Source code for torchrl.collectors.utils
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
from typing import Callable
import torch
from tensordict import NestedKey, pad, set_lazy_legacy, TensorDictBase
def _stack_output(fun) -> Callable:
def stacked_output_fun(*args, **kwargs):
out = fun(*args, **kwargs)
return tuple(torch.stack(_o, 0) for _o in out)
return stacked_output_fun
def _stack_output_zip(fun) -> Callable:
def stacked_output_fun(*args, **kwargs):
out = fun(*args, **kwargs)
return tuple(torch.stack(_o, 0) for _o in zip(*out))
return stacked_output_fun
[docs]@set_lazy_legacy(False)
def split_trajectories(
rollout_tensordict: TensorDictBase,
*,
prefix=None,
trajectory_key: NestedKey | None = None,
done_key: NestedKey | None = None,
) -> TensorDictBase:
"""A util function for trajectory separation.
Takes a tensordict with a key traj_ids that indicates the id of each trajectory.
From there, builds a B x T x ... zero-padded tensordict with B batches on max duration T
Args:
rollout_tensordict (TensorDictBase): a rollout with adjacent trajectories
along the last dimension.
prefix (NestedKey, optional): the prefix used to read and write meta-data,
such as ``"traj_ids"`` (the optional integer id of each trajectory)
and the ``"mask"`` entry indicating which data are valid and which
aren't. Defaults to ``"collector"`` if the input has a ``"collector"``
entry, ``()`` (no prefix) otherwise.
``prefix`` is kept as a legacy feature and will be deprecated eventually.
Prefer ``trajectory_key`` or ``done_key`` whenever possible.
trajectory_key (NestedKey, optional): the key pointing to the trajectory
ids. Supersedes ``done_key`` and ``prefix``. If not provided, defaults
to ``(prefix, "traj_ids")``.
done_key (NestedKey, optional): the key pointing to the ``"done""`` signal,
if the trajectory could not be directly recovered. Defaults to ``"done"``.
Returns:
A new tensordict with a leading dimension corresponding to the trajectory.
A ``"mask"`` boolean entry sharing the ``trajectory_key`` prefix
and the tensordict shape is also added. It indicated the valid elements of the tensordict,
as well as a ``"traj_ids"`` entry if ``trajectory_key`` could not be found.
Examples:
>>> from tensordict import TensorDict
>>> import torch
>>> from torchrl.collectors.utils import split_trajectories
>>> obs = torch.cat([torch.arange(10), torch.arange(5)])
>>> obs_ = torch.cat([torch.arange(1, 11), torch.arange(1, 6)])
>>> done = torch.zeros(15, dtype=torch.bool)
>>> done[9] = True
>>> trajectory_id = torch.cat([torch.zeros(10, dtype=torch.int32),
... torch.ones(5, dtype=torch.int32)])
>>> data = TensorDict({"obs": obs, ("next", "obs"): obs_, ("next", "done"): done, "trajectory": trajectory_id}, batch_size=[15])
>>> data_split = split_trajectories(data, done_key="done")
>>> print(data_split)
TensorDict(
fields={
mask: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.bool, is_shared=False),
obs: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.int64, is_shared=False)},
batch_size=torch.Size([2, 10]),
device=None,
is_shared=False),
obs: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.int64, is_shared=False),
traj_ids: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.int64, is_shared=False),
trajectory: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.int32, is_shared=False)},
batch_size=torch.Size([2, 10]),
device=None,
is_shared=False)
>>> # check that split_trajectory got the trajectories right with the done signal
>>> assert (data_split["traj_ids"] == data_split["trajectory"]).all()
>>> print(data_split["mask"])
tensor([[ True, True, True, True, True, True, True, True, True, True],
[ True, True, True, True, True, False, False, False, False, False]])
>>> data_split = split_trajectories(data, trajectory_key="trajectory")
>>> print(data_split)
TensorDict(
fields={
mask: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.bool, is_shared=False),
obs: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.int64, is_shared=False)},
batch_size=torch.Size([2, 10]),
device=None,
is_shared=False),
obs: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.int64, is_shared=False),
trajectory: Tensor(shape=torch.Size([2, 10]), device=cpu, dtype=torch.int32, is_shared=False)},
batch_size=torch.Size([2, 10]),
device=None,
is_shared=False)
"""
mask_key = None
if trajectory_key is not None:
from torchrl.envs.utils import _replace_last
traj_ids_key = trajectory_key
mask_key = _replace_last(trajectory_key, "mask")
else:
if prefix is None and "collector" in rollout_tensordict.keys():
prefix = "collector"
if prefix is None:
traj_ids_key = "traj_ids"
mask_key = "mask"
else:
traj_ids_key = (prefix, "traj_ids")
mask_key = (prefix, "mask")
rollout_tensordict = rollout_tensordict.copy()
traj_ids = rollout_tensordict.get(traj_ids_key, None)
if traj_ids is None:
if done_key is None:
done_key = "done"
done_key = ("next", done_key)
done = rollout_tensordict.get(done_key)
idx = (slice(None),) * (rollout_tensordict.ndim - 1) + (slice(None, -1),)
done_sel = done[idx]
pads = [1, 0]
pads = [0, 0] * (done.ndim - rollout_tensordict.ndim) + pads
done_sel = torch.nn.functional.pad(done_sel, pads)
if done_sel.shape != done.shape:
raise RuntimeError(
f"done and done_sel have different shape {done.shape} - {done_sel.shape} "
)
traj_ids = done_sel.cumsum(rollout_tensordict.ndim - 1)
traj_ids = traj_ids.squeeze(-1)
if rollout_tensordict.ndim > 1:
for i in range(1, rollout_tensordict.shape[0]):
traj_ids[i] += traj_ids[i - 1].max() + 1
rollout_tensordict.set(traj_ids_key, traj_ids)
splits = traj_ids.reshape(-1)
splits = [(splits == i).sum().item() for i in splits.unique_consecutive()]
# if all splits are identical then we can skip this function
if len(set(splits)) == 1 and splits[0] == traj_ids.shape[-1]:
rollout_tensordict.set(
mask_key,
torch.ones(
rollout_tensordict.shape,
device=rollout_tensordict.device,
dtype=torch.bool,
),
)
if rollout_tensordict.ndimension() == 1:
rollout_tensordict = rollout_tensordict.unsqueeze(0)
return rollout_tensordict
out_splits = rollout_tensordict.reshape(-1).split(splits, 0)
for out_split in out_splits:
out_split.set(
mask_key,
torch.ones(
out_split.shape,
dtype=torch.bool,
device=out_split.device,
),
)
if len(out_splits) > 1:
MAX = max(*[out_split.shape[0] for out_split in out_splits])
else:
MAX = out_splits[0].shape[0]
td = torch.stack(
[pad(out_split, [0, MAX - out_split.shape[0]]) for out_split in out_splits], 0
)
return td
