add custom handler and modify pipeline

handler.py

@@ -1,150 +1,11 @@
-from typing import Dict, List, Any, Optional, Tuple, Union
+from typing import Dict, List, Any
 from dataclasses import dataclass
 import torch
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-import numpy as np
-import transformers
-from transformers import AutoTokenizer, BertTokenizer
-from transformers import Pipeline, pipeline
+from transformers import AutoTokenizer
+from transformers import pipeline
 from transformers.pipelines import PIPELINE_REGISTRY
-from transformers import models
-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,
-        )
+from bibert_multitask_classification import BiBert_MultiTaskPipeline
+from bert_for_sequence_classification import BertForSequenceClassification
 
 @dataclass
 class Task:
@@ -153,59 +14,6 @@ class Task:
   type: str
   num_labels: int
 
-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"]
-
-    postprocess_kwargs = {}
-    if "top_k" in kwargs:
-      postprocess_kwargs["top_k"] = kwargs["top_k"]
-      postprocess_kwargs["_legacy"] = False
-    return preprocess_kwargs, forward_kwargs, postprocess_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, top_k=1, _legacy=True):
-    outputs = model_outputs["logits"][0]
-    outputs = outputs.numpy()
-    scores = softmax(outputs)
-
-    if top_k == 1 and _legacy:
-      return {"label": self.model.config.id2label[scores.argmax().item()], "score": scores.max().item()}
-
-    dict_scores = [
-        {"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(scores)
-    ]
-    if not _legacy:
-      dict_scores.sort(key=lambda x: x["score"], reverse=True)
-      if top_k is not None:
-          dict_scores = dict_scores[:top_k]
-    return dict_scores
-
 
 class EndpointHandler():
   def __init__(self, path=""):