added TF2 model and tests - updated templates

templates/adding_a_new_model/modeling_tf_xxx.py

@@ -26,6 +26,8 @@ import logging
 import math
 import os
 import sys
+import copy
+import itertools
 from io import open
 
 import numpy as np

templates/adding_a_new_model/modeling_xxx.py

@@ -25,6 +25,8 @@ import logging
 import math
 import os
 import sys
+import copy
+import itertools
 from io import open
 
 import torch

transformers/__init__.py

@@ -158,6 +158,9 @@ if is_tf_available():
                                     TFCTRLLMHeadModel,
                                     TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
 
+    from .modeling_tf_t5 import (TFT5PreTrainedModel, TFT5Model, TFT5WithLMHeadModel,
+                                 TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP)
+
 # TF 2.0 <=> PyTorch conversion utilities
 from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
                                         load_pytorch_checkpoint_in_tf2_model,

transformers/configuration_auto.py

@@ -27,6 +27,7 @@ from .configuration_xlm import XLMConfig
 from .configuration_roberta import RobertaConfig
 from .configuration_distilbert import DistilBertConfig
 from .configuration_ctrl import CTRLConfig
+from .configuration_t5 import T5Config
 
 logger = logging.getLogger(__name__)
 
@@ -64,6 +65,7 @@ class AutoConfig(object):
 
         The configuration class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
+            - contains `t5`: T5Config (T5 model)
             - contains `distilbert`: DistilBertConfig (DistilBERT model)
             - contains `bert`: BertConfig (Bert model)
             - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
@@ -114,7 +116,9 @@ class AutoConfig(object):
             assert unused_kwargs == {'foo': False}
 
         """
-        if 'distilbert' in pretrained_model_name_or_path:
+        if 't5' in pretrained_model_name_or_path:
+            return T5Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        elif 'distilbert' in pretrained_model_name_or_path:
             return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
             return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)

transformers/configuration_t5.py

@@ -27,8 +27,7 @@ from .configuration_utils import PretrainedConfig
 logger = logging.getLogger(__name__)
 
 T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-config.json",
-    't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-config.json",
+    't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json",
 }
 
 

transformers/modeling_t5.py

@@ -41,8 +41,7 @@ logger = logging.getLogger(__name__)
 # for the pretrained weights provided with the models
 ####################################################
 T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-pytorch_model.bin",
-    't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-pytorch_model.bin",
+    't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-pytorch_model.bin",
 }
 
 ####################################################
@@ -442,7 +441,7 @@ class T5PreTrainedModel(PreTrainedModel):
         if isinstance(module, nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(factor*1.0)
-        elif isinstance(module, T5Model):
+        elif isinstance(module, (T5Model, T5WithLMHeadModel)):
             # Mesh TensorFlow embeddings initialization
             # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
             module.shared.weight.data.normal_(mean=0.0, std=factor*1.0)
@@ -502,11 +501,10 @@ class T5Stack(T5PreTrainedModel):
         # ourselves in which case we just need to make it broadcastable to all heads.
         if attention_mask.dim() == 3:
             extended_attention_mask = attention_mask[:, None, :, :]
-
+        elif attention_mask.dim() == 2:
         # Provided a padding mask of dimensions [batch_size, seq_length]
         # - if the model is a decoder, apply a causal mask in addition to the padding mask
         # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-        if attention_mask.dim() == 2:
             if self.config.is_decoder:
                 seq_ids = torch.arange(seq_length, device=hidden_states.device)
                 causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
@@ -593,7 +591,7 @@ class T5Stack(T5PreTrainedModel):
 T5_START_DOCSTRING = r"""    The T5 model was proposed in
     `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_
     by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
-    It's an encoder decoder pre-trained transformer.
+    It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting.
 
