let transformers handle adamw_bnb_8bit

src/axolotl/utils/trainer.py

@@ -10,19 +10,13 @@ from functools import partial
 from pathlib import Path
 from typing import Optional, Union
 
-import bitsandbytes as bnb
 import numpy as np
 import torch.cuda
-import transformers
 from datasets import Dataset, set_caching_enabled
-from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import DataLoader, DistributedSampler, RandomSampler
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
-from transformers.trainer_pt_utils import (
-    SequentialDistributedSampler,
-    get_parameter_names,
-)
+from transformers.trainer_pt_utils import SequentialDistributedSampler
 
 from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
@@ -32,10 +26,7 @@ from axolotl.utils.callbacks import (
 )
 from axolotl.utils.collators import DataCollatorForSeq2Seq
 from axolotl.utils.dataloader import MultipackDistributedDataloader
-from axolotl.utils.schedulers import (
-    InterpolatingLogScheduler,
-    get_cosine_schedule_with_quadratic_warmup,
-)
+from axolotl.utils.schedulers import get_cosine_schedule_with_quadratic_warmup
 
 LOG = logging.getLogger("axolotl")
 
@@ -570,66 +561,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         if Path(cfg.torchdistx_path).exists():
             sys.path.append(cfg.torchdistx_path)
             importlib.import_module("torchdistx")
-    if (
-        cfg.optimizer == "adamw_bnb_8bit"
-        and not cfg.gptq
-        and "deepspeed" not in training_arguments_kwargs
-        and not cfg.fsdp
-    ):
-        decay_parameters = get_parameter_names(model, [nn.LayerNorm])
-        decay_parameters = [name for name in decay_parameters if "bias" not in name]
-        optimizer_grouped_parameters = [
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if (n in decay_parameters and p.requires_grad)
-                ],
-                "weight_decay": training_args.weight_decay,
-            },
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if (n not in decay_parameters and p.requires_grad)
-                ],
-                "weight_decay": 0.0,
-            },
-        ]
-
-        optimizer = bnb.optim.Adam8bit(
-            optimizer_grouped_parameters,
-            betas=(training_args.adam_beta1, training_args.adam_beta2),
-            eps=training_args.adam_epsilon,
-            lr=training_args.learning_rate,
-        )
-
-        if cfg.lr_scheduler == "one_cycle":
-            lr_scheduler_kwargs = (
-                cfg.lr_scheduler_kwargs if cfg.lr_scheduler_kwargs else {}
-            )
-            lr_scheduler = OneCycleLR(
-                optimizer,
-                cfg.learning_rate,
-                total_steps=total_num_steps,
-                epochs=cfg.num_epochs,
-                div_factor=cfg.lr_div_factor if cfg.lr_div_factor else 6,
-                **lr_scheduler_kwargs,
-            )
-        elif cfg.lr_scheduler == "log_sweep":
-            lr_scheduler = InterpolatingLogScheduler(
-                optimizer,
-                cfg.warmup_steps,
-                cfg.log_sweep_min_lr if cfg.log_sweep_min_lr else 1e-10,
-                cfg.log_sweep_max_lr if cfg.log_sweep_max_lr else 10,
-            )
-        else:
-            lr_scheduler = transformers.get_cosine_schedule_with_warmup(
-                optimizer,
-                training_args.warmup_steps,
-                total_num_steps,
-            )
-        trainer_kwargs["optimizers"] = (optimizer, lr_scheduler)
 
     callbacks = []
     callbacks.append(GPUStatsCallback(cfg))