FEAT / Trainer: Add adamw 4bit optimizer (huggingface#31865)

* add 4bit optimizer

* style

* fix msg

* style

* add qgalore

* Revert "add qgalore"

This reverts commit 25278e8.

* style

* version check

src/transformers/trainer.py

@@ -168,6 +168,7 @@
  is_torch_npu_available,
  is_torch_xla_available,
  is_torch_xpu_available,
+ is_torchao_available,
  logging,
  strtobool,
 )
@@ -1451,7 +1452,23 @@ def optimizer_hook(param):
  "gradient_clipping": float(optim_args.get("gradient_clipping", 1.0)),
  }
  )
+ elif args.optim == OptimizerNames.ADAMW_TORCH_4BIT:
+ if not is_torchao_available() or version.parse(importlib.metadata.version("torchao")) < version.parse(
+ "0.4.0"
+ ):
+ raise ImportError(
+ "You need to have `torchao>=0.4.0` in order to use torch 4-bit optimizers."
+ "Install it with `pip install torchao` or follow the instructions here: https://github.com/pytorch/ao"
+ )
+ if version.parse(importlib.metadata.version("torch")) < version.parse("2.3"):
+ raise ImportError(
+ "You need to have `torch>=2.3` in order to use torch 4-bit optimizers. "
+ "Install it with `pip install --upgrade torch`"
+ )
+ from torchao.prototype.low_bit_optim import AdamW4bit
 
+ optimizer_cls = AdamW4bit
+ optimizer_kwargs.update(adam_kwargs)
  else:
  raise ValueError(f"Trainer cannot instantiate unsupported optimizer: {args.optim}")
  return optimizer_cls, optimizer_kwargs

src/transformers/training_args.py

@@ -154,6 +154,7 @@ class OptimizerNames(ExplicitEnum):
  ADAMW_APEX_FUSED = "adamw_apex_fused"
  ADAFACTOR = "adafactor"
  ADAMW_ANYPRECISION = "adamw_anyprecision"
+ ADAMW_TORCH_4BIT = "adamw_torch_4bit"
  SGD = "sgd"
  ADAGRAD = "adagrad"
  ADAMW_BNB = "adamw_bnb_8bit"

tests/trainer/test_trainer.py

@@ -99,6 +99,7 @@
  is_apex_available,
  is_bitsandbytes_available,
  is_safetensors_available,
+ is_torchao_available,
  is_torchdistx_available,
 )
 from transformers.utils.hp_naming import TrialShortNamer
@@ -4210,6 +4211,16 @@ def hp_name(trial):
  dict(default_adam_kwargs, **default_anyprecision_kwargs),
  )
  )
+ if is_torchao_available():
+ import torchao
+
+ optim_test_params.append(
+ (
+ TrainingArguments(optim=OptimizerNames.ADAMW_TORCH_4BIT, output_dir="None"),
+ torchao.prototype.low_bit_optim.AdamW4bit,
+ default_adam_kwargs,
+ )
+ )
 
 
 @require_torch