Paged Optimizer + Lion Optimizer for Trainer (#23217)

* Added lion and paged optimizers and made original tests pass.

* Added tests for paged and lion optimizers.

* Added and fixed optimizer tests.

* Style and quality checks.

---------

Co-authored-by: younesbelkada <younesbelkada@gmail.com>

src/transformers/trainer.py

@@ -1170,6 +1170,38 @@ class Trainer:
                 optimizer_kwargs.update(adam_kwargs)
             except ImportError:
                 raise ValueError("Trainer tried to instantiate apex FusedAdam but apex is not installed!")
+        elif args.optim in [
+            OptimizerNames.ADAMW_BNB,
+            OptimizerNames.ADAMW_8BIT,
+            OptimizerNames.PAGED_ADAMW,
+            OptimizerNames.PAGED_ADAMW_8BIT,
+            OptimizerNames.LION,
+            OptimizerNames.LION_8BIT,
+            OptimizerNames.PAGED_LION,
+            OptimizerNames.PAGED_LION_8BIT,
+        ]:
+            try:
+                from bitsandbytes.optim import AdamW, Lion
+
+                is_paged = False
+                optim_bits = 32
+                optimizer_cls = None
+                additional_optim_kwargs = adam_kwargs
+                if "paged" in args.optim:
+                    is_paged = True
+                if "8bit" in args.optim:
+                    optim_bits = 8
+                if "adam" in args.optim:
+                    optimizer_cls = AdamW
+                elif "lion" in args.optim:
+                    optimizer_cls = Lion
+                    additional_optim_kwargs = {"betas": (args.adam_beta1, args.adam_beta2)}
+
+                bnb_kwargs = {"is_paged": is_paged, "optim_bits": optim_bits}
+                optimizer_kwargs.update(additional_optim_kwargs)
+                optimizer_kwargs.update(bnb_kwargs)
+            except ImportError:
+                raise ValueError("Trainer tried to instantiate bnb optimizer but bnb is not installed!")
         elif args.optim == OptimizerNames.ADAMW_BNB:
             try:
                 from bitsandbytes.optim import Adam8bit

src/transformers/training_args.py

@@ -139,10 +139,17 @@ class OptimizerNames(ExplicitEnum):
     ADAMW_TORCH_XLA = "adamw_torch_xla"
     ADAMW_APEX_FUSED = "adamw_apex_fused"
     ADAFACTOR = "adafactor"
-    ADAMW_BNB = "adamw_bnb_8bit"
     ADAMW_ANYPRECISION = "adamw_anyprecision"
     SGD = "sgd"
     ADAGRAD = "adagrad"
+    ADAMW_BNB = "adamw_bnb_8bit"
+    ADAMW_8BIT = "adamw_8bit"  # just an alias for adamw_bnb_8bit
+    LION_8BIT = "lion_8bit"
+    LION = "lion_32bit"
+    PAGED_ADAMW = "paged_adamw_32bit"
+    PAGED_ADAMW_8BIT = "paged_adamw_8bit"
+    PAGED_LION = "paged_lion_32bit"
+    PAGED_LION_8BIT = "paged_lion_8bit"
 
 
 @dataclass

tests/trainer/test_trainer.py

@@ -2474,6 +2474,11 @@ if is_torch_available():
         "lr": TrainingArguments.learning_rate,
     }
 
+    default_lion_kwargs = {
+        "betas": (TrainingArguments.adam_beta1, TrainingArguments.adam_beta2),
+        "lr": TrainingArguments.learning_rate,
+    }
+
     default_anyprecision_kwargs = {
         "use_kahan_summation": False,
         "momentum_dtype": torch.float32,
@@ -2525,11 +2530,59 @@ if is_torch_available():
         optim_test_params.append(
             (
                 TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None"),
-                bnb.optim.Adam8bit,
+                bnb.optim.AdamW,
                 default_adam_kwargs,
             )
         )
 
+        optim_test_params.append(
+            (
+                TrainingArguments(optim=OptimizerNames.ADAMW_8BIT, output_dir="None"),
+                bnb.optim.AdamW,
+                default_adam_kwargs,
+            )
+        )
+
+        optim_test_params.append(
+            (
+                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None"),
+                bnb.optim.AdamW,
+                default_adam_kwargs,
+            )
+        )
+
+        optim_test_params.append(
+            (
+                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None"),
+                bnb.optim.AdamW,
+                default_adam_kwargs,
+            )
+        )
+
+        optim_test_params.append(
+            (
+                TrainingArguments(optim=OptimizerNames.LION, output_dir="None"),
+                bnb.optim.Lion,
+                default_lion_kwargs,
+            )
+        )
+
+        optim_test_params.append(
+            (
+                TrainingArguments(optim=OptimizerNames.LION_8BIT, output_dir="None"),
+                bnb.optim.Lion,
+                default_lion_kwargs,
+            )
+        )
+
+        optim_test_params.append(
+            (
+                TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None"),
+                bnb.optim.Lion,
+                default_lion_kwargs,
+            )
+        )
+
     if is_torchdistx_available():
         import torchdistx
 
@@ -2598,15 +2651,113 @@ class TrainerOptimizerChoiceTest(unittest.TestCase):
         modules = {
             "bitsandbytes": mock,
             "bitsandbytes.optim": mock.optim,
-            "bitsandbytes.optim.Adam8bit": mock.optim.Adam8bit,
+            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
         }
         with patch.dict("sys.modules", modules):
             self.check_optim_and_kwargs(
                 TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None"),
-                mock.optim.Adam8bit,
+                mock.optim.AdamW,
                 default_adam_kwargs,
             )
 
