Merge commit for internal changes

jtoy · Jul 13, 2016 · 3cb73e9 · 3cb73e9
2 parents e56888c + c87a7ca
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diff --git a/README.md b/README.md
@@ -31,11 +31,11 @@ and discussion.**
 ## Installation
 *See [Download and Setup](tensorflow/g3doc/get_started/os_setup.md) for instructions on how to install our release binaries or how to build from source.*
 
-People who are a little bit adventurous can also try our nightly binaries:
+People who are a little more adventurous can also try our nightly binaries:
 
-* Linux CPU only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py2-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3.4](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py2-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
-* Mac CPU only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py2-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/))
+* Linux CPU-only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-cp27-none-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3.4](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-cp35-cp35m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
+* Linux GPU: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-cp27-none-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/140/artifact/pip_test/whl/tensorflow-0.8.0-cp35-cp35m-linux_x86_64.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Mac CPU-only: [Python 2](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py2-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/)) / [Python 3](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.9.0-py3-none-any.whl) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/))
 * [Android](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/lastSuccessfulBuild/artifact/bazel-out/local_linux/bin/tensorflow/examples/android/tensorflow_demo.apk) ([build history](http://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/))
 
 #### *Try your first TensorFlow program*

diff --git a/WORKSPACE b/WORKSPACE
@@ -175,7 +175,7 @@ new_git_repository(
   name = "iron_menu_behavior",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymerelements/iron-menu-behavior.git",
-  tag = "v1.1.7",
+  tag = "v1.1.8",
 )
 
 new_git_repository(
@@ -189,7 +189,7 @@ new_git_repository(
   name = "iron_overlay_behavior",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymerelements/iron-overlay-behavior.git",
-  tag = "v1.8.2",
+  tag = "v1.8.3",
 )
 
 new_git_repository(
@@ -322,7 +322,7 @@ new_git_repository(
   name = "paper_menu_button",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymerelements/paper-menu-button.git",
-  tag = "v1.1.1",
+  tag = "v1.3.0",
 )
 
 new_git_repository(

diff --git a/eigen.BUILD b/eigen.BUILD
@@ -1,9 +1,9 @@
 package(default_visibility = ["//visibility:public"])
 
-archive_dir = "eigen-eigen-bbffe8ed5f26"
+archive_dir = "eigen-eigen-b4fa9622b809"
 cc_library(
     name = "eigen",
-    hdrs = glob([archive_dir+"/**/*.h", archive_dir+"/unsupported/Eigen/CXX11/*", archive_dir+"/Eigen/*"]),
+    hdrs = glob([archive_dir+"/**/*.h", archive_dir+"/unsupported/Eigen/*", archive_dir+"/unsupported/Eigen/CXX11/*", archive_dir+"/Eigen/*"]),
     includes = [ archive_dir ],
     visibility = ["//visibility:public"],
 )
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_graph_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_graph_test.py
@@ -150,6 +150,32 @@ def testDistributionEntropy(self):
         self.assertAllEqual(entropies[2], entropies[0])
         self.assertAllEqual(entropies[1], entropies[0])
 
+  def testSurrogateLoss(self):
+    with self.test_session():
+      mu = [[3.0, -4.0, 5.0], [6.0, -7.0, 8.0]]
+      sigma = tf.constant(1.0)
+
+      # With default
+      with sg.value_type(sg.MeanValue(stop_gradient=True)):
+        dt = sg.DistributionTensor(distributions.Normal, mu=mu, sigma=sigma)
+      surrogate_loss = dt.surrogate_loss([tf.constant(2.0)])
+      self.assertTrue(surrogate_loss is not None)
+      self.assertAllClose(
+          dt.distribution.log_prob(mu).eval() * 2.0, surrogate_loss.eval())
+
+      # With passed-in surrogate_loss_fn.
+      dt = sg.DistributionTensor(
+          distributions.Normal,
+          mu=mu,
+          sigma=sigma,
+          dist_value_type=sg.MeanValue(stop_gradient=True),
+          surrogate_loss_fn=sg.get_score_function_with_baseline(
+              baseline=tf.constant(8.0)))
+      surrogate_loss = dt.surrogate_loss([tf.constant(2.0)])
+      self.assertTrue(surrogate_loss is not None)
+      self.assertAllClose((dt.distribution.log_prob(mu) * (2.0 - 8.0)).eval(),
+                          surrogate_loss.eval())
+
 
 class ValueTypeTest(tf.test.TestCase):
 
@@ -298,6 +324,16 @@ def testSurrogateLoss(self):
                 * loss_nodeps_nograd)]),
           xs=[mu, sigma])
 
+  def testNoSurrogateLoss(self):
+    with self.test_session():
+      mu = tf.constant([0.0, 0.1, 0.2])
+      sigma = tf.constant([1.1, 1.2, 1.3])
+      with sg.value_type(sg.SampleAndReshapeValue()):
+        dt = sg.DistributionTensor(NormalNotParam,
+                                   mu=mu,
+                                   sigma=sigma,
+                                   surrogate_loss_fn=None)
+        self.assertEqual(None, dt.surrogate_loss(tf.constant([2.0])))
 
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/stochastic_graph.py b/tensorflow/contrib/bayesflow/python/ops/stochastic_graph.py
@@ -27,6 +27,11 @@
 @@value_type
 @@get_current_value_type
 
+## Stochastic Computation Surrogate Loss Functions
+
+@@score_function
+@@get_score_function_with_baseline
+
 ## Stochastic Computation Graph Helper Functions
 
 @@surrogate_losses
@@ -46,6 +51,7 @@
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import tf_logging as logging
 
 STOCHASTIC_TENSOR_COLLECTION = "_stochastic_tensor_collection_"
@@ -55,9 +61,7 @@
 class StochasticTensor(object):
   """Base Class for Tensor-like objects that emit stochastic values."""
 
-  def __init__(self, **kwargs):
-    self._inputs = kwargs
-
+  def __init__(self):
     # Add self to this graph's Stochsatic Tensor collection for
     # purposes of later performing correct surrogate loss calculation.
     ops.add_to_collection(STOCHASTIC_TENSOR_COLLECTION, self)
@@ -306,11 +310,53 @@ def get_current_value_type():
   return _STOCHASTIC_VALUE_STACK[thread_id][-1]
 
 
+def get_score_function_with_baseline(baseline):
+
+  def score_function_with_baseline(dist_tensor, value, losses):
+    advantage = math_ops.add_n(losses) - baseline
+    return dist_tensor.distribution.log_prob(value) * advantage
+
+  return score_function_with_baseline
+
+
+def score_function(dist_tensor, value, losses):
+  return dist_tensor.distribution.log_prob(value) * math_ops.add_n(losses)
+
+
 class DistributionTensor(StochasticTensor):
-  """The DistributionTensor is a StochasticTensor backed by a distribution.
-  """
+  """DistributionTensor is a StochasticTensor backed by a distribution."""
+
+  def __init__(self,
+               dist_cls,
+               name=None,
+               dist_value_type=None,
+               surrogate_loss_fn=score_function,
+               **dist_args):
+    """Construct a `DistributionTensor`.
+
+    `surrogate_loss_fn` controls what `surrogate_loss` returns, which is used
+    in conjunction with the `surrogate_losses` function in this module.
+    `surrogate_loss_fn` is a callable that takes this `DistributionTensor`, a
+    `Tensor` with this `DistributionTensor`'s value, and a list of `Tensor`
+    losses influenced by this `DistributionTensor`; it should return a `Tensor`
+    surrogate loss. If not provided, it defaults to the score function
+    surrogate loss: `log_prob(value) * sum(losses)`. If `surrogate_loss_fn` is
+    None, no surrogate loss will be returned. Currently, a surrogate loss will
+    only be used if `dist_value_type.stop_gradient=True` or if the value is a
+    sample from a non-reparameterized distribution.
 
