respond to review comments

Author: Matthew

include/tvm/ir/affine_type.h

@@ -71,7 +71,7 @@ class TensorAffineTypeNode : public AffineTypeNode {
   RelayExpr zero_point;
   /*! \brief The data type of this type */
   DataType dtype;
-  /*! \brief The data type of this type */
+  /*! \brief The axis for per-channel quantization */
   int axis;
 
   void VisitAttrs(tvm::AttrVisitor* v) {

python/tvm/relay/transform/fake_quantization_to_integer.py

@@ -255,12 +255,12 @@ def relu(expr, type_map):
     arg = expr.args[0]
     t = type_map[arg]
     scale_shape = infer_shape(t.scale)
-    zero = relay.const(0, dtype="float32")
+    z_p = t.zero_point
     if len(scale_shape) > 0 and scale_shape[0] > 1:
         b_shape = [1] * len(infer_shape(arg))
         b_shape[t.axis] = -1
-        zero = relay.op.reshape(relay.op.broadcast_to(zero, scale_shape), b_shape)
-    zero = relay.qnn.op.quantize(zero, t.scale, t.zero_point, t.axis, t.dtype)
+        z_p = relay.op.reshape(relay.op.broadcast_to(z_p, scale_shape), b_shape)
+    zero = relay.op.cast(z_p, t.dtype)
     return [relay.op.maximum(arg, fold_constant(zero)), t]