add gather_use_object arguments (#31514)

* add gather_use_object arguments

* fix name and pass the CI test for Seq2SeqTrainer

* make style

* make it to functools

* fix typo

* add accelerate version:

* adding warning

* Update src/transformers/trainer.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* make style

* Update src/transformers/training_args.py

* check function move to initial part

* add test for eval_use_gather_object

---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

src/transformers/trainer.py

@@ -4605,6 +4605,11 @@ class Trainer:
         # some Trainer classes need to use `gather` instead of `gather_for_metrics`, thus we store a flag
         self.gather_function = self.accelerator.gather_for_metrics
 
+        if "use_gather_object" in inspect.signature(self.gather_function).parameters.keys():
+            self.gather_function = functools.partial(
+                self.gather_function, use_gather_object=self.args.eval_use_gather_object
+            )
+
         # deepspeed and accelerate flags covering both trainer args and accelerate launcher
         self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
         self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None

src/transformers/training_args.py

@@ -773,8 +773,11 @@ class TrainingArguments:
             that takes a boolean argument `compute_result`, which when passed `True`, will trigger the final global
             summary statistics from the batch-level summary statistics you've accumulated over the evaluation set.
 
-        eval_on_start(`bool`, *optional*, defaults to `False`):
+        eval_on_start (`bool`, *optional*, defaults to `False`):
             Whether to perform a evaluation step (sanity check) before the training to ensure the validation steps works correctly.
+
+        eval_use_gather_object (`bool`, *optional*, defaults to `False`):
+            Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices.
     """
 
     framework = "pt"
@@ -1465,6 +1468,13 @@ class TrainingArguments:
         },
     )
 
+    eval_use_gather_object: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices."
+        },
+    )
+
     def __post_init__(self):
         # Parse in args that could be `dict` sent in from the CLI as a string
         for field in _VALID_DICT_FIELDS:
@@ -1992,6 +2002,12 @@ class TrainingArguments:
                 FutureWarning,
             )
 
+        if self.eval_use_gather_object and not is_accelerate_available("0.30.0"):
+            raise ValueError(
+                "--eval_use_gather_object requires Accelerate to be version of `accelerate` < 0.30.0."
+                "This is not supported and we recommend you to update your version."
+            )
+
     def __str__(self):
         self_as_dict = asdict(self)
 

tests/trainer/test_trainer.py

@@ -132,6 +132,7 @@ if is_torch_available():
 
 # for version specific tests in TrainerIntegrationTest
 require_accelerate_version_min_0_28 = partial(require_accelerate, min_version="0.28")
+require_accelerate_version_min_0_30 = partial(require_accelerate, min_version="0.30")
 GRAD_ACCUM_KWARGS_VERSION_AVAILABLE = is_accelerate_available("0.28")
 if is_accelerate_available():
     from accelerate import Accelerator
@@ -3565,6 +3566,17 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
                 self.assertIn("torch_dtype", args_dict)
                 self.assertEqual(args_dict["torch_dtype"], dtype)
 
+    @require_accelerate_version_min_0_30
+    def test_eval_use_gather_object(self):
+        train_dataset = RegressionDataset()
+        eval_dataset = RegressionDataset()
+        model = RegressionDictModel()
+        args = TrainingArguments("./regression", report_to="none", eval_use_gather_object=True)
+        trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
+        trainer.train()
+        _ = trainer.evaluate()
+        _ = trainer.predict(eval_dataset)
+
 
 @require_torch
 @is_staging_test