Shortcuts

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 json
import os
import textwrap
import warnings
from collections import OrderedDict
from copy import copy
from multiprocessing.context import get_spawning_popen
from pathlib import Path
from typing import Any, Dict, List, Sequence, Union

import numpy as np
import tensordict
import torch
from tensordict import is_tensor_collection, TensorDict, TensorDictBase
from tensordict.memmap import MemmapTensor, MemoryMappedTensor
from tensordict.utils import _STRDTYPE2DTYPE
from torch import multiprocessing as mp

from torch.utils._pytree import LeafSpec, tree_flatten, tree_map, tree_unflatten

from torchrl._utils import (
    _CKPT_BACKEND,
    implement_for,
    logger as torchrl_logger,
    VERBOSE,
)
from torchrl.data.replay_buffers.utils import _is_int, INT_CLASSES

try:
    from torchsnapshot.serialization import tensor_from_memoryview

    _has_ts = True
except ImportError:
    _has_ts = False

SINGLE_TENSOR_BUFFER_NAME = os.environ.get(
    "SINGLE_TENSOR_BUFFER_NAME", "_-single-tensor-_"
)


[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 def __init__(self, max_size: int) -> None: self.max_size = int(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): ... @abc.abstractmethod def get(self, index: int) -> Any: ... @abc.abstractmethod def dumps(self, path): ... @abc.abstractmethod def 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): ret = self.set(index, value) for ent in self._attached_entities: ent.mark_update(index) return ret 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,)) 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]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. """ def __init__(self, max_size: int): super().__init__(max_size) self._storage = [] def dumps(self, path): raise NotImplementedError( "ListStorage doesn't support serialization via `dumps` - `loads` API." ) def loads(self, path): raise NotImplementedError( "ListStorage doesn't support serialization via `dumps` - `loads` API." ) def set(self, cursor: Union[int, Sequence[int], slice], data: Any): 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) 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) 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 = copy(self.__dict__) return state
[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 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 = ( torch.device(device) if device != "auto" else storage.device if storage is not None else "auto" ) self._storage = storage def dumps(self, path): path = Path(path) path.mkdir(exist_ok=True) if not self.initialized: raise RuntimeError("Cannot save a non-initialized storage.") metadata = {} if is_tensor_collection(self._storage): # try to load the path and overwrite. self._storage.memmap( path, copy_existing=True, num_threads=torch.get_num_threads() ) is_pytree = False else: _save_pytree(self._storage, metadata, path) is_pytree = True with open(path / "storage_metadata.json", "w") as file: json.dump( { "metadata": metadata, "is_pytree": is_pytree, "len": self._len, }, file, ) def loads(self, path): with open(path / "storage_metadata.json", "r") as file: metadata = json.load(file) is_pytree = metadata["is_pytree"] _len = metadata["len"] if is_pytree: path = Path(path) for local_path, md in metadata["metadata"].items(): # load tensor local_path_dot = local_path.replace(".", "/") total_tensor_path = path / (local_path_dot + ".memmap") shape = torch.Size(md["shape"]) dtype = _STRDTYPE2DTYPE[md["dtype"]] tensor = MemoryMappedTensor.from_filename( filename=total_tensor_path, shape=shape, dtype=dtype ) # split path local_path = local_path.split(".") # replace potential dots local_path = [_path.replace("_<dot>_", ".") for _path in local_path] if self.initialized: # copy in-place _storage_tensor = self._storage # in this case there is a single tensor, so we skip if local_path != ["_-single-tensor-_"]: for _path in local_path: if _path.isdigit(): _path_attempt = int(_path) try: _storage_tensor = _storage_tensor[_path_attempt] continue except IndexError: pass _storage_tensor = _storage_tensor[_path] _storage_tensor.copy_(tensor) else: raise RuntimeError( "Cannot fill a non-initialized pytree-based TensorStorage." ) else: _storage = TensorDict.load_memmap(path) if not self.initialized: # this should not be reached if is_pytree=True self._storage = _storage self.initialized = True else: self._storage.copy_(_storage) self._len = _len @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, *_ = torch.utils._pytree.tree_leaves(self._storage) _total_shape = leaf.shape[: self.ndim] self.__dict__["_total_shape_value"] = _total_shape 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: len_along_dim = len_along_dim // self._total_shape[1:].numel() 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: 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 torch.utils._pytree.tree_leaves(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, turncated where needed to accomodate 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,)) for _dim in shape) def flatten(self): if self.ndim == 1: return self 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 = copy(self.