Source code for torchrl.data.replay_buffers.storages
# 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 abc
import os
import textwrap
import warnings
from collections import OrderedDict
from copy import copy
from multiprocessing.context import get_spawning_popen
from typing import Any, Dict, List, Sequence, Union
import numpy as np
import tensordict
import torch
from tensordict import (
is_tensor_collection,
LazyStackedTensorDict,
TensorDict,
TensorDictBase,
)
from tensordict.memmap import MemoryMappedTensor
from torch import multiprocessing as mp
from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
from torchrl._utils import _make_ordinal_device, implement_for, logger as torchrl_logger
from torchrl.data.replay_buffers.checkpointers import (
ListStorageCheckpointer,
StorageCheckpointerBase,
StorageEnsembleCheckpointer,
TensorStorageCheckpointer,
)
from torchrl.data.replay_buffers.utils import (
_init_pytree,
_is_int,
INT_CLASSES,
tree_iter,
)
[docs]class Storage:
"""A Storage is the container of a replay buffer.
Every storage must have a set, get and __len__ methods implemented.
Get and set should support integers as well as list of integers.
The storage does not need to have a definite size, but if it does one should
make sure that it is compatible with the buffer size.
"""
ndim = 1
max_size: int
_default_checkpointer: StorageCheckpointerBase = StorageCheckpointerBase
_rng: torch.Generator | None = None
def __init__(
self, max_size: int, checkpointer: StorageCheckpointerBase | None = None
) -> None:
self.max_size = int(max_size)
self.checkpointer = checkpointer
@property
def checkpointer(self):
return self._checkpointer
@checkpointer.setter
def checkpointer(self, value: StorageCheckpointerBase | None) -> None:
if value is None:
value = self._default_checkpointer()
self._checkpointer = value
@property
def _is_full(self):
return len(self) == self.max_size
@property
def _attached_entities(self):
# RBs that use a given instance of Storage should add
# themselves to this set.
_attached_entities = self.__dict__.get("_attached_entities_set", None)
if _attached_entities is None:
_attached_entities = set()
self.__dict__["_attached_entities_set"] = _attached_entities
return _attached_entities
@abc.abstractmethod
def set(self, cursor: int, data: Any, *, set_cursor: bool = True):
...
@abc.abstractmethod
def get(self, index: int) -> Any:
...
def dumps(self, path):
self.checkpointer.dumps(self, path)
def loads(self, path):
self.checkpointer.loads(self, path)
[docs] def attach(self, buffer: Any) -> None:
"""This function attaches a sampler to this storage.
Buffers that read from this storage must be included as an attached
entity by calling this method. This guarantees that when data
in the storage changes, components are made aware of changes even if the storage
is shared with other buffers (eg. Priority Samplers).
Args:
buffer: the object that reads from this storage.
"""
self._attached_entities.add(buffer)
def __getitem__(self, item):
return self.get(item)
def __setitem__(self, index, value):
"""Sets values in the storage without updating the cursor or length."""
return self.set(index, value, set_cursor=False)
def __iter__(self):
for i in range(len(self)):
yield self[i]
@abc.abstractmethod
def __len__(self):
...
@abc.abstractmethod
def state_dict(self) -> Dict[str, Any]:
...
@abc.abstractmethod
def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
...
@abc.abstractmethod
def _empty(self):
...
def _rand_given_ndim(self, batch_size):
# a method to return random indices given the storage ndim
if self.ndim == 1:
return torch.randint(0, len(self), (batch_size,), generator=self._rng)
raise RuntimeError(
f"Random number generation is not implemented for storage of type {type(self)} with ndim {self.ndim}. "
f"Please report this exception as well as the use case (incl. buffer construction) on github."
)
@property
def shape(self):
if self.ndim == 1:
return torch.Size([self.max_size])
raise RuntimeError(
f"storage.shape is not supported for storages of type {type(self)} when ndim > 1."
f"Please report this exception as well as the use case (incl. buffer construction) on github."
)
def _max_size_along_dim0(self, *, single_data=None, batched_data=None):
if self.ndim == 1:
return self.max_size
raise RuntimeError(
f"storage._max_size_along_dim0 is not supported for storages of type {type(self)} when ndim > 1."
f"Please report this exception as well as the use case (incl. buffer construction) on github."
)
def flatten(self):
if self.ndim == 1:
return self
raise RuntimeError(
f"storage.flatten is not supported for storages of type {type(self)} when ndim > 1."
f"Please report this exception as well as the use case (incl. buffer construction) on github."
)
[docs] def save(self, *args, **kwargs):
"""Alias for :meth:`~.dumps`."""
return self.dumps(*args, **kwargs)
[docs] def dump(self, *args, **kwargs):
"""Alias for :meth:`~.dumps`."""
return self.dumps(*args, **kwargs)
[docs] def load(self, *args, **kwargs):
"""Alias for :meth:`~.loads`."""
return self.loads(*args, **kwargs)
def __getstate__(self):
state = copy(self.__dict__)
state["_rng"] = None
return state
[docs]class ListStorage(Storage):
"""A storage stored in a list.
