Introduce Cache 1/n

.github/checkgroup.yml

@@ -173,9 +173,9 @@ subprojects:
       - "!*.md"
       - "!**/*.md"
     checks:
-      - "data-cpu (macOS-11, lightning, 3.10, 2.0)"
-      - "data-cpu (ubuntu-20.04, lightning, 3.10, 2.0)"
-      - "data-cpu (windows-2022, lightning, 3.10, 2.0)"
+      - "data-cpu (macOS-11, lightning, 3.10, 2.1)"
+      - "data-cpu (ubuntu-20.04, lightning, 3.10, 2.1)"
+      - "data-cpu (windows-2022, lightning, 3.10, 2.1)"
 
   # SECTION: lightning_fabric
 

.github/workflows/ci-tests-data.yml

@@ -34,9 +34,9 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - { os: "macOS-11", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.0" }
-          - { os: "ubuntu-20.04", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.0" }
-          - { os: "windows-2022", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.0" }
+          - { os: "macOS-11", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.1" }
+          - { os: "ubuntu-20.04", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.1" }
+          - { os: "windows-2022", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.1" }
           # "oldest" versions tests, only on minimum Python
           # - {os: "macOS-11", pkg-name: "lightning", python-version: "3.8", pytorch-version: "2.0", requires: "oldest"}
           # - {os: "ubuntu-20.04", pkg-name: "lightning", python-version: "3.8", pytorch-version: "2.0", requires: "oldest"}

requirements/data/data.txt

@@ -3,6 +3,6 @@
 
 lightning-utilities >=0.8.0, <0.10.0
 # to be able to include also 0.6 and preserve `>` needed for CI min version bypass
-torchdata >0.5.9, <0.7.0
+torchdata >0.5.9, <=0.7.0
 # to be able to include also PL 2.0 and preserve `>` needed for CI min version bypass
-torch >0.14.0, <2.1.0
+torch >0.14.0, <=2.1.0

src/lightning/data/cache/__init__.py

@@ -0,0 +1,17 @@
+# Copyright The Lightning AI team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from lightning.data.cache.cache import Cache
+from lightning.data.cache.dataloader import LightningDataLoader
+
+__all__ = ["Cache", "LightningDataLoader"]

src/lightning/data/cache/cache.py

@@ -0,0 +1,90 @@
+# Copyright The Lightning AI team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from lightning.data.cache.constants import _INDEX_FILENAME, _TORCH_2_1_0_AVAILABLE
+from lightning.data.cache.reader import BinaryReader
+from lightning.data.cache.sampler import ChunkedIndex
+from lightning.data.cache.writer import BinaryWriter
+from lightning.data.datasets.env import _DistributedEnv
+
+logger = logging.Logger(__name__)
+
+
+class Cache:
+    def __init__(
+        self,
+        cache_dir: str,
+        remote_dir: Optional[str] = None,
+        compression: Optional[str] = None,
+        chunk_size: Optional[int] = None,
+        chunk_bytes: Optional[int] = None,
+    ):
+        """The Cache enables to optimise dataset format for cloud training. This is done by grouping several elements
+        together in order to accelerate fetching.
+
+        Arguments:
+            cache_dir: The path to where the chunks will be stored.
+            remote_dir: The path to a remote folder where the data are located.
+                The scheme needs to be added to the path.
+            compression: The name of the algorithm to reduce the size of the chunks.
+            chunk_bytes: The maximum number of bytes within a chunk.
+            chunk_size: The maximum number of items within a chunk.
+
+        """
+        super().__init__()
+        if not _TORCH_2_1_0_AVAILABLE:
+            raise ModuleNotFoundError("PyTorch version 2.1 or higher is required to use the cache.")
+        self._writer = BinaryWriter(cache_dir, chunk_size=chunk_size, chunk_bytes=chunk_bytes, compression=compression)
+        self._reader = BinaryReader(cache_dir, remote_dir=remote_dir, compression=compression)
+        self._cache_dir = cache_dir
+        self._is_done = False
+        self._distributed_env = _DistributedEnv.detect()
+
+    @property
+    def filled(self) -> bool:
+        """Returns whether the caching phase is done."""
+        if self._is_done:
+            return True
+        self._is_done = os.path.exists(os.path.join(self._cache_dir, _INDEX_FILENAME))
+        return self._is_done
+
+    def __setitem__(self, index: int, data: Any) -> None:
+        """Store an item in the writer."""
+        self._writer[index] = data
+
+    def __getitem__(self, index: Union[int, ChunkedIndex]) -> Dict[str, Any]:
+        """Read an item in the reader."""
+        if isinstance(index, int):
+            index = ChunkedIndex(index, self._get_chunk_index_from_index(index))
+        return self._reader.read(index)
+
+    def done(self) -> None:
+        """Inform the writer the chunking phase is finished."""
+        self._writer.done()
+
+    def merge(self, num_workers: int = 1) -> None:
+        """Inform the writer the chunking phase is finished."""
+        self._writer.merge(num_workers)
+
+    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_index_from_index(self, index: int) -> int:
+        return self._reader._get_chunk_index_from_index(index)

