Sync SyntaxNet with TensorFlow r1.0 (#1062)

* Sync SyntaxNet with TensorFlow r1.0

* Fix typo back

* Fix Dockerfile to match TensorFlow 1.0

syntaxnet/Dockerfile

@@ -15,7 +15,7 @@ RUN mkdir -p $SYNTAXNETDIR \
     && ./bazel-0.4.3-installer-linux-x86_64.sh --user \
     && git clone --recursive https://github.com/tensorflow/models.git \
     && cd $SYNTAXNETDIR/models/syntaxnet/tensorflow \
-    && echo -e "\n\n\n\n\n\n" | ./configure \
+    && echo -e "\n\n\n\n\n\n\n\n\n" | ./configure \
     && apt-get autoremove -y \
     && apt-get clean
 

syntaxnet/README.md

@@ -628,9 +628,11 @@ Original authors of the code in this package include (in alphabetical order):
 *   David Weiss
 *   Emily Pitler
 *   Greg Coppola
+*   Ivan Bogatyy
 *   Ji Ma
 *   Keith Hall
 *   Kuzman Ganchev
+*   Livio Baldini Soares
 *   Michael Collins
 *   Michael Ringgaard
 *   Ryan McDonald

syntaxnet/WORKSPACE

@@ -4,8 +4,10 @@ local_repository(
 )
 
 load("@org_tensorflow//tensorflow:workspace.bzl", "tf_workspace")
-tf_workspace()
+tf_workspace(path_prefix="", tf_repo_name="org_tensorflow")
 
-# Specify the minimum required Bazel version.
-load("@org_tensorflow//tensorflow:tensorflow.bzl", "check_version")
+# Test that Bazel is the correct release, tested for compatibility with the
+# current SyntaxNet snapshot.
+load("//syntaxnet:syntaxnet.bzl", "check_version")
 check_version("0.4.3")
+

syntaxnet/syntaxnet/graph_builder.py

@@ -69,7 +69,7 @@ def EmbeddingLookupFeatures(params, sparse_features, allow_weights):
 
   if allow_weights:
     # Multiply by weights, reshaping to allow broadcast.
-    broadcast_weights_shape = tf.concat_v2([tf.shape(weights), [1]], 0)
+    broadcast_weights_shape = tf.concat([tf.shape(weights), [1]], 0)
     embeddings *= tf.reshape(weights, broadcast_weights_shape)
 
   # Sum embeddings by index.
@@ -251,7 +251,7 @@ class GreedyParser(object):
         self._averaging[name + '_avg_update'] = ema.apply([param])
         self.variables[name + '_avg_var'] = ema.average(param)
         self.inits[name + '_avg_init'] = state_ops.init_variable(
-            ema.average(param), tf.zeros_initializer)
+            ema.average(param), tf.zeros_initializer())
     return (self.variables[name + '_avg_var'] if return_average else
             self.params[name])
 
@@ -330,7 +330,7 @@ class GreedyParser(object):
                                            i,
                                            return_average=return_average))
 
-    last_layer = tf.concat_v2(embeddings, 1)
+    last_layer = tf.concat(embeddings, 1)
     last_layer_size = self.embedding_size
 
     # Create ReLU layers.
@@ -364,7 +364,7 @@ class GreedyParser(object):
         [self._num_actions],
         tf.float32,
         'softmax_bias',
-        tf.zeros_initializer,
+        tf.zeros_initializer(),
         return_average=return_average)
     logits = tf.nn.xw_plus_b(last_layer,
                              softmax_weight,
@@ -530,7 +530,7 @@ class GreedyParser(object):
       for param in trainable_params:
         slot = optimizer.get_slot(param, 'momentum')
         self.inits[slot.name] = state_ops.init_variable(slot,
-                                                        tf.zeros_initializer)
+                                                        tf.zeros_initializer())
         self.variables[slot.name] = slot
       numerical_checks = [
           tf.check_numerics(param,

syntaxnet/syntaxnet/structured_graph_builder.py

@@ -37,7 +37,7 @@ def AddCrossEntropy(batch_size, n):
 
   for beam_id in range(batch_size):
     beam_gold_slot = tf.reshape(
-        tf.strided_slice(n['gold_slot'], [beam_id], [beam_id + 1], [1]), [1])
+        tf.strided_slice(n['gold_slot'], [beam_id], [beam_id + 1]), [1])
     def _ComputeCrossEntropy():
       """Adds ops to compute cross entropy of the gold path in a beam."""
       # Requires a cast so that UnsortedSegmentSum, in the gradient,
@@ -144,7 +144,7 @@ class StructuredGraphBuilder(graph_builder.GreedyParser):
       n = self.training
       n['accumulated_alive_steps'] = self._AddVariable(
           [batch_size], tf.int32, 'accumulated_alive_steps',
-          tf.zeros_initializer)
+          tf.zeros_initializer())
       n.update(self._AddBeamReader(task_context, batch_size, corpus_name))
       # This adds a required 'step' node too:
       learning_rate = tf.constant(learning_rate, dtype=tf.float32)
@@ -200,7 +200,7 @@ class StructuredGraphBuilder(graph_builder.GreedyParser):
       for param in trainable_params.values():
         slot = optimizer.get_slot(param, 'momentum')
         self.inits[slot.name] = state_ops.init_variable(slot,
-                                                        tf.zeros_initializer)
+                                                        tf.zeros_initializer())
         self.variables[slot.name] = slot
 
       def NumericalChecks():

syntaxnet/syntaxnet/syntaxnet.bzl

@@ -16,6 +16,15 @@
 load("@protobuf//:protobuf.bzl", "cc_proto_library")
 load("@protobuf//:protobuf.bzl", "py_proto_library")
 
+def check_version(expected_version):
+  current_version = native.bazel_version.split(" ")[0].split("-")[0]
+  if current_version != expected_version:
+    fail("\nCurrent Bazel version is {}, expected {}.\n".format(
+             current_version, expected_version) +
+         "To try anyway, remove check_version() call from syntaxnet/WORKSPACE")
+  else:
+    print("Bazel OK")
+
 def if_cuda(if_true, if_false = []):
     """Shorthand for select()'ing on whether we're building with CUDA."""
     return select({

tensorflow @ 950db43b

-Subproject commit 45ab528211c962b19e12f6b77165848310271624
+Subproject commit 950db43bfce2110767d832138438f266732adca6