boundlab.linearop.SelectOp#

class boundlab.linearop.SelectOp[source]#

Bases: GetSliceOp

Select a single index along dim, removing that dimension. (Alias for GetSliceOp)

Examples

>>> import torch
>>> from boundlab.linearop import SelectOp
>>> op = SelectOp(torch.Size([2, 3]), dim=0, index=1)
>>> op.forward(torch.tensor([[1., 2., 3.], [4., 5., 6.]]))
tensor([4., 5., 6.])

Methods

`__init__`	Initialize a LinearOp wrapper.
`abs`	Return a LinearOp representing the element-wise absolute value of this LinearOp.
`backward`	Backward using F.pad for vmap compatibility.
`force_jacobian`	Materialize Jacobian via batched forward/backward application.
`forward`	Apply the original linear function to an input tensor.
`jacobian`	Return an explicit Jacobian tensor when efficiently available.
`jacobian_op`	Materialize this LinearOp as an explicit Jacobian tensor.
`jacobian_scatter`	Add this operator's Jacobian contribution into an existing tensor.
`sum_input`	Return a LinearOp that sums over the input dimensions, if supported.
`sum_output`	Return a LinearOp that sums over the output dimensions, if supported.
`vbackward`	Apply the transposed linear function to an input tensor, supporting additional leading dimensions for batching.
`vforward`	Apply the original linear function to an input tensor, supporting additional trailing dimensions for batching.

__init__(input_shape, dim, index)[source]#

Initialize a LinearOp wrapper.

Parameters:

input_shape – The expected shape of input tensors.
output_shape – The expected shape of output tensors.
flags – Flags indicating special properties of this LinearOp.

__add__(other)#

Add this LinearOp to another.

__call__(x)#: Apply this LinearOp to an expression, returning a Linear.

__mul__(other)#

Scale this LinearOp by a scalar factor.

abs()#

Return a LinearOp representing the element-wise absolute value of this LinearOp.

backward(grad)#

Backward using F.pad for vmap compatibility.

force_jacobian()#

Materialize Jacobian via batched forward/backward application.

This fallback constructs an identity basis and applies either vforward() or vbackward() depending on whether the input or output side is smaller.

Returns:: A dense Jacobian tensor with shape [*output_shape, *input_shape].

Notes

This may be expensive in time and memory and is mainly intended for debugging, validation, or rare paths that require explicit Jacobians.

forward(x)#

Apply the original linear function to an input tensor.

jacobian()#

Return an explicit Jacobian tensor when efficiently available.

Returns:: A tensor with shape [*output_shape, *input_shape] if the concrete Jacobian can be produced directly. Returns NotImplemented for operators that only support implicit application.
Return type:: torch.Tensor

jacobian_op()#

Materialize this LinearOp as an explicit Jacobian tensor.

Returns:: A tensor with shape [*output_shape, *input_shape] representing the Jacobian of this LinearOp.
Return type:: torch.Tensor

Notes

This may be expensive in time and memory and is mainly intended for debugging, validation, or rare paths that require explicit Jacobians.

jacobian_scatter(src)#

Add this operator’s Jacobian contribution into an existing tensor.

Parameters:: src (torch.Tensor) – A tensor with Jacobian layout [*output_shape, *input_shape] that acts as the accumulation buffer.
Returns:: A tensor with the same shape as src containing src + jacobian(self).
Return type:: torch.Tensor

Notes

Subclasses may override this to implement structured/sparse updates without materializing the full Jacobian first.

sum_input()#

Return a LinearOp that sums over the input dimensions, if supported.

sum_output()#

Return a LinearOp that sums over the output dimensions, if supported.

vbackward(grad)#

Apply the transposed linear function to an input tensor, supporting additional leading dimensions for batching.

vforward(x)#

Apply the original linear function to an input tensor, supporting additional trailing dimensions for batching.

input_shape: torch.Size#: Expected input tensor shape.

output_shape: torch.Size#: Computed output tensor shape.