minor spelling tweaks

tensorflow/python/autograph/converters/conditional_expressions.py

@@ -23,7 +23,7 @@ from tensorflow.python.autograph.pyct import templates
 
 
 class ConditionalExpressionTransformer(converter.Base):
-  """Converts contitional expressions to functional form."""
+  """Converts conditional expressions to functional form."""
 
   def visit_IfExp(self, node):
     return templates.replace_as_expression(

tensorflow/python/autograph/converters/logical_expressions.py

@@ -24,7 +24,7 @@ from tensorflow.python.autograph.core import converter
 from tensorflow.python.autograph.pyct import parser
 from tensorflow.python.autograph.pyct import templates
 
-# TODO(mdan): Properly extrack boolean ops according to lazy eval rules.
+# TODO(mdan): Properly extract boolean ops according to lazy eval rules.
 # Note that this isn't completely safe either, because tensors may have control
 # dependencies.
 # Note that for loops that should be done after the loop was converted to

tensorflow/python/autograph/converters/return_statements.py

@@ -39,7 +39,7 @@ class _RewriteBlock(object):
 
 
 class ConditionalReturnRewriter(converter.Base):
-  """Rewrites a a pattern where it's unbovious that all paths return a value.
+  """Rewrites a a pattern where it's unobvious that all paths return a value.
 
   This rewrite allows avoiding intermediate None return values.
 

tensorflow/python/autograph/converters/return_statements_test.py

@@ -67,7 +67,7 @@ class SingleReturnTest(converter_testing.TestCase):
     self.assertTransformedEquivalent(test_fn, 2)
     self.assertTransformedEquivalent(test_fn, -2)
 
-  def test_contitional_missing_else(self):
+  def test_conditional_missing_else(self):
 
     def test_fn(x):
       if x > 0:

tensorflow/python/autograph/core/converter.py

@@ -23,10 +23,10 @@ The class hierarchy is as follows:
       [extends] converter.Base
         [extends] transformer.Base
             [extends] gast.nodeTransformer
-          [uses] transfomer.SourceInfo
+          [uses] transformer.SourceInfo
         [uses] converter.EntityContext
           [uses] converter.ProgramContext
-          [uses] transfomer.SourceInfo
+          [uses] transformer.SourceInfo
 
 converter.Base is a specialization of transformer.Base for AutoGraph. It's a
 very lightweight subclass that adds a `ctx` attribute holding the corresponding

tensorflow/python/autograph/core/function_wrappers.py

@@ -34,7 +34,7 @@ class FunctionScope(object):
     * optional TF name scopes - these name scopes match the name of the
         function, for easy visualization in tensorBoard;
     * optional automatic control dependencies - this adds the same mechanism
-        for control dependenecies that is used by `@tf.function`; it can be
+        for control dependencies that is used by `@tf.function`; it can be
         optionally enabled when using `tf.autograph.to_graph`;
     * tracking of autograph conversion state (whether it's enabled by the user,
         conversion options;

tensorflow/python/autograph/core/function_wrappers_test.py

@@ -39,7 +39,7 @@ class FunctionWrappersTest(test.TestCase):
       t = constant_op.constant(1)
     self.assertIn('test_name', t.name)
 
-  def test_auto_cotrol_deps(self):
+  def test_auto_control_deps(self):
     v = variables.Variable(1)
     with function_wrappers.FunctionScope(
         '_', None,

tensorflow/python/autograph/core/naming.py

@@ -30,7 +30,7 @@ class _NamingStyle(enum.Enum):
 
 
 class Namer(object):
-  """Symbol name generartor."""
+  """Symbol name generator."""
 
   def __init__(self, global_namespace):
     self.global_namespace = global_namespace

tensorflow/python/autograph/impl/api.py

@@ -192,7 +192,7 @@ def tf_convert(f, ctx, convert_by_default=True, user_requested=False):
 
   # TODO(mdan): Grab features from context.
   # Note: we pass the original context through to convert to properly handle the
-  # following scenario, which can be used insite TF implementations:
+  # following scenario, which can be used inside TF implementations:
   #
   #   ctx = ag_ctx.control_status_ctx()
   #   @function(autograph=False)  # Low-level graph code

tensorflow/python/autograph/impl/api_test.py

@@ -1021,7 +1021,7 @@ class ApiTest(test.TestCase):
                        ag_ctx.Status.ENABLED)
       return 0
 
-    # Note: the autograph=False sets the contect to Status.DISABLED. The test
+    # Note: the autograph=False sets the connect to Status.DISABLED. The test
     # verifies that to_graph overrides that.
     @def_function.function(autograph=False)
     def f():

tensorflow/python/autograph/impl/conversion.py

@@ -167,7 +167,7 @@ class _UnboundInstanceCache(_FunctionCache):
 
 
 # Using a re-entrant lock to guard against the unlikely possibility that the
-# conversion process tiggers additional code execution.
+# conversion process triggers additional code execution.
 _CACHE_LOCK = threading.RLock()
 
 
@@ -253,7 +253,7 @@ def _wrap_into_dynamic_factory(nodes, entity_name, factory_factory_name,
 
 def _convert_with_cache(entity, program_ctx, free_nonglobal_var_names):
   """Returns a (possibly cached) factory for the converted result of entity."""
-  # The cache subkey encompases any conversion options on which the generated
+  # The cache subkey encompasses any conversion options on which the generated
   # code may depend.
   # The cached factory includes the necessary definitions to distinguish
   # between the global and non-global free variables. For this reason, the

tensorflow/python/autograph/operators/control_flow.py

@@ -773,7 +773,7 @@ class _PythonLoopChecker(object):
     self.check_op_count_after_iteration = False
     self.ops_before_iteration = None
 
-  def _verify_ineffcient_unroll(self):
+  def _verify_inefficient_unroll(self):
     """Checks for possibly-inefficient creation of ops in a Python loop."""
     assert self.ops_before_iteration is not None
     ops_after_iteration = self._get_ops()
@@ -810,7 +810,7 @@ class _PythonLoopChecker(object):
     self._check_unroll_limits()
 
     if self.check_op_count_after_iteration:
-      did_warn = self._verify_ineffcient_unroll()
+      did_warn = self._verify_inefficient_unroll()
       if did_warn:
         self._stop_checking_inefficient_unroll()  # Only warn once.
       elif self.iterations > INEFFICIENT_UNROLL_MIN_ITERATIONS + 3:

tensorflow/python/autograph/operators/py_builtins.py

@@ -54,7 +54,7 @@ UNSPECIFIED = object()
 
 def overload_of(f):
   if f in SUPPORTED_BUILTINS:
-    return BUILTIN_FUINCTIONS_MAP[f.__name__]
+    return BUILTIN_FUNCTIONS_MAP[f.__name__]
   return f
 
 
@@ -441,7 +441,7 @@ def all_(iterable):
   return _py_all(iterable)
 
 
-# all() operation is similiar to any() and could be translated
+# all() operation is similar to any() and could be translated
 # to `filter(False)` then `take(1)`, and check if `False` exists.
 def _tf_dataset_all(iterable):
   # check and make sure iterable.element_spec only consists of one
@@ -467,7 +467,7 @@ SUPPORTED_BUILTINS = (abs, float, int, len, print, range, enumerate, zip, map,
 if six.PY2:
   SUPPORTED_BUILTINS += (xrange,)
 
-BUILTIN_FUINCTIONS_MAP = {
+BUILTIN_FUNCTIONS_MAP = {
     'abs': abs_,
     'float': float_,
     'int': int_,

tensorflow/python/autograph/pyct/cfg.py

@@ -248,7 +248,7 @@ class GraphBuilder(object):
   This builder ignores the flow generated by exceptions, which are assumed to
   always be catastrophic and present purely for diagnostic purposes (e.g. to
   print debug information). Statements like raise and try/catch sections are
-  allowed and will generate control flow edges, but ordinaty statements are
+  allowed and will generate control flow edges, but ordinary statements are
   assumed not to raise exceptions.
 
