Templates overhaul 1 (#8993)

.github/workflows/model-templates.yml

+name: Model templates runner
+
+on:
+  push:
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+
+jobs:
+  run_tests_templates:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v1
+
+      - name: Install Python
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.6
+
+      - name: Loading cache.
+        uses: actions/cache@v2
+        id: cache
+        with:
+          path: ~/.cache/pip
+          key: v1.2-tests_templates
+          restore-keys: |
+            v1.2-tests_templates-${{ hashFiles('setup.py') }}
+            v1.2-tests_templates
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install .[dev]
+      - name: Create model files
+        run: |
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          make style
+          python utils/check_table.py --fix_and_overwrite
+          python utils/check_dummies.py --fix_and_overwrite
+
+      - name: Run all non-slow tests
+        run: |
+          python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_templates tests/*template*
+
+      - name: Run style changes
+        run: |
+          git fetch origin master:master
+          make fixup
+
+      - name: Failure short reports
+        if: ${{ always() }}
+        run: cat reports/tests_templates_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: run_all_tests_templates_test_reports
+          path: reports

src/transformers/commands/add_new_model.py

@@ -19,12 +19,18 @@ from argparse import ArgumentParser, Namespace
 from pathlib import Path
 from typing import List
 
-from cookiecutter.main import cookiecutter
 from transformers.commands import BaseTransformersCLICommand
 
 from ..utils import logging
 
 
+try:
+    from cookiecutter.main import cookiecutter
+
+    _has_cookiecutter = True
+except ImportError:
+    _has_cookiecutter = False
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -49,6 +55,11 @@ class AddNewModelCommand(BaseTransformersCLICommand):
         self._path = path
 
     def run(self):
+        if not _has_cookiecutter:
+            raise ImportError(
+                "Model creation dependencies are required to use the `add_new_model` command. Install them by running "
+                "the folowing at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"
+            )
         # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
         directories = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
         if len(directories) > 0:
@@ -153,6 +164,11 @@ class AddNewModelCommand(BaseTransformersCLICommand):
             f"{model_dir}/tokenization_{lowercase_model_name}.py",
         )
 
+        shutil.move(
+            f"{directory}/tokenization_fast_{lowercase_model_name}.py",
+            f"{model_dir}/tokenization_{lowercase_model_name}_fast.py",
+        )
+
         from os import fdopen, remove
         from shutil import copymode, move
         from tempfile import mkstemp

src/transformers/file_utils.py

@@ -849,7 +849,7 @@ def add_code_sample_docstrings(
         elif "MaskedLM" in model_class or model_class in ["FlaubertWithLMHeadModel", "XLMWithLMHeadModel"]:
             doc_kwargs["mask"] = "[MASK]" if mask is None else mask
             code_sample = TF_MASKED_LM_SAMPLE if is_tf_class else PT_MASKED_LM_SAMPLE
-        elif "LMHead" in model_class:
+        elif "LMHead" in model_class or "CausalLM" in model_class:
             code_sample = TF_CAUSAL_LM_SAMPLE if is_tf_class else PT_CAUSAL_LM_SAMPLE
         elif "Model" in model_class or "Encoder" in model_class:
             code_sample = TF_BASE_MODEL_SAMPLE if is_tf_class else PT_BASE_MODEL_SAMPLE

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/__init__.py

@@ -17,20 +17,24 @@
 # limitations under the License.
 
 {%- if cookiecutter.generate_tensorflow_and_pytorch == "PyTorch & TensorFlow" %}
-from ...file_utils import is_tf_available, is_torch_available
+from ...file_utils import is_tf_available, is_torch_available, is_tokenizers_available
 {%- elif cookiecutter.generate_tensorflow_and_pytorch == "PyTorch" %}
-from ...file_utils import is_torch_available
+from ...file_utils import is_torch_available, is_tokenizers_available
 {%- elif cookiecutter.generate_tensorflow_and_pytorch == "TensorFlow" %}
-from ...file_utils import is_tf_available
+from ...file_utils import is_tf_available, is_tokenizers_available
 {% endif %}
 from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP, {{cookiecutter.camelcase_modelname}}Config
 from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
 
+if is_tokenizers_available():
+    from .tokenization_{{cookiecutter.lowercase_modelname}}_fast import {{cookiecutter.camelcase_modelname}}TokenizerFast
+
 {%- if (cookiecutter.generate_tensorflow_and_pytorch == "PyTorch & TensorFlow" or cookiecutter.generate_tensorflow_and_pytorch == "PyTorch") %}
 if is_torch_available():
     from .modeling_{{cookiecutter.lowercase_modelname}} import (
         {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
         {{cookiecutter.camelcase_modelname}}ForMaskedLM,
+        {{cookiecutter.camelcase_modelname}}ForCausalLM,
         {{cookiecutter.camelcase_modelname}}ForMultipleChoice,
         {{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
         {{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -46,6 +50,7 @@ if is_tf_available():
     from .modeling_tf_{{cookiecutter.lowercase_modelname}} import (
         TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
+        TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
         TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
         TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
         TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -17,6 +17,7 @@
 
 import tensorflow as tf
 
+from transformers.modeling_tf_outputs import TFCausalLMOutput
 from ...activations_tf import get_tf_activation
 from ...file_utils import (
     MULTIPLE_CHOICE_DUMMY_INPUTS,
@@ -40,6 +41,7 @@ from ...modeling_tf_utils import (
     TFQuestionAnsweringLoss,
     TFSequenceClassificationLoss,
     TFTokenClassificationLoss,
+    TFCausalLanguageModelingLoss,
     TFSequenceSummary,
     get_initializer,
     input_processing,
@@ -111,19 +113,23 @@ class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
         mode="embedding",
         training=False,
     ):
-        """Get token embeddings of inputs.
+        """
+        Get token embeddings of inputs.
+
         Args:
             inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
             mode: string, a valid value is one of "embedding" and "linear".
+
         Returns:
-            outputs: (1) If mode == "embedding", output embedding tensor, float32 with
-                shape [batch_size, length, embedding_size]; (2) mode == "linear", output
-                linear tensor, float32 with shape [batch_size, length, vocab_size].
+            outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
+            embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
+            vocab_size].
+
         Raises:
             ValueError: if mode is not valid.
 
         Shared weights logic adapted from
-            https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
+        https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
         """
         if mode == "embedding":
             return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
@@ -161,9 +167,12 @@ class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
         return embeddings
 
     def _linear(self, inputs):
-        """Computes logits by running inputs through a linear layer.
+        """
+        Computes logits by running inputs through a linear layer.
+
         Args:
-            inputs: A float32 tensor with shape [batch_size, length, hidden_size]
+            inputs: A float32 tensor with shape [batch_size, length, hidden_size].
+
         Returns:
             float32 tensor with shape [batch_size, length, vocab_size].
         """
@@ -327,7 +336,6 @@ class TF{{cookiecutter.camelcase_modelname}}Output(tf.keras.layers.Layer):
         return hidden_states
 
 
-# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->{{cookiecutter.camelcase_modelname}}
 class TF{{cookiecutter.camelcase_modelname}}Layer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -336,6 +344,7 @@ class TF{{cookiecutter.camelcase_modelname}}Layer(tf.keras.layers.Layer):
         self.intermediate = TF{{cookiecutter.camelcase_modelname}}Intermediate(config, name="intermediate")
         self.{{cookiecutter.lowercase_modelname}}_output = TF{{cookiecutter.camelcase_modelname}}Output(config, name="output")
 
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer.call with bert->{{cookiecutter.lowercase_modelname}}
     def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False):
         attention_outputs = self.attention(
             hidden_states, attention_mask, head_mask, output_attentions, training=training
@@ -347,7 +356,7 @@ class TF{{cookiecutter.camelcase_modelname}}Layer(tf.keras.layers.Layer):
 
         return outputs
 
-
+# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->{{cookiecutter.camelcase_modelname}}
 class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
@@ -453,20 +462,6 @@ class TF{{cookiecutter.camelcase_modelname}}MLMHead(tf.keras.layers.Layer):
         return prediction_scores
 
