<reponame>mk-hasan/tensorflow-noscope
# 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.
# ==============================================================================
"""Functions called by the generated code to execute an eager-mode op."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import six

from google.protobuf import text_format
from tensorflow.core.framework import tensor_pb2
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.eager import context
from tensorflow.python.eager import core
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.util import compat


def execute(op_name, num_outputs, inputs, attrs=None, name=None):
  """Execute a TensorFlow operation.

  Args:
    op_name: Name of the TensorFlow operation (see REGISTER_OP in C++ code) to
      execute.
    num_outputs: The number of outputs of the operation to fetch.
                 (Explicitly provided instead of being inferred for performance
                 reasons).
    inputs: A list of inputs to the operation. Each entry should be a Tensor, or
      a value which can be passed to the Tensor constructor to create one.
    attrs: A tuple with alternating string attr names and attr values for this
      operation.
    name: Customized name for the operation.

  Returns:
    None if there are no outputs, a single Tensor object if there is one output
    and a list of Tensor objects if there are multiple outputs.

  Raises:
    An exception on error.
  """
  ctx = context.get_default_context()
  # TODO(apassos) move this to convert_to_tensor
  # pylint: disable=protected-access
  input_handles = [c._handle for c in inputs]
  device_name = ctx.device_name
  try:
    outh = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,
                                            op_name, input_handles, attrs,
                                            num_outputs)
  except core._NotOkStatusException as e:
    if name is not None:
      message = e.message + " name: " + name
    else:
      message = e.message
    raise core._status_to_exception(e.code, message)
  # pylint: enable=protected-access

  tensors = [ops._tensor_from_handle(x) for x in outh]  # pylint: disable=protected-access
  # TODO(alive, cais): Use the execution callback mechanism.
  if core.active_trace() is not None:
    trace_name = name if name else op_name
    for t in tensors:
      # pylint: disable=protected-access
      core.active_trace().record_tensor(trace_name,
                                        ops.tensor_id(t),
                                        t._device_name(),
                                        t.shape.num_elements())
      # pylint: enable=protected-access

  # TODO(cais): Optimize this, perhaps by replacing this execute function with
  # a different one when there are execution callback(s).
  for callback in ctx.post_execution_callbacks:
    callback(op_name, name, attrs, inputs, tensors)

  return tensors


def record_gradient(unused_op_name, unused_inputs, unused_attrs, unused_results,
                    unused_name):
  """Import backprop if you want gradients recorded."""
  pass


def make_float(v, arg_name):
  if not isinstance(v, compat.real_types):
    raise TypeError("Expected float for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return float(v)


def make_int(v, arg_name):
  if isinstance(v, six.string_types):
    raise TypeError("Expected int for argument '%s' not %s." %
                    (arg_name, repr(v)))
  try:
    return int(v)
  except (ValueError, TypeError):
    raise TypeError("Expected int for argument '%s' not %s." %
                    (arg_name, repr(v)))


def make_str(v, arg_name):
  if not isinstance(v, compat.bytes_or_text_types):
    raise TypeError("Expected string for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return compat.as_bytes(v)  # Convert unicode strings to bytes.


def make_bool(v, arg_name):
  if not isinstance(v, bool):
    raise TypeError("Expected bool for argument '%s' not %s." %
                    (arg_name, repr(v)))
  return v


def make_type(v, arg_name):
  try:
    v = dtypes.as_dtype(v).base_dtype
  except TypeError:
    raise TypeError("Expected DataType for argument '%s' not %s." %
                    (arg_name, repr(v)))
  i = v.as_datatype_enum
  return i


def make_shape(v, arg_name):
  """Convert v into a list."""
  # Args:
  #   v: A TensorShapeProto, a list of ints, or a tensor_shape.TensorShape.
  #   arg_name: String, for error messages.

  # Returns:
  #   None if the rank is unknown, otherwise a list of ints (or Nones in the
  #   position where the dimension is unknown).
  try:
    shape = tensor_shape.as_shape(v)
  except TypeError as e:
    raise TypeError("Error converting %s to a TensorShape: %s." % (arg_name, e))
  except ValueError as e:
    raise ValueError("Error converting %s to a TensorShape: %s." % (arg_name,
                                                                    e))
  if shape.ndims is None:
    return None
  else:
    return shape.as_list()


def make_tensor(v, arg_name):
  """Ensure v is a TensorProto."""
  if isinstance(v, tensor_pb2.TensorProto):
    return v
  elif isinstance(v, six.string_types):
    pb = tensor_pb2.TensorProto()
    text_format.Merge(v, pb)
    return pb
  raise TypeError(
      "Don't know how to convert %s to a TensorProto for argument '%s'." %
      (repr(v), arg_name))


def args_to_matching_eager(l, default_dtype=None):
  """Convert sequence `l` to eager same-type Tensors."""
  # TODO(josh11b): Could we do a better job if we also passed in the
  # allowed dtypes when that was known?

  # Is some input already a Tensor with a dtype?
  dtype = None
  for t in l:
    if isinstance(t, ops.EagerTensor):
      dtype = t.dtype
      break

  if dtype is None:
    # Infer a dtype based on the first value, and use that dtype for the
    # remaining values.
    ret = []
    for t in l:
      ret.append(ops.convert_to_tensor(t, dtype, preferred_dtype=default_dtype))
      if dtype is None:
        dtype = ret[-1].dtype
  else:
    ret = [ops.convert_to_tensor(t, dtype) for t in l]

  return dtype, ret


def convert_to_mixed_eager_tensors(values):
  v = [t if isinstance(t, ops.EagerTensor) else ops.EagerTensor(t)
       for t in values]
  types = [t.dtype for t in v]
  return types, v


def args_to_mixed_eager_tensors(lists):
  """Converts a list of same-length lists of values to eager tensors."""
  assert len(lists) > 1

  # Generate an error if len(lists[i]) is not the same for all i.
  lists_ret = []
  for l in lists[1:]:
    if len(l) != len(lists[0]):
      raise ValueError(
          "Expected list arguments to be the same length: %d != %d (%r vs. %r)."
          % (len(lists[0]), len(l), lists[0], l))
    lists_ret.append([])

  # Convert the first element of each list first, then the second element, etc.
  types = []
  for i in range(len(lists[0])):
    dtype = None
    # If any list has a Tensor, use that dtype
    for l in lists:
      if isinstance(l[i], ops.EagerTensor):
        dtype = l[i].dtype
        break
    if dtype is None:
      # Convert the first one and use its dtype.
      lists_ret[0].append(ops.convert_to_tensor(lists[0][i]))
      dtype = lists_ret[0][i].dtype
      for j in range(1, len(lists)):
        lists_ret[j].append(
            ops.convert_to_tensor(lists[j][i], dtype=dtype))
    else:
      # Convert everything to the found dtype.
      for j in range(len(lists)):
        lists_ret[j].append(
            ops.convert_to_tensor(lists[j][i], dtype=dtype))
    types.append(dtype)
  return types, lists_ret
