[FIX] upgrade to tf 2.16.2

tensorflow_recommenders_addons/dynamic_embedding/core/kernels/segment_reduction_ops.h

@@ -19,8 +19,11 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/tensor_types.h"
+#if TF_VERSION_INTEGER >= 2160
+#include "unsupported/Eigen/CXX11/Tensor"
+#else
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-
+#endif
 namespace tensorflow {
 
 class OpKernelContext;

tensorflow_recommenders_addons/dynamic_embedding/core/kernels/segment_reduction_ops_impl.h

@@ -41,9 +41,13 @@ limitations under the License.
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/util/util.h"
+#if TF_VERSION_INTEGER >= 2160
+#include "Eigen/Core"
+#include "unsupported/Eigen/CXX11/Tensor"
+#else
 #include "third_party/eigen3/Eigen/Core"
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-
+#endif
 #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
 #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h"
 #endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

tensorflow_recommenders_addons/dynamic_embedding/python/keras/callbacks_test.py

@@ -1,15 +1,13 @@
 import pytest
+from tensorflow.python.framework import test_util
+
 from tensorflow_recommenders_addons.dynamic_embedding.python.keras.callbacks import \
   DEHvdBroadcastGlobalVariablesCallback
 
 
-@pytest.fixture
-def broadcast_callback(root_rank=0, device='/gpu:0'):
-  # Instantiate with the corrected parameters
-  return DEHvdBroadcastGlobalVariablesCallback(root_rank=root_rank,
-                                               device=device)
-
-
-def test_on_batch_end_subsequent_calls(broadcast_callback):
-  broadcast_callback.broadcast_done = True
-  broadcast_callback.on_batch_end(1)
+def test_on_batch_end_subsequent_calls():
+  if test_util.is_gpu_available():
+    broadcast_callback = DEHvdBroadcastGlobalVariablesCallback(root_rank=0,
+                                                               device='/gpu:0')
+    broadcast_callback.broadcast_done = True
+    broadcast_callback.on_batch_end(1)

tensorflow_recommenders_addons/dynamic_embedding/python/kernel_tests/dynamic_embedding_variable_test.py

@@ -62,8 +62,13 @@
 except:
   from tensorflow.python.training.tracking.util import Checkpoint
 from tensorflow.python.util import compat
-from tensorflow_estimator.python.estimator import estimator
-from tensorflow_estimator.python.estimator import estimator_lib
+
+try:  # tf version <= 2.15
+  from tensorflow_estimator.python.estimator import estimator
+  from tensorflow_estimator.python.estimator import estimator_lib
+except:
+  # do nothing
+  pass
 
 try:  # The data_structures has been moved to the new package in tf 2.11
   from tensorflow.python.trackable import data_structures
@@ -970,52 +975,56 @@ def test_table_save_load_local_file_system(self):
 
       del table
 
-  def test_table_save_load_local_file_system_for_estimator(self):
+  try:  # only test for tensorflow <= 2.15
 
-    def input_fn():
-      return {"x": constant_op.constant([1], dtype=dtypes.int64)}
+    def test_table_save_load_local_file_system_for_estimator(self):
 
-    def model_fn(features, labels, mode, params):
-      file_system_saver = de.FileSystemSaver()
-      embedding = de.get_variable(
-          name="embedding",
-          dim=3,
-          trainable=False,
-          key_dtype=dtypes.int64,
-          value_dtype=dtypes.float32,
-          initializer=-1.0,
-          kv_creator=de.CuckooHashTableCreator(saver=file_system_saver),
-      )
-      lookup = de.embedding_lookup(embedding, features["x"])
-      upsert = embedding.upsert(features["x"],
-                                constant_op.constant([[1.0, 2.0, 3.0]]))
-
-      with ops.control_dependencies([lookup, upsert]):
-        train_op = state_ops.assign_add(training.get_global_step(), 1)
-
-      scaffold = training.Scaffold(
-          saver=saver.Saver(sharded=True,
-                            max_to_keep=1,
-                            keep_checkpoint_every_n_hours=None,
-                            defer_build=True,
-                            save_relative_paths=True))
-      est = estimator_lib.EstimatorSpec(mode=mode,
-                                        scaffold=scaffold,
-                                        loss=constant_op.constant(0.),
-                                        train_op=train_op,
-                                        predictions=lookup)
-      return est
+      def input_fn():
+        return {"x": constant_op.constant([1], dtype=dtypes.int64)}
 
