LazyMemmapStorage

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

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.

• 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.

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.