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_tf_t5.py

@@ -22,24 +22,21 @@ import logging
 import math
 import os
 import sys
+import copy
+import itertools
 from io import open
 
 import numpy as np
 import tensorflow as tf
 
 from .configuration_t5 import T5Config
-from .modeling_tf_utils import TFPreTrainedModel, get_initializer
+from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list, get_initializer, DUMMY_INPUTS
 from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 
-####################################################
-# This dict contrains shortcut names and associated url
-# for the pretrained weights provided with the models
-####################################################
 TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-tf_model.h5",
-    't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-tf_model.h5",
+    't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-tf_model.h5",
 }
 
 ####################################################
@@ -48,33 +45,294 @@ TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
 # - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model)
 ####################################################
 
-####################################################
-# Here is an example of typical layer in a TF 2.0 model of the library
-# The classes are usually identical to the PyTorch ones and prefixed with 'TF'.
-#
-# Note that class __init__ parameters includes **kwargs (send to 'super').
-# This let us have a control on class scope and variable names:
-# More precisely, we set the names of the class attributes (lower level layers) to
-# to the equivalent attributes names in the PyTorch model so we can have equivalent
-# class and scope structure between PyTorch and TF 2.0 models and easily load one in the other.
-#
-# See the conversion methods in modeling_tf_pytorch_utils.py for more details
-####################################################
-class TFT5Layer(tf.keras.layers.Layer):
+class TFT5DenseReluDense(tf.keras.layers.Layer):
+    def __init__(self, config, **kwargs):
+        super(TFT5DenseReluDense, self).__init__(**kwargs)
+        self.wi = tf.keras.layers.Dense(config.d_ff, use_bias=False, name='wi')
+        self.wo = tf.keras.layers.Dense(config.d_model, use_bias=False, name='wo')
+        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
+        self.act = tf.keras.activations.relu
+
+    def call(self, hidden_states, training=False):
+        h = self.wi(hidden_states)
+        h = self.act(h)
+        h = self.dropout(h, training=training)
+        h = self.wo(h)
+        return h
+
+
+class TFT5LayerFF(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
-        super(TFT5Layer, self).__init__(**kwargs)
-        self.attention = TFT5Attention(config, name='attention')
-        self.intermediate = TFT5Intermediate(config, name='intermediate')
-        self.transformer_output = TFT5Output(config, name='output')
-
-    def call(self, inputs, training=False):
-        hidden_states, attention_mask, head_mask = inputs
-
-        attention_outputs = self.attention([hidden_states, attention_mask, head_mask], training=training)
-        attention_output = attention_outputs[0]
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.transformer_output([intermediate_output, attention_output], training=training)
-        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
+        super(TFT5LayerFF, self).__init__(**kwargs)
+        self.DenseReluDense = TFT5DenseReluDense(config, name='DenseReluDense')
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon,
+                                                             name='layer_norm')
+        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
+
+    def call(self, hidden_states, training=False):
+        norm_x = self.layer_norm(hidden_states)
+        y = self.DenseReluDense(norm_x, training=training)
+        layer_output = hidden_states + self.dropout(y, training=training)
+        return layer_output
+
+
+class TFT5Attention(tf.keras.layers.Layer):
+    NEW_ID = itertools.count()
+
+    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
+        super(TFT5Attention, self).__init__(**kwargs)
+        self.layer_id = next(TFT5Attention.NEW_ID)
+        self.is_decoder = config.is_decoder
+        self.has_relative_attention_bias = has_relative_attention_bias
+
+        self.output_attentions = config.output_attentions
+        self.relative_attention_num_buckets = config.relative_attention_num_buckets
+        self.dim = config.d_model
+        self.d_kv = config.d_kv
+        self.n_heads = config.num_heads
+        assert self.dim % self.n_heads == 0
+        assert self.dim // self.n_heads == self.d_kv
+
+        # Mesh TensorFlow initialization to avoid scaling before softmax
+        self.q = tf.keras.layers.Dense(self.dim, use_bias=False, name='q')
+        self.k = tf.keras.layers.Dense(self.dim, use_bias=False, name='k')
+        self.v = tf.keras.layers.Dense(self.dim, use_bias=False, name='v')
+        self.o = tf.keras.layers.Dense(self.dim, use_bias=False, name='o')
+        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
+
+        if self.has_relative_attention_bias:
+            self.relative_attention_bias = tf.keras.layers.Embedding(self.relative_attention_num_buckets,
+                                                                     self.n_heads,
+                                                                     name='relative_attention_bias')
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        raise NotImplementedError
+
+    @staticmethod
+    def _relative_position_bucket(relative_position,
+                                  bidirectional=True,
+                                  num_buckets=32,
