Fix TensorFlow dataset generator (#4881)

* fix TensorFlow generator

* Better features handling

* Apply style

* Apply style

* Fix squad as well

* Apply style

* Better factorization of TF Tensors creation

src/transformers/data/processors/glue.py

@@ -17,6 +17,7 @@
 
 import logging
 import os
+from dataclasses import asdict
 from enum import Enum
 from typing import List, Optional, Union
 
@@ -81,26 +82,16 @@ if is_tf_available():
 
         def gen():
             for ex in features:
-                yield (
-                    {
-                        "input_ids": ex.input_ids,
-                        "attention_mask": ex.attention_mask,
-                        "token_type_ids": ex.token_type_ids,
-                    },
-                    ex.label,
-                )
+                d = {k: v for k, v in asdict(ex).items() if v is not None}
+                label = d.pop("label")
+                yield (d, label)
+
+        input_names = ["input_ids"] + tokenizer.model_input_names
 
         return tf.data.Dataset.from_generator(
             gen,
-            ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
-            (
-                {
-                    "input_ids": tf.TensorShape([None]),
-                    "attention_mask": tf.TensorShape([None]),
-                    "token_type_ids": tf.TensorShape([None]),
-                },
-                tf.TensorShape([]),
-            ),
+            ({k: tf.int32 for k in input_names}, tf.int64),
+            ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),
         )
 
 

src/transformers/data/processors/squad.py

@@ -389,6 +389,23 @@ def squad_convert_examples_to_features(
 
         def gen():
             for i, ex in enumerate(features):
+                if ex.token_type_ids is None:
+                    yield (
+                        {
+                            "input_ids": ex.input_ids,
+                            "attention_mask": ex.attention_mask,
+                            "feature_index": i,
+                            "qas_id": ex.qas_id,
+                        },
+                        {
+                            "start_positions": ex.start_position,
+                            "end_positions": ex.end_position,
+                            "cls_index": ex.cls_index,
+                            "p_mask": ex.p_mask,
+                            "is_impossible": ex.is_impossible,
+                        },
+                    )
+                else:
                     yield (
                         {
                             "input_ids": ex.input_ids,
@@ -407,6 +424,7 @@ def squad_convert_examples_to_features(
                     )
 
         # Why have we split the batch into a tuple? PyTorch just has a list of tensors.
+        if "token_type_ids" in tokenizer.model_input_names:
             train_types = (
                 {
                     "input_ids": tf.int32,
@@ -440,6 +458,33 @@ def squad_convert_examples_to_features(
                     "is_impossible": tf.TensorShape([]),
                 },
             )
+        else:
+            train_types = (
+                {"input_ids": tf.int32, "attention_mask": tf.int32, "feature_index": tf.int64, "qas_id": tf.string},
+                {
+                    "start_positions": tf.int64,
+                    "end_positions": tf.int64,
+                    "cls_index": tf.int64,
+                    "p_mask": tf.int32,
+                    "is_impossible": tf.int32,
+                },
+            )
+
+            train_shapes = (
+                {
+                    "input_ids": tf.TensorShape([None]),
+                    "attention_mask": tf.TensorShape([None]),
+                    "feature_index": tf.TensorShape([]),
+                    "qas_id": tf.TensorShape([]),
+                },
+                {
+                    "start_positions": tf.TensorShape([]),
+                    "end_positions": tf.TensorShape([]),
+                    "cls_index": tf.TensorShape([]),
+                    "p_mask": tf.TensorShape([None]),
+                    "is_impossible": tf.TensorShape([]),
+                },
+            )
 
         return tf.data.Dataset.from_generator(gen, train_types, train_shapes)
     else: