TensorFlow: upstream changes to git.

Change 109418220
	Update WORKSPACE to use gmock.BUILD from google/protobuf instead of a duplicate.
	Update google/protobuf's commit hash to include damieng@'s commit.
Change 109417314
	TensorFlow: add .gitignore to ignore some in-tree modified files.
Change 109400051
	Optionally build full TensorFlow for Android.
	1. --define ANDROID_TYPES=__ANDROID_TYPES_FULL__ to register ops
	   for all types, not just float. Today this increases codesize
	   by ~700K when compiled for ARM, though only for clients who
	   request full type support.
	2. Add more ops to android_extended_ops, sufficient to train on
	   the linear regression baseball codelab.
Change 109388118
	Fix the option changed in templatize. Oops.
Change 109382553
	Allows setting a function name in an op's attr in the py frontend.
Change 109380896
	Remove assert_same_graph in favor of op_scope. Change the latter to handle tensor-like objects such as SparseTensor, IndexedSlices, and Variable.

Base CL: 109418322

.gitignore

@@ -0,0 +1,11 @@
+node_modules
+/bazel-bin
+/bazel-genfiles
+/bazel-out
+/bazel-tensorflow
+/bazel-testlogs
+/bazel-tf
+/third_party/py/numpy/numpy_include
+/tools/bazel.rc
+/util/python/python_include
+/util/python/python_lib

WORKSPACE

@@ -16,7 +16,7 @@ new_http_archive(
   name = "gmock_archive",
   url = "https://googlemock.googlecode.com/files/gmock-1.7.0.zip",
   sha256 = "26fcbb5925b74ad5fc8c26b0495dfc96353f4d553492eb97e85a8a6d2f43095b",
-  build_file = "gmock.BUILD",
+  build_file = "google/protobuf/gmock.BUILD",
 )
 
 bind(

tensorflow/core/BUILD

@@ -632,6 +632,7 @@ filegroup(
     srcs = [
         "//tensorflow/core:kernels/avgpooling_op.cc",
         "//tensorflow/core:kernels/avgpooling_op.h",
+        "//tensorflow/core:kernels/bcast_ops.cc",
         "//tensorflow/core:kernels/control_flow_ops.cc",
         "//tensorflow/core:kernels/control_flow_ops.h",
         "//tensorflow/core:kernels/conv_2d.h",
@@ -642,19 +643,23 @@ filegroup(
         "//tensorflow/core:kernels/cwise_op_less.cc",
         "//tensorflow/core:kernels/cwise_op_log.cc",
         "//tensorflow/core:kernels/cwise_op_mul.cc",
+        "//tensorflow/core:kernels/cwise_op_neg.cc",
         "//tensorflow/core:kernels/cwise_op_sigmoid.cc",
         "//tensorflow/core:kernels/cwise_op_sqrt.cc",
         "//tensorflow/core:kernels/cwise_op_square.cc",
         "//tensorflow/core:kernels/cwise_op_sub.cc",
         "//tensorflow/core:kernels/cwise_op_tanh.cc",
         "//tensorflow/core:kernels/dynamic_partition_op.cc",
+        "//tensorflow/core:kernels/dynamic_stitch_op.cc",
         "//tensorflow/core:kernels/lrn_op.cc",
         "//tensorflow/core:kernels/maxpooling_op.cc",
         "//tensorflow/core:kernels/maxpooling_op.h",
         "//tensorflow/core:kernels/reduction_ops.h",
         "//tensorflow/core:kernels/reduction_ops_common.h",
         "//tensorflow/core:kernels/reduction_ops_max.cc",
+        "//tensorflow/core:kernels/reduction_ops_mean.cc",
         "//tensorflow/core:kernels/reduction_ops_min.cc",
+        "//tensorflow/core:kernels/reduction_ops_prod.cc",
         "//tensorflow/core:kernels/reduction_ops_sum.cc",
         "//tensorflow/core:kernels/relu_op.cc",
         "//tensorflow/core:kernels/relu_op.h",
@@ -663,6 +668,8 @@ filegroup(
         "//tensorflow/core:kernels/softsign_op.cc",
         "//tensorflow/core:kernels/softsign_op.h",
         "//tensorflow/core:kernels/stack_ops.cc",
+        "//tensorflow/core:kernels/tile_ops.cc",
+        "//tensorflow/core:kernels/tile_ops.h",
         "//tensorflow/core:kernels/transpose_op.cc",
         "//tensorflow/core:kernels/transpose_op.h",
         "//tensorflow/core:kernels/transpose_op_functor.h",

