Lightning Data: Refactor files

src/lightning/data/__init__.py

@@ -1,7 +1,7 @@
+from lightning.data.processing.functions import map, optimize, walk
 from lightning.data.streaming.combined import CombinedStreamingDataset
 from lightning.data.streaming.dataloader import StreamingDataLoader
 from lightning.data.streaming.dataset import StreamingDataset
-from lightning.data.streaming.functions import map, optimize, walk
 
 __all__ = [
     "LightningDataset",

src/lightning/data/streaming/functions.py → src/lightning/data/processing/functions.py

@@ -22,9 +22,9 @@
 
 import torch
 
+from lightning.data.processing.data_processor import DataChunkRecipe, DataProcessor, DataTransformRecipe
 from lightning.data.processing.readers import BaseReader
 from lightning.data.streaming.constants import _IS_IN_STUDIO, _TORCH_GREATER_EQUAL_2_1_0
-from lightning.data.streaming.data_processor import DataChunkRecipe, DataProcessor, DataTransformRecipe
 from lightning.data.streaming.resolver import (
     Dir,
     _assert_dir_has_index_file,

src/lightning/data/processing/readers.py

@@ -5,8 +5,8 @@
 
 from lightning_utilities.core.imports import RequirementCache
 
-from lightning.data.streaming.shuffle import _associate_chunks_and_internals_to_ranks
 from lightning.data.utilities.env import _DistributedEnv
+from lightning.data.utilities.shuffle import _associate_chunks_and_internals_to_ranks
 
 _POLARS_AVAILABLE = RequirementCache("polars")
 _PYARROW_AVAILABLE = RequirementCache("pyarrow")

src/lightning/data/streaming/__init__.py

@@ -13,18 +13,14 @@
 
 from lightning.data.streaming.cache import Cache
 from lightning.data.streaming.combined import CombinedStreamingDataset
-from lightning.data.streaming.data_processor import DataChunkRecipe, DataProcessor, DataTransformRecipe
 from lightning.data.streaming.dataloader import StreamingDataLoader
 from lightning.data.streaming.dataset import StreamingDataset
 from lightning.data.streaming.item_loader import TokensLoader
 
 __all__ = [
     "Cache",
-    "DataProcessor",
     "StreamingDataset",
     "CombinedStreamingDataset",
     "StreamingDataLoader",
-    "DataTransformRecipe",
-    "DataChunkRecipe",
     "TokensLoader",
 ]

src/lightning/data/streaming/shuffle.py

@@ -19,6 +19,7 @@
 
 from lightning.data.streaming import Cache
 from lightning.data.utilities.env import _DistributedEnv
+from lightning.data.utilities.shuffle import _associate_chunks_and_internals_to_ranks, _intra_node_chunk_shuffle
 
 
 class Shuffle(ABC):
@@ -129,76 +130,3 @@ def get_chunks_and_intervals_per_ranks(self, distributed_env: _DistributedEnv, c
 
