Support `DeepSpeed` when using auto find batch size (#28088)

Fixup test

src/transformers/integrations/deepspeed.py

@@ -129,7 +129,7 @@ class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig):
 
     fill_only = partialmethod(fill_match, must_match=False)
 
-    def trainer_config_process(self, args):
+    def trainer_config_process(self, args, auto_find_batch_size=False):
         """
         Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object
         creation.
@@ -138,10 +138,15 @@ class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig):
         # train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps
         train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
         self.fill_match(
-            "train_micro_batch_size_per_gpu", args.per_device_train_batch_size, "per_device_train_batch_size"
+            "train_micro_batch_size_per_gpu",
+            args.per_device_train_batch_size,
+            "per_device_train_batch_size",
+            not auto_find_batch_size,
         )
         self.fill_match("gradient_accumulation_steps", args.gradient_accumulation_steps, "gradient_accumulation_steps")
-        self.fill_match("train_batch_size", train_batch_size, "train_batch_size (calculated)")
+        self.fill_match(
+            "train_batch_size", train_batch_size, "train_batch_size (calculated)", not auto_find_batch_size
+        )
         self.fill_match("gradient_clipping", args.max_grad_norm, "max_grad_norm")
 
         self.fill_match("optimizer.params.lr", args.learning_rate, "learning_rate")
@@ -336,6 +341,8 @@ def deepspeed_init(trainer, num_training_steps, inference=False):
         num_training_steps: per single gpu
         resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load
         inference: launch in inference mode (no optimizer and no lr scheduler)
+        auto_find_batch_size: whether to ignore the `train_micro_batch_size_per_gpu` argument as it's being
+            set automatically by the auto batch size finder
 
     Returns: optimizer, lr_scheduler
 

src/transformers/trainer.py

@@ -1506,12 +1506,8 @@ class Trainer:
             if resume_from_checkpoint is None:
                 raise ValueError(f"No valid checkpoint found in output directory ({args.output_dir})")
 
-        if (
-            resume_from_checkpoint is not None
-            and not is_sagemaker_mp_enabled()
-            and not self.is_deepspeed_enabled
-            and not self.is_fsdp_enabled
-        ):
+        if resume_from_checkpoint is not None:
+            if not is_sagemaker_mp_enabled() and not self.is_deepspeed_enabled and not self.is_fsdp_enabled:
                 self._load_from_checkpoint(resume_from_checkpoint)
             # In case of repeating the find_executable_batch_size, set `self._train_batch_size` properly
             state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
@@ -1553,6 +1549,19 @@ class Trainer:
         self.accelerator.free_memory()
         self._train_batch_size = batch_size
         if self.args.auto_find_batch_size:
+            if self.state.train_batch_size != self._train_batch_size:
+                from accelerate.utils import release_memory
+
+                (self.model_wrapped,) = release_memory(self.model_wrapped)
+                self.model_wrapped = self.model
+
+                # Check for DeepSpeed *after* the intial pass and modify the config
+                if self.is_deepspeed_enabled:
+                    # Temporarily unset `self.args.train_batch_size`
+                    original_bs = self.args.per_device_train_batch_size
+                    self.args.per_device_train_batch_size = self._train_batch_size // max(1, self.args.n_gpu)
+                    self.propagate_args_to_deepspeed(True)
+                    self.args.per_device_train_batch_size = original_bs
             self.state.train_batch_size = self._train_batch_size
         logger.debug(f"Currently training with a batch size of: {self._train_batch_size}")
         # Data loader and number of training steps
@@ -3944,12 +3953,17 @@ class Trainer:
                         "when using FSDP."
                     )
 
-        if self.is_deepspeed_enabled:
-            if getattr(self.args, "hf_deepspeed_config", None) is None:
+        if self.is_deepspeed_enabled and getattr(self.args, "hf_deepspeed_config", None) is None:
+            self.propagate_args_to_deepspeed()
+
+    def propagate_args_to_deepspeed(self, auto_find_batch_size=False):
+        """
+        Sets values in the deepspeed plugin based on the Trainer args
+        """
         from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
 
         ds_plugin = self.accelerator.state.deepspeed_plugin
 
         ds_plugin.hf_ds_config = HfTrainerDeepSpeedConfig(ds_plugin.hf_ds_config.config)
         ds_plugin.deepspeed_config = ds_plugin.hf_ds_config.config
-                ds_plugin.hf_ds_config.trainer_config_process(self.args)
+        ds_plugin.hf_ds_config.trainer_config_process(self.args, auto_find_batch_size)

tests/trainer/test_trainer.py

@@ -57,6 +57,7 @@ from transformers.testing_utils import (
     get_tests_dir,
     is_staging_test,
     require_accelerate,
+    require_deepspeed,
     require_intel_extension_for_pytorch,
     require_optuna,
     require_ray,
@@ -1551,6 +1552,51 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
         with patch.object(sys, "argv", testargs):
             run_glue.main()
 
+    @require_deepspeed
+    def test_auto_batch_size_with_resume_from_checkpoint_with_deepspeed(self):
+        train_dataset = RegressionDataset(length=128)
+
+        config = RegressionModelConfig(a=0, b=2)
+        model = RegressionRandomPreTrainedModel(config)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+
+        class MockCudaOOMCallback(TrainerCallback):
+            def on_step_end(self, args, state, control, **kwargs):
+                # simulate OOM on the first step
+                if state.train_batch_size >= 16:
+                    raise RuntimeError("CUDA out of memory.")
+
+        deepspeed = {
+            "zero_optimization": {
+                "stage": 1,
+            },
+            "train_batch_size": "auto",
+            "train_micro_batch_size_per_gpu": "auto",
+        }
+
+        args = RegressionTrainingArguments(
+            tmp_dir,
+            do_train=True,
+            max_steps=2,
+            save_steps=1,
+            per_device_train_batch_size=16,
+            auto_find_batch_size=True,
+            deepspeed=deepspeed,
+        )
+        trainer = Trainer(model, args, train_dataset=train_dataset, callbacks=[MockCudaOOMCallback()])
+        trainer.train()
+        # After `auto_find_batch_size` is ran we should now be at 8
+        self.assertEqual(trainer._train_batch_size, 8)
+
+        # We can then make a new Trainer
+        trainer = Trainer(model, args, train_dataset=train_dataset)
+        # Check we are at 16 to start
+        self.assertEqual(trainer._train_batch_size, 16 * max(trainer.args.n_gpu, 1))
+        trainer.train(resume_from_checkpoint=True)
+        # We should be back to 8 again, picking up based upon the last ran Trainer
+        self.assertEqual(trainer._train_batch_size, 8)
+
     def test_auto_batch_size_with_resume_from_checkpoint(self):
         train_dataset = RegressionDataset(length=128)