add SOAP optimizer

src/axolotl/core/trainer_builder.py

@@ -663,6 +663,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
                 optimizer_cls = MuonOptimizerFactory
                 optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "soap":
+                from axolotl.utils.optimizers.soap import SOAP
+
+                optimizer_cls = SOAP
+                optimizer_kwargs.update(adam_kwargs)
             elif self.cfg.optimizer == "optimi_adamw":
                 from optimi import AdamW
 

src/axolotl/utils/schemas/enums.py

@@ -52,3 +52,4 @@ class CustomSupportedOptimizers(str, Enum):
     ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
     adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
     muon = "muon"  # pylint: disable=invalid-name
+    soap = "soap"  # pylint: disable=invalid-name

tests/e2e/test_optimizers.py

@@ -201,3 +201,46 @@ class TestCustomOptimizers(unittest.TestCase):
 
         train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+
+    @with_temp_dir
+    def test_soap(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM-135M",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "vicgalle/alpaca-gpt4",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "soap",
+                "adam_beta1": 0.9,
+                "adam_beta2": 0.95,
+                "lr_scheduler": "cosine",
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)