as_node':
adjs = list(
map(subgraph_make_adj_edge_in(cuda=cuda),
zip(triples, node_lists)))
else:
adjs = list(
map(subgraph_make_adj(cuda=cuda), zip(triples, node_lists)))
nodefreq = pad_batch_tensorize(nodefreq, pad=pad, cuda=cuda)
word_freq = pad_batch_tensorize(word_freq_feat, pad=pad, cuda=cuda)
feature_dict = {'word_inpara_freq': word_freq, 'node_freq': nodefreq}
node_num = [len(_node) for _node in nodes]
_nodes = pad_batch_tensorize_3d(nodes, pad=0, cuda=cuda)
nmask = pad_batch_tensorize_3d(nodes, pad=-1, cuda=cuda).ne(-1).float()
src_lens = [len(src) for src in sources]
sources = [src for src in sources]
ext_srcs = [ext for ext in ext_srcs]
source = pad_batch_tensorize(sources, pad, cuda)
ext_src = pad_batch_tensorize(ext_srcs, pad, cuda)
ext_vsize = ext_src.max().item() + 1
extend_vsize = len(ext_word2id)
ext_id2word = {_id: _word for _word, _id in ext_word2id.items()}
#print('ext_size:', ext_vsize, extend_vsize)
if docgraph:
fw_args = (source, src_lens, ext_src, extend_vsize, _nodes, nmask,
node_num, feature_dict, adjs, START, END, UNK, 100)
else:
fw_args = (source, src_lens, ext_src, extend_vsize, _nodes, nmask,
node_num, feature_dict, node_lists, adjs, START, END, UNK,
100)
loss_args = (raw_articles, ext_id2word, raw_targets, questions)
return fw_args, loss_args
def batchify_fn_gat_copy_from_graph(pad,
start,
end,
data,
cuda=True,
adj_type='concat_triple',
mask_type='none',
decoder_supervision=False):
sources, ext_srcs, tar_ins, targets, \
nodes, nodelengths, sum_worthy, relations, rlengths, triples, \
all_node_words, ext_node_aligns, gold_copy_mask = tuple(map(list, unzip(data)))
if adj_type == 'concat_triple':
adjs = [
make_adj_triple(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
elif adj_type == 'edge_as_node':
adjs = [
make_adj_edge_in(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
else:
adjs = [
make_adj(triple, len(node), len(node), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
src_lens = [len(src) for src in sources]
sources = [src for src in sources]
ext_srcs = [ext for ext in ext_srcs]
tar_ins = [[start] + tgt for tgt in tar_ins]
targets = [tgt + [end] for tgt in targets]
source = pad_batch_tensorize(sources, pad, cuda)
tar_in = pad_batch_tensorize(tar_ins, pad, cuda)
target = pad_batch_tensorize(targets, pad, cuda)
ext_src = pad_batch_tensorize(ext_srcs, pad, cuda)
all_node_word = pad_batch_tensorize(all_node_words, pad, cuda)
all_node_mask = pad_batch_tensorize(all_node_words, pad=-1,
cuda=cuda).ne(-1).float()
ext_node_aligns = pad_batch_tensorize(ext_node_aligns, pad=0, cuda=cuda)
gold_copy_mask = pad_batch_tensorize(gold_copy_mask, pad=0,
cuda=cuda).float()
sum_worthy_label = pad_batch_tensorize(sum_worthy, pad=-1, cuda=cuda)
sum_worthy = pad_batch_tensorize(sum_worthy, pad=0, cuda=cuda).float()
node_num = [len(_node) for _node in nodes]
_nodes = pad_batch_tensorize_3d(nodes, pad=0, cuda=cuda)
_relations = pad_batch_tensorize_3d(relations, pad=0, cuda=cuda)
nmask = pad_batch_tensorize_3d(nodes, pad=-1, cuda=cuda).ne(-1).float()
rmask = pad_batch_tensorize_3d(relations, pad=-1, cuda=cuda).ne(-1).float()
ext_vsize = ext_src.max().item() + 1
fw_args = (source, src_lens, tar_in, ext_src, ext_vsize,
(_nodes, nmask, node_num, sum_worthy), (_relations, rmask,
triples, adjs),
(all_node_word, all_node_mask, ext_node_aligns, gold_copy_mask))
if 'soft' in mask_type and decoder_supervision:
raise Exception('not implemented yet')
#loss_args = (target, sum_worthy_label, extracted_labels)
elif 'soft' in mask_type:
loss_args = (target, sum_worthy_label)
elif decoder_supervision:
raise Exception('not implemented yet')
#loss_args = (target, extracted_labels)
else:
loss_args = (target, )
return fw_args, loss_args
def batchify_fn_gat(pad,
start,
end,
data,
cuda=True,
adj_type='concat_triple',
