feat: add support for adammini

src/llamafactory/hparams/finetuning_args.py

@@ -342,6 +342,10 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
         default=False,
         metadata={"help": "Whether or not to save the training loss curves."},
     )
+    use_adammini: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use AdamMini optimizer."},
+    )
 
     def __post_init__(self):
         def split_arg(arg):

src/llamafactory/train/trainer_utils.py

@@ -365,6 +365,32 @@ def _create_badam_optimizer(
 
     return optimizer
 
+def _create_adammini_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> "torch.optim.Optimizer":
+    from adam_mini import Adam_mini
+
+    n_embd = model.config.hidden_size
+    n_head = model.config.num_attention_heads
+    n_query_groups = getattr(model.config, "num_key_value_heads", n_head)
+
+    print("n_embd", n_embd, "n_head", n_head, "n_query_groups", n_query_groups)
+
+    optimizer = Adam_mini(
+        named_parameters=model.named_parameters(),
+        lr=training_args.learning_rate,
+        betas=(training_args.adam_beta1, training_args.adam_beta2),
+        eps=training_args.adam_epsilon,
+        weight_decay=training_args.weight_decay,
+        model_sharding=False,
+        dim=n_embd,
+        n_heads=n_head,
+        n_kv_heads=n_query_groups,
+    )
+
+    return optimizer
 
 def create_custom_optimizer(
     model: "PreTrainedModel",
@@ -380,6 +406,9 @@ def create_custom_optimizer(
     if finetuning_args.use_badam:
         return _create_badam_optimizer(model, training_args, finetuning_args)
 
+    if finetuning_args.use_adammini:
+        return _create_adammini_optimizer(model, training_args, finetuning_args)
+    
 
 def create_custom_scheduler(
     training_args: "Seq2SeqTrainingArguments",