+                                  max_distance=128):
+        """
+        Adapted from Mesh Tensorflow:
+        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+        Translate relative position to a bucket number for relative attention.
+        The relative position is defined as memory_position - query_position, i.e.
+        the distance in tokens from the attending position to the attended-to
+        position.  If bidirectional=False, then positive relative positions are
+        invalid.
+        We use smaller buckets for small absolute relative_position and larger buckets
+        for larger absolute relative_positions.  All relative positions >=max_distance
+        map to the same bucket.  All relative positions <=-max_distance map to the
+        same bucket.  This should allow for more graceful generalization to longer
+        sequences than the model has been trained on.
+        Args:
+            relative_position: an int32 Tensor
+            bidirectional: a boolean - whether the attention is bidirectional
+            num_buckets: an integer
+            max_distance: an integer
+        Returns:
+            a Tensor with the same shape as relative_position, containing int32
+            values in the range [0, num_buckets)
+        """
+        ret = 0
+        n = -relative_position
+        if bidirectional:
+            num_buckets //= 2
+            ret += tf.dtypes.cast(tf.math.less(n, 0), tf.int32) * num_buckets
+            n = tf.math.abs(n)
+        else:
+            n = tf.math.maximum(n, 0)
+        # now n is in the range [0, inf)
+        max_exact = num_buckets // 2
+        is_small = tf.math.less(n, max_exact)
+        val_if_large = max_exact + tf.dtypes.cast(
+            tf.math.log(tf.dtypes.cast(n, tf.float32) / max_exact)
+            / math.log(max_distance / max_exact) * (num_buckets - max_exact), tf.int32)
+        val_if_large = tf.math.minimum(val_if_large, num_buckets - 1)
+        ret += tf.where(is_small, n, val_if_large)
+        return ret
+
+    def compute_bias(self, qlen, klen):
+        """ Compute binned relative position bias """
+        context_position = tf.range(qlen)[:, None]
+        memory_position = tf.range(klen)[None, :]
+        relative_position = memory_position - context_position  # shape (qlen, klen)
+        rp_bucket = self._relative_position_bucket(relative_position,
+                                                   bidirectional=not self.is_decoder,
+                                                   num_buckets=self.relative_attention_num_buckets)
+        values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
+        values = tf.expand_dims(tf.transpose(values, [2, 0, 1]), axis=0) # shape (1, num_heads, qlen, klen)
+        return values
+
+    def call(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None, training=False):
+        """
+        Self-attention (if kv is None) or attention over source sentence (provided by kv).
+        """
+        # Input is (bs, qlen, dim)
+        # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
+        bs, qlen, dim = shape_list(input)
+        if kv is None:
+            klen = qlen if cache is None else cache['slen'] + qlen
+        else:
+            klen = shape_list(kv)[1]
+        # assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
+        n_heads = self.n_heads
+        dim_per_head = self.dim // n_heads
+
+        def shape(x):
+            """  projection """
+            return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
+
+        def unshape(x):
+            """  compute context """
+            return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
+
+        q = shape(self.q(input))                                          # (bs, n_heads, qlen, dim_per_head)
+        if kv is None:
+            k = shape(self.k(input))                                      # (bs, n_heads, qlen, dim_per_head)
+            v = shape(self.v(input))                                      # (bs, n_heads, qlen, dim_per_head)
+        elif cache is None or self.layer_id not in cache:
+            k = v = kv
+            k = shape(self.k(k))                                          # (bs, n_heads, qlen, dim_per_head)
+            v = shape(self.v(v))                                          # (bs, n_heads, qlen, dim_per_head)
+
+        if cache is not None:
+            if self.layer_id in cache:
+                if kv is None:
+                    k_, v_ = cache[self.layer_id]
+                    k = tf.concat([k_, k], axis=2)                             # (bs, n_heads, klen, dim_per_head)
+                    v = tf.concat([v_, v], axis=2)                             # (bs, n_heads, klen, dim_per_head)
+                else:
+                    k, v = cache[self.layer_id]
+            cache[self.layer_id] = (k, v)
+
+        # q = q / math.sqrt(dim_per_head)                                     # No scaling in T5
+        scores = tf.matmul(q, k, transpose_b=True)                            # (bs, n_heads, qlen, klen)
+
+        if position_bias is None:
+            if not self.has_relative_attention_bias:
+                raise ValueError("No position_bias provided and no weights to compute position_bias")
+            position_bias = self.compute_bias(qlen, klen)
+        scores += position_bias
+
+        if mask is not None:
+            scores += mask
+            # mask = (mask == 0).expand_as(scores)                              # (bs, n_heads, qlen, klen)
+            # scores.masked_fill_(mask, -float('inf'))                          # (bs, n_heads, qlen, klen)
+
+        weights = tf.nn.softmax(scores, axis=-1)                              # (bs, n_heads, qlen, klen)