This class cannot be extended with PyTrees, the data provided during calls to
:meth:`~torchrl.data.replay_buffers.ReplayBuffer.extend` should be iterables
(like lists, tuples, tensors or tensordicts with non-empty batch-size).
Args:
max_size (int): the maximum number of elements stored in the storage.
"""
_default_checkpointer = ListStorageCheckpointer
def __init__(self, max_size: int):
super().__init__(max_size)
self._storage = []
def set(
self,
cursor: Union[int, Sequence[int], slice],
data: Any,
*,
set_cursor: bool = True,
):
if not isinstance(cursor, INT_CLASSES):
if (isinstance(cursor, torch.Tensor) and cursor.numel() <= 1) or (
isinstance(cursor, np.ndarray) and cursor.size <= 1
):
self.set(int(cursor), data, set_cursor=set_cursor)
return
if isinstance(cursor, slice):
self._storage[cursor] = data
return
if isinstance(
data,
(
list,
tuple,
torch.Tensor,
TensorDictBase,
*tensordict.base._ACCEPTED_CLASSES,
range,
set,
np.ndarray,
),
):
for _cursor, _data in zip(cursor, data):
self.set(_cursor, _data, set_cursor=set_cursor)
else:
raise TypeError(
f"Cannot extend a {type(self)} with data of type {type(data)}. "
f"Provide a list, tuple, set, range, np.ndarray, tensor or tensordict subclass instead."
)
return
else:
if cursor > len(self._storage):
raise RuntimeError(
"Cannot append data located more than one item away from "
f"the storage size: the storage size is {len(self)} "
f"and the index of the item to be set is {cursor}."
)
if cursor >= self.max_size:
raise RuntimeError(
f"Cannot append data to the list storage: "
f"maximum capacity is {self.max_size} "
f"and the index of the item to be set is {cursor}."
)
if cursor == len(self._storage):
self._storage.append(data)
else:
self._storage[cursor] = data
def get(self, index: Union[int, Sequence[int], slice]) -> Any:
if isinstance(index, (INT_CLASSES, slice)):
return self._storage[index]
else:
return [self._storage[i] for i in index]
def __len__(self):
return len(self._storage)
def state_dict(self) -> Dict[str, Any]:
return {
"_storage": [
elt if not hasattr(elt, "state_dict") else elt.state_dict()
for elt in self._storage
]
}
def load_state_dict(self, state_dict):
_storage = state_dict["_storage"]
self._storage = []
for elt in _storage:
if isinstance(elt, torch.Tensor):
self._storage.append(elt)
elif isinstance(elt, (dict, OrderedDict)):
self._storage.append(
TensorDict({}, []).load_state_dict(elt, strict=False)
)
else:
raise TypeError(
f"Objects of type {type(elt)} are not supported by ListStorage.load_state_dict"
)
def _empty(self):
self._storage = []
def __getstate__(self):
if get_spawning_popen() is not None:
raise RuntimeError(
f"Cannot share a storage of type {type(self)} between processes."
)
state = super().__getstate__()
return state
def __repr__(self):
return f"{self.__class__.__name__}(items=[{self._storage[0]}, ...])"
[docs]class TensorStorage(Storage):
"""A storage for tensors and tensordicts.
Args:
storage (tensor or TensorDict): the data buffer to be used.
max_size (int): size of the storage, i.e. maximum number of elements stored
in the buffer.
Keyword Args:
device (torch.device, optional): device where the sampled tensors will be
stored and sent. Default is :obj:`torch.device("cpu")`.
If "auto" is passed, the device is automatically gathered from the
first batch of data passed. This is not enabled by default to avoid
data placed on GPU by mistake, causing OOM issues.
ndim (int, optional): the number of dimensions to be accounted for when
measuring the storage size. For instance, a storage of shape ``[3, 4]``
has capacity ``3`` if ``ndim=1`` and ``12`` if ``ndim=2``.
Defaults to ``1``.
Examples:
>>> data = TensorDict({
... "some data": torch.randn(10, 11),
... ("some", "nested", "data"): torch.randn(10, 11, 12),
... }, batch_size=[10, 11])
>>> storage = TensorStorage(data)
>>> len(storage) # only the first dimension is considered as indexable
10
>>> storage.get(0)
TensorDict(
fields={
some data: Tensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
some: TensorDict(
fields={
nested: TensorDict(
fields={
data: Tensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([11]),
device=None,
is_shared=False)},
batch_size=torch.Size([11]),
device=None,
is_shared=False)},
batch_size=torch.Size([11]),
device=None,
is_shared=False)
>>> storage.set(0, storage.get(0).zero_()) # zeros the data along index ``0``
This class also supports tensorclass data.
