Resume training (#958)

paddleslim/dygraph/prune/unstructured_pruner.py

@@ -304,6 +304,7 @@ class GMPUnstructuredPruner(UnstructuredPruner):
         self.cur_iteration = configs.get('resume_iteration')
 
         assert self.pruning_iterations / self.pruning_steps > 10, "To guarantee the performance of GMP pruner, pruning iterations must be larger than pruning steps by a margin."
+        self._need_prune_once = False
         self._prepare_training_hyper_parameters()
 
     def _prepare_training_hyper_parameters(self):
@@ -330,6 +331,7 @@ class GMPUnstructuredPruner(UnstructuredPruner):
 
         # pop out used ratios to resume training
         for i in range(self.cur_iteration):
+            self._need_prune_once = True
             if len(self.
                    ratios_stack) > 0 and i % self.ratio_increment_period == 0:
                 self.ratio = self.ratios_stack.pop()
@@ -344,7 +346,8 @@ class GMPUnstructuredPruner(UnstructuredPruner):
 
         # Update the threshold and masks only when a new ratio has been set.
         # This condition check would save training time dramatically since we only update the threshold by the triger of self.ratio_increment_period.
-        if ori_ratio != self.ratio:
+        if ori_ratio != self.ratio or self._need_prune_once:
             self.update_threshold()
             self._update_masks()
+            self._need_prune_once = False
         self.cur_iteration += 1

paddleslim/prune/unstructured_pruner.py

@@ -349,6 +349,7 @@ class GMPUnstructuredPruner(UnstructuredPruner):
         self.cur_iteration = configs.get('resume_iteration')
 
         assert self.pruning_iterations / self.pruning_steps > 10, "To guarantee the performance of GMP pruner, pruning iterations must be larger than pruning steps by a margin."
+        self._need_prune_once = False
         self._prepare_training_hyper_parameters()
 
     def _prepare_training_hyper_parameters(self):
@@ -375,6 +376,7 @@ class GMPUnstructuredPruner(UnstructuredPruner):
 
         # pop out used ratios to resume training
         for i in range(self.cur_iteration):
+            self._need_prune_once = True
             if len(self.
                    ratios_stack) > 0 and i % self.ratio_increment_period == 0:
                 self.ratio = self.ratios_stack.pop()
@@ -392,7 +394,8 @@ class GMPUnstructuredPruner(UnstructuredPruner):
 
         # Update the threshold and masks only when a new ratio has been set.
         # This condition check would save training time dramatically since we only update the threshold by the triger of self.ratio_increment_period.
-        if ori_ratio != self.ratio:
+        if ori_ratio != self.ratio or self._need_prune_once:
             self.update_threshold()
             self._update_masks()
+            self._need_prune_once = False
         self.cur_iteration += 1