From 06175aeaf62b460d46aa619bde2c12e8a40d468e Mon Sep 17 00:00:00 2001
From: minghaoBD <79566150+minghaoBD@users.noreply.github.com>
Date: Wed, 22 Dec 2021 21:31:50 +0800
Subject: [PATCH] Resume training (#958)

---
 paddleslim/dygraph/prune/unstructured_pruner.py | 5 ++++-
 paddleslim/prune/unstructured_pruner.py         | 5 ++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/paddleslim/dygraph/prune/unstructured_pruner.py b/paddleslim/dygraph/prune/unstructured_pruner.py
index 4e7ef182..deaa7e3a 100644
--- a/paddleslim/dygraph/prune/unstructured_pruner.py
+++ b/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
diff --git a/paddleslim/prune/unstructured_pruner.py b/paddleslim/prune/unstructured_pruner.py
index 2f3c840f..ca2fb2fd 100644
--- a/paddleslim/prune/unstructured_pruner.py
+++ b/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
-- 
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