Improve DatasetOptimizer API (#18827)

Co-authored-by: Justus Schock <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: thomas <[email protected]>
(cherry picked from commit e59dc41)

Author: 4 people

requirements/app/app.txt

@@ -1,4 +1,4 @@
-lightning-cloud ==0.5.42  # Must be pinned to ensure compatibility
+lightning-cloud ==0.5.43  # Must be pinned to ensure compatibility
 packaging
 typing-extensions >=4.0.0, <4.8.0
 deepdiff >=5.7.0, <6.6.0

src/lightning/app/cli/commands/cp.py

@@ -113,7 +113,7 @@ def _upload_files(live, client: LightningClient, local_src: str, remote_dst: str
     else:
         upload_paths = [local_src]
 
-    upload_urls = []
+    _upload_urls = []
 
     clusters = client.projects_service_list_project_cluster_bindings(project_id)
 
@@ -129,9 +129,11 @@ def _upload_files(live, client: LightningClient, local_src: str, remote_dst: str
                 body=ProjectIdStorageBody(cluster_id=cluster.cluster_id, filename=filename),
                 async_req=True,
             )
-            upload_urls.append(response)
+            _upload_urls.append(response)
 
-    upload_urls = [upload_url.get().upload_url for upload_url in upload_urls]
+    upload_urls = []
+    for upload_url in _upload_urls:
+        upload_urls.extend(upload_url.get().urls)
 
     live.stop()
 

src/lightning/data/__init__.py

@@ -1,5 +1,12 @@
 from lightning.data.datasets import LightningDataset, LightningIterableDataset
 from lightning.data.streaming.dataloader import StreamingDataLoader
 from lightning.data.streaming.dataset import StreamingDataset
+from lightning.data.streaming.dataset_optimizer import DatasetOptimizer
 
-__all__ = ["LightningDataset", "StreamingDataset", "StreamingDataLoader", "LightningIterableDataset"]
+__all__ = [
+    "LightningDataset",
+    "StreamingDataset",
+    "StreamingDataLoader",
+    "LightningIterableDataset",
+    "DatasetOptimizer",
+]

src/lightning/data/streaming/dataset.py

@@ -11,36 +11,135 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Literal, Optional, Union
+from typing import Any, List, Literal, Optional, Union
 
-from torch.utils.data import Dataset
+import numpy as np
+from torch.utils.data import IterableDataset
 
+from lightning.data.datasets.env import _DistributedEnv, _WorkerEnv
 from lightning.data.streaming import Cache
+from lightning.data.streaming.item_loader import BaseItemLoader
+from lightning.data.streaming.sampler import ChunkedIndex
 
 
-class StreamingDataset(Dataset):
+class StreamingDataset(IterableDataset):
     """The streaming dataset can be used once your data have been optimised using the DatasetOptimiser class."""
 
     def __init__(
-        self, name: str, version: Optional[Union[int, Literal["latest"]]] = "latest", cache_dir: Optional[str] = None
+        self,
+        name: str,
+        version: Optional[Union[int, Literal["latest"]]] = "latest",
+        cache_dir: Optional[str] = None,
+        item_loader: Optional[BaseItemLoader] = None,
+        shuffle: bool = True,
+        seed: int = 42,
     ) -> None:
         """The streaming dataset can be used once your data have been optimised using the DatasetOptimiser class.
 
         Arguments:
             name: The name of the optimised dataset.
             version: The version of the dataset to use.
             cache_dir: The cache dir where the data would be stored.
+            item_loader: The logic to load an item from a chunk.
+            shuffle: Whether to shuffle the data.
+            seed: Random seed for shuffling.
 
         """
         super().__init__()
-        self.cache = Cache(name=name, version=version, cache_dir=cache_dir)
+        self.cache = Cache(name=name, version=version, cache_dir=cache_dir, item_loader=item_loader, chunk_bytes=1)
+
+        self.cache._reader._try_load_config()
+
+        if not self.cache.filled:
+            raise ValueError(f"The provided dataset `{name}` isn't filled up.")
+
+        self.shuffle = shuffle
+        self.distributed_env = _DistributedEnv.detect()
+        self.worker_env: Optional[_WorkerEnv] = None
+
+        chunk_intervals = self.cache.get_chunk_interval()
+        self.L = sum([(interval[-1] - interval[0]) for interval in chunk_intervals])
+
+        self.worker_chunks: List[int] = []
+        self.worker_intervals: List[List[int]] = []
+        self.current_indexes: List[int] = []
+        self.chunk_index = 0
+        self.index = 0
+        self.has_triggered_download = False
+        self.min_items_per_replica: Optional[int] = None
+        self.seed = seed
+        self.num_iter = 0
+        self.random_state = None
 
     def __len__(self) -> int:
-        return len(self.cache)
+        return self.L
+
+    def __iter__(self) -> "StreamingDataset":
+        self.random_state = np.random.RandomState(seed=self.seed + self.num_iter)  # type: ignore
+        chunk_intervals = self.cache.get_chunk_interval()
+        indexes = range(len(chunk_intervals))
+        shuffled_indexes = self.random_state.permutation(indexes) if self.shuffle else list(indexes)
+        shuffled_chunk_intervals = np.asarray(chunk_intervals)[shuffled_indexes]
+
+        chunks_per_replica: List[List[int]] = [[] for _ in range(self.distributed_env.world_size)]
+        intervals_per_replica: List[List[List[int]]] = [[] for _ in range(self.distributed_env.world_size)]
+        for index, (chunk_index, chunk_interval) in enumerate(zip(shuffled_indexes, shuffled_chunk_intervals)):
+            replica_index = index % self.distributed_env.world_size
+            chunks_per_replica[replica_index].append(chunk_index)
+            intervals_per_replica[replica_index].append(chunk_interval)
+
+        current_chunks = chunks_per_replica[self.distributed_env.global_rank % self.distributed_env.world_size]
+        current_intervals = intervals_per_replica[self.distributed_env.global_rank % self.distributed_env.world_size]
+
+        if self.worker_env is None:
+            self.worker_env = _WorkerEnv.detect()
+
+        self.worker_chunks = []
+        self.worker_intervals = []
+
+        for i, (chunk_index, chunk_interval) in enumerate(zip(current_chunks, current_intervals)):
+            if i % self.worker_env.world_size != self.worker_env.rank:
+                continue
+            self.worker_chunks.append(chunk_index)
+            self.worker_intervals.append(chunk_interval)
+
+        self.current_indexes = []
+        self.chunk_index = 0
+        self.num_iter += 1
+
+        return self
+
+    def __getitem__(self, index: Union[ChunkedIndex, int]) -> Any:
+        if isinstance(index, int):
+            index = ChunkedIndex(index, self.cache._get_chunk_index_from_index(index))
+        return self.cache[index]
+
+    def __next__(self) -> Any:
+        # Lazily re-populate the interval to reduce memory usage.
+        if len(self.current_indexes) == 0:
+            if self.chunk_index == len(self.worker_intervals):
+                raise StopIteration
+
+            interval = self.worker_intervals[self.chunk_index]
+            current_indexes = np.arange(0, interval[1] - interval[0])
+            if self.shuffle:
+                current_indexes = self.random_state.permutation(current_indexes)
+            self.current_indexes = current_indexes.tolist()
+            self.chunk_index += 1
+
+        # Get the first index
+        index = self.current_indexes.pop(0)
+
+        # Call the `__getitem__` method.
+        data = self.__getitem__(
+            ChunkedIndex(
+                index=index,
+                chunk_index=self.worker_chunks[self.chunk_index - 1],
+                chunk_indexes=None if self.has_triggered_download else self.worker_chunks,
+            )
+        )
 
-    def __getitem__(self, idx: int) -> Any:
-        return self.cache[idx]
+        self.has_triggered_download = True
+        self.index += 1
 
-    def getitem(self, obj: Any) -> Any:
-        """Override the getitem with your own logic to transform the cache object."""
-        return obj
+        return data

src/lightning/data/streaming/dataset_optimizer.py

@@ -3,15 +3,15 @@
 import signal
 import traceback
 import types
-from abc import ABC, abstractmethod
 from enum import Enum
 from multiprocessing import Process, Queue
 from pathlib import Path
 from queue import Empty
 from shutil import copyfile
+from textwrap import dedent
 from threading import Thread
 from time import sleep, time
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple
+from typing import Any, Callable, Dict, List, Literal, Optional, Protocol, Tuple, TypeVar, runtime_checkable
 from urllib import parse
 
 from tqdm.auto import tqdm
@@ -167,7 +167,7 @@ def __init__(
         start_index: int,
         dataset_name: str,
         node_rank: int,
-        dataset_optimizer: "DatasetOptimizer",
+        prepare_item: Callable,
         src_dir: str,
         remote_src_dir: str,
         remote_dst_dir: Optional[str],
@@ -187,7 +187,7 @@ def __init__(
         self.start_index = start_index
         self.dataset_name = dataset_name
         self.node_rank = node_rank
-        self.prepare_item = dataset_optimizer.prepare_item
+        self.prepare_item = prepare_item
         self.src_dir = src_dir
         self.remote_src_dir = remote_src_dir
         self.remote_dst_dir = remote_dst_dir
@@ -432,57 +432,21 @@ class WorkerType(Enum):
     PROCESS = "process"
 
 
-class DatasetOptimizer(ABC):
-    @abstractmethod
-    def prepare_dataset_structure(self, src_dir: str, filepaths: List[str]) -> List[Any]:
-        """This function is meant to return a list of item metadata. Each item metadata should be enough to prepare a
-        single item when called with the prepare_item.
+T = TypeVar("T")
 
-        Example::
 
-            # For a classification use case
-
-            def prepare_dataset_structure(self, src_dir, filepaths)
-                import numpy as np
-
-                filepaths = ['class_a/file_1.ext', ..., 'class_b/file_1.ext', ...]
-                classes = np.unique([filepath.split("/")[0] for filepath in filepaths])
-                classes_to_idx_map = {c: idx for idx, c in enumerate(classes)}
-
-                # Return pair with the filepath to the obj and its class
-                # [('class_a/file_1.ext', 0), ... ('class_b/file_1.ext', 1)]
-                return [(filepath, classes_to_idx_map[filepath.split("/")[0]]) for filepath in filepaths]
-
-        Example::
-
-            # For a image segmentation use case
-
-            def prepare_dataset_structure(self, src_dir, filepaths)
-                import numpy as np
-
-                filepaths = ['file_1.JPEG', 'file_1.mask', .... 'file_N.JPEG', 'file_N.mask', ...]
-
-                # [('file_1.JPEG', 'file_1.mask'), ... ('file_N.JPEG', 'file_N.mask')]
-                return [(x[i], x[i+1]) for i in range(len(filepaths) -1)]
-
-            def prepare_item(self, obj):
-                image_filepath, mask_filepath = obj
-
-                image = load_and_resize(image_filepath)
-                mask = load_and_resize(mask_filepath)
-                return (image, mask)
-
-        """
+@runtime_checkable
+class _OptimizableDataset(Protocol):
+    @staticmethod
+    def prepare_dataset_structure(root: str, filepaths: List[str]) -> List[T]:
         pass
 
-    def prepare_item(self, metadata_item: Any) -> Any:
-        """Using some metadata, prepare the associated item.
+    @staticmethod
+    def prepare_item(item_metadata: T) -> Any:
+        return item_metadata
 
-        The output of this function will be binarised
-
-        """
-        return metadata_item
 