__dict__) if get_spawning_popen() is None: len = self._len del state["_len_value"] state["len__context"] = len elif not self.initialized: # check that the storage is initialized raise RuntimeError( f"Cannot share a storage of type {type(self)} between processed 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 explicitely 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 is_tensor_collection(storage): if not storage.is_memmap() and not storage.is_shared(): raise RuntimeError(STORAGE_ERR) else: if ( not isinstance(storage, MemoryMappedTensor) and not storage.is_shared() ): raise RuntimeError(STORAGE_ERR) 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: # unfortunately tree_flatten isn't an iterator so we will have to flatten it all leaf, *_ = torch.utils._pytree.tree_leaves(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], ): 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." ) 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], ): 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." ) 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 accomodate 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 behaviour 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._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." )
[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) """ 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: if VERBOSE: torchrl_logger.info("Creating a TensorStorage...") if self.device == "auto": self.device = data.device def max_size_along_dim0(data_shape): if self.ndim > 1: return ( -(self.max_size // -data_shape[: self.ndim - 1].numel()), *data_shape, ) return (self.max_size, *data_shape) if is_tensor_collection(data): out = ( data.expand(max_size_along_dim0(data.shape)) .clone() .zero_() .to(self.device) ) elif is_tensor_collection(data): out = ( data.expand(max_size_along_dim0(data.shape)) .clone() .zero_() .to(self.device) ) 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. 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``. 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) """ def __init__( self, max_size: int, *, scratch_dir=None, device: torch.device = "cpu", ndim: int = 1, ): super().__init__(max_size, ndim=ndim) self.initialized = False self.scratch_dir = None if scratch_dir is not None: self.scratch_dir = str(scratch_dir) if self.scratch_dir[-1] != "/": self.scratch_dir += "/" self.device = torch.device(device) if device != "auto" else device 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: if VERBOSE: torchrl_logger.info("Creating a MemmapStorage...") if self.device == "auto": self.device = data.device if self.device.type != "cpu": warnings.warn( "Support for Memmap device other than CPU will be deprecated in v0.4.0. " "Using a 'cuda' device may be suboptimal.", category=DeprecationWarning, ) def max_size_along_dim0(data_shape): if self.ndim > 1: return ( -(self.max_size // -data_shape[: self.ndim - 1].numel()), *data_shape, ) 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) for key, tensor in sorted( out.items(include_nested=True, leaves_only=True), key=str ): if VERBOSE: filesize = os.path.getsize(tensor.filename) / 1024 / 1024 torchrl_logger.info( f"\t{key}: {tensor.filename}, {filesize} Mb of storage (size: {tensor.shape})." ) 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) # to be deprecated in v0.4 def map_device(tensor): if tensor.device != self.device: return tensor.to(self.device, non_blocking=True) return tensor if is_tensor_collection(result): return map_device(result) else: return tree_map(map_device, 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. """ def __init__( self, *storages: Storage, transforms: List["Transform"] = None, # noqa: F821 ): 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 _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 dumps(self, path: Path): path = Path(path).absolute() for i, storage in enumerate(self._storages): storage.dumps(path / str(i)) if self._transforms is not None: for i, transform in enumerate(self._transforms): torch.save(transform.state_dict(), path / f"{i}_transform.pt") def loads(self, path: Path): path = Path(path).absolute() for i, storage in enumerate(self._storages): storage.loads(path / str(i)) if self._transforms is not None: for i, transform in enumerate(self._transforms): transform.load_state_dict(torch.load(path / f"{i}_transform.pt")) 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: MemmapTensor) -> torch.Tensor: if _CKPT_BACKEND == "torchsnapshot" and not _has_ts: raise ImportError( "the checkpointing backend is set to torchsnapshot but the library is not installed. Consider installing the library or switch to another backend. " f"Supported backends are {_CKPT_BACKEND.backends}" ) if isinstance(mem_map_tensor, torch.Tensor): # This will account for MemoryMappedTensors return mem_map_tensor if _CKPT_BACKEND == "torchsnapshot": # TorchSnapshot doesn't know how to stream MemmapTensor, so we view MemmapTensor # as a Tensor for saving and loading purposes. This doesn't incur any copy. return tensor_from_memoryview( dtype=mem_map_tensor.dtype, shape=list(mem_map_tensor.shape), mv=memoryview(mem_map_tensor._memmap_array), ) elif _CKPT_BACKEND == "torch": return mem_map_tensor._tensor def _collate_list_tensordict(x): out = torch.stack(x, 0) return out 