src/lightning/data/cache/compression.py

@@ -0,0 +1,76 @@
+# Copyright The Lightning AI team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import ABC, abstractclassmethod, abstractmethod
+from typing import Dict, TypeVar
+
+from lightning_utilities.core.imports import RequirementCache, requires
+
+_ZSTD_AVAILABLE = RequirementCache("zstd")
+
+if _ZSTD_AVAILABLE:
+    import zstd
+
+TCompressor = TypeVar("TCompressor", bound="Compressor")
+
+
+class Compressor(ABC):
+    """Base class for compression algorithm."""
+
+    @abstractmethod
+    def compress(self, data: bytes) -> bytes:
+        pass
+
+    @abstractmethod
+    def decompress(self, data: bytes) -> bytes:
+        pass
+
+    @abstractclassmethod
+    def register(cls, compressors: Dict[str, "Compressor"]) -> None:
+        pass
+
+
+class ZSTDCompressor(Compressor):
+    """Compressor for the zstd package."""
+
+    @requires("zstd")
+    def __init__(self, level: int) -> None:
+        super().__init__()
+        self.level = level
+        self.extension = "zstd"
+
+    @property
+    def name(self) -> str:
+        return f"{self.extension}:{self.level}"
+
+    def compress(self, data: bytes) -> bytes:
+        return zstd.compress(data, self.level)
+
+    def decompress(self, data: bytes) -> bytes:
+        return zstd.decompress(data)
+
+    @classmethod
+    def register(cls, compressors: Dict[str, "Compressor"]) -> None:  # type: ignore
+        if not _ZSTD_AVAILABLE:
+            return
+
+        # default
+        compressors["zstd"] = ZSTDCompressor(4)
+
+        for level in list(range(1, 23)):
+            compressors[f"zstd:{level}"] = ZSTDCompressor(level)
+
+
+_COMPRESSORS: Dict[str, Compressor] = {}
+
+ZSTDCompressor.register(_COMPRESSORS)