   Finally sections are also correctly interleaved between break/continue/return

tensorflow/python/autograph/pyct/common_transformers/anf_test.py

@@ -47,7 +47,7 @@ class DummyGensym(object):
 
 # These two test functions have to be top-level, not nested, for compatibility
 # with some unknown version of Python 2.7 preceding 2.7.15.  Why?  Because
-# `exec` and nested function definitions _incomaptibly_ change the
+# `exec` and nested function definitions _incompatibly_ change the
 # representation of local variables, such that `exec` inside a nested function
 # definition is a syntax error in that version.  The tuple form of `exec` fixes
 # this problem, but apparently that was introduced in some unknown version of
@@ -465,7 +465,7 @@ class AnfNonTransformationTest(AnfTransformerTest):
     node, _ = parser.parse_entity(test_fn, future_features=())
     orig_source = parser.unparse(node, indentation='  ')
     orig_str = textwrap.dedent(orig_source).strip()
-    config = [(anf.ANY, anf.LEAVE)]  # Configuration to trasform nothing
+    config = [(anf.ANY, anf.LEAVE)]  # Configuration to transform nothing
     node = anf.transform(
         node, self._simple_context(),
         config=config, gensym_source=DummyGensym)

tensorflow/python/autograph/pyct/origin_info.py

@@ -252,7 +252,7 @@ def resolve(node, source, context_filepath, context_lineno, context_col_offset):
 
 
 def resolve_entity(node, source, entity):
-  """Like resolve, but extracts the context informartion from an entity."""
+  """Like resolve, but extracts the context information from an entity."""
   lines, lineno = tf_inspect.getsourcelines(entity)
   filepath = tf_inspect.getsourcefile(entity)
 

tensorflow/python/autograph/pyct/parser.py

@@ -91,7 +91,7 @@ def dedent_block(code_string):
         # See:
         # https://docs.python.org/3/reference/lexical_analysis.html#indentation
         raise errors.UnsupportedLanguageElementError(
-            'code mixing tabs and spaces for intentation is not allowed')
+            'code mixing tabs and spaces for indentation is not allowed')
       if len(tok_string) >= block_level:
         tok_string = tok_string[block_level:]
       tokens[i] = (tok_type, tok_string)

tensorflow/python/autograph/pyct/static_analysis/activity.py

@@ -44,7 +44,7 @@ class Scope(object):
 
   Scope objects are mutable during construction only, and must be frozen using
   `Scope.finalize()` before use. Furthermore, a scope is consistent only after
-  all its chiledren have been frozen. While analysing code blocks, scopes are
+  all its children have been frozen. While analysing code blocks, scopes are
   being gradually built, from the innermost scope outward. Freezing indicates
   that the analysis of a code block is complete. Once frozen, mutation is no
   longer allowed. `is_final` tracks whether the scope is frozen or not. Certain

tensorflow/python/autograph/pyct/static_analysis/reaching_definitions.py

@@ -234,7 +234,7 @@ class TreeAnnotator(transformer.Base):
     # Recursively process any remaining subfunctions.
     self.current_analyzer = analyzer
     # Note: not visiting name, decorator_list and returns because they don't
-    # apply to this anlysis.
+    # apply to this analysis.
     # TODO(mdan): Should we still process the function name?
     node.args = self.visit(node.args)
     node.body = self.visit_block(node.body)

tensorflow/python/autograph/pyct/transformer.py

@@ -253,7 +253,7 @@ class Base(gast.NodeTransformer):
     self.enter_local_scope()
 
     # Allows scoping of local variables to keep state across calls to visit_*
-    # methods. Multiple scope hierchies may exist and are keyed by tag. A scope
+    # methods. Multiple scope hierarchies may exist and are keyed by tag. A scope
     # is valid at one or more nodes and all its children. Scopes created in
     # child nodes supersede their parent. Scopes are isolated from one another.
     self.state = _State()

tensorflow/python/client/virtual_gpu_test.py

@@ -97,7 +97,7 @@ class VirtualGpuTestUtil(object):
 
   # Generates a list of 3-tuples, each tuple contains the source and destination
   # device index for a binary operation like 'add', like:
-  # (src_devcie_1, src_device_2, dst_device)
+  # (src_device_1, src_device_2, dst_device)
   def _GenerateOperationPlacement(self):
     result = []
     for unused_i in range(self._num_ops):