 
-class TF{{cookiecutter.camelcase_modelname}}NSPHead(tf.keras.layers.Layer):
-    def __init__(self, config, **kwargs):
-        super().__init__(**kwargs)
-
-        self.seq_relationship = tf.keras.layers.Dense(
-            2, kernel_initializer=get_initializer(config.initializer_range), name="seq_relationship"
-        )
-
-    def call(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-
-        return seq_relationship_score
-
-
 @keras_serializable
 class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
     config_class = {{cookiecutter.camelcase_modelname}}Config
@@ -600,7 +595,6 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
         )
 
 
-# Copied from transformers.models.bert.modeling_tf_bert.TFBertPreTrainedModel with Bert->{{cookiecutter.camelcase_modelname}}
 class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
     """An abstract class to handle weights initialization and
     a simple interface for downloading and loading pretrained models.
@@ -855,6 +849,97 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
             attentions=outputs.attentions,
         )
 
+@add_start_docstrings(
+    """{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
+)
+class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFCausalLanguageModelingLoss):
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        if not config.is_decoder:
+            logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
+
+        self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
+        self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
+
+    def get_output_embeddings(self):
+        return self.{{cookiecutter.lowercase_modelname}}.embeddings
+
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="{{cookiecutter.checkpoint_identifier}}",
+        output_type=TFCausalLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        labels=None,
+        training=False,
+        **kwargs,
+    ):
+        r"""
+        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
+            config.vocab_size - 1]``.
+        """
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            labels=labels,
+            training=training,
+            kwargs_call=kwargs,
+        )
+        outputs = self.{{cookiecutter.lowercase_modelname}}(
+            inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            token_type_ids=inputs["token_type_ids"],
+            position_ids=inputs["position_ids"],
+            head_mask=inputs["head_mask"],
+            inputs_embeds=inputs["inputs_embeds"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+        sequence_output = outputs[0]
+        logits = self.mlm(sequence_output, training=inputs["training"])
+        loss = None
+
+        if inputs["labels"] is not None:
+            # shift labels to the left and cut last logit token
+            logits = logits[:, :-1]
+            labels = inputs["labels"][:, 1:]
+            loss = self.compute_loss(labels, logits)
+
+        if not inputs["return_dict"]:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFCausalLMOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
 
 class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -1151,7 +1236,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
             training=training,
             kwargs_call=kwargs,
         )
-        outputs = self.{{cookiecutter.uppercase_modelname}}(
+        outputs = self.{{cookiecutter.lowercase_modelname}}(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
             token_type_ids=inputs["token_type_ids"],
@@ -1246,7 +1331,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
             training=training,
             kwargs_call=kwargs,
         )
-        outputs = self.{{cookiecutter.uppercase_modelname}}(
+        outputs = self.{{cookiecutter.lowercase_modelname}}(
             inputs["input_ids"],
             attention_mask=inputs["attention_mask"],
             token_type_ids=inputs["token_type_ids"],

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -18,7 +18,6 @@
 
 import math
 import os
-import warnings
 
 import torch
 import torch.utils.checkpoint
@@ -29,10 +28,11 @@ from ...file_utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
 )
 from ...modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPooling,
+    BaseModelOutputWithCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
     MaskedLMOutput,
     MultipleChoiceModelOutput,
     QuestionAnsweringModelOutput,
@@ -157,6 +157,7 @@ class {{cookiecutter.camelcase_modelname}}Embeddings(nn.Module):
 
         # 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)))
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
 
     def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
         if input_ids is not None:
@@ -174,10 +175,12 @@ class {{cookiecutter.camelcase_modelname}}Embeddings(nn.Module):
 
         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 = inputs_embeds + token_type_embeddings
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings += position_embeddings
         embeddings = self.LayerNorm(embeddings)
         embeddings = self.dropout(embeddings)
         return embeddings
@@ -202,6 +205,10 @@ class {{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module):
         self.value = nn.Linear(config.hidden_size, self.all_head_size)
 
         self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
 
     def transpose_for_scores(self, x):
         new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
@@ -236,6 +243,23 @@ class {{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module):
 
         # 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))
+
+        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
+            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
+            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
+                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
+                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
+
         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 {{cookiecutter.camelcase_modelname}}Model forward() function)
@@ -432,10 +456,11 @@ class {{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
         encoder_attention_mask=None,
         output_attentions=False,
         output_hidden_states=False,
-        return_dict=False,
+        return_dict=True,
     ):
         all_hidden_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
         for i, layer_module in enumerate(self.layer):
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
@@ -469,15 +494,24 @@ class {{cookiecutter.camelcase_modelname}}Encoder(nn.Module):
                 )
             hidden_states = layer_outputs[0]
             if output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
 
         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
+            return tuple(
+                v
+                for v in [hidden_states, all_hidden_states, all_self_attentions, all_cross_attentions]
+                if v is not None
+            )
+        return BaseModelOutputWithCrossAttentions(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
         )
 
 
@@ -664,7 +698,7 @@ class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelna
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="{{cookiecutter.checkpoint_identifier}}",
-        output_type=BaseModelOutput,
+        output_type=BaseModelOutputWithCrossAttentions,
         config_class=_CONFIG_FOR_DOC,
     )
     def forward(
@@ -755,10 +789,11 @@ class {{cookiecutter.camelcase_modelname}}Model({{cookiecutter.camelcase_modelna
         if not return_dict:
             return (sequence_output,) + encoder_outputs[1:]
 
-        return BaseModelOutput(
+        return BaseModelOutputWithCrossAttentions(
             last_hidden_state=sequence_output,
             hidden_states=encoder_outputs.hidden_states,
             attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
         )
 
 
@@ -863,6 +898,127 @@ class {{cookiecutter.camelcase_modelname}}ForMaskedLM({{cookiecutter.camelcase_m
         return {"input_ids": input_ids, "attention_mask": attention_mask}
 
 
+@add_start_docstrings(
+    """{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
+)
+class {{cookiecutter.camelcase_modelname}}ForCausalLM({{cookiecutter.camelcase_modelname}}PreTrainedModel):
+
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if not config.is_decoder:
+            logger.warning("If you want to use `{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
+
+        self.{{cookiecutter.lowercase_modelname}} = {{cookiecutter.camelcase_modelname}}Model(config)
+        self.cls = {{cookiecutter.camelcase_modelname}}OnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
+    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,
+            labels=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 tokens that are **masked**.
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
+            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``.
+
+        Returns:
+
+        Example::
+
+            >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForCausalLM, {{cookiecutter.camelcase_modelname}}Config
+            >>> import torch
+
+            >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
+            >>> config = {{cookiecutter.camelcase_modelname}}Config.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
+            >>> config.is_decoder = True
+            >>> model = {{cookiecutter.camelcase_modelname}}ForCausalLM.from_pretrained('{{cookiecutter.checkpoint_identifier}}', config=config)
+
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+
+            >>> prediction_logits = outputs.logits
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.{{cookiecutter.lowercase_modelname}}(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        lm_loss = None
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[1:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
+
 class {{cookiecutter.camelcase_modelname}}ClassificationHead(nn.Module):
     """Head for sentence-level classification tasks."""
 

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -32,6 +32,7 @@ if is_tf_available():
         TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
         TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
         TF{{cookiecutter.camelcase_modelname}}ForTokenClassification,
+        TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
         TF{{cookiecutter.camelcase_modelname}}Model,
     )
 
@@ -134,6 +135,21 @@ class TF{{cookiecutter.camelcase_modelname}}ModelTester:
 
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
 
+    def create_and_check_lm_head(
+            self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.is_decoder = True
+        model = TF{{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        prediction_scores = model(inputs)["logits"]
+        self.parent.assertListEqual(
+            list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
+        )
+
     def create_and_check_for_masked_lm(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
@@ -224,6 +240,7 @@ class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unitte
     all_model_classes = (
         (
             TF{{cookiecutter.camelcase_modelname}}Model,
+            TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
             TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
             TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
             TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -249,6 +266,10 @@ class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unitte
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
 
+    def test_for_causal_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_lm_head(*config_and_inputs)
+
     def test_for_multiple_choice(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
@@ -269,3 +290,31 @@ class TF{{cookiecutter.camelcase_modelname}}ModelTest(TFModelTesterMixin, unitte
     def test_model_from_pretrained(self):
         model = TF{{cookiecutter.camelcase_modelname}}Model.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
         self.assertIsNotNone(model)
+
+@require_tf
+class TF{{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = TF{{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
+        input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
+        output = model(input_ids)[0]
+
+        # TODO Replace vocab size
+        vocab_size = 32000
+
+        expected_shape = [1, 6, vocab_size]
+        self.assertEqual(output.shape, expected_shape)
+
+        print(output[:, :3, :3])
+
+        # TODO Replace values below with what was printed above.
+        expected_slice = tf.constant(
+            [
+                [
+                    [-0.05243197, -0.04498899, 0.05512108],
+                    [-0.07444685, -0.01064632, 0.04352357],
+                    [-0.05020351, 0.05530146, 0.00700043],
+                ]
+            ]
+        )
+        tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -17,6 +17,7 @@
 
 import unittest
 
+from tests.test_modeling_common import floats_tensor
 from transformers import is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 from .test_configuration_common import ConfigTester
@@ -25,9 +26,12 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention
 
 
 if is_torch_available():
+    import torch
+
     from transformers import (
         {{cookiecutter.camelcase_modelname}}Config,
         {{cookiecutter.camelcase_modelname}}ForMaskedLM,
+        {{cookiecutter.camelcase_modelname}}ForCausalLM,
         {{cookiecutter.camelcase_modelname}}ForMultipleChoice,
         {{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
         {{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -122,6 +126,33 @@ class {{cookiecutter.camelcase_modelname}}ModelTester:
 
         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 
+    def prepare_config_and_inputs_for_decoder(self):
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = self.prepare_config_and_inputs()
+
+        config.is_decoder = True
+        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
+        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        return (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
     def create_and_check_model(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
@@ -133,6 +164,56 @@ class {{cookiecutter.camelcase_modelname}}ModelTester:
         result = model(input_ids)
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
 
+    def create_and_check_model_as_decoder(
+            self,
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+    ):
+        config.add_cross_attention = True
+        model = {{cookiecutter.camelcase_modelname}}Model(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+        )
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_causal_lm(
+            self,
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+    ):
+        model = {{cookiecutter.camelcase_modelname}}ForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
     def create_and_check_for_masked_lm(
             self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):
@@ -218,6 +299,7 @@ class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, unittest.T
         (
             {{cookiecutter.camelcase_modelname}}Model,
             {{cookiecutter.camelcase_modelname}}ForMaskedLM,
+            {{cookiecutter.camelcase_modelname}}ForCausalLM,
             {{cookiecutter.camelcase_modelname}}ForMultipleChoice,
             {{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
             {{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -226,6 +308,7 @@ class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, unittest.T
         if is_torch_available()
         else ()
     )
+    all_generative_model_classes = ({{cookiecutter.camelcase_modelname}}ForCausalLM,) if is_torch_available() else ()
 
     def setUp(self):
         self.model_tester = {{cookiecutter.camelcase_modelname}}ModelTester(self)
@@ -238,6 +321,12 @@ class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, unittest.T
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_model_various_embeddings(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        for type in ["absolute", "relative_key", "relative_key_query"]:
+            config_and_inputs[0].position_embedding_type = type
+            self.model_tester.create_and_check_model(*config_and_inputs)
+
     def test_for_masked_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
@@ -258,6 +347,38 @@ class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, unittest.T
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
 
