62 lines
2.6 KiB
Python
62 lines
2.6 KiB
Python
# Copyright 2015 The TensorFlow Authors. 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.
|
|
# ==============================================================================
|
|
"""Shared utilities related to backprop."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from tensorflow.core.framework import types_pb2
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.framework import tensor_util
|
|
from tensorflow.python.ops import handle_data_util
|
|
|
|
|
|
def _DTypeFromTensor(tensor):
|
|
"""Extract either `tensor.dtype` or the unanimous sub-type of a variant."""
|
|
dtype = tensor.dtype
|
|
if dtype.base_dtype == dtypes.variant:
|
|
# If we know statically that the data a variant points to is non-trainable
|
|
# then the variant itself is non-trainable.
|
|
if isinstance(tensor, ops.EagerTensor):
|
|
handle_data = tensor._handle_data # pylint: disable=protected-access
|
|
else:
|
|
handle_data = handle_data_util.get_resource_handle_data(tensor)
|
|
if (handle_data is not None
|
|
and handle_data.is_set
|
|
and handle_data.shape_and_type):
|
|
first_type = handle_data.shape_and_type[0].dtype
|
|
# Some variants have statically unknown dtypes; we can't make inferences
|
|
# about trainability, so we conservatively assume they're trainable
|
|
# (which may waste memory passing zeros around, but will be correct).
|
|
if (first_type != types_pb2.DT_INVALID
|
|
and all(shape_and_type.dtype == first_type
|
|
for shape_and_type in handle_data.shape_and_type)):
|
|
return first_type
|
|
return dtype
|
|
|
|
|
|
def IsTrainable(tensor_or_dtype):
|
|
"""Determines whether a tensor or dtype supports infinitesimal changes."""
|
|
if tensor_util.is_tf_type(tensor_or_dtype):
|
|
dtype = _DTypeFromTensor(tensor_or_dtype)
|
|
else:
|
|
dtype = tensor_or_dtype
|
|
dtype = dtypes.as_dtype(dtype)
|
|
return dtype.base_dtype in (dtypes.float16, dtypes.float32, dtypes.float64,
|
|
dtypes.complex64, dtypes.complex128,
|
|
dtypes.resource, dtypes.variant, dtypes.bfloat16)
|