tensorflow/python/compat/compat.py

@@ -96,7 +96,7 @@ def forward_compatible(year, month, day):
     if compat.forward_compatible(year, month, day):
       # Can use the awesome new implementation.
       return gen_math_ops.my_new_awesome_add(inputs, name)
-    # To maintain forward compatibiltiy, use the old implementation.
+    # To maintain forward compatibility, use the old implementation.
     return gen_math_ops.add(inputs, name)
   ```
 

tensorflow/python/compat/compat_test.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for forward and backwards compatibility utilties."""
+"""Tests for forward and backwards compatibility utilities."""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/python/compat/disable_v2_behavior_test.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for forward and backwards compatibility utilties."""
+"""Tests for forward and backwards compatibility utilities."""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/python/compiler/tensorrt/test/biasadd_matmul_test.py

@@ -114,7 +114,7 @@ class BiasaddMatMulTest(trt_test.TfTrtIntegrationTestBase):
         run_params=run_params,
         conversion_params=conversion_params,
         # Disable layout optimizer, since it will convert BiasAdd with NHWC
-        # format to NCHW format under four dimentional input.
+        # format to NCHW format under four dimensional input.
         disable_non_trt_optimizers=True)
     return conversion_params._replace(
         rewriter_config_template=rewrite_config_with_trt)

tensorflow/python/compiler/tensorrt/test/dynamic_input_shapes_test.py

@@ -89,7 +89,7 @@ class DynamicInputShapesTest(trt_test.TfTrtIntegrationTestBase):
         run_params=run_params,
         conversion_params=conversion_params,
         # Disable layout optimizer, since it will convert BiasAdd with NHWC
-        # format to NCHW format under four dimentional input.
+        # format to NCHW format under four dimensional input.
         disable_non_trt_optimizers=True)
     return conversion_params._replace(
         rewriter_config_template=rewrite_config_with_trt)

tensorflow/python/compiler/tensorrt/test/identity_output_test.py

@@ -14,7 +14,7 @@
 # ==============================================================================
 """This test checks a situation where the same tensor is considered as an output
 
-multiple times because it has been duplicated by 2+ indentity ops. Previously,
+multiple times because it has been duplicated by 2+ identity ops. Previously,
 the tensor would be renamed multiple times, overwriting the output binding name
 which resulted in a runtime error when the binding would not be found.
 """

tensorflow/python/compiler/tensorrt/test/int32_test.py

@@ -55,7 +55,7 @@ class ExcludeUnsupportedInt32Test(trt_test.TfTrtIntegrationTestBase):
         run_params=run_params,
         conversion_params=conversion_params,
         # Disable layout optimizer, since it will convert BiasAdd with NHWC
-        # format to NCHW format under four dimentional input.
+        # format to NCHW format under four dimensional input.
         disable_non_trt_optimizers=True)
     return conversion_params._replace(
         rewriter_config_template=rewrite_config_with_trt)

tensorflow/python/compiler/tensorrt/trt_convert.py

@@ -145,7 +145,7 @@ class TrtConversionParams(collections.namedtuple("TrtConversionParams", [
       missing ranges. The calibration graph must be converted to an inference
       graph by running calibration with calibrate(). If set to False,
       quantization nodes will be expected for every tensor in the graph
-      (exlcuding those which will be fused). If a range is missing, an error
+      (excluding those which will be fused). If a range is missing, an error
       will occur. Please note that accuracy may be negatively affected if
       there is a mismatch between which tensors TRT quantizes and which
       tensors were trained with fake quantization.
@@ -207,7 +207,7 @@ def _check_conversion_params(conversion_params, is_v2=False):
               "Found more than one TensorRTOptimizer in "
               "rewriter_config_template while only one is allowed.")
         trt_optimizer = optimizer
-    # If rewriter_config_template is set, it should inculde TensorRTOptimizer.
+    # If rewriter_config_template is set, it should include TensorRTOptimizer.
     # It is possible to remove this requirement if needed.
     if not trt_optimizer:
       raise ValueError(
@@ -327,7 +327,7 @@ def get_tensorrt_rewriter_config(conversion_params,
     rewriter_config_with_trt.CopyFrom(
         conversion_params.rewriter_config_template)
 
-  # Disabling optimizers should happen after CopyFrom the temaplte
+  # Disabling optimizers should happen after CopyFrom the template
   # otherwise the template can overwrite the disablement.
   if disable_non_trt_optimizers:
     off = rewriter_config_pb2.RewriterConfig.OFF
@@ -443,7 +443,7 @@ class TrtGraphConverter(object):
         missing ranges. The calibration graph must be converted to an inference
         graph by running calibration with calibrate(). If set to False,
         quantization nodes will be expected for every tensor in the graph
-        (exlcuding those which will be fused). If a range is missing, an error
+        (excluding those which will be fused). If a range is missing, an error
         will occur. Please note that accuracy may be negatively affected if
         there is a mismatch between which tensors TRT quantizes and which
         tensors were trained with fake quantization.

tensorflow/python/compiler/tensorrt/trt_convert_test.py

@@ -500,7 +500,7 @@ class TrtConvertTest(test_util.TensorFlowTestCase, parameterized.TestCase):
     # Load and verify the converted model.
     #
     # TODO(laigd): the name of the new input_signature of the
-    # `root_with_trt.run` function is empty string (originaly was None),
+    # `root_with_trt.run` function is empty string (originally was None),
     # investigate why.
     root_with_trt = load.load(output_saved_model_dir)
     # TODO(laigd): `root_with_trt.run` is still using the original graph without

tensorflow/python/data/benchmarks/map_benchmark.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Bechmarks for `tf.data.Dataset.map()`."""
+"""Benchmarks for `tf.data.Dataset.map()`."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

tensorflow/python/data/experimental/ops/readers.py

@@ -440,7 +440,7 @@ def make_csv_dataset_v2(
     if compression_type is not None:
       compression_type_value = tensor_util.constant_value(compression_type)
       if compression_type_value is None:
-        raise ValueError("Received unkown compression_type")
+        raise ValueError("Received unknown compression_type")
       if compression_type_value == "GZIP":
         file_io_fn = lambda filename: gzip.open(filename, "rt")
       elif compression_type_value == "ZLIB":

tensorflow/python/data/kernel_tests/from_generator_test.py

@@ -122,7 +122,7 @@ class FromGeneratorTest(test_base.DatasetTestBase, parameterized.TestCase):
 
     # The interleave transformation is essentially a flat map that
     # draws from multiple input datasets concurrently (in a cyclic
-    # fashion). By placing `Datsaet.from_generator()` inside an
+    # fashion). By placing `Dataset.from_generator()` inside an
     # interleave, we test its behavior when multiple iterators are
     # active at the same time; by additionally prefetching inside the
     # interleave, we create the possibility of parallel (modulo GIL)

tensorflow/python/data/ops/dataset_ops.py

@@ -2526,7 +2526,7 @@ def get_structure(dataset_or_iterator):
 
   Returns:
     A nested structure of `tf.TypeSpec` objects matching the structure of an
-    element of `dataset_or_iterator` and spacifying the type of individal
+    element of `dataset_or_iterator` and specifying the type of individual
     components.
 
   Raises:
@@ -3013,7 +3013,7 @@ class StructuredFunctionWrapper(object):
       use_legacy_function: (Optional.) A boolean that determines whether the
         function be created using `tensorflow.python.eager.function.defun`
         (default behavior) or `tensorflow.python.framework.function.Defun`
-        (legacy beheavior).
+        (legacy behavior).
       defun_kwargs: (Optional.) A dictionary mapping string argument names to
         values. If supplied, will be passed to `function` as keyword arguments.
 
@@ -3049,7 +3049,7 @@ class StructuredFunctionWrapper(object):
     # There is no graph to add in eager mode.
     add_to_graph &= not context.executing_eagerly()
     # There are some lifetime issues when a legacy function is not added to a
-    # out-living graph. It's already deprecated so de-priotizing the fix.
+    # out-living graph. It's already deprecated so de-prioritizing the fix.
     add_to_graph |= use_legacy_function
 
     if defun_kwargs is None:

tensorflow/python/data/util/sparse.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Python dataset sparse tensor utility functitons."""
+"""Python dataset sparse tensor utility functions."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

tensorflow/python/data/util/structure_test.py

