Merge pull request #7 from huggingface/smangrul/add-examples-fixes-docs

Adding `PETModelForTokenClassification` and 🐛 fixes

src/pet/pet_model.py

@@ -4,7 +4,7 @@ import warnings
 import torch
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from transformers import PreTrainedModel
-from transformers.modeling_outputs import SequenceClassifierOutput
+from transformers.modeling_outputs import SequenceClassifierOutput, TokenClassifierOutput
 
 from .tuners import LoRAModel, PrefixEncoder, PromptEmbedding, PromptEncoder
 from .utils import PETConfig, PETType, TaskType, _set_trainable, shift_tokens_right
@@ -502,3 +502,157 @@ class PETModelForSeq2SeqLM(PETModel):
                 (prompts[:, self.pet_config.num_virtual_tokens :], decoder_inputs_embeds), dim=1
             )
             return self.base_model(inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, **kwargs)
+
+
+class PETModelForTokenClassification(PETModel):
+    """
+    PET model for sequence classification tasks.
+
+    Args:
+        model (:obj:`PreTrainedModel`): Base transformer model
+        pet_config (:obj:`PETConfig`): PET config.
+
+    Attributes:
+        config (:obj:`PretrainedConfig`): The configuration object of the base model. cls_layer_name (:obj:`str`): The
+        name of the classification layer.
+
+    Example::
+
+        >>> from transformers import AutoModelForSequenceClassification >>> from pet import
+        PETModelForTokenClassification, get_pet_config >>> config = {
+                'pet_type': 'PREFIX_TUNING', 'task_type': 'TOKEN_CLS', 'inference_mode': False, 'num_virtual_tokens':
+                20, 'token_dim': 768, 'num_transformer_submodules': 1, 'num_attention_heads': 12, 'num_layers': 12,
+                'encoder_hidden_size': 768, 'prefix_projection': False, 'postprocess_past_key_value_function': None
+            }
+        >>> pet_config = get_pet_config(config) >>> model =
+        AutoModelForSequenceClassification.from_pretrained("bert-base-cased") >>> pet_model =
+        PETModelForSequenceClassification(model, pet_config) >>> pet_model.print_trainable_parameters() trainable
+        params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
+    """
+
+    def __init__(self, model, pet_config: PETConfig):
+        super().__init__(model, pet_config)
+        self.modules_to_save = ["classifier"]
+
+        for name, module in self.base_model.named_children():
+            if isinstance(module, torch.nn.Linear):
+                self.cls_layer_name = name
+                break
+
+        # to make sure classifier layer is trainable
+        _set_trainable(self.base_model)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.pet_config.pet_type == PETType.LORA:
+            return self.base_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                labels=labels,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs,
+            )
+
+        batch_size = input_ids.shape[0]
+        if attention_mask is not None:
+            # concat prompt attention mask
+            prefix_attention_mask = torch.ones(batch_size, self.pet_config.num_virtual_tokens).to(self.device)
+            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
+        if kwargs.get("position_ids", None) is not None:
+            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
+            kwargs["position_ids"] = None
+        kwargs.update(
+            {
+                "attention_mask": attention_mask,
+                "labels": labels,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+            }
+        )
+
+        if self.pet_config.pet_type == PETType.PREFIX_TUNING:
+            return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
+        else:
+            if kwargs.get("token_type_ids", None) is not None:
+                kwargs["token_type_ids"] = torch.cat(
+                    (
+                        torch.zeros(batch_size, self.pet_config.num_virtual_tokens).to(self.device),
+                        kwargs["token_type_ids"],
+                    ),
+                    dim=1,
+                ).long()
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+            prompts = self.get_prompt(batch_size=batch_size)
+            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
+            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
+
+    def _prefix_tuning_forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        batch_size = input_ids.shape[0]
+        past_key_values = self.get_prompt(batch_size)
+        fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
+        kwargs.update(
+            {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "inputs_embeds": inputs_embeds,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+                "past_key_values": past_key_values,
+            }
+        )
+        if "past_key_values" in fwd_params:
+            return self.base_model(labels=labels, **kwargs)
+        else:
+            transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
+            fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
+            if "past_key_values" not in fwd_params:
+                raise ValueError("Model does not support past key values which are required for prefix tuning.")
+            outputs = transformer_backbone_name(**kwargs)
+            sequence_output = outputs[0]
+            if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
+                sequence_output = self.base_model.dropout(sequence_output)
+            logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output)
+
+            loss = None
+            loss = None
+            if labels is not None:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return TokenClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )

src/pet/utils/config.py

@@ -14,6 +14,7 @@ class TaskType(str, enum.Enum):
     SEQ_CLS = "SEQ_CLS"
     SEQ_2_SEQ_LM = "SEQ_2_SEQ_LM"
     CAUSAL_LM = "CAUSAL_LM"
+    TOKEN_CLS = "TOKEN_CLS"
 
 
 @dataclass

src/pet/utils/save_and_load.py

@@ -10,12 +10,11 @@ def get_pet_model_state_dict(model):
     Args:
         model (:obj:`PETModel`): The PET model.
     """
-
+    state_dict = model.state_dict()
     if model.pet_config.pet_type == PETType.LORA:
         to_return = lora_state_dict(model, bias=model.pet_config.bias)
     else:
         to_return = {}
-        state_dict = model.state_dict()
         prompt_embeddings = model.get_prompt_embedding_to_save()
         to_return["prompt_embeddings"] = prompt_embeddings
     if model.modules_to_save is not None: