revise cachedataset runtime_cache modes

monai/apps/datasets.py

@@ -71,7 +71,7 @@ class MedNISTDataset(Randomizable, CacheDataset):
         as_contiguous: whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
             it may help improve the performance of following logic.
         runtime_cache: whether to compute cache at the runtime, default to `False` to prepare
-            the cache content at initializaiton.
+            the cache content at initialization. See: :py:class:`monai.data.CacheDataset`.
 
     Raises:
         ValueError: When ``root_dir`` is not a directory.
@@ -99,7 +99,7 @@ def __init__(
         progress: bool = True,
         copy_cache: bool = True,
         as_contiguous: bool = True,
-        runtime_cache: bool = False,
+        runtime_cache=False,
     ) -> None:
         root_dir = Path(root_dir)
         if not root_dir.is_dir():
@@ -228,7 +228,7 @@ class DecathlonDataset(Randomizable, CacheDataset):
         as_contiguous: whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
             it may help improve the performance of following logic.
         runtime_cache: whether to compute cache at the runtime, default to `False` to prepare
-            the cache content at initializaiton.
+            the cache content at initialization. See: :py:class:`monai.data.CacheDataset`.
 
     Raises:
         ValueError: When ``root_dir`` is not a directory.
@@ -296,7 +296,7 @@ def __init__(
         progress: bool = True,
         copy_cache: bool = True,
         as_contiguous: bool = True,
-        runtime_cache: bool = False,
+        runtime_cache=False,
     ) -> None:
         root_dir = Path(root_dir)
         if not root_dir.is_dir():
@@ -458,7 +458,7 @@ class TciaDataset(Randomizable, CacheDataset):
         as_contiguous: whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
             it may help improve the performance of following logic.
         runtime_cache: whether to compute cache at the runtime, default to `False` to prepare
-            the cache content at initializaiton.
+            the cache content at initialization. See: :py:class:`monai.data.CacheDataset`.
 
     Example::
 
@@ -514,7 +514,7 @@ def __init__(
         progress: bool = True,
         copy_cache: bool = True,
         as_contiguous: bool = True,
-        runtime_cache: bool = False,
+        runtime_cache=False,
     ) -> None:
         root_dir = Path(root_dir)
         if not root_dir.is_dir():

monai/data/dataloader.py

@@ -80,12 +80,6 @@ def __init__(self, dataset: Dataset, num_workers: int = 0, **kwargs) -> None:
             init_seed = _g.initial_seed()
             _seed = torch.empty((), dtype=torch.int64).random_(generator=_g).item()
             set_rnd(dataset, int(_seed))
-            # disable unnecessary multiprocessing caching
-            from monai.data.dataset import CacheDataset  # avoid circular import
-
-            if isinstance(dataset, CacheDataset):
-                dataset.disable_share_memory_cache()
-
             _g.manual_seed(init_seed)
         if "collate_fn" not in kwargs:
             kwargs["collate_fn"] = list_data_collate

