tensordict-nightly 2022.11.7

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TensorDict

TensorDict is a dictionary-like class that inherits properties from tensors, such as indexing, shape operations, casting to device etc.

The main purpose of TensorDict is to make code-bases more readable and modular by abstracting away tailored operations:

for i, tensordict in enumerate(dataset):
    # the model reads and writes tensordicts
    tensordict = model(tensordict)
    loss = loss_module(tensordict)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

With this level of abstraction, one can recycle a training loop for highly heterogeneous task. Each individual step of the training loop (data collection and transform, model prediction, loss computation etc.) can be tailored to the use case at hand without impacting the others. For instance, the above example can be easily used across classification and segmentation tasks, among many others.

Installation

To install the latest stable version of tensordict, simply run

pip install tensordict

This will work with python 3.7 and upward as well as pytorch 1.12 and upward.

To enjoy the latest features, one can use

pip install tensordict-nightly

Features

General

A tensordict is primarily defined by its batch_size (or shape) and its key-value pairs:

from tensordict import TensorDict
import torch
tensordict = TensorDict({
    "key 1": torch.ones(3, 4, 5),
    "key 2": torch.zeros(3, 4, 5, dtype=torch.bool),
}, batch_size=[3, 4])

The batch_size and the first dimensions of each of the tensors must be compliant. The tensors can be of any dtype and device. Optionally, one can restrict a tensordict to live on a dedicated device, which will send each tensor that is written there:

tensordict = TensorDict({
    "key 1": torch.ones(3, 4, 5),
    "key 2": torch.zeros(3, 4, 5, dtype=torch.bool),
}, batch_size=[3, 4], device="cuda:0")
tensordict["key 3"] = torch.randn(3, 4, device="cpu")
assert tensordict["key 3"].device is torch.device("cuda:0")

Tensor-like features

TensorDict objects can be indexed exactly like tensors. The resulting of indexing a TensorDict is another TensorDict containing tensors indexed along the required dimension:

tensordict = TensorDict({
    "key 1": torch.ones(3, 4, 5),
    "key 2": torch.zeros(3, 4, 5, dtype=torch.bool),
}, batch_size=[3, 4])
sub_tensordict = tensordict[..., :2]
assert sub_tensordict.shape == torch.Size([3, 2])
assert sub_tensordict["key 1"].shape == torch.Size([3, 2, 5])

Similarly, one can build tensordicts by stacking or concatenating single tensordicts:

tensordicts = [TensorDict({
    "key 1": torch.ones(3, 4, 5),
    "key 2": torch.zeros(3, 4, 5, dtype=torch.bool),
}, batch_size=[3, 4]) for _ in range(2)]
stack_tensordict = torch.stack(tensordicts, 1)
assert stack_tensordict.shape == torch.Size([3, 2, 4])
assert stack_tensordict["key 1"].shape == torch.Size([3, 2, 4, 5])
cat_tensordict = torch.cat(tensordicts, 0)
assert cat_tensordict.shape == torch.Size([6, 4])
assert cat_tensordict["key 1"].shape == torch.Size([6, 4, 5])

TensorDict instances can also be reshaped, viewed, squeezed and unsqueezed:

tensordict = TensorDict({
    "key 1": torch.ones(3, 4, 5),
    "key 2": torch.zeros(3, 4, 5, dtype=torch.bool),
}, batch_size=[3, 4])
print(tensordict.view(-1))  # prints torch.Size([12])
print(tensordict.reshape(-1))  # prints torch.Size([12])
print(tensordict.unsqueeze(-1))  # prints torch.Size([3, 4, 1])

One can also send tensordict from device to device, place them in shared memory, clone them, update them in-place or not, split them, unbind them, expand them etc.

If a functionality is missing, it is easy to call it using apply() or apply_():

tensordict_uniform = tensordict.apply(lambda tensor: tensor.uniform_())

TensorDict for functional programming using FuncTorch

We also provide an API to use TensorDict in conjunction with FuncTorch. For instance, TensorDict makes it easy to concatenate model weights to do model ensembling:

from torch import nn
from tensordict import TensorDict
from copy import deepcopy
from tensordict.nn.functional_modules import FunctionalModule
import torch
from functorch import vmap
layer1 = nn.Linear(3, 4)
layer2 = nn.Linear(4, 4)
model1 = nn.Sequential(layer1, layer2)
model2 = deepcopy(model1)
# we represent the weights hierarchically
weights1 = TensorDict(model1.state_dict(), []).unflatten_keys(".")
weights2 = TensorDict(model2.state_dict(), []).unflatten_keys(".")
weights = torch.stack([weights1, weights2], 0)
fmodule, _ = FunctionalModule._create_from(model1)
# an input we'd like to pass through the model
x = torch.randn(10, 3)
y = vmap(fmodule, (0, None))(weights, x)
y.shape  # torch.Size([2, 10, 4])

First-class dimensions

Note: first-class dimensions are themselves experimental, you will need to install torch-nightly to try this out.

We also support use of first-class dimensions from functorch. Indexing a TensorDict with first-class dimensions will result in all items in the TensorDict being indexed in the same way. Once a TensorDict has been indexed with first-class dimensions, any new entries must themselves have been indexed in a compatible way. First-class dimensions can be added to any of the batch dimensions, since they are guaranteed to exist for all entries. You can call order directly on the TensorDict, or access items individually according to your need.

Here's a simple example. Create a TensorDict as usual

import torch
from functorch.dim import dims
from tensordict import TensorDict

td = TensorDict(
    {"mask": torch.randint(2, (10, 28, 28), dtype=torch.uint8)},
    batch_size=[10, 28, 28],
)

You can then index the TensorDict with first class dimensions as you would a tensor

batch, width, height, channel = dims(4)
td_fc = td[batch, width, height]

All entries of the TensorDict will now have been indexed in the same way

td_fc["mask"]
# tensor(..., dtype=torch.uint8)
# with dims=(batch, width, height, 0) sizes=(10, 28, 28, 1)

You can add new items provided they have compatible first class dimensions, i.e. the new item must have all of the first-class dimensions of the TensorDict, though the item can have additional first-class non-batch dimensions, and the remaining positional dimensions are compatible with the TensorDict's batch size.

td_fc["input"] = torch.rand(10, 28, 28, 3)[batch, width, height, channel]

You can now take advantage of first-class dimensions when accessing the items

(td_fc["input"] * td_fc["mask"]).mean(channel)

Or can call order on the TensorDict to arrange dimensions of all items

td_ordered = td_fc.order(batch, height, width)
torch.testing.assert_close(
    td_ordered["mask"], td_fc["mask"].order(batch, height, width)
)

Nesting TensorDicts

It is possible to nest tensordict and switch easily between hierarchical and flat representations. For instance, the following code will result in a single-level tensordict with keys "key 1" and "key 2.sub-key":

>>> tensordict = TensorDict({
...     "key 1": torch.ones(3, 4, 5),
...     "key 2": TensorDict({"sub-key": torch.randn(3, 4, 5, 6)}, [3, 4, 5])
... }, batch_size=[3, 4])
>>> tensordict = tensordict.unflatten_keys(separator=".")

Disclaimer

TensorDict is at the alpha-stage, meaning that there may be bc-breaking changes introduced at any moment without warranty. Hopefully that should not happen too often, as the current roadmap mostly involves adding new features and building compatibility with the broader pytorch ecosystem.

License

TorchRL is licensed under the MIT License. See LICENSE for details.