[QNN] Doc fix on convolution and dequantize (#4799)

* QNN doc fix on conv and dequantize

* fix param name in tflite frontend

* make different fix

diff --git a/python/tvm/relay/qnn/op/qnn.py b/python/tvm/relay/qnn/op/qnn.py
index 739ee97..f76d7b3 100644 (file)
--- a/python/tvm/relay/qnn/op/qnn.py
+++ b/python/tvm/relay/qnn/op/qnn.py
@@ -131,9 +131,9 @@ def dequantize(data,
     data : tvm.relay.Expr
         The input tensor to be dequantized. Can be of type [int8, uint8].
     input_zero_point : tvm.relay.Expr
-        The output zero_point.
+        The input zero_point.
     input_scale : tvm.relay.Expr
-        The output scale.
+        The input scale.
     Returns
     -------
     result : tvm.relay.Expr
@@ -201,12 +201,12 @@ def conv2d(data,
            kernel_zero_point,
            input_scale,
            kernel_scale,
+           kernel_size,
            strides=(1, 1),
            padding=(0, 0),
            dilation=(1, 1),
            groups=1,
            channels=None,
-           kernel_size=None,
            data_layout="NCHW",
            kernel_layout="OIHW",
            out_layout="",
@@ -244,6 +244,9 @@ def conv2d(data,
            needed in the pass pipeline after qnn.conv2d is lowered to the
            sequence of steps as in nn.conv2d. See also input_scale in Requantize.
 
+    kernel_size : tuple of int
+        The spatial width and height of the convolution kernel.
+
     strides : tuple of int, optional
         The strides of convolution.
 
@@ -259,9 +262,6 @@ def conv2d(data,
     channels : int, optional
         Number of output channels of this convolution.
 
-    kernel_size : tuple of int, optional
-        The spatial of the convolution kernel.
-
     data_layout : str, optional
         Layout of the input.