-    save_dir = os.path.join(self.get_temp_dir(), "save_restore")
-    save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
+      def model_fn(features, labels, mode, params):
+        file_system_saver = de.FileSystemSaver()
+        embedding = de.get_variable(
+            name="embedding",
+            dim=3,
+            trainable=False,
+            key_dtype=dtypes.int64,
+            value_dtype=dtypes.float32,
+            initializer=-1.0,
+            kv_creator=de.CuckooHashTableCreator(saver=file_system_saver),
+        )
+        lookup = de.embedding_lookup(embedding, features["x"])
+        upsert = embedding.upsert(features["x"],
+                                  constant_op.constant([[1.0, 2.0, 3.0]]))
+
+        with ops.control_dependencies([lookup, upsert]):
+          train_op = state_ops.assign_add(training.get_global_step(), 1)
+
+        scaffold = training.Scaffold(
+            saver=saver.Saver(sharded=True,
+                              max_to_keep=1,
+                              keep_checkpoint_every_n_hours=None,
+                              defer_build=True,
+                              save_relative_paths=True))
+        est = estimator_lib.EstimatorSpec(mode=mode,
+                                          scaffold=scaffold,
+                                          loss=constant_op.constant(0.),
+                                          train_op=train_op,
+                                          predictions=lookup)
+        return est
+
+      save_dir = os.path.join(self.get_temp_dir(), "save_restore")
+      save_path = os.path.join(tempfile.mkdtemp(prefix=save_dir), "hash")
 
-    # train and save
-    est = estimator.Estimator(model_fn=model_fn, model_dir=save_path)
-    est.train(input_fn=input_fn, steps=1)
+      # train and save
+      est = estimator.Estimator(model_fn=model_fn, model_dir=save_path)
+      est.train(input_fn=input_fn, steps=1)
 
-    # restore and predict
-    predict_results = next(est.predict(input_fn=input_fn))
-    self.assertAllEqual(predict_results, [1.0, 2.0, 3.0])
+      # restore and predict
+      predict_results = next(est.predict(input_fn=input_fn))
+      self.assertAllEqual(predict_results, [1.0, 2.0, 3.0])
+  except:
+    pass
 
   def test_save_restore_only_table(self):
     if context.executing_eagerly():

tensorflow_recommenders_addons/dynamic_embedding/python/kernel_tests/horovod_sync_train_test.py

@@ -33,12 +33,10 @@
 from tensorflow.python.framework import test_util
 from tensorflow.python.framework.errors_impl import NotFoundError
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
 from tensorflow.python.training import adam
 from tensorflow.python.training import checkpoint_management
 from tensorflow.python.training import monitored_session
-from tensorflow.python.training.optimizer import Optimizer as tf1_opt
 from tensorflow.python.training import training_util
 try:
   from tensorflow.keras.optimizers.legacy import Adam
@@ -77,7 +75,8 @@ def test_adam_minimize_trainable(self):
       self.skipTest(
           "Apple silicon devices don't support synchronous training based on Horovod."
       )
-
+    if not is_gpu_available:
+      self.skipTest('Only test when gpu is available.')
     base_opt = adam.AdamOptimizer(1.0)
     test_opt = adam.AdamOptimizer(1.0)
     self.common_minimize_trainable_v1(base_opt, test_opt, name="adam")

tensorflow_recommenders_addons/dynamic_embedding/python/kernel_tests/warm_start_util_test.py

@@ -34,30 +34,12 @@
   kinit2 = None
   pass  # for compatible with TF < 2.3.x
 
-from tensorflow.core.protobuf import cluster_pb2
-from tensorflow.core.protobuf import config_pb2
-from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import sparse_tensor
-from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
-from tensorflow.python.keras import initializers as keras_init_ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import embedding_ops
-from tensorflow.python.ops import gen_array_ops
-from tensorflow.python.ops import init_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import resources
-from tensorflow.python.ops import script_ops
-from tensorflow.python.ops import variables
-from tensorflow.python.ops import variable_scope
 from tensorflow.python.platform import test
-from tensorflow.python.training import device_setter
 from tensorflow.python.training import saver
-from tensorflow.python.training import server_lib
-from tensorflow.python.util import compat
 from tensorflow_recommenders_addons import dynamic_embedding as de
 
 import tensorflow as tf
@@ -213,10 +195,14 @@ def test_warm_start_rename(self):
       self._test_warm_start_rename(num_shards, True)
       self._test_warm_start_rename(num_shards, False)
 
-  def test_warm_start_estimator(self):
-    for num_shards in [1, 3]:
-      self._test_warm_start_estimator(num_shards, True)
-      self._test_warm_start_estimator(num_shards, False)
+  try:  # tf version <= 2.15
+
+    def test_warm_start_estimator(self):
+      for num_shards in [1, 3]:
+        self._test_warm_start_estimator(num_shards, True)
+        self._test_warm_start_estimator(num_shards, False)
+  except:
+    print(f"estimator is not supported in this version of tensorflow")
 
 
 if __name__ == "__main__":