Align sparsity with block-wise masks in progressive pruning. (#1250)

Signed-off-by: YIYANGCAI <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: YIYANGCAI

neural_compressor/compression/pruner/pruners/progressive.py

@@ -60,7 +60,8 @@ def _init(self):
         self.progressive_steps = self.progressive_configs["progressive_steps"]
         self.progressive_type = self.progressive_configs["progressive_type"]
         self.use_global = self.progressive_configs["use_global"]
-        self.progressive_logger = False
+        self.progressive_logger = True
+        self.align_masks_flag = False
         self._init_for_progressive()
 
     def _init_for_progressive(self):
@@ -77,6 +78,11 @@ def _init_for_progressive(self):
             self.use_progressive = False
             return
 
+        if self.pruning_frequency == 1:
+            logger.info("Current progressive setting will degrading to non-progressive pruning.")
+            self.use_progressive = False
+            return
+
         # step 3: log hyper-parameters. and check validity.
         if self.use_progressive:
             logger.info("Progressive pruning is enabled!")
@@ -225,6 +231,10 @@ def on_step_begin(self, local_step):
 
         Implement at the start of each step.
         """
+        if self.global_step > self.end_step and self.align_masks_flag is False:
+            self.align_masks_after_pruning()
+            self.align_masks_flag = True
+
         if self.handled_global_step == self.global_step:
             return
 
@@ -270,3 +280,24 @@ def print_progressive_sparsity(self):
         """Output the progressive sparsity."""
         cur_sp = self.pattern.get_sparsity_ratio_progressive(self.progressive_masks)
         logger.info("Step: {} -> Current progressive sparsity: {}".format(self.global_step, cur_sp))
+
+    def obtain_weight_sparsity(self, modules):
+        total_numels = 0
+        sparse_numels = 0
+        for key in modules.keys():
+            total_numels += modules[key].weight.data.numel()
+            sparse_numels += torch.sum(torch.where(modules[key].weight.data == 0, 1, 0)).item()
+        return sparse_numels / total_numels
+
+    def align_masks_after_pruning(self):
+        if not self.use_progressive:
+            return
+        """Implement at the end of training phase."""
+        # If training ends while a progressive masks is applying, we have to use self.masks to align
+        # step 1 calculate sparsity under progressive masks
+        sparsity1 = self.obtain_weight_sparsity(self.modules)
+        # step 2 use block-wise masks to remask weights
+        self.mask_weights_general(self.masks)
+        # step 3 calculate sparsity under progressive masks
+        sparsity2 = self.obtain_weight_sparsity(self.modules)
+        logger.info(f"Replace progressive mask with complete masks: Sparsity Update: {sparsity1} => {sparsity2}")