Step 1 toward automatically assigning # of TPU cores.

Also fix the API as num_cores is not a good API. Changed to a debugging oriented argument using_single_core. Ideally we should not need that in future.

The updated lstm example cannot work with TF 1.10 anymore.

PiperOrigin-RevId: 210632592

Author: Jianwei Xie

tensorflow/contrib/tpu/python/tpu/keras_support.py

@@ -61,6 +61,7 @@
 from tensorflow.contrib.tpu.python.tpu import tpu
 from tensorflow.contrib.tpu.python.tpu import tpu_function
 from tensorflow.contrib.tpu.python.tpu import tpu_optimizer
+from tensorflow.contrib.tpu.python.tpu import tpu_system_metadata as tpu_system_metadata_lib
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python.client import session as tf_session
 from tensorflow.python.data.ops import dataset_ops
@@ -80,7 +81,6 @@
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import tf_logging as logging
-from tensorflow.python.util import tf_inspect
 
 
 _SESSIONS = {}
@@ -110,31 +110,52 @@ def reset_tpu_sessions():
   _SESSIONS.clear()
 
 
-# Work-around dependency cycle between DistributionStrategy and TPU lib.
-def TPUDistributionStrategy(tpu_cluster_resolver=None, num_cores=None):  # pylint: disable=invalid-name
-  """Construct a TPUDistributionStrategy."""
-  from tensorflow.contrib.distribute.python import tpu_strategy  # pylint: disable=g-import-not-at-top
-  # TODO(b/112705069): Remove this when TPUStrategy API is consistent.
-  # We are including this for (a) backwards compatibility for open sourced
-  # releases of TensorFlow and (b) to work around a circular dependency
-  # where keras_support and tpu_strategy depends on each other. Once we release
-  # a final version and remove support for the old API, this will be deleted.
-  # (See bug above for more details)
-  if tpu_cluster_resolver is None:
-    tpu_cluster_resolver = tpu_cluster_resolver_lib.TPUClusterResolver('')
-
-  args, _, _, _ = tf_inspect.getargspec(tpu_strategy.TPUStrategy.__init__)
-  if len(args) == 4:
-    logging.info('Detected new TPUStrategy API.')
-    return tpu_strategy.TPUStrategy(tpu_cluster_resolver,
-                                    steps_per_run=1,
-                                    num_cores=num_cores)
-  else:
-    logging.info('Detected old TPUStrategy API.')
-    strategy = tpu_strategy.TPUStrategy(num_cores_per_host=8)
-    strategy._tpu_cluster_resolver = tpu_cluster_resolver
+def get_tpu_system_metadata(tpu_cluster_resolver):
+  """Retrieves TPU system metadata given a TPUClusterResolver."""
+  master = tpu_cluster_resolver.master()
+
+  # pylint: disable=protected-access
+  cluster_spec = tpu_cluster_resolver.cluster_spec()
+  cluster_def = cluster_spec.as_cluster_def() if cluster_spec else None
+  tpu_system_metadata = (
+      tpu_system_metadata_lib._query_tpu_system_metadata(
+          master,
+          cluster_def=cluster_def,
+          query_topology=False))
+
+  return tpu_system_metadata
+
+
+class TPUDistributionStrategy(object):
+  """The strategy to run Keras model on TPU."""
+
+  def __init__(self, tpu_cluster_resolver=None, using_single_core=False):
+    """Construct a TPUDistributionStrategy.
+
+    Args:
+      tpu_cluster_resolver: Any instance of `TPUClusterResolver`. If None, will
+        create one with '' as master address.
+      using_single_core: Bool. This is the debugging option, which might be
+        removed in future once the model replication functionality is mature
+        enough. If `False` (default behavior), the system automatically finds
+        the best configuration, in terms of number of TPU cores, for the model
+        replication, typically using all avaiable TPU cores. If overwrites as
+        `True`, force the model replication using single core, i.e., no
+        replication.
+    """
 
-  return strategy
+    if tpu_cluster_resolver is None:
+      tpu_cluster_resolver = tpu_cluster_resolver_lib.TPUClusterResolver('')
+
+    num_cores = (1 if using_single_core else
+                 get_tpu_system_metadata(tpu_cluster_resolver).num_cores)
+
+    self._tpu_cluster_resolver = tpu_cluster_resolver
+    self._num_cores = num_cores
+
+  @property
+  def num_towers(self):
+    return self._num_cores
 
 
 class TPUEmbedding(embeddings.Embedding):
@@ -1212,5 +1233,10 @@ def tpu_model(model, strategy=None):
 
   if strategy is None:
     strategy = TPUDistributionStrategy()
+  else:
+    if not isinstance(strategy, TPUDistributionStrategy):
+      raise TypeError(
+          '`strategy` must have type `tf.contrib.tpu.TPUDistributionStrategy`. '
+          'Got: {}'.format(type(strategy)))
 
   return KerasTPUModel(cpu_model=model, strategy=strategy)