Add `qas_id` to SquadResult and SquadExample (#3745)

* Add qas_id

* Fix incorrect name in squad.py

* Make output files optional for squad eval

examples/run_squad.py

@@ -307,7 +307,7 @@ def evaluate(args, model, tokenizer, prefix=""):
             if args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                 del inputs["token_type_ids"]
 
-            example_indices = batch[3]
+            feature_indices = batch[3]
 
             # XLNet and XLM use more arguments for their predictions
             if args.model_type in ["xlnet", "xlm"]:
@@ -320,8 +320,9 @@ def evaluate(args, model, tokenizer, prefix=""):
 
             outputs = model(**inputs)
 
-        for i, example_index in enumerate(example_indices):
-            eval_feature = features[example_index.item()]
+        for i, feature_index in enumerate(feature_indices):
+            # TODO: i and feature_index are the same number! Simplify by removing enumerate?
+            eval_feature = features[feature_index.item()]
             unique_id = int(eval_feature.unique_id)
 
             output = [to_list(output[i]) for output in outputs]

src/transformers/data/metrics/squad_metrics.py

@@ -384,8 +384,12 @@ def compute_predictions_logits(
     tokenizer,
 ):
     """Write final predictions to the json file and log-odds of null if needed."""
-    logger.info("Writing predictions to: %s" % (output_prediction_file))
-    logger.info("Writing nbest to: %s" % (output_nbest_file))
+    if output_prediction_file:
+        logger.info(f"Writing predictions to: {output_prediction_file}")
+    if output_nbest_file:
+        logger.info(f"Writing nbest to: {output_nbest_file}")
+    if output_null_log_odds_file and version_2_with_negative:
+        logger.info(f"Writing null_log_odds to: {output_null_log_odds_file}")
 
     example_index_to_features = collections.defaultdict(list)
     for feature in all_features:
@@ -554,13 +558,15 @@ def compute_predictions_logits(
                 all_predictions[example.qas_id] = best_non_null_entry.text
         all_nbest_json[example.qas_id] = nbest_json
 
-    with open(output_prediction_file, "w") as writer:
-        writer.write(json.dumps(all_predictions, indent=4) + "\n")
+    if output_prediction_file:
+        with open(output_prediction_file, "w") as writer:
+            writer.write(json.dumps(all_predictions, indent=4) + "\n")
 
-    with open(output_nbest_file, "w") as writer:
-        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+    if output_nbest_file:
+        with open(output_nbest_file, "w") as writer:
+            writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
 
-    if version_2_with_negative:
+    if output_null_log_odds_file and version_2_with_negative:
         with open(output_null_log_odds_file, "w") as writer:
             writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
 

src/transformers/data/processors/squad.py

@@ -251,6 +251,7 @@ def squad_convert_example_to_features(example, max_seq_length, doc_stride, max_q
                 start_position=start_position,
                 end_position=end_position,
                 is_impossible=span_is_impossible,
+                qas_id=example.qas_id,
             )
         )
     return features
@@ -344,9 +345,9 @@ def squad_convert_examples_to_features(
         all_is_impossible = torch.tensor([f.is_impossible for f in features], dtype=torch.float)
 
         if not is_training:
-            all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+            all_feature_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
             dataset = TensorDataset(
-                all_input_ids, all_attention_masks, all_token_type_ids, all_example_index, all_cls_index, all_p_mask
+                all_input_ids, all_attention_masks, all_token_type_ids, all_feature_index, all_cls_index, all_p_mask
             )
         else:
             all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
@@ -368,12 +369,14 @@ def squad_convert_examples_to_features(
             raise RuntimeError("TensorFlow must be installed to return a TensorFlow dataset.")
 
         def gen():
-            for ex in features:
+            for i, ex in enumerate(features):
                 yield (
                     {
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
+                        "feature_index": i,
+                        "qas_id": ex.qas_id,
                     },
                     {
                         "start_position": ex.start_position,
@@ -384,35 +387,44 @@ def squad_convert_examples_to_features(
                     },
                 )
 
-        return tf.data.Dataset.from_generator(
-            gen,
-            (
-                {"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32},
-                {
-                    "start_position": tf.int64,
-                    "end_position": tf.int64,
-                    "cls_index": tf.int64,
-                    "p_mask": tf.int32,
-                    "is_impossible": tf.int32,
-                },
-            ),
-            (
-                {
-                    "input_ids": tf.TensorShape([None]),
-                    "attention_mask": tf.TensorShape([None]),
-                    "token_type_ids": tf.TensorShape([None]),
-                },
-                {
-                    "start_position": tf.TensorShape([]),
-                    "end_position": tf.TensorShape([]),
-                    "cls_index": tf.TensorShape([]),
-                    "p_mask": tf.TensorShape([None]),
-                    "is_impossible": tf.TensorShape([]),
-                },
-            ),
+        # Why have we split the batch into a tuple? PyTorch just has a list of tensors.
+        train_types = (
+            {
+                "input_ids": tf.int32,
+                "attention_mask": tf.int32,
+                "token_type_ids": tf.int32,
+                "feature_index": tf.int64,
+                "qas_id": tf.string,
+            },
+            {
+                "start_position": tf.int64,
+                "end_position": tf.int64,
+                "cls_index": tf.int64,
+                "p_mask": tf.int32,
+                "is_impossible": tf.int32,
+            },
         )
 
-    return features
+        train_shapes = (
+            {
+                "input_ids": tf.TensorShape([None]),
+                "attention_mask": tf.TensorShape([None]),
+                "token_type_ids": tf.TensorShape([None]),
+                "feature_index": tf.TensorShape([]),
+                "qas_id": tf.TensorShape([]),
+            },
+            {
+                "start_position": tf.TensorShape([]),
+                "end_position": 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:
+        return features
 
 
 class SquadProcessor(DataProcessor):
@@ -678,6 +690,7 @@ class SquadFeatures(object):
         start_position,
         end_position,
         is_impossible,
+        qas_id: str = None,
     ):
         self.input_ids = input_ids
         self.attention_mask = attention_mask
@@ -695,6 +708,7 @@ class SquadFeatures(object):
         self.start_position = start_position
         self.end_position = end_position
         self.is_impossible = is_impossible
+        self.qas_id = qas_id
 
 
 class SquadResult(object):