def _pad_batch_records(self, batch_records, is_infer, phase=None):
"""Padding a batch of records and construct model's inputs."""
batch = {}
batch_token_ids = [record.token_ids for record in batch_records]
batch_type_ids = [record.type_ids for record in batch_records]
batch_pos_ids = [record.pos_ids for record in batch_records]
batch["token_ids"] = pad_batch_data(batch_token_ids,
pad_id=self.pad_id)
batch["type_ids"] = pad_batch_data(batch_type_ids, pad_id=0)
batch["pos_ids"] = pad_batch_data(batch_pos_ids, pad_id=0)
if self.use_role:
batch_role_ids = [record.role_ids for record in batch_records]
batch["role_ids"] = pad_batch_data(batch_role_ids, pad_id=0)
attention_mask = self._gen_self_attn_mask(batch_token_ids,
is_unidirectional=False)
batch["attention_mask"] = attention_mask
if not is_infer:
batch_label = [record.label for record in batch_records]
batch["label"] = np.array(batch_label).astype("int64").reshape(
[-1, 1])
else:
batch_data_id = [record.data_id for record in batch_records]
batch["data_id"] = np.array(batch_data_id).astype("int64").reshape(
[-1, 1])
return batch
def _pad_batch_records(self, batch_records, is_infer, phase=None):
"""Padding a batch of records and construct model's inputs.
This function can be override by its subclass if necessary.
"""
batch_size = len(batch_records)
batch = {}
batch_token_ids = [record.token_ids for record in batch_records]
batch_type_ids = [record.type_ids for record in batch_records]
batch_pos_ids = [record.pos_ids for record in batch_records]
if self.use_role:
batch_role_ids = [record.role_ids for record in batch_records]
batch["token_ids"] = pad_batch_data(batch_token_ids,
pad_id=self.pad_id)
batch["type_ids"] = pad_batch_data(batch_type_ids, pad_id=0)
batch["pos_ids"] = pad_batch_data(batch_pos_ids, pad_id=0)
if self.use_role:
batch["role_ids"] = pad_batch_data(batch_role_ids, pad_id=0)
batch_tgt_start_idx = [
record.tgt_start_idx for record in batch_records
]
batch["generation_mask"] = self._gen_self_attn_mask(
batch_token_ids, batch_tgt_start_idx=batch_tgt_start_idx)
if is_infer:
tgt_ids = np.array([[[self.bos_id]]] * len(batch_token_ids),
dtype="int64")
if self.position_style == "continuous":
tgt_pos = np.array(batch_tgt_start_idx, dtype="int64")
else:
tgt_pos = np.zeros_like(batch_tgt_start_idx, dtype="int64")
tgt_pos = tgt_pos.reshape(-1, 1, 1)
batch["init_score"] = np.zeros_like(tgt_ids,
dtype="float32").reshape(
-1, 1).tolist()
batch["tgt_ids"] = tgt_ids.tolist()
batch["tgt_pos"] = tgt_pos.tolist()
batch["parent_idx"] = np.array(range(batch_size), dtype="int32")
batch["tgt_generation_mask"] = batch[
"generation_mask"][:, 0:1, :].astype("float32")
batch_data_id = [record.data_id for record in batch_records]
batch["data_id"] = np.array(batch_data_id).astype("int64").reshape(
[-1, 1])
else:
batch["tgt_label"], batch["tgt_idx"] = mask(
batch_tokens=batch_token_ids,
vocab_size=self.vocab_size,
tgt_starts=batch_tgt_start_idx,
bos_id=self.bos_id,
eos_id=self.eos_id,
mask_id=self.mask_id,
is_unidirectional=True)
return batch
def _pad_batch_records(self, batch_records, is_infer, phase=None):
"""Padding a batch of records and construct model's inputs."""
