schedulefree optimizers

docs/source/en/trainer.md

@@ -518,6 +518,51 @@ trainer.train()
 
 This script demonstrates how to fine-tune the `google/gemma-2b` model on the IMDB dataset using the GrokAdamW optimizer. The `TrainingArguments` are configured to use GrokAdamW, and the dataset is passed to the `Trainer` for training.
 
+## Schedule Free Optimizer
+
+The Schedule Free optimizers have been introduced in [The Road Less Scheduled](https://hf.co/papers/2405.15682).
+Schedule-Free learning replaces the momentum of the base optimizer with a combination of averaging and interpolation, to completely remove the need to anneal the learning rate with a traditional schedule.
+Supported optimizers for SFO are `"schedule_free_adamw"` and `"schedule_free_sgd"`. First install schedulefree from pypi `pip install schedulefree`.
+
+Below is a simple script to demonstrate how to fine-tune [google/gemma-2b](https://huggingface.co/google/gemma-2b) on IMDB dataset in full precision:
+
+```python
+import torch
+import datasets
+from transformers import TrainingArguments, AutoTokenizer, AutoModelForCausalLM
+import trl
+
+train_dataset = datasets.load_dataset('imdb', split='train')
+
+args = TrainingArguments(
+    output_dir="./test-schedulefree",
+    max_steps=1000,
+    per_device_train_batch_size=4,
+    optim="schedule_free_adamw",
+    gradient_checkpointing=True,
+    logging_strategy="steps",
+    logging_steps=1,
+    learning_rate=2e-6,
+    save_strategy="no",
+    run_name="sfo-imdb",
+)
+
+model_id = "google/gemma-2b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)
+
+trainer = trl.SFTTrainer(
+    model=model, 
+    args=args,
+    train_dataset=train_dataset,
+    dataset_text_field='text',
+    max_seq_length=1024,
+)
+
+trainer.train()
+```
+
 ## Accelerate and Trainer
 
 The [`Trainer`] class is powered by [Accelerate](https://hf.co/docs/accelerate), a library for easily training PyTorch models in distributed environments with support for integrations such as [FullyShardedDataParallel (FSDP)](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/) and [DeepSpeed](https://www.deepspeed.ai/).

setup.py

@@ -163,6 +163,7 @@
     "sacremoses",
     "safetensors>=0.4.1",
     "sagemaker>=2.31.0",
+    "schedulefree>=1.2.6",
     "scikit-learn",
     "scipy<1.13.0",  # SciPy >= 1.13.0 is not supported with the current jax pin (`jax>=0.4.1,<=0.4.13`)
     "sentencepiece>=0.1.91,!=0.1.92",

src/transformers/dependency_versions_table.py

@@ -69,6 +69,7 @@
     "sacremoses": "sacremoses",
     "safetensors": "safetensors>=0.4.1",
     "sagemaker": "sagemaker>=2.31.0",
+    "schedulefree": "schedulefree>=1.2.6",
     "scikit-learn": "scikit-learn",
     "scipy": "scipy<1.13.0",
     "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",

src/transformers/testing_utils.py

@@ -103,6 +103,7 @@
     is_rjieba_available,
     is_sacremoses_available,
     is_safetensors_available,
+    is_schedulefree_available,
     is_scipy_available,
     is_sentencepiece_available,
     is_seqio_available,
@@ -370,6 +371,14 @@ def require_grokadamw(test_case):
     return unittest.skipUnless(is_grokadamw_available(), "test requires GrokAdamW")(test_case)
 
 
+def require_schedulefree(test_case):
+    """
+    Decorator marking a test that requires schedulefree. These tests are skipped when schedulefree isn't installed.
+    https://github.com/facebookresearch/schedule_free
+    """
+    return unittest.skipUnless(is_schedulefree_available(), "test requires schedulefree")(test_case)
+
+
 def require_cv2(test_case):
     """
     Decorator marking a test that requires OpenCV.

src/transformers/trainer.py

@@ -161,6 +161,7 @@
     is_safetensors_available,
     is_sagemaker_dp_enabled,
     is_sagemaker_mp_enabled,
+    is_schedulefree_available,
     is_torch_compile_available,
     is_torch_mlu_available,
     is_torch_mps_available,
@@ -1488,6 +1489,34 @@ def optimizer_hook(param):
 
