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# Copyright 2022-2025 MetaOPT Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Integration with NumPy."""
# pragma: numpy cover file
from __future__ import annotations
import functools
import itertools
import warnings
from typing import Any, Callable
from typing_extensions import TypeAlias # Python 3.10+
import numpy as np # pylint: disable=import-error
from numpy.typing import ArrayLike # pylint: disable=import-error
from optree.ops import tree_flatten, tree_unflatten
from optree.typing import PyTreeSpec, PyTreeTypeVar
from optree.utils import safe_zip
__all__ = ['ArrayLikeTree', 'ArrayTree', 'tree_ravel']
ArrayLikeTree: TypeAlias = PyTreeTypeVar('ArrayLikeTree', ArrayLike) # type: ignore[valid-type]
ArrayTree: TypeAlias = PyTreeTypeVar('ArrayTree', np.ndarray) # type: ignore[valid-type]
def tree_ravel(
tree: ArrayLikeTree,
/,
is_leaf: Callable[[Any], bool] | None = None,
*,
none_is_leaf: bool = False,
namespace: str = '',
) -> tuple[np.ndarray, Callable[[np.ndarray], ArrayTree]]:
r"""Ravel (flatten) a pytree of arrays down to a 1D array.
>>> tree = {
... 'layer1': {
... 'weight': np.arange(0, 6, dtype=np.float32).reshape((2, 3)),
... 'bias': np.arange(6, 8, dtype=np.float32).reshape((2,)),
... },
... 'layer2': {
... 'weight': np.arange(8, 10, dtype=np.float32).reshape((1, 2)),
... 'bias': np.arange(10, 11, dtype=np.float32).reshape((1,)),
... },
... }
>>> tree # doctest: +IGNORE_WHITESPACE
{
'layer1': {
'weight': array([[0., 1., 2.],
[3., 4., 5.]], dtype=float32),
'bias': array([6., 7.], dtype=float32)
},
'layer2': {
'weight': array([[8., 9.]], dtype=float32),
'bias': array([10.], dtype=float32)
}
}
>>> flat, unravel_func = tree_ravel(tree)
>>> flat
array([ 6., 7., 0., 1., 2., 3., 4., 5., 10., 8., 9.], dtype=float32)
>>> unravel_func(flat) # doctest: +IGNORE_WHITESPACE
{
'layer1': {
'weight': array([[0., 1., 2.],
[3., 4., 5.]], dtype=float32),
'bias': array([6., 7.], dtype=float32)
},
'layer2': {
'weight': array([[8., 9.]], dtype=float32),
'bias': array([10.], dtype=float32)
}
}
Args:
tree (pytree): a pytree of arrays and scalars to ravel.
is_leaf (callable, optional): An optionally specified function that will be called at each
flattening step. It should return a boolean, with :data:`True` stopping the traversal
and the whole subtree being treated as a leaf, and :data:`False` indicating the
flattening should traverse the current object.
none_is_leaf (bool, optional): Whether to treat :data:`None` as a leaf. If :data:`False`,
:data:`None` is a non-leaf node with arity 0. Thus :data:`None` is contained in the
treespec rather than in the leaves list and :data:`None` will be remain in the result
pytree. (default: :data:`False`)
namespace (str, optional): The registry namespace used for custom pytree node types.
(default: :const:`''`, i.e., the global namespace)
Returns:
A pair ``(array, unravel_func)`` where the first element is a 1D array representing the
flattened and concatenated leaf values, with ``dtype`` determined by promoting the
``dtype``\s of leaf values, and the second element is a callable for unflattening a 1D array
of the same length back to a pytree of the same structure as the input ``tree``. If the
input pytree is empty (i.e. has no leaves) then as a convention a 1D empty array of the
default dtype is returned in the first component of the output.
"""
leaves, treespec = tree_flatten(
tree,
is_leaf=is_leaf,
none_is_leaf=none_is_leaf,
namespace=namespace,
)
flat, unravel_flat = _ravel_leaves(leaves)
return flat, functools.partial(_tree_unravel, treespec, unravel_flat)
ravel_pytree = tree_ravel
def _tree_unravel(
treespec: PyTreeSpec,
unravel_flat: Callable[[np.ndarray], list[np.ndarray]],
flat: np.ndarray,
/,
) -> ArrayTree:
return tree_unflatten(treespec, unravel_flat(flat))
def _ravel_leaves(
leaves: list[np.ndarray],
/,
) -> tuple[
np.ndarray,
Callable[[np.ndarray], list[np.ndarray]],
]:
if not leaves:
return (np.zeros(0), _unravel_empty)
from_dtypes = tuple(np.result_type(leaf) for leaf in leaves)
to_dtype = np.result_type(*leaves)
sizes = tuple(np.size(leaf) for leaf in leaves)
shapes = tuple(np.shape(leaf) for leaf in leaves)
indices = tuple(itertools.accumulate(sizes))
if all(dt == to_dtype for dt in from_dtypes):
# Skip any dtype conversion, resulting in a dtype-polymorphic `unravel`.
raveled = np.concatenate([np.ravel(leaf) for leaf in leaves])
return (
raveled,
functools.partial(_unravel_leaves_single_dtype, indices, shapes),
)
# When there is more than one distinct input dtype, we perform type conversions and produce a
# dtype-specific unravel function.
raveled = np.concatenate([np.ravel(leaf).astype(to_dtype) for leaf in leaves])
return (
raveled,
functools.partial(_unravel_leaves, indices, shapes, from_dtypes, to_dtype),
)
def _unravel_empty(flat: np.ndarray, /) -> list[np.ndarray]:
if np.shape(flat) != (0,):
raise ValueError(
f'The unravel function expected an array of shape {(0,)}, got shape {np.shape(flat)}.',
)
return []
def _unravel_leaves_single_dtype(
indices: tuple[int, ...],
shapes: tuple[tuple[int, ...], ...],
flat: np.ndarray,
/,
) -> list[np.ndarray]:
if np.shape(flat) != (indices[-1],):
raise ValueError(
f'The unravel function expected an array of shape {(indices[-1],)}, '
f'got shape {np.shape(flat)}.',
)
chunks = np.split(flat, indices[:-1])
return [chunk.reshape(shape) for chunk, shape in safe_zip(chunks, shapes)]
def _unravel_leaves(
indices: tuple[int, ...],
shapes: tuple[tuple[int, ...], ...],
from_dtypes: tuple[np.dtype, ...],
to_dtype: np.dtype,
flat: np.ndarray,
/,
) -> list[np.ndarray]:
if np.shape(flat) != (indices[-1],):
raise ValueError(
f'The unravel function expected an array of shape {(indices[-1],)}, '
f'got shape {np.shape(flat)}.',
)
array_dtype = np.result_type(flat)
if array_dtype != to_dtype:
raise ValueError(
f'The unravel function expected an array of dtype {to_dtype}, got dtype {array_dtype}.',
)
chunks = np.split(flat, indices[:-1])
with warnings.catch_warnings():
warnings.simplefilter('ignore') # ignore complex-to-real cast warning
return [
chunk.reshape(shape).astype(dtype)
for chunk, shape, dtype in safe_zip(chunks, shapes, from_dtypes)
]
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