mask_type='none',
decoder_supervision=False,
docgraph=True):
sources, ext_srcs, tar_ins, targets, \
nodes, nodelengths, sum_worthy, word_freq_feat, nodefreq, relations, rlengths, triples = tuple(map(list, unzip(data)))
if not docgraph:
node_lists = nodelengths
if adj_type == 'edge_as_node':
adjs = list(
map(subgraph_make_adj_edge_in(cuda=cuda),
zip(triples, node_lists)))
else:
adjs = list(
map(subgraph_make_adj(cuda=cuda), zip(triples, node_lists)))
else:
if adj_type == 'concat_triple':
adjs = [
make_adj_triple(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
elif adj_type == 'edge_as_node':
adjs = [
make_adj_edge_in(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
else:
adjs = [
make_adj(triple, len(node), len(node), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
src_lens = [len(src) for src in sources]
sources = [src for src in sources]
ext_srcs = [ext for ext in ext_srcs]
tar_ins = [[start] + tgt for tgt in tar_ins]
targets = [tgt + [end] for tgt in targets]
nodefreq = pad_batch_tensorize(nodefreq, pad=pad, cuda=cuda)
word_freq = pad_batch_tensorize(word_freq_feat, pad=pad, cuda=cuda)
feature_dict = {'word_inpara_freq': word_freq, 'node_freq': nodefreq}
source = pad_batch_tensorize(sources, pad, cuda)
tar_in = pad_batch_tensorize(tar_ins, pad, cuda)
target = pad_batch_tensorize(targets, pad, cuda)
ext_src = pad_batch_tensorize(ext_srcs, pad, cuda)
sum_worthy_label = pad_batch_tensorize(sum_worthy, pad=-1, cuda=cuda)
sum_worthy = pad_batch_tensorize(sum_worthy, pad=0, cuda=cuda).float()
node_num = [len(_node) for _node in nodes]
_nodes = pad_batch_tensorize_3d(nodes, pad=0, cuda=cuda)
_relations = pad_batch_tensorize_3d(relations, pad=0, cuda=cuda)
nmask = pad_batch_tensorize_3d(nodes, pad=-1, cuda=cuda).ne(-1).float()
rmask = pad_batch_tensorize_3d(relations, pad=-1, cuda=cuda).ne(-1).float()
ext_vsize = ext_src.max().item() + 1
if docgraph:
fw_args = (source, src_lens, tar_in, ext_src, ext_vsize,
(_nodes, nmask, node_num, sum_worthy,
feature_dict), (_relations, rmask, triples, adjs))
else:
fw_args = (source, src_lens, tar_in, ext_src, ext_vsize,
(_nodes, nmask, node_num, sum_worthy, feature_dict,
node_lists), (_relations, rmask, triples, adjs))
if 'soft' in mask_type and decoder_supervision:
raise Exception('not implemented yet')
#loss_args = (target, sum_worthy_label, extracted_labels)
elif 'soft' in mask_type:
loss_args = (target, sum_worthy_label)
elif decoder_supervision:
raise Exception('not implemented yet')
#loss_args = (target, extracted_labels)
else:
loss_args = (target, )
return fw_args, loss_args
def forward(self, raw_input, n_abs=None, sample_time=1, validate=False):
if self._enable_docgraph:
if self._bert:
raise NotImplementedError
else:
raw_article_sents, nodes, nodefreq, word_freq_feat, sent_word_freq, triples, relations, sent_aligns = raw_input
if self._adj_type == 'concat_triple':
adjs = [
make_adj_triple(triples, len(nodes), len(relations),
self._cuda)
]
elif self._adj_type == 'edge_as_node':
adjs = [
make_adj_edge_in(triples, len(nodes), len(relations),
self._cuda)
]
else:
adjs = [make_adj(triples, len(nodes), len(nodes), self._cuda)]
else:
if self._bert:
_, raw_article_sents, nodes, nodefreq, triples, relations, node_lists, word_nums = raw_input
else:
raw_article_sents, nodes, nodefreq, word_freq_feat, sent_word_freq, triples, relations, sent_aligns, node_lists = raw_input
if self._adj_type == 'edge_as_node':
adjs = [
subgraph_make_adj_edge_in((triples, node_lists),
cuda=self._cuda)
]
else:
adjs = [
subgraph_make_adj((triples, node_lists), cuda=self._cuda)
]
if not self._bert:
sent_word_freq = pad_batch_tensorize(sent_word_freq,
pad=0,
max_num=5,
cuda=self._cuda)
word_freq_feat = pad_batch_tensorize([word_freq_feat],
pad=0,
cuda=self._cuda)
nodenum = [len(nodes)]
sentnum = [len(raw_article_sents)]
nmask = pad_batch_tensorize(
nodes, pad=-1, cuda=self._cuda).ne(-1).float().unsqueeze(0)
nodes = pad_batch_tensorize(nodes, pad=0, cuda=self._cuda).unsqueeze(0)
nodefreq = pad_batch_tensorize([nodefreq], pad=0, cuda=self._cuda)
if self._bert:
articles = self._batcher(raw_article_sents)
articles, article_sent = self._encode_bert(articles, [word_nums])
enc_sent = self._sent_enc(article_sent[0], None, None,
None).unsqueeze(0)
enc_art = self._art_enc(enc_sent)
sent_aligns = None
else:
article_sent, articles = self._batcher(raw_article_sents)
articles = self._sent_enc._embedding(articles)
sent_aligns = [sent_aligns]
if self._pe:
bs, max_art_len, _ = articles.size()
src_pos = torch.tensor([[i for i in range(max_art_len)]
for _ in range(bs)
]).to(articles.device)
src_pos = self._sent_enc.poisition_enc(src_pos)
articles = torch.cat([articles, src_pos], dim=-1)
if 'inpara_freq' in self._feature_banks:
word_inpara_freq = self._sent_enc._inpara_embedding(
word_freq_feat)
articles = torch.cat([articles, word_inpara_freq], dim=-1)
enc_sent = self._sent_enc(article_sent, None, sent_word_freq,
None).unsqueeze(0)
enc_art = self._art_enc(enc_sent)
# print('enc_Art:', enc_art)
if self._enable_docgraph:
nodes = self._encode_docgraph(articles,
nodes,
nmask,
adjs,
nodenum,
enc_out=enc_art,
sent_nums=sentnum,
nodefreq=nodefreq)
else:
outputs = self._encode_paragraph(articles,
nodes,
nmask,
adjs, [node_lists],
enc_out=enc_art,
sent_nums=sentnum,
nodefreq=nodefreq)
if self._hierarchical_attn:
(topics, topic_length), masks, (nodes, node_length,
node_align_paras) = outputs
node_align_paras = pad_batch_tensorize(node_align_paras,
pad=0,
cuda=False).to(
nodes.device)
elif 'soft' in self._mask_type:
(nodes, topic_length), masks = outputs
topics = None
node_align_paras = None
else:
nodes, topic_length = outputs
topics = None
node_align_paras = None
nodes = nodes.squeeze(0)
# print('entity out:', entity_out)
if not validate:
greedy = self._net(enc_art,
nodes,
aligns=sent_aligns,
paras=(topics, node_align_paras, topic_length))
samples = []
probs = []
sample, prob = self._net.sample(enc_art,
nodes,
aligns=sent_aligns,
paras=(topics, node_align_paras,
topic_length))
samples.append(sample)
probs.append(prob)
else:
greedy = self._net(enc_art,
nodes,
aligns=sent_aligns,
paras=(topics, node_align_paras, topic_length))
samples = []
probs = []
# for i in range(sample_time):
# sample, prob = self._net.sample(enc_art, nodes, aligns=sent_aligns, paras=(topics, node_align_paras, topic_length))
# samples.append(sample)
# probs.append(prob)
return greedy, samples, probs
def batchify_fn_gat_bert(tokenizer,
data,
cuda=True,
adj_type='concat_triple',
mask_type='none',
docgraph=True):
sources, ext_srcs, tar_ins, targets, \
nodes, nodelengths, sum_worthy, nodefreq, relations, rlengths, triples, src_lens = (data[0], ) + tuple(map(list, unzip(data[1])))
start = tokenizer.encoder[tokenizer._bos_token]
end = tokenizer.encoder[tokenizer._eos_token]
pad = tokenizer.encoder[tokenizer._pad_token]
if not docgraph:
node_lists = nodelengths
if adj_type == 'edge_as_node':
adjs = list(
map(subgraph_make_adj_edge_in(cuda=cuda),
zip(triples, node_lists)))
else:
adjs = list(
map(subgraph_make_adj(cuda=cuda), zip(triples, node_lists)))
else:
if adj_type == 'concat_triple':
adjs = [
make_adj_triple(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
elif adj_type == 'edge_as_node':
adjs = [
make_adj_edge_in(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
else:
adjs = [
make_adj(triple, len(node), len(node), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
#src_lens = [len(src) for src in sources]
sources = [src for src in sources]
ext_srcs = [ext for ext in ext_srcs]
tar_ins = [[start] + tgt for tgt in tar_ins]
targets = [tgt + [end] for tgt in targets]
