def ceil_ste(x: Tensor) -> Tensor:
"""
Function that implements :func:`torch.ceil` with a straight-through gradient estimator.
Notes:
Wrapper for either :func:`~brevitas.function.autograd_ste_ops.ceil_ste_impl` (with env
``BREVITAS_JIT=0``) or its native just-in-time compiled variant (with ``BREVITAS_JIT=1``).
Examples:
>>> x = torch.tensor([1.7, -1.7], requires_grad=True)
>>> y = ceil_ste(x)
>>> y
tensor([ 2., -1.], grad_fn=<CeilSteFnBackward>)
>>> grad = torch.tensor([0.1, -0.1])
>>> y.backward(grad)
>>> (x.grad == grad).all().item()
True
"""
return fn_prefix.ceil_ste_impl(x)
def ceil_ste(x: Tensor) -> Tensor:
""" Perform ceil operation with Straight Trough Estimation (STE) of the Gradient
This operation behaves like an identity on the backward pass.
For Pytorch version >= 1.3.0, the STE operator is implemented in C++ using the
torch::autograd::Function class and compiled. At execution time, the Just-In-Time (JIT) compiler of Pytorch
is used to speed-up the computation.
For Pytorch version < 1.3.0, the STE operator is implemented using the
torch.autograd.Function class in python, and the JIT cannot be used.
Parameters
----------
x : Tensor
Tensor on which to apply the ceil operation
Returns
-------
Tensor
Tensor after applying ceil operation.
When backpropagating through this value, a straight through estimator is applied.
"""
return fn_prefix.ceil_ste_impl(x)
def ceil_ste(x: Tensor) -> Tensor:
"""
Wrapper for either :func:`~brevitas.function.autograd_ste_ops.ceil_ste_impl` (with env
BREVITAS_JIT=0) or its native just-in-time compiled variant (with BREVITAS_JIT=1).
"""
return fn_prefix.ceil_ste_impl(x)