better configuration for quadratic warmup

src/axolotl/utils/trainer.py

@@ -5,6 +5,7 @@ import logging
 import math
 import os
 import sys
+from dataclasses import field
 from pathlib import Path
 from typing import Optional
 
@@ -13,7 +14,7 @@ import torch.cuda
 import transformers
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
-from transformers import EarlyStoppingCallback, Trainer
+from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_pt_utils import get_parameter_names
 
 from axolotl.utils.callbacks import SavePeftModelCallback
@@ -23,11 +24,24 @@ from axolotl.utils.schedulers import (
 )
 
 
+class AxolotlTrainingArguments(TrainingArguments):
+    """
+    Extend the base TrainingArguments for axolotl helpers
+    """
+
+    lr_quadratic_warmup: bool = field(
+        default=False,
+        metadata={"help": "Use quadratic warmup for cosine scheduling."},
+    )
+
+
 class AxolotlTrainer(Trainer):
     """
     Extend the base Trainer for axolotl helpers
     """
 
+    args = None  # type: AxolotlTrainingArguments
+
     def create_scheduler(
         self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
     ):
@@ -37,11 +51,16 @@ class AxolotlTrainer(Trainer):
 
         Args:
             num_training_steps (int): The number of training steps to do.
+            optimizer (torch.optim.Optimizer): The training optimizer
         """
 
-        if self.lr_scheduler is None:  # pylint: disable=access-member-before-definition
-            """# type: ignore"""
-            if self.args.lr_scheduler_type == "cosine_with_quadratic":
+        # fmt: off
+        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+            # fmt: on
+            if (
+                self.args.lr_scheduler_type == "cosine"
+                and self.args.lr_quadratic_warmup is True
+            ):
                 self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
                     optimizer,
                     num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
@@ -132,6 +151,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         if cfg.fsdp_config:
             training_arguments_kwargs["fsdp_config"] = dict(cfg.fsdp_config)
 
+    if cfg.lr_quadratic_warmup is not None:
+        training_arguments_kwargs["lr_quadratic_warmup"] = cfg.lr_quadratic_warmup
+
     # deepspeed
     if (
         os.environ.get("ACCELERATE_USE_DEEPSPEED") == "true"
@@ -144,7 +166,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
             # TODO search Path("./") for one
             training_arguments_kwargs["deepspeed"] = "./ds_config.json"
 
-    training_args = transformers.TrainingArguments(
+    training_args = AxolotlTrainingArguments(
         per_device_train_batch_size=cfg.micro_batch_size,
         per_device_eval_batch_size=cfg.eval_batch_size
         if cfg.eval_batch_size is not None