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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 logging
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.base import _NESTED_TENSORS_AS_LISTS
from tensordict.memmap import MemoryMappedTensor
from tensordict.utils import _zip_strict
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, compilable: bool = False, ) -> None: self.max_size = int(max_size) self.checkpointer = checkpointer self._compilable = compilable self._attached_entities_list = [] @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) -> List: # RBs that use a given instance of Storage should add # themselves to this set. _attached_entities_list = getattr(self, "_attached_entities_list", None) if _attached_entities_list is None: self._attached_entities_list = _attached_entities_list = [] return _attached_entities_list @torch._dynamo.assume_constant_result def _attached_entities_iter(self): return self._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. """ if buffer not in self._attached_entities: self._attached_entities.append(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, device=getattr(self, "device", None), ) 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 def __contains__(self, item): return self.contains(item) @abc.abstractmethod def contains(self, item): ...
[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, optional): the maximum number of elements stored in the storage. If not provided, an unlimited storage is created. """ _default_checkpointer = ListStorageCheckpointer def __init__(self, max_size: int | None = None, compilable: bool = False): if max_size is None: max_size = torch.iinfo(torch.int64).max super().__init__(max_size, compilable=compilable) 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.ndim == 0) or ( isinstance(cursor, np.ndarray) and cursor.ndim == 0 ): 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_strict(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]}, ...])" def contains(self, item): if isinstance(item, int): if item < 0: item += len(self._storage) return 0 <= item < len(self._storage) if isinstance(item, torch.Tensor): return torch.tensor( [self.contains(elt) for elt in item.tolist()], dtype=torch.bool, device=item.device, ).reshape_as(item) raise NotImplementedError(f"type {type(item)} is not supported yet.")
[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``. compilable (bool, optional): whether the storage is compilable. If ``True``, the writer cannot be shared between multiple processes. Defaults to ``False``. 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, compilable: bool = False, ): 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, compilable=compilable) 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 not self._compilable: if _len_value is None: _len_value = self._len_value = mp.Value("i", 0) return _len_value.value else: if _len_value is None: _len_value = self._len_value = 0 return _len_value @_len.setter def _len(self, value): if not self._compilable: _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 else: self._len_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:])) # TODO: Without this disable, compiler recompiles for back-to-back calls. # Figuring out a way to avoid this disable would give better performance. @torch._dynamo.disable() def _rand_given_ndim(self, batch_size): return self._rand_given_ndim_impl(batch_size) # At the moment, this is separated into its own function so that we can test # it without the `torch._dynamo.disable` and detect if future updates to the # compiler fix the recompile issue. def _rand_given_ndim_impl(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, device=self.device) 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: if not state["_compilable"]: state["_len_value"] = len else: _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", compilable=True) 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", compilable=True) 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, compilable=True) 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", compilable=True) 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})" def contains(self, item): if isinstance(item, int): if item < 0: item += self._len_along_dim0 return 0 <= item < self._len_along_dim0 if isinstance(item, torch.Tensor): def _is_valid_index(idx): try: torch.zeros(self.shape, device="meta")[idx] return True except IndexError: return False if item.ndim: return torch.tensor( [_is_valid_index(idx) for idx in item], dtype=torch.bool, device=item.device, ) return torch.tensor(_is_valid_index(item), device=item.device) raise NotImplementedError(f"type {type(item)} is not supported yet.")
[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``. compilable (bool, optional): whether the storage is compilable. If ``True``, the writer cannot be shared between multiple processes. Defaults to ``False``. consolidated (bool, optional): if ``True``, the storage will be consolidated after its first expansion. Defaults to ``False``. 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, compilable: bool = False, consolidated: bool = False, ): super().__init__( storage=None, max_size=max_size, device=device, ndim=ndim, compilable=compilable, ) self.consolidated = consolidated def _init( self, data: Union[TensorDictBase, torch.Tensor, "PyTree"], # noqa: F821 ) -> None: if not self._compilable: # TODO: Investigate why this seems to have a performance impact with # the compiler 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: TensorDictBase = torch.empty_like( out.expand(max_size_along_dim0(data.shape)) ) if self.consolidated: out = out.consolidate() 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, ) if self.consolidated: raise ValueError("Cannot consolidate non-tensordict storages.") 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, compilable: bool = False, ): super().__init__(max_size, ndim=ndim, compilable=compilable) 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) if torchrl_logger.isEnabledFor(logging.DEBUG): for key, tensor in sorted( out.items( include_nested=True, leaves_only=True, is_leaf=_NESTED_TENSORS_AS_LISTS, ), 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 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 @implement_for("torch", "2.4") def _stack_anything(data): if is_tensor_collection(data[0]): return LazyStackedTensorDict.maybe_dense_stack(data) return tree_map( lambda *x: torch.stack(x), *data, is_leaf=lambda x: isinstance(x, torch.Tensor) or is_tensor_collection(x), ) @implement_for("torch", None, "2.4") def _stack_anything(data): # noqa: F811 from tensordict import _pytree if not _pytree.PYTREE_REGISTERED_TDS: raise RuntimeError( "TensorDict is not registered within PyTree. " "If you see this error, it means tensordicts instances cannot be natively stacked using tree_map. " "To solve this issue, (a) upgrade pytorch to a version > 2.4, or (b) make sure TensorDict is registered in PyTree. " "If this error persists, open an issue on https://github.com/pytorch/rl/issues" ) if is_tensor_collection(data[0]): return LazyStackedTensorDict.maybe_dense_stack(data) flat_trees = [] spec = None for d in data: flat_tree, spec = tree_flatten(d) flat_trees.append(flat_tree) leaves = [] for leaf in zip(*flat_trees): leaf = torch.stack(leaf) leaves.append(leaf) return tree_unflatten(leaves, spec) def _collate_id(x): return x def _get_default_collate(storage, _is_tensordict=False): if isinstance(storage, ListStorage): return _stack_anything 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])

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