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TensorFlow: upstream changes to git.

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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
This commit is contained in:
Manjunath Kudlur 2015-12-04 09:54:09 -08:00
parent 54a644f33f
commit 3dfd14421d
7 changed files with 194 additions and 94 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
.gitignore vendored Normal file
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@ -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

2
WORKSPACE
View File

@ -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(

7
tensorflow/core/BUILD
View File

@ -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",

13
tensorflow/core/kernels/cwise_ops_common.h
View File

@ -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

143
tensorflow/python/framework/ops.py
View File

@ -39,6 +39,7 @@ from tensorflow.python.framework import registry
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 @@ class IndexedSlices(object):
"""The `DType` of elements in this tensor."""
return self.values.dtype
@property
def graph(self):
"""The `Graph` that contains the values, indices, and shape tensors."""
return self._values.graph
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.
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
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 @@ class Operation(object):
"""
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 @@ class Operation(object):
"""
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 @@ class Operation(object):
"""
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 @@ class Graph(object):
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
if graph and not isinstance(graph, Graph):
raise TypeError("Input graph needs to be a Graph: %s" % graph)
# 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
# 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 op_scope(values, name, default_name):
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

108
tensorflow/python/framework/ops_test.py
View File

@ -27,6 +27,7 @@ from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_kernel_label_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import common_shapes
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
@ -356,19 +357,19 @@ class NameTest(test_util.TensorFlowTestCase):
self.assertEqual("my_op", op2.name)
self.assertEqual("my_op:0", op2.outputs[0].name)
def testname_scope(self):
def testNameScope(self):
g = ops.Graph()
with g.name_scope("foo") as foo:
self.assertEqual(foo, "foo/")
self.assertEqual("foo/", foo)
with g.name_scope("foo2") as foo2:
self.assertEqual(foo2, "foo/foo2/")
self.assertEqual("foo/foo2/", foo2)
with g.name_scope(None) as empty1:
self.assertEqual(empty1, "")
self.assertEqual("", empty1)
with g.name_scope("foo3") as foo3:
self.assertEqual(foo3, "foo3/")
self.assertEqual("foo3/", foo3)
with g.name_scope("") as empty2:
self.assertEqual(empty2, "")
self.assertEqual("", empty2)
self.assertEqual("const",
g.create_op("const", [], [dtypes.float32]).name)
@ -792,6 +793,80 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase):
self.assertEqual(b.op.control_inputs, [])
class OpScopeTest(test_util.TensorFlowTestCase):
def testNoScopeName(self):
g0 = ops.Graph()
values = [
g0.create_op("a", [], [dtypes.float32]),
g0.create_op("b", [], [dtypes.float32])]
with self.assertRaises(ValueError):
with ops.op_scope(values, None):
pass
with self.assertRaises(ValueError):
with ops.op_scope(values, None, None):
pass
def testEmptyScopeName(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
with ops.op_scope([a, b], "") as scope:
self.assertEqual("", scope)
self.assertEqual(g0, ops.get_default_graph())
with ops.op_scope([a, b], "", "my_default_scope") as scope:
self.assertEqual("", scope)
self.assertEqual(g0, ops.get_default_graph())
def testDefaultScopeName(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
scope_name = "my_scope"
default_scope_name = "my_default_scope"
with ops.op_scope([a, b], scope_name, default_scope_name) as scope:
self.assertEqual("%s/" % scope_name, scope)
self.assertEqual(g0, ops.get_default_graph())
with ops.op_scope([a, b], None, default_scope_name) as scope:
self.assertEqual("%s/" % default_scope_name, scope)
self.assertEqual(g0, ops.get_default_graph())
def _testGraphElements(self, graph_elements):
scope_name = "my_scope"
with ops.op_scope(graph_elements, scope_name) as scope:
self.assertEqual("%s/" % scope_name, scope)
self.assertEqual(graph_elements[0].graph, ops.get_default_graph())
g1 = ops.Graph()
c = g1.create_op("c", [], [dtypes.float32])
with self.assertRaises(ValueError):
with ops.op_scope(graph_elements + [c], scope_name):
pass
def testTensor(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
self._testGraphElements([a, b])
def testSparseTensor(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
sparse = ops.SparseTensor(
_apply_op(g0, "const", [], [dtypes.int64]),
_apply_op(g0, "const", [], [dtypes.float32]),
_apply_op(g0, "const", [], [dtypes.int64]))
self._testGraphElements([a, sparse, b])
def testVariable(self):
g0 = ops.Graph()
with g0.as_default():
variable = variables.Variable([1.0])
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
self._testGraphElements([a, variable, b])
class GraphTest(test_util.TensorFlowTestCase):
def setUp(self):
@ -835,27 +910,6 @@ class GraphTest(test_util.TensorFlowTestCase):
with self.assertRaises(TypeError):
g.as_graph_element(NonConvertibleObj())
def testAssertSameGraph(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
ops.assert_same_graph([a, b])
ops.assert_same_graph([a, b], g0)
g1 = ops.Graph()
c = g1.create_op("c", [], [dtypes.float32])
self.assertRaises(ValueError, ops.assert_same_graph, [a, b, c])
self.assertRaises(ValueError, ops.assert_same_graph, [c], g0)
self.assertRaises(ValueError, ops.assert_same_graph, [a], g1)
sparse = ops.SparseTensor(
_apply_op(g0, "const", [], [dtypes.int64]),
_apply_op(g0, "const", [], [dtypes.float32]),
_apply_op(g0, "const", [], [dtypes.int64]))
ops.assert_same_graph([sparse, a, b])
ops.assert_same_graph([sparse, a, b], g0)
self.assertRaises(ValueError, ops.assert_same_graph, [sparse, a, c])
self.assertRaises(ValueError, ops.assert_same_graph, [sparse, a, c], g1)
ops.RegisterShape("KernelLabel")(common_shapes.scalar_shape)

4
tensorflow/python/ops/op_def_library.py
View File

@ -616,6 +616,10 @@ class OpDefLibrary(object):
elif attr_def.type == "list(tensor)":
attr_value.list.tensor.extend(
[_MakeTensor(x, key) for x in value])
elif attr_def.type == "func":
if not isinstance(value, compat.bytes_or_text_types):
raise TypeError("Expects a string for the func name")
attr_value.func.name = value
else:
raise TypeError("Unrecognized Attr type " + attr_def.type)