             optimizer_cls = AdamW4bit
             optimizer_kwargs.update(adam_kwargs)
+        elif args.optim in [
+            OptimizerNames.SCHEDULE_FREE_ADAMW,
+            OptimizerNames.SCHEDULE_FREE_SGD,
+        ]:
+            if not is_schedulefree_available():
+                raise ImportError(
+                    "You need to install `schedulefree` in order to use schedulefree optimizers"
+                    " install it with `pip install schedulefree`"
+                )
+            from schedulefree import AdamWScheduleFree, SGDScheduleFree
+
+            additional_optim_kwargs = {}
+            if args.optim == OptimizerNames.SCHEDULE_FREE_ADAMW:
+                optimizer_cls = AdamWScheduleFree
+                additional_optim_kwargs = adam_kwargs
+            elif args.optim == OptimizerNames.SCHEDULE_FREE_SGD:
+                optimizer_cls = SGDScheduleFree
+            else:
+                raise ValueError("Invalid schedulefree optimizer")
+            additional_optim_kwargs["weight_decay"] = args.weight_decay
+            additional_optim_kwargs["warmup_steps"] = args.warmup_steps
+            additional_optim_kwargs.update(
+                {
+                    "weight_lr_power": float(optim_args.get("weight_lr_power", 2.0)),
+                    "r": float(optim_args.get("r", 0.0)),
+                }
+            )
+            optimizer_kwargs.update(additional_optim_kwargs)
         else:
             raise ValueError(f"Trainer cannot instantiate unsupported optimizer: {args.optim}")
         return optimizer_cls, optimizer_kwargs
@@ -3410,6 +3439,9 @@ def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor,
             `torch.Tensor`: The tensor with training loss on this batch.
         """
         model.train()
+        if hasattr(self.optimizer, "train") and callable(self.optimizer.train):
+            self.optimizer.train()
+
         inputs = self._prepare_inputs(inputs)
         if is_sagemaker_mp_enabled():
             loss_mb = smp_forward_backward(model, inputs, self.args.gradient_accumulation_steps)
@@ -3960,6 +3992,8 @@ def evaluation_loop(
         logger.info(f"  Batch size = {batch_size}")
 
         model.eval()
+        if hasattr(self.optimizer, "eval") and callable(self.optimizer.eval):
+            self.optimizer.eval()
 
         self.callback_handler.eval_dataloader = dataloader
         # Do this before wrapping.
@@ -4573,6 +4607,8 @@ def prediction_loop(
             inputs_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)
 
         model.eval()
+        if hasattr(self.optimizer, "eval") and callable(self.optimizer.eval):
+            self.optimizer.eval()
 
         if args.past_index >= 0:
             self._past = None

src/transformers/training_args.py

@@ -178,6 +178,8 @@ class OptimizerNames(ExplicitEnum):
     LOMO = "lomo"
     ADALOMO = "adalomo"
     GROKADAMW = "grokadamw"
+    SCHEDULE_FREE_ADAMW = "schedule_free_adamw"
+    SCHEDULE_FREE_SGD = "schedule_free_sgd"
 
 
 # Sometimes users will pass in a `str` repr of a dict in the CLI

src/transformers/utils/__init__.py

@@ -175,6 +175,7 @@
     is_safetensors_available,
     is_sagemaker_dp_enabled,
     is_sagemaker_mp_enabled,
+    is_schedulefree_available,
     is_scipy_available,
     is_sentencepiece_available,
     is_seqio_available,

src/transformers/utils/import_utils.py

@@ -103,6 +103,7 @@ def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[
 _galore_torch_available = _is_package_available("galore_torch")
 _lomo_available = _is_package_available("lomo_optim")
 _grokadamw_available = _is_package_available("grokadamw")
+_schedulefree_available = _is_package_available("schedulefree")
 # `importlib.metadata.version` doesn't work with `bs4` but `beautifulsoup4`. For `importlib.util.find_spec`, reversed.
 _bs4_available = importlib.util.find_spec("bs4") is not None
 _coloredlogs_available = _is_package_available("coloredlogs")
@@ -364,6 +365,10 @@ def is_grokadamw_available():
     return _grokadamw_available
 
 
+def is_schedulefree_available():
+    return _schedulefree_available
+
+
 def is_pyctcdecode_available():
     return _pyctcdecode_available
 

tests/trainer/test_trainer.py

@@ -70,6 +70,7 @@
     require_peft,
     require_ray,
     require_safetensors,
+    require_schedulefree,
     require_sentencepiece,
     require_sigopt,
     require_tensorboard,
@@ -1442,6 +1443,27 @@ def test_grokadamw():
             # Check this works
             _ = trainer.train()
 
+    @require_schedulefree
+    @require_torch_gpu
+    def test_schedulefree_adam(self):
+        config = LlamaConfig(vocab_size=100, hidden_size=32, num_hidden_layers=3, num_attention_heads=4)
+        tiny_llama = LlamaForCausalLM(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,
+                optim="schedule_free_adamw",
+            )
+            trainer = Trainer(tiny_llama, args, train_dataset=train_dataset)
+
+            # Check this works
+            _ = trainer.train()
+
     def test_galore_matched_modules(self):
         regex_patterns = [r".*.attn.*", r".*.mlp.*"]