+    def test_bnb_paged_adam8bit_alias(self):
+        mock = Mock()
+        modules = {
+            "bitsandbytes": mock,
+            "bitsandbytes.optim": mock.optim,
+            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
+        }
+        with patch.dict("sys.modules", modules):
+            self.check_optim_and_kwargs(
+                TrainingArguments(optim=OptimizerNames.ADAMW_8BIT, output_dir="None"),
+                mock.optim.AdamW,
+                default_adam_kwargs,
+            )
+
+    def test_bnb_paged_adam(self):
+        mock = Mock()
+        modules = {
+            "bitsandbytes": mock,
+            "bitsandbytes.optim": mock.optim,
+            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
+        }
+        with patch.dict("sys.modules", modules):
+            self.check_optim_and_kwargs(
+                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None"),
+                mock.optim.AdamW,
+                default_adam_kwargs,
+            )
+
+    def test_bnb_paged_adam8bit(self):
+        mock = Mock()
+        modules = {
+            "bitsandbytes": mock,
+            "bitsandbytes.optim": mock.optim,
+            "bitsandbytes.optim.AdamW": mock.optim.AdamW,
+        }
+        with patch.dict("sys.modules", modules):
+            self.check_optim_and_kwargs(
+                TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None"),
+                mock.optim.AdamW,
+                default_adam_kwargs,
+            )
+
+    def test_bnb_lion(self):
+        mock = Mock()
+        modules = {
+            "bitsandbytes": mock,
+            "bitsandbytes.optim": mock.optim,
+            "bitsandbytes.optim.Lion": mock.optim.Lion,
+        }
+        with patch.dict("sys.modules", modules):
+            self.check_optim_and_kwargs(
+                TrainingArguments(optim=OptimizerNames.LION, output_dir="None"),
+                mock.optim.Lion,
+                default_lion_kwargs,
+            )
+
+    def test_bnb_lion8bit(self):
+        mock = Mock()
+        modules = {
+            "bitsandbytes": mock,
+            "bitsandbytes.optim": mock.optim,
+            "bitsandbytes.optim.Lion": mock.optim.Lion,
+        }
+        with patch.dict("sys.modules", modules):
+            self.check_optim_and_kwargs(
+                TrainingArguments(optim=OptimizerNames.LION_8BIT, output_dir="None"),
+                mock.optim.Lion,
+                default_lion_kwargs,
+            )
+
+    def test_bnb_paged_lion8bit(self):
+        mock = Mock()
+        modules = {
+            "bitsandbytes": mock,
+            "bitsandbytes.optim": mock.optim,
+            "bitsandbytes.optim.Lion": mock.optim.Lion,
+        }
+        with patch.dict("sys.modules", modules):
+            self.check_optim_and_kwargs(
+                TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None"),
+                mock.optim.Lion,
+                default_lion_kwargs,
+            )
+
+    def test_bnb_paged_lion(self):
+        mock = Mock()
+        modules = {
+            "bitsandbytes": mock,
+            "bitsandbytes.optim": mock.optim,
+            "bitsandbytes.optim.Lion": mock.optim.Lion,
+        }
+        with patch.dict("sys.modules", modules):
+            self.check_optim_and_kwargs(
+                TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir="None"),
+                mock.optim.Lion,
+                default_lion_kwargs,
+            )
+
     def test_bnb_adam8bit_no_bnb(self):
         args = TrainingArguments(optim=OptimizerNames.ADAMW_BNB, output_dir="None")
 
@@ -2616,6 +2767,42 @@ class TrainerOptimizerChoiceTest(unittest.TestCase):
             with self.assertRaises(ValueError):
                 Trainer.get_optimizer_cls_and_kwargs(args)
 
+    def test_bnb_paged_adam_no_bnb(self):
+        args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW, output_dir="None")
+
+        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+        # bnb will fail even if bnb is installed.
+        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+            with self.assertRaises(ValueError):
+                Trainer.get_optimizer_cls_and_kwargs(args)
+
+    def test_bnb_paged_adam8bit_no_bnb(self):
+        args = TrainingArguments(optim=OptimizerNames.PAGED_ADAMW_8BIT, output_dir="None")
+
+        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+        # bnb will fail even if bnb is installed.
+        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+            with self.assertRaises(ValueError):
+                Trainer.get_optimizer_cls_and_kwargs(args)
+
+    def test_bnb_paged_lion_no_bnb(self):
+        args = TrainingArguments(optim=OptimizerNames.PAGED_LION, output_dir="None")
+
+        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+        # bnb will fail even if bnb is installed.
+        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+            with self.assertRaises(ValueError):
+                Trainer.get_optimizer_cls_and_kwargs(args)
+
+    def test_bnb_paged_lion8bit_no_bnb(self):
+        args = TrainingArguments(optim=OptimizerNames.PAGED_LION_8BIT, output_dir="None")
+
+        # Pretend that bnb does not exist, even if installed. By setting bnb to None, importing
+        # bnb will fail even if bnb is installed.
+        with patch.dict("sys.modules", {"bitsandbytes.optim": None}):
+            with self.assertRaises(ValueError):
+                Trainer.get_optimizer_cls_and_kwargs(args)
+
     def test_anyprecision_adamw(self):
         # Pretend that torchdistx is installed and mock torchdistx.optimizers.AnyPrecisionAdamW exists.
         # Trainer.get_optimizer_cls_and_kwargs does not use AnyPrecisioinAdamW. It only has to return the