[Callbacks] Remove MagnitudePruningModifier.leave_enabled (#1198)

## Purpose ##
* Simplify the modifier lifecycle by removing the ability for modifiers
to affect the model after the modifier's `end` event
* This allows the `on_event` method to be removed in a future change

## Background ##
* The `leave_enabled` option was originally intended as a shortcut to
simplify recipes which used magnitude pruning during the iterative
pruning, then needed the masks to stay enabled during stabilization SFT
* This change proposes making the recipe clearer by requiring the
ConstantPruningModifier after the MagnitudePruningModifier becomes
inactive

## Changes ##
* Remove `MagnitudePruningModifier.leave_enabled` with a deprecation
warning

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

src/llmcompressor/modifiers/pruning/magnitude/base.py

@@ -1,5 +1,8 @@
+import warnings
 from typing import Any, Dict, List, Union
 
+from pydantic import field_validator
+
 from llmcompressor.core import Event, EventType, ModelParameterizedLayer, State
 from llmcompressor.modifiers import Modifier
 from llmcompressor.modifiers.pruning.helpers import (
@@ -25,7 +28,7 @@ class MagnitudePruningModifier(Modifier, LayerParamMasking):
     update_scheduler: str = "cubic"
     scheduler_args: Dict[str, Any] = {}
     mask_structure: str = "unstructured"
-    leave_enabled: bool = True
+    leave_enabled: bool = False
     apply_globally: bool = False
 
     parameterized_layers_: Dict[str, ModelParameterizedLayer] = None
@@ -35,6 +38,14 @@ class MagnitudePruningModifier(Modifier, LayerParamMasking):
     mask_creator_function_: MaskCreatorType = None
     current_sparsity_: float = None
 
+    @field_validator("leave_enabled")
+    def validate_leave_enabled(value: bool) -> bool:
+        warnings.warn(
+            "MagnitudePruningModifier.leave_enable has been deprecated",
+            DeprecationWarning,
+        )
+        return False
+
     def on_initialize(self, state: State, **kwargs) -> bool:
         if self.apply_globally:
             raise NotImplementedError("global pruning not implemented yet for PyTorch")
@@ -75,9 +86,8 @@ def on_initialize(self, state: State, **kwargs) -> bool:
         return True
 
     def on_finalize(self, state: State, **kwargs) -> bool:
-        if not self.leave_enabled:
-            for layer_param_name, _ in self.parameterized_layers_.items():
-                self.remove_mask(layer_param_name)
+        for layer_param_name, _ in self.parameterized_layers_.items():
+            self.remove_mask(layer_param_name)
 
         return True
 
@@ -119,12 +129,7 @@ def on_update(self, state: State, event: Event, **kwargs):
             self._update_masks(event)
 
     def on_end(self, state: State, event: Event, **kwargs):
-        if not self.leave_enabled:
-            self.disable_masks()
-
-    def on_event(self, state: State, event: Event, **kwargs):
-        if event.current_index >= self.end and self.leave_enabled:
-            self._update_masks(event)
+        self.disable_masks()
 
     def _update_masks(self, event: Event):
         if event.type_ == EventType.OPTIM_PRE_STEP and not self._use_hooks: