EVOLUTION-MANAGER

Edit File: prefetching_ops.py

# 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.
# ==============================================================================
"""Python wrapper for prefetching_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.ops import iterator_ops
from tensorflow.python.data.util import structure
from tensorflow.python.eager import function
from tensorflow.python.framework import device as framework_device
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_spec
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.util.tf_export import tf_export

@tf_export("data.experimental.prefetch_to_device")
def prefetch_to_device(device, buffer_size=None):
  """A transformation that prefetches dataset values to the given `device`.

NOTE: Although the transformation creates a `tf.data.Dataset`, the
  transformation must be the final `Dataset` in the input pipeline.

Args:
    device: A string. The name of a device to which elements will be prefetched.
    buffer_size: (Optional.) The number of elements to buffer on `device`.
      Defaults to an automatically chosen value.

Returns:
    A `Dataset` transformation function, which can be passed to
    `tf.data.Dataset.apply`.
  """
  def _apply_fn(dataset):
    return dataset.apply(
        copy_to_device(target_device=device)).prefetch(buffer_size)

return _apply_fn

@tf_export("data.experimental.copy_to_device")
def copy_to_device(target_device, source_device="/cpu:0"):
  """A transformation that copies dataset elements to the given `target_device`.

Args:
    target_device: The name of a device to which elements will be copied.
    source_device: The original device on which `input_dataset` will be placed.

Returns:
    A `Dataset` transformation function, which can be passed to
    `tf.data.Dataset.apply`.
  """

def _apply_fn(dataset):
    options = dataset_ops.Options()
    options.experimental_optimization.apply_default_optimizations = False
    options.experimental_optimization.autotune = False
    return _CopyToDeviceDataset(
        dataset, target_device=target_device,
        source_device=source_device).with_options(options)

return _apply_fn

# TODO(rohanj): Use the _input_hostmem attr on the RemoteCall ops to indicate
# all inputs to the Op are in host memory, thereby avoiding some unnecessary
# Sends and Recvs.
class _CopyToDeviceDataset(dataset_ops.UnaryUnchangedStructureDataset):
  """A `Dataset` that copies elements to another device."""

def __init__(self, input_dataset, target_device, source_device="/cpu:0"):
    """Constructs a _CopyToDeviceDataset.

Args:
      input_dataset: `Dataset` to be copied
      target_device: The name of the device to which elements would be copied.
      source_device: Device where input_dataset would be placed.
    """
    self._input_dataset = input_dataset
    self._target_device = target_device
    spec = framework_device.DeviceSpec().from_string(self._target_device)
    self._is_gpu_target = (spec.device_type == "GPU")
    self._source_device_string = source_device
    self._source_device = ops.convert_to_tensor(source_device)

wrap_ds_variant = gen_dataset_ops.wrap_dataset_variant(
        self._input_dataset._variant_tensor)  # pylint: disable=protected-access

@function.defun()
    def _init_func():
      """Creates an iterator for the input dataset.

Returns:
        A `string` tensor that encapsulates the iterator created.
      """
      ds_variant = gen_dataset_ops.unwrap_dataset_variant(wrap_ds_variant)
      resource = gen_dataset_ops.anonymous_iterator(
          **self._input_dataset._flat_structure)  # pylint: disable=protected-access
      with ops.control_dependencies(
          [gen_dataset_ops.make_iterator(ds_variant, resource)]):
        return gen_dataset_ops.iterator_to_string_handle(resource)

init_func_concrete = _init_func._get_concrete_function_internal()  # pylint: disable=protected-access

@function.defun()
    def _remote_init_func():
      return functional_ops.remote_call(
          target=self._source_device,
          args=init_func_concrete.captured_inputs,
          Tout=[dtypes.string],
          f=init_func_concrete)

self._init_func = _remote_init_func._get_concrete_function_internal()  # pylint: disable=protected-access
    self._init_captured_args = self._init_func.captured_inputs

@function.defun(input_signature=[tensor_spec.TensorSpec([], dtypes.string)])
    def _next_func(string_handle):
      """Calls get_next for created iterator.

