Add `ElectraForCausalLM` -> Enable Electra encoder-decoder model (#14729)

* Add ElectraForCausalLM and cover some basic tests & need to fix a few tests

* Fix bugs

* make style

* make fix-copies

* Update doc

* Change docstring to markdown format

* Remove redundant update_keys_to_ignore

docs/source/model_doc/electra.mdx

@@ -83,6 +83,11 @@ This model was contributed by [lysandre](https://huggingface.co/lysandre). The o
 [[autodoc]] ElectraForPreTraining
     - forward
 
+## ElectraForCausalLM
+
+[[autodoc]] ElectraForCausalLM
+    - forward
+
 ## ElectraForMaskedLM
 
 [[autodoc]] ElectraForMaskedLM

src/transformers/__init__.py

@@ -885,6 +885,7 @@ if is_torch_available():
     _import_structure["models.electra"].extend(
         [
             "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "ElectraForCausalLM",
             "ElectraForMaskedLM",
             "ElectraForMultipleChoice",
             "ElectraForPreTraining",
@@ -2830,6 +2831,7 @@ if TYPE_CHECKING:
         )
         from .models.electra import (
             ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            ElectraForCausalLM,
             ElectraForMaskedLM,
             ElectraForMultipleChoice,
             ElectraForPreTraining,

src/transformers/models/auto/modeling_auto.py

@@ -218,6 +218,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
         ("transfo-xl", "TransfoXLLMHeadModel"),
         ("xlnet", "XLNetLMHeadModel"),
         ("xlm", "XLMWithLMHeadModel"),
+        ("electra", "ElectraForCausalLM"),
         ("ctrl", "CTRLLMHeadModel"),
         ("reformer", "ReformerModelWithLMHead"),
         ("bert-generation", "BertGenerationDecoder"),

src/transformers/models/electra/__init__.py

@@ -32,6 +32,7 @@ if is_tokenizers_available():
 if is_torch_available():
     _import_structure["modeling_electra"] = [
         "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "ElectraForCausalLM",
         "ElectraForMaskedLM",
         "ElectraForMultipleChoice",
         "ElectraForPreTraining",
@@ -79,6 +80,7 @@ if TYPE_CHECKING:
     if is_torch_available():
         from .modeling_electra import (
             ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            ElectraForCausalLM,
             ElectraForMaskedLM,
             ElectraForMultipleChoice,
             ElectraForPreTraining,

src/transformers/models/electra/modeling_electra.py

@@ -36,6 +36,7 @@ from ...file_utils import (
 from ...modeling_outputs import (
     BaseModelOutputWithCrossAttentions,
     BaseModelOutputWithPastAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
     MaskedLMOutput,
     MultipleChoiceModelOutput,
     QuestionAnsweringModelOutput,
@@ -846,6 +847,10 @@ class ElectraModel(ElectraPreTrainedModel):
         position_ids=None,
         head_mask=None,
         inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
         output_attentions=None,
         output_hidden_states=None,
         return_dict=None,
@@ -868,6 +873,9 @@ class ElectraModel(ElectraPreTrainedModel):
         batch_size, seq_length = input_shape
         device = input_ids.device if input_ids is not None else inputs_embeds.device
 
+        # past_key_values_length
+        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+
         if attention_mask is None:
             attention_mask = torch.ones(input_shape, device=device)
         if token_type_ids is None:
@@ -879,10 +887,26 @@ class ElectraModel(ElectraPreTrainedModel):
                 token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
 
         extended_attention_mask = 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
+
         head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
 
         hidden_states = self.embeddings(
-            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
         )
 
         if hasattr(self, "embeddings_project"):
@@ -892,6 +916,10 @@ class ElectraModel(ElectraPreTrainedModel):
             hidden_states,
             attention_mask=extended_attention_mask,
             head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
@@ -969,14 +997,14 @@ class ElectraForSequenceClassification(ElectraPreTrainedModel):
 
         discriminator_hidden_states = self.electra(
             input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            inputs_embeds,
-            output_attentions,
-            output_hidden_states,
-            return_dict,
+            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,
         )
 
         sequence_output = discriminator_hidden_states[0]
@@ -1075,14 +1103,14 @@ class ElectraForPreTraining(ElectraPreTrainedModel):
 
         discriminator_hidden_states = self.electra(
             input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            inputs_embeds,
-            output_attentions,
-            output_hidden_states,
-            return_dict,
+            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,
         )
         discriminator_sequence_output = discriminator_hidden_states[0]
 
@@ -1166,14 +1194,14 @@ class ElectraForMaskedLM(ElectraPreTrainedModel):
 
         generator_hidden_states = self.electra(
             input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            inputs_embeds,
-            output_attentions,
-            output_hidden_states,
-            return_dict,
+            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,
         )
         generator_sequence_output = generator_hidden_states[0]
 
