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TensorClass

class tensordict.TensorClass

TensorClass is the inheritance-based version of the @tensorclass decorator.

TensorClass allows you to code dataclasses that are better type-checked and more pythonic than those built with the @tensorclass decorator.

Examples

>>> from typing import Any
>>> import torch
>>> from tensordict import TensorClass
>>> class Foo(TensorClass):
...     tensor: torch.Tensor
...     non_tensor: Any
...     nested: Any = None
>>> foo = Foo(tensor=torch.randn(3), non_tensor="a string!", nested=None, batch_size=[3])
>>> print(foo)
Foo(
    non_tensor=NonTensorData(data=a string!, batch_size=torch.Size([3]), device=None),
    tensor=Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
    nested=None,
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)
Keyword Arguments:

  • batch_size (torch.Size, optional) – The batch size of the TensorDict. Defaults to None.

  • device (torch.device, optional) – The device on which the TensorDict will be created. Defaults to None.

  • frozen (bool, optional) – If True, the resulting class or instance will be immutable. Defaults to False.

  • autocast (bool, optional) – If True, enables automatic type casting for the resulting class or instance. Defaults to False.

  • nocast (bool, optional) – If True, disables any type casting for the resulting class or instance. Defaults to False.

  • tensor_only (bool, optional) – if True, it is expected that all items in tensorclass will be tensor instances (tensor-compatible, since non-tensor data is converted to tensors if possible). This can bring significant speed-ups at the cost of flexible interactions with non-tensor data. Defaults to False.

  • shadow (bool, optional) – Disables the validation of field names against TensorDict’s reserved attributes. Use with caution, as this may cause unintended consequences. Defaults to False.

You can pass boolean keyword arguments (“autocast”, “nocast”, “frozen”, “tensor_only”, “shadow”) in two ways: using

brackets or keyword arguments.

Examples

>>> class Foo(TensorClass["autocast"]):
...     integer: int
>>> Foo(integer=torch.ones(())).integer
1
>>> class Foo(TensorClass, autocast=True):  # equivalent
...     integer: int
>>> Foo(integer=torch.ones(())).integer
1
>>> class Foo(TensorClass["nocast"]):
...     integer: int
>>> Foo(integer=1).integer
1
>>> class Foo(TensorClass["nocast", "frozen"]):  # multiple keywords can be used
...     integer: int
>>> Foo(integer=1).integer
1
>>> class Foo(TensorClass, nocast=True):  # equivalent
...     integer: int
>>> Foo(integer=1).integer
1
>>> class Foo(TensorClass):
...     integer: int
>>> Foo(integer=1).integer
tensor(1)

Warning

TensorClass itself is not decorated as a tensorclass, but subclasses will be. This is because we cannot anticipate if the frozen argument will be set, and if it is, it may conflict with the parent class (a subclass cannot be frozen if the parent class isn’t).

dumps(prefix: Optional[str] = None, copy_existing: bool = False, *, num_threads: int = 0, return_early: bool = False, share_non_tensor: bool = False) → T

Saves the tensordict to disk.

This function is a proxy to memmap().

from_tensordict(tensordict, non_tensordict=None, safe=True)

Tensor class wrapper to instantiate a new tensor class object.

Parameters:

  • tensordict (TensorDict) – Dictionary of tensor types

  • non_tensordict (dict) – Dictionary with non-tensor and nested tensor class objects

get(key: NestedKey, *args, **kwargs)

Gets the value stored with the input key.

Parameters:

  • key (str, tuple of str) – key to be queried. If tuple of str it is equivalent to chained calls of getattr.

  • default – default value if the key is not found in the tensorclass.

Returns:

value stored with the input key

classmethod load(prefix: str | pathlib.Path, *args, **kwargs) → T

Loads a tensordict from disk.

This class method is a proxy to load_memmap().

load_(prefix: str | pathlib.Path, *args, **kwargs)

Loads a tensordict from disk within the current tensordict.

This class method is a proxy to load_memmap_().

classmethod load_memmap(prefix: str | pathlib.Path, device: Optional[device] = None, non_blocking: bool = False, *, out: Optional[TensorDictBase] = None) → T

Loads a memory-mapped tensordict from disk.

Parameters:

  • prefix (str or Path to folder) – the path to the folder where the saved tensordict should be fetched.

  • device (torch.device or equivalent, optional) – if provided, the data will be asynchronously cast to that device. Supports “meta” device, in which case the data isn’t loaded but a set of empty “meta” tensors are created. This is useful to get a sense of the total model size and structure without actually opening any file.

