simplify pipeline inference logic, add comment

Signed-off-by: Kyle Sayers <[email protected]>

Author: kylesayrs

src/llmcompressor/pipelines/basic/pipeline.py

@@ -38,7 +38,7 @@ def __call__(
         :param dataloader: loads data for calibration
         :param dataset_args: dataset arguments relevant to pipelines
         """
-        dispatch_for_generation(model)
+        dispatch_for_generation(model)  # basic dispatch is identical to generation
         model_device = get_execution_device(model)
 
         LifecycleCallbacks.calibration_epoch_start()

src/llmcompressor/pipelines/registry.py

@@ -7,23 +7,13 @@
 from torch.utils.data.dataloader import DataLoader
 
 from llmcompressor.modifiers import Modifier
-from llmcompressor.modifiers.awq import AWQModifier
-from llmcompressor.modifiers.obcq.sgpt_base import SparsityModifierBase
-from llmcompressor.modifiers.quantization import GPTQModifier, QuantizationMixin
-from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+from llmcompressor.modifiers.quantization import QuantizationModifier
 
 if TYPE_CHECKING:
     from llmcompressor.args.dataset_arguments import DatasetArguments
 
 __all__ = ["CalibrationPipeline"]
 
-CALIBRATION_MODIFIERS = (
-    SmoothQuantModifier,
-    AWQModifier,
-    GPTQModifier,
-    SparsityModifierBase,
-)
-
 
 class CalibrationPipeline(ABC, RegistryMixin):
     @staticmethod
@@ -48,7 +38,7 @@ def from_modifiers(
         :return: CalibrationPipeline instance to be called with data (if not datafree)
         """
         user = standardize_lookup_name(user) if user else None
-        inferred = standardize_lookup_name(cls._validate_infer_pipeline(modifiers))
+        inferred = standardize_lookup_name(cls._infer_pipeline(modifiers))
         independent = standardize_lookup_name("independent")
 
         if user == independent:
@@ -64,30 +54,11 @@ def from_modifiers(
         return cls.load_from_registry(pipeline)
 
     @staticmethod
-    def _validate_infer_pipeline(modifiers: List[Modifier]) -> str:
-        if any(isinstance(modifier, CALIBRATION_MODIFIERS) for modifier in modifiers):
-            return "sequential"
-
-        active_qmods = _get_active_quant_modifiers(modifiers)
-        if len(active_qmods) > 1:
-            raise ValueError(
-                f"Recipe contains more than one active quantization config "
-                f"({active_qmods}). These configs may be conflicting, Please modify "
-                "your recipe to use at most one quantization config"
-            )
-
-        if len(active_qmods) == 1:
-            quant_modifier = active_qmods[0]
-            config = quant_modifier.resolve_quantization_config()
-            if config.requires_calibration_data():
-                return "sequential"
-
-        return "datafree"
-
-
-def _get_active_quant_modifiers(modifiers: List[Modifier]) -> List[QuantizationMixin]:
-    return [
-        modifier
-        for modifier in modifiers
-        if isinstance(modifier, QuantizationMixin) and modifier.has_config()
-    ]
+    def _infer_pipeline(modifiers: List[Modifier]) -> str:
+        # only in the case of weight-only qmod quantization can we skip calibration
+        if len(modifiers) == 1 and isinstance(modifiers[0], QuantizationModifier):
+            config = modifiers[0].resolve_quantization_config()
+            if not config.requires_calibration_data():
+                return "datafree"
+
+        return "sequential"