display quant delay in ir when run with ascend

mindspore/nn/layer/quant.py

@@ -372,7 +372,8 @@ class FakeQuantWithMinMax(Cell):
         if self.is_ascend:
             self.fake_quant_train = quant_fun(num_bits=self.num_bits,
                                               symmetric=self.symmetric,
-                                              narrow_range=self.narrow_range)
+                                              narrow_range=self.narrow_range,
+                                              quant_delay=self.quant_delay)
             self.fake_quant_infer = self.fake_quant_train
         else:
             quant_fun = partial(quant_fun,
@@ -679,28 +680,40 @@ class Conv2dBnWithoutFoldQuant(Cell):
         self.group = group
         self.quant_delay = quant_delay
 
-        weight_shape = [out_channels, in_channels // group, *self.kernel_size]
-        self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
-
         self.bias_add = P.BiasAdd()
         if check_bool(has_bias):
             self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
         else:
             self.bias = None
-
-        self.conv = P.Conv2D(out_channel=self.out_channels,
-                             kernel_size=self.kernel_size,
-                             mode=1,
-                             pad_mode=self.pad_mode,
-                             pad=self.padding,
-                             stride=self.stride,
-                             dilation=self.dilation,
-                             group=self.group)
+        # initialize convolution op and Parameter
+        if context.get_context('device_target') == "Ascend" and group > 1:
+            validator.check_integer('group', group, in_channels, Rel.EQ)
+            validator.check_integer('group', group, out_channels, Rel.EQ)
+            self.conv = P.DepthwiseConv2dNative(channel_multiplier=1,
+                                                kernel_size=self.kernel_size,
+                                                pad_mode=pad_mode,
+                                                pad=padding,
+                                                stride=self.stride,
+                                                dilation=self.dilation)
+            weight_shape = [1, in_channels, *self.kernel_size]
+            channel_axis = 1
+        else:
+            self.conv = P.Conv2D(out_channel=self.out_channels,
+                                 kernel_size=self.kernel_size,
+                                 mode=1,
+                                 pad_mode=self.pad_mode,
+                                 pad=self.padding,
+                                 stride=self.stride,
+                                 dilation=self.dilation,
+                                 group=self.group)
+            weight_shape = [out_channels, in_channels // group, *self.kernel_size]
+            channel_axis = 0
+        self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
         self.fake_quant_weight = FakeQuantWithMinMax(min_init=-6,
                                                      max_init=6,
                                                      ema=False,
                                                      per_channel=per_channel,
-                                                     channel_axis=0,
+                                                     channel_axis=channel_axis,
                                                      num_channels=out_channels,
                                                      num_bits=num_bits,
                                                      symmetric=symmetric,
@@ -1009,6 +1022,7 @@ class ActQuant(_QuantActivation):
     def get_origin(self):
         return self.act
 
+
 class LeakyReLUQuant(_QuantActivation):
     r"""
     LeakyReLUQuant activation function. Add Fake Quant OP after HSwish OP.
@@ -1078,7 +1092,6 @@ class LeakyReLUQuant(_QuantActivation):
         return self.act
 
 
-
 class HSwishQuant(_QuantActivation):
     r"""
     HSwishQuant activation function. Add Fake Quant OP after HSwish OP.