@@ -398,7 +398,7 @@ class StructureTest(test_base.DatasetTestBase, parameterized.TestCase,
     # Define three mutually incompatible values/structures, and assert that:
     # 1. Using one structure to flatten a value with an incompatible structure
     #    fails.
-    # 2. Using one structure to restructre a flattened value with an
+    # 2. Using one structure to restructure a flattened value with an
     #    incompatible structure fails.
     value_tensor = constant_op.constant(42.0)
     s_tensor = structure.type_spec_from_value(value_tensor)

tensorflow/python/debug/cli/curses_ui.py

@@ -1214,7 +1214,7 @@ class CursesUI(base_ui.BaseUI):
     """Pad the whitespace at the end of a line with the default color pair.
 
     Prevents spurious color pairs from appearing at the end of the lines in
-    certain text terimnals.
+    certain text terminals.
 
     Args:
       pad: The curses pad object to operate on.

tensorflow/python/debug/cli/debugger_cli_common.py

@@ -115,7 +115,7 @@ def rich_text_lines_from_rich_line_list(rich_text_list, annotations=None):
 
   Args:
     rich_text_list: a list of RichLine objects or strings
-    annotations: annotatoins for the resultant RichTextLines object.
+    annotations: annotations for the resultant RichTextLines object.
 
   Returns:
     A corresponding RichTextLines object.

tensorflow/python/debug/cli/debugger_cli_common_test.py

@@ -476,7 +476,7 @@ class CommandHandlerRegistryTest(test_util.TensorFlowTestCase):
     help_lines = registry.get_help().lines
 
     # The help info should list commands in alphabetically sorted order,
-    # regardless of order in which the commands are reigstered.
+    # regardless of order in which the commands are registered.
     self.assertEqual("cols", help_lines[0])
     self.assertTrue(help_lines[1].endswith("Aliases: c"))
     self.assertFalse(help_lines[2])
@@ -790,7 +790,7 @@ class SliceRichTextLinesTest(test_util.TensorFlowTestCase):
     self.assertEqual(["Roses are red"], sliced.lines)
     self.assertEqual({0: [(0, 5, "red")]}, sliced.font_attr_segs)
 
-    # Non-line-number metadata should be preseved.
+    # Non-line-number metadata should be preserved.
     self.assertEqual({
         0: "longer wavelength",
         "foo_metadata": "bar"
@@ -1024,7 +1024,7 @@ class CommandHistoryTest(test_util.TensorFlowTestCase):
       self.assertEqual(
           ["help 2\n", "help 3\n", "help 4\n"], f.readlines())
 
-  def testCommandHistoryHandlesReadingIOErrorGracoiusly(self):
+  def testCommandHistoryHandlesReadingIOErrorGraciously(self):
     with open(self._history_file_path, "wt") as f:
       f.write("help\n")
 
@@ -1037,7 +1037,7 @@ class CommandHistoryTest(test_util.TensorFlowTestCase):
 
     self._restoreFileReadWritePermissions(self._history_file_path)
 
-  def testCommandHistoryHandlesWritingIOErrorGracoiusly(self):
+  def testCommandHistoryHandlesWritingIOErrorGraciously(self):
     with open(self._history_file_path, "wt") as f:
       f.write("help\n")
 

tensorflow/python/debug/cli/evaluator.py

@@ -55,7 +55,7 @@ def _parse_debug_tensor_name(debug_tensor_name):
       `None`.
     node_name: Name of the node.
     output_slot: Output slot index as an `int`.
-    debug_op: If the debug op suffix exists, the debug op name; otheriwse,
+    debug_op: If the debug op suffix exists, the debug op name; otherwise,
       `None`.
     exec_index: Execution index (applicable to cases in which a debug tensor
       is computed multiple times in a `tf.Session.run` call, e.g., due to

tensorflow/python/debug/cli/profile_analyzer_cli_test.py

@@ -273,11 +273,11 @@ class ProfileAnalyzerListProfileTest(test_util.TensorFlowTestCase):
     prof_output = prof_analyzer.list_profile(["-f", ".*file2"]).lines
     _assert_at_least_one_line_matches(r"Add/123", prof_output)
     _assert_no_lines_match(r"Mul/456", prof_output)
-    # Fitler by execution time.
+    # Filter by execution time.
     prof_output = prof_analyzer.list_profile(["-e", "[5, 10]"]).lines
     _assert_at_least_one_line_matches(r"Mul/456", prof_output)
     _assert_no_lines_match(r"Add/123", prof_output)
-    # Fitler by op time.
+    # Filter by op time.
     prof_output = prof_analyzer.list_profile(["-o", ">=2"]).lines
     _assert_at_least_one_line_matches(r"Add/123", prof_output)
     _assert_no_lines_match(r"Mul/456", prof_output)

tensorflow/python/debug/cli/tensor_format_test.py

@@ -78,7 +78,7 @@ class RichTextLinesTest(test_util.TensorFlowTestCase):
   def _checkTensorElementLocations(self, out, a):
     """Check the results of locate_tensor_element on an ndarray representation.
 
-    that represents a numpy.ndaray.
+    that represents a numpy.ndarray.
 
     Args:
       out: An instance of RichTextLines representing a numpy.ndarray.

tensorflow/python/debug/lib/check_numerics_callback.py

@@ -426,7 +426,7 @@ def disable_check_numerics():
   """Disable the eager/graph unified numerics checking mechanism.
 
   This method can be used after a call to `tf.debugging.enable_check_numerics()`
-  to disable the numerics-checking mechanism that catches inifnity and NaN
+  to disable the numerics-checking mechanism that catches infinity and NaN
   values output by ops executed eagerly or in tf.function-compiled graphs.
 
   This method is idempotent. Calling it multiple times has the same effect

tensorflow/python/debug/lib/check_numerics_callback_test.py

@@ -430,7 +430,7 @@ class CheckNumericsCallbackUnhealthyTest(test_util.TensorFlowTestCase):
     self.assertIn("one_over_x = 1.0 / x", message)
 
   @test_util.run_in_graph_and_eager_modes
-  def testInfInCustomKerasLayerWithoutTfFuntionPredictCall(self):
+  def testInfInCustomKerasLayerWithoutTfFunctionPredictCall(self):
     """Test catching Infinity in a custom layer, w/o tf.function."""
     check_numerics_callback.enable_check_numerics()
 
@@ -483,7 +483,7 @@ class CheckNumericsCallbackUnhealthyTest(test_util.TensorFlowTestCase):
     check_numerics_callback.enable_check_numerics()
 
     def generate_nan(x):
-      """Intetionally generates NaNs by taking log of negative number."""
+      """Intentionally generates NaNs by taking log of negative number."""
       casted_x = math_ops.cast(x, dtypes.float32)
       return math_ops.log([[-1.0, 1.0], [3.0, 5.0]]) + casted_x
 
@@ -503,7 +503,7 @@ class CheckNumericsCallbackUnhealthyTest(test_util.TensorFlowTestCase):
         message)
 
   @test_util.run_in_graph_and_eager_modes
-  def testCustomGradietWithNaNWithTfFunction(self):
+  def testCustomGradientWithNaNWithTfFunction(self):
     """Test that callback catches NaN in a gradient function during backprop."""
     check_numerics_callback.enable_check_numerics()
 

tensorflow/python/debug/lib/debug_data.py

@@ -296,7 +296,7 @@ class DebugTensorDatum(object):
         directory is `/tmp/tfdbg_1` and the dump file is at
         `/tmp/tfdbg_1/<device_path>/>ns_1/node_a_0_DebugIdentity_123456789`,
         then the value of the debug_dump_rel_path should be
-        `<device_path>/ns_1/node_a_0_DebugIdenity_1234456789`.
+        `<device_path>/ns_1/node_a_0_DebugIdentity_1234456789`.
 
     Raises:
       ValueError: If the base file name of the dump file does not conform to

tensorflow/python/debug/lib/debug_gradients.py

@@ -116,7 +116,7 @@ class GradientsDebugger(object):
 
     The side effect of this method is that when gradient tensor(s) are created
     with respect to the any paths that include the `input_tensor`, the gradient
-    tensor(s) with repsect to `input_tensor` will be registered with this
+    tensor(s) with respect to `input_tensor` will be registered with this
     this `GradientsDebugger` instance and can later be retrieved, with the
     methods `gradient_tensor` and `gradient_tensors`.
 
@@ -141,7 +141,7 @@ class GradientsDebugger(object):
 
     Args:
       input_tensor: the input `tf.Tensor` object whose related gradient tensors
-        are to be reigstered with this `GradientsDebugger` instance when they
+        are to be registered with this `GradientsDebugger` instance when they
         are created, e.g., during `tf.gradients` calls or the construction
         of optimization (training) op that uses `tf.gradients`.
 
@@ -173,7 +173,7 @@ class GradientsDebugger(object):
 
     The side effect of this method is that when gradient tensor(s) are created
     with respect to the any paths that include the `x_tensor`s, the gradient
-    tensor(s) with repsect to the tensor will be registered with this
+    tensor(s) with respect to the tensor will be registered with this
     this `GradientsDebugger` instance and can later be retrieved, with the
     methods `gradient_tensor` and `gradient_tensors`.
 

tensorflow/python/debug/lib/debug_graph_reconstruction_test.py

@@ -144,7 +144,7 @@ class ReconstructNonDebugGraphTest(test_util.TensorFlowTestCase):
       self._compareOriginalAndReconstructedGraphDefs(
           sess, c, expected_output=400.0)
 
-  def testReonstructGraphWithCond(self):
+  def testReconstructGraphWithCond(self):
     with session.Session(config=self._no_rewrite_session_config()) as sess:
       x = variables.Variable(10.0, name="x")
       y = variables.Variable(20.0, name="y")

tensorflow/python/debug/lib/debug_v2_ops_test.py

@@ -658,7 +658,7 @@ class DebugIdentityV2OpTest(dumping_callback_test_lib.DumpingCallbackTestBase):
               tensor_id=x._id,
               output_dtype=dtypes.float64)), x._id
 
-    # Assert the same op is returns a consistant value
+    # Assert the same op is returns a consistent value
     x = np.zeros([100, 100], dtype=np.float16)
     x[32, 47] = np.nan
     x[0:4, 3] = np.inf

tensorflow/python/debug/lib/dumping_callback.py

@@ -67,7 +67,7 @@ def _concrete_tensor_to_proto(tensor):
 
 
 class _DumpingCallback(object):
-  """An object holding the states surrouding the dumping callback."""
+  """An object holding the states surrounding the dumping callback."""
 
   def __init__(self,
                dump_root,

tensorflow/python/debug/lib/dumping_callback_test.py

@@ -1352,7 +1352,7 @@ class TracingCallbackTest(
       ("FullTensor", "FULL_TENSOR"),
   )
   @test_util.run_in_graph_and_eager_modes
-  def testMobiletNetV2Fit(self, tensor_debug_mode):
+  def testMobileNetV2Fit(self, tensor_debug_mode):
     """Test training Keras MobileNetV2 works with dumping."""
     # Use a large circular-buffer to make sure we capture all the executed ops.
     writer = dumping_callback.enable_dump_debug_info(

tensorflow/python/debug/lib/session_debug_grpc_test.py

@@ -574,7 +574,7 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase):
         if i in (0, 2):
           # During runs 0 and 2, the server should have received the published
           # debug tensor delta:0:DebugIdentity. The breakpoint should have been
-          # unblocked by EventReply reponses from the server.
+          # unblocked by EventReply responses from the server.
           self.assertAllClose(
               [5.0],
               self._server_1.debug_tensor_values["delta_1:0:DebugIdentity"])
@@ -628,7 +628,7 @@ class SessionDebugGrpcGatingTest(test_util.TensorFlowTestCase):
         if i in (0, 2):
           # During runs 0 and 2, the server should have received the published
           # debug tensor delta:0:DebugIdentity. The breakpoint should have been
-          # unblocked by EventReply reponses from the server.
+          # unblocked by EventReply responses from the server.
           self.assertAllClose(
               [5.0],
               self._server_1.debug_tensor_values["delta_1:0:DebugIdentity"])

tensorflow/python/debug/lib/source_remote.py

@@ -116,7 +116,7 @@ def _send_call_tracebacks(destinations,
     origin_stack: The traceback stack for the origin of the execution call. For
       graph execution, this is the traceback of the `tf.Session.run()`
       invocation. For eager execution, this is the traceback of the Python
-      line that executes the eager opertion.
+      line that executes the eager operation.
     is_eager_execution: (`bool`) whether an eager execution call (i.e., not a
       `tf.Session.run` or derived methods) is being sent.
     call_key: The key of the execution call, as a string. For graph execution,

tensorflow/python/debug/lib/source_utils.py

@@ -73,7 +73,7 @@ def guess_is_tensorflow_py_library(py_file_path):
 
   Raises:
     ValueError: if the extension name of py_file_path does not indicate a Python
-      source file (compiled or uncomplied).
+      source file (compiled or uncompiled).
   """
   if (not is_extension_uncompiled_python_source(py_file_path) and
       not is_extension_compiled_python_source(py_file_path)):

tensorflow/python/debug/lib/source_utils_test.py

@@ -341,7 +341,7 @@ class ListSourceAgainstDumpTest(test_util.TensorFlowTestCase):
     #   while/Less:0           4
     #   while/LoopCond:0       4
     #   while/Switch:0         1
-    #   while/Swtich:1         3
+    #   while/Switch:1         3
     #   while/Identity:0       3
     #   while/Add/y:0          3
     #   while/Add:0            3

tensorflow/python/debug/wrappers/dumping_wrapper.py

@@ -90,7 +90,7 @@ class DumpingDebugWrapperSession(framework.NonInteractiveDebugWrapperSession):
     self._run_counter_lock = threading.Lock()
 
   def prepare_run_debug_urls(self, fetches, feed_dict):
-    """Implementation of abstrat method in superclass.
+    """Implementation of abstract method in superclass.
 
     See doc of `NonInteractiveDebugWrapperSession.prepare_run_debug_urls()`
     for details. This implementation creates a run-specific subdirectory under

tensorflow/python/debug/wrappers/framework.py

@@ -44,7 +44,7 @@ c) (To be implemented in a future CL) Enter an instruction loop to let an
 3) The callback handles the request and returns a OnSessionInitResponse
    object with an action field, directing the wrapper session what to do next.
 
-If the action field in the OnSessionInitResponse is PROCEED, the constuctor
+If the action field in the OnSessionInitResponse is PROCEED, the constructor
 returns. Control is released back to the caller of the constructor, which can
 invoke run() method of wrapper session with the same syntax as a non-wrapped
 session, e.g.,:
@@ -69,7 +69,7 @@ A1) Right at the start of each run() call, the on_run_start() callback is
 
 A2) Right before the run() returns, the on_run_end() callback is invoked,
     with an OnRunEndRequest object as the argument, which carries information
-    including the actual action performed in the warpper run() call and the
+    including the actual action performed in the wrapper run() call and the
     run_metadata from the run() call.
 
 However, if the action field in OnSessionInitResponse is

tensorflow/python/debug/wrappers/local_cli_wrapper.py

@@ -393,7 +393,7 @@ class LocalCLIDebugWrapperSession(framework.BaseDebugWrapperSession):
         and caused the preparation of this run-end CLI (if any).
       passed_filter_exclude_node_names: (None or str) Regular expression used
         with the tensor filter to exclude ops with names matching the regular
-        expresssion.
+        expression.
     """
 
     if tf_error:

tensorflow/python/debug/wrappers/local_cli_wrapper_test.py

@@ -345,7 +345,7 @@ class LocalCLIDebugWrapperSessionTest(test_util.TensorFlowTestCase):
     self.assertEqual(0, len(wrapped_sess.observers["debug_dumps"]))
     self.assertEqual([], wrapped_sess.observers["tf_errors"])
 
-  def testRunMixingDebugModeAndMultpleTimes(self):
+  def testRunMixingDebugModeAndMultipleTimes(self):
     wrapped_sess = LocalCLIDebuggerWrapperSessionForTest(
         [["run", "-n"], ["run", "-t", "2"], ["run"], ["run"]],
         self.sess, dump_root=self._tmp_dir)

tensorflow/python/distribute/all_reduce.py

@@ -519,7 +519,7 @@ def _build_recursive_hd_gather(input_tensors, devices, red_op):
 
 
 def _build_recursive_hd_scatter(input_tensors, devices):
-  """Construct the scatter phase of recursive halving-doublng all-reduce.
+  """Construct the scatter phase of recursive halving-doubling all-reduce.
 
   Args:
     input_tensors: list of T `tf.Tensor` that are fully-reduced shards.

tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py

@@ -190,7 +190,7 @@ class SlurmClusterResolver(ClusterResolver):
     defaults to None.
 
     Returns:
-      A string specifying job name the process belongs to and an integner
+      A string specifying job name the process belongs to and an integer
         specifying the task index the process belongs to in that job.
     """
     return self.task_type, self.task_id
@@ -200,7 +200,7 @@ class SlurmClusterResolver(ClusterResolver):
 
     Args:
       task_type: (Optional) Overrides the default auto-selected task type.
-      task_id: (Optional) Overrides the default auto-slected task index.
+      task_id: (Optional) Overrides the default auto-selected task index.
       rpc_layer: (Optional) Overrides the default RPC protocol TensorFlow uses
         to communicate across nodes.
 

tensorflow/python/distribute/collective_all_reduce_strategy.py

@@ -63,7 +63,7 @@ class CollectiveAllReduceStrategy(distribute_lib.Strategy):
 
   When 'TF_CONFIG' environment variable is set, it parses cluster_spec,
   task_type and task_id from 'TF_CONFIG' and turns into a multi-worker strategy
-  which mirrores models on GPUs of all machines in a cluster. In the current
+  which mirrored models on GPUs of all machines in a cluster. In the current
   implementation, it uses all GPUs in a cluster and it assumes all workers have
   the same number of GPUs.
 
@@ -111,7 +111,7 @@ class CollectiveAllReduceStrategy(distribute_lib.Strategy):
 
   @classmethod
   def _from_local_devices(cls, devices):
-    """A convenience method to create an obejct with a list of devices."""
+    """A convenience method to create an object with a list of devices."""
     obj = cls()
     obj.extended._initialize_local(TFConfigClusterResolver(), devices=devices)  # pylint: disable=protected-access
     return obj

tensorflow/python/distribute/cross_device_ops.py

@@ -435,7 +435,7 @@ def _group_value_by_device(per_replica_values):
     ]
 
   Args:
-    per_replica_values: a list of PerReplica obejcts.
+    per_replica_values: a list of PerReplica objects.
 
   Returns:
     a list of lists, each sublist has components for its corresponding device of

tensorflow/python/distribute/cross_device_utils.py

@@ -761,7 +761,7 @@ def stitch_values(values_and_indices_list):
 
   Args:
