Rate this Page
★ ★ ★ ★ ★
reference/generated/torchrl.data.RemoteTensorDictReplayBuffer

RemoteTensorDictReplayBuffer#

class torchrl.data.RemoteTensorDictReplayBuffer(*args, use_ray_service=False, service_backend=None, service_backend_options=None, **kwargs)[source]#

A remote invocation friendly ReplayBuffer class. Public methods can be invoked by remote agents using torch.rpc or called locally as normal.

add(data: TensorDictBase) → int[source]#

Add a single element to the replay buffer.

Parameters:

data (Any) – data to be added to the replay buffer

Returns:

index where the data lives in the replay buffer.

append_transform(transform: Transform, *, invert: bool = False) → ReplayBuffer#

Appends transform at the end.

Transforms are applied in order when sample is called.

Parameters:

transform (Transform) – The transform to be appended

Keyword Arguments:

invert (bool, optional) – if True, the transform will be inverted (forward calls will be called during writing and inverse calls during reading). Defaults to False.

Example

>>> rb = ReplayBuffer(storage=LazyMemmapStorage(10), batch_size=4)
>>> data = TensorDict({"a": torch.zeros(10)}, [10])
>>> def t(data):
...     data += 1
...     return data
>>> rb.append_transform(t, invert=True)
>>> rb.extend(data)
>>> assert (data == 1).all()
classmethod as_remote(remote_config=None)#

Creates an instance of a remote ray class.

Parameters:

  • cls (Python Class) – class to be remotely instantiated.

  • remote_config (dict) – the quantity of CPU cores to reserve for this class. Defaults to torchrl.collectors.distributed.ray.DEFAULT_REMOTE_CLASS_CONFIG.

Returns:

A function that creates ray remote class instances.

property batch_size#

The batch size of the replay buffer.

The batch size can be overridden by setting the batch_size parameter in the sample() method.

It defines both the number of samples returned by sample() and the number of samples that are yielded by the ReplayBuffer iterator.

client() → T#

Return self for the zero-overhead direct backend.

dump(*args, **kwargs)#

Alias for dumps().

dumps(path)#

Saves the replay buffer on disk at the specified path.

Parameters:

path (Path or str) – path where to save the replay buffer.

Examples

>>> import tempfile
>>> import tqdm
>>> from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer
>>> from torchrl.data.replay_buffers.samplers import PrioritizedSampler, RandomSampler
>>> import torch
>>> from tensordict import TensorDict
>>> # Build and populate the replay buffer
>>> S = 1_000_000
>>> sampler = PrioritizedSampler(S, 1.1, 1.0)
>>> # sampler = RandomSampler()
>>> storage = LazyMemmapStorage(S)
>>> rb = TensorDictReplayBuffer(storage=storage, sampler=sampler)
>>>
>>> for _ in tqdm.tqdm(range(100)):
...     td = TensorDict({"obs": torch.randn(100, 3, 4), "next": {"obs": torch.randn(100, 3, 4)}, "td_error": torch.rand(100)}, [100])
...     rb.extend(td)
...     sample = rb.sample(32)
...     rb.update_tensordict_priority(sample)
>>> # save and load the buffer
>>> with tempfile.TemporaryDirectory() as tmpdir:
...     rb.dumps(tmpdir)
...
...     sampler = PrioritizedSampler(S, 1.1, 1.0)
...     # sampler = RandomSampler()
...     storage = LazyMemmapStorage(S)
...     rb_load = TensorDictReplayBuffer(storage=storage, sampler=sampler)
...     rb_load.loads(tmpdir)
...     assert len(rb) == len(rb_load)
empty(empty_write_count: bool = True)#

Empties the replay buffer and reset cursor to 0.

Parameters:

empty_write_count (bool, optional) – Whether to empty the write_count attribute. Defaults to True.

extend(tensordicts: list | TensorDictBase, *, update_priority: bool | None = None) → Tensor[source]#

Extends the replay buffer with a batch of data.

Parameters:

tensordicts (TensorDictBase) – The data to extend the replay buffer with.

Keyword Arguments:

update_priority (bool, optional) – Whether to update the priority of the data. Defaults to True.