src/lightning/data/cache/config.py

@@ -0,0 +1,125 @@
+# Copyright The Lightning AI team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from typing import Any, Dict, List, Optional, Tuple
+
+from lightning.data.cache.constants import _INDEX_FILENAME, _TORCH_2_1_0_AVAILABLE
+from lightning.data.cache.downloader import get_downloader_cls
+from lightning.data.cache.sampler import ChunkedIndex
+
+if _TORCH_2_1_0_AVAILABLE:
+    from torch.utils._pytree import treespec_loads
+
+
+class ChunksConfig:
+    def __init__(self, cache_dir: str, remote_dir: Optional[str]):
+        """The ChunksConfig reads the index files associated a chunked dataset and enables to map an index to its
+        chunk.
+
+        Arguments:
+            cache_dir: The path to cache folder.
+            remote_dir: The path to a remote folder where the data are located.
+                The scheme needs to be added to the path.
+
+        """
+        self._cache_dir = cache_dir
+        self._intervals: List[Tuple[int, int]] = []
+        self._config = None
+        self._chunks = []
+        self._remote_dir = remote_dir
+
+        with open(os.path.join(self._cache_dir, _INDEX_FILENAME)) as f:
+            data = json.load(f)
+
+            self._config = data["config"]
+
+            self._chunks.extend(data["chunks"])
+
+        self._config["data_spec"] = treespec_loads(self._config["data_spec"])
+
+        for chunk in self._chunks:
+            start, end = chunk["interval"]
+            if (end - start) != chunk["chunk_size"]:
+                raise Exception(
+                    "The config intervals doesn't match the number of samples. This shouldn't have happened."
+                )
+            self._intervals.append((chunk["interval"][0], chunk["interval"][1]))
+
+        self._length = sum([chunk["chunk_size"] for chunk in self._chunks])
+
+        self._downloader = None
+
+        if remote_dir:
+            self._downloader = get_downloader_cls(remote_dir)(remote_dir, cache_dir, self._chunks)
+
+    def download_chunk_from_index(self, chunk_index: int) -> None:
+        chunk_filename = self._chunks[chunk_index]["filename"]
+
+        local_chunkpath = os.path.join(self._cache_dir, chunk_filename)
+
+        if os.path.exists(local_chunkpath):
+            return
+
+        if self._downloader is None:
+            raise RuntimeError("The downloader should be defined.")
+
+        self._downloader.download_chunk_from_index(chunk_index)
+
+    @property
+    def intervals(self) -> List[Tuple[int, int]]:
+        if self._intervals is None:
+            raise RuntimeError("The intervals should be defined.")
+        return self._intervals
+
+    @property
+    def data_format(self) -> Any:
+        if self._config is None:
+            raise RuntimeError("The config should be defined.")
+        return self._config["data_format"]
+
+    @property
+    def config(self) -> Dict[str, Any]:
+        if self._config is None:
+            raise RuntimeError("The config should be defined.")
+        return self._config
+
+    def _get_chunk_index_from_index(self, index: int) -> int:
+        for chunk_index, internal in enumerate(self._intervals):
+            if internal[0] <= index < internal[1]:
+                return chunk_index
+        raise ValueError(
+            f"The provided index {index} didn't find a match within the chunk intervals {self._intervals}."
+        )
+
+    def __getitem__(self, index: ChunkedIndex) -> Tuple[str, int, int]:
+        """Find the associated chunk metadata."""
+        chunk = self._chunks[index.chunk_index]
+        return os.path.join(self._cache_dir, chunk["filename"]), *self._intervals[index.chunk_index]
+
+    @classmethod
+    def load(cls, cache_dir: str, remote_dir: Optional[str] = None) -> Optional["ChunksConfig"]:
+        cache_index_filepath = os.path.join(cache_dir, _INDEX_FILENAME)
+
+        if isinstance(remote_dir, str):
+            downloader = get_downloader_cls(remote_dir)(remote_dir, cache_dir, [])
+            downloader.download_file(os.path.join(remote_dir, _INDEX_FILENAME), cache_index_filepath)
+
+        if not os.path.exists(cache_index_filepath):
+            return None
+
+        return ChunksConfig(cache_dir, remote_dir)
+
+    def __len__(self) -> int:
+        return self._length

src/lightning/data/cache/constants.py

@@ -0,0 +1,21 @@
+# Copyright The Lightning AI team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from lightning_utilities.core.imports import RequirementCache
+
+_INDEX_FILENAME = "index.json"
+_DEFAULT_CHUNK_BYTES = 1 << 26  # 64M B
+
+# This is required for full pytree serialization / deserialization support
+_TORCH_2_1_0_AVAILABLE = RequirementCache("torch>=2.1.0")
+_VIZ_TRACKER_AVAILABLE = RequirementCache("viztracer")