+        weights = self.dropout(weights, training=training)                    # (bs, n_heads, qlen, klen)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            weights = weights * head_mask
+
+        context = tf.matmul(weights, v)                                    # (bs, n_heads, qlen, dim_per_head)
+        context = unshape(context)                                            # (bs, qlen, dim)
+
+        context = self.o(context)
+
+        outputs = (context,)
+        if self.output_attentions:
+            outputs = outputs + (weights,)
+        if self.has_relative_attention_bias:
+            outputs = outputs + (position_bias,)
+        return outputs
+
+
+class TFT5LayerSelfAttention(tf.keras.layers.Layer):
+    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
+        super(TFT5LayerSelfAttention, self).__init__(**kwargs)
+        self.SelfAttention = TFT5Attention(config,
+                                           has_relative_attention_bias=has_relative_attention_bias,
+                                           name='SelfAttention')
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon,
+                                                             name='layer_norm')
+        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
+
+    def call(self, hidden_states, attention_mask=None, position_bias=None,
+             head_mask=None, training=False):
+        norm_x = self.layer_norm(hidden_states)
+        attention_output = self.SelfAttention(norm_x,
+                                              mask=attention_mask,
+                                              position_bias=position_bias,
+                                              head_mask=head_mask,
+                                              training=training)
+        y = attention_output[0]
+        layer_output = hidden_states + self.dropout(y, training=training)
+        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
+        return outputs
+
+
+class TFT5LayerCrossAttention(tf.keras.layers.Layer):
+    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
+        super(TFT5LayerCrossAttention, self).__init__(**kwargs)
+        self.EncDecAttention = TFT5Attention(config,
+                                           has_relative_attention_bias=has_relative_attention_bias,
+                                           name='EncDecAttention')
+        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon,
+                                                             name='layer_norm')
+        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
+
+    def call(self, hidden_states, kv, attention_mask=None, position_bias=None,
+             head_mask=None, training=False):
+        norm_x = self.layer_norm(hidden_states)
+        attention_output = self.EncDecAttention(norm_x,
+                                                mask=attention_mask,
+                                                kv=kv,
+                                                position_bias=position_bias,
+                                                head_mask=head_mask,
+                                                training=training)
+        y = attention_output[0]
+        layer_output = hidden_states + self.dropout(y, training=training)
+        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
+        return outputs
+
+
+class TFT5Block(tf.keras.layers.Layer):
+    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
+        super(TFT5Block, self).__init__(**kwargs)
+        self.is_decoder = config.is_decoder
+        self.layer = []
+        self.layer.append(TFT5LayerSelfAttention(config,
+                                                 has_relative_attention_bias=has_relative_attention_bias,
+                                                 name='layer_._0'))
+        if self.is_decoder:
+            self.layer.append(TFT5LayerCrossAttention(config,
+                                                      has_relative_attention_bias=has_relative_attention_bias,
+                                                      name='layer_._1'))
+            self.layer.append(TFT5LayerFF(config, name='layer_._2'))
+        else:
+            self.layer.append(TFT5LayerFF(config, name='layer_._1'))
+
+    def call(self, hidden_states, attention_mask=None, position_bias=None,
+             encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None,
+             head_mask=None, training=False):
+        self_attention_outputs = self.layer[0](hidden_states,
+                                                attention_mask=attention_mask,
+                                                position_bias=position_bias,
+                                                head_mask=head_mask,
+                                                training=training)
+        hidden_states = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]
+
+        if not self.is_decoder:
+            hidden_states = self.layer[1](hidden_states, training=training)
+        else:
+            cross_attention_outputs = self.layer[1](hidden_states,
+                                                    kv=encoder_hidden_states,
+                                                    attention_mask=encoder_attention_mask,
+                                                    position_bias=encoder_decoder_position_bias,
+                                                    head_mask=head_mask,
+                                                    training=training)
+            hidden_states = cross_attention_outputs[0]
+            outputs = cross_attention_outputs[1:] + outputs
+            hidden_states = self.layer[2](hidden_states, training=training)
+
+        outputs = (hidden_states,) + outputs  # add attentions if we output them
         return outputs
 