batch = {}
batch_token_ids = [record.token_ids for record in batch_records]
batch_type_ids = [record.type_ids for record in batch_records]
batch_pos_ids = [record.pos_ids for record in batch_records]
if self.use_role:
batch_role_ids = [record.role_ids for record in batch_records]
batch_tgt_start_idx = [record.tgt_start_idx for record in batch_records]
batch_label = [record.label for record in batch_records]
batch_mask_token_ids, tgt_label, tgt_idx, label_idx = mask(
batch_tokens=batch_token_ids,
vocab_size=self.vocab_size,
bos_id=self.bos_id,
eos_id=self.eos_id,
mask_id=self.mask_id,
tgt_starts=batch_tgt_start_idx,
labels=batch_label,
is_unidirectional=False)
if not is_infer:
batch_token_ids = batch_mask_token_ids
batch["token_ids"] = pad_batch_data(batch_token_ids, pad_id=self.pad_id)
batch["type_ids"] = pad_batch_data(batch_type_ids, pad_id=0)
batch["pos_ids"] = pad_batch_data(batch_pos_ids, pad_id=0)
if self.use_role:
batch["role_ids"] = pad_batch_data(batch_role_ids, pad_id=0)
attention_mask = self._gen_self_attn_mask(batch_token_ids, is_unidirectional=False)
batch["attention_mask"] = attention_mask
batch["label_idx"] = label_idx
if not is_infer:
batch_label = np.array(batch_label).astype("int64").reshape([-1, 1])
batch["label"] = batch_label
batch["tgt_label"] = tgt_label
batch["tgt_idx"] = tgt_idx
else:
batch_data_id = [record.data_id for record in batch_records]
batch["data_id"] = np.array(batch_data_id).astype("int64").reshape([-1, 1])
return batch
def _pad_batch_records(self, batch_records, is_infer, phase=None):
"""pad batch records and mask"""
batch = {}
batch_token_ids = [record.token_ids for record in batch_records]
batch_type_ids = [record.type_ids for record in batch_records]
batch_pos_ids = [record.pos_ids for record in batch_records]
if self.use_role:
batch_role_ids = [record.role_ids for record in batch_records]
batch["token_ids"] = pad_batch_data(batch_token_ids, pad_id=self.pad_id)
batch["type_ids"] = pad_batch_data(batch_type_ids, pad_id=self.pad_id)
batch["pos_ids"] = pad_batch_data(batch_pos_ids, pad_id=self.pad_id)
if self.use_role:
batch["role_ids"] = pad_batch_data(batch_role_ids, pad_id=self.pad_id)
batch["attention_mask"] = self._gen_self_attn_mask(batch_token_ids, is_unidirectional=False)
batch_data_id = [record.data_id for record in batch_records]
batch["data_id"] = np.array(batch_data_id).astype("int64").reshape([-1, 1])
return batch
def _pad_batch_records(self, batch_records, is_infer, **kwargs):
"""Padding a batch of records and construct model's inputs."""
batch = {}
batch_token_ids = [record.token_ids for record in batch_records]
batch_type_ids = [record.type_ids for record in batch_records]
batch_pos_ids = [record.pos_ids for record in batch_records]
if self.use_role:
batch_role_ids = [record.role_ids for record in batch_records]
batch_tgt_start_idx = [record.tgt_start_idx for record in batch_records]
batch_size = len(batch_token_ids)
# padding
batch["token_ids"] = pad_batch_data(batch_token_ids, pad_id=self.pad_id)
batch["type_ids"] = pad_batch_data(batch_type_ids, pad_id=0)
batch["pos_ids"] = pad_batch_data(batch_pos_ids, pad_id=0)
if self.use_role:
batch["role_ids"] = pad_batch_data(batch_role_ids, pad_id=0)
batch["generation_mask"] = self._gen_self_attn_mask(
batch_token_ids,
batch_tgt_start_idx=batch_tgt_start_idx,
is_unidirectional=True,
num_aux_token=1)
if not is_infer:
batch["recognition_mask"] = self._gen_self_attn_mask(
batch_token_ids,
is_unidirectional=False,
num_aux_token=1)
if is_infer:
tgt_ids = np.array([[[self.bos_id]]] * batch_size, dtype="int64")
if self.position_style == "continuous":
tgt_pos = np.array(batch_tgt_start_idx, dtype="int64")
else:
tgt_pos = np.zeros_like(batch_tgt_start_idx, dtype="int64")
tgt_pos = tgt_pos.reshape(-1, 1, 1)
batch["init_score"] = np.zeros_like(tgt_ids, dtype="float32").reshape(-1, 1).tolist()
batch["tgt_ids"] = tgt_ids.tolist()
batch["tgt_pos"] = tgt_pos.tolist()
batch["parent_idx"] = np.array(range(batch_size), dtype="int32")
batch["latent_id"] = np.zeros([batch_size], dtype="int32")
batch["tgt_generation_mask"] = batch["generation_mask"][:, 0:1, :].astype("float32")
batch_data_id = [record.data_id for record in batch_records]
batch["data_id"] = np.array(batch_data_id).astype("int64").reshape([-1, 1])
else:
mask_return_list = mask(
batch_tokens=batch_token_ids,
vocab_size=self.vocab_size,
tgt_starts=batch_tgt_start_idx,
is_unidirectional=True,
use_latent=True,
use_bow=self.use_bow)
batch["tgt_label"] = mask_return_list[0]
batch["tgt_idx"] = mask_return_list[1]
if self.use_bow:
batch["bow_label"] = mask_return_list[2]
batch["bow_idx"] = mask_return_list[3]
return batch
def _pad_batch_records_for_training(self, batch_records):
"""Pad batch records and mask for KAG training."""
batch = {}
batch_data_id = [record.data_id for record in batch_records]
batch["data_id"] = np.array(batch_data_id).astype("int64").reshape(
[-1, 1])
# token_ids, [n, len, 1]
dual_src_batch_token_ids = [
record.dual_src_token_ids for record in batch_records
]
# [n * k, len, 1]
dual_knowledge_batch_token_ids = self._get_batch_knowledge_ids(
batch_records, "token_ids")
# [n, k, len, 1]
single_batch_token_ids = self._get_batch_single_item(
batch_records, "token_ids")
# type_ids
dual_src_batch_type_ids = [
record.dual_src_type_ids for record in batch_records
]
dual_knowledge_batch_type_ids = self._get_batch_knowledge_ids(
batch_records, "type_ids")
single_batch_type_ids = self._get_batch_single_item(
batch_records, "type_ids")
# pos_ids
dual_src_batch_pos_ids = [
record.dual_src_pos_ids for record in batch_records
]
dual_knowledge_batch_pos_ids = self._get_batch_knowledge_ids(
batch_records, "pos_ids")
single_batch_pos_ids = self._get_batch_single_item(
batch_records, "pos_ids")
if self.use_role:
dual_src_batch_role_ids = [
record.dual_src_role_ids for record in batch_records
]
dual_knowledge_batch_role_ids = self._get_batch_knowledge_ids(
batch_records, "role_ids")
single_batch_role_ids = self._get_batch_single_item(
batch_records, "role_ids")
batch_tgt_start_idx = [
record.tgt_start_idx for record in batch_records
]
batch_tgt_mask_pos = [record.tgt_mask_pos for record in batch_records]
batch_exact_k_lens = [record.exact_k_len for record in batch_records]
batch_size = len(batch_records)
tgt_label, tgt_idx = self._mask_batch_as_list_for_topk_gen(
batch_size=batch_size,
exact_k_lens=batch_exact_k_lens,
batch_tokens=single_batch_token_ids,
vocab_size=self.vocab_size,
bos_id=self.bos_id,
eos_id=self.eos_id,
mask_id=self.mask_id,
batch_mask_start_pos=batch_tgt_start_idx,
batch_tgt_mask_pos=batch_tgt_mask_pos)
flatten_batch_tgt_start_idx = [
j for i in batch_tgt_start_idx for j in i
]
batch["single_attention_mask"] = self._gen_self_attn_mask(
single_batch_token_ids,
batch_tgt_start_idx=flatten_batch_tgt_start_idx)
given_len = self.max_tgt_len
batch["tgt_label"] = self._pad_batch_data_to_len(tgt_label,
pad_id=self.bos_id,
given_len=given_len)
batch["tgt_idx"] = self._pad_batch_data_to_len_for_topk(
tgt_idx, pad_id=self.pad_id, given_len=given_len)
batch["single_token_ids"] = pad_batch_data(single_batch_token_ids,
pad_id=self.pad_id)
batch["single_type_ids"] = pad_batch_data(single_batch_type_ids,
pad_id=self.pad_id)
batch["single_pos_ids"] = pad_batch_data(single_batch_pos_ids,
pad_id=self.pad_id)
if self.use_role:
batch["single_role_ids"] = pad_batch_data(single_batch_role_ids,
pad_id=self.pad_id)
max_len = to_optimized_size(max(map(len, single_batch_token_ids)))
batch["tgt_label"] = batch["tgt_label"].reshape(
[-1, self.max_knowledge_num, given_len, 1])
batch["single_attention_mask"] = batch[
"single_attention_mask"].reshape(
[-1, self.max_knowledge_num, max_len, max_len])
batch["single_token_ids"] = batch["single_token_ids"].reshape(
[-1, self.max_knowledge_num, max_len, 1])
batch["single_type_ids"] = batch["single_type_ids"].reshape(
[-1, self.max_knowledge_num, max_len, 1])
batch["single_pos_ids"] = batch["single_pos_ids"].reshape(
[-1, self.max_knowledge_num, max_len, 1])
if self.use_role:
batch["single_role_ids"] = batch["single_role_ids"].reshape(
[-1, self.max_knowledge_num, max_len, 1])
# for dual
batch["dual_src_token_ids"] = pad_batch_data(dual_src_batch_token_ids,
pad_id=self.pad_id)
batch["dual_knowledge_token_ids"] = pad_batch_data(
dual_knowledge_batch_token_ids, pad_id=self.pad_id)
batch["dual_src_type_ids"] = pad_batch_data(dual_src_batch_type_ids,
pad_id=self.pad_id)
batch["dual_knowledge_type_ids"] = pad_batch_data(
dual_knowledge_batch_type_ids, pad_id=self.pad_id)
batch["dual_src_pos_ids"] = pad_batch_data(dual_src_batch_pos_ids,
pad_id=self.pad_id)
batch["dual_knowledge_pos_ids"] = pad_batch_data(
dual_knowledge_batch_pos_ids, pad_id=self.pad_id)
if self.use_role:
batch["dual_src_role_ids"] = pad_batch_data(
dual_src_batch_role_ids, pad_id=self.pad_id)
batch["dual_knowledge_role_ids"] = pad_batch_data(
dual_knowledge_batch_role_ids, pad_id=self.pad_id)
batch["dual_src_attention_mask"] = self._gen_self_attn_mask(
dual_src_batch_token_ids, is_unidirectional=False)
batch["dual_knowledge_attention_mask"] = self._gen_self_attn_mask(
dual_knowledge_batch_token_ids, is_unidirectional=False)
return batch
