Comment and error message consistency cleanup.

PiperOrigin-RevId: 168422582

tensorflow/python/eager/backprop.py

@@ -88,14 +88,14 @@ def _magic_gradient_function(op_name, attr_tuple, num_inputs, num_outputs,
 
   Args:
     op_name: the name of the op to be differentiated.
-    attr_tuple: the attrs, as a tuple
-    num_inputs: the number of inputs to the op
-    num_outputs: the number of outputs of the op
+    attr_tuple: the attrs, as a tuple.
+    num_inputs: the number of inputs to the op.
+    num_outputs: the number of outputs of the op.
     *tensors: a list of tensors, composed of, in order, the inputs, the outputs,
       and the gradients with respect to the outputs.
 
   Returns:
-    the gradients with respect to the inputs of the function, as a list.
+    The gradients with respect to the inputs of the function, as a list.
   """
   inputs = tensors[:num_inputs]
   outputs = tensors[num_inputs:num_inputs + num_outputs]
@@ -232,9 +232,9 @@ def implicit_val_and_grad(f):
     ag_core.active_progenitors.remove(start_node)
     if not ag_core.isnode(end_node):
       raise ValueError(
-          "Target not part of a computation being traced. %s" % end_node)
+          "Target not part of a computation being traced. %s." % end_node)
     if start_node not in end_node.progenitors:
-      raise ValueError("Target not derived from source. %s %s" %
+      raise ValueError("Target not derived from source. %s %s." %
                        (end_node.progenitors, repr(start_node)))
     output_gradients = kwds.get("output_gradients", None)
     if output_gradients is None:
@@ -282,7 +282,7 @@ def _get_arg_spec(f, params):
     return params
   else:
     raise ValueError(
-        "params must be all strings or all integers; got %s" % params)
+        "params must be all strings or all integers; got %s." % params)
 
 
 def gradients_function(f, params=None):

tensorflow/python/eager/context.py

@@ -286,8 +286,8 @@ class Context(object):
         it is unset.
       `attrs` contains the attributes of the operation as a `tuple` of
         alternating attribute names and attribute values.
-      `inputs` is the `list` of input `tfe.Tensor`(s) to the op.
-      `outputs` is the `list` of output `tfe.Tensor`(s) from the op.
+      `inputs` is the `list` of input `Tensor`(s) to the op.
+      `outputs` is the `list` of output `Tensor`(s) from the op.
        Return value(s) from the callback are ignored.
     """
     # TODO(cais): (b/64674139) Allow access to function-internal operations.
@@ -314,7 +314,7 @@ def _initialize_context():
 
 
 def context():
-  """Returns a singleton Context object."""
+  """Returns a singleton context object."""
   if _context is None:
     _initialize_context()
   return _context
@@ -373,7 +373,7 @@ def device(name):
   ```python
   with tfe.device('gpu:0'):
     with tfe.device('cpu:0'):
-      shape = tfe.Tensor([], dtype=tf.int32)
+      shape = Tensor([], dtype=tf.int32)
     x = ops.truncated_normal(shape, tf.float32)
   ```
   will ensure that the `shape` Tensor is on CPU but the `truncated_normal`
@@ -390,13 +390,13 @@ def device(name):
 
 
 def run(main=None, argv=None):
-  """Runs the program with an optional 'main' function and 'argv' list.
+  """Runs the program with an optional main function and argv list.
 
   The program will run with eager execution enabled.
 
   Args:
-    main: the main function to run
-    argv: the arguments to pass to it
+    main: the main function to run.
+    argv: the arguments to pass to it.
   """
   enable_eager_execution()
   app.run(main, argv)

tensorflow/python/eager/custom_gradient.py

@@ -38,7 +38,7 @@ def custom_gradient(f):
   """Decorator to define a function with a custom gradient.
 
   The input function is expected to return the tuple
-    (results, gradient_function)
+    (results, gradient_function).
 
   The output function will return results while possibly recording the
   gradient_function and inputs in the tape.

tensorflow/python/eager/execute.py

@@ -153,9 +153,10 @@ def make_shape(v, arg_name):
   try:
     shape = tensor_shape.as_shape(v)
   except TypeError as e:
-    raise TypeError("Error converting %s to a TensorShape: %s" % (arg_name, 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))
+    raise ValueError("Error converting %s to a TensorShape: %s." % (arg_name,
+                                                                    e))
   if shape.ndims is None:
     return None
   else:
@@ -171,7 +172,7 @@ def make_tensor(v, arg_name):
     text_format.Merge(v, pb)
     return pb
   raise TypeError(
-      "Don't know how to convert %s to a TensorProto for argument '%s'" %
+      "Don't know how to convert %s to a TensorProto for argument '%s'." %
       (repr(v), arg_name))
 
