Misc. fixes for Pytorch QA examples:

examples/flax/question-answering/utils_qa.py

@@ -158,7 +158,7 @@ def postprocess_qa_predictions(
                             "end_logit": end_logits[end_index],
                         }
                     )
-        if version_2_with_negative:
+        if version_2_with_negative and min_null_prediction is not None:
             # Add the minimum null prediction
             prelim_predictions.append(min_null_prediction)
             null_score = min_null_prediction["score"]
@@ -167,7 +167,11 @@ def postprocess_qa_predictions(
         predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
 
         # Add back the minimum null prediction if it was removed because of its low score.
-        if version_2_with_negative and not any(p["offsets"] == (0, 0) for p in predictions):
+        if (
+            version_2_with_negative
+            and min_null_prediction is not None
+            and not any(p["offsets"] == (0, 0) for p in predictions)
+        ):
             predictions.append(min_null_prediction)
 
         # Use the offsets to gather the answer text in the original context.
@@ -350,9 +354,12 @@ def postprocess_qa_predictions_with_beam_search(
                         start_index >= len(offset_mapping)
                         or end_index >= len(offset_mapping)
                         or offset_mapping[start_index] is None
+                        or len(offset_mapping[start_index]) < 2
                         or offset_mapping[end_index] is None
+                        or len(offset_mapping[end_index]) < 2
                     ):
                         continue
+
                     # Don't consider answers with a length negative or > max_answer_length.
                     if end_index < start_index or end_index - start_index + 1 > max_answer_length:
                         continue
@@ -381,7 +388,9 @@ def postprocess_qa_predictions_with_beam_search(
         # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
         # failure.
         if len(predictions) == 0:
-            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": -2e-6})
+            # Without predictions min_null_score is going to be None and None will cause an exception later
+            min_null_score = -2e-6
+            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": min_null_score})
 
         # Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
         # the LogSumExp trick).

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -19,6 +19,7 @@
 # You can also adapt this script on your own question answering task. Pointers for this are left as comments.
 
 import argparse
+import json
 import logging
 import math
 import os
@@ -60,6 +61,29 @@
 logger = logging.getLogger(__name__)
 
 
+def save_prefixed_metrics(results, output_dir, file_name: str = "all_results.json", metric_key_prefix: str = "eval"):
+    """
+    Save results while prefixing metric names.
+
+    Args:
+        results: (:obj:`dict`):
+            A dictionary of results.
+        output_dir: (:obj:`str`):
+            An output directory.
+        file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`):
+            An output file name.
+        metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`):
+            A metric name prefix.
+    """
+    # Prefix all keys with metric_key_prefix + '_'
+    for key in list(results.keys()):
+        if not key.startswith(f"{metric_key_prefix}_"):
+            results[f"{metric_key_prefix}_{key}"] = results.pop(key)
+
+    with open(os.path.join(output_dir, file_name), "w") as f:
+        json.dump(results, f, indent=4)
+
+
 def parse_args():
     parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering task")
     parser.add_argument(
@@ -171,8 +195,7 @@ def parse_args():
     )
     parser.add_argument(
         "--version_2_with_negative",
-        type=bool,
-        default=False,
+        action="store_true",
         help="If true, some of the examples do not have an answer.",
     )
     parser.add_argument(
@@ -807,6 +830,9 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
     all_end_top_log_probs = []
     all_end_top_index = []
     all_cls_logits = []
+
+    model.eval()
+
     for step, batch in enumerate(eval_dataloader):
         with torch.no_grad():
             outputs = model(**batch)
@@ -864,6 +890,9 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
         all_end_top_log_probs = []
         all_end_top_index = []
         all_cls_logits = []
+
+        model.eval()
+
         for step, batch in enumerate(predict_dataloader):
             with torch.no_grad():
                 outputs = model(**batch)
@@ -938,6 +967,9 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
 
+            logger.info(json.dumps(eval_metric, indent=4))
+            save_prefixed_metrics(eval_metric, args.output_dir)
+
 
 if __name__ == "__main__":
     main()

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -66,6 +66,29 @@
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
 
 
+def save_prefixed_metrics(results, output_dir, file_name: str = "all_results.json", metric_key_prefix: str = "eval"):
+    """
+    Save results while prefixing metric names.
+
+    Args:
+        results: (:obj:`dict`):
+            A dictionary of results.
+        output_dir: (:obj:`str`):
+            An output directory.
+        file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`):
+            An output file name.
+        metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`):
+            A metric name prefix.
+    """
+    # Prefix all keys with metric_key_prefix + '_'
+    for key in list(results.keys()):
+        if not key.startswith(f"{metric_key_prefix}_"):
+            results[f"{metric_key_prefix}_{key}"] = results.pop(key)
+
+    with open(os.path.join(output_dir, file_name), "w") as f:
+        json.dump(results, f, indent=4)
+
+
 def parse_args():
     parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering task")
     parser.add_argument(
@@ -194,8 +217,7 @@ def parse_args():
     )
     parser.add_argument(
         "--version_2_with_negative",
-        type=bool,
-        default=False,
+        action="store_true",
         help="If true, some of the examples do not have an answer.",
     )
     parser.add_argument(
@@ -824,6 +846,9 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
 
     all_start_logits = []
     all_end_logits = []
+
+    model.eval()
+
     for step, batch in enumerate(eval_dataloader):
         with torch.no_grad():
             outputs = model(**batch)
@@ -860,6 +885,9 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
 
