from functools import update_wrapper
from numbers import Number
import torch
import torch.nn.functional as F
from typing import Dict, Any
from torch.overrides import is_tensor_like
euler_constant = 0.57721566490153286060 # Euler Mascheroni Constant
__all__ = ["broadcast_all", "logits_to_probs", "clamp_probs", "probs_to_logits", "lazy_property",
"tril_matrix_to_vec", "vec_to_tril_matrix"]
def broadcast_all(*values):
r"""
Given a list of values (possibly containing numbers), returns a list where each
value is broadcasted based on the following rules:
- `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`.
- numbers.Number instances (scalars) are upcast to tensors having
the same size and type as the first tensor passed to `values`. If all the
values are scalars, then they are upcasted to scalar Tensors.
Args:
values (list of `numbers.Number`, `torch.*Tensor` or objects implementing __torch_function__)
Raises:
ValueError: if any of the values is not a `numbers.Number` instance,
a `torch.*Tensor` instance, or an instance implementing __torch_function__
"""
if not all(is_tensor_like(v) or isinstance(v, Number)
for v in values):
raise ValueError('Input arguments must all be instances of numbers.Number, '
'torch.Tensor or objects implementing __torch_function__.')
if not all(is_tensor_like(v) for v in values):
options: Dict[str, Any] = dict(dtype=torch.get_default_dtype())
for value in values:
if isinstance(value, torch.Tensor):
options = dict(dtype=value.dtype, device=value.device)
break
new_values = [v if is_tensor_like(v) else torch.tensor(v, **options)
for v in values]
return torch.broadcast_tensors(*new_values)
return torch.broadcast_tensors(*values)
def _standard_normal(shape, dtype, device):
if torch._C._get_tracing_state():
# [JIT WORKAROUND] lack of support for .normal_()
return torch.normal(torch.zeros(shape, dtype=dtype, device=device),
torch.ones(shape, dtype=dtype, device=device))
return torch.empty(shape, dtype=dtype, device=device).normal_()
def _sum_rightmost(value, dim):
r"""
Sum out ``dim`` many rightmost dimensions of a given tensor.
Args:
value (Tensor): A tensor of ``.dim()`` at least ``dim``.
dim (int): The number of rightmost dims to sum out.
"""
if dim == 0:
return value
required_shape = value.shape[:-dim] + (-1,)
return value.reshape(required_shape).sum(-1)
def logits_to_probs(logits, is_binary=False):
r"""
Converts a tensor of logits into probabilities. Note that for the
binary case, each value denotes log odds, whereas for the
multi-dimensional case, the values along the last dimension denote
the log probabilities (possibly unnormalized) of the events.
"""
if is_binary:
return torch.sigmoid(logits)
return F.softmax(logits, dim=-1)
def clamp_probs(probs):
eps = torch.finfo(probs.dtype).eps
return probs.clamp(min=eps, max=1 - eps)
def probs_to_logits(probs, is_binary=False):
r"""
Converts a tensor of probabilities into logits. For the binary case,
this denotes the probability of occurrence of the event indexed by `1`.
For the multi-dimensional case, the values along the last dimension
denote the probabilities of occurrence of each of the events.
"""
ps_clamped = clamp_probs(probs)
if is_binary:
return torch.log(ps_clamped) - torch.log1p(-ps_clamped)
return torch.log(ps_clamped)
class lazy_property:
r"""
Used as a decorator for lazy loading of class attributes. This uses a
non-data descriptor that calls the wrapped method to compute the property on
first call; thereafter replacing the wrapped method into an instance
attribute.
"""
def __init__(self, wrapped):
self.wrapped = wrapped
update_wrapper(self, wrapped)
def __get__(self, instance, obj_type=None):
if instance is None:
return _lazy_property_and_property(self.wrapped)
with torch.enable_grad():
value = self.wrapped(instance)
setattr(instance, self.wrapped.__name__, value)
return value
class _lazy_property_and_property(lazy_property, property):
"""We want lazy properties to look like multiple things.
* property when Sphinx autodoc looks
* lazy_property when Distribution validate_args looks
"""
def __init__(self, wrapped):
return property.__init__(self, wrapped)
def tril_matrix_to_vec(mat, diag=0):
r"""
Convert a `D x D` matrix or a batch of matrices into a (batched) vector
which comprises of lower triangular elements from the matrix in row order.
"""
n = mat.shape[-1]
if not torch._C._get_tracing_state() and (diag < -n or diag >= n):
raise ValueError(f'diag ({diag}) provided is outside [{-n}, {n-1}].')
arange = torch.arange(n, device=mat.device)
tril_mask = arange < arange.view(-1, 1) + (diag + 1)
vec = mat[..., tril_mask]
return vec
def vec_to_tril_matrix(vec, diag=0):
r"""
Convert a vector or a batch of vectors into a batched `D x D`
lower triangular matrix containing elements from the vector in row order.
"""
# +ve root of D**2 + (1+2*diag)*D - |diag| * (diag+1) - 2*vec.shape[-1] = 0
n = (-(1 + 2 * diag) + ((1 + 2 * diag)**2 + 8 * vec.shape[-1] + 4 * abs(diag) * (diag + 1))**0.5) / 2
eps = torch.finfo(vec.dtype).eps
if not torch._C._get_tracing_state() and (round(n) - n > eps):
raise ValueError(f'The size of last dimension is {vec.shape[-1]} which cannot be expressed as ' +
'the lower triangular part of a square D x D matrix.')
n = torch.round(n).long() if isinstance(n, torch.Tensor) else round(n)
mat = vec.new_zeros(vec.shape[:-1] + torch.Size((n, n)))
arange = torch.arange(n, device=vec.device)
tril_mask = arange < arange.view(-1, 1) + (diag + 1)
mat[..., tril_mask] = vec
return mat
