| |
| from litgpt.data import Alpaca |
| from litgpt.prompts import Alpaca as AlpacaPromptStyle |
|
|
|
|
| def test_alpaca(mock_tokenizer, alpaca_path): |
| alpaca = Alpaca(val_split_fraction=0.5, download_dir=alpaca_path.parent, file_name=alpaca_path.name, num_workers=0) |
| assert isinstance(alpaca.prompt_style, AlpacaPromptStyle) |
| alpaca.connect(mock_tokenizer, batch_size=2, max_seq_length=10) |
| alpaca.prepare_data() |
| alpaca.setup() |
|
|
| train_dataloader = alpaca.train_dataloader() |
| val_dataloader = alpaca.val_dataloader() |
|
|
| assert len(train_dataloader) == 6 |
| assert len(val_dataloader) == 6 |
|
|
| train_batch = next(iter(train_dataloader)) |
| val_batch = next(iter(val_dataloader)) |
|
|
| assert train_batch.keys() == val_batch.keys() == {"input_ids", "labels", "token_counts"} |
| for key in ["input_ids", "labels"]: |
| assert train_batch[key].shape == (2, 10), f"Unexpected shape for train_batch[{key}]" |
| assert val_batch[key].shape == (2, 10), f"Unexpected shape for val_batch[{key}]" |
|
|
| assert isinstance(train_dataloader.dataset.prompt_style, AlpacaPromptStyle) |
| assert isinstance(val_dataloader.dataset.prompt_style, AlpacaPromptStyle) |
|
|
| |
| assert alpaca.prepare_data_per_node |
|
|
