Added Sequence Classification class in GPTNeo (#11906)

* seq classification changes

* fix tests

datasets

@@ -0,0 +1 @@
+Subproject commit d95b95f8cf3cb0cff5f77a675139b584dcfcf719

docs/source/model_doc/gpt_neo.rst

@@ -65,3 +65,9 @@ GPTNeoForCausalLM
 
 .. autoclass:: transformers.GPTNeoForCausalLM
     :members: forward
+
+GPTNeoForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.GPTNeoForSequenceClassification
+    :members: forward

src/transformers/__init__.py

@@ -746,6 +746,7 @@ if is_torch_available():
         [
             "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
             "GPTNeoForCausalLM",
+            "GPTNeoForSequenceClassification",
             "GPTNeoModel",
             "GPTNeoPreTrainedModel",
             "load_tf_weights_in_gpt_neo",
@@ -2129,6 +2130,7 @@ if TYPE_CHECKING:
         from .models.gpt_neo import (
             GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoForCausalLM,
+            GPTNeoForSequenceClassification,
             GPTNeoModel,
             GPTNeoPreTrainedModel,
             load_tf_weights_in_gpt_neo,

src/transformers/models/auto/modeling_auto.py

@@ -145,7 +145,7 @@ from ..funnel.modeling_funnel import (
     FunnelModel,
 )
 from ..gpt2.modeling_gpt2 import GPT2ForSequenceClassification, GPT2LMHeadModel, GPT2Model
-from ..gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM, GPTNeoModel
+from ..gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM, GPTNeoForSequenceClassification, GPTNeoModel
 from ..ibert.modeling_ibert import (
     IBertForMaskedLM,
     IBertForMultipleChoice,
@@ -632,6 +632,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (DebertaConfig, DebertaForSequenceClassification),
         (DebertaV2Config, DebertaV2ForSequenceClassification),
         (GPT2Config, GPT2ForSequenceClassification),
+        (GPTNeoConfig, GPTNeoForSequenceClassification),
         (OpenAIGPTConfig, OpenAIGPTForSequenceClassification),
         (ReformerConfig, ReformerForSequenceClassification),
         (CTRLConfig, CTRLForSequenceClassification),

src/transformers/models/gpt_neo/__init__.py

@@ -28,6 +28,7 @@ if is_torch_available():
     _import_structure["modeling_gpt_neo"] = [
         "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoForCausalLM",
+        "GPTNeoForSequenceClassification",
         "GPTNeoModel",
         "GPTNeoPreTrainedModel",
         "load_tf_weights_in_gpt_neo",
@@ -41,6 +42,7 @@ if TYPE_CHECKING:
         from .modeling_gpt_neo import (
             GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoForCausalLM,
+            GPTNeoForSequenceClassification,
             GPTNeoModel,
             GPTNeoPreTrainedModel,
             load_tf_weights_in_gpt_neo,

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -22,7 +22,7 @@ import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
 from torch import nn
-from torch.nn import CrossEntropyLoss
+from torch.nn import CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
@@ -31,6 +31,7 @@ from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
     CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
 )
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
@@ -1027,3 +1028,120 @@ class GPTNeoForCausalLM(GPTNeoPreTrainedModel):
             tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
             for layer_past in past
         )
+
+
+@add_start_docstrings(
+    """
+    The GPTNeo Model transformer with a sequence classification head on top (linear layer).
+
+    :class:`~transformers.GPTNeoForSequenceClassification` uses the last token in order to do the classification, as
+    other causal models (e.g. GPT-1) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    :obj:`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each
+    row. If no :obj:`pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot
+    guess the padding tokens when :obj:`inputs_embeds` are passed instead of :obj:`input_ids`, it does the same (take
+    the last value in each row of the batch).
+    """,
+    GPT_NEO_START_DOCSTRING,
+)
+class GPTNeoForSequenceClassification(GPTNeoPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.transformer = GPTNeoModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            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).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size, sequence_length = input_ids.shape[:2]
+        else:
+            batch_size, sequence_length = inputs_embeds.shape[:2]
+
+        assert (
+            self.config.pad_token_id is not None or batch_size == 1
+        ), "Cannot handle batch sizes > 1 if no padding token is defined."
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
+            else:
+                sequence_lengths = -1
+                logger.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    f"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+
+        pooled_logits = logits[range(batch_size), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            if self.num_labels == 1:
+                #  We are doing regression
+                loss_fct = MSELoss()
+                loss = loss_fct(pooled_logits.view(-1), labels.to(self.dtype).view(-1))
+            else:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -1603,6 +1603,15 @@ class GPTNeoForCausalLM:
         requires_backends(self, ["torch"])
 
 
+class GPTNeoForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class GPTNeoModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])

tests/test_modeling_gpt2.py

@@ -361,7 +361,6 @@ class GPT2ModelTester:
         model = GPT2ForSequenceClassification(config)
         model.to(torch_device)
         model.eval()
-        print(config.num_labels, sequence_labels.size())
         result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
 

tests/test_modeling_gpt_neo.py

@@ -34,6 +34,7 @@ if is_torch_available():
         GPT2Tokenizer,
         GPTNeoConfig,
         GPTNeoForCausalLM,
+        GPTNeoForSequenceClassification,
         GPTNeoModel,
     )
     from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoAttentionMixin
@@ -238,6 +239,16 @@ class GPTNeoModelTester:
         self.parent.assertEqual(result.loss.shape, ())
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
+    def create_and_check_gpt_neo_for_sequence_classification(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        model = GPTNeoForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
     def create_and_check_forward_and_backwards(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
         model = GPTNeoForCausalLM(config)
         model.to(torch_device)
@@ -274,7 +285,9 @@ class GPTNeoModelTester:
 @require_torch
 class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
 
-    all_model_classes = (GPTNeoModel, GPTNeoForCausalLM) if is_torch_available() else ()
+    all_model_classes = (
+        (GPTNeoModel, GPTNeoForCausalLM, GPTNeoForSequenceClassification) if is_torch_available() else ()
+    )
     all_generative_model_classes = (GPTNeoForCausalLM,) if is_torch_available() else ()
     fx_ready_model_classes = all_model_classes
     test_missing_keys = False
@@ -305,6 +318,10 @@ class GPTNeoModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
 
+    def test_gpt_neo_sequence_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_gpt_neo_for_sequence_classification(*config_and_inputs)
+
     def test_gpt_neo_gradient_checkpointing(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
         self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)