 
@@ -85,6 +343,19 @@ class TFT5Layer(tf.keras.layers.Layer):
 class TFT5MainLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFT5MainLayer, self).__init__(**kwargs)
+        self.output_attentions = config.output_attentions
+        self.output_hidden_states = config.output_hidden_states
+        self.is_decoder = config.is_decoder
+        self.config = config
+        self.num_hidden_layers = config.num_layers
+
+        self.block = [TFT5Block(config,
+                                has_relative_attention_bias=bool(i == 0),
+                                name='block_._{}'.format(i))
+                        for i in range(config.num_layers)]
+        self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon,
+                                                                   name='final_layer_norm')
+        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
 
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
@@ -92,51 +363,56 @@ class TFT5MainLayer(tf.keras.layers.Layer):
     def _prune_heads(self, heads_to_prune):
         raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
 
-    def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
-        # We allow three types of multi-inputs:
-        # - traditional keyword arguments in the call method
-        # - all the arguments provided as a dict in the first positional argument of call
-        # - all the arguments provided as a list/tuple (ordered) in the first positional argument of call
-        # The last two options are useful to use the tf.keras fit() method.
-
-        if isinstance(inputs, (tuple, list)):
-            input_ids = inputs[0]
-            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
-            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
-            position_ids = inputs[3] if len(inputs) > 3 else position_ids
-            head_mask = inputs[4] if len(inputs) > 4 else head_mask
-            assert len(inputs) <= 5, "Too many inputs."
-        elif isinstance(inputs, dict):
-            input_ids = inputs.get('input_ids')
-            attention_mask = inputs.get('attention_mask', attention_mask)
-            token_type_ids = inputs.get('token_type_ids', token_type_ids)
-            position_ids = inputs.get('position_ids', position_ids)
-            head_mask = inputs.get('head_mask', head_mask)
-            assert len(inputs) <= 5, "Too many inputs."
-        else:
-            input_ids = inputs
+    def call(self, hidden_states, attention_mask=None, encoder_hidden_states=None,
+             encoder_attention_mask=None, head_mask=None, training=False):
 
