Feature BAdam

examples/extras/badam/sft.sh

@@ -0,0 +1,36 @@
+# BAdam layer-wise
+export CUDA_VISIBLE_DEVICES=0
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+python ../../../src/train_bash.py \
+--stage sft \
+--do_train \
+--model_name_or_path meta-llama/Llama-2-7b-hf \
+--dataset alpaca_gpt4_en,glaive_toolcall \
+--dataset_dir ../../../data \
+--template default \
+--finetuning_type full \
+--output_dir ../../../saves/LLaMA2-7B/badam \
+--overwrite_cache \
+--overwrite_output_dir \
+--cutoff_len 1024 \
+--preprocessing_num_workers 32 \
+--per_device_train_batch_size 8 \
+--per_device_eval_batch_size 5 \
+--gradient_accumulation_steps 2 \
+--lr_scheduler_type cosine \
+--logging_steps 10 \
+--warmup_steps 20 \
+--save_steps 100 \
+--eval_steps 100 \
+--evaluation_strategy steps \
+--load_best_model_at_end \
+--learning_rate 5e-5 \
+--num_train_epochs 3.0 \
+--val_size 0.1 \
+--plot_loss \
+--use_badam \
+--switch_mode descending \
+--badam_verbose 2 \
+--switch_block_every 50 \
+--pure_bf16 \
+

requirements.txt

@@ -15,3 +15,4 @@ fastapi
 sse-starlette
 matplotlib
 fire
+badam

src/llmtuner/hparams/finetuning_args.py

@@ -163,6 +163,47 @@ class RLHFArguments:
         metadata={"help": "The type of the reward model in PPO training. Lora model only supports lora training."},
     )
 
+@dataclass
+class BAdamArgument:
+    r"""
+    Arguments for BAdam optimizer.
+    """
+    use_badam: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use BAdam optimizer."},
+    )
+    badam_mode: Literal["layer", "ratio"] = field(
+        default="layer",
+        metadata={"help": "The mode of BAdam optimizer. 'layer' for layer-wise, 'ratio' for ratio-wise."},
+    )
+    
+    # ======== Arguments for layer-wise update ========
+    start_block: Optional[int] = field(
+        default=None,
+        metadata={"help": "The starting block index for block-wise fine-tuning."}
+    )
+    switch_block_every: Optional[int] = field(
+        default=50,
+        metadata={"help": "how often to switch model's block update. Set to -1 to disable the block update."}
+    )
+    switch_mode: Optional[Literal["ascending", "descending", "random", "fixed"]] = field(
+        default="ascending",
+        metadata={"help": "the strategy of picking block to update."}
+    )
+    
+    # ======== Arguments for ratio-wise update ========
+    badam_update_ratio: float = field(
+        default=0.,
+        metadata={"help": "The ratio of the update for the BAdam optimizer."}
+    )
+    badam_mask_mode: Literal["adjacent", "scatter"] = field(
+        default="adjacent",
+        metadata={"help": "The mode of the mask for BAdam optimizer. `adjacent` means that the trainable parameters are adjacent to each other; `scatter` means that trainable parameters are randomly choosed from the weight."}
+    )
+    badam_verbose: int = field(
+        default=0,
+        metadata={"help": "The verbosity level of BAdam optimizer. 0 for no print, 1 for print the block prefix, 2 for print trainable parameters"}
+    )
 
 @dataclass
 class GaloreArguments:
@@ -204,7 +245,7 @@ class GaloreArguments:
 
 
 @dataclass
-class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments):
+class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments, BAdamArgument):
     r"""
     Arguments pertaining to which techniques we are going to fine-tuning with.
     """

src/llmtuner/hparams/parser.py

@@ -171,6 +171,12 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if finetuning_args.use_galore and training_args.deepspeed is not None:
         raise ValueError("GaLore is incompatible with DeepSpeed.")
 
