| 102 | |
| 103 | |
| 104 | class StreamingTrainingParquet(torch.utils.data.Dataset): |
| 105 | |
| 106 | def __init__(self, data_root, tokenizer, label_name, train_length=4096, min_length=512, num_data=-1, seed=42, dataset_ckpt_path=None, file_depth=1): |
| 107 | |
| 108 | self.data_root = data_root |
| 109 | |
| 110 | self.data_path = sorted([f'{data_root}/{path}' for path in os.listdir(data_root) if not (os.path.isdir(f'{data_root}/{path}') and 'git' in path)]) |
| 111 | |
| 112 | for _ in range(file_depth): |
| 113 | self.data_path = sorted(sum([[f'{data_root}/{path}' for path in os.listdir(data_root)] for data_root in self.data_path], [])) |
| 114 | |
| 115 | random.shuffle(self.data_path) |
| 116 | |
| 117 | self.tokenizer = tokenizer |
| 118 | self.label_name = label_name |
| 119 | |
| 120 | self.len = num_data |
| 121 | self.train_length = train_length |
| 122 | self.min_length = min_length |
| 123 | |
| 124 | self.pivot = torch.distributed.get_rank() |
| 125 | self.size = torch.distributed.get_world_size() |
| 126 | |
| 127 | self.token_buffer, self.file_buffer = [], None |
| 128 | |
| 129 | self.file_buffer = pq.ParquetFile(self.data_path[self.pivot]) |
| 130 | self.table_idx, self.table_num = 0, self.file_buffer.num_row_groups |
| 131 | self.table_buffer = self.file_buffer.read_row_group(self.table_idx) |
| 132 | self.sample_idx, self.sample_num = 0, len(self.table_buffer[self.label_name]) |
| 133 | |
| 134 | if dataset_ckpt_path is not None: |
| 135 | dataset_ckpt_path = f"{dataset_ckpt_path}/dataset_ckpt-{self.pivot:{len(str(self.size))}d}-{self.size}.pt" |
| 136 | dataset_ckpt = torch.load(dataset_ckpt_path, weights_only=False) |
| 137 | self.data_path = dataset_ckpt['data_path'] |
| 138 | self.label_name = dataset_ckpt['label_name'] |
| 139 | self.pivot = dataset_ckpt['pivot'] |
| 140 | self.size = dataset_ckpt['size'] |
| 141 | self.file_buffer = pq.ParquetFile(self.data_path[self.pivot]) |
| 142 | self.table_idx = dataset_ckpt['table_idx'] |
| 143 | self.table_num = dataset_ckpt['table_num'] |
| 144 | self.table_buffer = dataset_ckpt['table_buffer'] |
| 145 | self.sample_idx = dataset_ckpt['sample_idx'] |
| 146 | self.sample_num = dataset_ckpt['sample_num'] |
| 147 | self.token_buffer = dataset_ckpt['token_buffer'] |
| 148 | |
| 149 | def __len__(self): |
| 150 | return self.len |
| 151 | |
| 152 | def __getitem__(self, _): |
| 153 | |
| 154 | if len(self.token_buffer) > self.train_length: |
| 155 | input_ids = torch.tensor(self.token_buffer[:self.train_length]).long() |
| 156 | position_ids = torch.tensor(list(range(self.train_length))).long() |
| 157 | self.token_buffer = self.token_buffer[self.train_length:] |
| 158 | |
| 159 | else: |
| 160 | input_ids = self.token_buffer |
| 161 | position_ids = list(range(self.train_length)) |
nothing calls this directly
no outgoing calls
no test coverage detected