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) @implement_for("torch", "2.3", None) def _path2str(path, default_name=None): # Uses the Keys defined in pytree to build a path from torch.utils._pytree import MappingKey, SequenceKey if default_name is None: default_name = SINGLE_TENSOR_BUFFER_NAME if not path: return default_name if isinstance(path, tuple): return "/".join([_path2str(_sub, default_name=default_name) for _sub in path]) if isinstance(path, MappingKey): if not isinstance(path.key, (int, str, bytes)): raise ValueError("Values must be of type int, str or bytes in PyTree maps.") result = str(path.key) if result == default_name: raise RuntimeError( "A tensor had the same identifier as the default name used when the buffer contains " f"a single tensor (name={default_name}). This behaviour is not allowed. Please rename your " f"tensor in the map/dict or set a new default name with the environment variable SINGLE_TENSOR_BUFFER_NAME." ) return result if isinstance(path, SequenceKey): return str(path.idx) @implement_for("torch", None, "2.3") def _path2str(path, default_name=None): # noqa: F811 raise RuntimeError def _get_paths(spec, cumulpath=""): # alternative way to build a path without the keys if isinstance(spec, LeafSpec): yield cumulpath if cumulpath else SINGLE_TENSOR_BUFFER_NAME contexts = spec.context children_specs = spec.children_specs if contexts is None: contexts = range(len(children_specs)) for context, spec in zip(contexts, children_specs): cpath = "/".join((cumulpath, str(context))) if cumulpath else str(context) yield from _get_paths(spec, cpath) def _save_pytree_common(tensor_path, path, tensor, metadata): if "." in tensor_path: tensor_path.replace(".", "_<dot>_") total_tensor_path = path / (tensor_path + ".memmap") if os.path.exists(total_tensor_path): MemoryMappedTensor.from_filename( shape=tensor.shape, filename=total_tensor_path, dtype=tensor.dtype, ).copy_(tensor) else: os.makedirs(total_tensor_path.parent, exist_ok=True) MemoryMappedTensor.from_tensor( tensor, filename=total_tensor_path, copy_existing=True, copy_data=True, ) key = tensor_path.replace("/", ".") if key in metadata: raise KeyError( "At least two values have conflicting representations in " f"the data structure to be serialized: {key}." ) metadata[key] = { "dtype": str(tensor.dtype), "shape": list(tensor.shape), } @implement_for("torch", "2.3", None) def _save_pytree(_storage, metadata, path): from torch.utils._pytree import tree_map_with_path def save_tensor( tensor_path: tuple, tensor: torch.Tensor, metadata=metadata, path=path ): tensor_path = _path2str(tensor_path) _save_pytree_common(tensor_path, path, tensor, metadata) tree_map_with_path(save_tensor, _storage) @implement_for("torch", None, "2.3") def _save_pytree(_storage, metadata, path): # noqa: F811 flat_storage, storage_specs = tree_flatten(_storage) storage_paths = _get_paths(storage_specs) def save_tensor( tensor_path: str, tensor: torch.Tensor, metadata=metadata, path=path ): _save_pytree_common(tensor_path, path, tensor, metadata) for tensor, tensor_path in zip(flat_storage, storage_paths): save_tensor(tensor_path, tensor) def _init_pytree_common(tensor_path, scratch_dir, max_size_fn, tensor): if "." in tensor_path: tensor_path.replace(".", "_<dot>_") if scratch_dir is not None: total_tensor_path = Path(scratch_dir) / (tensor_path + ".memmap") if os.path.exists(total_tensor_path): raise RuntimeError( f"The storage of tensor {total_tensor_path} already exists. " f"To load an existing replay buffer, use storage.loads. " f"Choose a different path to store your buffer or delete the existing files." ) os.makedirs(total_tensor_path.parent, exist_ok=True) else: total_tensor_path = None out = MemoryMappedTensor.empty( shape=max_size_fn(tensor.shape), filename=total_tensor_path, dtype=tensor.dtype, ) if VERBOSE: filesize = os.path.getsize(out.filename) / 1024 / 1024 torchrl_logger.info( f"The storage was created in {out.filename} and occupies {filesize} Mb of storage." ) return out @implement_for("torch", "2.3", None) def _init_pytree(scratch_dir, max_size_fn, data): from torch.utils._pytree import tree_map_with_path # If not a tensorclass/tensordict, it must be a tensor(-like) or a PyTree # if Tensor, we just create a MemoryMappedTensor of the desired shape, device and dtype def save_tensor(tensor_path: tuple, tensor: torch.Tensor): tensor_path = _path2str(tensor_path) return _init_pytree_common(tensor_path, scratch_dir, max_size_fn, tensor) out = tree_map_with_path(save_tensor, data) return out @implement_for("torch", None, "2.3") def _init_pytree(scratch_dir, max_size, data): # noqa: F811 flat_data, data_specs = tree_flatten(data) data_paths = _get_paths(data_specs) data_paths = list(data_paths) # If not a tensorclass/tensordict, it must be a tensor(-like) or a PyTree # if Tensor, we just create a MemoryMappedTensor of the desired shape, device and dtype def save_tensor(tensor_path: str, tensor: torch.Tensor): return _init_pytree_common(tensor_path, scratch_dir, max_size, tensor) out = [] for tensor, tensor_path in zip(flat_data, data_paths): out.append(save_tensor(tensor_path, tensor)) return tree_unflatten(out, data_specs) def _flip_list(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])

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources