Merge branch 'master' into bugfix/18394_batch_size_finder_max_val_bat…

…ches

src/lightning/__init__.py

@@ -1,5 +1,6 @@
 """Root package info."""
 import logging
+import sys
 
 # explicitly don't set root logger's propagation and leave this to subpackages to manage
 _logger = logging.getLogger(__name__)
@@ -28,3 +29,19 @@
     "seed_everything",
     "Fabric",
 ]
+
+
+def _cli_entry_point() -> None:
+    from lightning_utilities.core.imports import ModuleAvailableCache, RequirementCache
+
+    if not (
+        ModuleAvailableCache("lightning.app")
+        if RequirementCache("lightning-utilities<0.10.0")
+        else RequirementCache(module="lightning.app")  # type: ignore[call-arg]
+    ):
+        print("The `lightning` command requires additional dependencies: `pip install lightning[app]`")
+        sys.exit(1)
+
+    from lightning.app.cli.lightning_cli import main
+
+    main()

src/lightning/__setup__.py

@@ -115,7 +115,7 @@ def _setup_args() -> Dict[str, Any]:
         "python_requires": ">=3.8",  # todo: take the lowes based on all packages
         "entry_points": {
             "console_scripts": [
-                "lightning = lightning.app.cli.lightning_cli:main",
+                "lightning = lightning:_cli_entry_point",
             ],
         },
         "setup_requires": [],

src/lightning/data/streaming/cache.py

@@ -71,6 +71,10 @@ def __init__(
             if has_index_file and (chunk_size is None and chunk_bytes is None):
                 chunk_size = 2
 
+            # Add the version to the cache_dir to avoid collisions.
+            if remote_dir and os.path.basename(remote_dir).startswith("version_"):
+                cache_dir = os.path.join(cache_dir, os.path.basename(remote_dir))
+
             if cache_dir:
                 os.makedirs(cache_dir, exist_ok=True)
 
@@ -116,8 +120,8 @@ def _merge_no_wait(self, node_rank: Optional[int] = None) -> None:
     def __len__(self) -> int:
         return self._reader.get_length()
 
-    def get_chunk_interval(self) -> List[Tuple[int, int]]:
-        return self._reader.get_chunk_interval()
+    def get_chunk_intervals(self) -> List[Tuple[int, int]]:
+        return self._reader.get_chunk_intervals()
 
     def _get_chunk_index_from_index(self, index: int) -> int:
         return self._reader._get_chunk_index_from_index(index)

src/lightning/data/streaming/dataset.py

@@ -20,6 +20,7 @@
 from lightning.data.streaming import Cache
 from lightning.data.streaming.item_loader import BaseItemLoader
 from lightning.data.streaming.sampler import ChunkedIndex
+from lightning.data.streaming.shuffle import FullShuffle, NoShuffle, Shuffle, TruncatedShuffle
 
 
 class StreamingDataset(IterableDataset):
@@ -31,7 +32,7 @@ def __init__(
         version: Optional[Union[int, Literal["latest"]]] = "latest",
         cache_dir: Optional[str] = None,
         item_loader: Optional[BaseItemLoader] = None,
-        shuffle: bool = True,
+        shuffle: Union[bool, Literal["truncated", "full"]] = "truncated",
         seed: int = 42,
     ) -> None:
         """The streaming dataset can be used once your data have been optimised using the DatasetOptimiser class.
@@ -53,13 +54,21 @@ def __init__(
         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])
+        if isinstance(shuffle, bool):
+            _shuffle = TruncatedShuffle(self.cache, seed) if shuffle else NoShuffle(self.cache, seed)
+
+        if isinstance(shuffle, str):
+            if shuffle == "truncated":
+                _shuffle = TruncatedShuffle(self.cache, seed)
+            elif shuffle == "full":
+                _shuffle = FullShuffle(self.cache, seed)
+            else:
+                raise ValueError(f"The provided shuffle doesn't exist. Found {shuffle}")
 
+        self.shuffle: Shuffle = _shuffle
+        self.worker_env: Optional[_WorkerEnv] = None
         self.worker_chunks: List[int] = []
         self.worker_intervals: List[List[int]] = []
         self.current_indexes: List[int] = []
@@ -68,26 +77,16 @@ def __init__(
         self.has_triggered_download = False
         self.min_items_per_replica: Optional[int] = None
         self.seed = seed
-        self.num_iter = 0
+        self.current_epoch = 0
         self.random_state = None
 
     def __len__(self) -> int:
-        return self.L
+        return self.shuffle.get_len(self.distributed_env, self.current_epoch)
 
