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[Feature] TensorSpec.enumerate() #2354

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52 changes: 52 additions & 0 deletions test/test_specs.py
Original file line number Diff line number Diff line change
Expand Up @@ -3816,6 +3816,58 @@ def test_non_tensor(self):
assert not isinstance(non_tensor, MultiOneHot)


class TestSpecEnumerate:
def test_discrete(self):
spec = DiscreteTensorSpec(n=5, shape=(3,))
assert (
spec.enumerate()
== torch.tensor([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]])
).all()
assert spec.is_in(spec.enumerate())

def test_one_hot(self):
spec = OneHotDiscreteTensorSpec(n=5, shape=(2, 5))
assert (
spec.enumerate()
== torch.tensor(
[
[[1, 0, 0, 0, 0], [1, 0, 0, 0, 0]],
[[0, 1, 0, 0, 0], [0, 1, 0, 0, 0]],
[[0, 0, 1, 0, 0], [0, 0, 1, 0, 0]],
[[0, 0, 0, 1, 0], [0, 0, 0, 1, 0]],
[[0, 0, 0, 0, 1], [0, 0, 0, 0, 1]],
],
dtype=torch.bool,
)
).all()
assert spec.is_in(spec.enumerate())

def test_multi_discrete(self):
spec = MultiDiscreteTensorSpec([3, 4, 5], shape=(2, 3))
enum = spec.enumerate()
assert spec.is_in(enum)
assert enum.shape == torch.Size([60, 2, 3])

def test_multi_onehot(self):
spec = MultiOneHotDiscreteTensorSpec([3, 4, 5], shape=(2, 12))
enum = spec.enumerate()
assert spec.is_in(enum)
assert enum.shape == torch.Size([60, 2, 12])

def test_composite(self):
c = CompositeSpec(
{
"a": OneHotDiscreteTensorSpec(n=5, shape=(3, 5)),
("b", "c"): DiscreteTensorSpec(n=4, shape=(3,)),
},
shape=[3],
)
c_enum = c.enumerate()
assert c.is_in(c_enum)
assert c_enum.shape == torch.Size((20, 3))
assert c_enum["b"].shape == torch.Size((20, 3))


if __name__ == "__main__":
args, unknown = argparse.ArgumentParser().parse_known_args()
pytest.main([__file__, "--capture", "no", "--exitfirst"] + unknown)
113 changes: 111 additions & 2 deletions torchrl/data/tensor_specs.py
Original file line number Diff line number Diff line change
Expand Up @@ -834,6 +834,16 @@ def contains(self, item: torch.Tensor | TensorDictBase) -> bool:
"""
return self.is_in(item)

@abc.abstractmethod
def enumerate(self) -> Any:
"""Returns all the samples that can be obtained from the TensorSpec.

The samples will be stacked along the first dimension.

This method is only implemented for discrete specs.
"""
...

def project(
self, val: torch.Tensor | TensorDictBase
) -> torch.Tensor | TensorDictBase:
Expand Down Expand Up @@ -1271,6 +1281,11 @@ def __eq__(self, other):
return False
return True

def enumerate(self) -> torch.Tensor | TensorDictBase:
return torch.stack(
[spec.enumerate() for spec in self._specs], dim=self.stack_dim + 1
)

def __len__(self):
return self.shape[0]

Expand Down Expand Up @@ -1732,6 +1747,13 @@ def to_numpy(self, val: torch.Tensor, safe: bool = None) -> np.ndarray:
return np.array(vals).reshape(tuple(val.shape))
return val

def enumerate(self) -> torch.Tensor:
return (
torch.eye(self.n, dtype=self.dtype, device=self.device)
.expand(*self.shape, self.n)
.permute(-2, *range(self.ndimension() - 1), -1)
)

def index(self, index: INDEX_TYPING, tensor_to_index: torch.Tensor) -> torch.Tensor:
if not isinstance(index, torch.Tensor):
raise ValueError(
Expand Down Expand Up @@ -2056,6 +2078,11 @@ def __init__(
domain=domain,
)

def enumerate(self) -> Any:
raise NotImplementedError(
f"enumerate is not implemented for spec of class {type(self).__name__}."
)

def __eq__(self, other):
return (
type(other) == type(self)
Expand Down Expand Up @@ -2375,6 +2402,9 @@ def __init__(
shape=shape, space=None, device=device, dtype=dtype, domain=domain, **kwargs
)

def enumerate(self) -> Any:
raise NotImplementedError("Cannot enumerate a NonTensorSpec.")

def to(self, dest: Union[torch.dtype, DEVICE_TYPING]) -> NonTensor:
if isinstance(dest, torch.dtype):
dest_dtype = dest
Expand Down Expand Up @@ -2611,6 +2641,9 @@ def is_in(self, val: torch.Tensor) -> bool:
def _project(self, val: torch.Tensor) -> torch.Tensor:
return torch.as_tensor(val, dtype=self.dtype).reshape(self.shape)

def enumerate(self) -> Any:
raise NotImplementedError("enumerate cannot be called with continuous specs.")

def expand(self, *shape):
if len(shape) == 1 and isinstance(shape[0], (tuple, list, torch.Size)):
shape = shape[0]
Expand Down Expand Up @@ -2775,6 +2808,18 @@ def __init__(
)
self.update_mask(mask)

def enumerate(self) -> torch.Tensor:
nvec = self.nvec
enum_disc = self.to_categorical_spec().enumerate()
enums = torch.cat(
[
torch.nn.functional.one_hot(enum_unb, nv).to(self.dtype)
for nv, enum_unb in zip(nvec, enum_disc.unbind(-1))
],
-1,
)
return enums

def update_mask(self, mask):
"""Sets a mask to prevent some of the possible outcomes when a sample is taken.

