from typing import List, Optional, Union
from . import _dispatch
from ._classes import TensorBlock, TensorMap
from ._utils import _check_gradient_presence_raise
[docs]
def empty_like(
tensor: TensorMap,
gradients: Optional[Union[List[str], str]] = None,
requires_grad: bool = False,
) -> TensorMap:
"""Return a new :py:class:`TensorMap` with the same metadata as tensor,
and all values uninitialized.
:param tensor:
Input tensor from which the metadata is taken.
:param gradients:
Which gradients should be present in the output. If this is
:py:obj:`None` (default) all gradient of ``tensor`` are present in the
new :py:class:`TensorMap`. If this is an empty list ``[]``, no gradients
information is copied.
:param requires_grad:
If autograd should record operations for the returned tensor. This
option is only relevant for torch.
>>> import numpy as np
>>> import metatensor
>>> from metatensor import TensorBlock, TensorMap, Labels
>>> np.random.seed(1)
First we create a :py:class:`TensorMap` with just one block with two
gradients, named ``alpha`` and ``beta``, containing random data:
>>> block = TensorBlock(
... values=np.random.rand(4, 3),
... samples=Labels.range("sample", 4),
... components=[],
... properties=Labels.range("property", 3),
... )
>>> block.add_gradient(
... parameter="alpha",
... gradient=TensorBlock(
... values=np.random.rand(2, 3, 3),
... samples=Labels(["sample", "atom"], np.array([[0, 0], [0, 2]])),
... components=[Labels.range("component", 3)],
... properties=block.properties,
... ),
... )
>>> block.add_gradient(
... parameter="beta",
... gradient=TensorBlock(
... values=np.random.rand(1, 3),
... samples=Labels(["sample"], np.array([[0]])),
... components=[],
... properties=block.properties,
... ),
... )
>>> keys = Labels(names=["key"], values=np.array([[0]]))
>>> tensor = TensorMap(keys, [block])
>>> print(tensor.block(0))
TensorBlock
samples (4): ['sample']
components (): []
properties (3): ['property']
gradients: ['alpha', 'beta']
Then we use ``empty_like`` to create a :py:class:`TensorMap` with the same
metadata as ``tensor``, but with all values uninitialized.
>>> tensor_empty = metatensor.empty_like(tensor)
>>> print(tensor_empty.block(0))
TensorBlock
samples (4): ['sample']
components (): []
properties (3): ['property']
gradients: ['alpha', 'beta']
Note that if we copy just the gradient ``alpha``, ``beta`` is no longer
available.
>>> tensor_empty = metatensor.empty_like(tensor, gradients="alpha")
>>> print(tensor_empty.block(0).gradients_list())
['alpha']
"""
blocks: List[TensorBlock] = []
for block in tensor.blocks():
blocks.append(
empty_like_block(
block=block, gradients=gradients, requires_grad=requires_grad
)
)
return TensorMap(tensor.keys, blocks)
[docs]
def empty_like_block(
block: TensorBlock,
gradients: Optional[Union[List[str], str]] = None,
requires_grad: bool = False,
) -> TensorBlock:
"""Return a new :py:class:`TensorBlock` with the same metadata as block,
and all values uninitialized.
:param block:
Input block from which the metadata is taken.
:param gradients:
Which gradients should be present in the output. If this is
:py:obj:`None` (default) all gradient of ``block`` are present in the
new :py:class:`TensorBlock`. If this is an empty list ``[]``, no
gradients information is copied.
:param requires_grad:
If autograd should record operations for the returned tensor. This
option is only relevant for torch.
"""
values = _dispatch.empty_like(block.values, requires_grad=requires_grad)
result_block = TensorBlock(
values=values,
samples=block.samples,
components=block.components,
properties=block.properties,
)
if isinstance(gradients, str):
gradients = [gradients]
if gradients is None:
gradients = block.gradients_list()
else:
_check_gradient_presence_raise(
block=block, parameters=gradients, fname="empty_like"
)
for parameter in gradients:
gradient = block.gradient(parameter)
if len(gradient.gradients_list()) != 0:
raise NotImplementedError("gradients of gradients are not supported")
gradient_values = _dispatch.empty_like(gradient.values)
result_block.add_gradient(
parameter=parameter,
gradient=TensorBlock(
values=gradient_values,
samples=gradient.samples,
components=gradient.components,
properties=gradient.properties,
),
)
return result_block