Format

src/targets/gpu/jit/concat.cpp

@@ -63,9 +63,8 @@ struct concat_compiler : compiler<concat_compiler>
 
     static std::size_t get_min_elements(const std::vector<shape>& inputs)
     {
-        auto it = std::min_element(inputs.begin(), inputs.end(), by(std::less<>{}, [](auto s) {
-            return s.elements();
-        }));
+        auto it = std::min_element(
+            inputs.begin(), inputs.end(), by(std::less<>{}, [](auto s) { return s.elements(); }));
         return it->elements();
     }
 
@@ -80,15 +79,13 @@ struct concat_compiler : compiler<concat_compiler>
         auto vec               = vectorize::elements(axis, options.virtual_inputs);
         options.kernel_name    = v.get("kernel", "concat_kernel");
         options.set_launch_params(
-            v,
-            compute_global_for(ctx,
-                               get_min_elements(options.inputs) / vec.size, 256));
+            v, compute_global_for(ctx, get_min_elements(options.inputs) / vec.size, 256));
         auto src = interpolate_string(concat_kernel,
                                       {{"kernel", options.kernel_name},
                                        {"params", enum_params(inputs.size(), "void * private_p")},
                                        {"args", enum_params(inputs.size(), "private_p")},
                                        {"transformers", make_transformer_args(vec)},
-                                        {"axis", v.at("axis").to<std::string>()}});
+                                       {"axis", v.at("axis").to<std::string>()}});
         return compile_hip_code_object(src, options);
     }
 

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

@@ -31,35 +31,33 @@
 
 namespace migraphx {
 
-template<index_int Axis, class Output, class Input, class Start>
+template <index_int Axis, class Output, class Input, class Start>
 constexpr auto concat_slice(Output out, Input, Start)
 {
-    constexpr auto lens = get_shape_c<Input>{}.lens;
+    constexpr auto lens    = get_shape_c<Input>{}.lens;
     constexpr auto strides = get_shape_c<Output>{}.strides;
-    constexpr auto offset = return_c([] {
+    constexpr auto offset  = return_c([] {
         constexpr auto output_shape = get_shape_c<Output>{};
         return Start{} * output_shape.strides[Axis];
     });
-    constexpr auto s = make_shape(lens, strides);
+    constexpr auto s       = make_shape(lens, strides);
     return make_tensor_view(&out[offset], s);
 }
 
-template<index_int Axis, class Input>
+template <index_int Axis, class Input>
 constexpr auto concat_ends(Input)
 {
     constexpr auto lens = get_shape_c<Input>{}.lens;
     return _c<lens[Axis]>;
 }
 
-template<index_int Axis, class Output, class... Inputs>
+template <index_int Axis, class Output, class... Inputs>
 __device__ void concat(Output output, Inputs... inputs)
 {
     auto idx = make_index();
     fold([&](auto start, auto input) {
         auto y = concat_slice<Axis>(output, input, start);
-        idx.global_stride(input.get_shape().elements(), [&](auto i) {
-            y[i] = input[i];
-        });
+        idx.global_stride(input.get_shape().elements(), [&](auto i) { y[i] = input[i]; });
         return start + concat_ends<Axis>(input);
     })(_c<0>, inputs...);
 }