+    def test_model_as_decoder(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
+
+    def test_model_as_decoder_with_default_input_mask(self):
+        # This regression test was failing with PyTorch < 1.3
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        ) = self.model_tester.prepare_config_and_inputs_for_decoder()
+
+        input_mask = None
+
+        self.model_tester.create_and_check_model_as_decoder(
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
@@ -265,3 +386,23 @@ class {{cookiecutter.camelcase_modelname}}ModelTest(ModelTesterMixin, unittest.T
             self.assertIsNotNone(model)
 
 
+@require_torch
+class {{cookiecutter.camelcase_modelname}}ModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = {{cookiecutter.camelcase_modelname}}ForMaskedLM.from_pretrained("{{cookiecutter.checkpoint_identifier}}")
+        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
+        output = model(input_ids)[0]
+
+        # TODO Replace vocab size
+        vocab_size = 32000
+
+        expected_shape = torch.Size((1, 6, vocab_size))
+        self.assertEqual(output.shape, expected_shape)
+
+        # TODO Replace values below with what was printed above.
+        expected_slice = torch.tensor(
+            [[[-0.0483, 0.1188, -0.0313], [-0.0606, 0.1435, 0.0199], [-0.0235, 0.1519, 0.0175]]]
+        )
+
+        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/to_replace_{{cookiecutter.lowercase_modelname}}.py

@@ -31,6 +31,7 @@
     from .models.{{cookiecutter.lowercase_modelname}} import (
         {{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
         {{cookiecutter.camelcase_modelname}}ForMaskedLM,
+        {{cookiecutter.camelcase_modelname}}ForCausalLM,
         {{cookiecutter.camelcase_modelname}}ForMultipleChoice,
         {{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
         {{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -47,6 +48,7 @@
     from .models.{{cookiecutter.lowercase_modelname}} import (
         TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
+        TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
         TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
         TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
         TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -57,10 +59,13 @@
     )
 # End.
 
+# Below: "if is_tokenizers_available():"
+# Replace with:
+    from models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}TokenizerFast
 
 # Below: "from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig"
 # Replace with:
-from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP, {{cookiecutter.camelcase_modelname}}Config
+from .models.{{cookiecutter.lowercase_modelname}} import {{cookiecutter.uppercase_modelname}}_PRETRAINED_CONFIG_ARCHIVE_MAP, {{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}Tokenizer
 # End.
 
 
@@ -96,9 +101,9 @@ from ..{{cookiecutter.lowercase_modelname}}.configuration_{{cookiecutter.lowerca
 
 # Below: "# Add modeling imports here"
 # Replace with:
-
 from ..{{cookiecutter.lowercase_modelname}}.modeling_{{cookiecutter.lowercase_modelname}} import (
     {{cookiecutter.camelcase_modelname}}ForMaskedLM,
+    {{cookiecutter.camelcase_modelname}}ForCausalLM,
     {{cookiecutter.camelcase_modelname}}ForMultipleChoice,
     {{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
     {{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -117,6 +122,11 @@ from ..{{cookiecutter.lowercase_modelname}}.modeling_{{cookiecutter.lowercase_mo
         ({{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}ForMaskedLM),
 # End.
 
+# Below: "# Model for Causal LM mapping"
+# Replace with:
+        ({{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}ForCausalLM),
+# End.
+
 # Below: "# Model for Masked LM mapping"
 # Replace with:
         ({{cookiecutter.camelcase_modelname}}Config, {{cookiecutter.camelcase_modelname}}ForMaskedLM),
@@ -151,9 +161,9 @@ from ..{{cookiecutter.lowercase_modelname}}.modeling_{{cookiecutter.lowercase_mo
 
 # Below: "# Add modeling imports here"
 # Replace with:
-
 from ..{{cookiecutter.lowercase_modelname}}.modeling_tf_{{cookiecutter.lowercase_modelname}} import (
     TF{{cookiecutter.camelcase_modelname}}ForMaskedLM,
+    TF{{cookiecutter.camelcase_modelname}}ForCausalLM,
     TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice,
     TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering,
     TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification,
@@ -172,6 +182,11 @@ from ..{{cookiecutter.lowercase_modelname}}.modeling_tf_{{cookiecutter.lowercase
         ({{cookiecutter.camelcase_modelname}}Config, TF{{cookiecutter.camelcase_modelname}}ForMaskedLM),
 # End.
 
