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2449566 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | from torch.nn.functional import pad
from torch.utils.data import Dataset
import torch
import json
from transformers import PreTrainedTokenizer
from dataclasses import dataclass
def longest_common_subsequence(a, b, s_i=0, s_j=0) -> list:
a = a.numpy()
b = b.numpy()
m, n = len(a), len(b)
i = s_i
j = s_j
result = []
while i < m and j < n:
if a[i][1] == 0:
i += 1
continue
if b[j] == 0:
j += 1
continue
if a[i][1] == b[j]:
result.append(i+1)
i += 1
j += 1
elif a[i][1] < b[j]:
i += 1
else:
j += 1
return result
def get_pooler_tensor(segments_idxs):
# Tạo chỉ số segment đã pad cho toàn bộ batch
padded_idx_batch = []
max_seg, max_len_all = 0, 0
pad_multiple = 4
for seg_idx, max_len in segments_idxs:
max_len_all = max(max_len_all, max_len)
max_seg = max(max_seg, len(seg_idx))
padded = torch.stack([
pad(x, (0, max_len - len(x)), value=-1)
for x in seg_idx
]) # (num_segments, max_len)
padded_idx_batch.append(padded)
# Pad toàn bộ batch về cùng shape (B, max_seg, max_len_all)
def pad2d(t, h, w):
return pad(t, (0, w - t.size(1), 0, h - t.size(0)), value=-1)
# max_seg = int(math.ceil(max_seg / pad_multiple) * pad_multiple)
padded_idx_batch = torch.stack([
pad2d(p, max_seg, max_len_all) for p in padded_idx_batch
]) # (B, max_seg, max_len_all)
# Tạo mask và gather từ X
mask = padded_idx_batch != -1
safe_idx = padded_idx_batch.masked_fill(~mask, 0)
return {'safe_idx': safe_idx, 'mask': mask}
def prepare_pooler(offset_mapping, starts, phrases_offsets):
seg_idxs = []
for offset, start, phrases_offset in zip(offset_mapping, starts, phrases_offsets):
seg_idx = []
token_offset_start = [start.item()]
longest_common_offset = token_offset_start + longest_common_subsequence(offset, phrases_offset, start)
student_max_len = 1
for i in range(1, len(longest_common_offset)):
seg_idx.append(torch.arange(longest_common_offset[i - 1], longest_common_offset[i]))
student_max_len = max(student_max_len, seg_idx[-1].size(0))
seg_idxs.append((seg_idx, student_max_len))
return get_pooler_tensor(seg_idxs)
class LLMDataset(Dataset):
def __init__(self, file_path, tokenizer, syntactic_file, prompt_max_len=512):
self.dataset = []
with open(file_path, "r", encoding="utf-8") as f:
for line in f:
data = json.loads(line)
self.dataset.append(data)
s_prompt = tokenizer(
data['prompt'],
max_length=prompt_max_len,
truncation=True,
add_special_tokens=False
)
data['prompt'] = tokenizer.decode(s_prompt['input_ids'])
data['prompt_len'] = len(s_prompt['input_ids'])
with open(syntactic_file, "r", encoding="utf-8") as f:
idx = 0
for line in f:
prompt_end = len(self.dataset[idx]['prompt'])
data = json.loads(line)
phrases_lvl1 = [prompt_end] + [prompt_end + item['end_char'] for item in data['phrases_lvl1']]
phrases_lvl2 = [prompt_end] + [prompt_end + item['end_char'] for item in data['phrases_lvl2']]
self.dataset[idx]['phrases_lvl1_offset'] = torch.tensor(phrases_lvl1)
self.dataset[idx]['phrases_lvl2_offset'] = torch.tensor(phrases_lvl2)
idx += 1
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
return (self.dataset[index]['prompt'],
self.dataset[index]['output'],
self.dataset[index]['prompt_len'],
self.dataset[index]['phrases_lvl1_offset'],
self.dataset[index]['phrases_lvl2_offset'])
@dataclass
class LLMDataCollator:
tokenizer: PreTrainedTokenizer = None
model_type: str = ''
do_train: bool = True
max_len: int = 512
pad_to_multiple_of: int = 4
return_tensors: str = 'pt'
padding: bool = True
return_offsets_mapping: bool = True
n_span: int = 4
def __call__(self, batch):
prompts, fulls, prompt_lengths, phrases_lvl1_offsets, phrases_lvl2_offsets = [], [], [], [], []
for prompt, output, prompt_length, phrases_lvl1_offset, phrases_lvl2_offset in batch:
prompts.append(prompt)
fulls.append(prompt + output)
prompt_lengths.append(prompt_length)
phrases_lvl1_offsets.append(phrases_lvl1_offset)
phrases_lvl2_offsets.append(phrases_lvl2_offset)
inputs = self.tokenizer(
fulls,
truncation=True,
padding=self.padding,
max_length=self.max_len - 1,
return_tensors=self.return_tensors,
pad_to_multiple_of=self.pad_to_multiple_of,
return_offsets_mapping=self.return_offsets_mapping and self.do_train,
add_special_tokens=False
)
eos_tokens = torch.full((inputs["input_ids"].size(0), 1), self.tokenizer.eos_token_id, dtype=torch.long)
inputs["input_ids"] = torch.cat([inputs["input_ids"], eos_tokens], dim=1)
inputs["attention_mask"] = torch.cat([inputs["attention_mask"],
torch.zeros((inputs["attention_mask"].size(0), 1), dtype=torch.long)], dim=1)
labels = inputs["input_ids"][:, 1:].clone().detach()
labels = torch.cat([labels, torch.full((labels.size(0), 1), -100, dtype=torch.long)], dim=1)
input_lengths = inputs["attention_mask"].sum(dim=1)
prompt_lengths = torch.tensor(prompt_lengths)
if self.model_type in ["gpt2"]:
position_ids = torch.zeros(inputs['input_ids'].size(), dtype=torch.long)
for i in range(input_lengths.size(0)):
position_ids[i, :input_lengths[i]] = torch.arange(0, input_lengths[i], dtype=torch.long)
inputs["position_ids"] = position_ids
for i in range(len(labels)):
labels[i, :(prompt_lengths[i] -1)] = -100
labels[i, input_lengths[i]:] = -100
if not self.do_train:
return inputs, None, labels
token_offset_mapping = inputs.pop('offset_mapping', None)
if token_offset_mapping is not None :
starts = torch.zeros_like(prompt_lengths)
pooler_tensor = prepare_pooler(token_offset_mapping, starts, phrases_lvl2_offsets)
inputs['pooler_safe_idx'] = pooler_tensor['safe_idx']
inputs['pooler_mask'] = pooler_tensor['mask']
return inputs, labels
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