add support for adamw schedulefree

requirements.txt

@@ -41,3 +41,5 @@ gcsfs
 
 trl @ git+https://github.com/huggingface/trl.git@0ee349dcd43b0f4b3169449f16751c38ac4a609f
 zstandard==0.22.0
+
+schedulefree==1.2.1

src/axolotl/core/trainer_builder.py

@@ -14,11 +14,13 @@
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Type, Union
 
+import schedulefree
 import torch
 import transformers
 from datasets import Dataset
+from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import (
@@ -27,7 +29,7 @@
     TrainerCallback,
     TrainingArguments,
 )
-from transformers.trainer_utils import seed_worker
+from transformers.trainer_utils import EvalLoopOutput, seed_worker
 from transformers.utils import is_sagemaker_mp_enabled
 from trl import DPOTrainer
 from trl.trainer.utils import pad_to_length
@@ -486,6 +488,31 @@ def compute_loss(self, model, inputs, return_outputs=False):
             return self.orpo_compute_loss(model, inputs, return_outputs=return_outputs)
         return super().compute_loss(model, inputs, return_outputs=return_outputs)
 
+    def training_step(
+        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
+    ) -> torch.Tensor:
+        if self.optimizer.__class__.__name__ == "AdamWScheduleFree":
+            self.optimizer.train()
+        return super().training_step(model, inputs)
+
+    def evaluation_loop(
+        self,
+        dataloader: DataLoader,
+        description: str,
+        prediction_loss_only: Optional[bool] = None,
+        ignore_keys: Optional[List[str]] = None,
+        metric_key_prefix: str = "eval",
+    ) -> EvalLoopOutput:
+        if self.optimizer.__class__.__name__ == "AdamWScheduleFree":
+            self.optimizer.eval()
+        return super().evaluation_loop(
+            dataloader,
+            description,
+            prediction_loss_only=prediction_loss_only,
+            ignore_keys=ignore_keys,
+            metric_key_prefix=metric_key_prefix,
+        )
+
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
         concatenated_batch = {}
@@ -1297,6 +1324,33 @@ def build(self, total_num_steps):
                 sys.path.append(self.cfg.torchdistx_path)
                 importlib.import_module("torchdistx")
 
+        if self.cfg.optimizer == "schedule_free_adamw":
+            sfa_kwargs = {"lr": training_arguments_kwargs["learning_rate"]}
+            if "adam_epsilon" in training_arguments_kwargs:
+                sfa_kwargs["eps"] = training_arguments_kwargs["adam_epsilon"]
+
+            if "weight_decay" in training_arguments_kwargs:
+                sfa_kwargs["weight_decay"] = training_arguments_kwargs["weight_decay"]
+
+            sfa_kwargs["warmup_steps"] = training_arguments_kwargs["warmup_steps"]
+
+            if (
+                "adam_beta1" in training_arguments_kwargs
+                and "adam_beta2" in training_arguments_kwargs
+            ):
+                sfa_kwargs["betas"] = (
+                    training_arguments_kwargs["adam_beta1"],
+                    training_arguments_kwargs["adam_beta2"],
+                )
+
+            trainer_kwargs["optimizers"] = (
+                schedulefree.AdamWScheduleFree(
+                    params=self.model.parameters(), **sfa_kwargs
+                ),
+                None,
+            )
+            training_arguments_kwargs["optim"] = "adamw_hf"
+
         training_args = (
             AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
                 **training_arguments_kwargs,

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -314,7 +314,7 @@ class HyperparametersConfig(BaseModel):
     learning_rate: Union[str, float]
     weight_decay: Optional[float] = 0.0
     optimizer: Optional[
-        Union[OptimizerNames, Literal["lion_pytorch"]]
+        Union[OptimizerNames, Literal["lion_pytorch", "schedule_free_adamw"]]
     ] = OptimizerNames.ADAMW_HF.value
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
         default=None, metadata={"help": "Optional arguments to supply to optimizer."}