File size: 5,127 Bytes
778d47d | 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 | import json
import torch
import gc
from datasets import Dataset
from torch.utils.data import Dataset
from schema_item_filter import SchemaItemClassifierInference, filter_schema, filter_schema_purple
from utils.db_utils import get_db_schema_sequence, get_matched_content_sequence
def prepare_text2sql_prefix_sequence(data):
prompt = f"""Convert the question to SQL query.
{data["schema_sequence"]}
{data["content_sequence"]}
question: {data["text"]}"""
return prompt
def prepare_inputs_and_labels(prefix_seq, target_seq, tokenizer, max_tokens):
prefix_ids = [tokenizer.bos_token_id] + tokenizer(prefix_seq , truncation = False)["input_ids"]
target_ids = tokenizer(target_seq, truncation = False)["input_ids"] + [tokenizer.eos_token_id]
seq_length = len(prefix_ids) + len(target_ids)
if seq_length <= max_tokens: # pad inputs with pad_token_id
pad_length = max_tokens - seq_length
input_ids = prefix_ids + target_ids + [tokenizer.pad_token_id] * pad_length
# tell the model to ignore the padding tokens when performing (masked) self-attention
attention_mask = [1] * seq_length + [0] * pad_length
# only target_ids produces gradients
labels = [-100] * len(prefix_ids) + target_ids + [-100] * pad_length
else: # no padding
print("the current input sequence exceeds the max_tokens, we will truncate it.")
input_ids = prefix_ids + target_ids
# pre-truncate input ids
input_ids = [tokenizer.bos_token_id] + input_ids[-(max_tokens-1):]
attention_mask = [1] * max_tokens
# only target_ids produces gradients
labels = [-100] * len(prefix_ids) + target_ids
# pre-truncate labels
labels = labels[-max_tokens:]
return {
"input_ids": torch.tensor(input_ids, dtype = torch.int64),
"attention_mask": torch.tensor(attention_mask, dtype = torch.int64),
"labels": torch.tensor(labels, dtype = torch.int64)
}
# def prepare_inputs(prefix_seq, tokenizer, max_prefix_length):
# input_ids = [tokenizer.bos_token_id] + tokenizer(prefix_seq , truncation = False)["input_ids"]
# if len(input_ids) > max_prefix_length:
# print("the current input sequence exceeds the max_tokens, we will truncate it.")
# input_ids = [tokenizer.bos_token_id] + input_ids[-(max_prefix_length-1):]
# attention_mask = [1] * len(input_ids)
# return {
# "input_ids": torch.tensor(input_ids, dtype = torch.int64),
# "attention_mask": torch.tensor(attention_mask, dtype = torch.int64)
# }
def prepare_inputs(prefix_seq, tokenizer, max_prefix_length):
messages = [{
'role': 'user',
'content': prefix_seq
}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer(prompt , truncation = False)["input_ids"]
if len(input_ids) > max_prefix_length:
print("the current input sequence exceeds the max_tokens, we will truncate it.")
input_ids = input_ids[-(max_prefix_length-1):]
attention_mask = [1] * len(input_ids)
return {
"input_ids": torch.tensor(input_ids, dtype = torch.int64),
"attention_mask": torch.tensor(attention_mask, dtype = torch.int64)
}
class SFTSQLGenerationDataset(Dataset):
def __init__(self, text2sql_data_dir, tokenizer, max_tokens, mode, table_num, column_num, threshold, sic_path, do_filter_schema=True):
super().__init__()
dataset = json.load(open(text2sql_data_dir))
print("apply filtering strategies...")
if do_filter_schema:
if mode == "train":
dataset = filter_schema(dataset, "train", None, table_num, column_num, threshold=threshold)
elif mode == "eval":
sic = SchemaItemClassifierInference(sic_path)
dataset = filter_schema(dataset, "eval", sic, table_num, column_num, threshold=threshold)
# dataset = filter_schema_purple(dataset, "eval", "/home/datht/llmsql/selector/spider-dev/spider-dev-selector-t0.02-value-samples.json")
del sic
torch.cuda.empty_cache()
# prepare schema sequence and content sequence
for data in dataset:
data["schema_sequence"] = get_db_schema_sequence(data["schema"])
# data["content_sequence"] = get_matched_content_sequence(data["matched_contents"])
self.mode = mode
self.dataset = dataset
self.tokenizer = tokenizer
self.max_tokens = max_tokens
def __getitem__(self, index):
data = self.dataset[index]
prefix_seq = prepare_text2sql_prefix_sequence(data)
if index < 2:
print(prefix_seq)
if self.mode == "train":
target_seq = data["sql"]
return prepare_inputs_and_labels(prefix_seq, target_seq, self.tokenizer, self.max_tokens)
elif self.mode == "eval":
return prepare_inputs(prefix_seq, self.tokenizer, self.max_tokens)
def __len__(self):
return len(self.dataset) |