add custom pipeline

bert_for_sequence_classification.py

@@ -0,0 +1,145 @@
+import torch
+import transformers
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from typing import List, Optional, Tuple, Union
+
+from transformers import BertTokenizer
+from transformers import models, DataCollatorWithPadding, AutoTokenizer
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+from transformers.models.bert.configuration_bert import BertConfig
+from transformers.models.bert.modeling_bert import (
+    BertPreTrainedModel,
+    BERT_INPUTS_DOCSTRING,
+    _TOKENIZER_FOR_DOC,
+    _CHECKPOINT_FOR_DOC,
+    BERT_START_DOCSTRING,
+    _CONFIG_FOR_DOC,
+    _SEQ_CLASS_EXPECTED_OUTPUT,
+    _SEQ_CLASS_EXPECTED_LOSS,
+    BertModel,
+)
+
+from transformers.file_utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings_to_model_forward,
+    add_start_docstrings
+)
+
+@add_start_docstrings(
+    """
+    Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+    output) e.g. for GLUE tasks.
+    """,
+    BERT_START_DOCSTRING,
+)
+class BertForSequenceClassification(BertPreTrainedModel):
+  def __init__(self, config, **kwargs):
+    super().__init__(transformers.PretrainedConfig())
+    #task_labels_map={"binary_classification": 2, "label_classification": 5}
+    self.tasks = kwargs.get("tasks_map", {})
+    self.config = config
+
+    self.bert = BertModel(config)
+    classifier_dropout = (
+        config.classifier_dropout
+        if config.classifier_dropout is not None
+        else config.hidden_dropout_prob
+    )
+    self.dropout = nn.Dropout(classifier_dropout)
+    ## add task specific output heads
+    self.classifier1 = nn.Linear(
+        config.hidden_size, self.tasks[0].num_labels
+    )
+    self.classifier2 = nn.Linear(
+        config.hidden_size, self.tasks[1].num_labels
+    )
+
+    self.init_weights()
+
+  @add_start_docstrings_to_model_forward(
+  BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+  )
+  @add_code_sample_docstrings(
+      processor_class=_TOKENIZER_FOR_DOC,
+      checkpoint=_CHECKPOINT_FOR_DOC,
+      output_type=SequenceClassifierOutput,
+      config_class=_CONFIG_FOR_DOC,
+      expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
+      expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
+  )
+  def forward(
+    self,
+    input_ids: Optional[torch.Tensor] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    token_type_ids: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.Tensor] = None,
+    head_mask: Optional[torch.Tensor] = None,
+    inputs_embeds: Optional[torch.Tensor] = None,
+    labels: Optional[torch.Tensor] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    task_ids=None,
+) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+    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
+    )
+
+    outputs = self.bert(
+        input_ids,
+        attention_mask=attention_mask,
+        token_type_ids=token_type_ids,
+        position_ids=position_ids,
+        head_mask=head_mask,
+        inputs_embeds=inputs_embeds,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+    )
+
+    pooled_output = outputs[1]
+
+    pooled_output = self.dropout(pooled_output)
+  
+    unique_task_ids_list = torch.unique(task_ids).tolist()
+    loss_list = []
+    logits = None
+    for unique_task_id in unique_task_ids_list:
+
+      loss = None
+      task_id_filter = task_ids == unique_task_id 
+
+      if unique_task_id == 0:
+        logits = self.classifier1(pooled_output[task_id_filter])
+      elif unique_task_id == 1:
+        logits = self.classifier2(pooled_output[task_id_filter])
+
+      
+      if labels is not None: 
+        loss_fct = CrossEntropyLoss()
+        loss = loss_fct(logits.view(-1, self.tasks[unique_task_id].num_labels), labels[task_id_filter].view(-1))
+        loss_list.append(loss)
+    
+    # logits are only used for eval. and in case of eval the batch is not multi task
+    # For training only the loss is used
+
+    if loss_list:
+      loss = torch.stack(loss_list).mean()
+    if not return_dict:
+      output = (logits,) + outputs[2:]
+      return ((loss,) + output) if loss is not None else output
+    
+    return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

bibert_multitask_classification.py

@@ -0,0 +1,41 @@
+from transformers import Pipeline
+import numpy as np
+import torch
+
+def softmax(_outputs):
+  maxes = np.max(_outputs, axis=-1, keepdims=True)
+  shifted_exp = np.exp(_outputs - maxes)
+  return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
+
+class BiBert_MultiTaskPipeline(Pipeline):
+
+  def _sanitize_parameters(self, **kwargs):
+
+    preprocess_kwargs = {}
+    if "task_id" in kwargs:
+      preprocess_kwargs["task_id"] = kwargs["task_id"]
+    
+    forward_kwargs = {}
+    if "task_id" in kwargs:
+      forward_kwargs["task_id"] = kwargs["task_id"]
+    return preprocess_kwargs, forward_kwargs, {}
+
+  def preprocess(self, inputs, task_id):
+    return_tensors = self.framework
+    feature = self.tokenizer(inputs, padding = True, return_tensors=return_tensors).to(self.device)
+    task_ids = np.full(shape=1,fill_value=task_id, dtype=int)
+    feature["task_ids"] = torch.IntTensor(task_ids)
+    return feature
+  
+  def _forward(self, model_inputs, task_id):
+    return self.model(**model_inputs)
+
+  def postprocess(self, model_outputs):
+    outputs = model_outputs["logits"][0]
+    outputs = outputs.numpy()
+    scores = softmax(outputs)
+
+    dict_scores = [
+            {"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(scores)
+        ]
+    return dict_scores