[integration] Update Ray Tune integration for Ray 2.7

setup.py

@@ -149,7 +149,7 @@
     "pytest-timeout",
     "pytest-xdist",
     "python>=3.8.0",
-    "ray[tune]",
+    "ray[tune]>=2.7.0",
     "regex!=2019.12.17",
     "requests",
     "rhoknp>=1.1.0,<1.3.1",

src/transformers/dependency_versions_table.py

@@ -55,7 +55,7 @@
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",
     "python": "python>=3.8.0",
-    "ray[tune]": "ray[tune]",
+    "ray[tune]": "ray[tune]>=2.7.0",
     "regex": "regex!=2019.12.17",
     "requests": "requests",
     "rhoknp": "rhoknp>=1.1.0,<1.3.1",

src/transformers/hyperparameter_search.py

@@ -15,7 +15,7 @@
 
 from .integrations import (
     is_optuna_available,
-    is_ray_available,
+    is_ray_tune_available,
     is_sigopt_available,
     is_wandb_available,
     run_hp_search_optuna,
@@ -81,7 +81,7 @@ class RayTuneBackend(HyperParamSearchBackendBase):
 
     @staticmethod
     def is_available():
-        return is_ray_available()
+        return is_ray_tune_available()
 
     def run(self, trainer, n_trials: int, direction: str, **kwargs):
         return run_hp_search_ray(trainer, n_trials, direction, **kwargs)

src/transformers/integrations/integration_utils.py

@@ -236,8 +236,9 @@ def _objective(trial, checkpoint_dir=None):
 
 def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
     import ray
+    import ray.train
 
-    def _objective(trial, local_trainer, checkpoint_dir=None):
+    def _objective(trial: dict, local_trainer):
         try:
             from transformers.utils.notebook import NotebookProgressCallback
 
@@ -246,19 +247,34 @@ def _objective(trial, local_trainer, checkpoint_dir=None):
         except ModuleNotFoundError:
             pass
 
-        checkpoint = None
-        if checkpoint_dir:
-            for subdir in os.listdir(checkpoint_dir):
-                if subdir.startswith(PREFIX_CHECKPOINT_DIR):
-                    checkpoint = os.path.join(checkpoint_dir, subdir)
         local_trainer.objective = None
-        local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
+
+        checkpoint = ray.train.get_checkpoint()
+        if checkpoint:
+            # Upon trial resume, the local_trainer's objective gets reset to None.
+            # If `local_trainer.train` is a noop (training has already reached
+            # the target number of epochs/steps), then this would
+            # trigger an unnecessary extra checkpoint at the end of training.
+            # -> Set the objective to a dummy value upon resume as a workaround.
+            local_trainer.objective = "objective"
+
+            with checkpoint.as_directory() as checkpoint_dir:
+                checkpoint_path = next(Path(checkpoint_dir).glob(f"{PREFIX_CHECKPOINT_DIR}*")).as_posix()
+                local_trainer.train(resume_from_checkpoint=checkpoint_path, trial=trial)
+        else:
+            local_trainer.train(trial=trial)
+
         # If there hasn't been any evaluation during the training loop.
         if getattr(local_trainer, "objective", None) is None:
             metrics = local_trainer.evaluate()
             local_trainer.objective = local_trainer.compute_objective(metrics)
-            local_trainer._tune_save_checkpoint()
-            ray.tune.report(objective=local_trainer.objective, **metrics, done=True)
+
+            metrics.update({"objective": local_trainer.objective, "done": True})
+
+            with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
+                local_trainer._tune_save_checkpoint(checkpoint_dir=temp_checkpoint_dir)
+                checkpoint = ray.train.Checkpoint.from_directory(temp_checkpoint_dir)
+                ray.train.report(metrics, checkpoint=checkpoint)
 
     if not trainer._memory_tracker.skip_memory_metrics:
         from ..trainer_utils import TrainerMemoryTracker
@@ -296,28 +312,10 @@ def _objective(trial, local_trainer, checkpoint_dir=None):
         from ray.tune import CLIReporter
 
         kwargs["progress_reporter"] = CLIReporter(metric_columns=["objective"])
-    if "keep_checkpoints_num" in kwargs and kwargs["keep_checkpoints_num"] > 0:
-        # `keep_checkpoints_num=0` would disabled checkpointing
-        trainer.use_tune_checkpoints = True
-        if kwargs["keep_checkpoints_num"] > 1:
-            logger.warning(
-                f"Currently keeping {kwargs['keep_checkpoints_num']} checkpoints for each trial. "
-                "Checkpoints are usually huge, "
-                "consider setting `keep_checkpoints_num=1`."
-            )
+
     if "scheduler" in kwargs:
         from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining
 
