support seperate lr for embeddings, similar to loraplus (#1910) [skip…

… ci]

* support seperate lr for embeddings, similar to loraplus

* add test case for train w lr embedding scale

* use kwarg for optimizer

* make sure to handle the optimizer creation

* make sure to handle for embedding_lr too

* use smollm for e2e, check for embeddings lr first before wdecay

src/axolotl/core/trainer_builder.py

@@ -220,6 +220,14 @@ class AxolotlTrainingMixins:
         default=1e-6,
         metadata={"help": "loraplus learning rate for lora embedding layers."},
     )
+    embedding_lr_scale: Optional[float] = field(
+        default=None,
+        metadata={"help": "Scale the learning rate for the embedding layers."},
+    )
+    embedding_lr: Optional[float] = field(
+        default=None,
+        metadata={"help": "absolute learning rate for the embedding layers."},
+    )
     qlora: bool = field(
         default=False,
         metadata={"help": "whether this is a qlora training"},
@@ -386,7 +394,7 @@ def create_scheduler(
                     min_lr_ratio=self.args.cosine_min_lr_ratio,
                 )
             else:
-                return super().create_scheduler(num_training_steps, optimizer)
+                return super().create_scheduler(num_training_steps, optimizer=optimizer)
         else:
             if use_cosine_quadratic:
                 LOG.warning("axolotl's cosine scheduler with quadratic warmup not used (e.g., because of deepspeed).")
@@ -435,6 +443,8 @@ def _wrap_model(self, model, training=True, dataloader=None):
     def create_optimizer(self):
         if (
             self.args.loraplus_lr_ratio is None
+            and self.args.embedding_lr_scale is None
+            and self.args.embedding_lr is None
             and self.args.alternate_optimizer
             not in [
                 "optimi_adamw",
@@ -449,30 +459,59 @@ def create_optimizer(self):
         opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
         if self.optimizer is None:  # pylint: disable=access-member-before-definition
             decay_parameters = self.get_decay_parameter_names(opt_model)
-            optimizer_grouped_parameters = [
-                {
-                    "params": [
-                        p
-                        for n, p in opt_model.named_parameters()
-                        if (n in decay_parameters and p.requires_grad)
-                    ],
-                    "weight_decay": self.args.weight_decay,
-                },
-                {
-                    "params": [
-                        p
-                        for n, p in opt_model.named_parameters()
-                        if (n not in decay_parameters and p.requires_grad)
-                    ],
-                    "weight_decay": 0.0,
-                },
-            ]
+            params = {
+                "to_weight_decay": {},  # LayerNorm and bias
+                "embeddings": {},  # lm_head, embed_tokens,
+                "no_weight_decay": {},
+            }
 
             optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
                 self.args,
                 opt_model,
             )
 
+            for name, param in opt_model.named_parameters():
+                if not param.requires_grad:
+                    continue
+                if name.endswith("modules_to_save.default.weight") or any(
+                    embed_name in name for embed_name in ["embed_tokens", "lm_head"]
+                ):
+                    params["embeddings"][name] = param
+                elif name in decay_parameters:
+                    params["to_weight_decay"][name] = param
+                else:
+                    params["no_weight_decay"][name] = param
+            optimizer_grouped_parameters = []
+            if params["to_weight_decay"]:
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(params["to_weight_decay"].values()),
+                        "weight_decay": self.args.weight_decay,
+                        "lr": optimizer_kwargs["lr"],
+                    }
+                )
+            if params["embeddings"]:
+                lr = optimizer_kwargs["lr"]  # pylint: disable=invalid-name
+                if self.args.embedding_lr_scale:
+                    lr *= self.args.embedding_lr_scale  # pylint: disable=invalid-name
+                elif self.args.embedding_lr:
+                    lr = self.args.embedding_lr  # pylint: disable=invalid-name
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(params["embeddings"].values()),
+                        "weight_decay": 0.0,
+                        "lr": lr,
+                    }
+                )
+            if params["no_weight_decay"]:
+                optimizer_grouped_parameters.append(
+                    {
+                        "params": list(params["no_weight_decay"].values()),
+                        "weight_decay": 0.0,
+                        "lr": optimizer_kwargs["lr"],
+                    }
+                )
+
             if self.args.loraplus_lr_ratio is not None:
                 loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
                 loraplus_lr_embedding = getattr(
@@ -485,6 +524,13 @@ def create_optimizer(self):
                     loraplus_lr_embedding=loraplus_lr_embedding,
                     **optimizer_kwargs,
                 )
+            elif (
+                self.args.embedding_lr_scale is not None
+                or self.args.embedding_lr is not None
+            ):
+                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
+                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
+                )
             elif self.args.alternate_optimizer == "optimi_adamw":
                 from optimi import AdamW
 
@@ -1571,6 +1617,9 @@ def build(self, total_num_steps):
         training_arguments_kwargs[
             "loraplus_lr_embedding"
         ] = self.cfg.loraplus_lr_embedding
+        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
+        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
+
         if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
             training_arguments_kwargs[

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

@@ -430,6 +430,8 @@ class HyperparametersConfig(BaseModel):
     group_by_length: Optional[bool] = None
 
     learning_rate: Union[str, float]
+    embedding_lr: Optional[float] = None
+    embedding_lr_scale: Optional[float] = None
     weight_decay: Optional[float] = 0.0
     optimizer: Optional[
         Union[

tests/e2e/test_embeddings_lr.py

@@ -0,0 +1,121 @@
+"""
+E2E tests for llama pretrain
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+from tbparse import SummaryReader
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import most_recent_subdir, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestEmbeddingsLrScale(unittest.TestCase):
+    """
+    Test case for embedding_lr*
+    """
+
+    @with_temp_dir
+    def test_train_w_embedding_lr_scale(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "max_steps": 5,
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "embedding_lr_scale": 0.5,
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()
+
+        tb_log_path = most_recent_subdir(temp_dir + "/runs")
+        event_file = os.path.join(tb_log_path, sorted(os.listdir(tb_log_path))[0])
+        reader = SummaryReader(event_file)
+        df = reader.scalars  # pylint: disable=invalid-name
+        df = df[(df.tag == "train/train_loss")]  # pylint: disable=invalid-name
+        assert df.value.values[-1] < 2.0, "Loss is too high"
+
+    @with_temp_dir
+    def test_train_w_embedding_lr(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "max_steps": 5,
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "embedding_lr": 0.000005,
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()
+
+        tb_log_path = most_recent_subdir(temp_dir + "/runs")
+        event_file = os.path.join(tb_log_path, sorted(os.listdir(tb_log_path))[0])
+        reader = SummaryReader(event_file)
+        df = reader.scalars  # pylint: disable=invalid-name
+        df = df[(df.tag == "train/train_loss")]  # pylint: disable=invalid-name
+        assert df.value.values[-1] < 2.0, "Loss is too high"