Weight quantization support channel_wise_abs_max method to achieve higher accuracy (#23629)

* Weight quantization support channel_wise_abs_max method to achieve higher accuracy

python/paddle/fluid/contrib/slim/tests/test_weight_quantization_mobilenetv1.py

@@ -43,7 +43,8 @@ class TestWeightQuantization(unittest.TestCase):
             os.system(cmd)
 
     def run_test(self, model_name, model_data_url, model_data_md5, weight_bits,
-                 quantizable_op_type, threshold_rate):
+                 quantizable_op_type, weight_quantize_type, generate_test_model,
+                 threshold_rate):
 
         model_dir = self.download_model(model_name, model_data_url,
                                         model_data_md5)
@@ -57,6 +58,8 @@ class TestWeightQuantization(unittest.TestCase):
             save_model_dir=save_model_dir,
             weight_bits=weight_bits,
             quantizable_op_type=quantizable_op_type,
+            weight_quantize_type=weight_quantize_type,
+            generate_test_model=generate_test_model,
             threshold_rate=threshold_rate)
         print("finish weight quantization for " + model_name + "\n")
 
@@ -72,19 +75,45 @@ class TestWeightQuantizationMobilenetv1(TestWeightQuantization):
     model_data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz"
     model_data_md5 = "13892b0716d26443a8cdea15b3c6438b"
 
-    def test_weight_quantization_mobilenetv1_8bit(self):
+    def test_weight_quantization_mobilenetv1_8bit_abs_max(self):
         weight_bits = 8
         quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
+        weight_quantize_type = "abs_max"
+        generate_test_model = True
         threshold_rate = 0.0
         self.run_test(self.model_name, self.model_data_url, self.model_data_md5,
-                      weight_bits, quantizable_op_type, threshold_rate)
+                      weight_bits, quantizable_op_type, weight_quantize_type,
+                      generate_test_model, threshold_rate)
 
-    def test_weight_quantization_mobilenetv1_16bit(self):
+    def test_weight_quantization_mobilenetv1_8bit_channel_wise_abs_max(self):
+        weight_bits = 8
+        quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
+        weight_quantize_type = "channel_wise_abs_max"
+        generate_test_model = True
+        threshold_rate = 0.0
+        self.run_test(self.model_name, self.model_data_url, self.model_data_md5,
+                      weight_bits, quantizable_op_type, weight_quantize_type,
+                      generate_test_model, threshold_rate)
+
+    def test_weight_quantization_mobilenetv1_16bit_abs_max(self):
+        weight_bits = 16
+        quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
+        weight_quantize_type = "abs_max"
+        generate_test_model = False
+        threshold_rate = 1e-9
+        self.run_test(self.model_name, self.model_data_url, self.model_data_md5,
+                      weight_bits, quantizable_op_type, weight_quantize_type,
+                      generate_test_model, threshold_rate)
+
+    def test_weight_quantization_mobilenetv1_16bit_channel_wise_abs_max(self):
         weight_bits = 16
         quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
+        weight_quantize_type = "channel_wise_abs_max"
+        generate_test_model = False
         threshold_rate = 1e-9
         self.run_test(self.model_name, self.model_data_url, self.model_data_md5,
-                      weight_bits, quantizable_op_type, threshold_rate)
+                      weight_bits, quantizable_op_type, weight_quantize_type,
+                      generate_test_model, threshold_rate)
 
 
 if __name__ == '__main__':