Examples:
>>> from tensordict import tensorclass
>>> @tensorclass
... class MyClass:
... foo: torch.Tensor
... bar: torch.Tensor
>>> data = MyClass(foo=torch.randn(10, 11), bar=torch.randn(10, 11, 12), batch_size=[10, 11])
>>> storage = TensorStorage(data)
>>> storage.get(0)
MyClass(
bar=Tensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False),
foo=Tensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
batch_size=torch.Size([11]),
device=None,
is_shared=False)
"""
_storage = None
_default_checkpointer = TensorStorageCheckpointer
def __init__(
self,
storage,
max_size=None,
*,
device: torch.device = "cpu",
ndim: int = 1,
):
if not ((storage is None) ^ (max_size is None)):
if storage is None:
raise ValueError("Expected storage to be non-null.")
if max_size != storage.shape[0]:
raise ValueError(
"The max-size and the storage shape mismatch: got "
f"max_size={max_size} for a storage of shape {storage.shape}."
)
elif storage is not None:
if is_tensor_collection(storage):
max_size = storage.shape[0]
else:
max_size = tree_flatten(storage)[0][0].shape[0]
self.ndim = ndim
super().__init__(max_size)
self.initialized = storage is not None
if self.initialized:
self._len = max_size
else:
self._len = 0
self.device = (
_make_ordinal_device(torch.device(device))
if device != "auto"
else storage.device
if storage is not None
else "auto"
)
self._storage = storage
self._last_cursor = None
@property
def _len(self):
_len_value = self.__dict__.get("_len_value", None)
if _len_value is None:
_len_value = self._len_value = mp.Value("i", 0)
return _len_value.value
@_len.setter
def _len(self, value):
_len_value = self.__dict__.get("_len_value", None)
if _len_value is None:
_len_value = self._len_value = mp.Value("i", 0)
_len_value.value = value
@property
def _total_shape(self):
# Total shape, irrespective of how full the storage is
_total_shape = self.__dict__.get("_total_shape_value", None)
if _total_shape is None and self.initialized:
if is_tensor_collection(self._storage):
_total_shape = self._storage.shape[: self.ndim]
else:
leaf = next(tree_iter(self._storage))
_total_shape = leaf.shape[: self.ndim]
self.__dict__["_total_shape_value"] = _total_shape
self._len = torch.Size([self._len_along_dim0, *_total_shape[1:]]).numel()
return _total_shape
@property
def _is_full(self):
# whether the storage is full
return len(self) == self.max_size
@property
def _len_along_dim0(self):
# returns the length of the buffer along dim0
len_along_dim = len(self)
if self.ndim > 1:
_total_shape = self._total_shape
if _total_shape is not None:
len_along_dim = -(len_along_dim // -_total_shape[1:].numel())
else:
return None
return len_along_dim
def _max_size_along_dim0(self, *, single_data=None, batched_data=None):
# returns the max_size of the buffer along dim0
max_size = self.max_size
if self.ndim > 1:
shape = self.shape
if shape is None:
if single_data is not None:
data = single_data
elif batched_data is not None:
data = batched_data
else:
raise ValueError("single_data or batched_data must be passed.")
if is_tensor_collection(data):
datashape = data.shape[: self.ndim]
else:
for leaf in tree_iter(data):
datashape = leaf.shape[: self.ndim]
break
if batched_data is not None:
datashape = datashape[1:]
max_size = -(max_size // -datashape.numel())
else:
max_size = -(max_size // -self._total_shape[1:].numel())
return max_size
@property
def shape(self):
# Shape, truncated where needed to accommodate for the length of the storage
if self._is_full:
return self._total_shape
_total_shape = self._total_shape
if _total_shape is not None:
return torch.Size([self._len_along_dim0] + list(_total_shape[1:]))
def _rand_given_ndim(self, batch_size):
if self.ndim == 1:
return super()._rand_given_ndim(batch_size)
shape = self.shape
return tuple(
torch.randint(_dim, (batch_size,), generator=self._rng) for _dim in shape
)
def flatten(self):
if self.ndim == 1:
return self
if not self.initialized:
raise RuntimeError("Cannot flatten a non-initialized storage.")
if is_tensor_collection(self._storage):
if self._is_full:
return TensorStorage(self._storage.flatten(0, self.ndim - 1))
return TensorStorage(
self._storage[: self._len_along_dim0].flatten(0, self.ndim - 1)
)
if self._is_full:
return TensorStorage(
tree_map(lambda x: x.flatten(0, self.ndim - 1), self._storage)
)
return TensorStorage(
tree_map(
lambda x: x[: self._len_along_dim0].flatten(0, self.ndim - 1),
self._storage,
)
)
def __getstate__(self):
state = super().__getstate__()
if get_spawning_popen() is None:
length = self._len
del state["_len_value"]
state["len__context"] = length
elif not self.initialized:
# check that the storage is initialized
raise RuntimeError(
f"Cannot share a storage of type {type(self)} between processes if "
f"it has not been initialized yet. Populate the buffer with "
f"some data in the main process before passing it to the other "
f"subprocesses (or create the buffer explicitly with a TensorStorage)."
