Rework tests to compare trainer checkpoint args (#29883)

* Start rework

* Fix failing test

* Include max

* Update src/transformers/trainer.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/trainer.py

@@ -1565,23 +1565,28 @@ class Trainer:
             "logging_steps": "logging_steps",
             "eval_steps": "eval_steps",
             "save_steps": "save_steps",
-            "per_device_train_batch_size": "train_batch_size",
         }
 
-        warnings_list = []
+        has_warning = False
+        warning_str = "Warning: The following arguments do not match the ones in the `trainer_state.json` within the checkpoint directory: "
         for arg_attr, state_attr in attributes_map.items():
             arg_value = getattr(training_args, arg_attr, None)
             state_value = getattr(trainer_state, state_attr, None)
 
             if arg_value is not None and state_value is not None and arg_value != state_value:
-                warnings_list.append(
-                    f"Warning: The training argument '{arg_attr}' value ({arg_value}) does not match the trainer state '{state_attr}' value ({state_value}). "
-                    f"This argument will be overridden by the one found in trainer_state.json within the checkpoint directory."
-                )
+                warning_str += f"\n\t{arg_attr}: {arg_value} (from args) != {state_value} (from trainer_state.json)"
+                has_warning = True
+
+        # train bs is special as we need to account for multi-GPU
+        train_bs_args = training_args.per_device_train_batch_size
+        train_bs_state = trainer_state.train_batch_size // max(1, training_args.n_gpu)
+
+        if train_bs_args != train_bs_state:
+            warning_str += f"\n\tper_device_train_batch_size: {train_bs_args} (from args) != {train_bs_state} (from trainer_state.json)"
+            has_warning = True
 
-        if warnings_list:
-            for warning in warnings_list:
-                logger.warning(warning)
+        if has_warning:
+            logger.warning_once(warning_str)
 
     def _wrap_model(self, model, training=True, dataloader=None):
         if self.args.use_ipex:

tests/trainer/test_trainer.py

@@ -2540,16 +2540,14 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
             )
             checkpoint_trainer.train(resume_from_checkpoint=checkpoint)
 
+        self.assertIn("save_steps: 10 (from args) != 5 (from trainer_state.json)", cl.out)
+
         self.assertIn(
-            "Warning: The training argument 'save_steps' value (10) does not match the trainer state 'save_steps' value (5). This argument will be overridden by the one found in trainer_state.json within the checkpoint directory.",
-            cl.out,
-        )
-        self.assertIn(
-            "Warning: The training argument 'per_device_train_batch_size' value (8) does not match the trainer state 'train_batch_size' value (4). This argument will be overridden by the one found in trainer_state.json within the checkpoint directory.",
+            "per_device_train_batch_size: 8 (from args) != 4 (from trainer_state.json)",
             cl.out,
         )
         self.assertIn(
-            "Warning: The training argument 'eval_steps' value (10) does not match the trainer state 'eval_steps' value (5). This argument will be overridden by the one found in trainer_state.json within the checkpoint directory.",
+            "eval_steps: 10 (from args) != 5 (from trainer_state.json)",
             cl.out,
         )