!10274 details quant_config notes

From: @yuchaojie
Reviewed-by: @liangchenghui,@linqingke
Signed-off-by: @liangchenghui

mindspore/nn/layer/quant.py

@@ -362,8 +362,9 @@ class Conv2dBnFoldQuantOneConv(Cell):
         var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
             variance vector. Default: 'ones'.
         fake (bool): Whether Conv2dBnFoldQuant Cell adds FakeQuantWithMinMaxObserver. Default: True.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
     Inputs:
@@ -576,8 +577,9 @@ class Conv2dBnFoldQuant(Cell):
         var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
             variance vector. Default: 'ones'.
         fake (bool): Whether Conv2dBnFoldQuant Cell adds FakeQuantWithMinMaxObserver. Default: True.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
         freeze_bn (int): The quantization freeze BatchNormal op is according to the global step. Default: 100000.
 
@@ -753,8 +755,9 @@ class Conv2dBnWithoutFoldQuant(Cell):
         weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
             Default: 'normal'.
         bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Default: 'zeros'.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
     Inputs:
@@ -865,8 +868,9 @@ class Conv2dQuant(Cell):
         weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel.
             Default: 'normal'.
         bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the bias vector. Default: 'zeros'.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
     Inputs:
@@ -970,8 +974,9 @@ class DenseQuant(Cell):
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
         activation (Union[str, Cell, Primitive]): The regularization function applied to the output of the layer,
             eg. 'relu'. Default: None.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
     Inputs:
@@ -1080,8 +1085,9 @@ class ActQuant(_QuantActivation):
         ema (bool): The exponential Moving Average algorithm updates min and max. Default: False.
         ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
         fake_before (bool): Whether add fake quantized operation before activation. Default: False.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
     Inputs:
@@ -1144,8 +1150,9 @@ class TensorAddQuant(Cell):
 
     Args:
         ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
     Inputs:
@@ -1195,8 +1202,9 @@ class MulQuant(Cell):
 
     Args:
         ema_decay (float): Exponential Moving Average algorithm parameter. Default: 0.999.
-        quant_config (QuantConfig): Configs the oberser types and quant configs of weight and activation. Default:
-            both set to default FakeQuantWithMinMaxObserver.
+        quant_config (QuantConfig): Configs the oberser types and quant settings of weight and activation. Can be
+            generated by compression.quant.create_quant_config method.
+            Default: both set to default FakeQuantWithMinMaxObserver.
         quant_dtype (QuantDtype): Specifies the FakeQuant datatype. Default: QuantDtype.INT8.
 
     Inputs: