def do_combine(do_embed_mentions, do_embed_facts, embed_prefix):
"""Combines sharded DB into one single file."""
if FLAGS.shards_to_combine is None:
shard_range = range(FLAGS.num_shards + 1)
else:
shard_range = range(FLAGS.shards_to_combine)
if do_embed_mentions:
db_emb_str = "db_emb"
elif do_embed_facts:
db_emb_str = "fact_db_emb"
with tf.device("/cpu:0"):
all_db = []
for i in shard_range:
if do_embed_mentions:
embed_str = "%s_mention_feats_%d" % (embed_prefix, i)
elif do_embed_facts:
embed_str = "%s_fact_feats_%d" % (embed_prefix, i)
else:
tf.logging.info("Error choice")
return
ckpt_path = os.path.join(FLAGS.multihop_output_dir, embed_str)
if not tf.gfile.Exists(ckpt_path + ".meta"):
tf.logging.info("%s does not exist", ckpt_path)
continue
reader = tf.train.NewCheckpointReader(ckpt_path)
var_to_shape_map = reader.get_variable_to_shape_map()
tf.logging.info("Reading %s from %s with shape %s",
db_emb_str + "_%d" % i, ckpt_path,
str(var_to_shape_map[db_emb_str + "_%d" % i]))
tf_db = search_utils.load_database(
db_emb_str + "_%d" % i,
var_to_shape_map[db_emb_str + "_%d" % i], ckpt_path)
all_db.append(tf_db)
tf.logging.info("Reading all variables.")
session = tf.Session()
session.run(tf.global_variables_initializer())
session.run(tf.local_variables_initializer())
np_db = session.run(all_db)
tf.logging.info("Concatenating and storing.")
np_db = np.concatenate(np_db, axis=0)
if do_embed_mentions:
embed_feats_str = "%s_mention_feats" % embed_prefix
elif do_embed_facts:
embed_feats_str = "%s_fact_feats" % embed_prefix
search_utils.write_to_checkpoint(
db_emb_str, np_db, tf.float32,
os.path.join(FLAGS.multihop_output_dir, embed_feats_str))
def main(_):
if not tf.gfile.Exists(FLAGS.multihop_output_dir):
tf.gfile.MakeDirs(FLAGS.multihop_output_dir)
# Filenames.
paragraphs_file = os.path.join(FLAGS.data_dir, "processed_wiki.json")
train_file = os.path.join(FLAGS.qry_dir, "train.json")
dev_file = os.path.join(FLAGS.qry_dir, "dev.json")
test_file = os.path.join(FLAGS.qry_dir, "test.json")
entities_file = os.path.join(FLAGS.data_dir, "entities.txt")
# Initialize tokenizer.
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
# Read entities.
tf.logging.info("Reading entities.")
entity2id, entity2name = {}, {}
with tf.gfile.Open(entities_file) as f:
for ii, line in tqdm(enumerate(f)):
entity = line.strip()
entity2id[entity] = ii
entity2name[entity] = entity
# Read paragraphs, mentions and entities.
mentions = []
ent_rows, ent_cols, ent_vals = [], [], []
para_rows, para_cols, para_vals = [], [], []
mention2text = {}
total_sub_paras = [0]
all_sub_paras = []
entity_counts = collections.defaultdict(int)
tf.logging.info("Reading paragraphs from %s", paragraphs_file)
with tf.gfile.Open(paragraphs_file) as f:
for line in tqdm(f):
orig_para = json.loads(line.strip())
sub_para_objs = _get_sub_paras(orig_para, tokenizer,
FLAGS.max_seq_length,
FLAGS.doc_stride, total_sub_paras)
for para_obj in sub_para_objs:
# Add mentions from this paragraph.
my_entities = []
my_mentions = []
for m in para_obj["mentions"]:
# Para to mention matrix.
para_rows.append(para_obj["id"])
para_cols.append(len(mentions))
para_vals.append(1.)
# Create entity to mention sparse connections.
my_entities.append(m["kb_id"])
my_mentions.append(len(mentions))
entity_counts[m["kb_id"]] += 1
mention2text[len(mentions)] = m["text"]
mentions.append((m["kb_id"], para_obj["id"],
m["start_token"], m["end_token"]))
for entity in my_entities:
ent_rows.append(entity)
ent_cols.append(para_obj["id"])
ent_vals.append(1. / len(my_mentions))
all_sub_paras.append(para_obj["tokens"])
assert len(all_sub_paras) == total_sub_paras[0], (
len(all_sub_paras), total_sub_paras)
tf.logging.info("Num paragraphs = %d, Num mentions = %d",
total_sub_paras[0], len(mentions))
tf.logging.info("Saving coreference map.")
search_utils.write_to_checkpoint(
"coref", np.array([m[0] for m in mentions], dtype=np.int32), tf.int32,
os.path.join(FLAGS.multihop_output_dir, "coref.npz"))
tf.logging.info("Creating entity to mentions matrix.")
sp_entity2para = sp.csr_matrix((ent_vals, (ent_rows, ent_cols)),
shape=[len(entity2id),
len(all_sub_paras)])
sp_entity2para_filt = preprocess_utils.filter_sparse_rows(
sp_entity2para, FLAGS.max_paragraphs_per_entity)
sp_para2ment = sp.csr_matrix((para_vals, (para_rows, para_cols)),
shape=[len(all_sub_paras),
len(mentions)])
sp_entity2mention = sp_entity2para_filt.dot(sp_para2ment)
tf.logging.info("Num nonzero = %d", sp_entity2mention.getnnz())
tf.logging.info("Saving as ragged tensor %s.",
str(sp_entity2mention.shape))
search_utils.write_ragged_to_checkpoint(
"ent2ment", sp_entity2mention,
os.path.join(FLAGS.multihop_output_dir, "ent2ment.npz"))
tf.logging.info("Saving mentions metadata.")
np.save(
tf.gfile.Open(os.path.join(FLAGS.multihop_output_dir, "mentions.npy"),
"w"), np.array(mentions, dtype=np.int64))
json.dump(
mention2text,
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "mention2text.json"), "w"))
tf.logging.info("Saving entities metadata.")
json.dump([entity2id, entity2name],
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "entities.json"),
"w"))
json.dump(
entity_counts,
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "entity_counts.json"),
"w"))
tf.logging.info("Saving split paragraphs.")
json.dump(
all_sub_paras,
tf.gfile.Open(os.path.join(FLAGS.multihop_output_dir, "subparas.json"),
"w"))
# Store entity tokens.
tf.logging.info("Processing entities.")
entity_ids = np.zeros((len(entity2id), FLAGS.max_entity_len),
dtype=np.int32)
entity_mask = np.zeros((len(entity2id), FLAGS.max_entity_len),
dtype=np.float32)
num_exceed_len = 0.
for entity in tqdm(entity2id):
ei = entity2id[entity]
entity_tokens = tokenizer.tokenize(entity2name[entity])
entity_token_ids = tokenizer.convert_tokens_to_ids(entity_tokens)
if len(entity_token_ids) > FLAGS.max_entity_len:
num_exceed_len += 1
entity_token_ids = entity_token_ids[:FLAGS.max_entity_len]
entity_ids[ei, :len(entity_token_ids)] = entity_token_ids
entity_mask[ei, :len(entity_token_ids)] = 1.
tf.logging.info(
"Saving %d entity ids and mask. %d exceed max-length of %d.",
len(entity2id), num_exceed_len, FLAGS.max_entity_len)
search_utils.write_to_checkpoint(
"entity_ids", entity_ids, tf.int32,
os.path.join(FLAGS.multihop_output_dir, "entity_ids"))
search_utils.write_to_checkpoint(
"entity_mask", entity_mask, tf.float32,
os.path.join(FLAGS.multihop_output_dir, "entity_mask"))
# Pre-process question files.
def _preprocess_qrys(in_file, out_file):
tf.logging.info("Working on %s", in_file)
with tf.gfile.Open(in_file) as f_in, tf.gfile.Open(out_file,
"w") as f_out:
for line in f_in:
item = json.loads(line.strip())
# Sort entities in ascending order of their frequencies.
e_counts = [
entity_counts[e["kb_id"]] for e in item["entities"]
]
sorted_i = sorted(enumerate(e_counts), key=lambda x: x[1])
item["entities"] = [item["entities"][ii] for ii, _ in sorted_i]
f_out.write(json.dumps(item) + "\n")
_preprocess_qrys(train_file,
os.path.join(FLAGS.multihop_output_dir, "train.json"))
_preprocess_qrys(dev_file,
os.path.join(FLAGS.multihop_output_dir, "dev.json"))
_preprocess_qrys(test_file,
os.path.join(FLAGS.multihop_output_dir, "test.json"))
# Copy BERT checkpoint for future use.
tf.logging.info("Copying BERT checkpoint.")
if tf.gfile.Exists(os.path.join(FLAGS.pretrain_dir, "best_model.index")):
bert_ckpt = os.path.join(FLAGS.pretrain_dir, "best_model")
else:
bert_ckpt = tf.train.latest_checkpoint(FLAGS.pretrain_dir)
tf.logging.info("%s.data-00000-of-00001", bert_ckpt)
tf.gfile.Copy(bert_ckpt + ".data-00000-of-00001",
os.path.join(FLAGS.multihop_output_dir,
"bert_init.data-00000-of-00001"),
overwrite=True)
tf.logging.info("%s.index", bert_ckpt)
tf.gfile.Copy(bert_ckpt + ".index",
os.path.join(FLAGS.multihop_output_dir, "bert_init.index"),
overwrite=True)
tf.logging.info("%s.meta", bert_ckpt)
tf.gfile.Copy(bert_ckpt + ".meta",
os.path.join(FLAGS.multihop_output_dir, "bert_init.meta"),
overwrite=True)
# Get mention embeddings from BERT.
tf.logging.info("Computing mention embeddings for %d paras.",
len(all_sub_paras))
bert_predictor = bert_utils.BERTPredictor(tokenizer, bert_ckpt)
para_emb = bert_predictor.get_doc_embeddings(all_sub_paras)
mention_emb = np.empty((len(mentions), 2 * bert_predictor.emb_dim),
dtype=np.float32)
for im, mention in enumerate(mentions):
mention_emb[im, :] = np.concatenate([
para_emb[mention[1], mention[2], :], para_emb[mention[1],
mention[3], :]
])
del para_emb
tf.logging.info("Saving %d mention features to tensorflow checkpoint.",
mention_emb.shape[0])
with tf.device("/cpu:0"):
search_utils.write_to_checkpoint(
"db_emb", mention_emb, tf.float32,
os.path.join(FLAGS.multihop_output_dir, "mention_feats"))
def do_preprocess(tokenizer):
"""Loads and processes the data."""
# Read concepts.
tf.logging.info("Reading entities.")
entity2id = load_concept_vocab(FLAGS.entity_file)
entity2name = {concept: concept for (concept, _) in entity2id.items()}
# print("print # concepts:", len(entity2id))
tf.logging.info("# concepts: %d", len(entity2id))
if not tf.gfile.Exists(FLAGS.multihop_output_dir):
tf.gfile.MakeDirs(FLAGS.multihop_output_dir)
# Read paragraphs, mentions and entities.
mentions = []
ent_rows, ent_cols, ent_vals = [], [], []
ent2fact_rows, ent2fact_cols, ent2fact_vals = [], [], []
fact2ent_rows, fact2ent_cols, fact2ent_vals = [], [], []
ent2num_facts = collections.defaultdict(lambda: 0)
mention2text = {}
total_sub_paras = [0]
all_sub_paras = []
num_skipped_mentions = 0.
tf.logging.info("Reading paragraphs from %s", FLAGS.wiki_file)
with tf.gfile.Open(FLAGS.wiki_file) as f:
lines = f.read().split("\n")
if not lines[-1]:
lines = lines[:-1]
fact2entity = []
for ii, line in tqdm(enumerate(lines[:]),
total=len(lines),
desc="preprocessing lines"):
if ii == FLAGS.max_total_paragraphs:
tf.logging.info(
"Processed maximum number of paragraphs, breaking.")
break
orig_para = json.loads(line.strip())
if orig_para["kb_id"].lower() not in entity2id:
tf.logging.info("%s not in entities. Skipping %s para",
orig_para["kb_id"], orig_para["title"])
continue
sub_para_objs = index_util.get_sub_paras(orig_para, tokenizer,
FLAGS.max_seq_length,
FLAGS.doc_stride,
total_sub_paras)
assert len(sub_para_objs) == 1 # each doc is a single paragraph.
for para_obj in sub_para_objs:
# Add mentions from this paragraph.
local2global = {}
# title_entity_mention = None
assert para_obj["id"] == len(fact2entity)
fact2entity.append([])
for im, mention in enumerate(
para_obj["mentions"][:FLAGS.max_mentions_per_doc]):
if mention["kb_id"].lower() not in entity2id:
# tf.logging.info("%s not in entities. Skipping mention %s",
# mention["kb_id"], mention["text"])
num_skipped_mentions += 1
continue
mention2text[len(mentions)] = mention["text"]
# Map the index of a local mention to the global mention id.
local2global[im] = len(mentions)
# if mention["kb_id"] == orig_para["kb_id"]:
# title_entity_mention = len(mentions)
# The shape of 'mentions.npy' is thus #mentions * 4.
mentions.append(
(entity2id[mention["kb_id"].lower()], para_obj["id"],
mention["start_token"], mention["end_token"]))
# fact_id 2 entity_ids
fact2entity[para_obj["id"]].append(
entity2id[mention["kb_id"].lower()])
# Note: each pair of mention in this paragraph is recorded.
local_mentioned_entity_ids = set(
[mentions[gm][0] for _, gm in local2global.items()])
# Creating sparse entries for entity2mention matrix.
for _, gm in local2global.items():
for cur_entity_id in local_mentioned_entity_ids:
if cur_entity_id != gm:
ent_rows.append(cur_entity_id)
ent_cols.append(gm)
ent_vals.append(1.)
# Creating sparse entries for entity2fact matrix.
for cur_entity_id in local_mentioned_entity_ids:
fact2ent_rows.append(ii) # doc_id
fact2ent_cols.append(cur_entity_id)
fact2ent_vals.append(1.)
# Note: Use tf-idf to limit this in the future.
if ent2num_facts[cur_entity_id] >= FLAGS.max_facts_per_entity:
# We want to limit the number of the facts in ent2fact.
# Otherwise, the init_fact will be huge.
continue
ent2fact_rows.append(cur_entity_id)
ent2fact_cols.append(ii) # doc_id
ent2fact_vals.append(1.)
ent2num_facts[cur_entity_id] += 1
all_sub_paras.append(para_obj["tokens"])
assert len(all_sub_paras) == total_sub_paras[0], (len(all_sub_paras),
total_sub_paras)
tf.logging.info("Num paragraphs = %d, Num mentions = %d",
total_sub_paras[0], len(mentions))
tf.logging.info("Saving mention2entity coreference map.")
search_utils.write_to_checkpoint(
"coref", np.array([m[0] for m in mentions], dtype=np.int32), tf.int32,
os.path.join(FLAGS.multihop_output_dir, "coref.npz"))
tf.logging.info("Creating ent2men matrix with %d entries.", len(ent_vals))
# Fill a zero-inited sparse matrix for entity2mention.
sp_entity2mention = sp.csr_matrix((ent_vals, (ent_rows, ent_cols)),
shape=[len(entity2id),
len(mentions)])
tf.logging.info("Num nonzero in e2m = %d", sp_entity2mention.getnnz())
tf.logging.info("Saving as ragged e2m tensor %s.",
str(sp_entity2mention.shape))
search_utils.write_ragged_to_checkpoint(
"ent2ment", sp_entity2mention,
os.path.join(FLAGS.multihop_output_dir, "ent2ment.npz"))
tf.logging.info("Saving mentions metadata.")
np.save(
tf.gfile.Open(os.path.join(FLAGS.multihop_output_dir, "mentions.npy"),
"w"), np.array(mentions, dtype=np.int64))
json.dump(
mention2text,
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "mention2text.json"), "w"))
tf.logging.info("Saving entities metadata.")
assert len(lines) == len(all_sub_paras)
num_facts = len(all_sub_paras)
# Fill a zero-inited sparse matrix for entity2fact.
sp_entity2fact = sp.csr_matrix(
(ent2fact_vals, (ent2fact_rows, ent2fact_cols)),
shape=[len(entity2id), num_facts])
tf.logging.info("Num nonzero in e2f = %d", sp_entity2fact.getnnz())
tf.logging.info("Saving as ragged e2f tensor %s.",
str(sp_entity2fact.shape))
search_utils.write_ragged_to_checkpoint(
"ent2fact", sp_entity2fact,
os.path.join(FLAGS.multihop_output_dir,
"ent2fact_%d.npz" % FLAGS.max_facts_per_entity))
# Fill a zero-inited sparse matrix for fact2entity.
sp_fact2entity = sp.csr_matrix(
(ent2fact_vals, (ent2fact_cols, ent2fact_rows)), # Transpose.
shape=[num_facts, len(entity2id)])
tf.logging.info("Num nonzero in f2e = %d", sp_fact2entity.getnnz())
tf.logging.info("Saving as ragged f2e tensor %s.",
str(sp_fact2entity.shape))
search_utils.write_ragged_to_checkpoint(
"fact2ent", sp_fact2entity,
os.path.join(FLAGS.multihop_output_dir, "fact_coref.npz"))
json.dump([entity2id, entity2name],
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "entities.json"),
"w"))
tf.logging.info("Saving split paragraphs.")
json.dump(
all_sub_paras,
tf.gfile.Open(os.path.join(FLAGS.multihop_output_dir, "subparas.json"),
"w"))
# Store entity tokens.
tf.logging.info("Processing entities.")
entity_ids = np.zeros((len(entity2id), FLAGS.max_entity_length),
dtype=np.int32)
entity_mask = np.zeros((len(entity2id), FLAGS.max_entity_length),
dtype=np.float32)
num_exceed_len = 0.
for entity in tqdm(entity2id):
ei = entity2id[entity]
entity_tokens = tokenizer.tokenize(entity2name[entity])
entity_token_ids = tokenizer.convert_tokens_to_ids(entity_tokens)
if len(entity_token_ids) > FLAGS.max_entity_length:
num_exceed_len += 1
entity_token_ids = entity_token_ids[:FLAGS.max_entity_length]
entity_ids[ei, :len(entity_token_ids)] = entity_token_ids
entity_mask[ei, :len(entity_token_ids)] = 1.
tf.logging.info("Saving %d entity ids. %d exceed max-length of %d.",
len(entity2id), num_exceed_len, FLAGS.max_entity_length)
search_utils.write_to_checkpoint(
"entity_ids", entity_ids, tf.int32,
os.path.join(FLAGS.multihop_output_dir, "entity_ids"))
search_utils.write_to_checkpoint(
"entity_mask", entity_mask, tf.float32,
os.path.join(FLAGS.multihop_output_dir, "entity_mask"))
def do_embed(tokenizer, do_embed_mentions, do_embed_facts, embed_prefix):
"""Gets mention embeddings from BERT."""
# Start Embedding.
bert_ckpt = os.path.join(FLAGS.bert_ckpt_dir, FLAGS.ckpt_name)
with tf.gfile.Open(os.path.join(FLAGS.multihop_output_dir, "mentions.npy"),
"rb") as f:
mentions = np.load(f)
with tf.gfile.Open(os.path.join(FLAGS.multihop_output_dir,
"subparas.json")) as f:
all_sub_paras = json.load(f)
if do_embed_mentions:
tf.logging.info("Computing embeddings for %d mentions over %d paras.",
len(mentions), len(all_sub_paras))
shard_size = len(mentions) // FLAGS.num_shards
# Note that some FLAGS args are passed to the init function here.
tf.logging.info("Loading BERT from %s", bert_ckpt)
bert_predictor = bert_utils_v2.BERTPredictor(tokenizer, bert_ckpt)
if FLAGS.my_shard is None:
shard_range = range(FLAGS.num_shards + 1)
else:
shard_range = [FLAGS.my_shard]
for ns in shard_range:
min_ = ns * shard_size
max_ = (ns + 1) * shard_size
if min_ >= len(mentions):
break
if max_ > len(mentions):
max_ = len(mentions)
min_subp = mentions[min_][1] # the start sentence id
max_subp = mentions[max_ - 1][1] # the end sentence id
tf.logging.info("Processing shard %d of %d mentions and %d paras.",
ns, max_ - min_, max_subp - min_subp + 1)
# Get the embeddings of all the sentences.
# Note: this is always the last layer of the BERT.
para_emb = bert_predictor.get_doc_embeddings(
all_sub_paras[min_subp:max_subp + 1])
assert para_emb.shape[2] == 2 * FLAGS.projection_dim
mention_emb = np.empty((max_ - min_, 2 * bert_predictor.emb_dim),
dtype=np.float32)
for im, mention in enumerate(mentions[min_:max_]):
# mention[1] is the sentence id
# mention[2/3] is the start/end index of the token
mention_emb[im, :] = np.concatenate([
para_emb[mention[1] - min_subp,
mention[2], :FLAGS.projection_dim],
para_emb[mention[1] - min_subp, mention[3],
FLAGS.projection_dim:2 * FLAGS.projection_dim]
])
del para_emb
tf.logging.info(
"Saving %d mention features to tensorflow checkpoint.",
mention_emb.shape[0])
with tf.device("/cpu:0"):
search_utils.write_to_checkpoint(
"db_emb_%d" % ns, mention_emb, tf.float32,
os.path.join(FLAGS.multihop_output_dir,
"%s_mention_feats_%d" % (embed_prefix, ns)))
if do_embed_facts:
tf.logging.info("Computing embeddings for %d facts with %d mentions.",
len(all_sub_paras), len(mentions))
fact2mentions = collections.defaultdict(list)
for m in mentions:
fact2mentions[int(m[1])].append(m)
shard_size = len(all_sub_paras) // FLAGS.num_shards
# Note that some FLAGS args are passed to the init function here.
bert_predictor = bert_utils_v2.BERTPredictor(tokenizer, bert_ckpt)
if FLAGS.my_shard is None:
shard_range = range(FLAGS.num_shards + 1)
else:
shard_range = [FLAGS.my_shard]
for ns in shard_range:
min_ = ns * shard_size
max_ = (ns + 1) * shard_size
if min_ >= len(all_sub_paras):
break
if max_ > len(all_sub_paras):
max_ = len(all_sub_paras)
min_subp = min_ # the start sentence id
max_subp = max_ - 1 # the end sentence id
tf.logging.info("Processing shard %d of %d facts and %d paras.",
ns, max_ - min_, max_subp - min_subp + 1)
# Get the embeddings of all the sentences.
para_emb = bert_predictor.get_doc_embeddings(
all_sub_paras[min_subp:max_subp + 1])
assert para_emb.shape[2] == 2 * FLAGS.projection_dim
fact_emb = np.empty((max_ - min_, 2 * bert_predictor.emb_dim),
dtype=np.float32)
for ii, _ in enumerate(all_sub_paras[min_:max_]):
fact_id = min_ + ii
local_mentions = fact2mentions[fact_id]
mention_agg_emb = np.empty(
(len(local_mentions), 2 * bert_predictor.emb_dim),
dtype=np.float32)
for jj, m in enumerate(local_mentions):
mention_agg_emb[jj, :] = np.concatenate([
para_emb[ii, m[2], :FLAGS.projection_dim],
para_emb[ii, m[3],
FLAGS.projection_dim:2 * FLAGS.projection_dim]
])
fact_emb[ii, :] = np.mean(mention_agg_emb, axis=0)
del para_emb
tf.logging.info(
"Saving %d fact features to tensorflow checkpoint.",
fact_emb.shape[0])
with tf.device("/cpu:0"):
search_utils.write_to_checkpoint(
"fact_db_emb_%d" % ns, fact_emb, tf.float32,
os.path.join(FLAGS.multihop_output_dir,
"%s_fact_feats_%d" % (embed_prefix, ns)))
def main(_):
if not tf.gfile.Exists(FLAGS.multihop_output_dir):
tf.gfile.MakeDirs(FLAGS.multihop_output_dir)
# Initialize tokenizer.
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
# Read entities.
if FLAGS.do_preprocess:
tf.logging.info("Reading entities.")
entity2id, entity2name = {}, {}
with tf.gfile.Open(FLAGS.entity_file) as f:
entities = json.load(f)
tf.logging.info("Read %d entities", len(entities))
for e, (_, n) in entities.items():
if e.lower() in entity2id:
continue
# tf.logging.warn("%s entity repeated", e)
entity2id[e.lower()] = len(entity2id)
entity2name[e.lower()] = n
tf.logging.info("Kept %d entities", len(entity2id))
# Read paragraphs, mentions and entities.
if FLAGS.do_preprocess:
mentions = []
ent_rows, ent_cols, ent_vals = [], [], []
mention2text = {}
total_sub_paras = [0]
all_sub_paras = []
num_skipped_mentions = 0.
tf.logging.info("Reading paragraphs from %s", FLAGS.wiki_file)
with tf.gfile.Open(FLAGS.wiki_file) as f:
for ii, line in tqdm(enumerate(f)):
if ii == FLAGS.max_total_paragraphs:
tf.logging.info(
"Processed maximum number of paragraphs, breaking.")
