Remove experimental.PeepholeLSTMCell from the API.

PiperOrigin-RevId: 429503438

Author: fchollet

tensorflow_model_optimization/python/core/clustering/keras/cluster_integration_test.py

@@ -463,30 +463,6 @@ def testClusterGRU(self):
 
     self._train(stripped_model)
 
-  @keras_parameterized.run_all_keras_modes
-  def testClusterPeepholeLSTM(self):
-    model = keras.models.Sequential()
-    model.add(
-        keras.layers.Embedding(self.max_features, 16, input_length=self.maxlen))
-    model.add(keras.layers.RNN(tf.keras.experimental.PeepholeLSTMCell(16)))
-    model.add(keras.layers.Dense(1))
-    model.add(keras.layers.Activation("sigmoid"))
-
-    self._clusterTrainStrip(model)
-
-    self._assertNbUniqueWeights(
-        weight=model.layers[1].cell.kernel,
-        expected_unique_weights=self.params_clustering["number_of_clusters"],
-    )
-    self._assertNbUniqueWeights(
-        weight=model.layers[1].cell.recurrent_kernel,
-        expected_unique_weights=self.params_clustering["number_of_clusters"],
-    )
-    self._assertNbUniqueWeights(
-        weight=model.layers[0].embeddings,
-        expected_unique_weights=self.params_clustering["number_of_clusters"],
-    )
-
   @keras_parameterized.run_all_keras_modes
   def testClusterBidirectional(self):
     model = keras.models.Sequential()

tensorflow_model_optimization/python/core/clustering/keras/clustering_registry.py

@@ -89,7 +89,6 @@ class ClusteringRegistry(object):
       tf.compat.v2.keras.layers.SimpleRNNCell,
       tf.compat.v1.keras.layers.StackedRNNCells,
       tf.compat.v2.keras.layers.StackedRNNCells,
-      tf.keras.experimental.PeepholeLSTMCell,
   })
 
   _SUPPORTED_RNN_LAYERS = frozenset([

tensorflow_model_optimization/python/core/clustering/keras/clustering_registry_test.py

@@ -472,19 +472,6 @@ def testMakeClusterableWorksOnKerasRNNLayerWithRNNCellsParams(self):
                          layer.cell.cells[0].recurrent_kernel)]
     self.assertEqual(expected_weights[0], layer.get_clusterable_weights()[0])
 
-  def testMakeClusterableWorksOnKerasStackedRNNLayerWithPeepholeLSTMCell(self):
-    """Test stacked RNN layer with peephole LSTM cell.
-
-    Verifies that make_clusterable() works as expected on a built-in
-    RNN layer with a PeepholeLSTMCell
-    """
-    cell1 = layers.LSTMCell(10)
-    cell2 = keras.experimental.PeepholeLSTMCell(10)
-    cell_list = tf.keras.layers.StackedRNNCells([cell1, cell2])
-    layer = layers.RNN(cell_list)
-    with self.assertRaises(AttributeError):
-      layer.get_clusterable_weights()
-
     ClusterRegistry.make_clusterable(layer)
     keras.Sequential([layer]).build(input_shape=(2, 3, 4))
 
@@ -511,27 +498,6 @@ def testMakeClusterableWorksOnKerasBidirectionalLayerWithLSTM(self):
                          layer.forward_layer.cell.recurrent_kernel)]
     self.assertEqual(expected_weights[0], layer.get_clusterable_weights()[0])
 
