Keras was the only user of the fully-internal composite_tensor_utils, but it forked all of the symbols. So, this CL removes it from TF core. Also moves the test into the Keras training_v1_utils test now that all of the methods from composite_tensor_utils have been moved into training_v1_utils.
PiperOrigin-RevId: 342906866 Change-Id: I7f8d01009d37d9b5b68f569b7808e04808021723
This commit is contained in:
Tomer Kaftan
TensorFlower Gardener
parent
da495851f9
commit
40c932c10a
@ -2180,22 +2180,6 @@ py_library(
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],
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],
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)
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)
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py_library(
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name = "composite_tensor_utils",
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srcs = ["framework/composite_tensor_utils.py"],
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srcs_version = "PY2AND3",
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deps = [
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":array_ops",
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":composite_tensor",
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":sparse_ops",
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":sparse_tensor",
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"//tensorflow/python/ops/ragged:ragged_concat_ops",
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"//tensorflow/python/ops/ragged:ragged_tensor",
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"//tensorflow/python/ops/ragged:ragged_tensor_value",
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"//third_party/py/numpy",
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],
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)
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py_test(
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py_test(
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name = "framework_composite_tensor_test",
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name = "framework_composite_tensor_test",
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srcs = ["framework/composite_tensor_test.py"],
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srcs = ["framework/composite_tensor_test.py"],
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@ -2212,24 +2196,6 @@ py_test(
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],
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],
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)
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)
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tf_py_test(
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name = "framework_composite_tensor_utils_test",
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srcs = ["framework/composite_tensor_utils_test.py"],
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main = "framework/composite_tensor_utils_test.py",
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python_version = "PY3",
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deps = [
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":array_ops",
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":composite_tensor",
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":composite_tensor_utils",
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":framework_test_lib",
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":sparse_ops",
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":sparse_tensor",
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"//tensorflow/python/ops/ragged:ragged_tensor",
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"//tensorflow/python/ops/ragged:ragged_tensor_value",
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"//third_party/py/numpy",
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],
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)
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py_library(
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py_library(
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name = "tensor_shape",
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name = "tensor_shape",
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srcs = ["framework/tensor_shape.py"],
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srcs = ["framework/tensor_shape.py"],
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@ -1,160 +0,0 @@
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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# http://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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# ==============================================================================
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"""Helpers for handling composite tensors and composite tensor values."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.framework import composite_tensor
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.ops import sparse_ops
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from tensorflow.python.ops.ragged import ragged_concat_ops
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.ops.ragged import ragged_tensor_value
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def is_composite_or_composite_value(tensor):
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"""Returns true if 'tensor' is a CompositeTensor or a CT Value object."""
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# TODO(b/125094323): This should be isinstance(CompositeTensor) or
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# isinstance(CompositeTensorValue) once we support that.
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return isinstance(
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tensor,
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(composite_tensor.CompositeTensor, sparse_tensor.SparseTensorValue,
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ragged_tensor_value.RaggedTensorValue))
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def get_shape(tensor):
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"""Returns the shape of the passed composite tensor."""
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if isinstance(tensor, sparse_tensor.SparseTensorValue):
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# SparseTensorValues use a 'dense_shape' attribute
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return tensor.dense_shape
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else:
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return tensor.shape
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def _append_sparse_tensor_value(target, to_append):
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"""Append sparse tensor value objects."""
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# Make sure the sparse tensors are of the same size (except for the 0th dim).
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if len(target.dense_shape) != len(to_append.dense_shape):
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raise RuntimeError(
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'Unable to concatenate %s and %s. The inner dense shapes do not '
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'have the same number of dimensions (%s vs %s)' %
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(target, to_append, target.dense_shape, to_append.dense_shape))
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if target.dense_shape[1:] != to_append.dense_shape[1:]:
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raise RuntimeError(
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'Unable to concatenate %s and %s. The inner dense shapes do not '
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'match inner dimensions (%s vs %s)' %
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(target, to_append, target.dense_shape[1:], to_append.dense_shape[1:]))
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# Add the to_append indices to target, updating the 0th value, and keeping
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# track of the maximum so we know the final dense_shape of this tensor.
