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# Copyright 2024 The etils 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
#
# 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.
"""Array spec utils."""
# Is there a way of merging this with array_types ?
from __future__ import annotations
import functools
import sys
from typing import Any, Optional
from etils.enp import numpy_utils
from etils.enp.array_types import typing as array_types
from etils.enp.typing import Array
import numpy as np
lazy = numpy_utils.lazy
class UnknownArrayError(TypeError):
pass
@functools.lru_cache()
def _get_none_spec(): # -> tf.TypeSpec:
"""Returns the tf.NoneTensorSpec()."""
assert lazy.has_tf
# We need this hack as NoneTensorSpec is not exposed in the public API.
# (see: b/191132147)
ds = lazy.tf.data.Dataset.range(0)
ds = ds.map(lambda x: (x, None))
return ds.element_spec[-1]
class ArraySpec:
"""Structure containing shape/dtype."""
__slots__ = ['shape', 'dtype']
def __init__(self, shape, dtype):
if numpy_utils.is_dtype_str(dtype): # Normalize `str` dtype
dtype = np.dtype('O')
self.shape = tuple(shape)
self.dtype = np.dtype(dtype)
def __repr__(self) -> str:
array_type = array_types.ArrayAliasMeta(
dtype=self.dtype,
shape=self.shape,
)
return repr(array_type)
def __eq__(self, other) -> bool:
if not isinstance(other, type(self)):
return False
else:
return (other.shape, other.dtype) == (self.shape, self.dtype)
def __hash__(self) -> int:
return hash((self.shape, self.dtype))
@classmethod
def is_array(cls, array: Any) -> bool:
"""Returns `True` if the given value can be converted to `ArraySpec`."""
try:
cls.from_array(array)
except UnknownArrayError:
return False
else:
return True
@classmethod
def from_array(cls, array: Array) -> Optional[ArraySpec]:
"""Construct the `ArraySpec` from the given array."""
# Could refactor with some dynamic registration mechanism.
if isinstance(array, (np.ndarray, np.generic, ArraySpec)):
shape = array.shape
dtype = array.dtype
elif (
lazy.has_jax
and isinstance(array, lazy.jax.Array)
and lazy.jax.dtypes.issubdtype(array.dtype, lazy.jax.dtypes.prng_key)
):
shape = array.shape
dtype = np.uint32 # `jax.random.PRNGKeyArray` is a constant
elif lazy.has_jax and isinstance(
array,
(lazy.jax.ShapeDtypeStruct, lazy.jax.Array),
):
shape = array.shape
dtype = array.dtype
elif lazy.has_tf and isinstance(
array,
(lazy.tf.TensorSpec, lazy.tf.Tensor),
):
shape = array.shape
# In graph mode, `.shape` values can be `Dimension(32)`
shape = (int(s) if s is not None else s for s in shape)
dtype = array.dtype.as_numpy_dtype
elif lazy.has_tf and isinstance(array, type(_get_none_spec())):
return None # Special case for `NoneTensorSpec()`
elif _is_grain(array):
shape = array.shape
dtype = array.dtype
elif _is_orbax(array):
shape = array.shape
dtype = array.dtype
elif _is_flax_summarry(array):
shape = array.shape
dtype = array.dtype
elif isinstance(array, array_types.ArrayAliasMeta):
try:
shape = (int(s) for s in array.shape.split())
except ValueError:
raise UnknownArrayError(
f'Not supported dynamic shape: {array}'
) from None
dtype = array.dtype.np_dtype
else:
raise UnknownArrayError(f'Unknown array-like type: {type(array)}')
# Should we also handle `bytes` case ?
return cls(shape=shape, dtype=dtype)
def is_fake_array(array: Array) -> bool:
"""Returns `True` if the given array is a fake array."""
return (
(lazy.has_jax and isinstance(array, lazy.jax.ShapeDtypeStruct))
or (lazy.has_tf and isinstance(array, lazy.tf.TensorSpec))
or isinstance(array, ArraySpec)
or _is_orbax(array)
or _is_grain(array)
or _is_flax_summarry(array)
or isinstance(array, array_types.ArrayAliasMeta)
)
def _is_flax_summarry(value: Array) -> bool:
if 'flax.linen' not in sys.modules:
return False
from flax import linen as nn # pylint: disable=g-import-not-at-top # pytype: disable=import-error
return isinstance(value, nn.summary._ArrayRepresentation) # pylint: disable=protected-access
def _is_grain(array: Array) -> bool:
if 'grain.tensorflow' not in sys.modules:
return False
from grain import tensorflow as grain # pylint: disable=g-import-not-at-top # pytype: disable=import-error
return isinstance(array, grain.ArraySpec)
def _is_orbax(array: Array) -> bool:
if 'orbax.checkpoint' not in sys.modules:
return False
from orbax import checkpoint as ocp # pylint: disable=g-import-not-at-top # pytype: disable=import-error
return isinstance(
array,
(
ocp.type_handlers.ArrayMetadata,
ocp.type_handlers.ScalarMetadata,
),
)
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