[Reformer classification head] Implement the reformer model classification...

[Reformer classification head] Implement the reformer model classification head for text classification (#5198)

* Reformer model head classification implementation for text classification

* Reformat the reformer model classification code

* PR review comments, and test case implementation for reformer for classification head changes

* CI/CD reformer for classification head test import error fix

* CI/CD test case implementation  added ReformerForSequenceClassification to all_model_classes

* Code formatting- fixed

* Normal test cases added for reformer classification head

* Fix test cases implementation for the reformer classification head

* removed token_type_id parameter from the reformer classification head

* fixed the test case for reformer classification head

* merge conflict with master fixed

* merge conflict, changed reformer classification to accept the choice_label parameter added in latest code

* refactored the the reformer classification head test code

* reformer classification head, common transform test cases fixed

* final set of the review comment, rearranging the reformer classes and docstring add to classification forward method

* fixed the compilation error and text case fix for reformer classification head

* Apply suggestions from code review

Remove unnecessary dup
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/transformers/__init__.py

@@ -378,6 +378,7 @@ if is_torch_available():
         ReformerModel,
         ReformerForMaskedLM,
         ReformerModelWithLMHead,
+        ReformerForSequenceClassification,
         ReformerForQuestionAnswering,
         REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )

src/transformers/modeling_reformer.py

@@ -25,7 +25,7 @@ import numpy as np
 import torch
 from torch import nn
 from torch.autograd.function import Function
-from torch.nn import CrossEntropyLoss
+from torch.nn import CrossEntropyLoss, MSELoss
 
 from .activations import gelu, gelu_fast, gelu_new, swish
 from .configuration_reformer import ReformerConfig
@@ -36,7 +36,13 @@ from .file_utils import (
     add_start_docstrings,
     add_start_docstrings_to_callable,
 )
-from .modeling_outputs import BaseModelOutput, CausalLMOutput, MaskedLMOutput, QuestionAnsweringModelOutput
+from .modeling_outputs import (
+    BaseModelOutput,
+    CausalLMOutput,
+    MaskedLMOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+)
 from .modeling_utils import PreTrainedModel, apply_chunking_to_forward
 
 
@@ -1858,6 +1864,108 @@ class ReformerForMaskedLM(ReformerPreTrainedModel):
         )
 
 
+@add_start_docstrings(
+    """Reformer Model transformer with a sequence classification/regression head on top (a linear layer
+    on top of the pooled output) e.g. for GLUE tasks. """,
+    REFORMER_START_DOCSTRING,
+)
+class ReformerForSequenceClassification(ReformerPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.reformer = ReformerModel(config)
+        self.classifier = ReformerClassificationHead(config)
+        if config.is_decoder is True:
+            logger.warning("You might want to disable causal masking for sequence classification")
+
+        self.init_weights()
+
+    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",
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    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,
+        return_tuple=None,
+    ):
+        r"""
+            labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+                Labels for computing the sequence classification/regression loss.
+                Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
+                If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+                If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        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,
+            return_tuple=return_tuple,
+        )
+
+        sequence_output = outputs[0]
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(logits.view(-1), labels.view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if return_tuple:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+        )
+
+
+class ReformerClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(2 * config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, hidden_states, **kwargs):
+        hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.dense(hidden_states)
+        hidden_states = torch.tanh(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+        return hidden_states
+
+
 @add_start_docstrings(
     """Reformer Model with a span classification head on top for
     extractive question-answering tasks like SQuAD / TriviaQA ( a linear layer on

tests/test_modeling_reformer.py

@@ -28,6 +28,7 @@ if is_torch_available():
         ReformerForMaskedLM,
         ReformerModel,
         ReformerModelWithLMHead,
+        ReformerForSequenceClassification,
         ReformerTokenizer,
         ReformerLayer,
         ReformerForQuestionAnswering,
@@ -77,6 +78,7 @@ class ReformerModelTester:
         eos_token_id=None,
         scope=None,
         hash_seed=None,
+        num_labels=None,
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -124,6 +126,7 @@ class ReformerModelTester:
         self.encoder_seq_length = seq_length // attn_chunk_length + (self.seq_length % attn_chunk_length != 0)
         self.key_length = (num_chunks_before + num_chunks_after + 1) * attn_chunk_length
         self.chunk_length = attn_chunk_length
+        self.num_labels = num_labels
 
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -443,6 +446,22 @@ class ReformerModelTester:
         inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict
 
+    def create_and_check_reformer_for_sequence_classification(
+        self, config, input_ids, input_mask, choice_labels, is_decoder
+    ):
+        config.is_decoder = is_decoder
+        sequence_labels = ids_tensor([self.batch_size], config.num_labels)
+        model = ReformerForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        loss, logits = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
+        result = {
+            "loss": loss,
+            "logits": logits,
+        }
+        self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels])
+        self.check_loss_output(result)
+
 
 class ReformerTesterMixin:
     """
@@ -510,11 +529,17 @@ class ReformerTesterMixin:
         # Opt-out of this test.
         pass
 
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_reformer_for_sequence_classification(*config_and_inputs, is_decoder=False)
+
 
 @require_torch
 class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (ReformerModel, ReformerModelWithLMHead, ReformerForQuestionAnswering) if is_torch_available() else ()
+        (ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
+        if is_torch_available()
+        else ()
     )
     all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
     test_pruning = False
@@ -554,6 +579,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
             "eos_token_id": 2,
             "scope": None,
             "hash_seed": 0,
+            "num_labels": 2,
         }
 
     def setUp(self):
@@ -571,7 +597,9 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest
 @require_torch
 class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (ReformerModel, ReformerModelWithLMHead, ReformerForQuestionAnswering) if is_torch_available() else ()
+        (ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
+        if is_torch_available()
+        else ()
     )
     all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
     test_pruning = False
@@ -613,6 +641,7 @@ class ReformerLSHAttnModelTest(ReformerTesterMixin, ModelTesterMixin, unittest.T
             "eos_token_id": 2,
             "scope": None,
             "hash_seed": 0,
+            "num_labels": 2,
         }
 
     def setUp(self):