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[Doc] Better doc for SliceSampler
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ghstack-source-id: 7d79ef7d37c4dc2ffbdff5b422cf5da24d93c0da
Pull Request resolved: #2607
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@vmoens
vmoens committed Nov 25, 2024
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156 changes: 156 additions & 0 deletions torchrl/data/replay_buffers/samplers.py
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Expand Up @@ -802,6 +802,112 @@ class SliceSampler(Sampler):
attempt to find the ``traj_key`` entry in the storage. If it cannot be
found, the ``end_key`` will be used to reconstruct the episodes.
.. note:: When using `strict_length=False`, it is recommended to use
:func:`~torchrl.collectors.utils.split_trajectories` to split the sampled trajectories.
However, if two samples from the same episode are placed next to each other,
this may produce incorrect results. To avoid this issue, consider one of these solutions:
- using a :class:`~torchrl.data.TensorDictReplayBuffer` instance with the slice sampler
>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.collectors.utils import split_trajectories
>>> from torchrl.data import TensorDictReplayBuffer, ReplayBuffer, LazyTensorStorage, SliceSampler, SliceSamplerWithoutReplacement
>>>
>>> rb = TensorDictReplayBuffer(storage=LazyTensorStorage(max_size=1000),
... sampler=SliceSampler(
... slice_len=5, traj_key="episode",strict_length=False,
... ))
...
>>> ep_1 = TensorDict(
... {"obs": torch.arange(100),
... "episode": torch.zeros(100),},
... batch_size=[100]
... )
>>> ep_2 = TensorDict(
... {"obs": torch.arange(4),
... "episode": torch.ones(4),},
... batch_size=[4]
... )
>>> rb.extend(ep_1)
>>> rb.extend(ep_2)
>>>
>>> s = rb.sample(50)
>>> print(s)
TensorDict(
fields={
episode: Tensor(shape=torch.Size([46]), device=cpu, dtype=torch.float32, is_shared=False),
index: Tensor(shape=torch.Size([46, 1]), device=cpu, dtype=torch.int64, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([46, 1]), device=cpu, dtype=torch.bool, is_shared=False),
terminated: Tensor(shape=torch.Size([46, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([46, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([46]),
device=cpu,
is_shared=False),
obs: Tensor(shape=torch.Size([46]), device=cpu, dtype=torch.int64, is_shared=False)},
batch_size=torch.Size([46]),
device=cpu,
is_shared=False)
>>> t = split_trajectories(s, done_key="truncated")
>>> print(t["obs"])
tensor([[73, 74, 75, 76, 77],
[ 0, 1, 2, 3, 0],
[ 0, 1, 2, 3, 0],
[41, 42, 43, 44, 45],
[ 0, 1, 2, 3, 0],
[67, 68, 69, 70, 71],
[27, 28, 29, 30, 31],
[80, 81, 82, 83, 84],
[17, 18, 19, 20, 21],
[ 0, 1, 2, 3, 0]])
>>> print(t["episode"])
tensor([[0., 0., 0., 0., 0.],
[1., 1., 1., 1., 0.],
[1., 1., 1., 1., 0.],
[0., 0., 0., 0., 0.],
[1., 1., 1., 1., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[1., 1., 1., 1., 0.]])
- using a :class:`~torchrl.data.replay_buffers.samplers.SliceSamplerWithoutReplacement`
>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.collectors.utils import split_trajectories
>>> from torchrl.data import ReplayBuffer, LazyTensorStorage, SliceSampler, SliceSamplerWithoutReplacement
>>>
>>> rb = ReplayBuffer(storage=LazyTensorStorage(max_size=1000),
... sampler=SliceSamplerWithoutReplacement(
... slice_len=5, traj_key="episode",strict_length=False
... ))
...
>>> ep_1 = TensorDict(
... {"obs": torch.arange(100),
... "episode": torch.zeros(100),},
... batch_size=[100]
... )
>>> ep_2 = TensorDict(
... {"obs": torch.arange(4),
... "episode": torch.ones(4),},
... batch_size=[4]
... )
>>> rb.extend(ep_1)
>>> rb.extend(ep_2)
>>>
>>> s = rb.sample(50)
>>> t = split_trajectories(s, trajectory_key="episode")
>>> print(t["obs"])
tensor([[75, 76, 77, 78, 79],
[ 0, 1, 2, 3, 0]])
>>> print(t["episode"])
tensor([[0., 0., 0., 0., 0.],
[1., 1., 1., 1., 0.]])
Examples:
>>> import torch
>>> from tensordict import TensorDict
Expand Down Expand Up @@ -1427,6 +1533,10 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
class SliceSamplerWithoutReplacement(SliceSampler, SamplerWithoutReplacement):
"""Samples slices of data along the first dimension, given start and stop signals, without replacement.
In this context, ``without replacement`` means that the same element (NOT trajectory) will not be sampled twice
before the counter is automatically reset. Within a single sample, however, only one slice of a given trajectory
will appear (see example below).
This class is to be used with static replay buffers or in between two
replay buffer extensions. Extending the replay buffer will reset the
the sampler, and continuous sampling without replacement is currently not
Expand Down Expand Up @@ -1533,6 +1643,52 @@ class SliceSamplerWithoutReplacement(SliceSampler, SamplerWithoutReplacement):
tensor([ 1, 2, 7, 9, 10, 13, 15, 18, 21, 22])
tensor([ 0, 3, 4, 20, 23])
When requesting a large total number of samples with few trajectories and small span, the batch will contain
only at most one sample of each trajectory:
Examples:
>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.collectors.utils import split_trajectories
>>> from torchrl.data import ReplayBuffer, LazyTensorStorage, SliceSampler, SliceSamplerWithoutReplacement
>>>
>>> rb = ReplayBuffer(storage=LazyTensorStorage(max_size=1000),
... sampler=SliceSamplerWithoutReplacement(
... slice_len=5, traj_key="episode",strict_length=False
... ))
...
>>> ep_1 = TensorDict(
... {"obs": torch.arange(100),
... "episode": torch.zeros(100),},
... batch_size=[100]
... )
>>> ep_2 = TensorDict(
... {"obs": torch.arange(51),
... "episode": torch.ones(51),},
... batch_size=[51]
... )
>>> rb.extend(ep_1)
>>> rb.extend(ep_2)
>>>
>>> s = rb.sample(50)
>>> t = split_trajectories(s, trajectory_key="episode")
>>> print(t["obs"])
tensor([[14, 15, 16, 17, 18],
[ 3, 4, 5, 6, 7]])
>>> print(t["episode"])
tensor([[0., 0., 0., 0., 0.],
[1., 1., 1., 1., 1.]])
>>>
>>> s = rb.sample(50)
>>> t = split_trajectories(s, trajectory_key="episode")
>>> print(t["obs"])
tensor([[ 4, 5, 6, 7, 8],
[26, 27, 28, 29, 30]])
>>> print(t["episode"])
tensor([[0., 0., 0., 0., 0.],
[1., 1., 1., 1., 1.]])
"""

def __init__(
Expand Down

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