)
else:
# check that the content is shared, otherwise tell the user we can't help
storage = self._storage
STORAGE_ERR = "The storage must be place in shared memory or memmapped before being shared between processes."
# If the content is on cpu, it will be placed in shared memory.
# If it's on cuda it's already shared.
# If it's memmaped no worry in this case either.
# Only if the device is not "cpu" or "cuda" we may have a problem.
def assert_is_sharable(tensor):
if tensor.device is None or tensor.device.type in (
"cuda",
"cpu",
"meta",
):
return
raise RuntimeError(STORAGE_ERR)
if is_tensor_collection(storage):
storage.apply(assert_is_sharable, filter_empty=True)
else:
tree_map(storage, assert_is_sharable)
return state
def __setstate__(self, state):
len = state.pop("len__context", None)
if len is not None:
_len_value = mp.Value("i", len)
state["_len_value"] = _len_value
self.__dict__.update(state)
def state_dict(self) -> Dict[str, Any]:
_storage = self._storage
if isinstance(_storage, torch.Tensor):
pass
elif is_tensor_collection(_storage):
_storage = _storage.state_dict()
elif _storage is None:
_storage = {}
else:
raise TypeError(
f"Objects of type {type(_storage)} are not supported by {type(self)}.state_dict"
)
return {
"_storage": _storage,
"initialized": self.initialized,
"_len": self._len,
}
def load_state_dict(self, state_dict):
_storage = copy(state_dict["_storage"])
if isinstance(_storage, torch.Tensor):
if isinstance(self._storage, torch.Tensor):
self._storage.copy_(_storage)
elif self._storage is None:
self._storage = _storage
else:
raise RuntimeError(
f"Cannot copy a storage of type {type(_storage)} onto another of type {type(self._storage)}"
)
elif isinstance(_storage, (dict, OrderedDict)):
if is_tensor_collection(self._storage):
self._storage.load_state_dict(_storage, strict=False)
elif self._storage is None:
self._storage = TensorDict({}, []).load_state_dict(
_storage, strict=False
)
else:
raise RuntimeError(
f"Cannot copy a storage of type {type(_storage)} onto another of type {type(self._storage)}. If your storage is pytree-based, use the dumps/load API instead."
)
else:
raise TypeError(
f"Objects of type {type(_storage)} are not supported by ListStorage.load_state_dict"
)
self.initialized = state_dict["initialized"]
self._len = state_dict["_len"]
@implement_for("torch", "2.3")
def _set_tree_map(self, cursor, data, storage):
def set_tensor(datum, store):
store[cursor] = datum
# this won't be available until v2.3
tree_map(set_tensor, data, storage)
@implement_for("torch", "2.0", "2.3")
def _set_tree_map(self, cursor, data, storage): # noqa: 534
# flatten data and cursor
data_flat = tree_flatten(data)[0]
storage_flat = tree_flatten(storage)[0]
for datum, store in zip(data_flat, storage_flat):
store[cursor] = datum
def _get_new_len(self, data, cursor):
int_cursor = _is_int(cursor)
ndim = self.ndim - int_cursor
if is_tensor_collection(data) or isinstance(data, torch.Tensor):
numel = data.shape[:ndim].numel()
else:
leaf = next(tree_iter(data))
numel = leaf.shape[:ndim].numel()
self._len = min(self._len + numel, self.max_size)
@implement_for("torch", "2.0", None)
def set(
self,
cursor: Union[int, Sequence[int], slice],
data: Union[TensorDictBase, torch.Tensor],
*,
set_cursor: bool = True,
):
if set_cursor:
self._last_cursor = cursor
if isinstance(data, list):
# flip list
try:
data = _flip_list(data)
except Exception:
raise RuntimeError(
"Stacking the elements of the list resulted in "
"an error. "
f"Storages of type {type(self)} expect all elements of the list "
f"to have the same tree structure. If the list is compact (each "
f"leaf is itself a batch with the appropriate number of elements) "
f"consider using a tuple instead, as lists are used within `extend` "
f"for per-item addition."
)
if set_cursor:
self._get_new_len(data, cursor)
if not self.initialized:
if not isinstance(cursor, INT_CLASSES):
if is_tensor_collection(data):
self._init(data[0])
else:
self._init(tree_map(lambda x: x[0], data))
else:
self._init(data)
if is_tensor_collection(data):
self._storage[cursor] = data
else:
self._set_tree_map(cursor, data, self._storage)
@implement_for("torch", None, "2.0")
def set( # noqa: F811
self,
cursor: Union[int, Sequence[int], slice],
data: Union[TensorDictBase, torch.Tensor],
*,
set_cursor: bool = True,
):
if set_cursor:
self._last_cursor = cursor
if isinstance(data, list):
# flip list
try:
data = _flip_list(data)
except Exception:
raise RuntimeError(
"Stacking the elements of the list resulted in "
"an error. "
f"Storages of type {type(self)} expect all elements of the list "
f"to have the same tree structure. If the list is compact (each "
f"leaf is itself a batch with the appropriate number of elements) "
f"consider using a tuple instead, as lists are used within `extend` "
f"for per-item addition."