Returns:

The indices of the data that were added to the replay buffer.

property initialized: bool#

Whether the replay buffer has been initialized.

insert_transform(index: int, transform: Transform, *, invert: bool = False) → ReplayBuffer#

Inserts transform.

Transforms are executed in order when sample is called.

Parameters:

  • index (int) – Position to insert the transform.

  • transform (Transform) – The transform to be appended

Keyword Arguments:

invert (bool, optional) – if True, the transform will be inverted (forward calls will be called during writing and inverse calls during reading). Defaults to False.

property is_alive: bool#

Whether this direct replay buffer remains available.

load(*args, **kwargs)#

Alias for loads().

loads(path)#

Loads a replay buffer state at the given path.

The buffer should have matching components and be saved using dumps().

Parameters:

path (Path or str) – path where the replay buffer was saved.

See dumps() for more info.

next()#

Returns the next item in the replay buffer.

This method is used to iterate over the replay buffer in contexts where __iter__ is not available, such as RayReplayBuffer.

query(predicate: Callable[[Trajectory], bool] | None = None, *, trajectory_key: NestedKey | None = None) → list[Trajectory]#

Filters the stored trajectories with a query predicate.

Splits the buffer content into trajectories (see iter_trajectories()) and returns those matching the predicate as Trajectory views.

Parameters:

predicate (Callable[[Trajectory], bool], optional) – a TrajectoryPredicate built from traj, or any callable mapping a trajectory to a boolean. Defaults to None (return all trajectories).

Keyword Arguments:

trajectory_key (NestedKey, optional) – entry holding per-transition trajectory ids. Defaults to None (auto-detection from ("collector", "traj_ids"), "traj_ids", "episode" or the done/terminated/truncated flags).

Returns:

A list of matching trajectory views, ordered chronologically (oldest trajectory first; for multi-dimensional storages, grouped by batch coordinate).

The trajectory boundaries are computed from the stored (untransformed) data with the same machinery SliceSampler uses, so samplers and queries always agree on where trajectories start and stop. This includes storages with ndim > 1 (e.g. LazyTensorStorage(..., ndim=2) holding [B, T] batches), whose trajectories are recovered per batch coordinate.

Predicates built from traj report the keys they read via required_keys(); evaluation then only fetches those entries from the storage and only runs the transforms that can affect them. Matching trajectories are extracted in full with the complete transform chain applied, so predicates and results see the same values a sampler would produce. Opaque callables are evaluated against the fully transformed content.

Note

Once the buffer has wrapped around (it is at capacity and older entries have been overwritten), the oldest trajectory may have lost its first transitions to overwriting and will appear truncated at the front. A trajectory written across the wrap point is followed through it and returned whole, in time order.

Examples

>>> from torchrl.data import traj
>>> good_trajs = rb.query((traj.reward.sum() > 100) & (traj.length >= 50))
>>> observations = good_trajs[0].observation
read_all_in_order(end: int | None = None) → Any#

Read storage contents in physical order.

This is equivalent to rb[:] when end is None.

Parameters:

end (int, optional) – Number of leading storage entries to read. Defaults to the entire storage slice.

Returns:

A storage slice containing entries [:end].

register_load_hook(hook: Callable[[Any], Any])#

Registers a load hook for the storage.

Note

Hooks are currently not serialized when saving a replay buffer: they must be manually re-initialized every time the buffer is created.

register_save_hook(hook: Callable[[Any], Any])#

Registers a save hook for the storage.

Note

Hooks are currently not serialized when saving a replay buffer: they must be manually re-initialized every time the buffer is created.

sample(batch_size: int | None = None, include_info: bool | None = None, return_info: bool = False) → TensorDictBase[source]#

Samples a batch of data from the replay buffer.

Uses Sampler to sample indices, and retrieves them from Storage.

Parameters:

  • batch_size (int, optional) – size of data to be collected. If none is provided, this method will sample a batch-size as indicated by the sampler.

  • return_info (bool) – whether to return info. If True, the result is a tuple (data, info). If False, the result is the data.

  • include_info (bool, optional) – deprecated alias for return_info.

