fix wandb hp search unable to resume from sweep_id

src/transformers/integrations/integration_utils.py

@@ -585,11 +585,19 @@ def _objective():
 
         return trainer.objective
 
-    sweep_id = wandb.sweep(sweep_config, project=project, entity=entity) if not sweep_id else sweep_id
+    if not sweep_id:
+        sweep_id = wandb.sweep(sweep_config, project=project, entity=entity)
+    else:
+        import wandb.env
+
+        if entity:
+            wandb.env.set_entity(entity)
+        wandb.env.set_project(project)
+
     logger.info(f"wandb sweep id - {sweep_id}")
     wandb.agent(sweep_id, function=_objective, count=n_trials)
 
-    return BestRun(best_trial["run_id"], best_trial["objective"], best_trial["hyperparameters"])
+    return BestRun(best_trial["run_id"], best_trial["objective"], best_trial["hyperparameters"], sweep_id)
 
 
 def get_available_reporting_integrations():

tests/trainer/test_trainer.py

@@ -5707,9 +5707,6 @@ def setUp(self):
         self.batch_size = args.train_batch_size
 
     def test_hyperparameter_search(self):
-        class MyTrialShortNamer(TrialShortNamer):
-            DEFAULTS = {"a": 0, "b": 0}
-
         def hp_space(trial):
             return {
                 "method": "random",
@@ -5731,9 +5728,6 @@ def model_init(config):
 
             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,
@@ -5748,9 +5742,31 @@ def hp_name(params):
                 run_name="test",
                 model_init=model_init,
             )
-            trainer.hyperparameter_search(
-                direction="minimize", hp_space=hp_space, hp_name=hp_name, backend="wandb", n_trials=4, anonymous="must"
-            )
+            sweep_kwargs = {
+                "direction": "minimize",
+                "hp_space": hp_space,
+                "backend": "wandb",
+                "n_trials": 4,
+            }
+            best_run = trainer.hyperparameter_search(**sweep_kwargs)
+
+            self.assertIsNotNone(best_run.run_id)
+            self.assertIsNotNone(best_run.run_summary)
+            hp_keys = set(best_run.hyperparameters.keys())
+            self.assertSetEqual(hp_keys, {"a", "b", "assignments", "metric"})
+
+            # pretend restarting the process purged the environ
+            import os
+
+            del os.environ["WANDB_ENTITY"]
+            del os.environ["WANDB_PROJECT"]
+            sweep_kwargs["sweep_id"] = best_run.run_summary
+            updated_best_run = trainer.hyperparameter_search(**sweep_kwargs)
+
+            self.assertIsNotNone(updated_best_run.run_id)
+            self.assertEqual(updated_best_run.run_summary, best_run.run_summary)
+            updated_hp_keys = set(updated_best_run.hyperparameters.keys())
+            self.assertSetEqual(updated_hp_keys, {"a", "b", "assignments", "metric"})
 
 
 class HyperParameterSearchBackendsTest(unittest.TestCase):