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encode.py
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encode.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from copy import deepcopy
import numpy as np
def convert_example(tokenizer,
attn_id,
tgt_type_id=3,
max_encode_len=512,
max_decode_len=128,
is_test=False,
noise_prob=0.,
use_random_noice=False):
def warpper(example):
"""convert an example into necessary features"""
tokens = example['tokens']
labels = example['labels']
encoded_src = tokenizer(
tokens, max_seq_len=max_encode_len, pad_to_max_seq_len=False)
src_ids, src_sids = encoded_src["input_ids"], encoded_src[
"token_type_ids"]
src_pids = np.arange(len(src_ids))
if not is_test:
encoded_tgt = tokenizer(
labels, max_seq_len=max_decode_len, pad_to_max_seq_len=False)
tgt_ids, tgt_sids = encoded_tgt["input_ids"], encoded_tgt[
"token_type_ids"]
tgt_ids = np.array(tgt_ids)
tgt_sids = np.array(tgt_sids) + tgt_type_id
tgt_pids = np.arange(len(tgt_ids)) + len(src_ids)
attn_ids = np.ones_like(tgt_ids) * attn_id
if noise_prob > 0.:
tgt_labels = deepcopy(tgt_ids)
if use_random_noice:
noice_ids = np.random.randint(
1, len(tokenizer.vocab), size=tgt_ids.shape)
else:
noice_ids = np.ones_like(tgt_ids) * tokenizer.vocab['[NOISE]']
pos, = np.where(np.ones_like(tgt_ids))
np.random.shuffle(pos)
pos = pos[:int(noise_prob * len(pos))]
tgt_ids[pos, ] = noice_ids[pos, ]
else:
tgt_labels = tgt_ids
return (src_ids, src_pids, src_sids, tgt_ids, tgt_pids, tgt_sids,
attn_ids, tgt_labels)
return warpper
def gen_mask(batch_ids, mask_type='bidi', query_len=None, pad_value=0):
if query_len is None:
query_len = batch_ids.shape[1]
if mask_type != 'empty':
mask = (batch_ids != pad_value).astype(np.float32)
mask = np.tile(np.expand_dims(mask, 1), [1, query_len, 1])
if mask_type == 'causal':
assert query_len == batch_ids.shape[1]
mask = np.tril(mask)
elif mask_type == 'causal_without_diag':
assert query_len == batch_ids.shape[1]
mask = np.tril(mask, -1)
elif mask_type == 'diag':
assert query_len == batch_ids.shape[1]
# import pdb; pdb.set_trace()
mask = np.stack([np.diag(np.diag(m)) for m in mask], 0)
else:
mask_type == 'empty'
mask = np.zeros_like(batch_ids).astype(np.float32)
mask = np.tile(np.expand_dims(mask, 1), [1, query_len, 1])
return mask
def after_padding(args):
'''
attention mask:
*** src, tgt, attn
src 00, 01, 11
tgt 10, 11, 12
attn 20, 21, 22
*** s1, s2 | t1 t2 t3| attn1 attn2 attn3
s1 1, 1 | 0, 0, 0,| 0, 0, 0,
s2 1, 1 | 0, 0, 0,| 0, 0, 0,
-
t1 1, 1, | 1, 0, 0,| 0, 0, 0,
t2 1, 1, | 1, 1, 0,| 0, 0, 0,
t3 1, 1, | 1, 1, 1,| 0, 0, 0,
-
attn1 1, 1, | 0, 0, 0,| 1, 0, 0,
attn2 1, 1, | 1, 0, 0,| 0, 1, 0,
attn3 1, 1, | 1, 1, 0,| 0, 0, 1,
for details, see Fig3. https://arxiv.org/abs/2001.11314
'''
src_ids, src_pids, src_sids, tgt_ids, tgt_pids, tgt_sids, attn_ids, tgt_labels = args
src_len = src_ids.shape[1]
tgt_len = tgt_ids.shape[1]
mask_00 = gen_mask(src_ids, 'bidi', query_len=src_len)
mask_01 = gen_mask(tgt_ids, 'empty', query_len=src_len)
mask_02 = gen_mask(attn_ids, 'empty', query_len=src_len)
mask_10 = gen_mask(src_ids, 'bidi', query_len=tgt_len)
mask_11 = gen_mask(tgt_ids, 'causal', query_len=tgt_len)
mask_12 = gen_mask(attn_ids, 'empty', query_len=tgt_len)
mask_20 = gen_mask(src_ids, 'bidi', query_len=tgt_len)
mask_21 = gen_mask(tgt_ids, 'causal_without_diag', query_len=tgt_len)
mask_22 = gen_mask(attn_ids, 'diag', query_len=tgt_len)
mask_src_2_src = mask_00
mask_tgt_2_srctgt = np.concatenate([mask_10, mask_11], 2)
mask_attn_2_srctgtattn = np.concatenate([mask_20, mask_21, mask_22], 2)
raw_tgt_labels = deepcopy(tgt_labels)
tgt_labels = tgt_labels[np.where(tgt_labels != 0)]
return (src_ids, src_sids, src_pids, tgt_ids, tgt_sids, tgt_pids, attn_ids,
mask_src_2_src, mask_tgt_2_srctgt, mask_attn_2_srctgtattn,
tgt_labels, raw_tgt_labels)