Source code for torchrl.data.datasets.vd4rl
# 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
import functools
import importlib
import json
import os
import pathlib
import shutil
import tempfile
from collections import defaultdict
from pathlib import Path
from typing import Callable, List
import numpy as np
import torch
from tensordict import PersistentTensorDict, TensorDict
from torch import multiprocessing as mp
from torchrl._utils import KeyDependentDefaultDict, logger as torchrl_logger
from torchrl.data.datasets.common import BaseDatasetExperienceReplay
from torchrl.data.datasets.utils import _get_root_dir
from torchrl.data.replay_buffers.samplers import Sampler
from torchrl.data.replay_buffers.storages import TensorStorage
from torchrl.data.replay_buffers.writers import ImmutableDatasetWriter, Writer
from torchrl.envs.transforms import Compose, Resize, ToTensorImage
from torchrl.envs.utils import _classproperty
_has_tqdm = importlib.util.find_spec("tqdm", None) is not None
_has_h5py = importlib.util.find_spec("h5py", None) is not None
_has_hf_hub = importlib.util.find_spec("huggingface_hub", None) is not None
THIS_DIR = pathlib.Path(__file__).parent
[docs]class VD4RLExperienceReplay(BaseDatasetExperienceReplay):
"""V-D4RL experience replay dataset.
This class downloads the H5/npz data from V-D4RL and processes it in a mmap
format, which makes indexing (and therefore sampling) faster.
Learn more about V-D4RL here: https://arxiv.org/abs/2206.04779
The `"pixels"` entry is located at the root of the data, and all the data
that is not reward, done-state, action or pixels is moved under a `"state"`
node.
The data format follows the :ref:`TED convention <TED-format>`.
Args:
dataset_id (str): the dataset to be downloaded. Must be part of
VD4RLExperienceReplay.available_datasets.
batch_size (int): Batch-size used during sampling. Can be overridden by
`data.sample(batch_size)` if necessary.
Keyword Args:
root (Path or str, optional): The V-D4RL dataset root directory.
The actual dataset memory-mapped files will be saved under
`<root>/<dataset_id>`. If none is provided, it defaults to
``~/.cache/torchrl/vd4rl`.
download (bool or str, optional): Whether the dataset should be downloaded if
not found. Defaults to ``True``. Download can also be passed as ``"force"``,
in which case the downloaded data will be overwritten.
sampler (Sampler, optional): the sampler to be used. If none is provided
a default RandomSampler() will be used.
writer (Writer, optional): the writer to be used. If none is provided
a default :class:`~torchrl.data.replay_buffers.writers.ImmutableDatasetWriter` will be used.
collate_fn (callable, optional): merges a list of samples to form a
mini-batch of Tensor(s)/outputs. Used when using batched
loading from a map-style dataset.
pin_memory (bool): whether pin_memory() should be called on the rb
samples.
prefetch (int, optional): number of next batches to be prefetched
using multithreading.
transform (Transform, optional): Transform to be executed when sample() is called.
To chain transforms use the :class:`~torchrl.envs.transforms.transforms.Compose` class.
split_trajs (bool, optional): if ``True``, the trajectories will be split
along the first dimension and padded to have a matching shape.
To split the trajectories, the ``"done"`` signal will be used, which
is recovered via ``done = truncated | terminated``. In other words,
it is assumed that any ``truncated`` or ``terminated`` signal is
equivalent to the end of a trajectory. For some datasets from
``D4RL``, this may not be true. It is up to the user to make
accurate choices regarding this usage of ``split_trajs``.
Defaults to ``False``.
totensor (bool, optional): if ``True``, a :class:`~torchrl.envs.transforms.ToTensorImage`
transform will be included in the transform list (if not automatically
detected). Defaults to ``True``.
image_size (int, list of ints or None): if not ``None``, this argument
will be used to create a :class:`~torchrl.envs.transforms.Resize`
transform that will be appended to the transform list. Supports
`int` types (square resizing) or a list/tuple of `int` (rectangular
resizing). Defaults to ``None`` (no resizing).
num_workers (int, optional): the number of workers to download the files.
Defaults to ``0`` (no multiprocessing).
Attributes:
available_datasets: a list of accepted entries to be downloaded. These
names correspond to the directory path in the huggingface dataset
repository. If possible, the list will be dynamically retrieved from
huggingface. If no internet connection is available, it a cached
version will be used.
.. note:: Since not all experience replay have start and stop signals, we
do not mark the episodes in the retrieved dataset.