-        # Check if checkpointing is enabled for PopulationBasedTraining
-        if isinstance(kwargs["scheduler"], PopulationBasedTraining):
-            if not trainer.use_tune_checkpoints:
-                logger.warning(
-                    "You are using PopulationBasedTraining but you haven't enabled checkpointing. "
-                    "This means your trials will train from scratch everytime they are exploiting "
-                    "new configurations. Consider enabling checkpointing by passing "
-                    "`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`."
-                )
-
         # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting.
         if isinstance(
             kwargs["scheduler"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining)

src/transformers/trainer.py

@@ -28,6 +28,7 @@
 import re
 import shutil
 import sys
+import tempfile
 import time
 import warnings
 from collections.abc import Mapping
@@ -595,7 +596,6 @@ def __init__(
         # returned to 0 every time flos need to be logged
         self.current_flos = 0
         self.hp_search_backend = None
-        self.use_tune_checkpoints = False
         default_label_names = find_labels(self.model.__class__)
         self.label_names = default_label_names if self.args.label_names is None else self.args.label_names
         self.can_return_loss = can_return_loss(self.model.__class__)
@@ -1201,7 +1201,8 @@ def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
     def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, Any]], step: int, metrics: Dict[str, float]):
         if self.hp_search_backend is None or trial is None:
             return
-        self.objective = self.compute_objective(metrics.copy())
+        metrics = metrics.copy()
+        self.objective = self.compute_objective(metrics)
         if self.hp_search_backend == HPSearchBackend.OPTUNA:
             import optuna
 
@@ -1211,24 +1212,23 @@ def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, Any]], ste
                     self.callback_handler.on_train_end(self.args, self.state, self.control)
                     raise optuna.TrialPruned()
         elif self.hp_search_backend == HPSearchBackend.RAY:
-            from ray import tune
-
-            if self.control.should_save:
-                self._tune_save_checkpoint()
-            tune.report(objective=self.objective, **metrics)
-
-    def _tune_save_checkpoint(self):
-        from ray import tune
-
-        if not self.use_tune_checkpoints:
-            return
-        with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
-            output_dir = os.path.join(checkpoint_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
-            self.save_model(output_dir, _internal_call=True)
-            if self.args.should_save:
-                self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
-                torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
-                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
+            import ray.train
+
+            with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
+                checkpoint = None
+                if self.control.should_save:
+                    self._tune_save_checkpoint(checkpoint_dir=temp_checkpoint_dir)
+                    checkpoint = ray.train.Checkpoint.from_directory(temp_checkpoint_dir)
+                metrics["objective"] = self.objective
+                ray.train.report(metrics, checkpoint=checkpoint)
+
+    def _tune_save_checkpoint(self, checkpoint_dir: str):
+        output_dir = os.path.join(checkpoint_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
+        self.save_model(output_dir, _internal_call=True)
+        if self.args.should_save:
+            self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
+            torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
+            torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
 
     def call_model_init(self, trial=None):
         model_init_argcount = number_of_arguments(self.model_init)
@@ -1997,9 +1997,9 @@ def _get_output_dir(self, trial):
             if self.hp_search_backend == HPSearchBackend.OPTUNA:
                 run_id = trial.number
             elif self.hp_search_backend == HPSearchBackend.RAY:
-                from ray import tune
+                import ray.train
 
-                run_id = tune.get_trial_id()
+                run_id = ray.train.get_context().get_trial_id()
             elif self.hp_search_backend == HPSearchBackend.SIGOPT:
                 run_id = trial.id
             elif self.hp_search_backend == HPSearchBackend.WANDB: