Return analysis for hyperparameter_search with Ray backend (#22040)

* return analysis for hyperparameter_search with ray backend

* Revert "return analysis for hyperparameter_search with ray backend"

This reverts commit cd5179070930e03020d96d98eb51dec3eb21ef75.

* add run_summary attribute to BestRun and return analysis for ray backend

* fix typo

* add doc for run_summary for ray backend

src/transformers/integrations.py

@@ -346,7 +346,7 @@ def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestR
         **kwargs,
     )
     best_trial = analysis.get_best_trial(metric="objective", mode=direction[:3], scope=trainer.args.ray_scope)
-    best_run = BestRun(best_trial.trial_id, best_trial.last_result["objective"], best_trial.config)
+    best_run = BestRun(best_trial.trial_id, best_trial.last_result["objective"], best_trial.config, analysis)
     if _tb_writer is not None:
         trainer.add_callback(_tb_writer)
     return best_run

src/transformers/trainer.py

@@ -2492,7 +2492,8 @@ class Trainer:
                 - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)
 
         Returns:
-            [`trainer_utils.BestRun`]: All the information about the best run.
+            [`trainer_utils.BestRun`]: All the information about the best run. Experiment summary can be found in
+            `run_summary` attribute for Ray backend.
         """
         if backend is None:
             backend = default_hp_search_backend()

src/transformers/trainer_utils.py

@@ -192,7 +192,7 @@ class HubStrategy(ExplicitEnum):
 
 class BestRun(NamedTuple):
     """
-    The best run found by an hyperparameter search (see [`~Trainer.hyperparameter_search`]).
+    The best run found by a hyperparameter search (see [`~Trainer.hyperparameter_search`]).
 
     Parameters:
         run_id (`str`):
@@ -202,11 +202,14 @@ class BestRun(NamedTuple):
             The objective that was obtained for this run.
         hyperparameters (`Dict[str, Any]`):
             The hyperparameters picked to get this run.
+        run_summary (`Optional[Any]`):
+            A summary of tuning experiments. `ray.tune.ExperimentAnalysis` object for Ray backend.
     """
 
     run_id: str
     objective: float
     hyperparameters: Dict[str, Any]
+    run_summary: Optional[Any] = None
 
 
 def default_compute_objective(metrics: Dict[str, float]) -> float: