90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
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# Copyright 2018 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import warnings
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import numpy as np
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from jax import lax
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import jax.numpy as jnp
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from jax._src import dtypes
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from jax._src.tree_util import tree_flatten, tree_unflatten
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from jax._src.util import safe_zip, unzip2, HashablePartial
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zip = safe_zip
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def ravel_pytree(pytree):
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"""Ravel (flatten) a pytree of arrays down to a 1D array.
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Args:
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pytree: a pytree of arrays and scalars to ravel.
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Returns:
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A pair where the first element is a 1D array representing the flattened and
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concatenated leaf values, with dtype determined by promoting the dtypes of
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leaf values, and the second element is a callable for unflattening a 1D
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vector of the same length back to a pytree of of the same structure as the
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input ``pytree``. If the input pytree is empty (i.e. has no leaves) then as
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a convention a 1D empty array of dtype float32 is returned in the first
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component of the output.
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For details on dtype promotion, see
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https://jax.readthedocs.io/en/latest/type_promotion.html.
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"""
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leaves, treedef = tree_flatten(pytree)
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flat, unravel_list = _ravel_list(leaves)
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return flat, HashablePartial(unravel_pytree, treedef, unravel_list)
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def unravel_pytree(treedef, unravel_list, flat):
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return tree_unflatten(treedef, unravel_list(flat))
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def _ravel_list(lst):
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if not lst: return jnp.array([], jnp.float32), lambda _: []
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from_dtypes = tuple(dtypes.dtype(l) for l in lst)
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to_dtype = dtypes.result_type(*from_dtypes)
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sizes, shapes = unzip2((jnp.size(x), jnp.shape(x)) for x in lst)
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indices = tuple(np.cumsum(sizes))
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if all(dt == to_dtype for dt in from_dtypes):
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# Skip any dtype conversion, resulting in a dtype-polymorphic `unravel`.
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# See https://github.com/google/jax/issues/7809.
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del from_dtypes, to_dtype
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raveled = jnp.concatenate([jnp.ravel(e) for e in lst])
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return raveled, HashablePartial(_unravel_list_single_dtype, indices, shapes)
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# When there is more than one distinct input dtype, we perform type
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# conversions and produce a dtype-specific unravel function.
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ravel = lambda e: jnp.ravel(lax.convert_element_type(e, to_dtype))
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raveled = jnp.concatenate([ravel(e) for e in lst])
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unrav = HashablePartial(_unravel_list, indices, shapes, from_dtypes, to_dtype)
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return raveled, unrav
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def _unravel_list_single_dtype(indices, shapes, arr):
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chunks = jnp.split(arr, indices[:-1])
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return [chunk.reshape(shape) for chunk, shape in zip(chunks, shapes)]
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def _unravel_list(indices, shapes, from_dtypes, to_dtype, arr):
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arr_dtype = dtypes.dtype(arr)
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if arr_dtype != to_dtype:
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raise TypeError(f"unravel function given array of dtype {arr_dtype}, "
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f"but expected dtype {to_dtype}")
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chunks = jnp.split(arr, indices[:-1])
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with warnings.catch_warnings():
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warnings.simplefilter("ignore") # ignore complex-to-real cast warning
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return [lax.convert_element_type(chunk.reshape(shape), dtype)
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for chunk, shape, dtype in zip(chunks, shapes, from_dtypes)]
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