support galore once upstreamed into transformers (#1409)

* support galore once upstreamed into transformers

* update module name for llama in readme and fix typing for all linear

* bump trl for deprecation fixes from newer transformers

* include galore as an extra and install in docker image

* fix optim_args type

* fix optim_args

* update dependencies for galore

* add galore to cicd dockerfile

README.md

@@ -907,7 +907,26 @@ lr_div_factor: # Learning rate div factor
 # - paged_adamw_8bit
 # - paged_lion_32bit
 # - paged_lion_8bit
+# - galore_adamw
+# - galore_adamw_8bit
+# - galore_adafactor
+# - galore_adamw_layerwise
+# - galore_adamw_8bit_layerwise
+# - galore_adafactor_layerwise
 optimizer:
+# Dictionary of arguments to pass to the optimizer
+optim_args:
+# For Galore Optimizers the following optim_args are available
+# rank:  # type: int
+# update_proj_gap  # type: int
+# scale  # type: float
+# proj_type:  # type: str, default = std
+
+# The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm
+optim_target_modules:
+# - self_attn  # for llama
+# - mlp
+
 # Specify weight decay
 weight_decay:
 # adamw hyperparams

cicd/Dockerfile.jinja

@@ -23,9 +23,9 @@ RUN git fetch origin +$GITHUB_REF && \
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

docker/Dockerfile

@@ -21,9 +21,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install -e .[deepspeed,flash-attn,mamba-ssm] $AXOLOTL_ARGS; \
+        pip install -e .[deepspeed,flash-attn,mamba-ssm,galore] $AXOLOTL_ARGS; \
     fi
 
 # So we can test the Docker image

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.9.0
-transformers==4.38.2
+transformers @ git+https://github.com/huggingface/transformers.git@f6261d7d81edd036fc53bfede65fe91f01a661aa
 tokenizers==0.15.0
 bitsandbytes>=0.43.0
 accelerate==0.26.1
@@ -39,5 +39,5 @@ s3fs
 gcsfs
 # adlfs
 
-trl>=0.7.9
+trl @ git+https://github.com/huggingface/trl.git@304e208f778a5442c30cdda500348226cdc97d90
 fastcore>=1.5.29

setup.py

@@ -89,5 +89,8 @@ setup(
         "lion-pytorch": [
             "lion-pytorch==0.1.2",
         ],
+        "galore": [
+            "galore_torch",
+        ],
     },
 )

src/axolotl/core/trainer_builder.py

@@ -220,7 +220,7 @@ class AxolotlTrainer(Trainer):
         num_epochs=1,
         bench_data_collator=None,
         eval_data_collator=None,
-        **kwargs
+        **kwargs,
     ):
         self.num_epochs = num_epochs
         self.bench_data_collator = bench_data_collator
@@ -239,6 +239,7 @@ class AxolotlTrainer(Trainer):
         if self.optimizer is None:  # pylint: disable=access-member-before-definition
             optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
                 self.args,
+                opt_model,
             )
 
             loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
@@ -1150,6 +1151,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["optim"] = (
             self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
         )
+        if self.cfg.optim_args:
+            if isinstance(self.cfg.optim_args, dict):
+                optim_args = ",".join(
+                    [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
+                )
+            else:
+                optim_args = self.cfg.optim_args
+            training_arguments_kwargs["optim_args"] = optim_args
+        if self.cfg.optim_target_modules:
+            training_arguments_kwargs[
+                "optim_target_modules"
+            ] = self.cfg.optim_target_modules
         training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
         training_arguments_kwargs[
             "loraplus_lr_embedding"

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

@@ -313,6 +313,15 @@ class HyperparametersConfig(BaseModel):
     learning_rate: Union[str, float]
     weight_decay: Optional[float] = None
     optimizer: Optional[Union[OptimizerNames, Literal["lion_pytorch"]]] = None
+    optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
+        default=None, metadata={"help": "Optional arguments to supply to optimizer."}
+    )
+    optim_target_modules: Optional[Union[List[str], Literal["all_linear"]]] = Field(
+        default=None,
+        metadata={
+            "help": "The target modules to optimize, i.e. the module names that you would like to train."
+        },
+    )
     torchdistx_path: Optional[str] = None
     lr_scheduler: Optional[SchedulerType] = None
     lr_scheduler_kwargs: Optional[Dict[str, Any]] = None