  • non_blocking (bool, optional) – if True, synchronize won’t be called after loading tensors on device. Defaults to False.

  • out (TensorDictBase, optional) – optional tensordict where the data should be written.

Examples

>>> from tensordict import TensorDict
>>> td = TensorDict.fromkeys(["a", "b", "c", ("nested", "e")], 0)
>>> td.memmap("./saved_td")
>>> td_load = TensorDict.load_memmap("./saved_td")
>>> assert (td == td_load).all()

This method also allows loading nested tensordicts.

Examples

>>> nested = TensorDict.load_memmap("./saved_td/nested")
>>> assert nested["e"] == 0

A tensordict can also be loaded on “meta” device or, alternatively, as a fake tensor.

Examples

>>> import tempfile
>>> td = TensorDict({"a": torch.zeros(()), "b": {"c": torch.zeros(())}})
>>> with tempfile.TemporaryDirectory() as path:
...     td.save(path)
...     td_load = TensorDict.load_memmap(path, device="meta")
...     print("meta:", td_load)
...     from torch._subclasses import FakeTensorMode
...     with FakeTensorMode():
...         td_load = TensorDict.load_memmap(path)
...         print("fake:", td_load)
meta: TensorDict(
    fields={
        a: Tensor(shape=torch.Size([]), device=meta, dtype=torch.float32, is_shared=False),
        b: TensorDict(
            fields={
                c: Tensor(shape=torch.Size([]), device=meta, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([]),
            device=meta,
            is_shared=False)},
    batch_size=torch.Size([]),
    device=meta,
    is_shared=False)
fake: TensorDict(
    fields={
        a: FakeTensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        b: TensorDict(
            fields={
                c: FakeTensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([]),
            device=cpu,
            is_shared=False)},
    batch_size=torch.Size([]),
    device=cpu,
    is_shared=False)
load_state_dict(state_dict: dict[str, Any], strict=True, assign=False, from_flatten=False)

Loads a state_dict attemptedly in-place on the destination tensorclass.

memmap(prefix: Optional[str] = None, copy_existing: bool = False, *, num_threads: int = 0, return_early: bool = False, share_non_tensor: bool = False, existsok: bool = True) → T

Writes all tensors onto a corresponding memory-mapped Tensor in a new tensordict.

Parameters:

  • prefix (str) – directory prefix where the memory-mapped tensors will be stored. The directory tree structure will mimic the tensordict’s.

  • copy_existing (bool) – If False (default), an exception will be raised if an entry in the tensordict is already a tensor stored on disk with an associated file, but is not saved in the correct location according to prefix. If True, any existing Tensor will be copied to the new location.

Keyword Arguments:

  • num_threads (int, optional) – the number of threads used to write the memmap tensors. Defaults to 0.

  • return_early (bool, optional) – if True and num_threads>0, the method will return a future of the tensordict.

  • share_non_tensor (bool, optional) – if True, the non-tensor data will be shared between the processes and writing operation (such as inplace update or set) on any of the workers within a single node will update the value on all other workers. If the number of non-tensor leaves is high (e.g., sharing large stacks of non-tensor data) this may result in OOM or similar errors. Defaults to False.

  • existsok (bool, optional) – if False, an exception will be raised if a tensor already exists in the same path. Defaults to True.

The TensorDict is then locked, meaning that any writing operations that isn’t in-place will throw an exception (eg, rename, set or remove an entry). Once the tensordict is unlocked, the memory-mapped attribute is turned to False, because cross-process identity is not guaranteed anymore.

Returns:

A new tensordict with the tensors stored on disk if return_early=False, otherwise a TensorDictFuture instance.

Note

Serialising in this fashion might be slow with deeply nested tensordicts, so it is not recommended to call this method inside a training loop.

memmap_(prefix: Optional[str] = None, copy_existing: bool = False, *, num_threads: int = 0, return_early: bool = False, share_non_tensor: bool = False, existsok: bool = True) → T

Writes all tensors onto a corresponding memory-mapped Tensor, in-place.

Parameters:

  • prefix (str) – directory prefix where the memory-mapped tensors will be stored. The directory tree structure will mimic the tensordict’s.

  • copy_existing (bool) – If False (default), an exception will be raised if an entry in the tensordict is already a tensor stored on disk with an associated file, but is not saved in the correct location according to prefix. If True, any existing Tensor will be copied to the new location.

Keyword Arguments:

  • num_threads (int, optional) – the number of threads used to write the memmap tensors. Defaults to 0.

  • return_early (bool, optional) – if True and num_threads>0, the method will return a future of the tensordict. The resulting tensordict can be queried using future.result().

