Fix TF CTRL model naming (#6134)

setup.py

@@ -70,14 +70,14 @@ extras["sklearn"] = ["scikit-learn"]
 
 # keras2onnx and onnxconverter-common version is specific through a commit until 1.7.0 lands on pypi
 extras["tf"] = [
-    "tensorflow<=2.2",
+    "tensorflow",
     # "onnxconverter-common",
     # "keras2onnx"
     "onnxconverter-common @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
     "keras2onnx @ git+git://github.com/onnx/keras-onnx.git@cbdc75cb950b16db7f0a67be96a278f8d2953b48#egg=keras2onnx"
 ]
 extras["tf-cpu"] = [
-    "tensorflow-cpu<=2.2",
+    "tensorflow-cpu",
     # "onnxconverter-common",
     # "keras2onnx"
     "onnxconverter-common @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
@@ -86,7 +86,7 @@ extras["tf-cpu"] = [
 extras["torch"] = ["torch"]
 
 extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
-extras["all"] = extras["serving"] + ["tensorflow<=2.2", "torch"]
+extras["all"] = extras["serving"] + ["tensorflow", "torch"]
 
 extras["testing"] = ["pytest", "pytest-xdist", "timeout-decorator", "psutil"]
 # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
@@ -97,7 +97,7 @@ extras["quality"] = [
     "isort @ git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort",
     "flake8",
 ]
-extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3<1", "scikit-learn", "tensorflow<=2.2", "torch"]
+extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3<1", "scikit-learn", "tensorflow", "torch"]
 
 setup(
     name="transformers",

src/transformers/modeling_tf_ctrl.py

@@ -141,11 +141,18 @@ class TFMultiHeadAttention(tf.keras.layers.Layer):
         return outputs
 
 
-def point_wise_feed_forward_network(d_model_size, dff, name=""):
-    return tf.keras.Sequential(
-        [tf.keras.layers.Dense(dff, activation="relu", name="0"), tf.keras.layers.Dense(d_model_size, name="2")],
-        name="ffn",
-    )
+class TFPointWiseFeedForwardLayer(tf.keras.layers.Layer):
+    def __init__(self, d_model_size, dff, **kwargs):
+        super().__init__(**kwargs)
+
+        self.dense_0 = tf.keras.layers.Dense(dff, activation="relu", name="0")
+        self.dense_2 = tf.keras.layers.Dense(d_model_size, name="2")
+
+    def call(self, inputs, trainable=False):
+        dense_0_output = self.dense_0(inputs)
+        dense_2_output = self.dense_2(dense_0_output)
+
+        return dense_2_output
 
 
 class TFEncoderLayer(tf.keras.layers.Layer):
@@ -153,7 +160,7 @@ class TFEncoderLayer(tf.keras.layers.Layer):
         super().__init__(**kwargs)
 
         self.multi_head_attention = TFMultiHeadAttention(d_model_size, num_heads, name="multi_head_attention")
-        self.ffn = point_wise_feed_forward_network(d_model_size, dff, name="ffn")
+        self.ffn = TFPointWiseFeedForwardLayer(d_model_size, dff, name="ffn")
 
         self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm1")
         self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm2")