+        batch_size, seq_length = shape_list(hidden_states)[:2]
         if attention_mask is None:
-            attention_mask = tf.fill(tf.shape(input_ids), 1)
-        if token_type_ids is None:
-            token_type_ids = tf.fill(tf.shape(input_ids), 0)
-
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # this attention mask is more simple than the triangular masking of causal attention
-        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
+            attention_mask = tf.fill((batch_size, seq_length), 1)
+        if self.is_decoder and encoder_attention_mask is None:
+            encoder_seq_length = encoder_hidden_states.shape[1]
+            encoder_attention_mask = tf.fill((batch_size, encoder_seq_length), 1)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        attention_mask = tf.cast(attention_mask, dtype=tf.float32)
+        num_dims_attention_mask = len(shape_list(attention_mask))
+        if num_dims_attention_mask == 3:
+            extended_attention_mask = attention_mask[:, None, :, :]
+        elif num_dims_attention_mask == 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 self.config.is_decoder:
+                seq_ids = tf.range(seq_length)
+                causal_mask = tf.less_equal(tf.tile(seq_ids[None, None, :], (batch_size, seq_length, 1)),
+                                            seq_ids[None, :, None])
+                causal_mask = tf.cast(causal_mask, dtype=tf.float32)
+                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+            else:
+                extended_attention_mask = attention_mask[:, None, None, :]
 
         # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
         # masked positions, this operation will create a tensor which is 0.0 for
         # positions we want to attend and -10000.0 for masked positions.
         # Since we are adding it to the raw scores before the softmax, this is
         # effectively the same as removing these entirely.
-
-        extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
         extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
 
+        if self.is_decoder:
+            # If a 2D ou 3D attention mask is provided for the cross-attention
+            # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
+            encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=tf.float32)
+            num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
+            if num_dims_encoder_attention_mask == 3:
+                encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
+            if num_dims_encoder_attention_mask == 2:
+                encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
+
+            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
+        else:
+            encoder_extended_attention_mask = None
+
         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
         # attention_probs has shape bsz x n_heads x N x N
@@ -148,14 +424,44 @@ class TFT5MainLayer(tf.keras.layers.Layer):
             head_mask = [None] * self.num_hidden_layers
             # head_mask = tf.constant([0] * self.num_hidden_layers)
 
-        ##################################
-        # Replace this with your model code
-        embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
-        encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
-        sequence_output = encoder_outputs[0]
-        outputs = (sequence_output,) + encoder_outputs[1:]  # add hidden_states and attentions if they are here
-
-        return outputs  # sequence_output, (hidden_states), (attentions)
+        all_hidden_states = ()
+        all_attentions = ()
+        position_bias = None
+        encoder_decoder_position_bias = None
+        for i, layer_module in enumerate(self.block):
+            if self.output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_outputs = layer_module(hidden_states,
+                                         attention_mask=extended_attention_mask,
+                                         position_bias=position_bias,
+                                         encoder_hidden_states=encoder_hidden_states,
+                                         encoder_attention_mask=encoder_extended_attention_mask,
+                                         encoder_decoder_position_bias=encoder_decoder_position_bias,
+                                         head_mask=head_mask[i],
+                                         training=training)
+            hidden_states = layer_outputs[0]
+            if i == 0:
+                position_bias = layer_outputs[2 if self.output_attentions else 1]
+                if self.is_decoder:
+                    encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2]
+
+            if self.output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        hidden_states = self.final_layer_norm(hidden_states)
+        layer_output = self.dropout(hidden_states, training=training)
+
+        # Add last layer
+        if self.output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        outputs = (hidden_states,)
+        if self.output_hidden_states:
+            outputs = outputs + (all_hidden_states,)
+        if self.output_attentions:
+            outputs = outputs + (all_attentions,)
+        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
 
 
 ####################################################
@@ -173,18 +479,26 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
     pretrained_model_archive_map = TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
     base_model_prefix = "transformer"
 
+    @property
+    def dummy_inputs(self):
+        input_ids = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
+        input_mask = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
+        dummy_inputs = {'decoder_input_ids': input_ids,
+                        'encoder_input_ids': input_ids,
+                        'decoder_attention_mask': input_mask}
+        return dummy_inputs
 
-T5_START_DOCSTRING = r"""    The XXX model was proposed in
-    `XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`_
-    by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer
-    pre-trained using a combination of masked language modeling objective and next sentence prediction
-    on a large corpus comprising the Toronto Book Corpus and Wikipedia.
+
+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 transformer pre-trained in a text-to-text denoising generative setting.
 
