Merge pull request huggingface#26 from jamesthesnake/pop

Pop

docs/source/en/model_doc/gpt_neox.mdx

@@ -78,3 +78,8 @@ The `generate()` method can be used to generate text using GPT Neo model.
 
 [[autodoc]] GPTNeoXForCausalLM
     - forward
+
+## GPTNeoXForSequenceClassification
+
+[[autodoc]] GPTNeoXForSequenceClassification
+    - forward

docs/source/en/tasks/sequence_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), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [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), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [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), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [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), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [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), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [LLaMA](../model_doc/llama), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [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), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [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-->

src/transformers/__init__.py

@@ -1666,6 +1666,7 @@
         [
             "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
             "GPTNeoXForCausalLM",
+            "GPTNeoXForSequenceClassification",
             "GPTNeoXLayer",
             "GPTNeoXModel",
             "GPTNeoXPreTrainedModel",
@@ -5164,6 +5165,7 @@
         from .models.gpt_neox import (
             GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoXForCausalLM,
+            GPTNeoXForSequenceClassification,
             GPTNeoXLayer,
             GPTNeoXModel,
             GPTNeoXPreTrainedModel,

src/transformers/models/auto/modeling_auto.py

@@ -659,6 +659,7 @@
         ("gpt2", "GPT2ForSequenceClassification"),
         ("gpt_bigcode", "GPTBigCodeForSequenceClassification"),
         ("gpt_neo", "GPTNeoForSequenceClassification"),
+        ("gpt_neox", "GPTNeoXForSequenceClassification"),
         ("gptj", "GPTJForSequenceClassification"),
         ("ibert", "IBertForSequenceClassification"),
         ("layoutlm", "LayoutLMForSequenceClassification"),

src/transformers/models/data2vec/modeling_data2vec_text.py

@@ -999,6 +999,8 @@ def forward(
             shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
             labels = labels[:, 1:].contiguous()
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(shifted_prediction_scores.device)
             lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
         if not return_dict:
@@ -1114,6 +1116,8 @@ def forward(
         masked_lm_loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
             masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
         if not return_dict:
@@ -1224,6 +1228,8 @@ def forward(
 
         loss = None
         if labels is not None:
+            labels = labels.to(logits.device)
+
             if self.config.problem_type is None:
                 if self.num_labels == 1:
                     self.config.problem_type = "regression"
@@ -1337,6 +1343,8 @@ def forward(
         loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(reshaped_logits.device)
             loss = loss_fct(reshaped_logits, labels)
 
         if not return_dict:
@@ -1421,6 +1429,8 @@ def forward(
         loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(logits.device)
             loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
 
         if not return_dict:

src/transformers/models/esm/modeling_esm.py

@@ -1032,6 +1032,8 @@ def forward(
         masked_lm_loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
             masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
         if not return_dict:
@@ -1131,6 +1133,8 @@ def forward(
 
         loss = None
         if labels is not None:
+            labels = labels.to(logits.device)
+
             if self.config.problem_type is None:
                 if self.num_labels == 1:
                     self.config.problem_type = "regression"
@@ -1228,6 +1232,8 @@ def forward(
         loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(logits.device)
             loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
 
         if not return_dict:

src/transformers/models/gpt_neox/__init__.py

@@ -36,6 +36,7 @@
     _import_structure["modeling_gpt_neox"] = [
         "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoXForCausalLM",
+        "GPTNeoXForSequenceClassification",
         "GPTNeoXLayer",
         "GPTNeoXModel",
         "GPTNeoXPreTrainedModel",
@@ -62,6 +63,7 @@
         from .modeling_gpt_neox import (
             GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoXForCausalLM,
+            GPTNeoXForSequenceClassification,
             GPTNeoXLayer,
             GPTNeoXModel,
             GPTNeoXPreTrainedModel,

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -19,7 +19,7 @@
 import torch
 import torch.utils.checkpoint
 from torch import nn
-from torch.nn import CrossEntropyLoss
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...file_utils import (
@@ -28,7 +28,7 @@
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...utils import logging
 from .configuration_gpt_neox import GPTNeoXConfig
@@ -730,3 +730,131 @@ def _reorder_cache(self, past_key_values, beam_idx):
                 tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
             )
         return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The GPTNeoX Model transformer with a sequence classification head on top (linear layer).
+
+    [`GPTNeoXForSequenceClassification`] 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
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `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 `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    GPT_NEOX_START_DOCSTRING,
+)
+class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.gpt_neox = GPTNeoXModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[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[torch.Tensor], SequenceClassifierOutputWithPast]:
+        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
+
+        outputs = self.gpt_neox(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = 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]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("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).to(logits.device)
+            else:
+                sequence_lengths = -1
+                logger.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                print(pooled_logits.shape, labels.shape)
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/models/longformer/modeling_longformer.py

@@ -1863,6 +1863,8 @@ def forward(
         masked_lm_loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
             masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
         if not return_dict:
@@ -1952,6 +1954,8 @@ def forward(
 
         loss = None
         if labels is not None:
+            labels = labels.to(logits.device)
+
             if self.config.problem_type is None:
                 if self.num_labels == 1:
                     self.config.problem_type = "regression"
@@ -2217,6 +2221,8 @@ def forward(
         loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(logits.device)
             loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
 
         if not return_dict:
@@ -2329,6 +2335,8 @@ def forward(
         loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(reshaped_logits.device)
             loss = loss_fct(reshaped_logits, labels)
 
         if not return_dict:

src/transformers/models/longt5/modeling_longt5.py

@@ -2074,6 +2074,8 @@ def forward(
         loss = None
         if labels is not None:
             loss_fct = CrossEntropyLoss(ignore_index=-100)
+
+            labels = labels.to(lm_logits.device)
             loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
             # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
 

src/transformers/utils/dummy_pt_objects.py

@@ -3236,6 +3236,13 @@ def __init__(self, *args, **kwargs):
         requires_backends(self, ["torch"])
 
 
+class GPTNeoXForSequenceClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class GPTNeoXLayer(metaclass=DummyObject):
     _backends = ["torch"]