Source code for jax._src.flatten_util

# Copyright 2018 Google LLC
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import warnings

import numpy as np

from jax._src.tree_util import tree_flatten, tree_unflatten
from jax._src.util import safe_zip, unzip2

import jax.numpy as jnp
from jax._src import dtypes
from jax import lax

zip = safe_zip

[docs]def ravel_pytree(pytree): """Ravel (i.e. 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 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 """ leaves, treedef = tree_flatten(pytree) flat, unravel_list = _ravel_list(leaves) unravel_pytree = lambda flat: tree_unflatten(treedef, unravel_list(flat)) return flat, unravel_pytree
def _ravel_list(lst): if not lst: return jnp.array([], jnp.float32), lambda _: [] from_dtypes = [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 = np.cumsum(sizes) def unravel(arr): 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)] ravel = lambda e: jnp.ravel(lax.convert_element_type(e, to_dtype)) raveled = jnp.concatenate([ravel(e) for e in lst]) return raveled, unravel