     This model is a tf.keras.Model `tf.keras.Model`_ sub-class. Use it as a regular TF 2.0 Keras Model and
     refer to the TF 2.0 documentation for all matter related to general usage and behavior.
 
-    .. _`XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`:
-        https://arxiv.org/abs/1810.04805
+    .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`:
+        https://arxiv.org/abs/1910.10683
 
     .. _`tf.keras.Model`:
         https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
@@ -206,67 +520,50 @@ T5_START_DOCSTRING = r"""    The XXX model was proposed in
             `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
-        config (:class:`~transformers.XxxConfig`): Model configuration class with all the parameters of the model. 
+        config (:class:`~transformers.T5Config`): Model configuration class with all the parameters of the model. 
             Initializing with a config file does not load the weights associated with the model, only the configuration.
             Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
-XXX_INPUTS_DOCSTRING = r"""
+T5_INPUTS_DOCSTRING = r"""
     Inputs:
         **input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
             Indices of input sequence tokens in the vocabulary.
-            To match pre-training, XXX input sequence should be formatted with [CLS] and [SEP] tokens as follows:
+            To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows:
 
             (a) For sequence pairs:
 
                 ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
-                
-                ``token_type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1``
 
             (b) For single sequences:
 
                 ``tokens:         [CLS] the dog is hairy . [SEP]``
-                
-                ``token_type_ids:   0   0   0   0  0     0   0``
 
-            Xxx is a model with absolute position embeddings so it's usually advised to pad the inputs on
-            the right rather than the left.
 
-            Indices can be obtained using :class:`transformers.XxxTokenizer`.
+            T5 is a model with relative position embeddings so you should be able to pad the inputs on
+            the right or the left.
+
+            Indices can be obtained using :class:`transformers.T5Tokenizer`.
             See :func:`transformers.PreTrainedTokenizer.encode` and
             :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
             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.
-        **token_type_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
-            Segment token indices to indicate first and second portions of the inputs.
-            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
-            corresponds to a `sentence B` token
-            (see `XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
-        **position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` 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`) ``Numpy array`` or ``tf.Tensor`` 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 Xxx Model transformer outputing raw hidden-states without any specific head on top.",
-                      XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
-class TFXxxModel(TFXxxPreTrainedModel):
+@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 TFT5Model(TFT5PreTrainedModel):
     r"""
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
         **last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
             Sequence of hidden-states at the output of the last layer of the model.
-        **pooler_output**: ``tf.Tensor`` of shape ``(batch_size, hidden_size)``
-            Last layer hidden-state of the first token of the sequence (classification token)
-            further processed by a Linear layer and a Tanh activation function. The Linear
-            layer weights are trained from the next sentence prediction (classification)
-            objective during Xxx pretraining. This output is usually *not* a good summary
-            of the semantic content of the input, you're often better with averaging or pooling
-            the sequence of hidden-states for the whole input sequence.
         **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
             list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
             of shape ``(batch_size, sequence_length, hidden_size)``:
@@ -278,127 +575,72 @@ class TFXxxModel(TFXxxPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from transformers import XxxTokenizer, TFXxxModel
+        from transformers import T5Tokenizer, TFT5Model
 
-        tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
-        model = TFXxxModel.from_pretrained('xxx-base-uncased')
+        tokenizer = T5Tokenizer.from_pretrained('t5-small')
+        model = TFT5Model.from_pretrained('t5-small')
         input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
         outputs = model(input_ids)
         last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
 