monai/data/dataset.py

@@ -26,7 +26,6 @@
 
 import numpy as np
 import torch
-import torch.distributed as dist
 from torch.multiprocessing import Manager
 from torch.serialization import DEFAULT_PROTOCOL
 from torch.utils.data import Dataset as _TorchDataset
@@ -751,7 +750,7 @@ def __init__(
         as_contiguous: bool = True,
         hash_as_key: bool = False,
         hash_func: Callable[..., bytes] = pickle_hashing,
-        runtime_cache: bool = False,
+        runtime_cache: Union[bool, str, List, ListProxy] = False,
     ) -> None:
         """
         Args:
@@ -777,18 +776,21 @@ def __init__(
                 the dataset has duplicated items or augmented dataset.
             hash_func: if `hash_as_key`, a callable to compute hash from data items to be cached.
                 defaults to `monai.data.utils.pickle_hashing`.
-            runtime_cache: whether to compute cache at the runtime, default to `False` to prepare
-                the cache content at initialization, if `True`, it will cache during the first epoch
-                of model training, so it can start the first mini-batch earlier. please note that:
-                1. when using this option in multi-gpu distributed training,
-                `torch.cuda.set_device()` must be called before initializing this class.
-                2. if caching data that is in GPU memory during multi-gpu distributed training, this option
-                should not be used, since the underlying shared cache only works for CPU shared memory.
-                3. to execute `runtime cache` on GPU memory, must co-work with
-                `monai.data.DataLoader`, and can't work with `monai.data.DistributedSampler`
-                as GPU Tensor usually can't be shared in the multiprocessing context.
-                (try ``cache_dataset.disable_share_memory_cache()`` in case of GPU caching issues.)
+            runtime_cache: mode of cache at the runtime. Default to `False` to prepare
+                the cache content for the entire ``data`` during initialization, this potentially largely increase the
+                time required between the constructor called and first mini-batch generated.
+                Three options are provided to compute the cache on the fly after the dataset initialization:
 
+                1. ``"threads"`` or ``True``: use a regular ``list`` to store the cache items.
+                2. ``"processes"``: use a ListProxy to store the cache items, it can be shared among processes.
+                3. A list-like object: a users-provided container to be used to store the cache items.
+
+                For `thread-based` caching (typically for caching cuda tensors), option 1 is recommended.
+                For single process workflows with multiprocessing data loading, option 2 is recommended.
+                For multiprocessing workflows (typically for distributed training),
+                where this class is initialized in subprocesses, option 3 is recommended,
+                and the list-like object should be prepared in the main process and passed to all subprocesses.
+                Not following these recommendations may lead to runtime errors or duplicated cache across processes.
 
         """
         if not isinstance(transform, Compose):
@@ -808,10 +810,9 @@ def __init__(
         self.cache_num = 0
         self._cache: Union[List, ListProxy] = []
         self._hash_keys: List = []
-        self._is_dist = dist.is_available() and dist.is_initialized()
         self.set_data(data)
 
-    def set_data(self, data: Sequence):
+    def set_data(self, data: Sequence) -> None:
         """
         Set the input data and run deterministic transforms to generate cache content.
 
@@ -825,44 +826,28 @@ def set_data(self, data: Sequence):
         def _compute_cache_num(data_len: int):
             self.cache_num = min(int(self.set_num), int(data_len * self.set_rate), data_len)
 
-        def _compute_cache(indices=None):
-            if self.runtime_cache:
-                cache = Manager().list([None for _ in range(self.cache_num)])
-                if self._is_dist:
-                    obj_list = [cache]
-                    # broadcast the ListProxy to all the ranks, then share the same cache content at runtime
-                    dist.broadcast_object_list(obj_list, src=0)
-                    cache = obj_list[0]
-            else:
-                cache = self._fill_cache(indices)
-            return cache
-
         if self.hash_as_key:
             # only compute cache for the unique items of dataset, and record the last index for duplicated items
-            mapping = {self.hash_func(v): i for i, v in enumerate(data)}
+            mapping = {self.hash_func(v): i for i, v in enumerate(self.data)}
             _compute_cache_num(len(mapping))
             self._hash_keys = list(mapping)[: self.cache_num]
             indices = list(mapping.values())[: self.cache_num]
         else:
             _compute_cache_num(len(self.data))
             indices = list(range(self.cache_num))
 
-        self._cache = _compute_cache(indices)
-
-    def disable_share_memory_cache(self):
-        """
-        If the cache content is a multiprocessing shared memory ListProxy, convert it to a regular python list.
-        Because multiprocessing ListProxy is not supported for the GPU caching,  explicitly disable it.
-
-        """
-        if self.runtime_cache:
-            if not self._is_dist:
-                self._cache = list(self._cache)
-            else:
-                warnings.warn(
-                    "Unable to disable shared cache in DDP, when runtime_cache==True."
-                    "Please use runtime_cache=False option to explicitly not use the shared cache."
-                )
+        if self.runtime_cache in (False, None):  # prepare cache content immediately
+            self._cache = self._fill_cache(indices)
+            return
+        if isinstance(self.runtime_cache, str) and "process" in self.runtime_cache:
+            # this must be in the main process, not in dataloader's workers
+            self._cache = Manager().list([None] * self.cache_num)
+            return
+        if (self.runtime_cache is True) or (isinstance(self.runtime_cache, str) and "thread" in self.runtime_cache):
+            self._cache = [None] * self.cache_num
+            return
+        self._cache = self.runtime_cache  # type: ignore
+        return
 
     def _fill_cache(self, indices=None) -> List:
         """
@@ -1006,6 +991,7 @@ class SmartCacheDataset(Randomizable, CacheDataset):
             may set `copy=False` for better performance.
         as_contiguous: whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
             it may help improve the performance of following logic.
+        runtime_cache: Default to `False`, other options are not implemented yet.
 
     """
 
@@ -1023,7 +1009,7 @@ def __init__(
         seed: int = 0,
         copy_cache: bool = True,
         as_contiguous: bool = True,
-        runtime_cache: bool = False,
+        runtime_cache=False,
     ) -> None:
         if shuffle:
             self.set_random_state(seed=seed)
@@ -1034,8 +1020,20 @@ def __init__(
         self._round: int = 1
         self._replace_done: bool = False
         self._replace_mgr: Optional[threading.Thread] = None
+        if runtime_cache is not False:
+            raise NotImplementedError("Options other than `runtime_cache=False` is not implemented yet.")
 
-        super().__init__(data, transform, cache_num, cache_rate, num_init_workers, progress, copy_cache, as_contiguous)
+        super().__init__(
+            data=data,
+            transform=transform,
+            cache_num=cache_num,
+            cache_rate=cache_rate,
+            num_workers=num_init_workers,
+            progress=progress,
+            copy_cache=copy_cache,
+            as_contiguous=as_contiguous,
+            runtime_cache=False,
+        )
         if self._cache is None:
             self._cache = self._fill_cache()
         if self.cache_num >= len(data):

tests/test_integration_segmentation_3d.py

@@ -84,7 +84,7 @@ def run_training_test(root_dir, device="cuda:0", cachedataset=0, readers=(None,
     # create a training data loader
     if cachedataset == 2:
         train_ds = monai.data.CacheDataset(
-            data=train_files, transform=train_transforms, cache_rate=0.8, runtime_cache=True
+            data=train_files, transform=train_transforms, cache_rate=0.8, runtime_cache="process"
         )
     elif cachedataset == 3:
         train_ds = monai.data.LMDBDataset(data=train_files, transform=train_transforms, cache_dir=root_dir)

tests/test_sampler_dist.py

@@ -14,6 +14,7 @@
 import numpy as np
 import torch
 import torch.distributed as dist
+from torch.multiprocessing import Manager
 
 from monai.data import CacheDataset, DataLoader, DistributedSampler
 from monai.transforms import ToTensor
@@ -48,9 +49,14 @@ def test_uneven(self):
     @DistCall(nnodes=1, nproc_per_node=2, timeout=120)
     def test_cachedataset(self):
         data = [1, 2, 3, 4, 5]
-        dataset = CacheDataset(data=data, transform=ToTensor(track_meta=False), cache_rate=1.0, runtime_cache=True)
+        obj_list = [Manager().list([None] * len(data))]
+        dist.broadcast_object_list(obj_list, src=0)
+        dataset = CacheDataset(
+            data=data, transform=ToTensor(track_meta=False), cache_rate=1.0, runtime_cache=obj_list[0]
+        )
         sampler = DistributedSampler(dataset=dataset, shuffle=False, even_divisible=False)
         dataloader = DataLoader(dataset=dataset, sampler=sampler, batch_size=1, num_workers=2)
+        dist.barrier()
         for i in range(3):
             if i > 0:
                 # verify the runtime cache content is completed after first epoch