Rework TF trainer (#6038)

* Fully rework training/prediction loops

* fix method name

* Fix variable name

* Fix property name

* Fix scope

* Fix method name

* Fix tuple index

* Fix tuple index

* Fix indentation

* Fix variable name

* fix eval before log

* Add drop remainder for test dataset

* Fix step number + fix logging datetime

* fix eval loss value

* use global step instead of step + fix logging at step 0

* Fix logging datetime

* Fix global_step usage

* Fix breaking loop + logging datetime

* Fix step in prediction loop

* Fix step breaking

* Fix train/test loops

* Force TF at least 2.2 for the trainer

* Use assert_cardinality to facilitate the dataset size computation

* Log steps per epoch

* Make tfds compliant with TPU

* Make tfds compliant with TPU

* Use TF dataset enumerate instead of the Python one

* revert previous commit

* Fix data_dir

* Apply style

* rebase on master

* Address Sylvain's comments

* Address Sylvain's and Lysandre comments

* Trigger CI

* Remove unused import

examples/token-classification/run_tf_ner.py

@@ -17,7 +17,6 @@
 
 import logging
 import os
-import warnings
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple
 
@@ -185,11 +184,6 @@ def main():
 
         for i in range(batch_size):
             for j in range(seq_len):
-                if label_ids[i, j] == -1:
-                    label_ids[i, j] = -100
-                    warnings.warn(
-                        "Using `-1` to mask the loss for the token is depreciated. Please use `-100` instead."
-                    )
                 if label_ids[i, j] != -100:
                     out_label_list[i].append(label_map[label_ids[i][j]])
                     preds_list[i].append(label_map[preds[i][j]])

examples/token-classification/utils_ner.py

@@ -146,7 +146,7 @@ if is_tf_available():
         """
 
         features: List[InputFeatures]
-        pad_token_label_id: int = -1
+        pad_token_label_id: int = -100
         # Use cross entropy ignore_index as padding label id so that only
         # real label ids contribute to the loss later.
 
@@ -221,6 +221,8 @@ if is_tf_available():
                 )
 
         def get_dataset(self):
+            self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
+
             return self.dataset
 
         def __len__(self):