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base_dim0_value = target.dense_shape[0]
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max_dim0_value = target.dense_shape[0]
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new_indices = target.indices
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for index in to_append.indices:
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# Here, we iterate through the sparse indices of the tensor to append. For
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# each index, we update its zeroth value (the batch index) by adding the
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# number of batch items in the tensor we are appending to (so an index
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# of [0, 0, 1] for a value that is being appended to a tensor with 0th dim
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# size 3 would become [3, 0, 1].)
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index[0] += base_dim0_value
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max_dim0_value = max(max_dim0_value, index[0])
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new_indices = np.append(new_indices, [index], axis=0)
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# Extend the values array to contain all of the appended values. These will
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# be in the same order as the indices added above.
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new_values = np.concatenate((target.values, to_append.values), axis=0)
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# Create a new dense shape by replacing the value for the 0th dimension
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# with the new max dim0 value.
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new_dense_shape = list(target.dense_shape)
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new_dense_shape[0] = max_dim0_value + 1
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new_dense_shape = tuple(new_dense_shape)
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return sparse_tensor.SparseTensorValue(
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indices=new_indices, values=new_values, dense_shape=new_dense_shape)
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def _append_ragged_tensor_value(target, to_append):
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"""Append ragged tensor value objects."""
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# Make sure the ragged tensors are of the same size (save for the 0th dim).
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if len(target.shape) != len(to_append.shape):
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raise RuntimeError('Unable to concatenate %s and %s' % (target, to_append))
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if target.shape[1:] != to_append.shape[1:]:
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raise RuntimeError('Unable to concatenate %s and %s' % (target, to_append))
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adjusted_row_splits = to_append.row_splits[1:] + target.row_splits[-1]
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new_row_splits = np.append(target.row_splits, adjusted_row_splits)
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if isinstance(target.values, ragged_tensor_value.RaggedTensorValue):
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new_values = _append_ragged_tensor_value(target.values, to_append.values)
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else:
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new_values = np.concatenate((target.values, to_append.values), axis=0)
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return ragged_tensor_value.RaggedTensorValue(new_values, new_row_splits)
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def append_composite_tensor(target, to_append):
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"""Helper function to append composite tensors to each other in the 0 axis.
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In order to support batching within a fit/evaluate/predict call, we need
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to be able to aggregate within a CompositeTensor. Unfortunately, the CT
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API currently does not make this easy - especially in V1 mode, where we're
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working with CompositeTensor Value objects that have no connection with the
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CompositeTensors that created them.
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Arguments:
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target: CompositeTensor or CompositeTensor value object that will be
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appended to.
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to_append: CompositeTensor or CompositeTensor value object to append to.
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'target'.
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Returns:
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A CompositeTensor or CompositeTensor value object.
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Raises:
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RuntimeError: if concatenation is not possible.
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"""
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if type(target) is not type(to_append):
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raise RuntimeError('Unable to concatenate %s and %s' %
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(type(target), type(to_append)))
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# Perform type-specific concatenation.
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# TODO(b/125094323): This should be replaced by a simple call to
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# target.append() that should work on all of the below classes.
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# If we're seeing a CompositeTensor here, we know it's because we're in
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# Eager mode (or else we'd have evaluated the CT to a CT Value object
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# already). Therefore, it's safe to call concat() on it without evaluating
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# the result any further. If not - that is, if we're seeing a
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# SparseTensorValue or a RaggedTensorValue - we need to hand-update it
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# since we're outside of the graph anyways.
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if isinstance(target, sparse_tensor.SparseTensor):
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# We need to invoke the sparse version of concatenate here - tf.concat
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# won't work.
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return sparse_ops.sparse_concat(sp_inputs=[target, to_append], axis=0)
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elif isinstance(target, ragged_tensor.RaggedTensor):
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return ragged_concat_ops.concat([target, to_append], axis=0)
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elif isinstance(target, sparse_tensor.SparseTensorValue):
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return _append_sparse_tensor_value(target, to_append)
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elif isinstance(target, ragged_tensor_value.RaggedTensorValue):
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return _append_ragged_tensor_value(target, to_append)
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else:
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raise RuntimeError('Attempted to concatenate unsupported object %s.' %
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type(target))
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@ -1,103 +0,0 @@
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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# http://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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# ==============================================================================
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"""Tests for tensorflow.python.framework.composite_tensor_utils."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.framework import composite_tensor_utils
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.ops.ragged import ragged_tensor_value
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from tensorflow.python.platform import googletest
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class CompositeTensorTest(test_util.TensorFlowTestCase):
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def test_is_composite(self):
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# Validate that all composite tensor and value types return true.
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self.assertTrue(
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composite_tensor_utils.is_composite_or_composite_value(
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sparse_tensor.SparseTensor([[0, 0]], [1], [1, 1])))
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self.assertTrue(
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composite_tensor_utils.is_composite_or_composite_value(
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sparse_tensor.SparseTensorValue([[0, 0]], [1], [1, 1])))
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self.assertTrue(
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composite_tensor_utils.is_composite_or_composite_value(
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ragged_tensor.RaggedTensor.from_row_splits(
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np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))))
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self.assertTrue(
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composite_tensor_utils.is_composite_or_composite_value(
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ragged_tensor_value.RaggedTensorValue(
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np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))))
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# Test that numpy arrays and tensors return false.
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self.assertFalse(
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composite_tensor_utils.is_composite_or_composite_value(
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np.ndarray([0, 1])))
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self.assertFalse(
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composite_tensor_utils.is_composite_or_composite_value(
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ops.convert_to_tensor([3, 1])))
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def test_sparse_concatenation(self):
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tensor_1 = sparse_tensor.SparseTensor([[0, 0]], [1], [1, 1])
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tensor_2 = sparse_tensor.SparseTensor([[0, 0]], [2], [1, 1])
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concatenated_tensor = composite_tensor_utils.append_composite_tensor(
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tensor_1, tensor_2)
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evaluated_tensor = self.evaluate(concatenated_tensor)
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self.assertAllEqual(evaluated_tensor.indices, [[0, 0], [1, 0]])
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self.assertAllEqual(evaluated_tensor.values, [1, 2])
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self.assertAllEqual(evaluated_tensor.dense_shape, [2, 1])
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def test_sparse_value_concatenation(self):
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tensor_1 = sparse_tensor.SparseTensorValue([[0, 0]], [1], [1, 1])
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tensor_2 = sparse_tensor.SparseTensorValue([[0, 0]], [2], [1, 1])
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concatenated_tensor = composite_tensor_utils.append_composite_tensor(
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tensor_1, tensor_2)
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self.assertAllEqual(concatenated_tensor.indices, [[0, 0], [1, 0]])
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self.assertAllEqual(concatenated_tensor.values, [1, 2])
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self.assertAllEqual(concatenated_tensor.dense_shape, [2, 1])
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def test_ragged_concatenation(self):
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tensor_1 = ragged_tensor.RaggedTensor.from_row_splits(
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np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))
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tensor_2 = ragged_tensor.RaggedTensor.from_row_splits(
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np.array([3, 4, 5]), np.array([0, 2, 3], dtype=np.int64))
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concatenated_tensor = composite_tensor_utils.append_composite_tensor(
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tensor_1, tensor_2)
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evaluated_tensor = self.evaluate(concatenated_tensor)
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self.assertAllEqual(evaluated_tensor.values, [0, 1, 2, 3, 4, 5])
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self.assertAllEqual(evaluated_tensor.row_splits, [0, 1, 3, 5, 6])
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def test_ragged_value_concatenation(self):
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tensor_1 = ragged_tensor_value.RaggedTensorValue(
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np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))
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tensor_2 = ragged_tensor_value.RaggedTensorValue(
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np.array([3, 4, 5]), np.array([0, 2, 3], dtype=np.int64))
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concatenated_tensor = composite_tensor_utils.append_composite_tensor(
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tensor_1, tensor_2)
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self.assertAllEqual(concatenated_tensor.values, [0, 1, 2, 3, 4, 5])
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self.assertAllEqual(concatenated_tensor.row_splits, [0, 1, 3, 5, 6])
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if __name__ == '__main__':
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googletest.main()
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@ -61,7 +61,6 @@ py_library(
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"//tensorflow/python:check_ops",
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"//tensorflow/python:check_ops",
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"//tensorflow/python:client",
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"//tensorflow/python:client",
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"//tensorflow/python:clip_ops",
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"//tensorflow/python:clip_ops",
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"//tensorflow/python:composite_tensor_utils",
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"//tensorflow/python:constant_op",
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"//tensorflow/python:constant_op",
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"//tensorflow/python:control_flow_ops",
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"//tensorflow/python:control_flow_ops",
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"//tensorflow/python:ctc_ops",
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"//tensorflow/python:ctc_ops",
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@ -47,7 +47,6 @@ py_library(
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":input_spec",
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":input_spec",
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":keras_tensor",
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":keras_tensor",
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":node",
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":node",
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"//tensorflow/python:composite_tensor_utils",
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"//tensorflow/python:py_checkpoint_reader",
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"//tensorflow/python:py_checkpoint_reader",
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"//tensorflow/python/data",
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"//tensorflow/python/data",
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||||||
"//tensorflow/python/distribute:distribute_coordinator",
|
"//tensorflow/python/distribute:distribute_coordinator",
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@ -221,7 +220,6 @@ py_library(
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|||||||
srcs_version = "PY2AND3",
|
srcs_version = "PY2AND3",
|
||||||
deps = [
|
deps = [
|
||||||
":base_layer",
|
":base_layer",
|
||||||
"//tensorflow/python:composite_tensor_utils",
|
|
||||||
"//tensorflow/python/data",
|
"//tensorflow/python/data",
|
||||||
"//tensorflow/python/eager:monitoring",
|
"//tensorflow/python/eager:monitoring",
|
||||||
"//tensorflow/python/keras:backend",
|
"//tensorflow/python/keras:backend",
|
||||||
|
|||||||
@ -30,6 +30,8 @@ from tensorflow.python.data.ops import dataset_ops
|
|||||||
from tensorflow.python.data.ops import readers
|
from tensorflow.python.data.ops import readers
|
||||||
from tensorflow.python.eager import context
|
from tensorflow.python.eager import context
|
||||||
from tensorflow.python.framework import dtypes
|
from tensorflow.python.framework import dtypes
|
||||||
|
from tensorflow.python.framework import ops
|
||||||
|
from tensorflow.python.framework import sparse_tensor
|
||||||
from tensorflow.python.framework import tensor_util
|
from tensorflow.python.framework import tensor_util
|
||||||
from tensorflow.python.keras import backend
|
from tensorflow.python.keras import backend
|
||||||
from tensorflow.python.keras import keras_parameterized
|
from tensorflow.python.keras import keras_parameterized
|
||||||
@ -37,6 +39,8 @@ from tensorflow.python.keras import testing_utils
|
|||||||
from tensorflow.python.keras.engine import keras_tensor
|
from tensorflow.python.keras.engine import keras_tensor
|
||||||
from tensorflow.python.keras.engine import training_utils_v1
|
from tensorflow.python.keras.engine import training_utils_v1
|
||||||
from tensorflow.python.keras.utils import tf_utils
|
from tensorflow.python.keras.utils import tf_utils
|
||||||
|
from tensorflow.python.ops.ragged import ragged_tensor
|
||||||
|
from tensorflow.python.ops.ragged import ragged_tensor_value
|
||||||
from tensorflow.python.platform import test
|
from tensorflow.python.platform import test
|
||||||
from tensorflow.python.platform import tf_logging as logging
|
from tensorflow.python.platform import tf_logging as logging
|
||||||
|
|
||||||
@ -392,5 +396,74 @@ class AggregationTest(keras_parameterized.TestCase):
|
|||||||
self._run_without_steps()
|
self._run_without_steps()
|
||||||
|
|
||||||
|
|
||||||
|
class CompositeTensorTestUtils(keras_parameterized.TestCase):
|
||||||
|
|
||||||
|
def test_is_composite(self):