     values_and_indices_list: a list of tuples of values and indices indicating
-      the values and postions in the returned list.
+      the values and positions in the returned list.
 
   Returns:
     a stitched list of values.

tensorflow/python/distribute/device_util.py

@@ -62,7 +62,7 @@ def canonicalize(d, default=None):
     result = result.make_merged_spec(
         tf_device.DeviceSpec.from_string(default))
 
-  # Apply `d` last, so that it's values take precidence over the defaults.
+  # Apply `d` last, so that it's values take precedence over the defaults.
   result = result.make_merged_spec(d)
   return result.to_string()
 

tensorflow/python/distribute/distribute_coordinator.py

@@ -284,12 +284,12 @@ class _WorkerContext(object):
 
   @property
   def task_type(self):
-    """Returns the role of the corresponing task."""
+    """Returns the role of the corresponding task."""
     return self._task_type
 
   @property
   def task_id(self):
-    """Returns the id or index of the corresponing task."""
+    """Returns the id or index of the corresponding task."""
     return self._task_id
 
   @property
@@ -364,7 +364,7 @@ def _split_cluster_for_evaluator(cluster_spec, task_type):
   """Split the cluster for evaluator since it needn't talk to other tasks."""
   # Splitting the cluster is important to prevent the evaluator from talking to
   # other tasks in the cluster. Since we allow evaluator not to use
-  # distribution strategies and as a result ops in the evalauator task may have
+  # distribution strategies and as a result ops in the evaluator task may have
   # unspecified devices. Those ops may end up on other tasks if we don't split
   # the cluster.
   # Note: if you bypass distribute coordinator and bring the cluster yourself,
@@ -694,7 +694,7 @@ def run_distribute_coordinator(worker_fn,
   operations.
 
   This method is intended to be invoked by high-level APIs so that users don't
-  have to explictly call it to run this coordinator. For those who don't use
+  have to explicitly call it to run this coordinator. For those who don't use
   high-level APIs, to change a program to use this coordinator, wrap everything
   in a the program after global data definitions such as commandline flag
   definition into the `worker_fn` and get task-specific configurations from

tensorflow/python/distribute/distribute_coordinator_test.py

@@ -593,7 +593,7 @@ class DistributeCoordinatorTestStandaloneMode(DistributeCoordinatorTestBase):
                      ("fake_evaluator", 3, True, False))
 
 
-class DistributeCoordinatorTestInpendentWorkerMode(
+class DistributeCoordinatorTestIndependentWorkerMode(
     DistributeCoordinatorTestBase):
 
   def testInGraph(self):
@@ -946,7 +946,7 @@ class RunStandardTensorflowServerTest(test.TestCase):
 
 
 if __name__ == "__main__":
-  # TODO(yuefengz): find a smart way to terminite std server threads.
+  # TODO(yuefengz): find a smart way to terminate std server threads.
   with test.mock.patch.object(sys, "exit", os._exit):
     # Reduce `recovery_wait_secs` from 30 seconds so the test completes quickly.
     orig_init = session_manager.SessionManager.__init__

tensorflow/python/distribute/distribute_lib.py

@@ -1998,7 +1998,7 @@ class StrategyExtendedV1(StrategyExtendedV2):
         - last_step_outputs: A dictionary containing tensors set using
         `context.set_last_step_output`. Evaluating this returns the value of
         the tensors after the last iteration.
-        - non_tensor_outputs: A dictionatry containing anything that was set by
+        - non_tensor_outputs: A dictionary containing anything that was set by
           `fn` by calling `context.set_non_tensor_output`.
     """
     _require_cross_replica_or_default_context_extended(self)

tensorflow/python/distribute/estimator_training.py

@@ -158,7 +158,7 @@ def init_run_config(config, tf_config):
     return
 
   # Don't use distribute coordinator if it is local training or cluster has a
-  # MASTER job or `train_distribute` is not specifed.
+  # MASTER job or `train_distribute` is not specified.
   if (not cluster_spec or 'master' in cluster_spec.jobs or
       not config._train_distribute):
     config._distribute_coordinator_mode = None

tensorflow/python/distribute/input_lib.py

@@ -176,7 +176,7 @@ def _get_next_as_optional(iterator, strategy, name=None):
     with ops.device(worker):
       worker_has_value, next_element = (
           iterator._iterators[i].get_next_as_list(new_name))  # pylint: disable=protected-access
-      # Collective all-reduce requires explict devices for inputs.
+      # Collective all-reduce requires explicit devices for inputs.
       with ops.device("/cpu:0"):
         # Converting to integers for all-reduce.
         worker_has_value = math_ops.cast(worker_has_value, dtypes.int32)

tensorflow/python/distribute/mirrored_strategy.py

@@ -260,7 +260,7 @@ def _group_device_list(devices):
 
   Returns:
     a dict of list of device strings mapping from task_type to a list of devices
-    for the task_type in the asceding order of task_id.
+    for the task_type in the ascending order of task_id.
   """
   assert not _is_device_list_single_worker(devices)
   device_dict = {}

tensorflow/python/distribute/mirrored_strategy_test.py

@@ -1319,7 +1319,7 @@ class MirroredVariableStopGradientTest(test.TestCase, parameterized.TestCase):
 
 class FunctionTest(test.TestCase):
 
-  def testBackwardFuctionDevicePlacement(self):
+  def testBackwardFunctionDevicePlacement(self):
     if context.num_gpus() < 1:
       self.skipTest("At least one GPU is required.")
     devices = [device_util.resolve("/device:GPU:0"),

tensorflow/python/distribute/model_collection/model_collection_base.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A base class to provid a model and corresponding input data for testing."""
+"""A base class to provide a model and corresponding input data for testing."""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

tensorflow/python/distribute/model_collection/simple_models.py

@@ -42,7 +42,7 @@ def _get_data_for_simple_models():
 
 
 class SimpleFunctionalModel(model_collection_base.ModelAndInput):
-  """A simple functinal model and its inputs."""
+  """A simple functional model and its inputs."""
 
   def get_model(self, **kwargs):
     output_name = 'output_layer'

tensorflow/python/distribute/parameter_server_strategy.py

@@ -550,7 +550,7 @@ class ParameterServerStrategyExtended(distribute_lib.StrategyExtendedV1):
                  cluster_spec=None,
                  task_type=None,
                  task_id=None):
-    """Configures the strategy class with `cluser_spec`.
+    """Configures the strategy class with `cluster_spec`.
 
     The strategy object will be re-initialized if `cluster_spec` is passed to
     `configure` but was not passed when instantiating the strategy.

tensorflow/python/distribute/parameter_server_strategy_test.py

@@ -186,7 +186,7 @@ class ParameterServerStrategyTestBase(
           g = e + 1.0
         self.assertEqual(g.device, worker_device + '/device:CPU:1')
 
-        # Ths ops.colocate_with will be ignored when defining a variale but not
+        # Ths ops.colocate_with will be ignored when defining a variable but not
         # for a normal tensor.
         with ops.colocate_with(x):
           u = variable_scope.get_variable('u', initializer=30.0)
@@ -340,7 +340,7 @@ class ParameterServerStrategyTestBase(
           g = e + 1.0
         self.assertEqual(g.device, device_util.canonicalize('/device:CPU:1'))
 
-        # Ths ops.colocate_with will be ignored when defining a variale but not
+        # Ths ops.colocate_with will be ignored when defining a variable but not
         # for a normal tensor.
         with ops.colocate_with(x):
           u = variable_scope.get_variable('u', initializer=30.0)

tensorflow/python/distribute/reduce_util.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Utilites for reduce operations."""
+"""Utilities for reduce operations."""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/python/distribute/saved_model_test_base.py

@@ -142,7 +142,7 @@ class TestSavedModelBase(test.TestCase, parameterized.TestCase):
   def _save_model(self, model, saved_dir):
     """Save the given model to the given saved_dir.
 
-    This method needs to be implemeted by the subclasses.
+    This method needs to be implemented by the subclasses.
 
     Args:
       model: a keras model object to save.

tensorflow/python/distribute/values_test.py

@@ -1409,7 +1409,7 @@ class PerReplicaTest(test.TestCase, parameterized.TestCase):
     vals = (constant_op.constant(1.), constant_op.constant([5., 6.0]),)
     per_replica = values.PerReplica(vals)
 
-    # Note: nest.map_structutre exercises nest.flatten and
+    # Note: nest.map_structure exercises nest.flatten and
     # nest.pack_sequence_as.
     result = nest.map_structure(
         lambda t: t + 10, per_replica, expand_composites=True)

tensorflow/python/eager/benchmarks_test.py

@@ -25,7 +25,7 @@ To run a subset of benchmarks using --benchmarks flag.
 --benchmarks: the list of benchmarks to run. The specified value is interpreted
 as a regular expression and any benchmark whose name contains a partial match
 to the regular expression is executed.
