FEAT [Trainer / bnb]: Add RMSProp from bitsandbytes to HF Trainer

src/transformers/trainer.py

@@ -1084,9 +1084,12 @@ def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any, Any]:
             OptimizerNames.LION_8BIT,
             OptimizerNames.PAGED_LION,
             OptimizerNames.PAGED_LION_8BIT,
+            OptimizerNames.RMSPROP_BNB,
+            OptimizerNames.RMSPROP_8BIT,
+            OptimizerNames.RMSPROP_32BIT,
         ]:
             try:
-                from bitsandbytes.optim import AdamW, Lion
+                from bitsandbytes.optim import AdamW, Lion, RMSprop
 
                 is_paged = False
                 optim_bits = 32
@@ -1101,8 +1104,16 @@ def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any, Any]:
                 elif "lion" in args.optim:
                     optimizer_cls = Lion
                     additional_optim_kwargs = {"betas": (args.adam_beta1, args.adam_beta2)}
+                elif "rmsprop" in args.optim:
+                    optimizer_cls = RMSprop
+                    # Above we pass all `adam_kwargs` to the optimizer, here
+                    # we only pass `optim_args` which can be passed by the user.
+                    additional_optim_kwargs = optim_args
+
+                bnb_kwargs = {"optim_bits": optim_bits}
+                if "rmsprop" not in args.optim:
+                    bnb_kwargs["is_paged"] = is_paged
 
-                bnb_kwargs = {"is_paged": is_paged, "optim_bits": optim_bits}
                 optimizer_kwargs.update(additional_optim_kwargs)
                 optimizer_kwargs.update(bnb_kwargs)
             except ImportError:

src/transformers/training_args.py

@@ -157,6 +157,9 @@ class OptimizerNames(ExplicitEnum):
     PAGED_LION = "paged_lion_32bit"
     PAGED_LION_8BIT = "paged_lion_8bit"
     RMSPROP = "rmsprop"
+    RMSPROP_BNB = "rmsprop_bnb"
+    RMSPROP_8BIT = "rmsprop_bnb_8bit"
+    RMSPROP_32BIT = "rmsprop_bnb_32bit"
 
 
 # TODO: `TrainingArguments` users rely on it being fully mutable. In the future see if we can narrow this to a few keys: https://github.com/huggingface/transformers/pull/25903

tests/trainer/test_trainer.py

@@ -58,6 +58,7 @@
     get_tests_dir,
     is_staging_test,
     require_accelerate,
+    require_bitsandbytes,
     require_deepspeed,
     require_intel_extension_for_pytorch,
     require_optuna,
@@ -872,6 +873,56 @@ def test_number_of_steps_in_training_with_ipex(self):
             train_output = trainer.train()
             self.assertEqual(train_output.global_step, 10)
 
+    @require_bitsandbytes
+    def test_rmsprop_bnb(self):
+        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
+        tiny_gpt2 = GPT2LMHeadModel(config)
+        x = torch.randint(0, 100, (128,))
+        train_dataset = RepeatDataset(x)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Trainer without inf/nan filter
+            args = TrainingArguments(
+                tmpdir, learning_rate=1e-9, logging_steps=5, logging_nan_inf_filter=False, optim="rmsprop_bnb"
+            )
+            trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
+
+            # Check that it trains without errors
+            trainer.train()
+
+    @require_bitsandbytes
+    def test_rmsprop_bnb_8bit(self):
+        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
+        tiny_gpt2 = GPT2LMHeadModel(config)
+        x = torch.randint(0, 100, (128,))
+        train_dataset = RepeatDataset(x)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Trainer without inf/nan filter
+            args = TrainingArguments(
+                tmpdir, learning_rate=1e-9, logging_steps=5, logging_nan_inf_filter=False, optim="rmsprop_bnb_8bit"
+            )
+            trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
+
+            # Check that it trains without errors
+            trainer.train()
+
+    @require_bitsandbytes
+    def test_rmsprop_bnb_32bit(self):
+        config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
+        tiny_gpt2 = GPT2LMHeadModel(config)
+        x = torch.randint(0, 100, (128,))
+        train_dataset = RepeatDataset(x)
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Trainer without inf/nan filter
+            args = TrainingArguments(
+                tmpdir, learning_rate=1e-9, logging_steps=5, logging_nan_inf_filter=False, optim="rmsprop_bnb_32bit"
+            )
+            trainer = Trainer(tiny_gpt2, args, train_dataset=train_dataset)
+
+            # Check that it trains without errors
+            trainer.train()
+
     def test_neftune(self):
         config = GPT2Config(vocab_size=100, n_positions=128, n_embd=32, n_layer=3, n_head=4)
         tiny_gpt2 = GPT2LMHeadModel(config)