+# Below: "# Model for Causal LM mapping"
+# Replace with:
+        ({{cookiecutter.camelcase_modelname}}Config, TF{{cookiecutter.camelcase_modelname}}ForCausalLM),
+# End.
+
 # Below: "# Model for Masked LM mapping"
 # Replace with:
         ({{cookiecutter.camelcase_modelname}}Config, TF{{cookiecutter.camelcase_modelname}}ForMaskedLM),

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_fast_{{cookiecutter.lowercase_modelname}}.py

+# coding=utf-8
+# Copyright {{cookiecutter.authors}} and The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+"""Tokenization classes for {{cookiecutter.modelname}}."""
+
+{%- if cookiecutter.tokenizer_type == "Based on BERT" %}
+from ...utils import logging
+from ..bert.tokenization_bert_fast import BertTokenizerFast
+from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "{{cookiecutter.checkpoint_identifier}}": "https://huggingface.co/{{cookiecutter.checkpoint_identifier}}/resolve/main/vocab.txt",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "{{cookiecutter.checkpoint_identifier}}": 512,
+}
+
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "{{cookiecutter.checkpoint_identifier}}": {"do_lower_case": False},
+}
+
+
+class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
+    r"""
+    Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).
+
+    :class:`~transformers.{{cookiecutter.camelcase_modelname}}TokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs
+    end-to-end tokenization: punctuation splitting and wordpiece.
+
+    Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
+    parameters.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
+{%- elif cookiecutter.tokenizer_type == "Standalone" %}
+from typing import List, Optional
+
+from tokenizers import ByteLevelBPETokenizer
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Tokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "{{cookiecutter.checkpoint_identifier}}": 1024,
+}
+
+class {{cookiecutter.camelcase_modelname}}TokenizerFast(PreTrainedTokenizerFast):
+    """
+    Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).
+
+    Args:
+        vocab_file (:obj:`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    slow_tokenizer_class = {{cookiecutter.camelcase_modelname}}Tokenizer
+
+    def __init__(
+            self,
+            vocab_file,
+            merges_file,
+            unk_token="<|endoftext|>",
+            bos_token="<|endoftext|>",
+            eos_token="<|endoftext|>",
+            add_prefix_space=False,
+            trim_offsets=True,
+            **kwargs
+    ):
+        super().__init__(
+            ByteLevelBPETokenizer(
+                vocab_file=vocab_file,
+                merges_file=merges_file,
+                add_prefix_space=add_prefix_space,
+                trim_offsets=trim_offsets,
+            ),
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            **kwargs,
+        )
+        self.add_prefix_space = add_prefix_space
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+        if token_ids_1 is None:
+            return output
+
+        return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]
+
+
+    def create_token_type_ids_from_sequences(
+            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task.
+        {{cookiecutter.modelname}} does not make use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`:  List of zeros.
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+
+{% endif %}

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/tokenization_{{cookiecutter.lowercase_modelname}}.py

