[examples/s2s] add test set predictions (huggingface#10085)

* add do_predict, pass eval_beams durig eval

* update help

* apply suggestions from code review

examples/seq2seq/run_seq2seq.py

@@ -167,9 +167,22 @@ class DataTrainingArguments:
             "value if set."
         },
     )
+    max_test_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of test examples to this "
+            "value if set."
+        },
+    )
     source_lang: Optional[str] = field(default=None, metadata={"help": "Source language id for translation."})
     target_lang: Optional[str] = field(default=None, metadata={"help": "Target language id for translation."})
-    eval_beams: Optional[int] = field(default=None, metadata={"help": "Number of beams to use for evaluation."})
+    num_beams: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
+            "which is used during ``evaluate`` and ``predict``."
+        },
+    )
     ignore_pad_token_for_loss: bool = field(
         default=True,
         metadata={
@@ -336,8 +349,13 @@ def main():
     # We need to tokenize inputs and targets.
     if training_args.do_train:
         column_names = datasets["train"].column_names
-    else:
+    elif training_args.do_eval:
         column_names = datasets["validation"].column_names
+    elif training_args.do_predict:
+        column_names = datasets["test"].column_names
+    else:
+        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
+        return
 
     # For translation we set the codes of our source and target languages (only useful for mBART, the others will
     # ignore those attributes).
@@ -440,6 +458,19 @@ def preprocess_function(examples):
             load_from_cache_file=not data_args.overwrite_cache,
         )
 
+    if training_args.do_predict:
+        max_target_length = data_args.val_max_target_length
+        test_dataset = datasets["test"]
+        if data_args.max_test_samples is not None:
+            test_dataset = test_dataset.select(range(data_args.max_test_samples))
+        test_dataset = test_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
+
     # Data collator
     label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
     if data_args.pad_to_max_length:
@@ -523,7 +554,7 @@ def compute_metrics(eval_preds):
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        results = trainer.evaluate()
+        results = trainer.evaluate(max_length=data_args.val_max_target_length, num_beams=data_args.num_beams)
 
         output_eval_file = os.path.join(training_args.output_dir, "eval_results_seq2seq.txt")
         if trainer.is_world_process_zero():
@@ -533,6 +564,34 @@ def compute_metrics(eval_preds):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 
+    if training_args.do_predict:
+        logger.info("*** Test ***")
+
+        test_results = trainer.predict(
+            test_dataset,
+            metric_key_prefix="test",
+            max_length=data_args.val_max_target_length,
+            num_beams=data_args.num_beams,
+        )
+        test_metrics = test_results.metrics
+
+        output_test_result_file = os.path.join(training_args.output_dir, "test_results_seq2seq.txt")
+        if trainer.is_world_process_zero():
+            with open(output_test_result_file, "w") as writer:
+                logger.info("***** Test results *****")
+                for key, value in sorted(test_metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            if training_args.predict_with_generate:
+                test_preds = tokenizer.batch_decode(
+                    test_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
+                )
+                test_preds = [pred.strip() for pred in test_preds]
+                output_test_preds_file = os.path.join(training_args.output_dir, "test_preds_seq2seq.txt")
+                with open(output_test_preds_file, "w") as writer:
+                    writer.write("\n".join(test_preds))
+
     return results