[T5, TF 2.2] change tf t5 argument naming (#3547)

* change tf t5 argument naming for TF 2.2

* correct bug in testing

src/transformers/modeling_tf_t5.py

@@ -592,8 +592,8 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
         input_ids = tf.constant(DUMMY_INPUTS)
         input_mask = tf.constant(DUMMY_MASK)
         dummy_inputs = {
+            "inputs": input_ids,
             "decoder_input_ids": input_ids,
-            "input_ids": input_ids,
             "decoder_attention_mask": input_mask,
         }
         return dummy_inputs
@@ -637,11 +637,9 @@ T5_START_DOCSTRING = r"""    The T5 model was proposed in
 
 T5_INPUTS_DOCSTRING = r"""
     Args:
-        decoder_input_ids are usually used as a `dict` (see T5 description above for more information) containing all the following.
-        decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
-            Provide for sequence to sequence training. T5 uses the pad_token_id as the starting token for decoder_input_ids generation.
+        inputs are usually used as a `dict` (see T5 description above for more information) containing all the following.
 
-        input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
+        inputs (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
             Indices of input sequence tokens in the vocabulary.
             T5 is a model with relative position embeddings so you should be able to pad the inputs on
             the right or the left.
@@ -650,6 +648,8 @@ T5_INPUTS_DOCSTRING = r"""
             `T5 Training <./t5.html#training>`_ .
             See :func:`transformers.PreTrainedTokenizer.encode` and
             :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+        decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
+            Provide for sequence to sequence training. T5 uses the pad_token_id as the starting token for decoder_input_ids generation.
         attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
@@ -706,7 +706,7 @@ class TFT5Model(TFT5PreTrainedModel):
         return self.shared
 
     @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
-    def call(self, decoder_input_ids, **kwargs):
+    def call(self, inputs, **kwargs):
         r"""
     Return:
         :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
@@ -736,13 +736,13 @@ class TFT5Model(TFT5PreTrainedModel):
 
         """
 
-        if isinstance(decoder_input_ids, dict):
-            kwargs.update(decoder_input_ids)
+        if isinstance(inputs, dict):
+            kwargs.update(inputs)
         else:
-            kwargs["decoder_input_ids"] = decoder_input_ids
+            kwargs["inputs"] = inputs
 
         # retrieve arguments
-        input_ids = kwargs.get("input_ids", None)
+        input_ids = kwargs.get("inputs", None)
         decoder_input_ids = kwargs.get("decoder_input_ids", None)
         attention_mask = kwargs.get("attention_mask", None)
         encoder_outputs = kwargs.get("encoder_outputs", None)
@@ -803,7 +803,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
         return self.encoder
 
     @add_start_docstrings_to_callable(T5_INPUTS_DOCSTRING)
-    def call(self, decoder_input_ids, **kwargs):
+    def call(self, inputs, **kwargs):
         r"""
     Return:
         :obj:`tuple(tf.Tensor)` comprising various elements depending on the configuration (:class:`~transformers.T5Config`) and inputs.
@@ -839,13 +839,13 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
 
         """
 
-        if isinstance(decoder_input_ids, dict):
-            kwargs.update(decoder_input_ids)
+        if isinstance(inputs, dict):
+            kwargs.update(inputs)
         else:
-            kwargs["decoder_input_ids"] = decoder_input_ids
+            kwargs["inputs"] = inputs
 
         # retrieve arguments
-        input_ids = kwargs.get("input_ids", None)
+        input_ids = kwargs.get("inputs", None)
         decoder_input_ids = kwargs.get("decoder_input_ids", None)
         attention_mask = kwargs.get("attention_mask", None)
         encoder_outputs = kwargs.get("encoder_outputs", None)
@@ -890,7 +890,8 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel):
             encoder_outputs = (past,)
 
         return {
-            "inputs": input_ids,
+            "inputs": None,  # inputs don't have to be defined, but still need to be passed to make Keras.layer.__call__ happy
+            "decoder_input_ids": input_ids,  # input_ids are the decoder_input_ids
             "encoder_outputs": encoder_outputs,
             "attention_mask": attention_mask,
         }

tests/test_modeling_tf_common.py

@@ -162,6 +162,10 @@ class TFModelTesterMixin:
             pt_inputs_dict = dict(
                 (name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
             )
+            # need to rename encoder-decoder "inputs" for PyTorch
+            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
+                pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
+
             with torch.no_grad():
                 pto = pt_model(**pt_inputs_dict)
             tfo = tf_model(inputs_dict, training=False)
@@ -201,6 +205,10 @@ class TFModelTesterMixin:
             pt_inputs_dict = dict(
                 (name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
             )
+            # need to rename encoder-decoder "inputs" for PyTorch
+            if "inputs" in pt_inputs_dict and self.is_encoder_decoder:
+                pt_inputs_dict["input_ids"] = pt_inputs_dict.pop("inputs")
+
             with torch.no_grad():
                 pto = pt_model(**pt_inputs_dict)
             tfo = tf_model(inputs_dict)
@@ -223,7 +231,7 @@ class TFModelTesterMixin:
         if self.is_encoder_decoder:
             input_ids = {
                 "decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"),
-                "input_ids": tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32"),
+                "inputs": tf.keras.Input(batch_shape=(2, 2000), name="inputs", dtype="int32"),
             }
         else:
             input_ids = tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32")
@@ -259,7 +267,7 @@ class TFModelTesterMixin:
             outputs_dict = model(inputs_dict)
 
             inputs_keywords = copy.deepcopy(inputs_dict)
-            input_ids = inputs_keywords.pop("input_ids" if not self.is_encoder_decoder else "decoder_input_ids", None,)
+            input_ids = inputs_keywords.pop("input_ids" if not self.is_encoder_decoder else "inputs", None,)
             outputs_keywords = model(input_ids, **inputs_keywords)
 
             output_dict = outputs_dict[0].numpy()
@@ -395,9 +403,9 @@ class TFModelTesterMixin:
             input_ids = inputs_dict["input_ids"]
             del inputs_dict["input_ids"]
         else:
-            encoder_input_ids = inputs_dict["input_ids"]
+            encoder_input_ids = inputs_dict["inputs"]
             decoder_input_ids = inputs_dict["decoder_input_ids"]
-            del inputs_dict["input_ids"]
+            del inputs_dict["inputs"]
             del inputs_dict["decoder_input_ids"]
 
         for model_class in self.all_model_classes:
@@ -415,7 +423,7 @@ class TFModelTesterMixin:
     def test_lm_head_model_random_generate(self):
 
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        input_ids = inputs_dict["input_ids"]
+        input_ids = inputs_dict["input_ids"] if "input_ids" in inputs_dict else inputs_dict["inputs"]
 
         if self.is_encoder_decoder:
             config.output_past = True  # needed for Bart TODO: might have to update for other encoder-decoder models

tests/test_modeling_tf_t5.py

@@ -107,13 +107,15 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
         def create_and_check_t5_model(self, config, input_ids, input_mask, token_labels):
             model = TFT5Model(config=config)
             inputs = {
-                "input_ids": input_ids,
+                "inputs": input_ids,
                 "decoder_input_ids": input_ids,
                 "decoder_attention_mask": input_mask,
             }
             encoder_output, decoder_output = model(inputs)
 
-            encoder_output, decoder_output = model(input_ids, decoder_attention_mask=input_mask, input_ids=input_ids)
+            encoder_output, decoder_output = model(
+                input_ids, decoder_attention_mask=input_mask, decoder_input_ids=input_ids
+            )
 
             result = {
                 "encoder_output": encoder_output.numpy(),
@@ -129,7 +131,7 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
         def create_and_check_t5_with_lm_head(self, config, input_ids, input_mask, token_labels):
             model = TFT5ForConditionalGeneration(config=config)
             inputs_dict = {
-                "input_ids": input_ids,
+                "inputs": input_ids,
                 "decoder_input_ids": input_ids,
                 "decoder_attention_mask": input_mask,
             }
@@ -147,7 +149,7 @@ class TFT5ModelTest(TFModelTesterMixin, unittest.TestCase):
             config_and_inputs = self.prepare_config_and_inputs()
             (config, input_ids, input_mask, token_labels) = config_and_inputs
             inputs_dict = {
-                "input_ids": input_ids,
+                "inputs": input_ids,
                 "decoder_input_ids": input_ids,
                 "decoder_attention_mask": input_mask,
             }