)
if set_cursor:
self._get_new_len(data, cursor)
if not is_tensor_collection(data) and not isinstance(data, torch.Tensor):
raise NotImplementedError(
"storage extension with pytrees is only available with torch >= 2.0. If you need this "
"feature, please open an issue on TorchRL's github repository."
)
if not self.initialized:
if not isinstance(cursor, INT_CLASSES):
self._init(data[0])
else:
self._init(data)
if not isinstance(cursor, (*INT_CLASSES, slice)):
if not isinstance(cursor, torch.Tensor):
cursor = torch.tensor(cursor, dtype=torch.long)
elif cursor.dtype != torch.long:
cursor = cursor.to(dtype=torch.long)
if len(cursor) > self._len_along_dim0:
warnings.warn(
"A cursor of length superior to the storage capacity was provided. "
"To accommodate for this, the cursor will be truncated to its last "
"element such that its length matched the length of the storage. "
"This may **not** be the optimal behavior for your application! "
"Make sure that the storage capacity is big enough to support the "
"batch size provided."
)
self._storage[cursor] = data
def get(self, index: Union[int, Sequence[int], slice]) -> Any:
_storage = self._storage
is_tc = is_tensor_collection(_storage)
if not self.initialized:
raise RuntimeError("Cannot get elements out of a non-initialized storage.")
if not self._is_full:
if is_tc:
storage = self._storage[: self._len_along_dim0]
else:
storage = tree_map(lambda x: x[: self._len_along_dim0], self._storage)
else:
storage = self._storage
if not self.initialized:
raise RuntimeError(
"Cannot get an item from an unitialized LazyMemmapStorage"
)
if is_tc:
return storage[index]
else:
return tree_map(lambda x: x[index], storage)
def __len__(self):
return self._len
def _empty(self):
# assuming that the data structure is the same, we don't need to to
# anything if the cursor is reset to 0
self._len = 0
def _init(self):
raise NotImplementedError(
f"{type(self)} must be initialized during construction."
)
def __repr__(self):
if not self.initialized:
storage_str = textwrap.indent("data=<empty>", 4 * " ")
elif is_tensor_collection(self._storage):
storage_str = textwrap.indent(f"data={self[:]}", 4 * " ")
else:
def repr_item(x):
if isinstance(x, torch.Tensor):
return f"{x.__class__.__name__}(shape={x.shape}, dtype={x.dtype}, device={x.device})"
return x.__class__.__name__
storage_str = textwrap.indent(
f"data={tree_map(repr_item, self[:])}", 4 * " "
)
shape_str = textwrap.indent(f"shape={self.shape}", 4 * " ")
len_str = textwrap.indent(f"len={len(self)}", 4 * " ")
maxsize_str = textwrap.indent(f"max_size={self.max_size}", 4 * " ")
return f"{self.__class__.__name__}(\n{storage_str}, \n{shape_str}, \n{len_str}, \n{maxsize_str})"
[docs]class LazyTensorStorage(TensorStorage):
"""A pre-allocated tensor storage for tensors and tensordicts.
Args:
max_size (int): size of the storage, i.e. maximum number of elements stored
in the buffer.
Keyword Args:
device (torch.device, optional): device where the sampled tensors will be
stored and sent. Default is :obj:`torch.device("cpu")`.
If "auto" is passed, the device is automatically gathered from the
first batch of data passed. This is not enabled by default to avoid
data placed on GPU by mistake, causing OOM issues.
ndim (int, optional): the number of dimensions to be accounted for when
measuring the storage size. For instance, a storage of shape ``[3, 4]``
has capacity ``3`` if ``ndim=1`` and ``12`` if ``ndim=2``.
Defaults to ``1``.
Examples:
>>> data = TensorDict({
... "some data": torch.randn(10, 11),
... ("some", "nested", "data"): torch.randn(10, 11, 12),
... }, batch_size=[10, 11])
>>> storage = LazyTensorStorage(100)
>>> storage.set(range(10), data)
>>> len(storage) # only the first dimension is considered as indexable
10
>>> storage.get(0)
TensorDict(
fields={
some data: Tensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
some: TensorDict(
fields={
nested: TensorDict(
fields={
data: Tensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)},
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)},
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)
>>> storage.set(0, storage.get(0).zero_()) # zeros the data along index ``0``
This class also supports tensorclass data.