-  def __init__(self, dist_cls, name=None, dist_value_type=None, **dist_args):
+    Args:
+      dist_cls: a class deriving from `BaseDistribution`.
+      name: a name for this `DistributionTensor` and its ops.
+      dist_value_type: a `_StochasticValueType`, which will determine what the
+          `value` of this `DistributionTensor` will be. If not provided, the
+          value type set with the `value_type` context manager will be used.
+      surrogate_loss_fn: callable that takes
+          `(dt, dt.value(), influenced_losses)`, where `dt` is this
+          `DistributionTensor`, and returns a `Tensor` surrogate loss.
+      **dist_args: keyword arguments to be passed through to `dist_cls` on
+          construction.
+    """
     self._dist_cls = dist_cls
     self._dist_args = dist_args
     if dist_value_type is None:
@@ -326,6 +372,10 @@ def __init__(self, dist_cls, name=None, dist_value_type=None, **dist_args):
 
     self._value_type.declare_inputs(self, dist_args)
 
+    if surrogate_loss_fn is not None and not callable(surrogate_loss_fn):
+      raise TypeError("surrogate_loss_fn must be callable")
+    self._surrogate_loss_fn = surrogate_loss_fn
+
     with ops.op_scope(dist_args.values(), name, "DistributionTensor") as scope:
       self._name = scope
       self._dist = dist_cls(**dist_args)
@@ -374,9 +424,7 @@ def _create_value(self):
       raise TypeError(
           "Unrecognized Distribution Value Type: %s", self._value_type)
 
-    stop_gradient = self._value_type.stop_gradient
-
-    if stop_gradient:
+    if self._value_type.stop_gradient:
       # stop_gradient is being enforced by the value type
       return array_ops.stop_gradient(value_tensor)
 
@@ -411,19 +459,23 @@ def mean(self, name="mean"):
   def value(self, name="value"):
     return self._value
 
-  def surrogate_loss(self, losses, name=None):
-    # Return a loss term based on losses and the distribution.  Return
-    # None if pathwise derivatives are supported
-    if self._dist.is_continuous and self._dist.is_reparameterized:
+  def surrogate_loss(self, losses, name="DistributionSurrogateLoss"):
+    # Return a loss term based on losses and the distribution. Returns
+    # None if pathwise derivatives are supported, if the surrogate_loss_fn
+    # was explicitly set to None, or if the value type is MeanValue.
+    if self._surrogate_loss_fn is None:
+      return None
+
+    if (self._dist.is_continuous and self._dist.is_reparameterized and
+        not self._value_type.stop_gradient):
       # Can perform pathwise-derivative on this one; no surrogate loss needed.
       return None
 
-    with ops.op_scope(losses, name, "DistributionSurrogateLoss"):
-      if isinstance(self._value_type, SampleAndReshapeValue):
-        # TODO(ebrevdo): use add_n instead of sum(losses) if shapes all match?
-        return self._dist.log_prob(self._value) * sum(losses)
-      elif isinstance(self._value_type, SampleValue):
-        return self._dist.log_prob(self._value) * sum(losses)
+    with ops.op_scope(losses, name):
+      if (self._value_type.stop_gradient or
+          isinstance(self._value_type, SampleAndReshapeValue) or
+          isinstance(self._value_type, SampleValue)):
+        return self._surrogate_loss_fn(self, self._value, losses)
       elif isinstance(self._value_type, MeanValue):
         return None  # MeanValue generally provides its own gradient
       else:
@@ -512,14 +564,14 @@ def surrogate_losses(sample_losses, name="SurrogateLosses"):
           "No collection of Stochastic Tensors found for current graph.")
       return []
 
-    surrogate_loss_losses = []
+    surrogate_losses_ = []
 
     # Iterate through all of the stochastic dependencies, adding
     # surrogate terms where necessary.
     for (stoch_node, dependent_losses) in stoch_dependencies_map.items():
-      surrogate_loss = stoch_node.surrogate_loss(list(dependent_losses))
-      if surrogate_loss is not None:
+      surrogate_loss_ = stoch_node.surrogate_loss(list(dependent_losses))
+      if surrogate_loss_ is not None:
         with ops.name_scope("SurrogateLoss_%s" % stoch_node.name):
-          surrogate_loss_losses.append(array_ops.identity(surrogate_loss))
+          surrogate_losses_.append(array_ops.identity(surrogate_loss_))
 
-    return surrogate_loss_losses
+    return surrogate_losses_