deprecate sequential_targets on modifiers

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

Author: kylesayrs

src/llmcompressor/args/dataset_arguments.py

@@ -186,3 +186,13 @@ class DatasetArguments(CustomDatasetArguments):
             "{module}.{method_name} or {function_name}"
         },
     )
+    sequential_targets: Optional[List[str]] = field(
+        default=None,
+        metadata={
+            "help": "List of layer targets for the sequential pipeline. "
+            "This is typically a single DecoderLayer. "
+            "Not specifying this argument will cause the sequential pipeline to "
+            "default to using the `no_split_params` specified by the HF model "
+            "definition"
+        },
+    )

src/llmcompressor/pipelines/registry.py

@@ -17,8 +17,12 @@
 
 __all__ = ["CalibrationPipeline"]
 
-SEQUENTIAL_MODIFIERS = (AWQModifier, GPTQModifier, SparsityModifierBase)
-CALIBRATION_MODIFIERS = (SmoothQuantModifier, *SEQUENTIAL_MODIFIERS)
+CALIBRATION_MODIFIERS = (
+    SmoothQuantModifier,
+    AWQModifier,
+    GPTQModifier,
+    SparsityModifierBase,
+)
 
 
 class CalibrationPipeline(ABC, RegistryMixin):

src/llmcompressor/pipelines/sequential/helpers.py

@@ -2,7 +2,7 @@
 import inspect
 from collections import deque
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Set
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set
 
 import torch
 from compressed_tensors import has_offloaded_params
@@ -23,7 +23,10 @@
 
 from .ast_helpers import autowrap_forwards
 
-__all__ = ["trace_subgraphs", "Subgraph", "get_targets_from_modifiers"]
+if TYPE_CHECKING:
+    from llmcompressor.args.dataset_arguments import DatasetArguments
+
+__all__ = ["trace_subgraphs", "Subgraph", "get_sequential_targets"]
 
 
 @dataclass
@@ -416,44 +419,59 @@ def match_modules(model: Module, target_names: List[str]) -> Set[Module]:
     )
 
 
-def get_targets_from_modifiers(
-    modifiers: List[Modifier], model: PreTrainedModel
+def get_sequential_targets(
+    modifiers: List[Modifier], model: PreTrainedModel, args: "DatasetArguments"
 ) -> List[str]:
     """
-    Infer sequential targets from modifiers list
+    Infer sequential targets from modifiers list and dataset args
 
     :param model: model being calibrated
     :param modifiers: list of modifiers being applied during calibration
+    :param dataset_args: dataset arguments passed by user
     :return: list of sequential targets
     """
-    # avoid circular import
-    from llmcompressor.pipelines.registry import SEQUENTIAL_MODIFIERS
-
-    sequential_modifiers = [
-        modifier for modifier in modifiers if isinstance(modifier, SEQUENTIAL_MODIFIERS)
+    modifier_targets = [
+        (modifier, modifier.sequential_targets)
+        for modifier in modifiers
+        if getattr(modifier, "sequential_targets", None) is not None
     ]
 
-    if len(sequential_modifiers) >= 2:
-        types = [type(modifier) for modifier in sequential_modifiers]
+    # deprecation warning
+    if len(modifier_targets) > 1:
         logger.warning(
+            "Passing sequential targets through modifiers is deprecated, "
+            "please use `oneshot(sequential_targets=...)`"
+        )
+
+    # cannot infer from multiple modifiers
+    if len(modifier_targets) >= 2:
+        types = [type(modifier) for modifier, _ in modifier_targets]
+        raise ValueError(
             "Cannot infer sequential targets from multiple sequential modifiers "
-            f"({types}). Defaulting to {types[0]}"
+            f"({types})"
         )
-    elif len(sequential_modifiers) <= 0:
-        types = [type(modifier) for modifier in modifiers]
-        raise ValueError(f"Cannot infer sequential targets from list of {types}")
 
-    modifier = sequential_modifiers[0]
+    # resolve single modifier
+    if len(modifier_targets) == 1:
+        if args.sequential_targets is not None:
+            raise ValueError(
+                f"Got sequential targets from both {type(modifier_targets[0][0])} "
+                "and dataset arguments `sequential_targets`"
+            )
+
+        sequential_targets = modifier_targets[0][1]
 
-    # infer sequential targets
-    if modifier.sequential_targets is None:
-        sequential_targets = get_no_split_params(model)
-    elif isinstance(modifier.sequential_targets, str):
-        sequential_targets = [modifier.sequential_targets]
+    # if no modifiers, use data args
     else:
-        sequential_targets = modifier.sequential_targets
+        sequential_targets = args.sequential_targets  # may be `None`
 
-    return sequential_targets
+    # validate and infer
+    if sequential_targets is None:
+        return get_no_split_params(model)
+    elif isinstance(sequential_targets, str):
+        return [sequential_targets]
+    else:
+        return sequential_targets
 
 
 def add_line_numbers(text: str) -> str:

src/llmcompressor/pipelines/sequential/pipeline.py

@@ -11,7 +11,7 @@
 from llmcompressor.pipelines.cache import IntermediatesCache
 from llmcompressor.pipelines.registry import CalibrationPipeline
 from llmcompressor.pipelines.sequential.helpers import (
-    get_targets_from_modifiers,
+    get_sequential_targets,
     trace_subgraphs,
 )
 from llmcompressor.utils.helpers import DisableQuantization, calibration_forward_context
@@ -64,7 +64,7 @@ def __call__(
 
         # prepare to trace subgraphs
         modifiers = session.get_modifiers()
-        sequential_targets = get_targets_from_modifiers(modifiers, model)
+        sequential_targets = get_sequential_targets(modifiers, model, dataset_args)
         ignore = dataset_args.tracing_ignore
 
         # trace subgraphs