# Copyright 2018 The JAX Authors.
#
# 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
#
# https://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.
import warnings
import numpy as np
from jax import lax
import jax.numpy as jnp
from jax._src import dtypes
from jax._src.tree_util import tree_flatten, tree_unflatten
from jax._src.util import safe_zip, unzip2, HashablePartial
zip = safe_zip
[docs]
def ravel_pytree(pytree):
"""Ravel (flatten) a pytree of arrays down to a 1D array.
Args:
pytree: a pytree of arrays and scalars to ravel.
Returns:
A pair where the first element is a 1D array representing the flattened and
concatenated leaf values, with dtype determined by promoting the dtypes of
leaf values, and the second element is a callable for unflattening a 1D
vector of the same length back to a pytree of the same structure as the
input ``pytree``. If the input pytree is empty (i.e. has no leaves) then as
a convention a 1D empty array of dtype float32 is returned in the first
component of the output.
For details on dtype promotion, see
https://jax.readthedocs.io/en/latest/type_promotion.html.
"""
leaves, treedef = tree_flatten(pytree)
flat, unravel_list = _ravel_list(leaves)
return flat, HashablePartial(unravel_pytree, treedef, unravel_list)
def unravel_pytree(treedef, unravel_list, flat):
return tree_unflatten(treedef, unravel_list(flat))
def _ravel_list(lst):
if not lst: return jnp.array([], jnp.float32), lambda _: []
from_dtypes = tuple(dtypes.dtype(l) for l in lst)
to_dtype = dtypes.result_type(*from_dtypes)
sizes, shapes = unzip2((jnp.size(x), jnp.shape(x)) for x in lst)
indices = tuple(np.cumsum(sizes))
if all(dt == to_dtype for dt in from_dtypes):
# Skip any dtype conversion, resulting in a dtype-polymorphic `unravel`.
# See https://github.com/google/jax/issues/7809.
del from_dtypes, to_dtype
raveled = jnp.concatenate([jnp.ravel(e) for e in lst])
return raveled, HashablePartial(_unravel_list_single_dtype, indices, shapes)
# When there is more than one distinct input dtype, we perform type
# conversions and produce a dtype-specific unravel function.
ravel = lambda e: jnp.ravel(lax.convert_element_type(e, to_dtype))
raveled = jnp.concatenate([ravel(e) for e in lst])
unrav = HashablePartial(_unravel_list, indices, shapes, from_dtypes, to_dtype)
return raveled, unrav
def _unravel_list_single_dtype(indices, shapes, arr):
chunks = jnp.split(arr, indices[:-1])
return [chunk.reshape(shape) for chunk, shape in zip(chunks, shapes)]
def _unravel_list(indices, shapes, from_dtypes, to_dtype, arr):
arr_dtype = dtypes.dtype(arr)
if arr_dtype != to_dtype:
raise TypeError(f"unravel function given array of dtype {arr_dtype}, "
f"but expected dtype {to_dtype}")
chunks = jnp.split(arr, indices[:-1])
with warnings.catch_warnings():
warnings.simplefilter("ignore") # ignore complex-to-real cast warning
return [lax.convert_element_type(chunk.reshape(shape), dtype)
for chunk, shape, dtype in zip(chunks, shapes, from_dtypes)]
