Naive Run Compressed Pt. 2 (#62)

* update auto model

* style and reload

* fixing tests

* sparseautomodel compatability

* revert un-needed change

* skip init

* quality

* remove recipe app

src/llmcompressor/modifiers/quantization/quantization/base.py

@@ -70,9 +70,7 @@ class QuantizationModifier(Modifier):
     calibration_function_: Any = None
 
     def on_initialize_structure(self, state: State, **kwargs):
-        module = state.model
-        self._apply_modifier_to_model(module)
-        module.apply(freeze_module_quantization)
+        pass
 
     def on_initialize(self, state: State, **kwargs) -> bool:
         if self.end and self.end != -1:

src/llmcompressor/transformers/sparsification/sparse_model.py

@@ -4,19 +4,20 @@
 from typing import Optional, Union
 
 import torch
+from accelerate import load_checkpoint_and_dispatch
 from compressed_tensors.compressors import ModelCompressor
+from compressed_tensors.quantization import (
+    QuantizationStatus,
+    apply_quantization_config,
+)
 from loguru import logger
 from torch.nn import Module
 from transformers import AutoModelForCausalLM, PreTrainedModel
 
-from llmcompressor.pytorch.model_load.helpers import initialize_recipe
 from llmcompressor.transformers.sparsification.compressed_tensors_utils import (
     modify_save_pretrained,
 )
-from llmcompressor.transformers.utils.helpers import (
-    download_model_directory,
-    resolve_recipe,
-)
+from llmcompressor.transformers.utils.helpers import download_model_directory
 
 __all__ = ["SparseAutoModel", "SparseAutoModelForCausalLM", "get_shared_tokenizer_src"]
 
@@ -40,6 +41,7 @@ class SparseAutoModelForCausalLM(AutoModelForCausalLM):
     def from_pretrained(
         cls,
         pretrained_model_name_or_path,
+        run_compressed: bool = False,
         recipe: Optional[Union[str, Path]] = None,
         *model_args,
         **kwargs,
@@ -115,12 +117,27 @@ def skip(*args, **kwargs):
         # If model is quantized or compressed on disk, initialize quantization
         # structure and run decompression
         if compressor is not None:
-            # initialize quantization and decompress weights
-            compressor.decompress(model_path=pretrained_model_name_or_path, model=model)
-
-        recipe = resolve_recipe(recipe=recipe, model_path=pretrained_model_name_or_path)
-        if recipe:
-            initialize_recipe(model=model, recipe_path=recipe)
+            quantization_config = compressor.quantization_config
+            is_compressed = (
+                quantization_config is not None
+                and quantization_config.quantization_status
+                == QuantizationStatus.COMPRESSED
+            )
+            if run_compressed and is_compressed:
+                # initialize quantization, don't decompress
+                apply_quantization_config(
+                    model, quantization_config, run_compressed=True
+                )
+                model = load_checkpoint_and_dispatch(
+                    model, pretrained_model_name_or_path
+                )
+            else:
+                # initialize quantization and decompress weights
+                if quantization_config is not None:
+                    quantization_config.quantization_status = QuantizationStatus.FROZEN
+                compressor.decompress(
+                    model_path=pretrained_model_name_or_path, model=model
+                )
 
         return model