         all_start_logits = []
         all_end_logits = []
+
+        model.eval()
+
         for step, batch in enumerate(predict_dataloader):
             with torch.no_grad():
                 outputs = model(**batch)
@@ -907,8 +935,9 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
-        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
-            json.dump({"eval_f1": eval_metric["f1"], "eval_exact": eval_metric["exact"]}, f)
+
+            logger.info(json.dumps(eval_metric, indent=4))
+            save_prefixed_metrics(eval_metric, args.output_dir)
 
 
 if __name__ == "__main__":

examples/pytorch/question-answering/utils_qa.py

@@ -158,7 +158,7 @@ def postprocess_qa_predictions(
                             "end_logit": end_logits[end_index],
                         }
                     )
-        if version_2_with_negative:
+        if version_2_with_negative and min_null_prediction is not None:
             # Add the minimum null prediction
             prelim_predictions.append(min_null_prediction)
             null_score = min_null_prediction["score"]
@@ -167,7 +167,11 @@ def postprocess_qa_predictions(
         predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
 
         # Add back the minimum null prediction if it was removed because of its low score.
-        if version_2_with_negative and not any(p["offsets"] == (0, 0) for p in predictions):
+        if (
+            version_2_with_negative
+            and min_null_prediction is not None
+            and not any(p["offsets"] == (0, 0) for p in predictions)
+        ):
             predictions.append(min_null_prediction)
 
         # Use the offsets to gather the answer text in the original context.
@@ -350,9 +354,12 @@ def postprocess_qa_predictions_with_beam_search(
                         start_index >= len(offset_mapping)
                         or end_index >= len(offset_mapping)
                         or offset_mapping[start_index] is None
+                        or len(offset_mapping[start_index]) < 2
                         or offset_mapping[end_index] is None
+                        or len(offset_mapping[end_index]) < 2
                     ):
                         continue
+
                     # Don't consider answers with a length negative or > max_answer_length.
                     if end_index < start_index or end_index - start_index + 1 > max_answer_length:
                         continue
@@ -381,7 +388,9 @@ def postprocess_qa_predictions_with_beam_search(
         # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
         # failure.
         if len(predictions) == 0:
-            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": -2e-6})
+            # Without predictions min_null_score is going to be None and None will cause an exception later
+            min_null_score = -2e-6
+            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": min_null_score})
 
         # Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
         # the LogSumExp trick).

examples/pytorch/test_accelerate_examples.py

@@ -200,7 +200,7 @@ def test_run_squad_no_trainer(self):
         testargs = f"""
             run_qa_no_trainer.py
             --model_name_or_path bert-base-uncased
-            --version_2_with_negative=False
+            --version_2_with_negative
             --train_file tests/fixtures/tests_samples/SQUAD/sample.json
             --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
             --output_dir {tmp_dir}
@@ -216,6 +216,7 @@ def test_run_squad_no_trainer(self):
         with patch.object(sys, "argv", testargs):
             run_squad_no_trainer.main()
             result = get_results(tmp_dir)
+            # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
             self.assertGreaterEqual(result["eval_f1"], 30)
             self.assertGreaterEqual(result["eval_exact"], 30)
             self.assertTrue(os.path.exists(os.path.join(tmp_dir, "epoch_0")))

examples/tensorflow/question-answering/utils_qa.py

@@ -158,7 +158,7 @@ def postprocess_qa_predictions(
                             "end_logit": end_logits[end_index],
                         }
                     )
-        if version_2_with_negative:
+        if version_2_with_negative and min_null_prediction is not None:
             # Add the minimum null prediction
             prelim_predictions.append(min_null_prediction)
             null_score = min_null_prediction["score"]
@@ -167,7 +167,11 @@ def postprocess_qa_predictions(
         predictions = sorted(prelim_predictions, key=lambda x: x["score"], reverse=True)[:n_best_size]
 
         # Add back the minimum null prediction if it was removed because of its low score.
-        if version_2_with_negative and not any(p["offsets"] == (0, 0) for p in predictions):
+        if (
+            version_2_with_negative
+            and min_null_prediction is not None
+            and not any(p["offsets"] == (0, 0) for p in predictions)
+        ):
             predictions.append(min_null_prediction)
 
         # Use the offsets to gather the answer text in the original context.
@@ -350,9 +354,12 @@ def postprocess_qa_predictions_with_beam_search(
                         start_index >= len(offset_mapping)
                         or end_index >= len(offset_mapping)
                         or offset_mapping[start_index] is None
+                        or len(offset_mapping[start_index]) < 2
                         or offset_mapping[end_index] is None
+                        or len(offset_mapping[end_index]) < 2
                     ):
                         continue
+
                     # Don't consider answers with a length negative or > max_answer_length.
                     if end_index < start_index or end_index - start_index + 1 > max_answer_length:
                         continue
@@ -381,7 +388,9 @@ def postprocess_qa_predictions_with_beam_search(
         # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
         # failure.
         if len(predictions) == 0:
-            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": -2e-6})
+            # Without predictions min_null_score is going to be None and None will cause an exception later
+            min_null_score = -2e-6
+            predictions.insert(0, {"text": "", "start_logit": -1e-6, "end_logit": -1e-6, "score": min_null_score})
 
         # Compute the softmax of all scores (we do it with numpy to stay independent from torch/tf in this file, using
         # the LogSumExp trick).