     def __call__(self, array: np.ndarray, num_chunks: int, current_epoch: int, chunk_index: int) -> List[int]:
         return np.random.RandomState([self.seed, num_chunks * current_epoch, chunk_index]).permutation(array).tolist()
-
-
-def _intra_node_chunk_shuffle(
-    distributed_env: _DistributedEnv,
-    chunks_per_ranks: List[List[int]],
-    seed: int,
-    current_epoch: int,
-) -> List[int]:
-    chunk_indexes_per_nodes: Any = [[] for _ in range(distributed_env.num_nodes)]
-    for rank, chunks_per_rank in enumerate(chunks_per_ranks):
-        chunk_indexes_per_nodes[0 if distributed_env.num_nodes == 1 else rank // distributed_env.num_nodes].extend(
-            chunks_per_rank
-        )
-
-    # shuffle the chunks associated to the node
-    for i in range(len(chunk_indexes_per_nodes)):
-        # permute the indexes within the node
-        chunk_indexes_per_nodes[i] = np.random.RandomState(seed=seed + current_epoch).permutation(
-            chunk_indexes_per_nodes[i]
-        )
-
-    return [index for chunks in chunk_indexes_per_nodes for index in chunks]
-
-
-def _associate_chunks_and_internals_to_ranks(
-    distributed_env: _DistributedEnv,
-    indexes: Any,
-    chunk_intervals: Any,
-    drop_last: bool,
-) -> Tuple[List[List[int]], List[Any]]:
-    num_items = sum([(interval[-1] - interval[0]) for interval in chunk_intervals])
-    num_items_per_ranks: List[int] = [
-        num_items // distributed_env.world_size + num_items % distributed_env.world_size
-        if rank == distributed_env.world_size - 1 and not drop_last
-        else num_items // distributed_env.world_size
-        for rank in range(distributed_env.world_size)
-    ]
-    chunks_per_ranks: List[List[int]] = [[] for _ in range(distributed_env.world_size)]
-    intervals_per_ranks: List[List[List[int]]] = [[] for _ in range(distributed_env.world_size)]
-
-    # 4. Assign the chunk & intervals to each rank
-    for chunk_index, chunk_interval in zip(indexes, chunk_intervals):
-        rank = 0
-
-        while True:
-            if rank == len(num_items_per_ranks):
-                break
-
-            items_left_to_assign = num_items_per_ranks[rank]
-
-            if items_left_to_assign == 0:
-                rank += 1
-                continue
-
-            items_in_chunk = chunk_interval[-1] - chunk_interval[0]
-
-            if items_in_chunk == 0:
-                break
-
-            if items_in_chunk > items_left_to_assign:
-                chunks_per_ranks[rank].append(chunk_index)
-                begin, end = chunk_interval
-                intervals_per_ranks[rank].append([begin, begin + items_left_to_assign])
-                chunk_interval = (begin + items_left_to_assign, end)
-                num_items_per_ranks[rank] = 0
-                rank += 1
-            else:
-                chunks_per_ranks[rank].append(chunk_index)
-                intervals_per_ranks[rank].append(chunk_interval)
-                num_items_per_ranks[rank] -= items_in_chunk
-                break
-
-    return chunks_per_ranks, intervals_per_ranks

src/lightning/data/utilities/shuffle.py

@@ -0,0 +1,78 @@
+from typing import Any, List, Tuple
+
+import numpy as np
+
+from lightning.data.utilities.env import _DistributedEnv
+
+
+def _intra_node_chunk_shuffle(
+    distributed_env: _DistributedEnv,
+    chunks_per_ranks: List[List[int]],
+    seed: int,
+    current_epoch: int,
+) -> List[int]:
+    chunk_indexes_per_nodes: Any = [[] for _ in range(distributed_env.num_nodes)]
+    for rank, chunks_per_rank in enumerate(chunks_per_ranks):
+        chunk_indexes_per_nodes[0 if distributed_env.num_nodes == 1 else rank // distributed_env.num_nodes].extend(
+            chunks_per_rank
+        )
+
+    # shuffle the chunks associated to the node
+    for i in range(len(chunk_indexes_per_nodes)):
+        # permute the indexes within the node
+        chunk_indexes_per_nodes[i] = np.random.RandomState(seed=seed + current_epoch).permutation(
+            chunk_indexes_per_nodes[i]
+        )
+
+    return [index for chunks in chunk_indexes_per_nodes for index in chunks]
+
+
+def _associate_chunks_and_internals_to_ranks(
+    distributed_env: _DistributedEnv,
+    indexes: Any,
+    chunk_intervals: Any,
+    drop_last: bool,
+) -> Tuple[List[List[int]], List[Any]]:
+    num_items = sum([(interval[-1] - interval[0]) for interval in chunk_intervals])
+    num_items_per_ranks: List[int] = [
+        num_items // distributed_env.world_size + num_items % distributed_env.world_size
+        if rank == distributed_env.world_size - 1 and not drop_last
+        else num_items // distributed_env.world_size
+        for rank in range(distributed_env.world_size)
+    ]
+    chunks_per_ranks: List[List[int]] = [[] for _ in range(distributed_env.world_size)]
+    intervals_per_ranks: List[List[List[int]]] = [[] for _ in range(distributed_env.world_size)]
+
+    # 4. Assign the chunk & intervals to each rank
+    for chunk_index, chunk_interval in zip(indexes, chunk_intervals):
+        rank = 0
+
+        while True:
+            if rank == len(num_items_per_ranks):
+                break
+
+            items_left_to_assign = num_items_per_ranks[rank]
+
+            if items_left_to_assign == 0:
+                rank += 1
+                continue
+
+            items_in_chunk = chunk_interval[-1] - chunk_interval[0]
+
+            if items_in_chunk == 0:
+                break
+
+            if items_in_chunk > items_left_to_assign:
+                chunks_per_ranks[rank].append(chunk_index)
+                begin, end = chunk_interval
+                intervals_per_ranks[rank].append([begin, begin + items_left_to_assign])
+                chunk_interval = (begin + items_left_to_assign, end)
+                num_items_per_ranks[rank] = 0
+                rank += 1
+            else:
+                chunks_per_ranks[rank].append(chunk_index)
+                intervals_per_ranks[rank].append(chunk_interval)
+                num_items_per_ranks[rank] -= items_in_chunk
+                break
+
+    return chunks_per_ranks, intervals_per_ranks

