Formatting

src/targets/gpu/jit/ck_gemm.cpp

@@ -38,7 +38,6 @@
 #include <migraphx/env.hpp>
 #include <migraphx/file_buffer.hpp>
 
-
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
@@ -64,7 +63,6 @@ MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_TUNING);
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_TUNING_VALUE);
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_CK_DEBUG);
 
-
 // NOLINTNEXTLINE
 static const char* const ck_gemm_kernel = R"__migraphx__(
 #include <args.hpp>
@@ -291,9 +289,9 @@ struct ck_gemm_compiler : compiler<ck_gemm_compiler>
 
     operation compile_op(context& /* ctx */, const std::vector<shape>& inputs, const value& v) const
     {
-        auto a_shape = inputs[0];
-        auto b_shape = inputs[1];
-        auto c_shape = inputs.back();
+        auto a_shape      = inputs[0];
+        auto b_shape      = inputs[1];
+        auto c_shape      = inputs.back();
         auto tuning_value = get_tuning_for({a_shape, b_shape, c_shape});
 
         auto rank           = a_shape.lens().size();
@@ -307,15 +305,17 @@ struct ck_gemm_compiler : compiler<ck_gemm_compiler>
         auto k           = a_shape.lens().back();
 
         const auto numDTensors = inputs.size() - 3;
-        const bool transA = transposed_matrix(a_shape);
-        const bool transB = transposed_matrix(b_shape);
-        const bool transCDE = transposed_matrix(c_shape);
-        const auto a_type = get_type(a_shape);
-        const auto b_type = get_type(b_shape);
-        const auto cde_type = ck_tuple(inputs.begin() + 2, inputs.end() - 1, &get_type);//get_type(c_shape);
+        const bool transA      = transposed_matrix(a_shape);
+        const bool transB      = transposed_matrix(b_shape);
+        const bool transCDE    = transposed_matrix(c_shape);
+        const auto a_type      = get_type(a_shape);
+        const auto b_type      = get_type(b_shape);
+        const auto cde_type =
+            ck_tuple(inputs.begin() + 2, inputs.end() - 1, &get_type); // get_type(c_shape);
         const auto cde_layout = ck_tuple(inputs.begin() + 2, inputs.end() - 1, &get_layout);
 
-        std::string ck_passthrough = "ck_passthrough";//"ck::tensor_operation::element_wise::PassThrough";
+        std::string ck_passthrough =
+            "ck_passthrough"; //"ck::tensor_operation::element_wise::PassThrough";
         std::string cde_op = ck_passthrough;
         assert(inputs.size() < 4 or v.contains("post"));
         if(v.contains("post"))
@@ -323,16 +323,28 @@ struct ck_gemm_compiler : compiler<ck_gemm_compiler>
             cde_op = v.at("post").to<std::string>();
         }
 
-        auto problem = ck::tensor_operation::device::instance::Problem{static_cast<ck::index_t>(m), static_cast<ck::index_t>(n), static_cast<ck::index_t>(k), static_cast<ck::index_t>(numDTensors), static_cast<ck::index_t>(tuning_value),  
-                                transA, transB, transCDE,
-                                a_type, b_type, cde_type,
-                                ck_passthrough, ck_passthrough, cde_op, cde_layout};
-        const auto solution = problem.GetSolution();
+        auto problem =
+            ck::tensor_operation::device::instance::Problem{static_cast<ck::index_t>(m),
+                                                            static_cast<ck::index_t>(n),
+                                                            static_cast<ck::index_t>(k),
+                                                            static_cast<ck::index_t>(numDTensors),
+                                                            static_cast<ck::index_t>(tuning_value),
+                                                            transA,
+                                                            transB,
+                                                            transCDE,
+                                                            a_type,
+                                                            b_type,
+                                                            cde_type,
+                                                            ck_passthrough,
+                                                            ck_passthrough,
+                                                            cde_op,
+                                                            cde_layout};
+        const auto solution   = problem.GetSolution();
         auto blocks_per_batch = problem.GetGridSize();
-        auto block_size = problem.GetBlockSize();
+        auto block_size       = problem.GetBlockSize();
 
         hip_compile_options options;
-        auto grid_size  = can_fold_batch ? blocks_per_batch : batch_count * blocks_per_batch;
+        auto grid_size = can_fold_batch ? blocks_per_batch : batch_count * blocks_per_batch;
         options.set_launch_params(v, grid_size * block_size, block_size);
         options.inputs         = inputs;
         options.output         = c_shape;
@@ -349,7 +361,7 @@ struct ck_gemm_compiler : compiler<ck_gemm_compiler>
 
         if(v.get("check", false) or enabled(MIGRAPHX_CK_DEBUG{}))
             options.params += " -DMIGRAPHX_CK_CHECK=1";
-        
+
         auto src = interpolate_string(ck_gemm_kernel,
                                       {{"solution", solution},
                                        {"params", enum_params(inputs.size(), "void * private_p")},