break
if ii > 0 and ii % 100000 == 0:
tf.logging.info("Skipped / Kept mentions = %.3f",
num_skipped_mentions / len(mentions))
orig_para = json.loads(line.strip())
if orig_para["kb_id"].lower() not in entity2id:
tf.logging.warn("%s not in entities. Skipping %s para",
orig_para["kb_id"], orig_para["title"])
continue
sub_para_objs = _get_sub_paras(orig_para, tokenizer,
FLAGS.max_seq_length,
FLAGS.doc_stride,
total_sub_paras)
for para_obj in sub_para_objs:
# Add mentions from this paragraph.
local2global = {}
title_entity_mention = None
for im, mention in enumerate(
para_obj["mentions"]
[:FLAGS.max_mentions_per_entity]):
if mention["kb_id"].lower() not in entity2id:
# tf.logging.warn("%s not in entities. Skipping mention %s",
# mention["kb_id"], mention["text"])
num_skipped_mentions += 1
continue
mention2text[len(mentions)] = mention["text"]
local2global[im] = len(mentions)
if mention["kb_id"] == orig_para["kb_id"]:
title_entity_mention = len(mentions)
mentions.append(
(entity2id[mention["kb_id"].lower()],
para_obj["id"], mention["start_token"],
mention["end_token"]))
for im, gm in local2global.items():
# entity to mention matrix.
ent_rows.append(entity2id[orig_para["kb_id"].lower()])
ent_cols.append(gm)
ent_vals.append(1.)
if title_entity_mention is not None:
ent_rows.append(mentions[gm][0])
ent_cols.append(title_entity_mention)
ent_vals.append(1.)
all_sub_paras.append(para_obj["tokens"])
assert len(all_sub_paras) == total_sub_paras[0], (
len(all_sub_paras), total_sub_paras)
tf.logging.info("Num paragraphs = %d, Num mentions = %d",
total_sub_paras[0], len(mentions))
tf.logging.info("Saving coreference map.")
search_utils.write_to_checkpoint(
"coref", np.array([m[0] for m in mentions], dtype=np.int32),
tf.int32, os.path.join(FLAGS.multihop_output_dir, "coref.npz"))
tf.logging.info("Creating entity to mentions matrix.")
sp_entity2mention = sp.csr_matrix(
(ent_vals, (ent_rows, ent_cols)),
shape=[len(entity2id), len(mentions)])
tf.logging.info("Num nonzero = %d", sp_entity2mention.getnnz())
tf.logging.info("Saving as ragged tensor %s.",
str(sp_entity2mention.shape))
search_utils.write_ragged_to_checkpoint(
"ent2ment", sp_entity2mention,
os.path.join(FLAGS.multihop_output_dir, "ent2ment.npz"))
tf.logging.info("Saving mentions metadata.")
np.save(
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "mentions.npy"), "w"),
np.array(mentions, dtype=np.int64))
json.dump(
mention2text,
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "mention2text.json"),
"w"))
tf.logging.info("Saving entities metadata.")
json.dump([entity2id, entity2name],
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "entities.json"),
"w"))
tf.logging.info("Saving split paragraphs.")
json.dump(
all_sub_paras,
tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir, "subparas.json"), "w"))
# Store entity tokens.
if FLAGS.do_preprocess:
tf.logging.info("Processing entities.")
entity_ids = np.zeros((len(entity2id), FLAGS.max_entity_length),
dtype=np.int32)
entity_mask = np.zeros((len(entity2id), FLAGS.max_entity_length),
dtype=np.float32)
num_exceed_len = 0.
for entity in tqdm(entity2id):
ei = entity2id[entity]
entity_tokens = tokenizer.tokenize(entity2name[entity])
entity_token_ids = tokenizer.convert_tokens_to_ids(entity_tokens)
if len(entity_token_ids) > FLAGS.max_entity_length:
num_exceed_len += 1
entity_token_ids = entity_token_ids[:FLAGS.max_entity_length]
entity_ids[ei, :len(entity_token_ids)] = entity_token_ids
entity_mask[ei, :len(entity_token_ids)] = 1.
tf.logging.info("Saving %d entity ids. %d exceed max-length of %d.",
len(entity2id), num_exceed_len,
FLAGS.max_entity_length)
search_utils.write_to_checkpoint(
"entity_ids", entity_ids, tf.int32,
os.path.join(FLAGS.multihop_output_dir, "entity_ids"))
search_utils.write_to_checkpoint(
"entity_mask", entity_mask, tf.float32,
os.path.join(FLAGS.multihop_output_dir, "entity_mask"))
# Copy BERT checkpoint for future use.
if FLAGS.do_preprocess:
tf.logging.info("Copying BERT checkpoint.")