Examples:
>>> from tensordict import tensorclass
>>> @tensorclass
... class MyClass:
... foo: torch.Tensor
... bar: torch.Tensor
>>> data = MyClass(foo=torch.randn(10, 11), bar=torch.randn(10, 11, 12), batch_size=[10, 11])
>>> storage = LazyTensorStorage(10)
>>> storage.set(range(10), data)
>>> storage.get(0)
MyClass(
bar=Tensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False),
foo=Tensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)
"""
_default_checkpointer = TensorStorageCheckpointer
def __init__(
self,
max_size: int,
*,
device: torch.device = "cpu",
ndim: int = 1,
):
super().__init__(storage=None, max_size=max_size, device=device, ndim=ndim)
def _init(
self,
data: Union[TensorDictBase, torch.Tensor, "PyTree"], # noqa: F821
) -> None:
torchrl_logger.debug("Creating a TensorStorage...")
if self.device == "auto":
self.device = data.device
def max_size_along_dim0(data_shape):
if self.ndim > 1:
result = (
-(self.max_size // -data_shape[: self.ndim - 1].numel()),
*data_shape,
)
self.max_size = torch.Size(result).numel()
return result
return (self.max_size, *data_shape)
if is_tensor_collection(data):
out = data.to(self.device)
out = out.expand(max_size_along_dim0(data.shape))
out = out.clone()
out = out.zero_()
else:
# if Tensor, we just create a MemoryMappedTensor of the desired shape, device and dtype
out = tree_map(
lambda data: torch.empty(
max_size_along_dim0(data.shape),
device=self.device,
dtype=data.dtype,
),
data,
)
self._storage = out
self.initialized = True
[docs]class LazyMemmapStorage(LazyTensorStorage):
"""A memory-mapped storage for tensors and tensordicts.
Args:
max_size (int): size of the storage, i.e. maximum number of elements stored
in the buffer.
Keyword Args:
scratch_dir (str or path): directory where memmap-tensors will be written.
device (torch.device, optional): device where the sampled tensors will be
stored and sent. Default is :obj:`torch.device("cpu")`.
If ``None`` is provided, the device is automatically gathered from the
first batch of data passed. This is not enabled by default to avoid
data placed on GPU by mistake, causing OOM issues.
ndim (int, optional): the number of dimensions to be accounted for when
measuring the storage size. For instance, a storage of shape ``[3, 4]``
has capacity ``3`` if ``ndim=1`` and ``12`` if ``ndim=2``.
Defaults to ``1``.
existsok (bool, optional): whether an error should be raised if any of the
tensors already exists on disk. Defaults to ``True``. If ``False``, the
tensor will be opened as is, not overewritten.
.. note:: When checkpointing a ``LazyMemmapStorage``, one can provide a path identical to where the storage is
already stored to avoid executing long copies of data that is already stored on disk.
This will only work if the default :class:`~torchrl.data.TensorStorageCheckpointer` checkpointer is used.
Example:
>>> from tensordict import TensorDict
>>> from torchrl.data import TensorStorage, LazyMemmapStorage, ReplayBuffer
>>> import tempfile
>>> from pathlib import Path
>>> import time
>>> td = TensorDict(a=0, b=1).expand(1000).clone()
>>> # We pass a path that is <main_ckpt_dir>/storage to LazyMemmapStorage
>>> rb_memmap = ReplayBuffer(storage=LazyMemmapStorage(10_000_000, scratch_dir="dump/storage"))
>>> rb_memmap.extend(td);
>>> # Checkpointing in `dump` is a zero-copy, as the data is already in `dump/storage`
>>> rb_memmap.dumps(Path("./dump"))
Examples:
>>> data = TensorDict({
... "some data": torch.randn(10, 11),
... ("some", "nested", "data"): torch.randn(10, 11, 12),
... }, batch_size=[10, 11])
>>> storage = LazyMemmapStorage(100)
>>> storage.set(range(10), data)
>>> len(storage) # only the first dimension is considered as indexable
10
>>> storage.get(0)
TensorDict(
fields={
some data: MemoryMappedTensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
some: TensorDict(
fields={
nested: TensorDict(
fields={
data: MemoryMappedTensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)},
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)},
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)
This class also supports tensorclass data.
Examples:
>>> from tensordict import tensorclass
>>> @tensorclass
... class MyClass:
... foo: torch.Tensor
... bar: torch.Tensor
>>> data = MyClass(foo=torch.randn(10, 11), bar=torch.randn(10, 11, 12), batch_size=[10, 11])
>>> storage = LazyMemmapStorage(10)
>>> storage.set(range(10), data)
>>> storage.get(0)
MyClass(
bar=MemoryMappedTensor(shape=torch.Size([11, 12]), device=cpu, dtype=torch.float32, is_shared=False),
foo=MemoryMappedTensor(shape=torch.Size([11]), device=cpu, dtype=torch.float32, is_shared=False),
batch_size=torch.Size([11]),
device=cpu,
is_shared=False)
"""
_default_checkpointer = TensorStorageCheckpointer
def __init__(
self,
max_size: int,
*,
scratch_dir=None,
device: torch.device = "cpu",
ndim: int = 1,
existsok: bool = False,
):
super().__init__(max_size, ndim=ndim)
self.initialized = False
self.scratch_dir = None
self.existsok = existsok
if scratch_dir is not None:
self.scratch_dir = str(scratch_dir)
if self.scratch_dir[-1] != "/":
self.scratch_dir += "/"
self.device = (
_make_ordinal_device(torch.device(device))
if device != "auto"
else torch.device("cpu")
)
if self.device.type != "cpu":
raise ValueError(
"Memory map device other than CPU isn't supported. To cast your data to the desired device, "
"use `buffer.append_transform(lambda x: x.to(device)` or a similar transform."
