Add generate() functionality to TF 2.0 (#3063)

* add first copy past test to tf 2 generate

* add tf top_k_top_p_filter fn

* add generate function for TF

* add generate function for TF

* implemented generate for all models expect transfoXL

* implemented generate for all models expect transfoXL

* implemented generate for all models expect transfoXL

* make style

* change permission of test file to correct ones

* delete ipdb

* delete ipdb

* fix bug and finish simple gpt2 integration test

* clean test file

* clean test file

* make style

* make style

* make style

* make style

* change import style

* change import style

* make style

* make style

* add decorators

* add decorators

* fix tf ctrl bug dim => axis in TF

* make style

* make style

* refactored test file

* refactored test file

* take out test_torch_tf_conversion if nothing is defined

* take out test_torch_tf_conversion if nothing is defined

* remove useless files

* remove useless files

* fix conflicts

* fix conflicts

* fix conflicts

* fix conflicts

* fix conflicts

* solve conflicts

* solve conflicts

* fix conflicts

* fix conflicts

* merge conflicts

* delete ipdb

* exposed top_k_top_p_filtering fns

* delete weirdly created w! file

* add comment to test tf common modeling

* fix conflicts

* fix conflicts

* make style

* merge conflicts

* make style

* change tf.tensor.shape to shape_list(tensor)

src/transformers/modeling_tf_ctrl.py

@@ -105,8 +105,8 @@ class TFMultiHeadAttention(tf.keras.layers.Layer):
         v = self.split_into_heads(v, batch_size)
         if layer_past is not None:
             past_key, past_value = tf.unstack(layer_past, axis=1)
-            k = tf.concat((past_key, k), dim=-2)
-            v = tf.concat((past_value, v), dim=-2)
+            k = tf.concat((past_key, k), axis=-2)
+            v = tf.concat((past_value, v), axis=-2)
         present = tf.stack((k, v), axis=1)
 
         output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
@@ -505,6 +505,13 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
     def get_output_embeddings(self):
         return self.lm_head.input_embeddings
 
+    def prepare_inputs_for_generation(self, inputs, past, **kwargs):
+        # only last token for inputs_ids if past is defined in kwargs
+        if past:
+            inputs = tf.expand_dims(inputs[:, -1], -1)
+
+        return {"inputs": inputs, "past": past}
+
     @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
     def call(self, inputs, **kwargs):
         r"""