Expand Down Expand Up @@ -3208,6 +3253,12 @@ def __init__(
)
self.update_mask(mask)

def enumerate(self) -> torch.Tensor:
arange = torch.arange(self.n, dtype=self.dtype, device=self.device)
if self.ndim:
arange = arange.view(-1, *(1,) * self.ndim)
return arange.expand(self.n, *self.shape)

@property
def n(self):
return self.space.n
Expand Down Expand Up @@ -3715,6 +3766,29 @@ def __init__(
self.update_mask(mask)
self.remove_singleton = remove_singleton

def enumerate(self) -> torch.Tensor:
if self.mask is not None:
raise RuntimeError(
"Cannot enumerate a masked TensorSpec. Submit an issue on github if this feature is requested."
)
if self.nvec._base.ndim == 1:
nvec = self.nvec._base
else:
# we have to use unique() to isolate the nvec
nvec = self.nvec.view(-1, self.nvec.shape[-1]).unique(dim=0).squeeze(0)
if nvec.ndim > 1:
raise ValueError(
f"Cannot call enumerate on heterogeneous nvecs: unique nvecs={nvec}."
)
arange = torch.meshgrid(
*[torch.arange(n, device=self.device, dtype=self.dtype) for n in nvec],
indexing="ij",
)
arange = torch.stack([arange_.reshape(-1) for arange_ in arange], dim=-1)
arange = arange.view(arange.shape[0], *(1,) * (self.ndim - 1), self.shape[-1])
arange = arange.expand(arange.shape[0], *self.shape)
return arange

def update_mask(self, mask):
"""Sets a mask to prevent some of the possible outcomes when a sample is taken.

Expand Down Expand Up @@ -3932,6 +4006,8 @@ def to_one_hot(

def to_one_hot_spec(self) -> MultiOneHot:
"""Converts the spec to the equivalent one-hot spec."""
if self.ndim > 1:
return torch.stack([spec.to_one_hot_spec() for spec in self.unbind(0)])
nvec = [_space.n for _space in self.space]
return MultiOneHot(
nvec,
Expand Down Expand Up @@ -4606,6 +4682,33 @@ def clone(self) -> Composite:
shape=self.shape,
)

def enumerate(self) -> TensorDictBase:
# We are going to use meshgrid to create samples of all the subspecs in here
# but first let's get rid of the batch size, we'll put it back later
self_without_batch = self
while self_without_batch.ndim:
self_without_batch = self_without_batch[0]
samples = {key: spec.enumerate() for key, spec in self_without_batch.items()}
if samples:
idx_rep = torch.meshgrid(
*(torch.arange(s.shape[0]) for s in samples.values()), indexing="ij"
)
idx_rep = tuple(idx.reshape(-1) for idx in idx_rep)
samples = {
key: sample[idx]
for ((key, sample), idx) in zip(samples.items(), idx_rep)
}
samples = TensorDict(
samples, batch_size=idx_rep[0].shape[:1], device=self.device
)
# Expand
if self.ndim:
samples = samples.reshape(-1, *(1,) * self.ndim)
samples = samples.expand(samples.shape[0], *self.shape)
else:
samples = TensorDict(batch_size=self.shape, device=self.device)
return samples

def empty(self):
"""Create a spec like self, but with no entries."""
try:
Expand Down Expand Up @@ -4856,6 +4959,12 @@ def update(self, dict) -> None:
self[key] = item
return self

def enumerate(self) -> TensorDictBase:
dim = self.stack_dim
return LazyStackedTensorDict.maybe_dense_stack(
[spec.enumerate() for spec in self._specs], dim + 1
)

def __eq__(self, other):
if not isinstance(other, StackedComposite):
return False
Expand Down Expand Up @@ -5150,7 +5259,7 @@ def rand(self, shape: torch.Size = None) -> TensorDictBase:


@TensorSpec.implements_for_spec(torch.stack)
def _stack_specs(list_of_spec, dim, out=None):
def _stack_specs(list_of_spec, dim=0, out=None):
if out is not None:
raise NotImplementedError(
"In-place spec modification is not a feature of torchrl, hence "
Expand Down Expand Up @@ -5187,7 +5296,7 @@ def _stack_specs(list_of_spec, dim, out=None):


@Composite.implements_for_spec(torch.stack)
def _stack_composite_specs(list_of_spec, dim, out=None):
def _stack_composite_specs(list_of_spec, dim=0, out=None):
if out is not None:
raise NotImplementedError(
"In-place spec modification is not a feature of torchrl, hence "
Expand Down
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