| import numpy as np |
| import torch |
|
|
| from kawk50m.binary_dataset import PackedTokenDataset |
|
|
|
|
| def test_dataset_builds_causal_lm_batches_and_epoch_permutation(tmp_path) -> None: |
| token_path = tmp_path / "tokens.bin" |
| np.arange(21, dtype=np.uint16).tofile(token_path) |
| dataset = PackedTokenDataset(token_path, sequence_length=4) |
|
|
| assert len(dataset) == 5 |
| inputs, labels = dataset.evaluation_batch( |
| batch_index=0, |
| batch_size=2, |
| rank=0, |
| world_size=1, |
| device=torch.device("cpu"), |
| ) |
| assert inputs.tolist() == [[0, 1, 2, 3], [4, 5, 6, 7]] |
| |
| |
| assert labels.tolist() == inputs.tolist() |
| assert labels.data_ptr() != inputs.data_ptr() |
|
|
| visited = [] |
| for micro_step in range(len(dataset)): |
| train_inputs, _ = dataset.training_batch( |
| micro_step=micro_step, |
| batch_size=1, |
| rank=0, |
| world_size=1, |
| seed=42, |
| device=torch.device("cpu"), |
| ) |
| visited.append(train_inputs[0, 0].item() // 4) |
| assert sorted(visited) == list(range(len(dataset))) |
|
|
| offset_inputs, _ = dataset.training_batch( |
| micro_step=0, |
| batch_size=1, |
| rank=0, |
| world_size=1, |
| seed=42, |
| sample_offset=1, |
| device=torch.device("cpu"), |
| ) |
| next_inputs, _ = dataset.training_batch( |
| micro_step=1, |
| batch_size=1, |
| rank=0, |
| world_size=1, |
| seed=42, |
| device=torch.device("cpu"), |
| ) |
| assert offset_inputs.tolist() == next_inputs.tolist() |
|
|
|
|
| def test_dataset_reads_multiple_binary_shards_from_glob(tmp_path) -> None: |
| np.arange(9, dtype=np.uint16).tofile(tmp_path / "part-000.bin") |
| np.arange(100, 109, dtype=np.uint16).tofile(tmp_path / "part-001.bin") |
|
|
| dataset = PackedTokenDataset( |
| str(tmp_path / "part-*.bin"), |
| sequence_length=4, |
| ) |
|
|
| assert len(dataset) == 4 |
| assert dataset.sequence(0).tolist() == [0, 1, 2, 3, 4] |
| assert dataset.sequence(1).tolist() == [4, 5, 6, 7, 8] |
| assert dataset.sequence(2).tolist() == [100, 101, 102, 103, 104] |
| assert dataset.sequence(3).tolist() == [104, 105, 106, 107, 108] |
|
|
