StreamingDataset improve deletion strategy

src/lightning/data/streaming/cache.py

@@ -51,7 +51,7 @@ def __init__(
         chunk_size: Optional[int] = None,
         chunk_bytes: Optional[Union[int, str]] = None,
         item_loader: Optional[BaseItemLoader] = None,
-        max_cache_size: Union[int, str] = "200GB",
+        max_cache_size: Union[int, str] = "100GB",
         serializers: Optional[Dict[str, Serializer]] = None,
     ):
         """The Cache enables to optimise dataset format for cloud training. This is done by grouping several elements

src/lightning/data/streaming/config.py

@@ -128,6 +128,13 @@ def __getitem__(self, index: ChunkedIndex) -> Tuple[str, int, int]:
         chunk = self._chunks[index.chunk_index]
         return os.path.join(self._cache_dir, chunk["filename"]), *self._intervals[index.chunk_index]
 
+    def _get_chunk_index_from_filename(self, chunk_filename: str) -> int:
+        """Retrieves the associated chunk_index for a given chunk filename."""
+        for chunk_index, chunk in enumerate(self._chunks):
+            if chunk["filename"] == chunk_filename:
+                return chunk_index
+        raise ValueError(f"The provided filename doesn't exist {chunk_filename}.")
+
     @classmethod
     def load(
         cls,

src/lightning/data/streaming/dataset.py

@@ -18,7 +18,6 @@
 import sys
 import tempfile
 from dataclasses import dataclass
-from datetime import datetime
 from time import time
 from typing import Any, Dict, List, Optional, Union
 
@@ -31,7 +30,6 @@
     _DEFAULT_CACHE_DIR,
     _INDEX_FILENAME,
     _LIGHTNING_CLOUD_LATEST,
-    _TIME_FORMAT,
 )
 from lightning.data.streaming.item_loader import BaseItemLoader
 from lightning.data.streaming.sampler import ChunkedIndex
@@ -56,6 +54,7 @@ def __init__(
         seed: int = 42,
         serializers: Optional[Dict[str, Serializer]] = None,
         checkpoint_interval: Optional[int] = None,
+        max_cache_size: Union[int, str] = "100GB",
     ) -> None:
         """The streaming dataset can be used once your data have been optimised using the DatasetOptimiser class.
 
@@ -68,6 +67,7 @@ def __init__(
             seed: Random seed for shuffling.
             serializers: The serializers used to serialize and deserialize the chunks.
             checkpoint_interval: Interval in seconds at which the workers are going to store their own progress.
+            max_cache_size: The maximum cache size used by the StreamingDataset.
 
         """
         super().__init__()
@@ -84,6 +84,7 @@ def __init__(
         self.shuffle: bool = shuffle
         self.drop_last = drop_last
         self.seed = seed
+        self.max_cache_size = max_cache_size
 
         self.cache: Optional[Cache] = None
         self.distributed_env = _DistributedEnv.detect()
@@ -118,7 +119,11 @@ def _create_cache(self, worker_env: _WorkerEnv) -> Cache:
                 self.input_dir.path = cache_path
 
         cache = Cache(
-            input_dir=self.input_dir, item_loader=self.item_loader, chunk_bytes=1, serializers=self.serializers
+            input_dir=self.input_dir,
+            item_loader=self.item_loader,
+            chunk_bytes=1,
+            serializers=self.serializers,
+            max_cache_size=self.max_cache_size,
         )
         cache._reader._try_load_config()
 
@@ -391,10 +396,6 @@ def _try_create_cache_dir(input_dir: str, shard_rank: int = 0) -> Optional[str]:
     return cache_dir
 
 
-def _string_to_datetime(item: str) -> datetime:
-    return datetime.strptime(item.split("checkpoint-")[1].split(".json")[0], _TIME_FORMAT)
-
-
 def _load_state_dict_from_checkpoint_dir(checkpoint_dir: str) -> Dict[str, Any]:
     state_dict: Dict[str, Any] = {}
     if not os.path.exists(checkpoint_dir):

src/lightning/data/streaming/item_loader.py

@@ -45,11 +45,21 @@ def generate_intervals(self) -> List[Tuple[int, int]]:
         """Returns a list of tuple describing the indexes intervals of the chunks."""
         pass
 
