From a8cfdd3adcdabcd4b694da3b199a78f0511d3d5e Mon Sep 17 00:00:00 2001
From: Guanghua Yu <742925032@qq.com>
Date: Tue, 17 May 2022 19:28:31 +0800
Subject: [PATCH] fix PACT quant_aware abnormal training accuracy (#1111)

---
 paddleslim/quant/quanter.py | 29 +++++++++++++++++++++++++++--
 1 file changed, 27 insertions(+), 2 deletions(-)

diff --git a/paddleslim/quant/quanter.py b/paddleslim/quant/quanter.py
index 9668ed34..2faa9a07 100755
--- a/paddleslim/quant/quanter.py
+++ b/paddleslim/quant/quanter.py
@@ -95,6 +95,31 @@ _quant_config_default = {
 }
 
 
+# TODO: Hard-code, remove it when Paddle 2.3.1
+class OutScaleForTrainingPassV2(OutScaleForTrainingPass):
+    def __init__(self, scope=None, place=None, moving_rate=0.9):
+        OutScaleForTrainingPass.__init__(
+            self, scope=scope, place=place, moving_rate=moving_rate)
+
+    def _scale_name(self, var_name):
+        """
+        Return the scale name for the var named `var_name`.
+        """
+        return "%s@scale" % (var_name)
+
+
+# TODO: Hard-code, remove it when Paddle 2.3.1
+class OutScaleForInferencePassV2(OutScaleForInferencePass):
+    def __init__(self, scope=None):
+        OutScaleForInferencePass.__init__(self, scope=scope)
+
+    def _scale_name(self, var_name):
+        """
+        Return the scale name for the var named `var_name`.
+        """
+        return "%s@scale" % (var_name)
+
+
 def load_dict():
     with open(VARS_MAPPING_TABLE, 'r') as file:
         data = file.read()
@@ -298,7 +323,7 @@ def quant_aware(program,
             quantizable_op_type=quant_dequant_ops)
         quant_dequant_pass.apply(main_graph)
 
-    out_scale_training_pass = OutScaleForTrainingPass(
+    out_scale_training_pass = OutScaleForTrainingPassV2(
         scope=scope, place=place, moving_rate=config['moving_rate'])
     out_scale_training_pass.apply(main_graph)
 
@@ -509,7 +534,7 @@ def convert(program,
         quant_weight_pass = QuantWeightPass(scope, place)
         quant_weight_pass.apply(test_graph)
     else:
-        out_scale_infer_pass = OutScaleForInferencePass(scope=scope)
+        out_scale_infer_pass = OutScaleForInferencePassV2(scope=scope)
         out_scale_infer_pass.apply(test_graph)
         # Freeze the graph after training by adjusting the quantize
         # operators' order for the inference.
-- 
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