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# Copyright 2017 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.
# ==============================================================================
"""Scan dataset transformation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import nest
from tensorflow.python.data.util import sparse
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_dataset_ops
class _ScanDataset(dataset_ops.Dataset):
"""A dataset that scans a function across its input."""
def __init__(self, input_dataset, initial_state, scan_func):
"""See `scan()` for details."""
super(_ScanDataset, self).__init__()
self._input_dataset = input_dataset
with ops.name_scope("initial_state"):
self._initial_state = nest.pack_sequence_as(initial_state, [
ops.convert_to_tensor(t, name="component_%d" % i)
for i, t in enumerate(nest.flatten(initial_state))
])
# Compute initial values for the state shapes and types based on
# the initial state. These will be refined by running
# `tf_scan_func` one or more times below.
# TODO(b/68937811): Allow the initial state to be a tf.SparseTensor.
self._state_shapes = nest.pack_sequence_as(
self._initial_state,
[t.shape for t in nest.flatten(self._initial_state)])
self._state_types = nest.pack_sequence_as(
self._initial_state,
[t.dtype for t in nest.flatten(self._initial_state)])
# Will be populated by calling `tf_scan_func`.
self._output_classes = None
self._output_shapes = None
self._output_types = None
# Iteratively rerun the scan function until reaching a fixed point on
# `self._state_shapes`.
need_to_rerun = True
while need_to_rerun:
flat_state_shapes = nest.flatten(self._state_shapes)
flat_state_types = nest.flatten(self._state_types)
# Create a list in which `tf_scan_func` will store the s
flat_new_state_shapes = []
@function.Defun(*(flat_state_types + nest.flatten(
sparse.as_dense_types(input_dataset.output_types,
input_dataset.output_classes))))
def tf_scan_func(*args):
"""A wrapper for Defun that facilitates shape inference."""
# Pass in shape information from the state and input_dataset.
# TODO(b/69424092): Check that neither inputs nor outputs are sparse.
dense_shapes = sparse.as_dense_shapes(input_dataset.output_shapes,
input_dataset.output_classes)
for arg, shape in zip(args,
flat_state_shapes + nest.flatten(dense_shapes)):
arg.set_shape(shape)
pivot = len(flat_state_shapes)
old_state = nest.pack_sequence_as(self._initial_state, args[:pivot])
input_value = nest.pack_sequence_as(input_dataset.output_types,
args[pivot:])
ret = scan_func(old_state, input_value)
if not isinstance(ret, collections.Sequence) or len(ret) != 2:
raise TypeError("The scan function must return a pair comprising the "
"new state and the output value.")
new_state, output_value = ret
flat_new_state = [
ops.convert_to_tensor(t) for t in nest.flatten(new_state)
]
flat_output_value = [
ops.convert_to_tensor(t) for t in nest.flatten(output_value)
]
# Extract shape information from the returned values.
flat_new_state_shapes.extend([t.shape for t in flat_new_state])
self._output_shapes = nest.pack_sequence_as(
output_value, [t.shape for t in flat_output_value])
# Extract and validate type information from the returned values.
for t, dtype in zip(flat_new_state, flat_state_types):
if t.dtype != dtype:
raise TypeError(
"The element types for the new state must match the initial "
"state. Expected %s; got %s." %
(self._state_types, nest.pack_sequence_as(
self._state_types, [t.dtype for t in flat_new_state])))
self._output_classes = nest.pack_sequence_as(
output_value, [ops.Tensor for _ in flat_output_value])
self._output_types = nest.pack_sequence_as(
output_value, [t.dtype for t in flat_output_value])
return flat_new_state + flat_output_value
# Use the private method that will execute `tf_scan_func` but delay
# adding it to the graph in case we need to rerun the function.
tf_scan_func._create_definition_if_needed() # pylint: disable=protected-access
weakened_state_shapes = [
original.most_specific_compatible_shape(new)
for original, new in zip(flat_state_shapes, flat_new_state_shapes)
]
need_to_rerun = False
for original_shape, weakened_shape in zip(flat_state_shapes,
weakened_state_shapes):
if original_shape.ndims is not None and (
weakened_shape.ndims is None or
original_shape.as_list() != weakened_shape.as_list()):
need_to_rerun = True
break
if need_to_rerun:
# NOTE(mrry): `self._output_shapes` will be overwritten when we rerun
# `tf_scan_func`.
self._state_shapes = nest.pack_sequence_as(self._state_shapes,
weakened_state_shapes)
self._scan_func = tf_scan_func
self._scan_func.add_to_graph(ops.get_default_graph())
def _as_variant_tensor(self):
input_t = self._input_dataset._as_variant_tensor() # pylint: disable=protected-access
return gen_dataset_ops.scan_dataset(
input_t,
nest.flatten(self._initial_state),
self._scan_func.captured_inputs,
f=self._scan_func,
output_types=nest.flatten(
sparse.as_dense_types(self.output_types, self.output_classes)),
output_shapes=nest.flatten(
sparse.as_dense_shapes(self.output_shapes, self.output_classes)))
@property
def output_classes(self):
return self._output_classes
@property
def output_shapes(self):
return self._output_shapes
@property
def output_types(self):
return self._output_types
def scan(initial_state, scan_func):
"""A transformation that scans a function across an input dataset.
This transformation is a stateful relative of @{tf.data.Dataset.map}.
In addition to mapping `scan_func` across the elements of the input dataset,
`scan()` accumulates one or more state tensors, whose initial values are
`initial_state`.
Args:
initial_state: A nested structure of tensors, representing the initial state
of the accumulator.
scan_func: A function that maps `(old_state, input_element)` to
`(new_state, output_element). It must take two arguments and return a
pair of nested structures of tensors. The `new_state` must match the
structure of `initial_state`.
Returns:
A `Dataset` transformation function, which can be passed to
@{tf.data.Dataset.apply}.
"""
def _apply_fn(dataset):
return _ScanDataset(dataset, initial_state, scan_func)
return _apply_fn
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