  • share_non_tensor (bool, optional) – if True, the non-tensor data will be shared between the processes and writing operation (such as inplace update or set) on any of the workers within a single node will update the value on all other workers. If the number of non-tensor leaves is high (e.g., sharing large stacks of non-tensor data) this may result in OOM or similar errors. Defaults to False.

  • existsok (bool, optional) – if False, an exception will be raised if a tensor already exists in the same path. Defaults to True.

The TensorDict is then locked, meaning that any writing operations that isn’t in-place will throw an exception (eg, rename, set or remove an entry). Once the tensordict is unlocked, the memory-mapped attribute is turned to False, because cross-process identity is not guaranteed anymore.

Returns:

self if return_early=False, otherwise a TensorDictFuture instance.

Note

Serialising in this fashion might be slow with deeply nested tensordicts, so it is not recommended to call this method inside a training loop.

memmap_like(prefix: Optional[str] = None, copy_existing: bool = False, *, existsok: bool = True, num_threads: int = 0, return_early: bool = False, share_non_tensor: bool = False) → T

Creates a contentless Memory-mapped tensordict with the same shapes as the original one.

Parameters:

  • prefix (str) – directory prefix where the memory-mapped tensors will be stored. The directory tree structure will mimic the tensordict’s.

  • copy_existing (bool) – If False (default), an exception will be raised if an entry in the tensordict is already a tensor stored on disk with an associated file, but is not saved in the correct location according to prefix. If True, any existing Tensor will be copied to the new location.

Keyword Arguments:

  • num_threads (int, optional) – the number of threads used to write the memmap tensors. Defaults to 0.

  • return_early (bool, optional) – if True and num_threads>0, the method will return a future of the tensordict.

  • share_non_tensor (bool, optional) – if True, the non-tensor data will be shared between the processes and writing operation (such as inplace update or set) on any of the workers within a single node will update the value on all other workers. If the number of non-tensor leaves is high (e.g., sharing large stacks of non-tensor data) this may result in OOM or similar errors. Defaults to False.

  • existsok (bool, optional) – if False, an exception will be raised if a tensor already exists in the same path. Defaults to True.

The TensorDict is then locked, meaning that any writing operations that isn’t in-place will throw an exception (eg, rename, set or remove an entry). Once the tensordict is unlocked, the memory-mapped attribute is turned to False, because cross-process identity is not guaranteed anymore.

Returns:

A new TensorDict instance with data stored as memory-mapped tensors if return_early=False, otherwise a TensorDictFuture instance.

Note

This is the recommended method to write a set of large buffers on disk, as memmap_() will copy the information, which can be slow for large content.

Examples

>>> td = TensorDict({
...     "a": torch.zeros((3, 64, 64), dtype=torch.uint8),
...     "b": torch.zeros(1, dtype=torch.int64),
... }, batch_size=[]).expand(1_000_000)  # expand does not allocate new memory
>>> buffer = td.memmap_like("/path/to/dataset")
memmap_refresh_()

Refreshes the content of the memory-mapped tensordict if it has a saved_path.

This method will raise an exception if no path is associated with it.

save(prefix: Optional[str] = None, copy_existing: bool = False, *, num_threads: int = 0, return_early: bool = False, share_non_tensor: bool = False) → T

Saves the tensordict to disk.

This function is a proxy to memmap().

set(key: NestedKey, value: Any, inplace: bool = False, non_blocking: bool = False)

Sets a new key-value pair.

Parameters:

  • key (str, tuple of str) – name of the key to be set. If tuple of str it is equivalent to chained calls of getattr followed by a final setattr.

  • value (Any) – value to be stored in the tensorclass

  • inplace (bool, optional) – if True, set will tentatively try to update the value in-place. If False or if the key isn’t present, the value will be simply written at its destination.

Returns:

self

state_dict(destination=None, prefix='', keep_vars=False, flatten=False) → dict[str, Any]

Returns a state_dict dictionary that can be used to save and load data from a tensorclass.

to_tensordict(*, retain_none: Optional[bool] = None) → TensorDict

Convert the tensorclass into a regular TensorDict.

Makes a copy of all entries. Memmap and shared memory tensors are converted to regular tensors.

Parameters:

retain_none (bool) – if True, the None values will be written in the tensordict. Otherwise they will be discrarded. Default: True.

Returns:

A new TensorDict object containing the same values as the tensorclass.

unbind(dim: int)

Returns a tuple of indexed tensorclass instances unbound along the indicated dimension.

Resulting tensorclass instances will share the storage of the initial tensorclass instance.

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