Returns:

A tensordict containing a batch of data selected in the replay buffer. A tuple containing this tensordict and info if return_info flag is set to True.

property sampler: Sampler#

The sampler of the replay buffer.

The sampler must be an instance of Sampler.

save(*args, **kwargs)#

Alias for dumps().

property service_backend: str#

The canonical deployment backend for this replay buffer.

set_(key, value)#

Sets the value of a key across the entire replay buffer in-place.

Parameters:

  • key (NestedKey) – the key to set.

  • value (torch.Tensor) – the value to write.

Returns:

self

set_at_(key, value, index)#

Sets the value of a key at specified indices in the replay buffer.

Parameters:

  • key (NestedKey) – the key to set.

  • value (torch.Tensor) – the value to write.

  • index – the indices where to write the value.

Returns:

self

set_sampler(sampler: Sampler)#

Sets a new sampler in the replay buffer and returns the previous sampler.

set_storage(storage: Storage, collate_fn: Callable | None = None)#

Sets a new storage in the replay buffer and returns the previous storage.

Parameters:

  • storage (Storage) – the new storage for the buffer.

  • collate_fn (callable, optional) – if provided, the collate_fn is set to this value. Otherwise it is reset to a default value.

set_writer(writer: Writer)#

Sets a new writer in the replay buffer and returns the previous writer.

shutdown(timeout: float | None = None) → None#

Mark this direct replay-buffer owner as shut down.

start() → T#

Return this already-started direct replay buffer.

stats() → dict[str, int | float | bool]#

Returns a cheap, serializable snapshot of the buffer’s operational state.

The snapshot only contains scalar counters and gauges. It never includes the storage content, does not modify the buffer state and is safe to call concurrently with writes and samples. Cumulative counters such as write_count are meant to be converted into rates by an external monitor such as LoggerMonitor.

Calling this method on an uninitialized buffer does not trigger its initialization; an empty snapshot with initialized=False is returned instead (capacity is still reported when the storage already advertises it).

Returns:

  • "size": current number of elements in the buffer (mirrors len(buffer));

  • "write_count": total number of items written through add and extend (0 for writers that do not track writes, such as ImmutableDatasetWriter);

  • "prefetch_queue_size": number of pending prefetched batches;

  • "initialized": whether the buffer components are initialized;

  • "capacity": maximum number of elements the storage can hold (only present when the storage advertises a max_size);

  • "utilization": size / capacity (only present alongside capacity).

Remote clients backed by the distributed transport report a subset of these entries (size and write_count).

Return type:

A dictionary with the following entries

Examples

>>> import torch
>>> from torchrl.data import LazyTensorStorage, ReplayBuffer
>>> rb = ReplayBuffer(storage=LazyTensorStorage(10))
>>> rb.extend(torch.arange(5))
>>> snapshot = rb.stats()
>>> print(snapshot["size"], snapshot["write_count"], snapshot["capacity"])
5 5 10
property storage: Storage#

The storage of the replay buffer.

The storage must be an instance of Storage.

property transform: Transform#

The transform of the replay buffer.

The transform must be an instance of Transform.

update_(input_dict_or_td, clone=False, *, keys_to_update=None)#

Updates the replay buffer in-place with the given dict or TensorDict.

Parameters:

  • input_dict_or_td (dict or TensorDictBase) – the data to update with.

  • clone (bool, optional) – whether to clone the values before writing. Defaults to False.

  • keys_to_update (sequence of NestedKey, optional) – if provided, only these keys will be updated.

Returns:

self

write_all(data: Any, end: int | None = None) → None#

Write data back to storage in physical order.

This is equivalent to rb[:end] = data. If end is None, end defaults to data.shape[0] for tensor collections and len(data) otherwise. If data spans the full storage, this is equivalent to rb[:] = data.

Parameters:

  • data – Data to write to storage.

  • end (int, optional) – Number of leading storage entries to update. Defaults to data.shape[0] for tensor collections and len(data) otherwise.

property write_count: int#

The total number of items written so far in the buffer through add and extend.

property writer: Writer#

The writer of the replay buffer.

The writer must be an instance of Writer.

On this page
Edit on GitHub
Show Source
PyTorch Libraries