)
self._len = 0
def state_dict(self) -> Dict[str, Any]:
_storage = self._storage
if isinstance(_storage, torch.Tensor):
_storage = _mem_map_tensor_as_tensor(_storage)
elif isinstance(_storage, TensorDictBase):
_storage = _storage.apply(_mem_map_tensor_as_tensor).state_dict()
elif _storage is None:
_storage = {}
else:
raise TypeError(
f"Objects of type {type(_storage)} are not supported by LazyTensorStorage.state_dict. If you are trying to serialize a PyTree, the storage.dumps/loads is preferred."
)
return {
"_storage": _storage,
"initialized": self.initialized,
"_len": self._len,
}
def load_state_dict(self, state_dict):
_storage = copy(state_dict["_storage"])
if isinstance(_storage, torch.Tensor):
if isinstance(self._storage, torch.Tensor):
_mem_map_tensor_as_tensor(self._storage).copy_(_storage)
elif self._storage is None:
self._storage = _make_memmap(
_storage,
path=self.scratch_dir + "/tensor.memmap"
if self.scratch_dir is not None
else None,
)
else:
raise RuntimeError(
f"Cannot copy a storage of type {type(_storage)} onto another of type {type(self._storage)}"
)
elif isinstance(_storage, (dict, OrderedDict)):
if is_tensor_collection(self._storage):
self._storage.load_state_dict(_storage, strict=False)
self._storage.memmap_()
elif self._storage is None:
warnings.warn(
"Loading the storage on an uninitialized TensorDict."
"It is preferable to load a storage onto a"
"pre-allocated one whenever possible."
)
self._storage = TensorDict({}, []).load_state_dict(
_storage, strict=False
)
self._storage.memmap_()
else:
raise RuntimeError(
f"Cannot copy a storage of type {type(_storage)} onto another of type {type(self._storage)}"
)
else:
raise TypeError(
f"Objects of type {type(_storage)} are not supported by ListStorage.load_state_dict"
)
self.initialized = state_dict["initialized"]
self._len = state_dict["_len"]
def _init(self, data: Union[TensorDictBase, torch.Tensor]) -> None:
torchrl_logger.debug("Creating a MemmapStorage...")
if self.device == "auto":
self.device = data.device
if self.device.type != "cpu":
raise RuntimeError("Support for Memmap device other than CPU is deprecated")
def max_size_along_dim0(data_shape):
if self.ndim > 1:
result = (
-(self.max_size // -data_shape[: self.ndim - 1].numel()),
*data_shape,
)
self.max_size = torch.Size(result).numel()
return result
return (self.max_size, *data_shape)
if is_tensor_collection(data):
out = data.clone().to(self.device)
out = out.expand(max_size_along_dim0(data.shape))
out = out.memmap_like(prefix=self.scratch_dir, existsok=self.existsok)
for key, tensor in sorted(
out.items(include_nested=True, leaves_only=True), key=str
):
try:
filesize = os.path.getsize(tensor.filename) / 1024 / 1024
torchrl_logger.debug(
f"\t{key}: {tensor.filename}, {filesize} Mb of storage (size: {tensor.shape})."
)
except (AttributeError, RuntimeError):
pass
else:
out = _init_pytree(self.scratch_dir, max_size_along_dim0, data)
self._storage = out
self.initialized = True
def get(self, index: Union[int, Sequence[int], slice]) -> Any:
result = super().get(index)
return result
[docs]class StorageEnsemble(Storage):
"""An ensemble of storages.
This class is designed to work with :class:`~torchrl.data.replay_buffers.replay_buffers.ReplayBufferEnsemble`.
Args:
storages (sequence of Storage): the storages to make the composite storage.
Keyword Args:
transforms (list of :class:`~torchrl.envs.Transform`, optional): a list of
transforms of the same length as storages.
.. warning::
This class signatures for :meth:`~.get` does not match other storages, as
it will return a tuple ``(buffer_id, samples)`` rather than just the samples.
.. warning::
This class does not support writing (similarly to :class:`~torchrl.data.replay_buffers.writers.WriterEnsemble`).
To extend one of the replay buffers, simply index the parent
:class:`~torchrl.data.ReplayBufferEnsemble` object.
