remove unnecessary prepare for inputs

src/transformers/__init__.py

@@ -366,6 +366,7 @@ if is_torch_available():
         ReformerAttention,
         ReformerLayer,
         ReformerModel,
+        ReformerForMaskedLM,
         ReformerModelWithLMHead,
         REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )

src/transformers/modeling_auto.py

@@ -122,7 +122,7 @@ from .modeling_mobilebert import (
     MobileBertModel,
 )
 from .modeling_openai import OpenAIGPTLMHeadModel, OpenAIGPTModel
-from .modeling_reformer import ReformerModel, ReformerModelWithLMHead
+from .modeling_reformer import ReformerForMaskedLM, ReformerModel, ReformerModelWithLMHead
 from .modeling_retribert import RetriBertModel
 from .modeling_roberta import (
     RobertaForMaskedLM,
@@ -266,6 +266,7 @@ MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
         (FlaubertConfig, FlaubertWithLMHeadModel),
         (XLMConfig, XLMWithLMHeadModel),
         (ElectraConfig, ElectraForMaskedLM),
+        (ReformerConfig, ReformerForMaskedLM),
     ]
 )
 

src/transformers/modeling_reformer.py

@@ -1704,6 +1704,7 @@ class ReformerModel(ReformerPreTrainedModel):
 class ReformerModelWithLMHead(ReformerPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
+        assert config.is_decoder, "If you want to use `ReformerLMHeadModel` make sure that `is_decoder=True`."
         self.reformer = ReformerModel(config)
         self.lm_head = ReformerOnlyLMHead(config)
 
@@ -1789,3 +1790,84 @@ class ReformerModelWithLMHead(ReformerPreTrainedModel):
             inputs_dict["num_hashes"] = kwargs["num_hashes"]
 
         return inputs_dict
+
+
+@add_start_docstrings("""Reformer Model with a `language modeling` head on top. """, REFORMER_START_DOCSTRING)
+class ReformerForMaskedLM(ReformerPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        assert (
+            not config.is_decoder
+        ), "If you want to use `ReformerForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention."
+        self.reformer = ReformerModel(config)
+        self.lm_head = ReformerOnlyLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.lm_head.decoder
+
+    def tie_weights(self):
+        # word embeddings are not tied in Reformer
+        pass
+
+    @add_start_docstrings_to_callable(REFORMER_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="google/reformer-crime-and-punishment")
+    def forward(
+        self,
+        input_ids=None,
+        position_ids=None,
+        attention_mask=None,
+        head_mask=None,
+        inputs_embeds=None,
+        num_hashes=None,
+        labels=None,
+        output_hidden_states=None,
+        output_attentions=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+                Labels for computing the masked language modeling loss.
+                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
+
+    Return:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification loss (cross entropy).
+        prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`)
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        all_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        all_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        """
+
+        reformer_outputs = self.reformer(
+            input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            num_hashes=num_hashes,
+            output_hidden_states=output_hidden_states,
+            output_attentions=output_attentions,
+        )
+
+        sequence_output = reformer_outputs[0]
+        logits = self.lm_head(sequence_output)
+        outputs = (logits,) + reformer_outputs[1:]
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+            outputs = (masked_lm_loss,) + outputs
+
+        return outputs  # (mlm_loss), lm_logits, (hidden_states), (attentions)

tests/test_modeling_reformer.py

@@ -25,6 +25,7 @@ from .utils import require_multigpu, require_torch, slow, torch_device
 if is_torch_available():
     from transformers import (
         ReformerConfig,
+        ReformerForMaskedLM,
         ReformerModel,
         ReformerModelWithLMHead,
         ReformerTokenizer,
@@ -207,7 +208,24 @@ class ReformerModelTester:
         )
         self.check_loss_output(result)
 
-    def create_and_check_reformer_model_with_attn_mask(self, config, input_ids, input_mask, is_decoder):
+    def create_and_check_reformer_with_mlm(
+        self, config, input_ids, input_mask,
+    ):
+        config.is_decoder = False
+        model = ReformerForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        loss, prediction_scores = model(input_ids, attention_mask=input_mask, labels=input_ids)
+        result = {
+            "loss": loss,
+            "prediction_scores": prediction_scores,
+        }
+        self.parent.assertListEqual(
+            list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size],
+        )
+        self.check_loss_output(result)
+
+    def create_and_check_reformer_model_with_attn_mask(self, config, input_ids, input_mask, is_decoder=False):
         # no special position embeddings
         config.axial_pos_embds = False
         config.is_decoder = is_decoder
@@ -248,7 +266,7 @@ class ReformerModelTester:
 
         self.parent.assertTrue(torch.allclose(output_padded, output_padded_rolled, atol=1e-3))
 
-    def create_and_check_reformer_layer_dropout_seed(self, config, input_ids, input_mask, is_decoder):
+    def create_and_check_reformer_layer_dropout_seed(self, config, input_ids, input_mask, is_decoder=False):
         config.is_decoder = is_decoder
         layer = ReformerLayer(config).to(torch_device)
         layer.train()
@@ -423,17 +441,21 @@ class ReformerTesterMixin:
 
     def test_reformer_model_attn_masking(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, True)
-        self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, False)
+        self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=True)
+        self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=False)
 
     def test_reformer_with_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_reformer_with_lm(*config_and_inputs)
 
+    def test_reformer_with_mlm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_reformer_with_mlm(*config_and_inputs)
+
     def test_reformer_layer_training_dropout(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, True)
-        self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, False)
+        self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=True)
+        self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=False)
 
     def test_reformer_chunking_forward_equality(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
@@ -481,7 +503,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
             "batch_size": 13,
             "seq_length": 32,
             "is_training": True,
-            "is_decoder": False,
+            "is_decoder": True,
             "use_input_mask": True,
             "vocab_size": 32,
             "attention_head_size": 16,
@@ -536,7 +558,7 @@ class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.T
             "seq_length": 13,
             "use_input_mask": True,
             "is_training": False,
-            "is_decoder": False,
+            "is_decoder": True,
             "vocab_size": 32,
             "attention_head_size": 16,
             "hidden_size": 64,
@@ -886,7 +908,7 @@ class ReformerIntegrationTests(unittest.TestCase):
         config["num_buckets"] = [2, 4]
         config["is_decoder"] = False
         torch.manual_seed(0)
-        model = ReformerModelWithLMHead(ReformerConfig(**config)).to(torch_device)
+        model = ReformerForMaskedLM(ReformerConfig(**config)).to(torch_device)
         model.eval()
         input_ids, attn_mask = self._get_input_ids_and_mask()
         hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]