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[Feature] Add PrioritizedSliceSampler #1875

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merged 11 commits into from
Feb 7, 2024
Merged

[Feature] Add PrioritizedSliceSampler #1875

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ac4fa9d
Add PrioritizedSliceSampler + few tests
Cadene Feb 6, 2024
74a4bee
Improve docstring + remove comments
Cadene Feb 6, 2024
ffba3dc
Merge remote-tracking branch 'origin/main' into prioritized_slice_sam…
vmoens Feb 6, 2024
58e1995
Add more tests + Fix + Add docstrings + Improved import in __init__
Cadene Feb 7, 2024
84962c6
Merge remote-tracking branch 'fork/prioritized_slice_sampler' into pr…
Cadene Feb 7, 2024
109dbb5
Revert bugfix traj_terminated
Cadene Feb 7, 2024
a9d1388
Update torchrl/data/replay_buffers/samplers.py
Cadene Feb 7, 2024
8463e09
amend
vmoens Feb 7, 2024
56de68a
Merge remote-tracking branch 'origin/main' into prioritized_slice_sam…
vmoens Feb 7, 2024
b647345
amend
vmoens Feb 7, 2024
6e68587
Merge remote-tracking branch 'origin/main' into prioritized_slice_sam…
vmoens Feb 7, 2024
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amend
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@vmoens
vmoens committed Feb 7, 2024
commit 8463e091748b1f7b5f1bb9b6ed7090e21dbccdd7
1 change: 1 addition & 0 deletions docs/source/reference/data.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -134,6 +134,7 @@ using the following components:

Sampler
PrioritizedSampler
PrioritizedSliceSampler
RandomSampler
SamplerWithoutReplacement
SliceSampler
Expand Down
24 changes: 12 additions & 12 deletions torchrl/data/replay_buffers/samplers.py
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Expand Up @@ -546,7 +546,7 @@ class SliceSampler(Sampler):
allowed to appear in the batch.
Be mindful that this can result in effective `batch_size` shorter
than the one asked for! Trajectories can be split using
:func:`torchrl.collectors.split_trajectories`. Defaults to ``True``.
:func:`~torchrl.collectors.split_trajectories`. Defaults to ``True``.

.. note:: To recover the trajectory splits in the storage,
:class:`~torchrl.data.replay_buffers.samplers.SliceSampler` will first
Expand Down Expand Up @@ -583,7 +583,7 @@ class SliceSampler(Sampler):
>>> print("episodes", sample.get("episode").unique())
episodes tensor([1, 2, 3, 4], dtype=torch.int32)

:class:`torchrl.data.replay_buffers.SliceSampler` is default-compatible with
:class:`~torchrl.data.replay_buffers.SliceSampler` is default-compatible with
most of TorchRL's datasets:

Examples:
Expand Down Expand Up @@ -962,7 +962,7 @@ class SliceSamplerWithoutReplacement(SliceSampler, SamplerWithoutReplacement):
allowed to appear in the batch.
Be mindful that this can result in effective `batch_size` shorter
than the one asked for! Trajectories can be split using
:func:`torchrl.collectors.split_trajectories`. Defaults to ``True``.
:func:`~torchrl.collectors.split_trajectories`. Defaults to ``True``.
shuffle (bool, optional): if ``False``, the order of the trajectories
is not shuffled. Defaults to ``True``.

Expand Down Expand Up @@ -1003,7 +1003,7 @@ class SliceSamplerWithoutReplacement(SliceSampler, SamplerWithoutReplacement):
>>> print("sample:", sample)
>>> print("trajectories in sample", sample.get("episode").unique())

:class:`torchrl.data.replay_buffers.SliceSamplerWithoutReplacement` is default-compatible with
:class:`~torchrl.data.replay_buffers.SliceSamplerWithoutReplacement` is default-compatible with
most of TorchRL's datasets, and allows users to consume datasets in a dataloader-like fashion:

Examples:
Expand Down Expand Up @@ -1131,7 +1131,7 @@ class PrioritizedSliceSampler(SliceSampler, PrioritizedSampler):
allowed to appear in the batch.
Be mindful that this can result in effective `batch_size` shorter
than the one asked for! Trajectories can be split using
:func:`torchrl.collectors.split_trajectories`. Defaults to ``True``.
:func:`~torchrl.collectors.split_trajectories`. Defaults to ``True``.

Examples:
>>> import torch
Expand All @@ -1154,19 +1154,19 @@ class PrioritizedSliceSampler(SliceSampler, PrioritizedSampler):
>>> rb.extend(data)
>>> sample, info = rb.sample(return_info=True)
>>> print("episode", sample["episode"].tolist())
episode [2, 2, 2, 2, 1, 1]
>>> print("steps", sample["steps"].tolist())
steps [1, 2, 0, 1, 1, 2]
>>> print("weight", info["_weight"].tolist())
weight [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
>>> priority = torch.tensor([0,3,3,0,0,0,1,1,1])
>>> rb.update_priority(torch.arange(0,9,1), priority=priority)
>>> sample, info = rb.sample(return_info=True)
>>> print("episode", sample["episode"].tolist())
>>> print("steps", sample["steps"].tolist())
>>> print("weight", info["_weight"].tolist())
episode [2, 2, 2, 2, 1, 1]
steps [1, 2, 0, 1, 1, 2]
weight [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
episode [2, 2, 2, 2, 2, 2]
>>> print("steps", sample["steps"].tolist())
steps [1, 2, 0, 1, 0, 1]
>>> print("weight", info["_weight"].tolist())
weight [9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06]
"""

Expand Down Expand Up @@ -1254,8 +1254,8 @@ def sample(self, storage: Storage, batch_size: int) -> Tuple[torch.Tensor, dict]
self, storage=storage, batch_size=batch_size // seq_length
)
# TODO: update PrioritizedSampler.sample to return torch tensors
starts = torch.from_numpy(starts).to(device=lengths.device)
info["_weight"] = torch.from_numpy(info["_weight"]).to(device=lengths.device)
starts = torch.as_tensor(starts, device=lengths.device)
info["_weight"] = torch.as_tensor(info["_weight"], device=lengths.device)

# extends starting indices of each slice with sequence_length to get indices of all steps
index = self._tensor_slices_from_startend(seq_length, starts)
Expand Down