formatting fix

include/ck/tensor_operation/gpu/device/impl/codegen_device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp

@@ -453,14 +453,12 @@ struct CodegenDeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
     // Use appropriate gridwise gemm
     using GridwiseGemm =
         ck::conditional_t<isMultiA || isMultiB,
-                           GridwiseGemmMultipleABD_xdl_cshuffle<GridwiseGemmTemplateParameters>,
-                           GridwiseGemmMultipleD_xdl_cshuffle<GridwiseGemmTemplateParameters>>;
+                          GridwiseGemmMultipleABD_xdl_cshuffle<GridwiseGemmTemplateParameters>,
+                          GridwiseGemmMultipleD_xdl_cshuffle<GridwiseGemmTemplateParameters>>;
 
     // If ADataTypes or BDataTypes is tuple, user has to pass ck::Array with pointers.
-    using APointers =
-        ck::conditional_t<isMultiA, ck::Array<const void*, NumATensor>&, const void*>;
-    using BPointers =
-        ck::conditional_t<isMultiB, ck::Array<const void*, NumBTensor>&, const void*>;
+    using APointers = ck::conditional_t<isMultiA, ck::Array<const void*, NumATensor>&, const void*>;
+    using BPointers = ck::conditional_t<isMultiB, ck::Array<const void*, NumBTensor>&, const void*>;
     // Use Tuple for the both cases for GridPointer to initialize it in Argument constructor (not
     // in initializer list what is required for single const pointer).
     using AGridPointer = remove_cvref_t<

include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp

@@ -340,8 +340,8 @@ struct Bilinear
     };
 
     template <>
-    __host__ __device__ constexpr void operator()<int8_t, int32_t, int8_t>(
-        int8_t& y, const int32_t& x0, const int8_t& x1) const
+    __host__ __device__ constexpr void
+    operator()<int8_t, int32_t, int8_t>(int8_t& y, const int32_t& x0, const int8_t& x1) const
     {
         y = type_convert<int8_t>(alpha_ * type_convert<float>(x0) +
                                  beta_ * type_convert<float>(x1));