Args:
        string_handle: An iterator string handle created by _init_func
      Returns:
        The elements generated from `input_dataset`
      """
      with ops.device(self._source_device_string):
        iterator = iterator_ops.Iterator.from_string_handle(
            string_handle,
            dataset_ops.get_legacy_output_types(self),
            dataset_ops.get_legacy_output_shapes(self),
            dataset_ops.get_legacy_output_classes(self))
      return structure.to_tensor_list(self.element_spec, iterator.get_next())

next_func_concrete = _next_func._get_concrete_function_internal()  # pylint: disable=protected-access

@function.defun_with_attributes(
        input_signature=[tensor_spec.TensorSpec([], dtypes.string)],
        attributes={"experimental_ints_on_device": True})
    def _remote_next_func(string_handle):
      return functional_ops.remote_call(
          target=self._source_device,
          args=[string_handle] + next_func_concrete.captured_inputs,
          Tout=self._input_dataset._flat_types,  # pylint: disable=protected-access
          f=next_func_concrete)

self._next_func = _remote_next_func._get_concrete_function_internal()  # pylint: disable=protected-access
    self._next_captured_args = self._next_func.captured_inputs

@function.defun(input_signature=[tensor_spec.TensorSpec([], dtypes.string)])
    def _finalize_func(string_handle):
      """Destroys the iterator resource created.

Args:
        string_handle: An iterator string handle created by _init_func
      Returns:
        Tensor constant 0
      """
      iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2(
          string_handle,
          **self._input_dataset._flat_structure)  # pylint: disable=protected-access
      with ops.control_dependencies([
          resource_variable_ops.destroy_resource_op(
              iterator_resource, ignore_lookup_error=True)]):
        return array_ops.constant(0, dtypes.int64)

finalize_func_concrete = _finalize_func._get_concrete_function_internal()  # pylint: disable=protected-access

@function.defun(input_signature=[tensor_spec.TensorSpec([], dtypes.string)])
    def _remote_finalize_func(string_handle):
      return functional_ops.remote_call(
          target=self._source_device,
          args=[string_handle] + finalize_func_concrete.captured_inputs,
          Tout=[dtypes.int64],
          f=finalize_func_concrete)

self._finalize_func = _remote_finalize_func._get_concrete_function_internal(  # pylint: disable=protected-access
    )
    self._finalize_captured_args = self._finalize_func.captured_inputs

g = ops.get_default_graph()
    self._init_func.add_to_graph(g)
    self._next_func.add_to_graph(g)
    self._finalize_func.add_to_graph(g)
    # pylint: enable=protected-scope

with ops.device(self._target_device):
      variant_tensor = 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,
          **self._input_dataset._flat_structure)  # pylint: disable=protected-access
    super(_CopyToDeviceDataset, self).__init__(input_dataset, variant_tensor)

# The one_shot_iterator implementation needs a 0 arg _make_dataset function
  # that thereby captures all the inputs required to create the dataset. Since
  # there are strings that are inputs to the GeneratorDataset which can't be
  # placed on a GPU, this fails for the GPU case. Therefore, disabling it for
  # GPU
  def make_one_shot_iterator(self):
    if self._is_gpu_target:
      raise ValueError("Cannot create a one shot iterator when using "
                       "`tf.data.experimental.copy_to_device()` on GPU. Please "
                       "use `Dataset.make_initializable_iterator()` instead.")
    else:
      return super(_CopyToDeviceDataset, self).make_one_shot_iterator()

class _MapOnGpuDataset(dataset_ops.UnaryDataset):
  """A `Dataset` that maps a function over elements in its using a GPU."""

def __init__(self, input_dataset, map_func, use_inter_op_parallelism=True):
    """See `Dataset.map()` for details."""
    self._input_dataset = input_dataset
    self._use_inter_op_parallelism = use_inter_op_parallelism

self._map_func = dataset_ops.StructuredFunctionWrapper(
        map_func,
        self._transformation_name(),
        dataset=input_dataset,
        defun_kwargs={"experimental_ints_on_device": True})
    variant_tensor = ged_ops.experimental_map_dataset(
        self._input_dataset._variant_tensor,  # pylint: disable=protected-access
        self._map_func.function.captured_inputs,
        f=self._map_func.function,
        use_inter_op_parallelism=self._use_inter_op_parallelism,
        **self._flat_structure)
    super(_MapOnGpuDataset, self).__init__(input_dataset, variant_tensor)

def _functions(self):
    return [self._map_func]

@property
  def element_spec(self):
    return self._map_func.output_structure

def _transformation_name(self):
    return "map_on_gpu()"

def map_on_gpu(map_func):
  """Maps `map_func` across the elements of this dataset.

NOTE: This is a highly experimental version of `tf.data.Dataset.map` that runs
  `map_func` on GPU. It must be used after applying the
  `tf.data.experimental.copy_to_device` transformation with a GPU device
  argument.

Args:
    map_func: A function mapping a nested structure of tensors (having shapes
      and types defined by `self.output_shapes` and `self.output_types`) to
      another nested structure of tensors.

Returns:
    A `Dataset` transformation function, which can be passed to
    `tf.data.Dataset.apply`.
  """

def _apply_fn(dataset):
    return _MapOnGpuDataset(dataset, map_func)

return _apply_fn