+    if (finetuning_args.use_badam
+        and finetuning_args.badam_mode == "layer"
+        and training_args.parallel_mode.value == "distributed"
+    ):
+        raise ValueError("BAdam with layer-wise mode is not supported in distributed training by now, use ratio mode instead.")
+    
     if model_args.infer_backend == "vllm":
         raise ValueError("vLLM backend is only available for API, CLI and Web.")
 

src/llmtuner/model/adapter.py

@@ -37,7 +37,7 @@ def init_adapter(
 
     if finetuning_args.finetuning_type == "full" and is_trainable:
         logger.info("Fine-tuning method: Full")
-        if not finetuning_args.pure_bf16:
+        if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam):
             model = model.float()
 
     if finetuning_args.finetuning_type == "freeze" and is_trainable:
@@ -82,7 +82,7 @@ def init_adapter(
 
         for name, param in model.named_parameters():
             if any(trainable_layer in name for trainable_layer in trainable_layers):
-                if not finetuning_args.pure_bf16:
+                if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam):
                     param.data = param.data.to(torch.float32)
             else:
                 param.requires_grad_(False)
@@ -162,7 +162,7 @@ def init_adapter(
                 )
                 model = get_peft_model(model, lora_config)
 
-        if not finetuning_args.pure_bf16:
+        if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam):
             for param in filter(lambda p: p.requires_grad, model.parameters()):
                 param.data = param.data.to(torch.float32)
 

src/llmtuner/model/patcher.py

@@ -17,7 +17,7 @@ from ..extras.logging import get_logger
 from ..extras.misc import get_current_device, infer_optim_dtype
 from ..extras.packages import is_flash_attn2_available
 from ..extras.patches.llama_patch import apply_llama_patch
-from .utils import QuantizationMethod, add_z3_leaf_module
+from .utils import QuantizationMethod, add_z3_leaf_module, gradient_checkpointing_enable
 
 
 if TYPE_CHECKING:
@@ -266,8 +266,9 @@ def _prepare_model_for_training(
         else:
             # use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
             # According to: https://github.com/huggingface/transformers/issues/28339
+            model.gradient_checkpointing_enable = MethodType(gradient_checkpointing_enable, model)
             model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
-            model.enable_input_require_grads()
+            # model.enable_input_require_grads()
             setattr(model.config, "use_cache", False)  # turn off when gradient checkpointing is enabled
             logger.info("Gradient checkpointing enabled.")
 

src/llmtuner/model/utils.py

@@ -135,3 +135,45 @@ def register_autoclass(config: "PretrainedConfig", model: "PreTrainedModel", tok
         model.__class__.register_for_auto_class()
     if "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
         tokenizer.__class__.register_for_auto_class()
+
+def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
+    """
+    Modification of the original method to enable gradient checkpointing for block-wise optimizer.
+    
+    Activates gradient checkpointing for the current model.
+
+    We pass the `__call__` method of the modules instead of `forward` because `__call__` attaches all the hooks of
+    the module. https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
+
+    Args:
+        gradient_checkpointing_kwargs (dict, *optional*):
+            Additional keyword arguments passed along to the `torch.utils.checkpoint.checkpoint` function.
+    """
+    from torch.utils.checkpoint import checkpoint
+    
+    if not self.supports_gradient_checkpointing:
+        raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
+
+    if gradient_checkpointing_kwargs is None:
+        gradient_checkpointing_kwargs = {}
+
+    # gradient_checkpointing_func = functools.partial(checkpoint, **gradient_checkpointing_kwargs)
+    
+    def gradient_checkpointing_func(func, *args, **kwargs):
+        module = func.__self__
+        
+        if any([p.requires_grad for p in module.parameters()]):
+            for arg in args:
+                if torch.is_tensor(arg) and torch.is_floating_point(arg):
+                    arg.requires_grad_(True)
+        
+        return checkpoint(func, *args, **kwargs)            
+        
+    self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
+
+    if getattr(self, "_hf_peft_config_loaded", False):
+        # When using PEFT + gradient checkpointing + Trainer we need to make sure the input has requires_grad=True
+        # we do it also on PEFT: https://github.com/huggingface/peft/blob/85013987aa82aa1af3da1236b6902556ce3e483e/src/peft/peft_model.py#L334
+        # When training with PEFT, only LoRA layers will have requires grad set to True, but the output of frozen layers need to propagate
+        # the gradients to make sure the gradient flows.
+        self.enable_input_require_grads()

src/llmtuner/train/sft/trainer.py

@@ -9,7 +9,8 @@ from transformers import Seq2SeqTrainer
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
 from ..utils import create_custom_optimzer, create_custom_scheduler
-
+from types import MethodType
+from packaging import version
 
 if TYPE_CHECKING:
     from transformers.trainer import PredictionOutput
@@ -28,6 +29,9 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
     def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
+        if version.parse(torch.__version__) >= version.parse("1.13"):
+            from badam import clip_grad_norm_for_sparse_tensor
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)
 
     def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:

src/llmtuner/train/utils.py

@@ -287,12 +287,69 @@ def _create_loraplus_optimizer(
     logger.info("Using LoRA+ optimizer with loraplus lr ratio {:.2f}.".format(finetuning_args.loraplus_lr_ratio))
     return optimizer
 
+def _create_badam_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> "torch.optim.Optimizer":
+    
+    from transformers.trainer_pt_utils import get_parameter_names
+    decay_parameters = list(filter(lambda n: "bias" not in n, get_parameter_names(model, ALL_LAYERNORM_LAYERS)))
+    # filter out the embedding layers when using badam ratio mode
+    if finetuning_args.badam_mode == "ratio":
+        decay_parameters = list(filter(lambda n: "embed" not in n, decay_parameters)) # TODO: make it more general
+    optimizer_grouped_parameters = [
+        {
+            "params": [p for n, p in model.named_parameters() if n in decay_parameters],
+            "weight_decay": training_args.weight_decay,
+        },
+        {
+            "params": [p for n, p in model.named_parameters() if n not in decay_parameters],
+            "weight_decay": 0.0,
+        },
+    ]
+
+    optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
+
+    # create BlockOptimizer
+    if finetuning_args.badam_mode == "layer":
+        from badam import BlockOptimizer
+        base_optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
+        optimizer = BlockOptimizer(base_optimizer=base_optimizer,
+                                named_parameters_list=list(model.named_parameters()),
+                                block_prefix_list=None,
+                                switch_block_every=finetuning_args.switch_block_every,
+                                start_block=finetuning_args.start_block,
+                                switch_mode=finetuning_args.switch_mode,
+                                verbose=finetuning_args.badam_verbose)
+        
+        logger.info(f"Using BAdam optimizer with layer-wise update, switch mode is {finetuning_args.switch_mode}, "
+                    f"switch block every {finetuning_args.switch_block_every} steps, "
+                    f"default start block is {finetuning_args.start_block}")
+    
+    elif finetuning_args.badam_mode == "ratio":
+        assert finetuning_args.badam_update_ratio > 0.
+        from badam import BlockOptimizerRatio
+        optimizer = BlockOptimizerRatio(param_groups=optimizer_grouped_parameters,
+                                        named_parameters_list=list(model.named_parameters()),
+                                        update_ratio=finetuning_args.badam_update_ratio,
+                                        mask_mode=finetuning_args.badam_mask_mode,
+                                        verbose=finetuning_args.badam_verbose,
+                                        **optimizer_kwargs)
+        
+        logger.info(f"Using BAdam optimizer with ratio update, update ratio is {finetuning_args.badam_update_ratio}, "
+                    f"mask mode is {finetuning_args.badam_mask_mode}")
+    
+    return optimizer
 
 def create_custom_optimzer(
     model: "PreTrainedModel",
     training_args: "Seq2SeqTrainingArguments",
     finetuning_args: "FinetuningArguments",
 ) -> Optional["torch.optim.Optimizer"]:
+    if finetuning_args.use_badam:
+        return _create_badam_optimizer(model, training_args, finetuning_args)
+    
     if finetuning_args.use_galore:
         return _create_galore_optimizer(model, training_args, finetuning_args)