tensorflow/core/kernels/cwise_ops_common.h

@@ -367,11 +367,14 @@ struct SelectFunctor<CPUDevice, T> {
                           OP<D##Device, F<T>>);
 
 // Macros to register kernels for multiple types (T0, T1, etc.)  on
-// device type "D" (CPU or GPU) for operatin "N" (e.g., sqrt) using
+// device type "D" (CPU or GPU) for operation "N" (e.g., sqrt) using
 // the functor "F" (e.g., functor:sqrt).
 
-#if defined(__ANDROID__)
-// On Android, only register the first type (float)
+#if defined(__ANDROID_TYPES_SLIM__)
+// Normally Android TensorFlow is built with a reduced number of types (float).
+// Override on the command-line "--define ANDROID_TYPES=__ANDROID_TYPES_FULL__"
+// to generate a library with full type support with a consequent increase in
+// code size.
 #define REGISTER2(OP, D, N, F, T0, T1) REGISTER(OP, D, N, F, T0)
 #define REGISTER3(OP, D, N, F, T0, T1, T2) REGISTER(OP, D, N, F, T0)
 #define REGISTER4(OP, D, N, F, T0, T1, T2, T3) REGISTER(OP, D, N, F, T0)
@@ -381,7 +384,7 @@ struct SelectFunctor<CPUDevice, T> {
   REGISTER(OP, D, N, F, T0)
 #define REGISTER8(OP, D, N, F, T0, T1, T2, T3, T4, T5, T6, T7) \
   REGISTER(OP, D, N, F, T0)
-#else  // !defined(__ANDROID__)
+#else  // !defined(__ANDROID_TYPES_SLIM__)
 #define REGISTER2(OP, D, N, F, T0, T1) \
   REGISTER(OP, D, N, F, T0)            \
   REGISTER(OP, D, N, F, T1)
@@ -403,7 +406,7 @@ struct SelectFunctor<CPUDevice, T> {
 #define REGISTER8(OP, D, N, F, T0, T1, T2, T3, T4, T5, T6, T7) \
   REGISTER4(OP, D, N, F, T0, T1, T2, T3)                       \
   REGISTER4(OP, D, N, F, T4, T5, T6, T7)
-#endif  // defined(__ANDROID__)
+#endif  // defined(__ANDROID_TYPES_SLIM__)
 
 }  // end namespace tensorflow
 

tensorflow/python/framework/ops.py

@@ -39,6 +39,7 @@
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import versions
 from tensorflow.python.util import compat
+from tensorflow.python.platform import logging
 
 
 def _convert_stack(stack):
@@ -95,6 +96,22 @@ def _extract_stack():
   return ret
 
 
+def _as_graph_element(obj):
+  """Convert `obj` to a graph element if possible, otherwise return `None`.
+
+  Args:
+    obj: Object to convert.
+
+  Returns:
+    The result of `obj._as_graph_element()` if that method is available;
+        otherwise `None`.
+  """
+  conv_fn = getattr(obj, "_as_graph_element", None)
+  if conv_fn and callable(conv_fn):
+    return conv_fn()
+  return None
+
+
 class Tensor(object):
   """Represents a value produced by an `Operation`.
 
