| import logging |
| import os |
| from tqdm import tqdm |
| import json |
|
|
| from dataclasses import dataclass |
| from transformers.tokenization_bart import BartTokenizer |
| from transformers.tokenization_roberta import RobertaTokenizer |
| from transformers.tokenization_utils import PreTrainedTokenizer |
| from relogic.pretrainkit.datasets.utils import pad_and_tensorize_sequence |
|
|
| from torch.utils.data.dataset import Dataset |
| import random |
|
|
| logger = logging.getLogger(__name__) |
|
|
| class MultiDDataset(Dataset): |
| """ |
| Dataset for training task: SQL (+ schema) -> text |
| """ |
| def __init__(self, tokenizer: PreTrainedTokenizer, file_path, block_size, local_rank=-1): |
| assert os.path.isfile(file_path) |
| logger.info("Creating features from dataset file at {}".format(file_path)) |
|
|
| self.examples = [] |
| total, valid = 0, 0 |
| add_prefix_space = isinstance(tokenizer, BartTokenizer) or isinstance(tokenizer, RobertaTokenizer) |
| with open(file_path, encoding="utf-8") as f: |
| for line in tqdm(f): |
| total += 1 |
| example = json.loads(line) |
|
|
| sql = " ".join(example["sql"].split()).lower() |
| text = example["question"].strip().lower() |
|
|
| text_tokens = [tokenizer.cls_token] + tokenizer.tokenize(text, add_prefix_space=add_prefix_space) + [tokenizer.sep_token] |
| sql_tokens = [tokenizer.cls_token] + tokenizer.tokenize(sql, add_prefix_space=add_prefix_space) + [tokenizer.sep_token] |
|
|
| text_token_ids = tokenizer.convert_tokens_to_ids(text_tokens) |
| sql_token_ids = tokenizer.convert_tokens_to_ids(sql_tokens) |
| if len(text_token_ids) > 800 or len(sql_token_ids) > 800: |
| continue |
|
|
| self.examples.append({ |
| "text_token_ids": text_token_ids, |
| "sql_token_ids": sql_token_ids}) |
| logger.info("Total {} examples.".format(total)) |
|
|
| def __len__(self): |
| return len(self.examples) |
|
|
| def __getitem__(self, i): |
| return self.examples[i] |
|
|
| @dataclass |
| class DataCollatorForMultiD: |
| """ |
| |
| """ |
| tokenizer: PreTrainedTokenizer |
| bi_direc: bool = False |
|
|
| def __post_init__(self): |
| |
| |
| |
| self.label_eos_id = self.tokenizer.sep_token_id |
| self.label_bos_id = self.tokenizer.cls_token_id |
|
|
|
|
| def collate_batch(self, examples): |
| text_ids_sequences = [example["text_token_ids"] for example in examples] |
| sql_ids_sequences = [example["sql_token_ids"] for example in examples] |
|
|
| padded_text_ids_tensor = pad_and_tensorize_sequence( |
| text_ids_sequences, padding_value=self.tokenizer.pad_token_id) |
|
|
| padded_sql_ids_tensor = pad_and_tensorize_sequence( |
| sql_ids_sequences, padding_value=self.tokenizer.pad_token_id) |
|
|
| if self.bi_direc: |
| if random.random() < 0.5: |
| return { |
| "input_ids": padded_sql_ids_tensor, |
| "labels": padded_text_ids_tensor, |
| "pad_token_id": self.tokenizer.pad_token_id, |
| "label_eos_id": self.label_eos_id, |
| "label_bos_id": self.label_bos_id, |
| "label_padding_id": self.tokenizer.pad_token_id |
| } |
|
|
| else: |
| return { |
| "input_ids": padded_text_ids_tensor, |
| "labels": padded_sql_ids_tensor, |
| "pad_token_id": self.tokenizer.pad_token_id, |
| "label_eos_id": self.label_eos_id, |
| "label_bos_id": self.label_bos_id, |
| "label_padding_id": self.tokenizer.pad_token_id |
| } |
| else: |
| return { |
| "input_ids": padded_text_ids_tensor, |
| "labels": padded_sql_ids_tensor, |
| "pad_token_id": self.tokenizer.pad_token_id, |
| "label_eos_id": self.label_eos_id, |
| "label_bos_id": self.label_bos_id, |
| "label_padding_id": self.tokenizer.pad_token_id |
| } |