@@ -1247,14 +1275,14 @@ class ElectraForTokenClassification(ElectraPreTrainedModel):
 
         discriminator_hidden_states = self.electra(
             input_ids,
-            attention_mask,
-            token_type_ids,
-            position_ids,
-            head_mask,
-            inputs_embeds,
-            output_attentions,
-            output_hidden_states,
-            return_dict,
+            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,
         )
         discriminator_sequence_output = discriminator_hidden_states[0]
 
@@ -1481,3 +1509,152 @@ class ElectraForMultipleChoice(ElectraPreTrainedModel):
             hidden_states=discriminator_hidden_states.hidden_states,
             attentions=discriminator_hidden_states.attentions,
         )
+
+
+@add_start_docstrings(
+    """ELECTRA Model with a `language modeling` head on top for CLM fine-tuning. """, ELECTRA_START_DOCSTRING
+)
+class ElectraForCausalLM(ElectraPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        if not config.is_decoder:
+            logger.warning("If you want to use `ElectraLMHeadModel` as a standalone, add `is_decoder=True.`")
+
+        self.electra = ElectraModel(config)
+        self.generator_predictions = ElectraGeneratorPredictions(config)
+        self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.generator_lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.generator_lm_head = new_embeddings
+
+    @add_start_docstrings_to_model_forward(ELECTRA_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,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        encoder_hidden_states  (`torch.FloatTensor` of shape `(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 (`torch.FloatTensor` of shape `(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 (`torch.LongTensor` of shape `(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 in `[0, ..., config.vocab_size]`
+        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
+            instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up
+            decoding (see `past_key_values`).
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import ElectraTokenizer, ElectraForCausalLM, ElectraConfig
+        >>> import torch
+
+        >>> tokenizer = ElectraTokenizer.from_pretrained("google/electra-base-generator")
+        >>> config = ElectraConfig.from_pretrained("google/electra-base-generator")
+        >>> config.is_decoder = True
+        >>> model = ElectraForCausalLM.from_pretrained("google/electra-base-generator", 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
+        if labels is not None:
+            use_cache = False
+
+        outputs = self.electra(
+            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,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.generator_lm_head(self.generator_predictions(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,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM.prepare_inputs_for_generation
+    def prepare_inputs_for_generation(self, input_ids, past=None, 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)
+
+        # cut decoder_input_ids if past is used
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
+
+    # Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM._reorder_cache
+    def _reorder_cache(self, past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past

src/transformers/utils/dummy_pt_objects.py

@@ -1924,6 +1924,18 @@ class DPRReader:
 ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class ElectraForCausalLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    def forward(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class ElectraForMaskedLM:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])

tests/test_modeling_electra.py

@@ -21,7 +21,7 @@ from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
-from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
 
 
 if is_torch_available():
@@ -29,6 +29,7 @@ if is_torch_available():
 
     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
+        ElectraForCausalLM,
         ElectraForMaskedLM,
         ElectraForMultipleChoice,
         ElectraForPreTraining,
@@ -117,6 +118,34 @@ class ElectraModelTester:
             initializer_range=self.initializer_range,
         )
 
+    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_electra_model(
         self,
         config,
@@ -136,6 +165,38 @@ class ElectraModelTester:
         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_electra_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 = ElectraModel(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_electra_for_masked_lm(
         self,
         config,
@@ -153,6 +214,24 @@ class ElectraModelTester:
         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_electra_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 = ElectraForCausalLM(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_electra_for_token_classification(
         self,
         config,
@@ -281,6 +360,7 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
             ElectraModel,
             ElectraForPreTraining,
             ElectraForMaskedLM,
+            ElectraForCausalLM,
             ElectraForMultipleChoice,
             ElectraForTokenClassification,
             ElectraForSequenceClassification,
@@ -289,6 +369,8 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
         if is_torch_available()
         else ()
     )
+    all_generative_model_classes = (ElectraForCausalLM,) if is_torch_available() else ()
+
     fx_ready_model_classes = all_model_classes
     fx_dynamic_ready_model_classes = all_model_classes
 
@@ -314,6 +396,10 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_electra_model(*config_and_inputs)
 
+    def test_electra_model_as_decoder(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_electra_model_as_decoder(*config_and_inputs)
+
     def test_electra_model_various_embeddings(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
@@ -350,6 +436,10 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
             model = ElectraModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    def test_for_causal_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_electra_for_causal_lm(*config_and_inputs)
+
 
 @require_torch
 class ElectraModelIntegrationTest(unittest.TestCase):