@@ -680,6 +697,7 @@ class IndexedSlices(object):
 
   def __init__(self, values, indices, dense_shape=None):
     """Creates an `IndexedSlices`."""
+    _get_graph_from_inputs([values, indices, dense_shape])
     self._values = values
     self._indices = indices
     self._dense_shape = dense_shape
@@ -719,30 +737,15 @@ def dtype(self):
     """The `DType` of elements in this tensor."""
     return self.values.dtype
 
-  def __str__(self):
-    return "IndexedSlices(indices=%s, values=%s)" % (
-        self._indices, self._values)
-
-
-def assert_same_graph(items, expected_graph=None):
-  """Asserts all items are from the same graph.
+  @property
+  def graph(self):
+    """The `Graph` that contains the values, indices, and shape tensors."""
+    return self._values.graph
 
-  Args:
-    items: List of graph items (e.g., Variable, Tensor, SparseTensor,
-        Operation, or IndexedSlices).
-    expected_graph: Expected graph. If not specified, assert all tensors are
-        from the same graph.
-  Returns:
-    items, for chaining.
-  Raises:
-    ValueError: If any graphs do not match.
-  """
-  for item in items:
-    if not expected_graph:
-      expected_graph = item.graph
-    elif expected_graph != item.graph:
-      raise ValueError("Items must be from the same graph.")
-  return items
+  def __str__(self):
+    return "IndexedSlices(indices=%s, values=%s%s)" % (
+        self._indices, self._values,
+        (", dense_shape=%s" % self._dense_shape) if self._dense_shape else "")
 
 
 class SparseTensor(object):
@@ -1106,7 +1109,7 @@ def _add_input(self, tensor, dtype=None):
     """
     if not isinstance(tensor, Tensor):
       raise TypeError("tensor must be a Tensor: %s" % tensor)
-    assert_same_graph([self, tensor])
+    _assert_same_graph(self, tensor)
     if dtype is None:
       dtype = tensor.dtype
     else:
@@ -1138,7 +1141,7 @@ def _update_input(self, index, tensor, dtype=None):
     """
     if not isinstance(tensor, Tensor):
       raise TypeError("tensor must be a Tensor: %s" % tensor)
-    assert_same_graph([self, tensor])
+    _assert_same_graph(self, tensor)
     if dtype is None:
       dtype = tensor.dtype
     else:
@@ -1166,7 +1169,7 @@ def _add_control_input(self, op):
     """
     if not isinstance(op, Operation):
       raise TypeError("op must be an Operation: %s" % op)
-    assert_same_graph([self, op])
+    _assert_same_graph(self, op)
     self._control_inputs.append(op)
     self._recompute_node_def()
 
@@ -1887,9 +1890,7 @@ def as_graph_element(self, obj, allow_tensor=True, allow_operation=True):
     else:
       raise ValueError("allow_tensor and allow_operation can't both be False.")
 
-    conv_fn = getattr(obj, "_as_graph_element", None)
-    if conv_fn and callable(conv_fn):
-      obj = conv_fn()
+    obj = _as_graph_element(obj) or obj
 
     # If obj appears to be a name...
     if isinstance(obj, compat.bytes_or_text_types):
@@ -2971,6 +2972,21 @@ def get_default_graph():
   return _default_graph_stack.get_default()
 
 
+def _assert_same_graph(original_item, item):
+  """Fail if the 2 items are from different graphs.
+
+  Args:
+    original_item: Original item to check against.
+    item: Item to check.
+
+  Raises:
+    ValueError: if graphs do not match.
+  """
+  if original_item.graph is not item.graph:
+    raise ValueError(
+        "%s must be from the same graph as %s." % (item, original_item))
+
+
 def _get_graph_from_inputs(op_input_list, graph=None):
   """Returns the appropriate graph to use for the given inputs.
 
@@ -2986,8 +3002,8 @@ def _get_graph_from_inputs(op_input_list, graph=None):
      "op_input_list", we attempt to use the default graph.
 