"""
_default_checkpointer = StorageEnsembleCheckpointer
def __init__(
self,
*storages: Storage,
transforms: List["Transform"] = None, # noqa: F821
):
self._rng_private = None
self._storages = storages
self._transforms = transforms
if transforms is not None and len(transforms) != len(storages):
raise TypeError(
"transforms must have the same length as the storages " "provided."
)
@property
def _rng(self):
return self._rng_private
@_rng.setter
def _rng(self, value):
self._rng_private = value
for storage in self._storages:
storage._rng = value
@property
def _attached_entities(self):
return set()
def extend(self, value):
raise RuntimeError
def add(self, value):
raise RuntimeError
def get(self, item):
# we return the buffer id too to be able to track the appropriate collate_fn
buffer_ids = item.get("buffer_ids")
index = item.get("index")
results = []
for (buffer_id, sample) in zip(buffer_ids, index):
buffer_id = self._convert_id(buffer_id)
results.append((buffer_id, self._get_storage(buffer_id).get(sample)))
if self._transforms is not None:
results = [
(buffer_id, self._transforms[buffer_id](result))
if self._transforms[buffer_id] is not None
else (buffer_id, result)
for buffer_id, result in results
]
return results
def _convert_id(self, sub):
if isinstance(sub, torch.Tensor):
sub = sub.item()
return sub
def _get_storage(self, sub):
return self._storages[sub]
def state_dict(self) -> Dict[str, Any]:
raise NotImplementedError
def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
raise NotImplementedError
_INDEX_ERROR = "Expected an index of type torch.Tensor, range, np.ndarray, int, slice or ellipsis, got {} instead."
def __getitem__(self, index):
if isinstance(index, tuple):
if index[0] is Ellipsis:
index = (slice(None), index[1:])
result = self[index[0]]
if len(index) > 1:
if result is self:
# then index[0] is an ellipsis/slice(None)
sample = [storage[index[1:]] for storage in self._storages]
return sample
if isinstance(result, StorageEnsemble):
new_index = (slice(None), *index[1:])
return result[new_index]
return result[index[1:]]
return result
if isinstance(index, slice) and index == slice(None):
return self
if isinstance(index, (list, range, np.ndarray)):
index = torch.as_tensor(index)
if isinstance(index, torch.Tensor):
if index.ndim > 1:
raise RuntimeError(
f"Cannot index a {type(self)} with tensor indices that have more than one dimension."
)
if index.is_floating_point():
raise TypeError(
"A floating point index was recieved when an integer dtype was expected."
)
if isinstance(index, int) or (not isinstance(index, slice) and len(index) == 0):
try:
index = int(index)
except Exception:
raise IndexError(self._INDEX_ERROR.format(type(index)))
try:
return self._storages[index]
except IndexError:
raise IndexError(self._INDEX_ERROR.format(type(index)))
if isinstance(index, torch.Tensor):
index = index.tolist()
storages = [self._storages[i] for i in index]
transforms = (
[self._transforms[i] for i in index]
if self._transforms is not None
else [None] * len(index)
)
else:
# slice
storages = self._storages[index]
transforms = (
self._transforms[index]
if self._transforms is not None
else [None] * len(storages)
)
return StorageEnsemble(*storages, transforms=transforms)
def __len__(self):
return len(self._storages)
def __repr__(self):
storages = textwrap.indent(f"storages={self._storages}", " " * 4)
transforms = textwrap.indent(f"transforms={self._transforms}", " " * 4)
return f"StorageEnsemble(\n{storages}, \n{transforms})"
# Utils
def _mem_map_tensor_as_tensor(mem_map_tensor) -> torch.Tensor:
if isinstance(mem_map_tensor, torch.Tensor):
# This will account for MemoryMappedTensors
return mem_map_tensor
def _collate_list_tensordict(x):
out = torch.stack(x, 0)
return out
def _stack_anything(x):
if is_tensor_collection(x[0]):
return LazyStackedTensorDict.maybe_dense_stack(x)
return torch.stack(x)
def _collate_id(x):
return x
def _get_default_collate(storage, _is_tensordict=False):
if isinstance(storage, ListStorage):
if _is_tensordict:
return _collate_list_tensordict
else:
return torch.utils.data._utils.collate.default_collate
elif isinstance(storage, TensorStorage):
return _collate_id
else:
raise NotImplementedError(
f"Could not find a default collate_fn for storage {type(storage)}."
)
def _make_memmap(tensor, path):
return MemoryMappedTensor.from_tensor(tensor, filename=path)
def _make_empty_memmap(shape, dtype, path):
return MemoryMappedTensor.empty(shape=shape, dtype=dtype, filename=path)
def _flip_list(data):
if all(is_tensor_collection(_data) for _data in data):
return torch.stack(data)
flat_data, flat_specs = zip(*[tree_flatten(item) for item in data])
flat_data = zip(*flat_data)
stacks = [torch.stack(item) for item in flat_data]
return tree_unflatten(stacks, flat_specs[0])