     This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
     refer to the PyTorch documentation for all matter related to general usage and behavior.
@@ -634,16 +632,13 @@ T5_INPUTS_DOCSTRING = r"""
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
             ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
-        **position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
-            Indices of positions of each input sequence tokens in the position embeddings.
-            Selected in the range ``[0, config.max_position_embeddings - 1]``.
         **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
             Mask to nullify selected heads of the self-attention modules.
             Mask values selected in ``[0, 1]``:
             ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
 """
 
-@add_start_docstrings("The bare single stack (encoder or decoder) of a T5 Model transformer outputting raw hidden-states"
+@add_start_docstrings("The bare T5 Model transformer outputting raw hidden-states"
                       "without any specific head on top.",
                       T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
 class T5Model(T5PreTrainedModel):
@@ -661,8 +656,8 @@ class T5Model(T5PreTrainedModel):
 
     Examples::
 
-        tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
-        model = T5Model.from_pretrained('t5-base-uncased')
+        tokenizer = T5Tokenizer.from_pretrained('t5-small')
+        model = T5Model.from_pretrained('t5-small')
         input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids)
         last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
@@ -752,8 +747,8 @@ class T5WithLMHeadModel(T5PreTrainedModel):
 
     Examples::
 
-        tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
-        model = T5WithLMHeadModel.from_pretrained('t5-base-uncased')
+        tokenizer = T5Tokenizer.from_pretrained('t5-small')
+        model = T5WithLMHeadModel.from_pretrained('t5-small')
         input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids, lm_labels=input_ids)
         loss, prediction_scores = outputs[:2]
@@ -763,31 +758,73 @@ class T5WithLMHeadModel(T5PreTrainedModel):
         super(T5WithLMHeadModel, self).__init__(config)
         self.model_dim = config.d_model
 
-        self.transformer = T5Model(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        self.encoder = T5Stack(encoder_config)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        self.decoder = T5Stack(decoder_config)
+
         self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
 
         self.init_weights()
 
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, new_embeddings):
+        self.shared = new_embeddings
+
     def get_output_embeddings(self):
         return self.lm_head
 
     def forward(self, **kwargs):
+        # keyword arguments come in 3 flavors: encoder-specific (prefixed by
+        # `encoder_`), decoder-specific (prefixed by `decoder_`) and those
+        # that apply to the model as whole.
+        # We let the specific kwargs override the common ones in case of conflict.
+
         lm_labels = kwargs.pop('decoder_lm_labels', None)
-        outputs = self.transformer(**kwargs)
 
-        sequence_output = outputs[0]
+        kwargs_common = dict((k, v) for k, v in kwargs.items()
+                             if not k.startswith("encoder_") and not k.startswith("decoder_"))
+        kwargs_encoder = kwargs_common.copy()
+        kwargs_decoder = kwargs_common.copy()
+        kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_")))
+        kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_")))
+
+        # Encode if needed (training, first prediction pass)
+        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
+        if encoder_hidden_states is None:
+            encoder_inputs_ids = kwargs_encoder.pop("input_ids")
+            hidden_states = self.shared(encoder_inputs_ids)  # Convert inputs in embeddings
+            encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
+            encoder_hidden_states = encoder_outputs[0]
+        else:
+            encoder_outputs = ()
+
+        # Decode
+        decoder_inputs_ids = kwargs_decoder.pop("input_ids")
+        hidden_states = self.shared(decoder_inputs_ids)  # Convert inputs in embeddings
+        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
+        kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
+        decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
+
+        sequence_output = decoder_outputs[0]
         # Rescale output before projecting on vocab
         # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
         sequence_output = sequence_output * (self.model_dim ** -0.5)
         lm_logits = self.lm_head(sequence_output)
 
-        outputs = (lm_logits,) + outputs[1:]  # Add hidden states and attention if they are here
+        decoder_outputs = (lm_logits,) + decoder_outputs[1:]  # Add hidden states and attention if they are here
         if lm_labels is not None:
             shift_logits = lm_logits[..., :-1, :].contiguous()
             shift_labels = lm_labels[..., 1:].contiguous()
             loss_fct = CrossEntropyLoss(ignore_index=-1)
             loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
                             shift_labels.view(-1))
-            outputs = (loss,) + outputs  # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
+            decoder_outputs = (loss,) + decoder_outputs  # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
 