     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)
-
+        chunks_per_replica, intervals_per_replica = self.shuffle.get_chunks_and_intervals_per_process(
+            self.distributed_env, self.current_epoch
+        )
         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]
 
@@ -105,7 +104,7 @@ def __iter__(self) -> "StreamingDataset":
 
         self.current_indexes = []
         self.chunk_index = 0
-        self.num_iter += 1
+        self.index = 0
 
         return self
 
@@ -115,16 +114,20 @@ def __getitem__(self, index: Union[ChunkedIndex, int]) -> Any:
         return self.cache[index]
 
     def __next__(self) -> Any:
+        # Prevent to create more batch on a given process
+        if self.index >= len(self):
+            self.current_epoch += 1
+            raise StopIteration
+
         # Lazily re-populate the interval to reduce memory usage.
         if len(self.current_indexes) == 0:
             if self.chunk_index == len(self.worker_intervals):
+                self.current_epoch += 1
                 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()
+            current_indexes = np.arange(interval[0], interval[1])
+            self.current_indexes = self.shuffle(current_indexes)
             self.chunk_index += 1
 
         # Get the first index

src/lightning/data/streaming/dataset_optimizer.py

@@ -485,7 +485,7 @@ def __init__(
         self.name = name
         self.src_dir = str(src_dir)
         self.num_workers = num_workers or (1 if fast_dev_run else (os.cpu_count() or 1) * 4)
-        self.num_downloaders = num_downloaders or (1 if fast_dev_run else 2)
+        self.num_downloaders = num_downloaders or 1
         if chunk_size is not None and chunk_bytes is not None:
             raise ValueError("Either one of the `chunk_size` or the `chunk_bytes` need to be provided.")
         self.chunk_size = chunk_size

src/lightning/data/streaming/item_loader.py

@@ -122,7 +122,7 @@ def generate_intervals(self) -> List[Tuple[int, int]]:
 
         return self._intervals
 
-    def load_item_from_chunk(self, index: int, chunk_index: int, chunk_filepath: str, _: int) -> torch.Tensor:
+    def load_item_from_chunk(self, index: int, chunk_index: int, chunk_filepath: str, begin: int) -> torch.Tensor:
         while not os.path.exists(chunk_filepath):
             sleep(0.0001)
 
@@ -137,5 +137,5 @@ def load_item_from_chunk(self, index: int, chunk_index: int, chunk_filepath: str
         assert self._dtype
 
         buffer: bytes = self._buffers[chunk_index]
-        offset = self._dtype.itemsize * index
+        offset = self._dtype.itemsize * ((index - begin) if index >= begin else index + 1)
         return torch.frombuffer(buffer, dtype=self._dtype, count=self._block_size, offset=offset)

src/lightning/data/streaming/reader.py

@@ -166,7 +166,7 @@ def get_length(self) -> int:
 
         return len(self.config)
 
-    def get_chunk_interval(self) -> List[Tuple[int, int]]:
+    def get_chunk_intervals(self) -> List[Tuple[int, int]]:
         """Get the index interval of each chunk."""
         if self._config is None and self._try_load_config() is None:
             raise Exception("The reader index isn't defined.")

src/lightning/data/streaming/sampler.py

@@ -146,13 +146,13 @@ def __iter_distributed__(self) -> Iterator[List[Union[int, ChunkedIndex]]]:
         yield from self.__iter_indices_per_workers__(worker_indices_batches)
 
     def __iter_from_chunks_non_distributed__(self) -> Iterator[List[Union[int, ChunkedIndex]]]:
-        chunk_intervals = self._cache.get_chunk_interval()
+        chunk_intervals = self._cache.get_chunk_intervals()
         shuffled_indexes = np.random.permutation(range(len(chunk_intervals)))
         shuffled_chunk_intervals = np.asarray(chunk_intervals)[shuffled_indexes]
         yield from self.__iter_from_shuffled_chunks(shuffled_indexes.tolist(), shuffled_chunk_intervals)
 
     def __iter_from_chunks_distributed__(self) -> Iterator[List[Union[int, ChunkedIndex]]]:
-        chunk_intervals = self._cache.get_chunk_interval()
+        chunk_intervals = self._cache.get_chunk_intervals()
         shuffled_indexes = np.random.permutation(range(len(chunk_intervals)))
         shuffled_chunk_intervals = np.asarray(chunk_intervals)[shuffled_indexes]