     """
     def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxModel, self).__init__(config, *inputs, **kwargs)
-        self.transformer = TFXxxMainLayer(config, name='transformer')
-
-    def call(self, inputs, **kwargs):
-        outputs = self.transformer(inputs, **kwargs)
-        return outputs
-
-
-@add_start_docstrings("""Xxx Model with a `language modeling` head on top. """,
-    XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
-class TFXxxForMaskedLM(TFXxxPreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **prediction_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        import tensorflow as tf
-        from transformers import XxxTokenizer, TFXxxForMaskedLM
-
-        tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
-        model = TFXxxForMaskedLM.from_pretrained('xxx-base-uncased')
-        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
-        outputs = model(input_ids)
-        prediction_scores = outputs[0]
-
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForMaskedLM, self).__init__(config, *inputs, **kwargs)
-
-        self.transformer = TFXxxMainLayer(config, name='transformer')
-        self.mlm = TFXxxMLMHead(config, self.transformer.embeddings, name='mlm')
-
-    def call(self, inputs, **kwargs):
-        outputs = self.transformer(inputs, **kwargs)
+        super(TFT5Model, self).__init__(config, *inputs, **kwargs)
+        self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model,
+                                         name='shared')
 
-        sequence_output = outputs[0]
-        prediction_scores = self.mlm(sequence_output, training=kwargs.get('training', False))
+        encoder_config = copy.deepcopy(config)
+        self.encoder = TFT5MainLayer(encoder_config, name='encoder')
 
-        outputs = (prediction_scores,) + outputs[2:]  # Add hidden states and attention if they are here
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        self.decoder = TFT5MainLayer(decoder_config, name='decoder')
 
-        return outputs  # prediction_scores, (hidden_states), (attentions)
-
-
-@add_start_docstrings("""Xxx Model transformer with a sequence classification/regression head on top (a linear layer on top of
-    the pooled output) e.g. for GLUE tasks. """,
-    XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
-class TFXxxForSequenceClassification(TFXxxPreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **logits**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, config.num_labels)``
-            Classification (or regression if config.num_labels==1) scores (before SoftMax).
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        import tensorflow as tf
-        from transformers import XxxTokenizer, TFXxxForSequenceClassification
-
-        tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
-        model = TFXxxForSequenceClassification.from_pretrained('xxx-base-uncased')
-        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
-        outputs = model(input_ids)
-        logits = outputs[0]
-
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForSequenceClassification, self).__init__(config, *inputs, **kwargs)
-        self.num_labels = config.num_labels
-
-        self.transformer = TFXxxMainLayer(config, name='transformer')
-        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(config.num_labels,
-                                                kernel_initializer=get_initializer(config.initializer_range),
-                                                name='classifier')
-
-    def call(self, inputs, **kwargs):
-        outputs = self.transformer(inputs, **kwargs)
-
-        pooled_output = outputs[1]
+    def call(self, decoder_input_ids, **kwargs):
+        # We allow two types of multi-inputs:
+        # - traditional keyword arguments in the call method
+        # - all the arguments provided as a dict in the first positional argument of call
+        # The last option is useful to use the tf.keras fit() method.
 
-        pooled_output = self.dropout(pooled_output, training=kwargs.get('training', False))
-        logits = self.classifier(pooled_output)
+        if isinstance(decoder_input_ids, dict):
+            kwargs.update(decoder_input_ids)
+        else:
+            kwargs['decoder_input_ids'] = decoder_input_ids
+
+        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 = ()
 
-        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
+        # 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)
 
-        return outputs  # logits, (hidden_states), (attentions)
+        return decoder_outputs + encoder_outputs
 
 
-@add_start_docstrings("""Xxx Model with a token classification head on top (a linear layer on top of
-    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
-    XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
-class TFXxxForTokenClassification(TFXxxPreTrainedModel):
+@add_start_docstrings("""T5 Model with a `language modeling` head on top. """,
+    T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
+class TFT5WithLMHeadModel(TFT5PreTrainedModel):
     r"""
     Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
-            Classification scores (before SoftMax).
+        **prediction_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
             list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
             of shape ``(batch_size, sequence_length, hidden_size)``:
@@ -410,87 +652,66 @@ class TFXxxForTokenClassification(TFXxxPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from transformers import XxxTokenizer, TFXxxForTokenClassification
+        from transformers import T5Tokenizer, TFT5WithLMHeadModel
 