@@ -17,7 +17,6 @@
 {%- if cookiecutter.tokenizer_type == "Based on BERT" %}
 from ...utils import logging
 from ..bert.tokenization_bert import BertTokenizer
-from ..bert.tokenization_bert_fast import BertTokenizerFast
 
 
 logger = logging.get_logger(__name__)
@@ -55,30 +54,12 @@ class {{cookiecutter.camelcase_modelname}}Tokenizer(BertTokenizer):
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-
-
-class {{cookiecutter.camelcase_modelname}}TokenizerFast(BertTokenizerFast):
-    r"""
-    Construct a "fast" {{cookiecutter.modelname}} tokenizer (backed by HuggingFace's `tokenizers` library).
-
-    :class:`~transformers.{{cookiecutter.camelcase_modelname}}TokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs
-    end-to-end tokenization: punctuation splitting and wordpiece.
-
-    Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
-    parameters.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
 {%- elif cookiecutter.tokenizer_type == "Standalone" %}
 from typing import List, Optional
 
 from tokenizers import ByteLevelBPETokenizer
 
 from ...tokenization_utils import AddedToken, PreTrainedTokenizer
-from ...tokenization_utils_base import BatchEncoding
 from ...tokenization_utils_fast import PreTrainedTokenizerFast
 from ...utils import logging
 

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/{{cookiecutter.lowercase_modelname}}.rst

@@ -58,6 +58,13 @@ Tips:
     :members: forward
 
 
+{{cookiecutter.camelcase_modelname}}ForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.{{cookiecutter.camelcase_modelname}}ForCausalLM
+    :members: forward
+
+
 {{cookiecutter.camelcase_modelname}}ForMaskedLM
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -109,6 +116,13 @@ TF{{cookiecutter.camelcase_modelname}}ForMaskedLM
     :members: call
 
 
+TF{{cookiecutter.camelcase_modelname}}ForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TF{{cookiecutter.camelcase_modelname}}ForCausalLM
+    :members: forward
+
+
 TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

templates/adding_a_new_model/tests/encoder-bert-tokenizer.json

 {
-  "modelname": "EncoderBERT",
-  "uppercase_modelname": "ENCODER_BERT",
-  "lowercase_modelname": "encoder_bert",
-  "camelcase_modelname": "EncoderBert",
+  "modelname": "Template",
+  "uppercase_modelname": "TEMPLATE",
+  "lowercase_modelname": "template",
+  "camelcase_modelname": "Template",
   "authors": "The HuggingFace Team",
   "checkpoint_identifier": "brand-new-bert-base-cased",
   "tokenizer_type": "Based on BERT",

templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json

 {
-  "modelname": "PTEncoderBERT",
-  "uppercase_modelname": "PT_ENCODER_BERT",
-  "lowercase_modelname": "pt_encoder_bert",
-  "camelcase_modelname": "PtEncoderBert",
+  "modelname": "TemplatePT",
+  "uppercase_modelname": "TEMPLATE_PT",
+  "lowercase_modelname": "template_pt",
+  "camelcase_modelname": "TemplatePt",
   "authors": "The HuggingFace Team",
   "checkpoint_identifier": "brand-new-bert-base-cased",
   "tokenizer_type": "Based on BERT",

templates/adding_a_new_model/tests/standalone.json

 {
-  "modelname": "BIEncoderBERT",
-  "uppercase_modelname": "BI_ENCODER_BERT",
-  "lowercase_modelname": "bi_encoder_bert",
-  "camelcase_modelname": "BiEncoderBert",
+  "modelname": "TemplateBI",
+  "uppercase_modelname": "TEMPLATE_BI",
+  "lowercase_modelname": "template_bi",
+  "camelcase_modelname": "TemplateBi",
   "authors": "The HuggingFace Team",
   "checkpoint_identifier": "bi-brand-new-bert-base-cased",
   "tokenizer_type": "Standalone",

templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json

 {
-  "modelname": "TFEncoderBERT",
-  "uppercase_modelname": "TF_ENCODER_BERT",
-  "lowercase_modelname": "tf_encoder_bert",
-  "camelcase_modelname": "TfEncoderBert",
+  "modelname": "TemplateTF",
+  "uppercase_modelname": "TEMPLATE_TF",
+  "lowercase_modelname": "template_tf",
+  "camelcase_modelname": "TemplateTf",
   "authors": "The HuggingFace Team",
   "checkpoint_identifier": "brand-new-bert-base-cased",
   "tokenizer_type": "Based on BERT",