clean pipelines (#3795)

src/transformers/pipelines.py

@@ -23,17 +23,12 @@ import sys
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from os.path import abspath, exists
-from typing import Dict, List, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoConfig
-from .configuration_bart import BartConfig
-from .configuration_distilbert import DistilBertConfig
-from .configuration_roberta import RobertaConfig
-from .configuration_t5 import T5Config
 from .configuration_utils import PretrainedConfig
-from .configuration_xlm import XLMConfig
 from .data import SquadExample, squad_convert_examples_to_features
 from .file_utils import is_tf_available, is_torch_available
 from .modelcard import ModelCard
@@ -423,27 +418,6 @@ class Pipeline(_ScikitCompat):
         """
         return {name: tensor.to(self.device) for name, tensor in inputs.items()}
 
-    def inputs_for_model(self, features: Union[dict, List[dict]]) -> Dict:
-        """
-        Generates the input dictionary with model-specific parameters.
-
-        Returns:
-            dict holding all the required parameters for model's forward
-        """
-        args = ["input_ids", "attention_mask"]
-
-        if not isinstance(self.model.config, (DistilBertConfig, XLMConfig, RobertaConfig, BartConfig, T5Config)):
-            args += ["token_type_ids"]
-
-        # PR #1548 (CLI) There is an issue with attention_mask
-        # if 'xlnet' in model_type or 'xlm' in model_type:
-        #     args += ['cls_index', 'p_mask']
-
-        if isinstance(features, dict):
-            return {k: features[k] for k in args}
-        else:
-            return {k: [feature[k] for feature in features] for k in args}
-
     def _parse_and_tokenize(self, *texts, pad_to_max_length=False, **kwargs):
         """
         Parse arguments and tokenize
@@ -458,9 +432,6 @@ class Pipeline(_ScikitCompat):
             pad_to_max_length=pad_to_max_length,
         )
 
-        # Filter out features not available on specific models
-        # inputs = self.inputs_for_model(inputs)
-
         return inputs
 
     def __call__(self, *texts, **kwargs):
@@ -995,7 +966,8 @@ class QuestionAnsweringPipeline(Pipeline):
         ]
         all_answers = []
         for features, example in zip(features_list, examples):
-            fw_args = self.inputs_for_model([f.__dict__ for f in features])
+            model_input_names = self.tokenizer.model_input_names + ["input_ids"]
+            fw_args = {k: [feature.__dict__[k] for feature in features] for k in model_input_names}
 
             # Manage tensor allocation on correct device
             with self.device_placement():