LazyMemmapStorage

class torchrl.data.replay_buffers.LazyMemmapStorage(max_size: int, *, scratch_dir=None, device: device = 'cpu', ndim: int = 1, existsok: bool = False)

A memory-mapped storage for tensors and tensordicts.

Parameters:

max_size (int) – size of the storage, i.e. maximum number of elements stored in the buffer.

Keyword Arguments:

• scratch_dir (str or path) – directory where memmap-tensors will be written.

• device (torch.device, optional) – device where the sampled tensors will be stored and sent. Default is torch.device("cpu"). If None is provided, the device is automatically gathered from the first batch of data passed. This is not enabled by default to avoid data placed on GPU by mistake, causing OOM issues.

• ndim (int, optional) – the number of dimensions to be accounted for when measuring the storage size. For instance, a storage of shape [3, 4] has capacity 3 if ndim=1 and 12 if ndim=2. Defaults to 1.

• existsok (bool, optional) – whether an error should be raised if any of the tensors already exists on disk. Defaults to True. If False, the tensor will be opened as is, not overewritten.

Note

When checkpointing a LazyMemmapStorage, one can provide a path identical to where the storage is already stored to avoid executing long copies of data that is already stored on disk. This will only work if the default TensorStorageCheckpointer checkpointer is used. Example:

>>> from tensordict import TensorDict
>>> from torchrl.data import TensorStorage, LazyMemmapStorage, ReplayBuffer
>>> import tempfile
>>> from pathlib import Path
>>> import time
>>> td = TensorDict(a=0, b=1).expand(1000).clone()
>>> # We pass a path that is <main_ckpt_dir>/storage to LazyMemmapStorage
>>> rb_memmap = ReplayBuffer(storage=LazyMemmapStorage(10_000_000, scratch_dir="dump/storage"))
>>> rb_memmap.extend(td);
>>> # Checkpointing in `dump` is a zero-copy, as the data is already in `dump/storage`
>>> rb_memmap.dumps(Path("./dump"))

Examples

>>> data = TensorDict({
...     "some data": torch.randn(10, 11),
...     ("some", "nested", "data"): torch.randn(10, 11, 12),
... }, batch_size=[10, 11])
>>> storage = LazyMemmapStorage(100)
>>> storage.set(range(10), data)
>>> len(storage)  # only the first dimension is considered as indexable
10
>>> storage.get(0)
TensorDict(
    fields={
        some data: MemoryMappedTensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
        some: TensorDict(
            fields={
                nested: TensorDict(
                    fields={
                        data: MemoryMappedTensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False)},
                    batch_size=torch.Size([11]),
                    device=cpu,
                    is_shared=False)},
            batch_size=torch.Size([11]),
            device=cpu,
            is_shared=False)},
    batch_size=torch.Size([11]),
    device=cpu,
    is_shared=False)

This class also supports tensorclass data.

Examples

>>> from tensordict import tensorclass
>>> @tensorclass
... class MyClass:
...     foo: torch.Tensor
...     bar: torch.Tensor
>>> data = MyClass(foo=torch.randn(10, 11), bar=torch.randn(10, 11, 12), batch_size=[10, 11])
>>> storage = LazyMemmapStorage(10)
>>> storage.set(range(10), data)
>>> storage.get(0)
MyClass(
    bar=MemoryMappedTensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False),
    foo=MemoryMappedTensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
    batch_size=torch.Size([11]),
    device=cpu,
    is_shared=False)

attach(buffer: Any) → None

This function attaches a sampler to this storage.

Buffers that read from this storage must be included as an attached entity by calling this method. This guarantees that when data in the storage changes, components are made aware of changes even if the storage is shared with other buffers (eg. Priority Samplers).

Parameters:

buffer – the object that reads from this storage.

dump(*args, **kwargs)

Alias for dumps().

load(*args, **kwargs)

Alias for loads().

save(*args, **kwargs)

Alias for dumps().