Revert "Fix DeepSpeed mixed precision precedence over Accelerate defaults"

src/transformers/integrations/deepspeed.py

@@ -130,58 +130,11 @@ def fill_match(self, ds_key_long, hf_val, hf_key=None, must_match=True):
 
     fill_only = partialmethod(fill_match, must_match=False)
 
-    def override_training_args_from_deepspeed(self, args):
-        """
-        Override TrainingArguments based on DeepSpeed config values to ensure compatibility.
-
-        This method ensures that the DeepSpeed config takes precedence over TrainingArguments
-        defaults when there are conflicts, particularly for mixed precision settings.
-
-        Args:
-            args: TrainingArguments object to potentially modify
-        """
-        # Check precision settings in DeepSpeed config and override TrainingArguments accordingly
-        # Only override defaults, not explicit user settings
-
-        # Check if user explicitly set precision options (we assume defaults are False)
-        user_set_fp16 = args.fp16 is True
-        user_set_bf16 = args.bf16 is True
-
-        if self.is_true("fp16.enabled"):
-            # DeepSpeed config explicitly enables fp16
-            if not user_set_fp16 and not user_set_bf16:
-                # User didn't explicitly set either, so apply DeepSpeed config
-                args.fp16 = True
-                args.bf16 = False
-            elif user_set_bf16 and not user_set_fp16:
-                # User explicitly chose bf16, but DeepSpeed config wants fp16
-                # This is a potential conflict - let user choice win but log a warning
-                pass  # Keep user's bf16=True, fp16=False
-        elif self.is_true("bf16.enabled"):
-            # DeepSpeed config explicitly enables bf16
-            if not user_set_fp16 and not user_set_bf16:
-                # User didn't explicitly set either, so apply DeepSpeed config
-                args.bf16 = True
-                args.fp16 = False
-            elif user_set_fp16 and not user_set_bf16:
-                # User explicitly chose fp16, but DeepSpeed config wants bf16
-                # This is a potential conflict - let user choice win but log a warning
-                pass  # Keep user's fp16=True, bf16=False
-        elif self.is_false("fp16.enabled") and self.is_false("bf16.enabled"):
-            # Both are explicitly disabled in DeepSpeed config
-            if not user_set_fp16 and not user_set_bf16:
-                # User didn't explicitly set either, so apply DeepSpeed config (fp32)
-                args.fp16 = False
-                args.bf16 = False
-
     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.
         """
-        # First, override TrainingArguments based on DeepSpeed config to ensure compatibility
-        self.override_training_args_from_deepspeed(args)
-
         # DeepSpeed does:
         # 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

src/transformers/training_args.py

@@ -1689,8 +1689,13 @@ def __post_init__(self):
                         torch.backends.cudnn.allow_tf32 = False
                 # no need to assert on else
 
-        # NOTE: Mixed precision environment variable setting moved to after DeepSpeed processing
-        # to ensure DeepSpeed config can override TrainingArguments defaults
+        # if training args is specified, it will override the one specified in the accelerate config
+        mixed_precision_dtype = os.environ.get("ACCELERATE_MIXED_PRECISION", "no")
+        if self.fp16:
+            mixed_precision_dtype = "fp16"
+        elif self.bf16:
+            mixed_precision_dtype = "bf16"
+        os.environ["ACCELERATE_MIXED_PRECISION"] = mixed_precision_dtype
 
         if self.report_to is None:
             logger.info(
@@ -1877,15 +1882,6 @@ def __post_init__(self):
             self.deepspeed_plugin.set_mixed_precision(mixed_precision)
             self.deepspeed_plugin.set_deepspeed_weakref()
 
-        # Set mixed precision environment variable after DeepSpeed processing
-        # This ensures DeepSpeed config overrides have been applied to fp16/bf16 settings
-        mixed_precision_dtype = os.environ.get("ACCELERATE_MIXED_PRECISION", "no")
-        if self.fp16:
-            mixed_precision_dtype = "fp16"
-        elif self.bf16:
-            mixed_precision_dtype = "bf16"
-        os.environ["ACCELERATE_MIXED_PRECISION"] = mixed_precision_dtype
-
         if self.use_cpu:
             self.dataloader_pin_memory = False
 

tests/deepspeed/test_deepspeed.py

@@ -1431,50 +1431,3 @@ def test_clm_from_config_zero3_fp16(self):
         with CaptureStderr() as cs:
             execute_subprocess_async(cmd, env=self.get_env())
         self.assertIn("Detected DeepSpeed ZeRO-3", cs.err)
-
-
-@require_deepspeed
-class TestDeepSpeedMixedPrecisionPrecedence(TestCasePlus):
-    """Test DeepSpeed mixed precision precedence over Accelerate defaults."""
-
-    def setUp(self):
-        super().setUp()
-        unset_hf_deepspeed_config()
-
-    def tearDown(self):
-        super().tearDown()
-        unset_hf_deepspeed_config()
-
-    def test_deepspeed_fp16_overrides_defaults(self):
-        """Test that DeepSpeed fp16 config overrides TrainingArguments defaults"""
-        from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
-
-        args = TrainingArguments(output_dir="./test_output", fp16=False, bf16=False)
-        ds_config = {"fp16": {"enabled": True}, "bf16": {"enabled": False}, "zero_optimization": {"stage": 2}}
-        hf_ds_config = HfTrainerDeepSpeedConfig(ds_config)
-        hf_ds_config.trainer_config_process(args)
-        self.assertTrue(args.fp16)
-        self.assertFalse(args.bf16)
-
-    def test_deepspeed_bf16_overrides_defaults(self):
-        """Test that DeepSpeed bf16 config overrides TrainingArguments defaults"""
-        from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
-
-        args = TrainingArguments(output_dir="./test_output", fp16=False, bf16=False)
-        ds_config = {"fp16": {"enabled": False}, "bf16": {"enabled": True}, "zero_optimization": {"stage": 2}}
-        hf_ds_config = HfTrainerDeepSpeedConfig(ds_config)
-        hf_ds_config.trainer_config_process(args)
-        self.assertTrue(args.bf16)
-        self.assertFalse(args.fp16)
-
-    def test_user_explicit_settings_preserved(self):
-        """Test that explicit user settings are preserved over DeepSpeed config"""
-        from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
-
-        args = TrainingArguments(output_dir="./test_output", fp16=True, bf16=False)  # User explicit
-        ds_config = {"fp16": {"enabled": False}, "bf16": {"enabled": True}, "zero_optimization": {"stage": 2}}
-        hf_ds_config = HfTrainerDeepSpeedConfig(ds_config)
-        hf_ds_config.trainer_config_process(args)
-        # User's explicit choice should be preserved
-        self.assertTrue(args.fp16)
-        self.assertFalse(args.bf16)