Transfoxl seq classification (#8868)

* Transfoxl sequence classification

* Transfoxl sequence classification

docs/source/model_doc/transformerxl.rst

@@ -75,6 +75,11 @@ TransfoXLLMHeadModel
 .. autoclass:: transformers.TransfoXLLMHeadModel
     :members: forward
 
+TransfoXLForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TransfoXLForSequenceClassification
+    :members: forward
 
 TFTransfoXLModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

src/transformers/__init__.py

@@ -578,6 +578,7 @@ if is_torch_available():
     from .models.transfo_xl import (
         TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
         AdaptiveEmbedding,
+        TransfoXLForSequenceClassification,
         TransfoXLLMHeadModel,
         TransfoXLModel,
         TransfoXLPreTrainedModel,

src/transformers/models/auto/modeling_auto.py

@@ -157,7 +157,7 @@ from ..squeezebert.modeling_squeezebert import (
     SqueezeBertModel,
 )
 from ..t5.modeling_t5 import T5ForConditionalGeneration, T5Model
-from ..transfo_xl.modeling_transfo_xl import TransfoXLLMHeadModel, TransfoXLModel
+from ..transfo_xl.modeling_transfo_xl import TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel
 from ..xlm.modeling_xlm import (
     XLMForMultipleChoice,
     XLMForQuestionAnsweringSimple,
@@ -416,6 +416,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
         (OpenAIGPTConfig, OpenAIGPTForSequenceClassification),
         (ReformerConfig, ReformerForSequenceClassification),
         (CTRLConfig, CTRLForSequenceClassification),
+        (TransfoXLConfig, TransfoXLForSequenceClassification),
     ]
 )
 

src/transformers/models/transfo_xl/__init__.py

@@ -11,6 +11,7 @@ if is_torch_available():
     from .modeling_transfo_xl import (
         TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
         AdaptiveEmbedding,
+        TransfoXLForSequenceClassification,
         TransfoXLLMHeadModel,
         TransfoXLModel,
         TransfoXLPreTrainedModel,

src/transformers/models/transfo_xl/modeling_transfo_xl.py

@@ -23,6 +23,7 @@ from typing import List, Optional, Tuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.nn import CrossEntropyLoss, MSELoss
 
 from ...file_utils import (
     ModelOutput,
@@ -632,6 +633,40 @@ class TransfoXLModelOutput(ModelOutput):
     attentions: Optional[Tuple[torch.FloatTensor]] = None
 
 
+@dataclass
+class TransfoXLSequenceClassifierOutputWithPast(ModelOutput):
+    """
+    Base class for outputs of sentence classification models.
+
+    Args:
+        loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        mems (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
+            Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see :obj:`mems`
+            input) to speed up sequential decoding. The token ids which have their past given to this model should not
+            be passed as input ids as they have already been computed.
+        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.
+        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.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    mems: List[torch.FloatTensor] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
 @dataclass
 class TransfoXLLMHeadModelOutput(ModelOutput):
     """
@@ -1101,3 +1136,110 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
         self.crit.cutoffs = new_cutoffs
         self.crit.cutoff_ends = [0] + new_cutoffs
         self.crit.n_token = new_num_tokens
+
+
+@add_start_docstrings(
+    """
+    The Transformer-XL Model transformer with a sequence classification head on top (linear layer).
+
+    :class:`~transformers.TransfoXLForSequenceClassification` 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).
+    """,
+    TRANSFO_XL_START_DOCSTRING,
+)
+class TransfoXLForSequenceClassification(TransfoXLPreTrainedModel):
+    _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 = TransfoXLModel(config)
+        self.score = nn.Linear(config.d_embed, self.num_labels, bias=False)
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="transfo-xl-wt103",
+        output_type=TransfoXLSequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        mems=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=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,
+            mems=mems,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            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:
+                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 TransfoXLSequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            mems=transformer_outputs.mems,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -1785,6 +1785,15 @@ class AdaptiveEmbedding:
         requires_pytorch(self)
 
 
+class TransfoXLForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
 class TransfoXLLMHeadModel:
     def __init__(self, *args, **kwargs):
         requires_pytorch(self)

tests/test_modeling_transfo_xl.py

@@ -27,7 +27,7 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor
 if is_torch_available():
     import torch
 
-    from transformers import TransfoXLConfig, TransfoXLLMHeadModel, TransfoXLModel
+    from transformers import TransfoXLConfig, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel
     from transformers.models.transfo_xl.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST
 
 
@@ -56,6 +56,8 @@ class TransfoXLModelTester:
         self.scope = None
         self.seed = 1
         self.eos_token_id = 0
+        self.num_labels = 3
+        self.pad_token_id = self.vocab_size - 1
 
     def prepare_config_and_inputs(self):
         input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
@@ -78,6 +80,7 @@ class TransfoXLModelTester:
             div_val=self.div_val,
             n_layer=self.num_hidden_layers,
             eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
         )
 
         return (config, input_ids_1, input_ids_2, lm_labels)
@@ -148,6 +151,14 @@ class TransfoXLModelTester:
             [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers,
         )
 
+    def create_and_check_transfo_xl_for_sequence_classification(self, config, input_ids_1, input_ids_2, lm_labels):
+        config.num_labels = self.num_labels
+        model = TransfoXLForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids_1)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
@@ -157,7 +168,9 @@ class TransfoXLModelTester:
 
 @require_torch
 class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
-    all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
+    all_model_classes = (
+        (TransfoXLModel, TransfoXLLMHeadModel, TransfoXLForSequenceClassification) if is_torch_available() else ()
+    )
     all_generative_model_classes = (TransfoXLLMHeadModel,) if is_torch_available() else ()
     test_pruning = False
     test_torchscript = False
@@ -204,6 +217,10 @@ class TransfoXLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestC
         output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
         self.model_tester.check_transfo_xl_lm_head_output(output_result)
 
+    def test_transfo_xl_sequence_classification_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*config_and_inputs)
+
     def test_retain_grad_hidden_states_attentions(self):
         # xlnet cannot keep gradients in attentions or hidden states
         return