Copy code from Bert to Roberta and add safeguard script (#7219)

* Copy code from Bert to Roberta and add safeguard script

* Fix docstring

* Comment code

* Formatting

* Update src/transformers/modeling_roberta.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Add test and fix bugs

* Fix style and make new comand
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

.circleci/config.yml

@@ -247,6 +247,7 @@ jobs:
             - run: black --check --line-length 119 --target-version py35 examples templates tests src utils
             - run: isort --check-only examples templates tests src utils
             - run: flake8 examples templates tests src utils
+            - run: python utils/check_copies.py
             - run: python utils/check_repo.py
     check_repository_consistency:
         working_directory: ~/transformers

Makefile

@@ -6,6 +6,7 @@ quality:
 	black --check --line-length 119 --target-version py35 examples templates tests src utils
 	isort --check-only examples templates tests src utils
 	flake8 examples templates tests src utils
+	python utils/check_copies.py
 	python utils/check_repo.py
 
 # Format source code automatically
@@ -14,6 +15,11 @@ style:
 	black --line-length 119 --target-version py35 examples templates tests src utils
 	isort examples templates tests src utils
 
+# Make marked copies of snippets of codes conform to the original
+
+fix-copies:
+	python utils/check_copies.py --fix_and_overwrite
+
 # Run tests for the library
 
 test:

src/transformers/modeling_roberta.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 """PyTorch RoBERTa model. """
 
-
+import math
 import warnings
 
 import torch
@@ -29,8 +29,10 @@ from .file_utils import (
     add_start_docstrings_to_callable,
     replace_return_docstrings,
 )
-from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
+from .modeling_bert import ACT2FN, gelu
 from .modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
     CausalLMOutput,
     MaskedLMOutput,
     MultipleChoiceModelOutput,
@@ -38,6 +40,12 @@ from .modeling_outputs import (
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
+from .modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
 from .utils import logging
 
 
@@ -57,15 +65,31 @@ ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
 ]
 
 
-class RobertaEmbeddings(BertEmbeddings):
+RobertaLayerNorm = torch.nn.LayerNorm
+
+
+class RobertaEmbeddings(nn.Module):
     """
     Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
     """
 
+    # Copied from transformers.modeling_bert.BertEmbeddings.__init__ with Bert->Roberta
     def __init__(self, config):
-        super().__init__(config)
+        super().__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+        # End copy
         self.padding_idx = config.pad_token_id
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
         self.position_embeddings = nn.Embedding(
             config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
         )
@@ -78,9 +102,29 @@ class RobertaEmbeddings(BertEmbeddings):
             else:
                 position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
 
-        return super().forward(
-            input_ids, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds
-        )
+        # Copied from transformers.modeling_bert.BertEmbeddings.forward
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
 
     def create_position_ids_from_inputs_embeds(self, inputs_embeds):
         """We are provided embeddings directly. We cannot infer which are padded so just generate
@@ -98,6 +142,343 @@ class RobertaEmbeddings(BertEmbeddings):
         return position_ids.unsqueeze(0).expand(input_shape)
 
 
+# Copied from transformers.modeling_bert.BertSelfAttention
+class RobertaSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        mixed_query_layer = self.query(hidden_states)
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        if encoder_hidden_states is not None:
+            mixed_key_layer = self.key(encoder_hidden_states)
+            mixed_value_layer = self.value(encoder_hidden_states)
+            attention_mask = encoder_attention_mask
+        else:
+            mixed_key_layer = self.key(hidden_states)
+            mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+        return outputs
+
+
+# Copied from transformers.modeling_bert.BertSelfOutput with Bert->Roberta
+class RobertaSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.modeling_bert.BertAttention with Bert->Roberta
+class RobertaAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = RobertaSelfAttention(config)
+        self.output = RobertaSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+# Copied from transformers.modeling_bert.BertIntermediate
+class RobertaIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.modeling_bert.BertOutput with Bert->Roberta
+class RobertaOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.modeling_bert.BertLayer with Bert->Roberta
+class RobertaLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = RobertaAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
+            self.crossattention = RobertaAttention(config)
+        self.intermediate = RobertaIntermediate(config)
+        self.output = RobertaOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+    ):
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        if self.is_decoder and encoder_hidden_states is not None:
+            assert hasattr(
+                self, "crossattention"
+            ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:]  # add cross attentions if we output attention weights
+
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        )
+        outputs = (layer_output,) + outputs
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+# Copied from transformers.modeling_bert.BertEncoder with Bert->Roberta
+class RobertaEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([RobertaLayer(config) for _ in range(config.num_hidden_layers)])
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=False,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if getattr(self.config, "gradient_checkpointing", False):
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions,
+                )
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
+        )
+
+
+# Copied from transformers.modeling_bert.BertPooler
+class RobertaPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class RobertaPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and
+    a simple interface for downloading and loading pretrained models.
+    """
+
+    config_class = RobertaConfig
+    base_model_prefix = "roberta"
+    authorized_missing_keys = [r"position_ids"]
+
+    # Copied from transformers.modeling_bert.BertPreTrainedModel._init_weights with Bert->Roberta
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, RobertaLayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
 ROBERTA_START_DOCSTRING = r"""
 
     This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
@@ -159,19 +540,34 @@ ROBERTA_INPUTS_DOCSTRING = r"""
     "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
     ROBERTA_START_DOCSTRING,
 )
-class RobertaModel(BertModel):
-    """
-    This class overrides :class:`~transformers.BertModel`. Please check the
-    superclass for the appropriate documentation alongside usage examples.
+class RobertaModel(RobertaPreTrainedModel):
     """
 
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
+    The model can behave as an encoder (with only self-attention) as well
+    as a decoder, in which case a layer of cross-attention is added between
+    the self-attention layers, following the architecture described in `Attention is all you need`_ by Ashish Vaswani,
+    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+    To behave as an decoder the model needs to be initialized with the
+    :obj:`is_decoder` argument of the configuration set to :obj:`True`.
+    To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
+    argument and :obj:`add_cross_attention` set to :obj:`True`; an
+    :obj:`encoder_hidden_states` is then expected as an input to the forward pass.
+
+    .. _`Attention is all you need`:
+        https://arxiv.org/abs/1706.03762
 
+    """
+
+    # Copied from transformers.modeling_bert.BertModel.__init__ with Bert->Roberta
     def __init__(self, config):
         super().__init__(config)
+        self.config = config
 
         self.embeddings = RobertaEmbeddings(config)
+        self.encoder = RobertaEncoder(config)
+        self.pooler = RobertaPooler(config)
+
         self.init_weights()
 
     def get_input_embeddings(self):
@@ -180,14 +576,121 @@ class RobertaModel(BertModel):
     def set_input_embeddings(self, value):
         self.embeddings.word_embeddings = value
 
+    def _prune_heads(self, heads_to_prune):
+        """Prunes heads of the model.
+        heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+        See base class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    @add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="roberta-base",
+        output_type=BaseModelOutputWithPooling,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    # Copied from transformers.modeling_bert.BertModel.forward
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+            if the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask
+            is used in the cross-attention if the model is configured as a decoder.
+            Mask values selected in ``[0, 1]``:
+            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        # 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.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        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
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output)
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
 
 @add_start_docstrings(
     """RoBERTa Model with a `language modeling` head on top for CLM fine-tuning. """, ROBERTA_START_DOCSTRING
 )
-class RobertaForCausalLM(BertPreTrainedModel):
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-
+class RobertaForCausalLM(RobertaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 
@@ -300,11 +803,7 @@ class RobertaForCausalLM(BertPreTrainedModel):
 
 
 @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
-class RobertaForMaskedLM(BertPreTrainedModel):
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-    authorized_missing_keys = [r"position_ids", r"lm_head\.decoder\.bias"]
-
+class RobertaForMaskedLM(RobertaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 
@@ -402,7 +901,7 @@ class RobertaLMHead(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.layer_norm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layer_norm = RobertaLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
 
         self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
         self.bias = nn.Parameter(torch.zeros(config.vocab_size))
@@ -426,10 +925,7 @@ class RobertaLMHead(nn.Module):
     on top of the pooled output) e.g. for GLUE tasks. """,
     ROBERTA_START_DOCSTRING,
 )
-class RobertaForSequenceClassification(BertPreTrainedModel):
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-
+class RobertaForSequenceClassification(RobertaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -509,10 +1005,7 @@ class RobertaForSequenceClassification(BertPreTrainedModel):
     the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
     ROBERTA_START_DOCSTRING,
 )
-class RobertaForMultipleChoice(BertPreTrainedModel):
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-
+class RobertaForMultipleChoice(RobertaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
 
@@ -600,10 +1093,7 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
     the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
     ROBERTA_START_DOCSTRING,
 )
-class RobertaForTokenClassification(BertPreTrainedModel):
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-
+class RobertaForTokenClassification(RobertaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels
@@ -708,10 +1198,7 @@ class RobertaClassificationHead(nn.Module):
     the hidden-states output to compute `span start logits` and `span end logits`). """,
     ROBERTA_START_DOCSTRING,
 )
-class RobertaForQuestionAnswering(BertPreTrainedModel):
-    config_class = RobertaConfig
-    base_model_prefix = "roberta"
-
+class RobertaForQuestionAnswering(RobertaPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
         self.num_labels = config.num_labels

tests/test_utils_check_copies.py

+import os
+import re
+import shutil
+import sys
+import tempfile
+import unittest
+
+
+git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
+sys.path.append(os.path.join(git_repo_path, "utils"))
+
+import check_copies  # noqa: E402
+
+
+# This is the reference code that will be used in the tests.
+# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
+REFERENCE_CODE = """    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+"""
+
+
+class CopyCheckTester(unittest.TestCase):
+    def setUp(self):
+        self.transformer_dir = tempfile.mkdtemp()
+        check_copies.TRANSFORMER_PATH = self.transformer_dir
+        shutil.copy(
+            os.path.join(git_repo_path, "src/transformers/modeling_bert.py"),
+            os.path.join(self.transformer_dir, "modeling_bert.py"),
+        )
+
+    def tearDown(self):
+        check_copies.TRANSFORMER_PATH = "src/transformers"
+        shutil.rmtree(self.transformer_dir)
+
+    def check_copy_consistency(self, comment, class_name, class_code, overwrite_result=None):
+        code = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
+        if overwrite_result is not None:
+            expected = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
+        fname = os.path.join(self.transformer_dir, "new_code.py")
+        with open(fname, "w") as f:
+            f.write(code)
+        if overwrite_result is None:
+            self.assertTrue(check_copies.is_copy_consistent(fname))
+        else:
+            check_copies.is_copy_consistent(f.name, overwrite=True)
+            with open(fname, "r") as f:
+                self.assertTrue(f.read(), expected)
+
+    def test_find_code_in_transformers(self):
+        code = check_copies.find_code_in_transformers("modeling_bert.BertLMPredictionHead")
+        self.assertEqual(code, REFERENCE_CODE)
+
+    def test_is_copy_consistent(self):
+        # Base copy consistency
+        self.check_copy_consistency(
+            "# Copied from transformers.modeling_bert.BertLMPredictionHead",
+            "BertLMPredictionHead",
+            REFERENCE_CODE + "\n",
+        )
+
+        # With no empty line at the end
+        self.check_copy_consistency(
+            "# Copied from transformers.modeling_bert.BertLMPredictionHead",
+            "BertLMPredictionHead",
+            REFERENCE_CODE,
+        )
+
+        # Copy consistency with rename
+        self.check_copy_consistency(
+            "# Copied from transformers.modeling_bert.BertLMPredictionHead with Bert->TestModel",
+            "TestModelLMPredictionHead",
+            re.sub("Bert", "TestModel", REFERENCE_CODE),
+        )
+
+        # Copy consistency with a really long name
+        long_class_name = "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReasonIReallyDontUnderstand"
+        self.check_copy_consistency(
+            f"# Copied from transformers.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}",
+            f"{long_class_name}LMPredictionHead",
+            re.sub("Bert", long_class_name, REFERENCE_CODE),
+        )
+
+        # Copy consistency with overwrite
+        self.check_copy_consistency(
+            "# Copied from transformers.modeling_bert.BertLMPredictionHead with Bert->TestModel",
+            "TestModelLMPredictionHead",
+            REFERENCE_CODE,
+            overwrite_result=re.sub("Bert", "TestModel", REFERENCE_CODE),
+        )