Examples:
>>> import torch
>>> torch.manual_seed(0)
>>> from torchrl.data.datasets import VD4RLExperienceReplay
>>> d = VD4RLExperienceReplay("main/walker_walk/random/64px", batch_size=32,
... image_size=50)
>>> for batch in d:
... break
>>> print(batch)
TensorDict(
fields={
action: Tensor(shape=torch.Size([32, 6]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
index: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.int64, is_shared=False),
is_init: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: TensorDict(
fields={
height: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.float32, is_shared=False),
orientations: Tensor(shape=torch.Size([32, 14]), device=cpu, dtype=torch.float32, is_shared=False),
velocity: Tensor(shape=torch.Size([32, 9]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([32]),
device=cpu,
is_shared=False),
pixels: Tensor(shape=torch.Size([32, 3, 50, 50]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([32]),
device=cpu,
is_shared=False),
observation: TensorDict(
fields={
height: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.float32, is_shared=False),
orientations: Tensor(shape=torch.Size([32, 14]), device=cpu, dtype=torch.float32, is_shared=False),
velocity: Tensor(shape=torch.Size([32, 9]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([32]),
device=cpu,
is_shared=False),
pixels: Tensor(shape=torch.Size([32, 3, 50, 50]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
truncated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([32]),
device=cpu,
is_shared=False)
"""
def __init__(
self,
dataset_id,
batch_size: int,
*,
root: str | Path | None = None,
download: bool = True,
sampler: Sampler | None = None,
writer: Writer | None = None,
collate_fn: Callable | None = None,
pin_memory: bool = False,
prefetch: int | None = None,
transform: "torchrl.envs.Transform" | None = None, # noqa-F821
split_trajs: bool = False,
totensor: bool = True,
image_size: int | List[int] | None = None,
num_workers: int = 0,
**env_kwargs,
):
if not _has_h5py or not _has_hf_hub:
raise ImportError(
"h5py and huggingface_hub are required for V-D4RL datasets."
)
if dataset_id not in self.available_datasets:
raise ValueError(
f"The dataset_id {dataset_id} isn't part of the accepted datasets. "
f"To check which dataset can be downloaded, call `{type(self)}.available_datasets`."
)
self.dataset_id = dataset_id
if root is None:
root = _get_root_dir("vd4rl")
os.makedirs(root, exist_ok=True)
self.root = root
self.split_trajs = split_trajs
self.download = download
self.num_workers = num_workers
if self.download == "force" or (self.download and not self._is_downloaded()):
if self.download == "force":
try:
if os.path.exists(self.data_path_root):
shutil.rmtree(self.data_path_root)
if self.data_path != self.data_path_root:
shutil.rmtree(self.data_path)
except FileNotFoundError:
pass
storage = self._download_and_preproc(
dataset_id, data_path=self.data_path, num_workers=self.num_workers
)
elif self.split_trajs and not os.path.exists(self.data_path):
storage = self._make_split()
else:
storage = self._load()
if totensor and transform is None:
transform = ToTensorImage(
in_keys=["pixels", ("next", "pixels")], shape_tolerant=True
)
elif totensor and (
not isinstance(transform, Compose)
or not any(isinstance(t, ToTensorImage) for t in transform)
):
transform = Compose(
transform,
ToTensorImage(
in_keys=["pixels", ("next", "pixels")], shape_tolerant=True
),
)
if image_size is not None:
transform = Compose(
transform, Resize(image_size, in_keys=["pixels", ("next", "pixels")])
)
storage = TensorStorage(storage)
if writer is None:
writer = ImmutableDatasetWriter()
super().__init__(
storage=storage,
sampler=sampler,
writer=writer,
collate_fn=collate_fn,
pin_memory=pin_memory,
prefetch=prefetch,
transform=transform,
batch_size=batch_size,
)
@classmethod
def _parse_datasets(cls):
from huggingface_hub import HfApi
dataset = HfApi().dataset_info("conglu/vd4rl")
sibs = defaultdict(list)
for sib in dataset.siblings:
if sib.rfilename.endswith("npz") or sib.rfilename.endswith("hdf5"):
path = Path(sib.rfilename)
sibs[path.parent].append(path)
return sibs
@classmethod
def _hf_hub_download(cls, subfolder, filename, *, tmpdir):
from huggingface_hub import hf_hub_download