-e.g. --benchmarks=".*matmul*." will run all matmul related benmarks.
+e.g. --benchmarks=".*matmul*." will run all matmul related benchmarks.
 
 """
 from __future__ import absolute_import

tensorflow/python/eager/def_function.py

@@ -65,7 +65,7 @@ class _CallCounter(object):
         break
 
   def called_without_tracing(self):
-    # We don't count tracing when users load a concrete function dicretly or
+    # We don't count tracing when users load a concrete function directly or
     # call get_concrete_function, so the first call can be not a tracing call.
     if not self._calls_per_tracings:
       self._calls_per_tracings = [0]
@@ -380,7 +380,7 @@ class Function(object):
         tensorflow.autograph.Feature values. Allows enabling additional
         conversion options when autograph is set to True.
       experimental_relax_shapes: When true, argument shapes may be relaxed to
-        avoid unecessary retracing.
+        avoid unnecessary retracing.
       experimental_compile: If false, execute the function in a regular way. The
         function is optimized by some graph rewrite passes (some ops might be
         clustered into a single op) and interpreted by the standard TensorFlow
@@ -728,7 +728,7 @@ class Function(object):
     @function_lib.defun(autograph=False)
     def initialize_variables():
       op_map = object_identity.ObjectIdentityDictionary()
-      # Stack all the var_is_initialized values into one tensor and intepret the
+      # Stack all the var_is_initialized values into one tensor and interpret the
       # numpy value. This will reduce the number of RPCs between client and
       # worker in the remote case.
       with ops.init_scope():

tensorflow/python/eager/forwardprop_test.py

@@ -936,7 +936,7 @@ class ForwardpropTest(test.TestCase, parameterized.TestCase):
 
   # NOTE: assert_no_new_pyobjects_executing_eagerly fails flakily on this
   # test... could be something wrong with the test decorator, or some sort of
-  # nondeterminstic caching.
+  # nondeterministic caching.
   def testMirroredVariableWatched(self):
 
     def _replicated(input_tangent):

tensorflow/python/eager/forwardprop_util.py

@@ -65,7 +65,7 @@ def push_forwardprop_state():
   temporarily reset its state. This is useful when building forwardprop versions
   of functions, where an accumulator will trigger function building and then
   must process captured symbolic tensors while building it. Without pushing and
-  poping, accumulators ignore operations executed as a direct result of their
+  popping, accumulators ignore operations executed as a direct result of their
   own jvp computations.
 
   Yields:

tensorflow/python/eager/function.py

@@ -358,7 +358,7 @@ def add_function_callback(function_callback):
 
   wherein `function` is the just-created _EagerDefinedFunction.
   The callback is invoked immediately after a new `_EagerDefinedFunction`
-  is created. The return value(s) of the callback fucntion (if any) is ignored.
+  is created. The return value(s) of the callback function (if any) is ignored.
 
   Repeated registration of the same callback function is idempotent.
   After a callback is added, it can be removed with the
@@ -850,7 +850,7 @@ class _DelayedRewriteGradientFunctions(object):
     higher-order symbolic gradients (tf.gradients).
 
     Args:
-      flat_outputs: The restult of running `forward`.
+      flat_outputs: The result of running `forward`.
       inference_args: A flat list of Tensors with inference inputs to the
         operation.
       input_tangents: A flat list of Tensors with input tangents consumed by the
@@ -1314,7 +1314,7 @@ class _TapeGradientFunctions(object):
     have produced tangents which need to be recorded.
 
     Args:
-      flat_outputs: The restult of running `forward`.
+      flat_outputs: The result of running `forward`.
       inference_args: A flat list of Tensors with inference inputs to the
         operation.
       input_tangents: A flat list of Tensors with input tangents consumed by the
@@ -1757,7 +1757,7 @@ class ConcreteFunction(object):
     return self._build_call_outputs(flat_outputs)
 
   def _experimental_with_cancellation_manager(self, cancellation_manager):
-    """Returns a callable that invokes a cancelable version of this function.
+    """Returns a callable that invokes a cancellable version of this function.
 
     Args:
       cancellation_manager: A `CancellationManager` object that can be used to
@@ -2376,7 +2376,7 @@ class Function(object):
         `when autograph=True`. See https://www.tensorflow.org/guide/autograph
         for more information.
       experimental_relax_shapes: When true, argument shapes may be relaxed to
-        avoid unecessary retracing.
+        avoid unnecessary retracing.
       capture_by_value: Experimental. Whether to capture resource variables by
         value or reference. If None, will inherit from a parent context or
         default to False.
@@ -2668,7 +2668,7 @@ class Function(object):
     return graph_function
 
   def _define_function_with_shape_relaxation(self, args, kwargs):
-    """Define a function, relaxing arg shapes to avoid unecessary retracing."""
+    """Define a function, relaxing arg shapes to avoid unnecessary retracing."""
 
     rank_only_cache_key = self._cache_key(
         args, kwargs, include_tensor_ranks_only=True)
@@ -2824,7 +2824,7 @@ def defun(func=None,
   the values of its non-Tensor Python objects.
 
   When eager execution is enabled, the ability to create graphs from Python
-  functions makes it possible to incrementally trade off debugability and
+  functions makes it possible to incrementally trade off debuggability and
   interactivity for performance.  Functions compiled with `defun` cannot be
   inspected with `pdb`; however, executing a graph
   generated by `defun` sometimes takes less time and memory than eagerly
@@ -3130,7 +3130,7 @@ def defun(func=None,
       of tensorflow.autograph.Feature values) to control behavior when
       autograph=True.
     experimental_relax_shapes: When true, argument shapes may be relaxed to
-      avoid unecessary retracing.
+      avoid unnecessary retracing.
 
   Returns:
      If `func` is not None, returns a callable that will execute the compiled

tensorflow/python/eager/function_test.py

@@ -3508,7 +3508,7 @@ class MultiDeviceTest(test.TestCase, parameterized.TestCase):
     self.assertEqual(r2.numpy(), 34000.0 + 13.0 * 7.0)
 
   @test_util.run_gpu_only
-  def testArgumentPrunning(self):
+  def testArgumentPruning(self):
     """Tests functions taking unnecessary arguments."""
     with ops.device('/device:CPU:0'):
       c1 = constant_op.constant(5.0)

tensorflow/python/eager/pywrap_tensor.cc

@@ -359,7 +359,7 @@ typedef struct EagerTensor {
   TFE_TensorHandle* handle;
   int64_t id;
   // This mirrors tensorflow.core.framework.ops.Tensor._handle_data Which will
-  // be None for tensors of type other than DT_REOSURCE. For DT_RESOURCE
+  // be None for tensors of type other than DT_RESOURCE. For DT_RESOURCE
   // tensors, this will contain a serialized HandleData proto with shape
   // inference metadata about shapes and dtypes of resources accessible from
   // this handle.
@@ -660,7 +660,7 @@ static PyObject* EagerTensor_backing_device(EagerTensor* self) {
 #endif
 }
 
-static PyGetSetDef EagerTensor_getseters[] = {
+static PyGetSetDef EagerTensor_getsetters[] = {
     {const_cast<char*>("_id"), (getter)EagerTensor_getid, nullptr,
      const_cast<char*>("Tensor ID."), nullptr},
     {const_cast<char*>("device"), (getter)EagerTensor_device, nullptr,
@@ -758,7 +758,7 @@ PyTypeObject* EagerTensorType = nullptr;
 static PyType_Slot EagerTensor_Type_slots[] = {
     {Py_tp_dealloc, reinterpret_cast<void*>(EagerTensor_dealloc)},
     {Py_tp_methods, reinterpret_cast<void*>(EagerTensor_methods)},
-    {Py_tp_getset, reinterpret_cast<void*>(EagerTensor_getseters)},
+    {Py_tp_getset, reinterpret_cast<void*>(EagerTensor_getsetters)},
     {Py_tp_init, reinterpret_cast<void*>(EagerTensor_init)},
     {0, nullptr},
 };
@@ -799,7 +799,7 @@ static PyTypeObject _EagerTensorType = {
     nullptr,                            /* tp_iternext */
     EagerTensor_methods,                /* tp_methods */
     EagerTensor_members,                /* tp_members */
-    EagerTensor_getseters,              /* tp_getset */
+    EagerTensor_getsetters,             /* tp_getset */
     nullptr,                            /* tp_base */
     nullptr,                            /* tp_dict */
     nullptr,                            /* tp_descr_get */

tensorflow/python/eager/pywrap_tfe.h

@@ -197,7 +197,7 @@ PyObject* TFE_Py_TapeSetIsStopped();
 //    forwardprop to, given the gradients of the output tensors, produce the
 //    gradients of the input tensors. This function is automatically transposed
 //    during forwardprop.
-//  - forward_function is an optional special-case for fowardprop, taking input
+//  - forward_function is an optional special-case for forwardprop, taking input
 //    jvps and returning output jvps.
 //
 // Records an operation both for backprop (gradient tape) and forwardprop
@@ -307,7 +307,7 @@ PyObject* TFE_Py_ForwardAccumulatorJVP(PyObject* accumulator, PyObject* tensor);