-        return outputs  # (lm_loss), lm_logits, (hidden_states), (attentions)
+        return decoder_outputs + encoder_outputs

transformers/modeling_tf_pytorch_utils.py

@@ -156,7 +156,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
             e.args += (symbolic_weight.shape, array.shape)
             raise e
 
-        logger.info("Initialize TF weight {}".format(symbolic_weight.name))
+        # logger.warning("Initialize TF weight {}".format(symbolic_weight.name))
 
         weight_value_tuples.append((symbolic_weight, array))
         all_pytorch_weights.discard(name)
@@ -269,7 +269,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
             e.args += (pt_weight.shape, array.shape)
             raise e
 
-        logger.info("Initialize PyTorch weight {}".format(pt_weight_name))
+        # logger.warning("Initialize PyTorch weight {}".format(pt_weight_name))
 
         new_pt_params_dict[pt_weight_name] = torch.from_numpy(array)
         loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array)

transformers/modeling_utils.py

@@ -160,7 +160,6 @@ class PreTrainedModel(nn.Module):
         base_model.vocab_size = new_num_tokens
 
         # Tie weights again if needed
-        if hasattr(self, 'tie_weights'):
         self.tie_weights()
 
         return model_embeds
@@ -458,8 +457,7 @@ class PreTrainedModel(nn.Module):
                 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
                                 model.__class__.__name__, "\n\t".join(error_msgs)))
 
-        if hasattr(model, 'tie_weights'):
-            model.tie_weights()  # make sure word embedding weights are still tied
+        model.tie_weights()  # make sure word embedding weights are still tied if needed
 
         # Set model in evaluation mode to desactivate DropOut modules by default
         model.eval()

transformers/tests/modeling_tf_common_test.py

@@ -69,6 +69,7 @@ class TFCommonTestCases:
         test_torchscript = True
         test_pruning = True
         test_resize_embeddings = True
+        is_encoder_decoder = False
 
         def test_initialization(self):
             pass
@@ -156,6 +157,10 @@ class TFCommonTestCases:
         def test_compile_tf_model(self):
             config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
+            if self.is_encoder_decoder:
+                input_ids = {'decoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='decoder_input_ids', dtype='int32'),
+                             'encoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='encoder_input_ids', dtype='int32')}
+            else:
                 input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32')
             optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
             loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
@@ -189,7 +194,7 @@ class TFCommonTestCases:
                 outputs_dict = model(inputs_dict)
 
                 inputs_keywords = copy.deepcopy(inputs_dict)
-                input_ids = inputs_keywords.pop('input_ids')
+                input_ids = inputs_keywords.pop('input_ids', inputs_keywords.pop('decoder_input_ids'))
                 outputs_keywords = model(input_ids, **inputs_keywords)
 
                 output_dict = outputs_dict[0].numpy()
@@ -216,12 +221,24 @@ class TFCommonTestCases:
                     self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
                 out_len = len(outputs)
 
+                if self.is_encoder_decoder:
+                    self.assertEqual(out_len % 2, 0)
+                    decoder_attentions = outputs[(out_len // 2)-1]
+                    self.assertEqual(model.config.output_attentions, True)
+                    self.assertEqual(model.config.output_hidden_states, False)
+                    self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
+                    self.assertListEqual(
+                        list(decoder_attentions[0].shape[-3:]),
+                        [self.model_tester.num_attention_heads,
+                         self.model_tester.seq_length,
+                         self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
+
                 # Check attention is always last and order is fine
                 config.output_attentions = True
                 config.output_hidden_states = True
                 model = model_class(config)
                 outputs = model(inputs_dict)
-                self.assertEqual(out_len+1, len(outputs))
+                self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
                 self.assertEqual(model.config.output_attentions, True)
                 self.assertEqual(model.config.output_hidden_states, True)
 

transformers/tests/modeling_tf_t5_test.py

@@ -26,7 +26,7 @@ from .configuration_common_test import ConfigTester
 
 from transformers import T5Config, is_tf_available
 
-if False:  # is_tf_available():
+if is_tf_available():
     import tensorflow as tf
     from transformers.modeling_tf_t5 import (TFT5Model, TFT5WithLMHeadModel,TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP)
 else:
@@ -35,7 +35,8 @@ else:
 
 class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
 
-    all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if False  else () # is_tf_available() else ()
+    is_encoder_decoder = True
+    all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if is_tf_available() else ()
 
     class TFT5ModelTester(object):
 
@@ -45,22 +46,16 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
                      seq_length=7,
                      is_training=True,
                      use_input_mask=True,
-                     use_token_type_ids=True,
                      use_labels=True,
                      vocab_size=99,
+                     n_positions=14,
                      hidden_size=32,
                      num_hidden_layers=5,
                      num_attention_heads=4,
-                     intermediate_size=37,
-                     hidden_act="gelu",
-                     hidden_dropout_prob=0.1,
-                     attention_probs_dropout_prob=0.1,
-                     max_position_embeddings=512,
-                     type_vocab_size=16,
-                     type_sequence_label_size=2,
-                     initializer_range=0.02,
-                     num_labels=3,
-                     num_choices=4,
+                     d_ff=37,
+                     relative_attention_num_buckets=8,
+                     dropout_rate=0.1,
+                     initializer_factor=0.002,
                      scope=None,
                     ):
             self.parent = parent
@@ -68,22 +63,16 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
             self.seq_length = seq_length
             self.is_training = is_training
             self.use_input_mask = use_input_mask
-            self.use_token_type_ids = use_token_type_ids
             self.use_labels = use_labels
             self.vocab_size = vocab_size
+            self.n_positions = n_positions
             self.hidden_size = hidden_size
             self.num_hidden_layers = num_hidden_layers
             self.num_attention_heads = num_attention_heads
-            self.intermediate_size = intermediate_size
-            self.hidden_act = hidden_act
-            self.hidden_dropout_prob = hidden_dropout_prob
-            self.attention_probs_dropout_prob = attention_probs_dropout_prob
-            self.max_position_embeddings = max_position_embeddings
-            self.type_vocab_size = type_vocab_size
-            self.type_sequence_label_size = type_sequence_label_size
-            self.initializer_range = initializer_range
-            self.num_labels = num_labels
-            self.num_choices = num_choices
+            self.d_ff = d_ff
+            self.relative_attention_num_buckets = relative_attention_num_buckets
+            self.dropout_rate = dropout_rate
+            self.initializer_factor = initializer_factor
             self.scope = scope
 
         def prepare_config_and_inputs(self):
@@ -93,61 +82,53 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
             if self.use_input_mask:
                 input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
 
-            token_type_ids = None
-            if self.use_token_type_ids:
-                token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
-
-            sequence_labels = None
             token_labels = None
-            choice_labels = None
             if self.use_labels:
-                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
-                token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
-                choice_labels = ids_tensor([self.batch_size], self.num_choices)
+                token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
 
             config = T5Config(
                 vocab_size_or_config_json_file=self.vocab_size,
-                hidden_size=self.hidden_size,
-                num_hidden_layers=self.num_hidden_layers,
-                num_attention_heads=self.num_attention_heads,
-                intermediate_size=self.intermediate_size,
-                hidden_act=self.hidden_act,
-                hidden_dropout_prob=self.hidden_dropout_prob,
-                attention_probs_dropout_prob=self.attention_probs_dropout_prob,
-                max_position_embeddings=self.max_position_embeddings,
-                type_vocab_size=self.type_vocab_size,
-                initializer_range=self.initializer_range)
-
-            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
-        def create_and_check_t5_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
+                n_positions=self.n_positions,
+                d_model=self.hidden_size,
+                d_ff=self.d_ff,
+                d_kv=self.hidden_size // self.num_attention_heads,
+                num_layers=self.num_hidden_layers,
+                num_heads=self.num_attention_heads,
+                relative_attention_num_buckets=self.relative_attention_num_buckets,
+                dropout_rate=self.dropout_rate,
+                initializer_factor=self.initializer_factor)
+
+            return (config, input_ids, input_mask, token_labels)
+
+        def create_and_check_t5_model(self, config, input_ids, input_mask, token_labels):
             model = TFT5Model(config=config)
-            inputs = {'input_ids': input_ids,
-                      'attention_mask': input_mask,
-                      'token_type_ids': token_type_ids}
-            sequence_output, pooled_output = model(inputs)
-
-            inputs = [input_ids, input_mask]
-            sequence_output, pooled_output = model(inputs)
+            inputs = {'encoder_input_ids': input_ids,
+                      'decoder_input_ids': input_ids,
+                      'decoder_attention_mask': input_mask}
+            encoder_output, decoder_output = model(inputs)
 
-            sequence_output, pooled_output = model(input_ids)
+            encoder_output, decoder_output = model(input_ids,
+                                                   decoder_attention_mask=input_mask,
+                                                   encoder_input_ids=input_ids)
 
             result = {
-                "sequence_output": sequence_output.numpy(),
-                "pooled_output": pooled_output.numpy(),
+                "encoder_output": encoder_output.numpy(),
+                "decoder_output": decoder_output.numpy(),
             }
             self.parent.assertListEqual(
-                list(result["sequence_output"].shape),
+                list(result["encoder_output"].shape),
+                [self.batch_size, self.seq_length, self.hidden_size])
+            self.parent.assertListEqual(
+                list(result["decoder_output"].shape),
                 [self.batch_size, self.seq_length, self.hidden_size])
-            self.parent.assertListEqual(list(result["pooled_output"].shape), [self.batch_size, self.hidden_size])
 
 
-        def create_and_check_t5_with_lm_head(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
+        def create_and_check_t5_with_lm_head(self, config, input_ids, input_mask, token_labels):
             model = TFT5WithLMHeadModel(config=config)
-            inputs = {'input_ids': input_ids,
-                      'attention_mask': input_mask,
-                      'token_type_ids': token_type_ids}
-            prediction_scores, = model(inputs)
+            inputs = {'encoder_input_ids': input_ids,
+                      'decoder_input_ids': input_ids,
+                      'decoder_attention_mask': input_mask}
+            prediction_scores, decoder_output = model(inputs)
             result = {
                 "prediction_scores": prediction_scores.numpy(),
             }
@@ -158,14 +139,15 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
 
         def prepare_config_and_inputs_for_common(self):
             config_and_inputs = self.prepare_config_and_inputs()
-            (config, input_ids, token_type_ids, input_mask,
-             sequence_labels, token_labels, choice_labels) = config_and_inputs
-            inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
+            (config, input_ids, input_mask, token_labels) = config_and_inputs
+            inputs_dict = {'encoder_input_ids': input_ids,
+                           'decoder_input_ids': input_ids,
+                           'decoder_attention_mask': input_mask}
             return config, inputs_dict
 
     def setUp(self):
         self.model_tester = TFT5ModelTest.TFT5ModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=T5Config, hidden_size=37)
+        self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
 
     def test_config(self):
         self.config_tester.run_common_tests()
@@ -181,7 +163,7 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
     @pytest.mark.slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
-        for model_name in ['t5-base']:
+        for model_name in ['t5-small']:
             model = TFT5Model.from_pretrained(model_name, cache_dir=cache_dir)
             shutil.rmtree(cache_dir)
             self.assertIsNotNone(model)