tests/tests_data/streaming/test_data_processor.py → tests/tests_data/processing/test_data_processor.py

@@ -10,10 +10,9 @@
 import pytest
 import torch
 from lightning import seed_everything
-from lightning.data.streaming import data_processor as data_processor_module
-from lightning.data.streaming import functions, resolver
-from lightning.data.streaming.cache import Cache, Dir
-from lightning.data.streaming.data_processor import (
+from lightning.data.processing import data_processor as data_processor_module
+from lightning.data.processing import functions
+from lightning.data.processing.data_processor import (
     DataChunkRecipe,
     DataProcessor,
     DataTransformRecipe,
@@ -26,7 +25,9 @@
     _wait_for_disk_usage_higher_than_threshold,
     _wait_for_file_to_exist,
 )
-from lightning.data.streaming.functions import LambdaDataTransformRecipe, map, optimize
+from lightning.data.processing.functions import LambdaDataTransformRecipe, map, optimize
+from lightning.data.streaming import resolver
+from lightning.data.streaming.cache import Cache, Dir
 from lightning_utilities.core.imports import RequirementCache
 
 _PIL_AVAILABLE = RequirementCache("PIL")
@@ -162,7 +163,7 @@ def fn(*_, **__):
 
 
 @pytest.mark.skipif(condition=sys.platform == "win32", reason="Not supported on windows")
-@mock.patch("lightning.data.streaming.data_processor._wait_for_disk_usage_higher_than_threshold")
+@mock.patch("lightning.data.processing.data_processor._wait_for_disk_usage_higher_than_threshold")
 def test_download_data_target(wait_for_disk_usage_higher_than_threshold_mock, tmpdir):
     input_dir = os.path.join(tmpdir, "input_dir")
     os.makedirs(input_dir, exist_ok=True)
@@ -201,7 +202,7 @@ def fn(*_, **__):
 
 def test_wait_for_disk_usage_higher_than_threshold():
     disk_usage_mock = mock.Mock(side_effect=[mock.Mock(free=10e9), mock.Mock(free=10e9), mock.Mock(free=10e11)])
-    with mock.patch("lightning.data.streaming.data_processor.shutil.disk_usage", disk_usage_mock):
+    with mock.patch("lightning.data.processing.data_processor.shutil.disk_usage", disk_usage_mock):
         _wait_for_disk_usage_higher_than_threshold("/", 10, sleep_time=0)
     assert disk_usage_mock.call_count == 3
 

tests/tests_data/streaming/test_functions.py → tests/tests_data/processing/test_functions.py

@@ -4,7 +4,7 @@
 
 import pytest
 from lightning.data import walk
-from lightning.data.streaming.functions import _get_input_dir
+from lightning.data.processing.functions import _get_input_dir
 
 
 @pytest.mark.skipif(sys.platform == "win32", reason="currently not supported for windows.")

tests/tests_data/streaming/test_dataset.py

@@ -20,7 +20,8 @@
 import pytest
 import torch
 from lightning import seed_everything
-from lightning.data.streaming import Cache, functions
+from lightning.data.processing import functions
+from lightning.data.streaming import Cache
 from lightning.data.streaming.dataloader import StreamingDataLoader
 from lightning.data.streaming.dataset import (
     _INDEX_FILENAME,

tests/tests_data/streaming/test_shuffle.py

@@ -1,5 +1,5 @@
-from lightning.data.streaming.shuffle import _associate_chunks_and_internals_to_ranks, _intra_node_chunk_shuffle
 from lightning.data.utilities.env import _DistributedEnv
+from lightning.data.utilities.shuffle import _associate_chunks_and_internals_to_ranks, _intra_node_chunk_shuffle
 
 
 def test_intra_node_chunk_shuffle():