to #24765941 fix lint

src/relay/qnn/op/add.cc

@@ -71,7 +71,7 @@ Expr QnnAddCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
     float real_lhs_scale_val = lhs_scale_val / twice_max_input_scale * (1 << 20);
     float real_rhs_scale_val = rhs_scale_val / twice_max_input_scale * (1 << 20);
     float real_out_scale_val = twice_max_input_scale / ((1 << 20) * out_scale_val);
-  
+
     auto real_lhs_scale = MakeConstantScalar<float>(
         DataType::Float(32), real_lhs_scale_val);
     auto real_rhs_scale = MakeConstantScalar<float>(
@@ -97,14 +97,14 @@ Expr QnnAddCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
                         itmd_out_scale, output_zero_point,
                         DataType::Int(32), rounding);
   } else {
-    // FIXME (anijain2305) - The lowering can be further optimized. Instead of inserting requantize in
-    // the start, we can insert requantize at the end if both input tensors have same qnn params. In
-    // that case, we can first add the tensors, subtract the zero point, and requantize at the end.
-    // This can be done in future.
-  
+    // FIXME (anijain2305) - The lowering can be further optimized. Instead of inserting requantize
+    // in the start, we can insert requantize at the end if both input tensors have same qnn params.
+    // In that case, we can first add the tensors, subtract the zero point, and requantize at the
+    // end. This can be done in future.
+
     // Since the input qnn params can be different than output qnn params, we first requantize the
-    // input tensors to the output qnn params. Then we call relay.add on the requantized inputs. This
-    // addition results in extra addition of the output zero point. We futher subtract the zero
+    // input tensors to the output qnn params. Then we call relay.add on the requantized inputs.
+    // This addition results in extra addition of the output zero point. We futher subtract the zero
     // point. The whole process can be represented using following equations
     //
     //          scale_c * (Q_c - zp_c) = scale_a * (Q_a - zp_a) + scale_b * (Q_b - zp_b)
@@ -116,7 +116,7 @@ Expr QnnAddCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
     // Comparing the LHS and RHS, it results in
     //          Q_c = Q_a' + Q_b' - zp_c
     // The add op is done in int32 precision.
-  
+
     // Requantize LHS if necessary.
     auto requantized_lhs = lhs;
     if (!IsEqualScalar(lhs_scale, output_scale) ||
@@ -126,7 +126,7 @@ Expr QnnAddCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
     } else {
       requantized_lhs = Cast(requantized_lhs, DataType::Int(32));
     }
-  
+
     // Requantize RHS if necessary.
     auto requantized_rhs = rhs;
     if (!IsEqualScalar(rhs_scale, output_scale) ||
@@ -138,28 +138,27 @@ Expr QnnAddCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
     }
 
     output = Add(requantized_lhs, requantized_rhs);
-  
+
     // Subtract zero point.
     auto zero_scalar = MakeConstantScalar(DataType::Int(32), 0);
     if (!IsEqualScalar(output_zero_point, zero_scalar)) {
       output = Subtract(output, output_zero_point);
     }
   }
-  
+
   // Go back to lower precision.
   auto q_min = GetQmin(input_dtype);
   auto q_max = GetQmax(input_dtype);
   output = Clip(output, q_min, q_max);
   return Cast(output, input_dtype);
-
 }
 
 Expr MakeQnnAdd(Expr lhs, Expr rhs, Expr lhs_scale, Expr lhs_zero_point,
                 Expr rhs_scale, Expr rhs_zero_point, Expr output_scale,
                 Expr output_zero_point, std::string rounding) {
   auto attrs = make_object<QnnAddAttrs>();
   attrs->rounding = std::move(rounding);
- 
+
   static const Op& op = Op::Get("qnn.add");
   return CallNode::make(op, {lhs, rhs,
                              lhs_scale, lhs_zero_point,

topi/include/topi/nn/pooling.h

@@ -578,7 +578,7 @@ inline Tensor adaptive_pool_impl(const Tensor& x,
         Array<PrimExpr> indices;
         Array<tir::IterVar> reduce_axes;
         std::tie(indices, reduce_axes) = get_iter_vars(output, false);
-  
+
         PrimExpr divide_factor = tvm::cast(x->dtype, 1);
         for (size_t i = 0; i < n_dim; ++i) {
           divide_factor *= tvm::cast(x->dtype, reduce_axes[i]->dom->extent);
@@ -592,12 +592,12 @@ inline Tensor adaptive_pool_impl(const Tensor& x,
         Array<PrimExpr> indices;
         Array<tir::IterVar> reduce_axes;
         std::tie(indices, reduce_axes) = get_iter_vars(output, false);
-  
+
         PrimExpr divide_factor = tvm::cast(x->dtype, 1);
         for (size_t i = 0; i < n_dim; ++i) {
           divide_factor *= tvm::cast(x->dtype, reduce_axes[i]->dom->extent);
         }
-  
+
         return div(pool_sum(indices), divide_factor);
       }, "tensor", kElementWise);
     }