Add Ray Tune hyperparameter search integration test (#10414)

tests/test_trainer.py

@@ -27,6 +27,7 @@ from transformers.testing_utils import (
     get_tests_dir,
     require_datasets,
     require_optuna,
+    require_ray,
     require_sentencepiece,
     require_tokenizers,
     require_torch,
@@ -80,6 +81,12 @@ class RegressionDataset:
         return result
 
 
+@dataclasses.dataclass
+class RegressionTrainingArguments(TrainingArguments):
+    a: float = 0.0
+    b: float = 0.0
+
+
 class RepeatDataset:
     def __init__(self, x, length=64):
         self.x = x
@@ -200,7 +207,8 @@ if is_torch_available():
         optimizers = kwargs.pop("optimizers", (None, None))
         output_dir = kwargs.pop("output_dir", "./regression")
         model_init = kwargs.pop("model_init", None)
-        args = TrainingArguments(output_dir, **kwargs)
+
+        args = RegressionTrainingArguments(output_dir, a=a, b=b, **kwargs)
         return Trainer(
             model,
             args,
@@ -973,7 +981,7 @@ class TrainerIntegrationTest(unittest.TestCase):
 
 @require_torch
 @require_optuna
-class TrainerHyperParameterIntegrationTest(unittest.TestCase):
+class TrainerHyperParameterOptunaIntegrationTest(unittest.TestCase):
     def setUp(self):
         args = TrainingArguments(".")
         self.n_epochs = args.num_train_epochs
@@ -1014,3 +1022,49 @@ class TrainerHyperParameterIntegrationTest(unittest.TestCase):
                 model_init=model_init,
             )
             trainer.hyperparameter_search(direction="minimize", hp_space=hp_space, hp_name=hp_name, n_trials=4)
+
+
+@require_torch
+@require_ray
+class TrainerHyperParameterRayIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        args = TrainingArguments(".")
+        self.n_epochs = args.num_train_epochs
+        self.batch_size = args.train_batch_size
+
+    def test_hyperparameter_search(self):
+        class MyTrialShortNamer(TrialShortNamer):
+            DEFAULTS = {"a": 0, "b": 0}
+
+        def hp_space(trial):
+            from ray import tune
+
+            return {
+                "a": tune.randint(-4, 4),
+                "b": tune.randint(-4, 4),
+            }
+
+        def model_init(config):
+            model_config = RegressionModelConfig(a=config["a"], b=config["b"], double_output=False)
+
+            return RegressionPreTrainedModel(model_config)
+
+        def hp_name(params):
+            return MyTrialShortNamer.shortname(params)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            trainer = get_regression_trainer(
+                output_dir=tmp_dir,
+                learning_rate=0.1,
+                logging_steps=1,
+                evaluation_strategy=EvaluationStrategy.EPOCH,
+                num_train_epochs=4,
+                disable_tqdm=True,
+                load_best_model_at_end=True,
+                logging_dir="runs",
+                run_name="test",
+                model_init=model_init,
+            )
+            trainer.hyperparameter_search(
+                direction="minimize", hp_space=hp_space, hp_name=hp_name, backend="ray", n_trials=4
+            )