[cherry-pick] 0.11.1 patch - global_sparsity backwards compatibility (#627)

Author: bfineran

src/sparseml/pytorch/sparsification/pruning/modifier_pruning_magnitude.py

@@ -16,13 +16,14 @@
 Modifiers classes related to magnitude pruning
 """
 
+import logging
 from typing import Dict, List, Union
 
 import torch
 from torch import Tensor
 from torch.nn import Parameter
 
-from sparseml.pytorch.optim.modifier import PyTorchModifierYAML
+from sparseml.pytorch.optim.modifier import ModifierProp, PyTorchModifierYAML
 from sparseml.pytorch.sparsification.pruning.mask_creator import (
     PruningMaskCreator,
     get_mask_creator_default,
@@ -43,6 +44,9 @@
 ]
 
 
+_LOGGER = logging.getLogger(__name__)
+
+
 class MagnitudePruningParamsScorer(PruningParamsScorer):
     """
     Scores parameters based on their magnitude
@@ -106,6 +110,9 @@ class GMPruningModifier(BaseGradualPruningModifier, BaseGMPruningModifier):
     :param mask_type: String to define type of sparsity to apply. May be 'unstructred'
         for unstructured pruning or 'block4' for four block pruning or a list of two
         integers for a custom block shape. Default is 'unstructured'
+    :param global_sparsity: set True to use global magnitude pruning, False for
+        layer-wise. Default is False. [DEPRECATED] - use GlobalMagnitudePruningModifier
+        for global magnitude pruning and MagnitudePruningModifier for layer-wise
     """
 
     def __init__(
@@ -120,7 +127,10 @@ def __init__(
         inter_func: str = "cubic",
         log_types: Union[str, List[str]] = ALL_TOKEN,
         mask_type: str = "unstructured",
+        global_sparsity: bool = False,
     ):
+        self._check_warn_global_sparsity(global_sparsity)
+
         super(GMPruningModifier, self).__init__(
             params=params,
             init_sparsity=init_sparsity,
@@ -132,8 +142,8 @@ def __init__(
             log_types=log_types,
             mask_type=mask_type,
             leave_enabled=leave_enabled,
+            global_sparsity=global_sparsity,
             end_comparator=-1,
-            global_sparsity=self._use_global_sparsity,
             allow_reintroduction=False,
             parent_class_kwarg_names=[
                 "init_sparsity",
@@ -161,10 +171,22 @@ def _get_scorer(self, params: List[Parameter]) -> PruningParamsScorer:
         """
         return MagnitudePruningParamsScorer(params)
 
-    @property
-    def _use_global_sparsity(self) -> bool:
-        # base GMPruningModifier will not support global sparsity
-        return False
+    @ModifierProp()
+    def global_sparsity(self) -> bool:
+        """
+        :return: True for global magnitude pruning, False for
+            layer-wise. [DEPRECATED] - use GlobalMagnitudePruningModifier
+            for global magnitude pruning and MagnitudePruningModifier for layer-wise
+        """
+        return self._global_sparsity
+
+    def _check_warn_global_sparsity(self, global_sparsity):
+        if self.__class__.__name__ == "GMPruningModifier" and global_sparsity is True:
+            _LOGGER.warning(
+                "Use of global_sparsity parameter in GMPruningModifier is now "
+                "deprecated. Use GlobalMagnitudePruningModifier instead for global "
+                "magnitude pruning"
+            )
 
 
 @PyTorchModifierYAML()
@@ -217,8 +239,41 @@ class MagnitudePruningModifier(GMPruningModifier):
         integers for a custom block shape. Default is 'unstructured'
     """
 
-    # just an alias for GMPruningModifier
-    pass
+    def __init__(
+        self,
+        init_sparsity: Union[float, str],
+        final_sparsity: Union[float, Dict[float, List[str]]],
+        start_epoch: float,
+        end_epoch: float,
+        update_frequency: float,
+        params: Union[str, List[str]],
+        leave_enabled: bool = True,
+        inter_func: str = "cubic",
+        log_types: Union[str, List[str]] = ALL_TOKEN,
+        mask_type: str = "unstructured",
+    ):
+        super(MagnitudePruningModifier, self).__init__(
+            params=params,
+            init_sparsity=init_sparsity,
+            final_sparsity=final_sparsity,
+            start_epoch=start_epoch,
+            end_epoch=end_epoch,
+            update_frequency=update_frequency,
+            inter_func=inter_func,
+            log_types=log_types,
+            mask_type=mask_type,
+            leave_enabled=leave_enabled,
+            global_sparsity=False,
+        )
+
+    @ModifierProp(serializable=False)
+    def global_sparsity(self) -> bool:
+        """
+        :return: True for global magnitude pruning, False for
+            layer-wise. [DEPRECATED] - use GlobalMagnitudePruningModifier
+            for global magnitude pruning and MagnitudePruningModifier for layer-wise
+        """
+        return self._global_sparsity
 
 
 @PyTorchModifierYAML()
@@ -295,8 +350,14 @@ def __init__(
             log_types=log_types,
             mask_type=mask_type,
             leave_enabled=leave_enabled,
+            global_sparsity=True,
         )
 
-    @property
-    def _use_global_sparsity(self) -> bool:
-        return True
+    @ModifierProp(serializable=False)
+    def global_sparsity(self) -> bool:
+        """
+        :return: True for global magnitude pruning, False for
+            layer-wise. [DEPRECATED] - use GlobalMagnitudePruningModifier
+            for global magnitude pruning and MagnitudePruningModifier for layer-wise
+        """
+        return self._global_sparsity

src/sparseml/pytorch/sparsification/pruning/modifier_pruning_movement.py

@@ -24,7 +24,7 @@
 from torch import Tensor
 from torch.nn import Parameter
 
-from sparseml.pytorch.optim.modifier import PyTorchModifierYAML
+from sparseml.pytorch.optim.modifier import ModifierProp, PyTorchModifierYAML
 from sparseml.pytorch.sparsification.pruning.modifier_pruning_magnitude import (
     GMPruningModifier,
 )
@@ -122,6 +122,15 @@ def _get_scorer(self, params: List[Parameter]) -> PruningParamsGradScorer:
         """
         return MovementPruningParamsScorer(params=params)
 
+    @ModifierProp(serializable=False)
+    def global_sparsity(self) -> bool:
+        """
+        :return: True for global magnitude pruning, False for
+            layer-wise. [DEPRECATED] - use GlobalMagnitudePruningModifier
+            for global magnitude pruning and MagnitudePruningModifier for layer-wise
+        """
+        return self._global_sparsity
+
 
 class MovementPruningParamsScorer(PruningParamsGradScorer):
     """

src/sparseml/pytorch/sparsification/pruning/modifier_pruning_structured.py

@@ -426,3 +426,12 @@ def param_groups(self) -> List[List[str]]:
             useful for structures such as residual blocks or grouped convolutions
         """
         return self._param_groups
+
+    @ModifierProp(serializable=False)
+    def global_sparsity(self) -> bool:
+        """
+        :return: True for global magnitude pruning, False for
+            layer-wise. [DEPRECATED] - use GlobalMagnitudePruningModifier
+            for global magnitude pruning and MagnitudePruningModifier for layer-wise
+        """
+        return self._global_sparsity

src/sparseml/version.py

@@ -19,7 +19,7 @@
 from datetime import date
 
 
-version_base = "0.11.0"
+version_base = "0.11.1"
 is_release = False  # change to True to set the generated version as a release version
 
 

tests/sparseml/pytorch/sparsification/pruning/test_modifier_pruning_magnitude.py

@@ -249,6 +249,7 @@ def test_gm_pruning_yaml(params, init_sparsity, final_sparsity):
     update_frequency = 1.0
     inter_func = "cubic"
     mask_type = "filter"
+    global_sparsity = False
     yaml_str = f"""
     !GMPruningModifier
         init_sparsity: {init_sparsity}
@@ -259,6 +260,7 @@ def test_gm_pruning_yaml(params, init_sparsity, final_sparsity):
         params: {params}
         inter_func: {inter_func}
         mask_type: {mask_type}
+        global_sparsity: {global_sparsity}
     """
     yaml_modifier = GMPruningModifier.load_obj(yaml_str)  # type: GMPruningModifier
     serialized_modifier = GMPruningModifier.load_obj(
@@ -273,12 +275,18 @@ def test_gm_pruning_yaml(params, init_sparsity, final_sparsity):
         params=params,
         inter_func=inter_func,
         mask_type=mask_type,
+        global_sparsity=global_sparsity,
     )
 
     assert isinstance(yaml_modifier, GMPruningModifier)
     pruning_modifier_serialization_vals_test(
         yaml_modifier, serialized_modifier, obj_modifier
     )
+    assert (
+        yaml_modifier.global_sparsity
+        == serialized_modifier.global_sparsity
+        == obj_modifier.global_sparsity
+    )
 
 
 def test_magnitude_pruning_yaml():