 
@@ -217,7 +218,7 @@ def args_to_mixed_eager_tensors(lists):
   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)"
+          "Expected list arguments to be the same length: %d != %d (%r vs. %r)."
           % (len(lists[0]), len(l), lists[0], l))
     lists_ret.append([])
 

tensorflow/python/eager/execution_callbacks.py

@@ -228,8 +228,8 @@ def add_execution_callback(callback):
         it is unset.
       `attrs` contains the attributes of the operation as a `tuple` of
         alternating attribute name and attribute value.
-      `inputs` is the `list` of input `tfe.Tensor`(s) to the op.
-      `outputs` is the `list` of output `tfe.Tensor`(s) from the op.
+      `inputs` is the `list` of input `Tensor`(s) to the op.
+      `outputs` is the `list` of output `Tensor`(s) from the op.
        Return value(s) from the callback are ignored.
   """
   context.get_default_context().add_post_execution_callback(callback)
@@ -246,8 +246,8 @@ def seterr(inf_or_nan=None):
   Example:
   ``` python
   tfe.seterr(inf_or_nan="raise")
-  a = tfe.Tensor(10.0)
-  b = tfe.Tensor(0.0)
+  a = Tensor(10.0)
+  b = Tensor(0.0)
   c = a / b  # <-- Raises InfOrNanError.
 
   tfe.seterr(inf_or_nan="ignore")

tensorflow/python/eager/function.py

@@ -41,7 +41,7 @@ from tensorflow.python.util import nest
 # Thread-local storage for tfe Tensors which are referenced while evaluating a
 # graph-mode function.
 _scoped_captures = threading.local()
-# _scoped_captures.tensors is either None or a map from tfe.Tensor id to a pair
+# _scoped_captures.tensors is either None or a map from Tensor id to a pair
 # of a tfe tensor and its corresponding placeholder to pass as a function
 # argument. The value should be None unless we're in function definition
 # context.
@@ -62,7 +62,7 @@ def _convert_to_graph_tensor(value, dtype=None, name=None, as_ref=False):
   """Captures a Tensor while building a graph mode function.
 
   Arguments:
-    value: A tfe.Tensor object
+    value: A Tensor object.
     dtype: The datatype of the value produced by the node in the graph.
     name:  Name of the node in the graph.
     as_ref: Ignored (required by register_tensor_conversion_function).
@@ -482,12 +482,12 @@ def defun(func):
   func must be a Python function that constructs a TensorFlow graph,
   typically using functions in the tensorflow module.
 
-  Arguments to func can be either tfe.Tensor objects or Python
+  Arguments to func can be either Tensor objects or Python
   objects. Non-Tensor python objects are treated as constants, and new function
   definitions are created internally based on their values.
 
-  func must return a tf.Tensor (NOT a tfe.Tensor) or a list of tf.Tensor (NOT a
-  tfe.Tensor). TODO(apassos) make the wrapped tfe ops return tf.Tensors when in
+  func must return a tf.Tensor (NOT a Tensor) or a list of tf.Tensor (NOT a
+  Tensor). TODO(apassos) make the wrapped tfe ops return tf.Tensors when in
   graph mode.
 
   TODO(apassos): deal with captured global state. Deal with control flow.
@@ -497,6 +497,6 @@ def defun(func):
 
   Returns:
      A callable that will execute the compiled function (and return zero
-     or more tfe.Tensor objects)
+     or more Tensor objects).
   """
   return named_defun(func, func.__name__)

tensorflow/python/eager/ops_test.py

@@ -99,7 +99,7 @@ class TargetTest(test_util.TensorFlowTestCase):
   # with tfe.device('/gpu:0'):
   #   ...  # code here
   #   with tfe.device('/cpu:0'):
-  #     shape = tfe.Tensor(...)
+  #     shape = Tensor(...)
   #   y = tfe.ops.random_uniform(.., shape)
   #
   # Without the CPU device block tfe.ops.random_uniform would fail since the
@@ -108,7 +108,7 @@ class TargetTest(test_util.TensorFlowTestCase):
   # After this change, we simplify the code:
   #
   # with tfe.device('/gpu:0'):
-  #   y = tfe.ops.random_uniform(, tfe.Tensor(...))
+  #   y = tfe.ops.random_uniform(, Tensor(...))
   #
   # The approximation is not exact since if there are GPU kernels which do not
   # require host memory for int32 tensors, there will be a discrepancy between

tensorflow/python/eager/pywrap_tfe.h

@@ -64,7 +64,7 @@ PyObject* TFE_Py_RegisterExceptionClass(PyObject* e);
 // class registered via TFE_Py_RegisterExceptionClass) and returns -1.
 int TFE_Py_MayBeRaiseException(TF_Status* status);
 
-// Returns the string associated with the passed-in python object/
+// Returns the string associated with the passed-in python object.
 char* TFE_GetPyThonString(PyObject* o);
 
 #endif  // TENSORFLOW_PYTHON_EAGER_PYWRAP_TFE_H_