| import os |
| import sys |
| from pathlib import Path |
|
|
| import pytest |
|
|
| from datasets import Dataset, IterableDataset |
| from datasets.distributed import split_dataset_by_node |
|
|
| from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch |
|
|
|
|
| def test_split_dataset_by_node_map_style(): |
| full_ds = Dataset.from_dict({"i": range(17)}) |
| full_size = len(full_ds) |
| world_size = 3 |
| datasets_per_rank = [ |
| split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size) |
| ] |
| assert sum(len(ds) for ds in datasets_per_rank) == full_size |
| assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size |
|
|
|
|
| def test_split_dataset_by_node_iterable(): |
| def gen(): |
| return ({"i": i} for i in range(17)) |
|
|
| world_size = 3 |
| full_ds = IterableDataset.from_generator(gen) |
| full_size = len(list(full_ds)) |
| datasets_per_rank = [ |
| split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size) |
| ] |
| assert sum(len(list(ds)) for ds in datasets_per_rank) == full_size |
| assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size |
|
|
|
|
| @pytest.mark.parametrize("shards_per_node", [1, 2, 3]) |
| def test_split_dataset_by_node_iterable_sharded(shards_per_node): |
| def gen(shards): |
| for shard in shards: |
| yield from ({"i": i, "shard": shard} for i in range(17)) |
|
|
| world_size = 3 |
| num_shards = shards_per_node * world_size |
| gen_kwargs = {"shards": [f"shard_{shard_idx}.txt" for shard_idx in range(num_shards)]} |
| full_ds = IterableDataset.from_generator(gen, gen_kwargs=gen_kwargs) |
| full_size = len(list(full_ds)) |
| assert full_ds.n_shards == world_size * shards_per_node |
| datasets_per_rank = [ |
| split_dataset_by_node(full_ds, rank=rank, world_size=world_size) for rank in range(world_size) |
| ] |
| assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size |
| assert sum(len(list(ds)) for ds in datasets_per_rank) == full_size |
| assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size |
|
|
|
|
| def test_distributed_shuffle_iterable(): |
| def gen(): |
| return ({"i": i} for i in range(17)) |
|
|
| world_size = 2 |
| full_ds = IterableDataset.from_generator(gen) |
| full_size = len(list(full_ds)) |
|
|
| ds_rank0 = split_dataset_by_node(full_ds, rank=0, world_size=world_size).shuffle(seed=42) |
| assert len(list(ds_rank0)) == 1 + full_size // world_size |
| with pytest.raises(RuntimeError): |
| split_dataset_by_node(full_ds, rank=0, world_size=world_size).shuffle() |
|
|
| ds_rank0 = split_dataset_by_node(full_ds.shuffle(seed=42), rank=0, world_size=world_size) |
| assert len(list(ds_rank0)) == 1 + full_size // world_size |
| with pytest.raises(RuntimeError): |
| split_dataset_by_node(full_ds.shuffle(), rank=0, world_size=world_size) |
|
|
|
|
| @pytest.mark.parametrize("streaming", [False, True]) |
| @require_torch |
| @pytest.mark.skipif(os.name == "nt", reason="execute_subprocess_async doesn't support windows") |
| @pytest.mark.integration |
| def test_torch_distributed_run(streaming): |
| nproc_per_node = 2 |
| master_port = get_torch_dist_unique_port() |
| test_script = Path(__file__).resolve().parent / "distributed_scripts" / "run_torch_distributed.py" |
| distributed_args = f""" |
| -m torch.distributed.run |
| --nproc_per_node={nproc_per_node} |
| --master_port={master_port} |
| {test_script} |
| """.split() |
| args = f""" |
| --streaming={streaming} |
| """.split() |
| cmd = [sys.executable] + distributed_args + args |
| execute_subprocess_async(cmd, env=os.environ.copy()) |
|
|
|
|
| @pytest.mark.parametrize( |
| "nproc_per_node, num_workers", |
| [ |
| (2, 2), |
| (3, 2), |
| ], |
| ) |
| @require_torch |
| @pytest.mark.skipif(os.name == "nt", reason="execute_subprocess_async doesn't support windows") |
| @pytest.mark.integration |
| def test_torch_distributed_run_streaming_with_num_workers(nproc_per_node, num_workers): |
| streaming = True |
| master_port = get_torch_dist_unique_port() |
| test_script = Path(__file__).resolve().parent / "distributed_scripts" / "run_torch_distributed.py" |
| distributed_args = f""" |
| -m torch.distributed.run |
| --nproc_per_node={nproc_per_node} |
| --master_port={master_port} |
| {test_script} |
| """.split() |
| args = f""" |
| --streaming={streaming} |
| --num_workers={num_workers} |
| """.split() |
| cmd = [sys.executable] + distributed_args + args |
| execute_subprocess_async(cmd, env=os.environ.copy()) |
|
|