+class DatasetOptimizer:
     def __init__(
         self,
         name: str,
@@ -547,9 +511,29 @@ def __init__(
         )
         self.random_seed = random_seed
 
-    def run(self) -> None:
+    def run(self, optimizable_dataset: _OptimizableDataset) -> None:
         """The `DatasetChunker.run(...)` method is used to trigger the data processing from your dataset into
         chunks."""
+        if not isinstance(optimizable_dataset, _OptimizableDataset):
+            raise ValueError(
+                dedent(
+                    """The provided argument to the DatasetOptimizer.run(...) needs to have the following format:
+
+                Example:
+
+                    class YourDataset:
+
+                        @staticmethod
+                        def prepare_dataset_structure(root: str, filepaths: List[str]) -> List[T]:
+                            return [...]
+
+                        @staticmethod
+                        def prepare_item(item_metadata: T) -> Any:
+                            return ...
+                """
+                )
+            )
+
         t0 = time()
         print(f"Setup started for `{self.name}` with fast_dev_run={self.fast_dev_run}.")
 
@@ -564,7 +548,7 @@ def run(self) -> None:
         seed_everything(self.random_seed)
 
         # Call the setup method of the user
-        user_items = self.prepare_dataset_structure(self.src_dir, filepaths)
+        user_items: List[Any] = optimizable_dataset.prepare_dataset_structure(self.src_dir, filepaths)
 
         if not isinstance(user_items, list):
             raise ValueError("The setup_fn should return a list of item metadata.")
@@ -588,9 +572,9 @@ def run(self) -> None:
         signal.signal(signal.SIGINT, self._signal_handler)
 
         if self.worker_type == WorkerType.THREAD.value:
-            self._create_thread_workers(begins, workers_user_items)
+            self._create_thread_workers(optimizable_dataset, begins, workers_user_items)
         else:
-            self._create_process_workers(begins, workers_user_items)
+            self._create_process_workers(optimizable_dataset, begins, workers_user_items)
 
         print("Workers are ready ! Starting data processing...")
 
@@ -634,7 +618,9 @@ def _exit_on_error(self, error: str) -> None:
             w.join(0)
         raise RuntimeError(f"We found the following error {error}.")
 
-    def _create_thread_workers(self, begins: List[int], workers_user_items: List[List[Any]]) -> None:
+    def _create_thread_workers(
+        self, optimizable_dataset: _OptimizableDataset, begins: List[int], workers_user_items: List[List[Any]]
+    ) -> None:
         current_total = 0
         total = sum([len(w) for w in workers_user_items])
         with tqdm(total=total, smoothing=0) as pbar:
@@ -649,7 +635,7 @@ def _create_thread_workers(self, begins: List[int], workers_user_items: List[Lis
                     begins[worker_idx],
                     self.name,
                     _get_node_rank(),
-                    self,
+                    optimizable_dataset.prepare_item,
                     self.src_dir,
                     self.remote_src_dir,
                     self.remote_dst_dir,
@@ -676,7 +662,9 @@ def _create_thread_workers(self, begins: List[int], workers_user_items: List[Lis
                 if current_total == total:
                     break
 
-    def _create_process_workers(self, begins: List[int], workers_user_items: List[List[Any]]) -> None:
+    def _create_process_workers(
+        self, optimizable_dataset: _OptimizableDataset, begins: List[int], workers_user_items: List[List[Any]]
+    ) -> None:
         self.progress_queue = Queue()
         workers: List[DataWorkerProcess] = []
         stop_queues: List[Queue] = []
@@ -688,7 +676,7 @@ def _create_process_workers(self, begins: List[int], workers_user_items: List[Li
                 begins[worker_idx],
                 self.name,
                 _get_node_rank(),
-                self,
+                optimizable_dataset.prepare_item,
                 self.src_dir,
                 self.remote_src_dir,
                 self.remote_dst_dir,