|
||||||
|
# Validate that all composite tensor and value types return true.
|
||||||
|
self.assertTrue(
|
||||||
|
training_utils_v1.is_composite_or_composite_value(
|
||||||
|
sparse_tensor.SparseTensor([[0, 0]], [1], [1, 1])))
|
||||||
|
self.assertTrue(
|
||||||
|
training_utils_v1.is_composite_or_composite_value(
|
||||||
|
sparse_tensor.SparseTensorValue([[0, 0]], [1], [1, 1])))
|
||||||
|
self.assertTrue(
|
||||||
|
training_utils_v1.is_composite_or_composite_value(
|
||||||
|
ragged_tensor.RaggedTensor.from_row_splits(
|
||||||
|
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))))
|
||||||
|
self.assertTrue(
|
||||||
|
training_utils_v1.is_composite_or_composite_value(
|
||||||
|
ragged_tensor_value.RaggedTensorValue(
|
||||||
|
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))))
|
||||||
|
|
||||||
|
# Test that numpy arrays and tensors return false.
|
||||||
|
self.assertFalse(
|
||||||
|
training_utils_v1.is_composite_or_composite_value(np.ndarray([0, 1])))
|
||||||
|
self.assertFalse(
|
||||||
|
training_utils_v1.is_composite_or_composite_value(
|
||||||
|
ops.convert_to_tensor_v2_with_dispatch([3, 1])))
|
||||||
|
|
||||||
|
def test_sparse_concatenation(self):
|
||||||
|
tensor_1 = sparse_tensor.SparseTensor([[0, 0]], [1], [1, 1])
|
||||||
|
tensor_2 = sparse_tensor.SparseTensor([[0, 0]], [2], [1, 1])
|
||||||
|
concatenated_tensor = training_utils_v1._append_composite_tensor(
|
||||||
|
tensor_1, tensor_2)
|
||||||
|
evaluated_tensor = self.evaluate(concatenated_tensor)
|
||||||
|
self.assertAllEqual(evaluated_tensor.indices, [[0, 0], [1, 0]])
|
||||||
|
self.assertAllEqual(evaluated_tensor.values, [1, 2])
|
||||||
|
self.assertAllEqual(evaluated_tensor.dense_shape, [2, 1])
|
||||||
|
|
||||||
|
def test_sparse_value_concatenation(self):
|
||||||
|
tensor_1 = sparse_tensor.SparseTensorValue([[0, 0]], [1], [1, 1])
|
||||||
|
tensor_2 = sparse_tensor.SparseTensorValue([[0, 0]], [2], [1, 1])
|
||||||
|
concatenated_tensor = training_utils_v1._append_composite_tensor(
|
||||||
|
tensor_1, tensor_2)
|
||||||
|
self.assertAllEqual(concatenated_tensor.indices, [[0, 0], [1, 0]])
|
||||||
|
self.assertAllEqual(concatenated_tensor.values, [1, 2])
|
||||||
|
self.assertAllEqual(concatenated_tensor.dense_shape, [2, 1])
|
||||||
|
|
||||||
|
def test_ragged_concatenation(self):
|
||||||
|
tensor_1 = ragged_tensor.RaggedTensor.from_row_splits(
|
||||||
|
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))
|
||||||
|
tensor_2 = ragged_tensor.RaggedTensor.from_row_splits(
|
||||||
|
np.array([3, 4, 5]), np.array([0, 2, 3], dtype=np.int64))
|
||||||
|
concatenated_tensor = training_utils_v1._append_composite_tensor(
|
||||||
|
tensor_1, tensor_2)
|
||||||
|
evaluated_tensor = self.evaluate(concatenated_tensor)
|
||||||
|
|
||||||
|
self.assertAllEqual(evaluated_tensor.values, [0, 1, 2, 3, 4, 5])
|
||||||
|
self.assertAllEqual(evaluated_tensor.row_splits, [0, 1, 3, 5, 6])
|
||||||
|
|
||||||
|
def test_ragged_value_concatenation(self):
|
||||||
|
tensor_1 = ragged_tensor_value.RaggedTensorValue(
|
||||||
|
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))
|
||||||
|
tensor_2 = ragged_tensor_value.RaggedTensorValue(
|
||||||
|
np.array([3, 4, 5]), np.array([0, 2, 3], dtype=np.int64))
|
||||||
|
concatenated_tensor = training_utils_v1._append_composite_tensor(
|
||||||
|
tensor_1, tensor_2)
|
||||||
|
|
||||||
|
self.assertAllEqual(concatenated_tensor.values, [0, 1, 2, 3, 4, 5])
|
||||||
|
self.assertAllEqual(concatenated_tensor.row_splits, [0, 1, 3, 5, 6])
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
test.main()
|
test.main()
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user