Rebase and updates!

examples/llama-3/sparse-finetuning.yaml

@@ -74,3 +74,4 @@ llmcompressor:
             're:.*down_proj.weight',
           ]
           start: 0
+  save_compressed: true

setup.py

@@ -149,8 +149,9 @@ extras_require = {
     "vllm": [
         "vllm==0.7.2",
     ],
+    # PENDING: https://github.com/vllm-project/llm-compressor/pull/1352
     "llmcompressor": [
-        "llmcompressor~=0.5.0",
+        "llmcompressor==0.5.1",
     ],
 }
 

src/axolotl/integrations/llm_compressor/args.py

@@ -20,9 +20,13 @@ class CompressionArgs(BaseModel):
         ),
     ]
 
-    model_config = ConfigDict(
+    save_compressed: Annotated[
-        validate_assignment=True,
+        bool,
-    )
+        Field(
+            default=False,
+            description="Whether to save the compressed model after training.",
+        ),
+    ]
 
 
 class LLMCompressorArgs(BaseModel):

src/axolotl/integrations/llm_compressor/plugin.py

@@ -7,7 +7,7 @@ import logging
 from functools import wraps
 from typing import Any, Callable, ParamSpec, TypeVar
 
-from llmcompressor import active_session
+from llmcompressor import active_session, create_session
 from llmcompressor.core import callbacks as session_callbacks
 from llmcompressor.recipe import Recipe
 from transformers.trainer import Trainer
@@ -43,6 +43,7 @@ class LLMCompressorCallbackHandler(TrainerCallback):
             Recipe.model_validate(recipe) if not isinstance(recipe, Recipe) else recipe
         )
         self.trainer.compute_loss = compute_loss_wrapper(self.trainer.compute_loss)
+        create_session()
 
     def on_train_begin(
         self,
@@ -60,13 +61,14 @@ class LLMCompressorCallbackHandler(TrainerCallback):
             control (TrainerControl): Trainer control.
         """
         super().on_train_begin(args, state, control, **kwargs)
-        session = active_session()
+        self.trainer.accelerator.wait_for_everyone()
-        session.initialize(
+        active_session().initialize(
             model=self.trainer.model,
             optimizer=self.trainer.optimizer,
             start=state.epoch,
             recipe=self.recipe,
         )
+        self.trainer.accelerator.wait_for_everyone()
 
     def on_step_begin(
         self,
@@ -107,8 +109,7 @@ class LLMCompressorCallbackHandler(TrainerCallback):
         Called at the end of training. Finalizes the compression session.
         """
         super().on_train_end(args, state, control, **kwargs)
-        session = active_session()
+        active_session().finalize()
-        session.finalize()
 
 
 class LLMCompressorPlugin(BasePlugin):
@@ -158,7 +159,8 @@ def compute_loss_wrapper(compute_loss_func: Callable[P, R]) -> Callable[P, R]:
     @wraps(compute_loss_func)
     def compute_and_notify(*args: P.args, **kwargs: P.kwargs) -> R:
         loss = compute_loss_func(*args, **kwargs)
-        session_callbacks.loss_calculated(loss=loss)
+        if active_session().lifecycle.initialized_:
+            session_callbacks.loss_calculated(loss=loss)
         return loss
 
     return compute_and_notify

src/axolotl/integrations/llm_compressor/utils.py

@@ -0,0 +1,15 @@
+from transformers import Trainer
+
+def save_compressed_model(
+        model, output_dir, trainer: Trainer, safe_serialization: bool, save_compressed:bool
+):
+    from llmcompressor.transformers.sparsification.compressed_tensors_utils import modify_save_pretrained
+    trainer.accelerator.wait_for_everyone()
+    if trainer.accelerator.is_main_process:
+        modify_save_pretrained(model)
+        model.save_pretrained(
+            output_dir,
+            safe_serialization=safe_serialization,
+            save_compressed=save_compressed,
+            skip_sparsity_compression_stats=not save_compressed,
+        )

src/axolotl/train.py

@@ -271,6 +271,19 @@ def save_trained_model(
                 os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
             except FileNotFoundError:
                 pass
+    elif hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
+        from axolotl.integrations.llm_compressor.utils import (
+            save_compressed_model,
+        )
+
+        save_compressed_model(
+            model=model,
+            output_dir=cfg.output_dir,
+            trainer=trainer,
+            safe_serialization=safe_serialization,
+            save_compressed=cfg.llmcompressor.save_compressed,
+        )
+
     elif cfg.local_rank == 0:
         if cfg.flash_optimum and BetterTransformer:
             model = BetterTransformer.reverse(model)
@@ -279,6 +292,7 @@ def save_trained_model(
             trainer.model.save_pretrained(
                 cfg.output_dir, safe_serialization=safe_serialization
             )
+
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)