Add speed metrics to all example scripts + template (#9260)

examples/language-modeling/run_clm.py

@@ -341,9 +341,20 @@ def main():
             if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
             else None
         )
-        trainer.train(model_path=model_path)
+        train_result = trainer.train(model_path=model_path)
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -358,7 +369,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

examples/language-modeling/run_mlm.py

@@ -376,9 +376,20 @@ def main():
             if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
             else None
         )
-        trainer.train(model_path=model_path)
+        train_result = trainer.train(model_path=model_path)
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -393,7 +404,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

examples/language-modeling/run_mlm_wwm.py

@@ -334,9 +334,20 @@ def main():
             if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
             else None
         )
-        trainer.train(model_path=model_path)
+        train_result = trainer.train(model_path=model_path)
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -351,7 +362,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

examples/language-modeling/run_plm.py

@@ -363,9 +363,20 @@ def main():
             if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
             else None
         )
-        trainer.train(model_path=model_path)
+        train_result = trainer.train(model_path=model_path)
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -380,7 +391,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

examples/multiple-choice/run_swag.py

@@ -317,11 +317,22 @@ def main():
 
     # Training
     if training_args.do_train:
-        trainer.train(
+        train_result = trainer.train(
             model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
         )
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -333,7 +344,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

examples/question-answering/run_qa.py

@@ -438,11 +438,22 @@ def main():
 
     # Training
     if training_args.do_train:
-        trainer.train(
+        train_result = trainer.train(
             model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
         )
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -453,7 +464,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

examples/question-answering/run_qa_beam_search.py

@@ -481,11 +481,22 @@ def main():
 
     # Training
     if training_args.do_train:
-        trainer.train(
+        train_result = trainer.train(
             model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
         )
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -496,7 +507,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

examples/token-classification/run_ner.py

@@ -340,11 +340,22 @@ def main():
 
     # Training
     if training_args.do_train:
-        trainer.train(
+        train_result = trainer.train(
             model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
         )
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -377,7 +388,7 @@ def main():
         output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
         if trainer.is_world_process_zero():
             with open(output_test_results_file, "w") as writer:
-                for key, value in metrics.items():
+                for key, value in sorted(metrics.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")
 

templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py

@@ -308,7 +308,7 @@ def main():
     # Training
     if training_args.do_train:
 {%- if cookiecutter.can_train_from_scratch == "False" %}
-        trainer.train(
+        train_result = trainer.train(
             model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
         )
 {%- elif cookiecutter.can_train_from_scratch == "True" %}
@@ -317,10 +317,21 @@ def main():
             if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
             else None
         )
-        trainer.train(model_path=model_path)
+        train_result = trainer.train(model_path=model_path)
 {% endif %}
         trainer.save_model()  # Saves the tokenizer too for easy upload
 
+        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_train_file, "w") as writer:
+                logger.info("***** Train results *****")
+                for key, value in sorted(train_result.metrics.items()):
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
+            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
+
     # Evaluation
     results = {}
     if training_args.do_eval:
@@ -332,7 +343,7 @@ def main():
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")
-                for key, value in results.items():
+                for key, value in sorted(results.items()):
                     logger.info(f"  {key} = {value}")
                     writer.write(f"{key} = {value}\n")