Format

src/targets/gpu/jit/reduce.cpp

@@ -101,7 +101,7 @@ struct reduce_compiler : compiler<reduce_compiler>
         auto faxis = find_fast_axis({inputs.front()});
         vectorize vec{};
         // Vectorize if the axis is a reduction axis
-        if (inputs.back().lens()[faxis] == 1)
+        if(inputs.back().lens()[faxis] == 1)
         {
             vec = vectorize::elements(faxis, inputs);
         }
@@ -111,11 +111,14 @@ struct reduce_compiler : compiler<reduce_compiler>
         {
             auto block_size = compute_block_size(reduce_elements, 256);
             options.set_launch_params(
-                v, compute_global_for(ctx, inputs.back().elements() * block_size / vec.size, 256), block_size);
+                v,
+                compute_global_for(ctx, inputs.back().elements() * block_size / vec.size, 256),
+                block_size);
         }
         else if(algo == "lane")
         {
-            options.set_launch_params(v, compute_global_for(ctx, inputs.back().elements() / vec.size, 256));
+            options.set_launch_params(
+                v, compute_global_for(ctx, inputs.back().elements() / vec.size, 256));
         }
         else
         {
@@ -124,7 +127,7 @@ struct reduce_compiler : compiler<reduce_compiler>
         options.inputs         = inputs;
         options.output         = inputs.back();
         options.virtual_inputs = reduce_dims(inputs);
-        options.kernel_name = "reduce_kernel";
+        options.kernel_name    = "reduce_kernel";
         std::string identity   = "[](auto x) { return x; }";
         auto src               = interpolate_string(simple_reduce_kernel,
                                       {{"reduction", v.at("reduction").to<std::string>()},

src/targets/gpu/kernels/include/migraphx/kernels/reduce.hpp

@@ -163,9 +163,12 @@ struct block
         __device__ auto reduce(Op op, T init, Read read) const
         {
             return sliced(slicer, [=](auto x, auto... xs) {
-                return vec_reduce(block_reduce(idx, op, init, x.get_shape().elements(), [&](auto j) {
-                    return read(x[j], xs[j]...);
-                }), op);
+                return vec_reduce(block_reduce(idx,
+                                               op,
+                                               init,
+                                               x.get_shape().elements(),
+                                               [&](auto j) { return read(x[j], xs[j]...); }),
+                                  op);
             });
         }
 

src/targets/gpu/kernels/include/migraphx/kernels/vec.hpp

@@ -146,7 +146,7 @@ constexpr auto vec_packed_transform(Ts... xs)
     };
 }
 
-template<class T, class Op>
+template <class T, class Op>
 constexpr auto vec_reduce(T x, Op op)
 {
     if constexpr(vec_size<T>() < 2)
@@ -155,7 +155,7 @@ constexpr auto vec_reduce(T x, Op op)
     {
         vec_type<T> result;
         for(int i = 1; i < vec_size<T>(); i++)
-            result = op(result[i-1], result[i]);
+            result = op(result[i - 1], result[i]);
         return result;
     }
 }