support fuse Conv and BN before QAT (#1146)

docs/zh_cn/api_cn/dygraph/quanter/qat.rst

@@ -80,6 +80,9 @@ QAT
 
         # 需要量化的算子类型
         'quantizable_layer_type': ['Conv2D', 'Linear'],
+
+        # 是否需要融合Conv + BN
+        'fuse_conv_bn': False,
     }
 ..
 

paddleslim/dygraph/quant/qat.py

@@ -56,6 +56,8 @@ _quant_config_default = {
     'moving_rate': 0.9,
     # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
     'quantizable_layer_type': ['Conv2D', 'Linear'],
+    # whether fuse conv and bn before QAT
+    'fuse_conv_bn': False,
 }
 
 
@@ -141,7 +143,8 @@ class PACT(paddle.nn.Layer):
 
 class QAT(object):
     """
-    Quant Aware Training(QAT): Add the fake quant logic for given quantizable layers, namely add the quant_dequant computational logic both for activation inputs and weight inputs.
+    Quant Aware Training(QAT): Add the fake quant logic for given quantizable layers,
+    namely add the quant_dequant computational logic both for activation inputs and weight inputs.
     """
 
     def __init__(self,
@@ -190,22 +193,42 @@ class QAT(object):
         self.act_preprocess = PACT if self.config[
             'activation_preprocess_type'] == 'PACT' else None
 
-        self.weight_preprocess = weight_preprocess if weight_preprocess is not None else self.weight_preprocess
-        self.act_preprocess = act_preprocess if act_preprocess is not None else self.act_preprocess
+        self.weight_preprocess = weight_preprocess if weight_preprocess is not None \
+            else self.weight_preprocess
+        self.act_preprocess = act_preprocess if act_preprocess is not None \
+            else self.act_preprocess
         self.weight_quantize = weight_quantize
         self.act_quantize = act_quantize
 
-        self.imperative_qat = ImperativeQuantAware(
-            weight_bits=self.config['weight_bits'],
-            activation_bits=self.config['activation_bits'],
-            weight_quantize_type=self.config['weight_quantize_type'],
-            activation_quantize_type=self.config['activation_quantize_type'],
-            moving_rate=self.config['moving_rate'],
-            quantizable_layer_type=self.config['quantizable_layer_type'],
-            weight_preprocess_layer=self.weight_preprocess,
-            act_preprocess_layer=self.act_preprocess,
-            weight_quantize_layer=self.weight_quantize,
-            act_quantize_layer=self.act_quantize)
+        # TODO: remove try-except when the version is stable
+        try:
+            self.imperative_qat = ImperativeQuantAware(
+                weight_bits=self.config['weight_bits'],
+                activation_bits=self.config['activation_bits'],
+                weight_quantize_type=self.config['weight_quantize_type'],
+                activation_quantize_type=self.config[
+                    'activation_quantize_type'],
+                moving_rate=self.config['moving_rate'],
+                quantizable_layer_type=self.config['quantizable_layer_type'],
+                fuse_conv_bn=self.config[
+                    'fuse_conv_bn'],  # support Paddle > 2.3
+                weight_preprocess_layer=self.weight_preprocess,
+                act_preprocess_layer=self.act_preprocess,
+                weight_quantize_layer=self.weight_quantize,
+                act_quantize_layer=self.act_quantize)
+        except:
+            self.imperative_qat = ImperativeQuantAware(
+                weight_bits=self.config['weight_bits'],
+                activation_bits=self.config['activation_bits'],
+                weight_quantize_type=self.config['weight_quantize_type'],
+                activation_quantize_type=self.config[
+                    'activation_quantize_type'],
+                moving_rate=self.config['moving_rate'],
+                quantizable_layer_type=self.config['quantizable_layer_type'],
+                weight_preprocess_layer=self.weight_preprocess,
+                act_preprocess_layer=self.act_preprocess,
+                weight_quantize_layer=self.weight_quantize,
+                act_quantize_layer=self.act_quantize)
 
     def quantize(self, model, inplace=True):
         """
@@ -224,11 +247,13 @@ class QAT(object):
         self._model = copy.deepcopy(model)
 
         if inplace:
-            self.imperative_qat.quantize(model)
-            quant_model = model
+            quantize_model = self.imperative_qat.quantize(model)
+            quant_model = quantize_model if quantize_model is not None else model
         else:
             quant_model = copy.deepcopy(model)
-            self.imperative_qat.quantize(quant_model)
+            quantize_model = self.imperative_qat.quantize(quant_model)
+            if quantize_model is not None:
+                quant_model = quantize_model
 
         return quant_model