tensordict.nn.distributions.CompositeDistribution

class tensordict.nn.distributions.CompositeDistribution(params: TensorDictBase, distribution_map: dict, *, name_map: Optional[dict] = None, extra_kwargs=None, aggregate_probabilities: Optional[bool] = None, log_prob_key: NestedKey = 'sample_log_prob', entropy_key: NestedKey = 'entropy')

A composition of distributions.

Groups distributions together with the TensorDict interface. Methods (log_prob_composite, entropy_composite, cdf, icdf, rsample, sample etc.) will return a tensordict, possibly modified in-place if the input was a tensordict.

Parameters:

• params (TensorDictBase) – a nested key-tensor map where the root entries point to the sample names, and the leaves are the distribution parameters. Entry names must match those of distribution_map.

• distribution_map (Dict[NestedKey, Type[torch.distribution.Distribution]]) – indicated the distribution types to be used. The names of the distributions will match the names of the samples in the tensordict.

Keyword Arguments:

• name_map (Dict[NestedKey, NestedKey]]) – a dictionary representing where each sample should be written. If not provided, the key names from distribution_map will be used.

• extra_kwargs (Dict[NestedKey, Dict]) – a possibly incomplete dictionary of extra keyword arguments for the distributions to be built.

• aggregate_probabilities (bool) – if True, the log_prob() and entropy() methods will sum the probabilities and entropies of the individual distributions and return a single tensor. If False, the single log-probabilities will be registered in the input tensordict (for log_prob()) or retuned as leaves of the output tensordict (for entropy()). This parameter can be overridden at runtime by passing the aggregate_probabilities argument to log_prob and entropy. Defaults to False.

• log_prob_key (NestedKey, optional) – key where to write the log_prob. Defaults to ‘sample_log_prob’.

• entropy_key (NestedKey, optional) – key where to write the entropy. Defaults to ‘entropy’.

Note

In this distribution class, the batch-size of the input tensordict containing the params (params) is indicative of the batch_shape of the distribution. For instance, the "sample_log_prob" entry resulting from a call to log_prob will be of the shape of the params (+ any supplementary batch dimension).

Examples

>>> params = TensorDict({
...     "cont": {"loc": torch.randn(3, 4), "scale": torch.rand(3, 4)},
...     ("nested", "disc"): {"logits": torch.randn(3, 10)}
... }, [3])
>>> dist = CompositeDistribution(params,
...     distribution_map={"cont": d.Normal, ("nested", "disc"): d.Categorical})
>>> sample = dist.sample((4,))
>>> sample = dist.log_prob(sample)
>>> print(sample)
TensorDict(
    fields={
        cont: Tensor(shape=torch.Size([4, 3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        cont_log_prob: Tensor(shape=torch.Size([4, 3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        nested: TensorDict(
            fields={
                disc: Tensor(shape=torch.Size([4, 3]), device=cpu, dtype=torch.int64, is_shared=False),
                disc_log_prob: Tensor(shape=torch.Size([4, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([4]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([4]),
    device=None,
    is_shared=False)