-        tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
-        model = TFXxxForTokenClassification.from_pretrained('xxx-base-uncased')
+        tokenizer = T5Tokenizer.from_pretrained('t5-small')
+        model = TFT5WithLMHeadModel.from_pretrained('t5-small')
         input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
         outputs = model(input_ids)
-        scores = outputs[0]
+        prediction_scores = outputs[0]
 
     """
     def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForTokenClassification, self).__init__(config, *inputs, **kwargs)
-        self.num_labels = config.num_labels
-
-        self.transformer = TFXxxMainLayer(config, name='transformer')
-        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(config.num_labels,
-                                                kernel_initializer=get_initializer(config.initializer_range),
-                                                name='classifier')
-
-    def call(self, inputs, **kwargs):
-        outputs = self.transformer(inputs, **kwargs)
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output, training=kwargs.get('training', False))
-        logits = self.classifier(sequence_output)
-
-        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
-
-        return outputs  # scores, (hidden_states), (attentions)
-
-
-@add_start_docstrings("""Xxx Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
-    the hidden-states output to compute `span start logits` and `span end logits`). """,
-    XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
-class TFXxxForQuestionAnswering(TFXxxPreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **start_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
-            Span-start scores (before SoftMax).
-        **end_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
-            Span-end scores (before SoftMax).
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
+        super(TFT5WithLMHeadModel, self).__init__(config, *inputs, **kwargs)
+        self.model_dim = config.d_model
 
-    Examples::
+        self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model,
+                                         name='shared')
 
-        import tensorflow as tf
-        from transformers import XxxTokenizer, TFXxxForQuestionAnswering
+        encoder_config = copy.deepcopy(config)
+        self.encoder = TFT5MainLayer(encoder_config, name='encoder')
 
-        tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
-        model = TFXxxForQuestionAnswering.from_pretrained('xxx-base-uncased')
-        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
-        outputs = model(input_ids)
-        start_scores, end_scores = outputs[:2]
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        self.decoder = TFT5MainLayer(decoder_config, name='decoder')
 
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
-        self.num_labels = config.num_labels
-
-        self.transformer = TFXxxMainLayer(config, name='transformer')
-        self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
-                                                kernel_initializer=get_initializer(config.initializer_range),
-                                                name='qa_outputs')
-
-    def call(self, inputs, **kwargs):
-        outputs = self.transformer(inputs, **kwargs)
+    def call(self, decoder_input_ids, **kwargs):
+        # We allow two types of multi-inputs:
+        # - traditional keyword arguments in the call method
+        # - all the arguments provided as a dict in the first positional argument of call
+        # The last option is useful to use the tf.keras fit() method.
 
-        sequence_output = outputs[0]
+        if isinstance(decoder_input_ids, dict):
+            kwargs.update(decoder_input_ids)
+        else:
+            kwargs['decoder_input_ids'] = decoder_input_ids
+
+        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 = ()
 
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = tf.split(logits, 2, axis=-1)
-        start_logits = tf.squeeze(start_logits, axis=-1)
-        end_logits = tf.squeeze(end_logits, axis=-1)
+        # 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)
 
-        outputs = (start_logits, end_logits,) + outputs[2:]
+        sequence_output = decoder_outputs[0] * (self.model_dim ** -0.5)
+        lm_logits = self.shared(sequence_output, mode="linear")
+        decoder_outputs = (lm_logits,) + decoder_outputs[1:]
 
-        return outputs  # start_logits, end_logits, (hidden_states), (attentions)
+        return decoder_outputs + encoder_outputs

transformers/modeling_utils.py

@@ -160,8 +160,7 @@ class PreTrainedModel(nn.Module):
         base_model.vocab_size = new_num_tokens
 
         # Tie weights again if needed
-        if hasattr(self, 'tie_weights'):
-            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
@@ -156,7 +157,11 @@ class TFCommonTestCases:
         def test_compile_tf_model(self):
             config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
-            input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32')
+            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)
             metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
@@ -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)