return hf_hub_download(
"conglu/vd4rl",
subfolder=subfolder,
filename=filename,
repo_type="dataset",
cache_dir=str(tmpdir),
)
@classmethod
def _download_and_preproc(cls, dataset_id, data_path, num_workers):
tds = []
with tempfile.TemporaryDirectory() as tmpdir:
sibs = cls._parse_datasets()
total_steps = 0
paths_to_proc = []
files_to_proc = []
for path in sibs:
if dataset_id not in str(path):
continue
for file in sibs[path]:
paths_to_proc.append(str(path))
files_to_proc.append(str(file.parts[-1]))
func = functools.partial(cls._hf_hub_download, tmpdir=tmpdir)
if num_workers > 0:
with mp.Pool(num_workers) as pool:
files = pool.starmap(
func,
zip(paths_to_proc, files_to_proc),
)
files = list(files)
else:
files = [
func(subfolder, filename)
for (subfolder, filename) in zip(paths_to_proc, files_to_proc)
]
torchrl_logger.info("Downloaded, processing files")
if _has_tqdm:
import tqdm
pbar = tqdm.tqdm(files)
else:
pbar = files
for local_path in pbar:
if _has_tqdm:
pbar.set_description(f"file={local_path}")
# we memmap temporarily the files for faster access later
if local_path.endswith("hdf5"):
td = (
PersistentTensorDict.from_h5(local_path)
.to_tensordict()
.memmap(num_threads=32)
)
else:
td = _from_npz(local_path).memmap(num_threads=32)
td.unlock_()
if total_steps == 0:
tdc = cls._process_data(td.clone())
td_save = tdc[0]
tds.append(td)
total_steps += td.shape[0]
# From this point, the local paths are non needed anymore
td_save = td_save.expand(total_steps).memmap_like(data_path, num_threads=32)
torchrl_logger.info(f"Saved tensordict: {td_save}")
idx0 = 0
idx1 = 0
while len(files):
_ = files.pop(0)
td = tds.pop(0)
td = cls._process_data(td)
idx1 += td.shape[0]
td_save[idx0:idx1] = td
idx0 = idx1
return td_save
@classmethod
def _process_data(cls, td: TensorDict):
for name in list(td.keys()):
# move remaining data
if name not in _NAME_MATCH:
td.rename_key_(name, ("state", name))
elif name != _NAME_MATCH[name]:
td.rename_key_(name, _NAME_MATCH[name])
if ("next", "reward") in td.keys(True):
td.set(("next", "reward"), td.get(("next", "reward")).unsqueeze(-1))
if ("next", "done") in td.keys(True) and ("next", "terminated") in td.keys(
True
):
# first unsqueeze
td.set(("next", "done"), td.get(("next", "done")).unsqueeze(-1))
td.set(("next", "terminated"), td.get(("next", "terminated")).unsqueeze(-1))
# create root vals
td.set("done", torch.zeros_like(td.get(("next", "done"))))
td.set("terminated", torch.zeros_like(td.get(("next", "terminated"))))
# Add truncated
td.set(
("next", "truncated"),
td.get(("next", "done")) & ~td.get(("next", "terminated")),
)
td.set("truncated", torch.zeros_like(td.get(("next", "truncated"))))
pixels = td.get("pixels")
subtd = td._get_sub_tensordict(slice(0, -1))
subtd.set(("next", "pixels"), pixels[1:], inplace=True)
state = td.get("state", None)
if state is not None:
subtd.set(("next", "state"), state[1:], inplace=True)
return td
@_classproperty
def available_datasets(cls):
return cls._available_datasets()
@classmethod
def _available_datasets(cls):
# try to gather paths from hf
try:
sibs = cls._parse_datasets()
return [str(path)[6:] for path in sibs]
except Exception:
# return the default datasets
with open(THIS_DIR / "vd4rl.json", "r") as file:
return json.load(file)
def _make_split(self):
from torchrl.collectors.utils import split_trajectories
td_data = TensorDict.load_memmap(self.data_path_root)
td_data = split_trajectories(td_data).memmap_(self.data_path)
return td_data
def _load(self):
return TensorDict.load_memmap(self.data_path)
@property
def data_path(self):
if self.split_trajs:
return Path(self.root) / (self.dataset_id + "_split")
return self.data_path_root
@property
def data_path_root(self):
return Path(self.root) / self.dataset_id
def _is_downloaded(self):
return os.path.exists(self.data_path_root)
def _from_npz(npz_path):
npz = np.load(npz_path)
npz_dict = {file: npz[file] for file in npz.files}
return TensorDict.from_dict(npz_dict)
_NAME_MATCH = KeyDependentDefaultDict(lambda x: x)
_NAME_MATCH.update(
{
"is_first": "is_init",
"is_last": ("next", "done"),
"is_terminal": ("next", "terminated"),
"reward": ("next", "reward"),
"image": "pixels",
"observation": "pixels",
"discount": "discount",
"action": "action",
}
)