pre commit hooks

examples/llama-3/sft.yaml

@@ -1,4 +1,4 @@
-base_model: "nm-testing/llama2.c-stories42M-gsm8k-sparse-only-uncompressed" 
+base_model: "nm-testing/llama2.c-stories42M-gsm8k-sparse-only-uncompressed"
 # TODO: change to
 # base_model: neuralmagic/Sparse-Llama-3.1-8B-2of4
 
@@ -68,11 +68,11 @@ llmcompressor:
         ConstantPruningModifier:
           targets: [
             're:.*q_proj.weight',
-            're:.*k_proj.weight', 
+            're:.*k_proj.weight',
             're:.*v_proj.weight',
             're:.*o_proj.weight',
             're:.*gate_proj.weight',
             're:.*up_proj.weight',
             're:.*down_proj.weight',
           ]
-          start: 0
+          start: 0

src/axolotl/integrations/llmcompressor_sft/__init__.py

@@ -5,19 +5,19 @@ by maintaining masks for zero weights during training.
 
 import logging
 from functools import wraps
-from typing import Callable, TypeVar, ParamSpec, Any
+from typing import Any, Callable, ParamSpec, TypeVar
 
-from transformers.trainer import Trainer
-from transformers.trainer_callback import TrainerCallback, TrainerState, TrainerControl
-from transformers.training_args import TrainingArguments
-
-from ..base import BasePlugin
 from llmcompressor import active_session
 from llmcompressor.core import callbacks as session_callbacks
 from llmcompressor.recipe import Recipe
+from transformers.trainer import Trainer
+from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
+from transformers.training_args import TrainingArguments
+
+from ..base import BasePlugin
 
 P = ParamSpec("P")  # Params for generic function signatures
-R = TypeVar("R")    # Return type for generic function signatures
+R = TypeVar("R")  # Return type for generic function signatures
 
 LOG = logging.getLogger("axolotl.integrations.llmcompressor_sft")
 
@@ -39,11 +39,17 @@ class SFTCallbackHandler(TrainerCallback):
         """
         super().__init__()
         self.trainer = trainer
-        self.recipe = Recipe.model_validate(recipe) if not isinstance(recipe, Recipe) else recipe
+        self.recipe = (
+            Recipe.model_validate(recipe) if not isinstance(recipe, Recipe) else recipe
+        )
         self.trainer.compute_loss = compute_loss_wrapper(self.trainer.compute_loss)
 
     def on_train_begin(
-        self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
     ) -> None:
         """
         Called at the beginning of training. Initializes the compression session.
@@ -63,7 +69,11 @@ class SFTCallbackHandler(TrainerCallback):
         )
 
     def on_step_begin(
-        self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
     ) -> None:
         """
         Called at the beginning of a training step. Triggers batch_start callback.
@@ -72,7 +82,11 @@ class SFTCallbackHandler(TrainerCallback):
         session_callbacks.batch_start()
 
     def on_step_end(
-        self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
     ) -> None:
         """
         Called at the end of a training step. Triggers optimizer and batch_end callbacks.
@@ -83,7 +97,11 @@ class SFTCallbackHandler(TrainerCallback):
         session_callbacks.batch_end()
 
     def on_train_end(
-        self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
     ) -> None:
         """
         Called at the end of training. Finalizes the compression session.

src/axolotl/integrations/llmcompressor_sft/args.py

@@ -2,19 +2,18 @@
 LLMCompressor and Sparse Finetuning config models.
 """
 
-from pydantic import BaseModel, Field, ConfigDict
 from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field
 from typing_extensions import Annotated
 
+
 class SFTArgs(BaseModel):
     """Sparse Finetuning config for LLMCompressor."""
 
     # Typing for recipe is set to Any due to:
     # https://github.com/vllm-project/llm-compressor/issues/1319
-    recipe: Annotated[
-        Any,
-        Field(description="Recipe config.")
-    ]
+    recipe: Annotated[Any, Field(description="Recipe config.")]
 
     model_config = ConfigDict(
         arbitrary_types_allowed=True,
@@ -25,10 +24,7 @@ class SFTArgs(BaseModel):
 class LLMCompressorArgs(BaseModel):
     """LLMCompressor configuration BaseModel."""
 
-    llmcompressor: Annotated[
-        SFTArgs,
-        Field(description="SFT llmcompressor args")
-    ]
+    llmcompressor: Annotated[SFTArgs, Field(description="SFT llmcompressor args")]
 
     model_config = ConfigDict(
         validate_assignment=True,

src/axolotl/utils/models.py

@@ -141,7 +141,7 @@ def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
         hasattr(model_config, "quantization_config")
         and model_config.quantization_config
     )
-    
+
     # Detect compressed-tensors config
     is_compressed_tensors_config = (
         quant_config_exists
@@ -156,7 +156,7 @@ def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
             )
         # Skip further quant checks for compressed-tensors
         return
-        
+
     quant_config_method_is_gptq = (
         quant_config_exists
         and "quant_method" in model_config.quantization_config