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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.
# ==============================================================================
"""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 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, ctx, 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.
ctx: The value of context.context().
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.
"""
device_name = ctx.device_name
# pylint: disable=protected-access
try:
tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,
op_name, inputs, attrs,
num_outputs)
except core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
six.raise_from(core._status_to_exception(e.code, message), None)
# TODO(alive, cais): Use the execution callback mechanism.
if core.active_trace() is not None:
for t in tensors:
core.active_trace().record_tensor(op_name,
ops.tensor_id(t),
t.device,
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, ctx, default_dtype=None):
"""Convert sequence `l` to eager same-type Tensors."""
EagerTensor = ops.EagerTensor # pylint: disable=invalid-name
if all(isinstance(x, EagerTensor) for x in l):
return l[0].dtype, l
# 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, EagerTensor):
dtype = t.dtype
break
internal_convert_to_tensor = ops.internal_convert_to_tensor
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(internal_convert_to_tensor(
t, dtype, preferred_dtype=default_dtype, ctx=ctx))
if dtype is None:
dtype = ret[-1].dtype
else:
ret = [internal_convert_to_tensor(t, dtype, ctx=ctx) for t in l]
return dtype, ret
def convert_to_mixed_eager_tensors(values, ctx):
v = [
t if isinstance(t, ops.EagerTensor) else ops.EagerTensor(
t, context=ctx._handle, device=ctx.device_name) # pylint: disable=protected-access
for t in values
]
types = [t.dtype for t in v]
return types, v
def args_to_mixed_eager_tensors(lists, ctx):
"""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.internal_convert_to_tensor(lists[0][i], ctx=ctx))
dtype = lists_ret[0][i].dtype
for j in range(1, len(lists)):
lists_ret[j].append(
ops.internal_convert_to_tensor(lists[j][i], dtype=dtype, ctx=ctx))
else:
# Convert everything to the found dtype.
for j in range(len(lists)):
lists_ret[j].append(
ops.internal_convert_to_tensor(lists[j][i], dtype=dtype, ctx=ctx))
types.append(dtype)
return types, lists_ret
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