Removing _PerDeviceGenerator and MultiDeviceIterator from contrib now that they're moved to core. I overlooked this in the CL to move to core.

PiperOrigin-RevId: 214699544

1 files changed, 0 insertions, 189 deletions

diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py
index f994425304..58395879e6 100644
--- a/tensorflow/contrib/data/python/ops/prefetching_ops.py
+++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py
@@ -31,7 +31,6 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import function
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import functional_ops
 from tensorflow.python.ops import gen_dataset_ops as core_gen_dataset_ops
 from tensorflow.python.ops import resource_variable_ops
@@ -526,191 +525,3 @@ class _CopyToDeviceDataset(dataset_ops.UnaryDataset):
   @property
   def output_classes(self):
     return self._input_dataset.output_classes
-
-
-class _PerDeviceGenerator(dataset_ops.Dataset):
-  """A `dummy` generator dataset."""
-
-  def __init__(self, shard_num, multi_device_iterator_resource, incarnation_id,
-               source_device, target_device, output_shapes, output_types,
-               output_classes):
-    self._target_device = target_device
-    self._output_types = output_types
-    self._output_shapes = output_shapes
-    self._output_classes = output_classes
-    self._flat_output_shapes = nest.flatten(
-        sparse.as_dense_shapes(self._output_shapes, self._output_classes))
-    self._flat_output_types = nest.flatten(
-        sparse.as_dense_types(self._output_types, self._output_classes))
-
-    multi_device_iterator_string_handle = (
-        gen_dataset_ops.multi_device_iterator_to_string_handle(
-            multi_device_iterator_resource))
-
-    @function.Defun()
-    def _init_func():
-      return multi_device_iterator_string_handle
-
-    @function.Defun()
-    def _remote_init_func():
-      return functional_ops.remote_call(
-          target=source_device,
-          args=_init_func.captured_inputs,
-          Tout=[dtypes.string],
-          f=_init_func)
-
-    self._init_func = _remote_init_func
-    self._init_captured_args = _remote_init_func.captured_inputs
-
-    @function.Defun(dtypes.string)
-    def _next_func(string_handle):
-      multi_device_iterator = (
-          gen_dataset_ops.multi_device_iterator_from_string_handle(
-              string_handle=string_handle,
-              output_types=self._flat_output_types,
-              output_shapes=self._flat_output_shapes))
-      return gen_dataset_ops.multi_device_iterator_get_next_from_shard(
-          multi_device_iterator=multi_device_iterator,
-          shard_num=shard_num,
-          incarnation_id=incarnation_id,
-          output_types=self._flat_output_types,
-          output_shapes=self._flat_output_shapes)
-
-    @function.Defun(dtypes.string)
-    def _remote_next_func(string_handle):
-      return functional_ops.remote_call(
-          target=source_device,
-          args=[string_handle] + _next_func.captured_inputs,
-          Tout=self._flat_output_types,
-          f=_next_func)
-
-    self._next_func = _remote_next_func
-    self._next_captured_args = _remote_next_func.captured_inputs
-
-    @function.Defun(dtypes.string)
-    def _finalize_func(unused_string_handle):
-      return array_ops.constant(0, dtypes.int64)
-
-    @function.Defun(dtypes.string)
-    def _remote_finalize_func(string_handle):
-      return functional_ops.remote_call(
-          target=source_device,
-          args=[string_handle] + _finalize_func.captured_inputs,
-          Tout=[dtypes.int64],
-          f=_finalize_func)
-
-    self._finalize_func = _remote_finalize_func
-    self._finalize_captured_args = _remote_finalize_func.captured_inputs
-
-  def _as_variant_tensor(self):
-    with ops.device(self._target_device):
-      return core_gen_dataset_ops.generator_dataset(
-          self._init_captured_args,
-          self._next_captured_args,
-          self._finalize_captured_args,
-          init_func=self._init_func,
-          next_func=self._next_func,
-          finalize_func=self._finalize_func,
-          output_types=self._flat_output_types,
-          output_shapes=self._flat_output_shapes)
-
-  def _inputs(self):
-    # TODO(b/116506223): Determine which datasets should be used as inputs here.
-    return []
-
-  @property
-  def output_types(self):
-    return self._output_types
-
-  @property
-  def output_shapes(self):
-    return self._output_shapes
-
-  @property
-  def output_classes(self):
-    return self._output_classes
-
-
-class MultiDeviceIterator(object):
-  """An iterator over multiple devices."""
-
-  def __init__(self,
-               dataset,
-               devices,
-               max_buffer_size=1,
-               prefetch_buffer_size=1,
-               source_device="/cpu:0"):
-    """Constructs a MultiDeviceIterator.
-
-    Args:
-      dataset: The input dataset to be iterated over.
-      devices: The list of devices to fetch data to.
-      max_buffer_size: Maximum size of the host side per device buffer to keep.
-      prefetch_buffer_size: if > 1, then we setup a buffer on each device
-        to prefetch into.
-      source_device: The host device to place the `dataset` on.
-    """
-    self._dataset = dataset
-    self._devices = devices
-    self._source_device = source_device
-    self._source_device_tensor = ops.convert_to_tensor(source_device)
-
-    self._flat_output_shapes = nest.flatten(
-        sparse.as_dense_shapes(self._dataset.output_shapes,
-                               self._dataset.output_classes))
-    self._flat_output_types = nest.flatten(
-        sparse.as_dense_types(self._dataset.output_types,
-                              self._dataset.output_classes))
-
-    # Create the MultiDeviceIterator.
-    with ops.device(self._source_device):
-      self._multi_device_iterator_resource = (
-          gen_dataset_ops.multi_device_iterator(
-              devices=self._devices,
-              shared_name="",
-              container="",
-              output_types=self._flat_output_types,
-              output_shapes=self._flat_output_shapes))
-
-      # The incarnation ID is used to ensure consistency between the per-device
-      # iterators and the multi-device iterator.
-      self._incarnation_id = gen_dataset_ops.multi_device_iterator_init(
-          self._dataset._as_variant_tensor(),  # pylint: disable=protected-access
-          self._multi_device_iterator_resource,
-          max_buffer_size=max_buffer_size)
-
-    # TODO(rohanj): Explore the possibility of the MultiDeviceIterator to
-    # initialize the device side of the pipeline. This would allow the
-    # MultiDeviceIterator to choose, for example, to move some transformations
-    # into the device side from its input. It might be useful in rewriting.
-    # Create the per device iterators.
-    self._device_iterators = []
-    i = 0
-    for device in self._devices:
-      ds = _PerDeviceGenerator(
-          i, self._multi_device_iterator_resource, self._incarnation_id,
-          self._source_device_tensor, device, self._dataset.output_shapes,
-          self._dataset.output_types, self._dataset.output_classes)
-      if prefetch_buffer_size > 0:
-        ds = ds.prefetch(prefetch_buffer_size)
-      with ops.device(device):
-        self._device_iterators.append(ds.make_initializable_iterator())
-      i += 1
-
-    device_iterator_initializers = [
-        iterator.initializer for iterator in self._device_iterators
-    ]
-    self._initializer = control_flow_ops.group(*device_iterator_initializers)
-
-  def get_next(self):
-    result = []
-    i = 0
-    for device in self._devices:
-      with ops.device(device):
-        result.append(self._device_iterators[i].get_next())
-      i += 1
-    return result
-
-  @property
-  def initializer(self):
-    return self._initializer