nodefreq = pad_batch_tensorize(nodefreq, pad=pad, cuda=cuda)
feature_dict = {'node_freq': nodefreq}
source = pad_batch_tensorize(sources, pad, cuda)
tar_in = pad_batch_tensorize(tar_ins, pad, cuda)
target = pad_batch_tensorize(targets, pad, cuda)
ext_src = pad_batch_tensorize(ext_srcs, pad, cuda)
sum_worthy_label = pad_batch_tensorize(sum_worthy, pad=-1, cuda=cuda)
sum_worthy = pad_batch_tensorize(sum_worthy, pad=0, cuda=cuda).float()
node_num = [len(_node) for _node in nodes]
_nodes = pad_batch_tensorize_3d(nodes, pad=0, cuda=cuda)
_relations = pad_batch_tensorize_3d(relations, pad=0, cuda=cuda)
nmask = pad_batch_tensorize_3d(nodes, pad=-1, cuda=cuda).ne(-1).float()
rmask = pad_batch_tensorize_3d(relations, pad=-1, cuda=cuda).ne(-1).float()
ext_vsize = ext_src.max().item() + 1
if docgraph:
fw_args = (source, src_lens, tar_in, ext_src, ext_vsize,
(_nodes, nmask, node_num, sum_worthy,
feature_dict), (_relations, rmask, triples, adjs))
else:
fw_args = (source, src_lens, tar_in, ext_src, ext_vsize,
(_nodes, nmask, node_num, sum_worthy, feature_dict,
node_lists), (_relations, rmask, triples, adjs))
if 'soft' in mask_type:
loss_args = (target, sum_worthy_label)
else:
loss_args = (target, )
return fw_args, loss_args
def batchify_fn_graph_rl_bert(tokenizer,
data,
cuda=True,
adj_type='concat_triple',
docgraph=True,
reward_data_dir=None):
start = tokenizer.encoder[tokenizer._bos_token]
end = tokenizer.encoder[tokenizer._eos_token]
pad = tokenizer.encoder[tokenizer._pad_token]
unk = tokenizer.encoder[tokenizer._unk_token]
if reward_data_dir is not None:
batch, ext_word2id, raw_articles, raw_targets, questions = data
else:
batch, ext_word2id, raw_articles, raw_targets = data
questions = []
if docgraph:
sources, ext_srcs, nodes, nodefreq, relations, triples, src_lens, tar_ins, targets = (
batch[0], ) + tuple(map(list, unzip(batch[1])))
if adj_type == 'concat_triple':
adjs = [
make_adj_triple(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
elif adj_type == 'edge_as_node':
adjs = [
make_adj_edge_in(triple, len(node), len(relation), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
else:
adjs = [
make_adj(triple, len(node), len(node), cuda)
for triple, node, relation in zip(triples, nodes, relations)
]
else:
sources, ext_srcs, nodes, nodefreq, relations, triples, node_lists, src_lens, tar_ins, targets = (
batch[0], ) + tuple(map(list, unzip(batch[1])))
if adj_type == 'edge_as_node':
adjs = list(
map(subgraph_make_adj_edge_in(cuda=cuda),
zip(triples, node_lists)))
else:
adjs = list(
map(subgraph_make_adj(cuda=cuda), zip(triples, node_lists)))
nodefreq = pad_batch_tensorize(nodefreq, pad=0, cuda=cuda)
feature_dict = {'node_freq': nodefreq}
node_num = [len(_node) for _node in nodes]
_nodes = pad_batch_tensorize_3d(nodes, pad=0, cuda=cuda)
nmask = pad_batch_tensorize_3d(nodes, pad=-1, cuda=cuda).ne(-1).float()
tar_ins = [[start] + tgt for tgt in tar_ins]
targets = [tgt + [end] for tgt in targets]
tar_in = pad_batch_tensorize(tar_ins, pad, cuda)
target = pad_batch_tensorize(targets, pad, cuda)
#src_lens = [len(src) for src in sources]
sources = [src for src in sources]
ext_srcs = [ext for ext in ext_srcs]
source = pad_batch_tensorize(sources, pad, cuda)
ext_src = pad_batch_tensorize(ext_srcs, pad, cuda)
ext_vsize = ext_src.max().item() + 1
extend_vsize = len(ext_word2id)
ext_id2word = {_id: _word for _word, _id in ext_word2id.items()}
#print('ext_size:', ext_vsize, extend_vsize)
if docgraph:
fw_args = (source, src_lens, ext_src, extend_vsize, _nodes, nmask,
node_num, feature_dict, adjs, start, end, unk, 150, tar_in)
else:
fw_args = (source, src_lens, ext_src, extend_vsize, _nodes, nmask,
node_num, feature_dict, node_lists, adjs, start, end, unk,
150, tar_in)
loss_args = (raw_articles, ext_id2word, raw_targets, questions, target)
return fw_args, loss_args