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import torch as t
class SparseAct:
"""
A SparseAct is a helper class which represents a vector in the sparse feature basis provided by an SAE, jointly with the SAE error term.
A SparseAct may have three fields:
act (... seq d_sae): the feature activations in the sparse basis
res (... seq d_model): the SAE error term
resc (... seq): a contracted SAE error term, useful for when we want one number per feature and error
(instead of having d_model numbers per error)
"""
def __init__(
self,
act: t.Tensor,
res: t.Tensor | None = None,
resc: t.Tensor | None = None, # contracted residual
) -> None:
self.act = act
self.res = res
self.resc = resc
def _map(self, f, aux=None) -> 'SparseAct':
kwargs = {}
# if isinstance(aux, SparseAct):
if aux.__class__.__name__ == 'SparseAct': # NOTE: not recommended but this is for fixing relative import
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None and getattr(aux, attr) is not None:
kwargs[attr] = f(getattr(self, attr), getattr(aux, attr))
else:
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
kwargs[attr] = f(getattr(self, attr), aux)
return SparseAct(**kwargs)
def __mul__(self, other) -> SparseAct:
return self._map(lambda x, y: x * y, other)
def __rmul__(self, other) -> SparseAct:
# This will handle float/int * SparseAct by reusing the __mul__ logic
return self.__mul__(other)
def __matmul__(self, other: SparseAct) -> SparseAct:
if other.res is not None and self.res is not None:
# Normal mode, where act is features, and res is error term of SAE
return SparseAct(act = self.act * other.act, resc=(self.res * other.res).sum(dim=-1))
else:
return SparseAct(act = self.act * other.act)
def __add__(self, other) -> SparseAct:
return self._map(lambda x, y: x + y, other)
def __radd__(self, other: SparseAct) -> SparseAct:
return self.__add__(other)
def __sub__(self, other) -> SparseAct:
return self._map(lambda x, y: x - y, other)
def __rsub__(self, other) -> SparseAct:
return self._map(lambda x, y: y - x, other)
def __truediv__(self, other) -> SparseAct:
if isinstance(other, SparseAct):
kwargs = {}
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
kwargs[attr] = getattr(self, attr) / getattr(other, attr)
else:
kwargs = {}
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
kwargs[attr] = getattr(self, attr) / other
return SparseAct(**kwargs)
def __rtruediv__(self, other) -> SparseAct:
if isinstance(other, SparseAct):
kwargs = {}
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
kwargs[attr] = other / getattr(self, attr)
else:
kwargs = {}
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
kwargs[attr] = other / getattr(self, attr)
return SparseAct(**kwargs)
def __neg__(self) -> SparseAct:
return self._map(lambda x, _: -x)
def __invert__(self) -> SparseAct:
return self._map(lambda x, _: ~x)
def __getitem__(self, index: int):
return self.act[index]
def __repr__(self):
return f"SparseAct(act={self.act}, res={self.res}), resc={self.resc}"
def sum(self, dim=None):
kwargs = {}
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
kwargs[attr] = getattr(self, attr).sum(dim)
return SparseAct(**kwargs)
def mean(self, dim: int):
kwargs = {}
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
kwargs[attr] = getattr(self, attr).mean(dim)
return SparseAct(**kwargs)
@property
def grad(self):
kwargs = {}
for attribute in ['act', 'res', 'resc']:
if getattr(self, attribute) is not None:
kwargs[attribute] = getattr(self, attribute).grad
return SparseAct(**kwargs)
def clone(self):
kwargs = {}
for attribute in ['act', 'res', 'resc']:
if getattr(self, attribute) is not None:
kwargs[attribute] = getattr(self, attribute).clone()
return SparseAct(**kwargs)
@property
def value(self):
kwargs = {}
for attribute in ['act', 'res', 'resc']:
if getattr(self, attribute) is not None:
kwargs[attribute] = getattr(self, attribute).value
return SparseAct(**kwargs)
def save(self):
return self._map(lambda x, _: x.save())
def detach(self):
return self._map(lambda x, _: x.detach())
def to_tensor(self, contracted: bool = True):
list_tens = [self.act]
if self.res is not None:
list_tens.append(self.res)
if self.resc is not None:
if contracted:
assert len(self.resc.shape)+1 == len(self.act.shape), "Unvalid resc shape."
list_tens.append(t.unsqueeze(self.resc, -1))
else:
assert len(self.resc.shape) == len(self.act.shape), "Unvalid resc shape."
list_tens.append(self.resc)
return t.cat(list_tens, dim=-1)
def to_sparse_like_self(self, tens: t.Tensor, contracted: bool = True):
assert tens.shape == self.to_tensor(contracted).shape
if self.res is None and self.resc is None:
return SparseAct(act=tens)
elif self.resc is None and self.res is not None:
return SparseAct(
act = tens[..., :self.act.shape[-1]],
res = tens[..., self.act.shape[-1]:]
)
elif self.res is None and self.resc is not None:
return SparseAct(
act = tens[..., :-1],
resc = tens[..., -1]
)
else:
return SparseAct(
act = tens[..., :self.act.shape[-1]],
res = tens[..., self.act.shape[-1]:(self.act.shape[-1]+self.res.shape[-1])], # type: ignore
resc = tens[..., -1]
)
def to(self, device):
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
setattr(self, attr, getattr(self, attr).to(device))
return self
def __eq__(self, other): # type: ignore
return self._map(lambda x, y: x == y, other)
def __gt__(self, other):
return self._map(lambda x, y: x > y, other)
def __lt__(self, other):
return self._map(lambda x, y: x < y, other)
def __le__(self, other):
return self._map(lambda x, y: x <= y, other)
def __ge__(self, other):
return self._map(lambda x, y: x >= y, other)
def nonzero(self):
return self._map(lambda x, _: x.nonzero())
def squeeze(self, dim):
return self._map(lambda x, _: x.squeeze(dim=dim))
def expand_as(self, other):
return self._map(lambda x, y: x.expand_as(y), other)
def zeros_like(self):
return self._map(lambda x, _: t.zeros_like(x))
def ones_like(self):
return self._map(lambda x, _: t.ones_like(x))
def abs(self):
return self._map(lambda x, _: x.abs())
def numel(self):
numel = 0
for attr in ['act', 'res', 'resc']:
if getattr(self, attr) is not None:
numel += getattr(self, attr).numel()
return numel
def item(self):
if self.numel() == 1:
return self.act.item()
def contract(self):
"""
Contract the residuals along the given dimension.
"""
if self.resc is not None:
self.resc = self.resc.sum(dim=-1)
return self |