   Args:
-    op_input_list: A list of inputs to an operation, which may include Tensor
-      and Operation objects.
+    op_input_list: A list of inputs to an operation, which may include `Tensor`,
+      `Operation`, and other objects that may be converted to a graph element.
     graph: (Optional) The explicit graph to use.
 
   Raises:
@@ -3001,37 +3017,35 @@ def _get_graph_from_inputs(op_input_list, graph=None):
     The appropriate graph to use for the given inputs.
   """
   op_input_list = tuple(op_input_list)  # Handle generators correctly
-
-  # 1. If the graph is specified explicitly, we validate that all of the inputs
-  #    are compatible with that graph.
-  if graph is not None:
-    if not isinstance(graph, Graph):
-      raise TypeError("Input graph needs to be a Graph: %s" % graph)
-    for op_input in op_input_list:
-      if isinstance(op_input, Operation):
-        if op_input.graph is not graph:
-          raise ValueError("Operation %s is not from the passed-in graph"
-                           % op_input)
-      elif isinstance(op_input, Tensor):
-        if op_input.graph is not graph:
-          raise ValueError("Tensor %s is not from the passed-in graph"
-                           % op_input)
-    return graph
-
-  # 2. Otherwise, we attempt to select a graph from one of the Operation-
-  #    or Tensor-valued inputs.
-  original_input = None
+  if graph and not isinstance(graph, Graph):
+    raise TypeError("Input graph needs to be a Graph: %s" % graph)
+
+  # 1. We validate that all of the inputs are from the same graph. This is
+  #    either the supplied graph parameter, or the first one selected from one
+  #    the graph-element-valued inputs. In the latter case, we hold onto
+  #    that input in original_graph_element so we can provide a more
+  #    informative error if a mismatch is found.
+  original_graph_element = None
   for op_input in op_input_list:
-    if isinstance(op_input, (Operation, Tensor)):
-      if original_input is None:
-        original_input = op_input
-      else:
-        assert_same_graph([original_input, op_input])
-  if original_input is not None:
-    return original_input.graph
+    # Determine if this is a valid graph_element.
+    graph_element = None
+    if isinstance(op_input, (Operation, Tensor, SparseTensor, IndexedSlices)):
+      graph_element = op_input
+    else:
+      graph_element = _as_graph_element(op_input)
 
-  # 3. If all else fails, we use the default graph, which is always there.
-  return get_default_graph()
+    if graph_element:
+      if not graph:
+        original_graph_element = graph_element
+        graph = graph_element.graph
+      elif original_graph_element:
+        _assert_same_graph(original_graph_element, graph_element)
+      elif graph_element.graph is not graph:
+        raise ValueError(
+            "%s is not from the passed-in graph." % graph_element)
+
+  # 2. If all else fails, we use the default graph, which is always there.
+  return graph or get_default_graph()
 
 
 class GraphKeys(object):
@@ -3115,7 +3129,7 @@ def get_collection(key, scope=None):
 
 # pylint: disable=g-doc-return-or-yield
 @contextlib.contextmanager
-def op_scope(values, name, default_name):
+def op_scope(values, name, default_name=None):
   """Returns a context manager for use when defining a Python op.
 
   This context manager validates that the given `values` are from the
@@ -3140,10 +3154,17 @@ def my_op(a, b, c, name=None):
     default_name: The default name to use if the `name` argument is `None`.
 
   Returns:
-    A context manager for use in defining a Python op.
+    A context manager for use in defining Python ops. Yields the name scope.
+
+  Raises:
+    ValueError: if neither `name` nor `default_name` is provided.
   """
   g = _get_graph_from_inputs(values)
   n = default_name if name is None else name
+  if n is None:
+    raise ValueError(
+        "At least one of name (%s) and default_name (%s) must be provided." % (
+            name, default_name))
   with g.as_default(), g.name_scope(n) as scope:
     yield scope
 # pylint: enable=g-doc-return-or-yield