utils/check_copies.py

+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import glob
+import os
+import re
+import tempfile
+
+
+# All paths are set with the intent you should run this script from the root of the repo with the command
+# python utils/check_copies.py
+TRANSFORMERS_PATH = "src/transformers"
+
+
+def find_code_in_transformers(object_name):
+    """ Find and return the code source code of `object_name`."""
+    parts = object_name.split(".")
+    i = 0
+
+    # First let's find the module where our object lives.
+    module = parts[i]
+    while i < len(parts) and not os.path.isfile(os.path.join(TRANSFORMERS_PATH, f"{module}.py")):
+        i += 1
+        module = os.path.join(module, parts[i])
+    if i >= len(parts):
+        raise ValueError(
+            f"`object_name` should begin with the name of a module of transformers but got {object_name}."
+        )
+
+    with open(os.path.join(TRANSFORMERS_PATH, f"{module}.py"), "r") as f:
+        lines = f.readlines()
+
+    # Now let's find the class / func in the code!
+    indent = ""
+    line_index = 0
+    for name in parts[i + 1 :]:
+        while line_index < len(lines) and re.search(f"^{indent}(class|def)\s+{name}", lines[line_index]) is None:
+            line_index += 1
+        indent += "    "
+        line_index += 1
+
+    if line_index >= len(lines):
+        raise ValueError(f" {object_name} does not match any function or class in {module}.")
+
+    # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
+    start_index = line_index
+    while line_index < len(lines) and (lines[line_index].startswith(indent) or len(lines[line_index]) <= 1):
+        line_index += 1
+    # Clean up empty lines at the end (if any).
+    while len(lines[line_index - 1]) <= 1:
+        line_index -= 1
+
+    code_lines = lines[start_index:line_index]
+    return "".join(code_lines)
+
+
+_re_copy_warning = re.compile(r"^(\s*)#\s*Copied from\s+transformers\.(\S+\.\S+)\s*($|\S.*$)")
+_re_replace_pattern = re.compile(r"with\s+(\S+)->(\S+)(?:\s|$)")
+
+
+def blackify(code):
+    """
+    Applies the black part of our `make style` command to `code`.
+    """
+    has_indent = code.startswith("    ")
+    if has_indent:
+        code = f"class Bla:\n{code}"
+    with tempfile.TemporaryDirectory() as d:
+        fname = os.path.join(d, "tmp.py")
+        with open(fname, "w") as f:
+            f.write(code)
+        os.system(f"black -q --line-length 119 --target-version py35 {fname}")
+        with open(fname, "r") as f:
+            result = f.read()
+            return result[len("class Bla:\n") :] if has_indent else result
+
+
+def is_copy_consistent(filename, overwrite=False):
+    """
+    Check if the code commented as a copy in `filename` matches the original.
+
+    Return the differences or overwrites the content depending on `overwrite`.
+    """
+    with open(filename) as f:
+        lines = f.readlines()
+    found_diff = False
+    line_index = 0
+    # Not a foor loop cause `lines` is going to change (if `overwrite=True`).
+    while line_index < len(lines):
+        search = _re_copy_warning.search(lines[line_index])
+        if search is None:
+            line_index += 1
+            continue
+
+        # There is some copied code here, let's retrieve the original.
+        indent, object_name, replace_pattern = search.groups()
+        theoretical_code = find_code_in_transformers(object_name)
+        theoretical_indent = re.search(r"^(\s*)\S", theoretical_code).groups()[0]
+
+        start_index = line_index + 1 if indent == theoretical_indent else line_index + 2
+        indent = theoretical_indent
+        line_index = start_index
+
+        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
+        should_continue = True
+        while line_index < len(lines) and should_continue:
+            line_index += 1
+            if line_index >= len(lines):
+                break
+            line = lines[line_index]
+            should_continue = (len(line) <= 1 or line.startswith(indent)) and re.search(
+                f"^{indent}# End copy", line
+            ) is None
+        # Clean up empty lines at the end (if any).
+        while len(lines[line_index - 1]) <= 1:
+            line_index -= 1
+
+        observed_code_lines = lines[start_index:line_index]
+        observed_code = "".join(observed_code_lines)
+
+        # Before comparing, use the `replace_pattern` on the original code.
+        if len(replace_pattern) > 0:
+            search_patterns = _re_replace_pattern.search(replace_pattern)
+            if search_patterns is not None:
+                obj1, obj2 = search_patterns.groups()
+                theoretical_code = re.sub(obj1, obj2, theoretical_code)
+
+        # Blackify each version before comparing them.
+        observed_code = blackify(observed_code)
+        theoretical_code = blackify(theoretical_code)
+
+        # Test for a diff and act accordingly.
+        if observed_code != theoretical_code:
+            found_diff = True
+            if overwrite:
+                lines = lines[:start_index] + [theoretical_code] + lines[line_index:]
+                line_index = start_index + 1
+
+    if overwrite and found_diff:
+        # Warn the user a file has been modified.
+        print(f"Detected changes, rewriting {filename}.")
+        with open(filename, "w") as f:
+            f.writelines(lines)
+    return not found_diff
+
+
+def check_copies(overwrite: bool = False):
+    all_files = glob.glob(os.path.join(TRANSFORMERS_PATH, "**/*.py"), recursive=True)
+    diffs = []
+    for filename in all_files:
+        consistent = is_copy_consistent(filename, overwrite)
+        if not consistent:
+            diffs.append(filename)
+    if not overwrite and len(diffs) > 0:
+        diff = "\n".join(diffs)
+        raise Exception(
+            "Found copy inconsistencies in the following files:\n"
+            + diff
+            + "\nRun `make fix-copies` or `python utils/check_copies --fix_and_overwrite` to fix them."
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
+    args = parser.parse_args()
+
+    check_copies(args.fix_and_overwrite)

utils/check_repo.py

+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import importlib
 import inspect
 import os