 // temporarily reset its state. This is useful when building forwardprop
 // versions of functions, where an accumulator will trigger function building
 // and then must process captured symbolic tensors while building it. Without
-// pushing and poping, accumulators ignore operations executed as a direct
+// pushing and popping, accumulators ignore operations executed as a direct
 // result of their own jvp computations.
 PyObject* TFE_Py_ForwardAccumulatorPushState();
 PyObject* TFE_Py_ForwardAccumulatorPopState();

tensorflow/python/eager/tape.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Gradient tape utilites."""
+"""Gradient tape utilities."""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/python/eager/wrap_function.py

@@ -270,7 +270,7 @@ class WrappedFunction(function.ConcreteFunction):
     tensor_fetches = []
     tensor_infos = []
 
-    def _fetch_preprocesing_callback(fetch):
+    def _fetch_preprocessing_callback(fetch):
       """Extract out lists of ops, tensors, and tensor type info.
 
       Turns TensorInfos into Tensors in the original `fetches` structure.
@@ -300,9 +300,9 @@ class WrappedFunction(function.ConcreteFunction):
         return fetch
       else:
         graph_element = self.graph.as_graph_element(fetch)
-        return _fetch_preprocesing_callback(graph_element)
+        return _fetch_preprocessing_callback(graph_element)
 
-    fetches = nest.map_structure(_fetch_preprocesing_callback, fetches)
+    fetches = nest.map_structure(_fetch_preprocessing_callback, fetches)
 
     # Expand composite tensors into their component dense Tensors.
     tensor_fetches = nest.flatten(tensor_fetches, expand_composites=True)

tensorflow/python/feature_column/feature_column_test.py

@@ -63,7 +63,7 @@ def _initialized_session(config=None):
 
 class LazyColumnTest(test.TestCase):
 
-  def test_transormations_called_once(self):
+  def test_transformations_called_once(self):
 
     class TransformCounter(_FeatureColumn):
 
@@ -1131,7 +1131,7 @@ class CrossedColumnTest(test.TestCase):
   def test_linear_model(self):
     """Tests linear_model.
 
-    Uses data from test_get_sparse_tesnsors_simple.
+    Uses data from test_get_sparse_tensors_simple.
     """
     a = fc._numeric_column('a', dtype=dtypes.int32, shape=(2,))
     b = fc._bucketized_column(a, boundaries=(0, 1))
@@ -1213,7 +1213,7 @@ class CrossedColumnTest(test.TestCase):
   def test_keras_linear_model(self):
     """Tests _LinearModel.
 
-    Uses data from test_get_sparse_tesnsors_simple.
+    Uses data from test_get_sparse_tensors_simple.
     """
     a = fc._numeric_column('a', dtype=dtypes.int32, shape=(2,))
     b = fc._bucketized_column(a, boundaries=(0, 1))

tensorflow/python/feature_column/feature_column_v2.py

@@ -683,7 +683,7 @@ class LinearModel(training.Model):
           }
           ```
 
-        with `sparse_combiner` as "mean", the linear model outputs conceptly are
+        with `sparse_combiner` as "mean", the linear model outputs conceptually are
         ```
         y_0 = 1.0 / 2.0 * ( w_a + w_ b) + w_c + b_0
         y_1 = w_d + 1.0 / 3.0 * ( w_e + w_ f + w_g) + b_1

tensorflow/python/feature_column/feature_column_v2_test.py

@@ -1369,7 +1369,7 @@ class CrossedColumnTest(test.TestCase):
   def test_linear_model(self):
     """Tests linear_model.
 
-    Uses data from test_get_sparse_tesnsors_simple.
+    Uses data from test_get_sparse_tensors_simple.
     """
     a = fc.numeric_column('a', dtype=dtypes.int32, shape=(2,))
     b = fc.bucketized_column(a, boundaries=(0, 1))
@@ -1463,7 +1463,7 @@ class CrossedColumnTest(test.TestCase):
   def test_old_linear_model(self):
     """Tests linear_model.
 
-    Uses data from test_get_sparse_tesnsors_simple.
+    Uses data from test_get_sparse_tensors_simple.
     """
     a = fc.numeric_column('a', dtype=dtypes.int32, shape=(2,))
     b = fc.bucketized_column(a, boundaries=(0, 1))
@@ -1573,7 +1573,7 @@ class CrossedColumnTest(test.TestCase):
   def test_old_linear_model_old_numeric(self):
     """Tests linear_model.
 
-    Uses data from test_get_sparse_tesnsors_simple.
+    Uses data from test_get_sparse_tensors_simple.
     """
     a = fc_old._numeric_column('a', dtype=dtypes.int32, shape=(2,))
     b = fc.bucketized_column(a, boundaries=(0, 1))

tensorflow/python/feature_column/serialization.py

@@ -197,7 +197,7 @@ def _column_name_with_class_name(fc):
   Without this two FeatureColumns that have the same name and where
   one wraps the other, such as an IndicatorColumn wrapping a
   SequenceCategoricalColumn, will fail to deserialize because they will have the
-  same name in colums_by_name, causing the wrong column to be returned.
+  same name in columns_by_name, causing the wrong column to be returned.
 
   Args:
     fc: A FeatureColumn.

tensorflow/python/framework/config_test.py

@@ -614,7 +614,7 @@ class DeviceTest(test.TestCase):
       self.assertIsNotNone(gpu.name)
 
   @reset_eager
-  def testV1CompatibilityDummyInivisibleDeviceList(self):
+  def testV1CompatibilityDummyInvisibleDeviceList(self):
     gpus = config.list_physical_devices('GPU')
     if gpus:
       self.skipTest('Test requires no GPUs')

tensorflow/python/framework/convert_to_constants.py

@@ -249,7 +249,7 @@ def _get_control_flow_function_data(node_defs, tensor_data, name_to_node):
 
   def get_source_node_name_through_identities(node_name):
     # Trace the source node along with a chain of Identity nodes.
-    # For example, given Plaecholder -> Identity -> Identity -> node_name
+    # For example, given Placeholder -> Identity -> Identity -> node_name
     # The function will return the name of the Placeholder.
     while name_to_node[node_name].op == "Identity":
       node_name = _get_tensor_name(name_to_node[node_name].input[0])

tensorflow/python/framework/device_spec.py

@@ -212,7 +212,7 @@ class DeviceSpecV2(object):
   def make_merged_spec(self, dev):
     """Returns a new DeviceSpec which incorporates `dev`.
 
-    When combining specs, `dev` will take precidence over the current spec.
+    When combining specs, `dev` will take precedence over the current spec.
     So for instance:
     ```
     first_spec = tf.DeviceSpec(job=0, device_type="CPU")
@@ -253,7 +253,7 @@ class DeviceSpecV2(object):
         job=self.job, replica=self.replica, task=self.task,
         device_type=self.device_type, device_index=self.device_index)
 
-    # Explicitly provided kwargs take precidence.
+    # Explicitly provided kwargs take precedence.
     init_kwargs.update(kwargs)
     return self.__class__(**init_kwargs)
 

tensorflow/python/framework/func_graph.py

@@ -77,7 +77,7 @@ def convert_structure_to_signature(structure, arg_names=None):
 
   Returns:
     Identical structure that has TensorSpec objects instead of Tensors and
-    UknownArgument instead of any unsupported types.
+    UnknownArgument instead of any unsupported types.
   """
   def encode_arg(arg, path):
     """A representation for this argument, for converting into signatures."""
@@ -1197,7 +1197,7 @@ def _get_defun_inputs(args, names, structure, flat_shapes=None):
                        "either zero or all names have to be specified.")
 
     for arg in flattened:
-      # We have a shape entry for each arg, regadless of whether it's a real
+      # We have a shape entry for each arg, regardless of whether it's a real
       # Tensor or not.  For non-tensor entries it should be None.
       shape = next(shapes_iter)
       if isinstance(arg, (ops.Tensor, tensor_spec.TensorSpec)):

tensorflow/python/framework/function.py

@@ -290,7 +290,7 @@ class _DefinedFunction(object):
     device_funcs = ops.get_default_graph()._device_functions_outer_to_inner
     # pylint: enable=protected-access
 
-    # Get the innermost device if possbile.
+    # Get the innermost device if possible.
     self._caller_device = device_funcs[-1] if device_funcs else None
 
     # Cached OpDef for this function. When C API is enabled, this is

tensorflow/python/framework/function_def_to_graph.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
-"""Utlity to convert FunctionDef to GraphDef and Graph."""
+"""Utility to convert FunctionDef to GraphDef and Graph."""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/python/framework/graph_util_impl.py

@@ -124,7 +124,7 @@ def _node_name(n):
 
 
 def _get_colocated_node_name(colocated_node_name):
-  """Decodes colocated node name and returns it without loc:@ preprended."""
+  """Decodes colocated node name and returns it without loc:@ prepended."""
   colocated_node_decoded = colocated_node_name.decode("utf-8")
   if colocated_node_decoded.startswith("loc:@"):
     return colocated_node_decoded[5:]

tensorflow/python/framework/meta_graph.py

@@ -1045,7 +1045,7 @@ def export_scoped_meta_graph(filename=None,
       name, _ = os.path.splitext(filename)
       debug_filename = "{name}{ext}".format(name=name, ext=".debug")
 
-      # Gets the operation from the graph by the name. Exludes variable nodes,
+      # Gets the operation from the graph by the name. Excludes variable nodes,
       # so only the nodes in the frozen models are included.
       # TODO(liufengdb): fix this for functions.
       ops_to_export = []