if tf.gfile.Exists(os.path.join(FLAGS.pretrain_dir,
"best_model.index")):
bert_ckpt = os.path.join(FLAGS.pretrain_dir, "best_model")
else:
bert_ckpt = tf.train.latest_checkpoint(FLAGS.pretrain_dir)
tf.logging.info("%s.data-00000-of-00001", bert_ckpt)
tf.gfile.Copy(bert_ckpt + ".data-00000-of-00001",
os.path.join(FLAGS.multihop_output_dir,
"bert_init.data-00000-of-00001"),
overwrite=True)
tf.logging.info("%s.index", bert_ckpt)
tf.gfile.Copy(bert_ckpt + ".index",
os.path.join(FLAGS.multihop_output_dir,
"bert_init.index"),
overwrite=True)
tf.logging.info("%s.meta", bert_ckpt)
tf.gfile.Copy(bert_ckpt + ".meta",
os.path.join(FLAGS.multihop_output_dir,
"bert_init.meta"),
overwrite=True)
if FLAGS.do_embed:
# Get mention embeddings from BERT.
bert_ckpt = os.path.join(FLAGS.multihop_output_dir, "bert_init")
if not FLAGS.do_preprocess:
with tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir,
"mentions.npy")) as f:
mentions = np.load(f)
with tf.gfile.Open(
os.path.join(FLAGS.multihop_output_dir,
"subparas.json")) as f:
all_sub_paras = json.load(f)
tf.logging.info("Computing embeddings for %d mentions over %d paras.",
len(mentions), len(all_sub_paras))
shard_size = len(mentions) // FLAGS.num_shards
bert_predictor = bert_utils_v2.BERTPredictor(tokenizer, bert_ckpt)
if FLAGS.my_shard is None:
shard_range = range(FLAGS.num_shards + 1)
else:
shard_range = [FLAGS.my_shard]
for ns in shard_range:
min_ = ns * shard_size
max_ = (ns + 1) * shard_size
if min_ >= len(mentions):
break
if max_ > len(mentions):
max_ = len(mentions)
min_subp = mentions[min_][1]
max_subp = mentions[max_ - 1][1]
tf.logging.info("Processing shard %d of %d mentions and %d paras.",
ns, max_ - min_, max_subp - min_subp + 1)
para_emb = bert_predictor.get_doc_embeddings(
all_sub_paras[min_subp:max_subp + 1])
assert para_emb.shape[2] == 2 * FLAGS.projection_dim
mention_emb = np.empty((max_ - min_, 2 * bert_predictor.emb_dim),
dtype=np.float32)
for im, mention in enumerate(mentions[min_:max_]):
mention_emb[im, :] = np.concatenate([
para_emb[mention[1] - min_subp,
mention[2], :FLAGS.projection_dim],
para_emb[mention[1] - min_subp, mention[3],
FLAGS.projection_dim:2 * FLAGS.projection_dim]
])
del para_emb
tf.logging.info(
"Saving %d mention features to tensorflow checkpoint.",
mention_emb.shape[0])
with tf.device("/cpu:0"):
search_utils.write_to_checkpoint(
"db_emb_%d" % ns, mention_emb, tf.float32,
os.path.join(FLAGS.multihop_output_dir,
"mention_feats_%d" % ns))
if FLAGS.do_combine:
# Combine sharded DB into one.
if FLAGS.shards_to_combine is None:
shard_range = range(FLAGS.num_shards + 1)
else:
shard_range = range(FLAGS.shards_to_combine)
with tf.device("/cpu:0"):
all_db = []
for i in shard_range:
ckpt_path = os.path.join(FLAGS.multihop_output_dir,
"mention_feats_%d" % i)
reader = tf.NewCheckpointReader(ckpt_path)
var_to_shape_map = reader.get_variable_to_shape_map()
tf.logging.info("Reading %s from %s with shape %s",
"db_emb_%d" % i, ckpt_path,
str(var_to_shape_map["db_emb_%d" % i]))
tf_db = search_utils.load_database(
"db_emb_%d" % i, var_to_shape_map["db_emb_%d" % i],
ckpt_path)
all_db.append(tf_db)
tf.logging.info("Reading all variables.")
session = tf.Session()
session.run(tf.global_variables_initializer())
session.run(tf.local_variables_initializer())
np_db = session.run(all_db)
tf.logging.info("Concatenating and storing.")
np_db = np.concatenate(np_db, axis=0)
search_utils.write_to_checkpoint(
"db_emb", np_db, tf.float32,
os.path.join(FLAGS.multihop_output_dir, "mention_feats"))