+    @abstractmethod
+    def pre_load_chunk(self, chunk_index: int, chunk_filepath: str) -> None:
+        """Logic to load the chunk in background to gain some time."""
+        pass
+
     @abstractmethod
     def load_item_from_chunk(self, index: int, chunk_index: int, chunk_filepath: str, begin: int) -> Any:
         """Returns an item loaded from a chunk."""
         pass
 
+    @abstractmethod
+    def delete(self, chunk_index: int, chunk_filepath: str) -> None:
+        """Delete a chunk from the local filesystem."""
+        pass
+
 
 class PyTreeLoader(BaseItemLoader):
     """The Pytree Loader is the default loader of the Cache object."""
@@ -67,6 +77,9 @@ def generate_intervals(self) -> List[Tuple[int, int]]:
             begin += chunk["chunk_size"]
         return intervals
 
+    def pre_load_chunk(self, chunk_index: int, chunk_filepath: str) -> None:
+        pass
+
     def load_item_from_chunk(self, index: int, chunk_index: int, chunk_filepath: str, begin: int) -> bytes:
         offset = (1 + (index - begin) if index >= begin else index + 1) * 4
 
@@ -106,6 +119,10 @@ def deserialize(self, raw_item_data: bytes) -> "PyTree":
             idx += size
         return tree_unflatten(data, self._config["data_spec"])
 
+    def delete(self, chunk_index: int, chunk_filepath: str) -> None:
+        if os.path.exists(chunk_filepath):
+            os.remove(chunk_filepath)
+
 
 class TokensLoader(BaseItemLoader):
     def __init__(self, block_size: int):
@@ -146,6 +163,26 @@ def generate_intervals(self) -> List[Tuple[int, int]]:
             begin += num_blocks
         return intervals
 
+    def _load_chunk(self, chunk_index: int, chunk_filepath: str) -> None:
+        if chunk_index not in self._mmaps:
+            # TODO: Add deletion and memmap close
+            chunk = self._chunks[chunk_index]
+
+            # Skip the header
+            # The number of items + the number of offsets (number of items in the chunk + 1)
+            # multiplied by the header encoding dtype (np.uint32)
+            offset = (1 + chunk["chunk_size"] + 1) * 4
+            mmap = np.memmap(chunk_filepath, mode="r", order="C", offset=offset)
+            self._mmaps[chunk_index] = mmap
+            self._buffers[chunk_index] = memoryview(mmap)  # type: ignore
+
+    def pre_load_chunk(self, chunk_index: int, chunk_filepath: str) -> None:
+        # This is called within the prepare chunks thread, so we overlap data loading with data reading.
+        if chunk_filepath not in self._chunk_filepaths:
+            self._chunk_filepaths[chunk_filepath] = True
+
+        self._load_chunk(chunk_index, chunk_filepath)
+
     def load_item_from_chunk(self, index: int, chunk_index: int, chunk_filepath: str, begin: int) -> torch.Tensor:
         if chunk_filepath in self._chunk_filepaths and not os.path.isfile(chunk_filepath):
             del self._chunk_filepaths[chunk_filepath]
@@ -163,20 +200,18 @@ def load_item_from_chunk(self, index: int, chunk_index: int, chunk_filepath: str
 
             self._chunk_filepaths[chunk_filepath] = True
 
-        if chunk_index not in self._mmaps:
-            # TODO: Add deletion and memmap close
-            chunk = self._chunks[chunk_index]
-
-            # Skip the header
-            # The number of items + the number of offsets (number of items in the chunk + 1)
-            # multiplied by the header encoding dtype (np.uint32)
-            offset = (1 + chunk["chunk_size"] + 1) * 4
-            mmap = np.memmap(chunk_filepath, mode="r", order="C", offset=offset)
-            self._mmaps[chunk_index] = mmap
-            self._buffers[chunk_index] = memoryview(mmap)  # type: ignore
+        self._load_chunk(chunk_index, chunk_filepath)
 
         assert self._dtype
 
         buffer: bytes = self._buffers[chunk_index]
         offset = self._dtype.itemsize * (index - begin) * self._block_size
         return torch.frombuffer(buffer, dtype=self._dtype, count=self._block_size, offset=offset)
+
+    def delete(self, chunk_index: int, chunk_filepath: str) -> None:
+        if os.path.exists(chunk_filepath):
+            if chunk_index in self._buffers:
+                del self._buffers[chunk_index]
+            if chunk_index in self._mmaps:
+                del self._mmaps[chunk_index]
+            os.remove(chunk_filepath)