-  def testMakeClusterableWorksOnRNNLayerWithPeepholeLSTMCell(self):
-    """Test RNN with peephole LSTM cell.
-
-    Verifies that make_clusterable() works as expected on a built-in
-    RNN layer with a PeepholeLSTMCell
-    """
-    cell1 = layers.LSTMCell(10)
-    cell2 = keras.experimental.PeepholeLSTMCell(10)
-    layer = layers.RNN([cell1, cell2])
-
-    with self.assertRaises(AttributeError):
-      layer.get_clusterable_weights()
-
-    ClusterRegistry.make_clusterable(layer)
-    keras.Sequential([layer]).build(input_shape=(2, 3, 4))
-
-    expected_weights = [('kernel/0', layer.cell.cells[0].kernel),
-                        ('recurrent_kernel/0',
-                         layer.cell.cells[0].recurrent_kernel)]
-    self.assertEqual(expected_weights[0], layer.get_clusterable_weights()[0])
-
   def testMakeClusterableDoesNotWorksOnKerasRNNLayerWithClusterableCell(self):
     """A built-in RNN layer with custom clusterable RNN cell is not clusterable."""
     cell1 = layers.LSTMCell(10)

tensorflow_model_optimization/python/core/sparsity/keras/prune_integration_test.py

@@ -469,7 +469,6 @@ def testRNNLayersWithRNNCellParams_ReachesTargetSparsity(self):
             keras.layers.RNN([
                 layers.LSTMCell(10),
                 layers.GRUCell(10),
-                tf.keras.experimental.PeepholeLSTMCell(10),
                 layers.SimpleRNNCell(10)
             ]),
             input_shape=(3, 4),

tensorflow_model_optimization/python/core/sparsity/keras/prune_registry.py

@@ -112,12 +112,6 @@ class PruneRegistry(object):
       tf.compat.v2.keras.layers.GRUCell: ['kernel', 'recurrent_kernel'],
       tf.compat.v1.keras.layers.LSTMCell: ['kernel', 'recurrent_kernel'],
       tf.compat.v2.keras.layers.LSTMCell: ['kernel', 'recurrent_kernel'],
-      tf.compat.v1.keras.experimental.PeepholeLSTMCell: [
-          'kernel', 'recurrent_kernel'
-      ],
-      tf.compat.v2.keras.experimental.PeepholeLSTMCell: [
-          'kernel', 'recurrent_kernel'
-      ],
       tf.compat.v1.keras.layers.SimpleRNNCell: ['kernel', 'recurrent_kernel'],
       tf.compat.v2.keras.layers.SimpleRNNCell: ['kernel', 'recurrent_kernel'],
   }

tensorflow_model_optimization/python/core/sparsity/keras/prune_registry_test.py

@@ -83,7 +83,6 @@ class PruneRegistryTest(tf.test.TestCase, parameterized.TestCase):
       layers.RNN([
           layers.LSTMCell(10),
           layers.GRUCell(10),
-          keras.experimental.PeepholeLSTMCell(10),
           layers.SimpleRNNCell(10)
       ]),
       keras.layers.RNN(MinimalRNNCellPrunable(32)),

tensorflow_model_optimization/python/examples/clustering/keras/imdb/imdb_multiple_cells.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""End-to-end tests for StackedRNNCells and PeepholeLSTMCell.
+"""End-to-end tests for StackedRNNCells.
 
 The dataset is actually too small for LSTM to be of any advantage
 compared to simpler, much faster methods such as TF-IDF+LogReg.
@@ -32,22 +32,6 @@
 
 x_train, y_train, x_test, y_test = prepare_dataset()
 
-print("Build a model with the StackedRNNCells with PeepholeLSTMCell...")
-model = tf.keras.models.Sequential()
-
-model.add(tf.keras.layers.Embedding(max_features, 128, input_length=maxlen))
-model.add(
-    tf.keras.layers.RNN(
-        tf.keras.layers.StackedRNNCells(
-            [tf.keras.experimental.PeepholeLSTMCell(128) for _ in range(2)])))
-model.add(tf.keras.layers.Dropout(0.5))
-model.add(tf.keras.layers.Dense(1))
-model.add(tf.keras.layers.Activation("sigmoid"))
-
-test_case = "StackedRNNCells_PeepholeLSTMCell"
-cluster_train_eval_strip(
-    model, x_train, y_train, x_test, y_test, batch_size, test_case)
-
 print("Build a model with the StackedRNNCells with LSTMCell...")
 model = tf.keras.models.Sequential()