added biogpt token classifier (#22447)

* added biogpt token classifier * fix reviews * Updated modeling_biogpt.py Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
2023-04-03 18:50:02 +05:30
parent 1194c3e315
commit 7d25c9c81e
8 changed files with 138 additions and 5 deletions
--- a/docs/source/en/model_doc/biogpt.mdx
+++ b/docs/source/en/model_doc/biogpt.mdx
@@ -53,4 +53,9 @@ This model was contributed by [kamalkraj](https://huggingface.co/kamalkraj). The
 ## BioGptForCausalLM

 [[autodoc]] BioGptForCausalLM
+    - forward
+    
+## BioGptForTokenClassification
+
+[[autodoc]] BioGptForTokenClassification
    - forward
--- a/docs/source/en/tasks/token_classification.mdx
+++ b/docs/source/en/tasks/token_classification.mdx
@@ -28,7 +28,7 @@ The task illustrated in this tutorial is supported by the following model archit

 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->

-[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)
+[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BioGpt](../model_doc/biogpt), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ErnieM](../model_doc/ernie_m), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod), [YOSO](../model_doc/yoso)

 <!--End of the generated tip-->

--- a/src/transformers/init.py
+++ b/src/transformers/init.py
@@ -1131,6 +1131,7 @@ else:
        [
            "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "BioGptForCausalLM",
+            "BioGptForTokenClassification",
            "BioGptModel",
            "BioGptPreTrainedModel",
        ]
@@ -4703,6 +4704,7 @@ if TYPE_CHECKING:
        from .models.biogpt import (
            BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BioGptForCausalLM,
+            BioGptForTokenClassification,
            BioGptModel,
            BioGptPreTrainedModel,
        )
--- a/src/transformers/models/auto/modeling_auto.py
+++ b/src/transformers/models/auto/modeling_auto.py
@@ -782,6 +782,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
        ("albert", "AlbertForTokenClassification"),
        ("bert", "BertForTokenClassification"),
        ("big_bird", "BigBirdForTokenClassification"),
+        ("biogpt", "BioGptForTokenClassification"),
        ("bloom", "BloomForTokenClassification"),
        ("camembert", "CamembertForTokenClassification"),
        ("canine", "CanineForTokenClassification"),
--- a/src/transformers/models/biogpt/init.py
+++ b/src/transformers/models/biogpt/init.py
@@ -30,6 +30,7 @@ else:
    _import_structure["modeling_biogpt"] = [
        "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BioGptForCausalLM",
+        "BioGptForTokenClassification",
        "BioGptModel",
        "BioGptPreTrainedModel",
    ]
@@ -48,6 +49,7 @@ if TYPE_CHECKING:
        from .modeling_biogpt import (
            BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BioGptForCausalLM,
+            BioGptForTokenClassification,
            BioGptModel,
            BioGptPreTrainedModel,
        )
--- a/src/transformers/models/biogpt/modeling_biogpt.py
+++ b/src/transformers/models/biogpt/modeling_biogpt.py
@@ -25,7 +25,11 @@ from torch import nn
 from torch.nn import CrossEntropyLoss

 from ...activations import ACT2FN
-from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
+from ...modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    TokenClassifierOutput,
+)
 from ...modeling_utils import PreTrainedModel
 from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
 from .configuration_biogpt import BioGptConfig
@@ -736,3 +740,95 @@ class BioGptForCausalLM(BioGptPreTrainedModel):
        for layer_past in past_key_values:
            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    BioGPT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    BIOGPT_START_DOCSTRING,
+)
+class BioGptForTokenClassification(BioGptPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.biogpt = BioGptModel(config)
+        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
+            classifier_dropout = config.classifier_dropout
+        else:
+            classifier_dropout = config.hidden_dropout_prob
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `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.biogpt(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            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]
+        hidden_states = self.dropout(hidden_states)
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # Only keep active parts of the loss
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)
+                active_labels = torch.where(
+                    active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
+                )
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + transformer_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
--- a/src/transformers/utils/dummy_pt_objects.py
+++ b/src/transformers/utils/dummy_pt_objects.py
@@ -1093,6 +1093,13 @@ class BioGptForCausalLM(metaclass=DummyObject):
        requires_backends(self, ["torch"])


+class BioGptForTokenClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class BioGptModel(metaclass=DummyObject):
    _backends = ["torch"]

--- a/tests/models/biogpt/test_modeling_biogpt.py
+++ b/tests/models/biogpt/test_modeling_biogpt.py
@@ -29,7 +29,7 @@ from ...test_pipeline_mixin import PipelineTesterMixin
 if is_torch_available():
    import torch

-    from transformers import BioGptForCausalLM, BioGptModel, BioGptTokenizer
+    from transformers import BioGptForCausalLM, BioGptForTokenClassification, BioGptModel, BioGptTokenizer
    from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST


@@ -247,6 +247,16 @@ class BioGptModelTester:
                self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
                self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)

+    def create_and_check_biogpt_for_token_classification(
+        self, config, input_ids, input_mask, head_mask, token_type_ids, *args
+    ):
+        config.num_labels = self.num_labels
+        model = BioGptForTokenClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
@@ -264,10 +274,16 @@ class BioGptModelTester:

@require_torch
 class BioGptModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (BioGptModel, BioGptForCausalLM) if is_torch_available() else ()
+    all_model_classes = (BioGptModel, BioGptForCausalLM, BioGptForTokenClassification) if is_torch_available() else ()
    all_generative_model_classes = (BioGptForCausalLM,) if is_torch_available() else ()
    pipeline_model_mapping = (
-        {"feature-extraction": BioGptModel, "text-generation": BioGptForCausalLM} if is_torch_available() else {}
+        {
+            "feature-extraction": BioGptModel,
+            "text-generation": BioGptForCausalLM,
+            "token-classification": BioGptForTokenClassification,
+        }
+        if is_torch_available()
+        else {}
    )
    test_pruning = False

@@ -304,6 +320,10 @@ class BioGptModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMix
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_weight_initialization(*config_and_inputs)

+    def test_biogpt_token_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_biogpt_for_token_classification(*config_and_inputs)
+
    @slow
    def test_batch_generation(self):
        model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")