from typing import List, Optional
from ._classes import TensorBlock, TensorMap
[docs]
def remove_gradients(
tensor: TensorMap,
remove: Optional[List[str]] = None,
) -> TensorMap:
"""Remove some or all of the gradients from a :py:class:`TensorMap`.
This function is related but different to :py:func:`metatensor.detach`. This
function removes the explicit forward mode gradients stored in the blocks, while
:py:func:`metatensor.detach` separate the values (as well as any potential
gradients) from the underlying computational graph use by PyTorch to run backward
differentiation.
:param tensor: :py:class:`TensorMap` with gradients to be removed
:param remove: which gradients should be excluded from the new tensor map. If this
is set to :py:obj:`None` (this is the default), all the gradients will be
removed.
:returns: A new :py:class:`TensorMap` without the gradients in ``remove``.
"""
if remove is None and len(tensor) != 0:
remove = tensor.block(0).gradients_list()
blocks: List[TensorBlock] = []
for block in tensor.blocks():
blocks.append(remove_gradients_block(block, remove))
return TensorMap(tensor.keys, blocks)
[docs]
def remove_gradients_block(
block: TensorBlock,
remove: Optional[List[str]] = None,
) -> TensorBlock:
"""Remove some or all of the gradients from a :py:class:`TensorBlock`.
This function is related but different to :py:func:`metatensor.detach_block`. This
function removes the explicit forward mode gradients stored in the ``block``, while
:py:func:`metatensor.detach_block` separate the values (as well as any potential
gradients) from the underlying computational graph use by PyTorch to run backward
differentiation.
:param block: :py:class:`TensorBlock` with gradients to be removed
:param remove: which gradients should be excluded from the new block. If this is set
to :py:obj:`None` (this is the default), all the gradients will be removed.
:returns: A new :py:class:`TensorBlock` without the gradients in ``remove``.
"""
if remove is None:
remove = block.gradients_list()
new_block = TensorBlock(
values=block.values,
samples=block.samples,
components=block.components,
properties=block.properties,
)
for parameter, gradient in block.gradients():
if parameter in remove:
continue
if len(gradient.gradients_list()) != 0:
raise NotImplementedError("gradients of gradients are not supported")
new_block.add_gradient(
parameter=parameter,
gradient=TensorBlock(
values=gradient.values,
samples=gradient.samples,
components=gradient.components,
properties=gradient.properties,
),
)
return new_block