clang format

src/targets/cpu/lowering.cpp

@@ -848,37 +848,37 @@ struct cpu_apply
         apply_map["dot"]         = extend_op<cpu_gemm, op::dot>();
         apply_map["batch_norm_inference"] =
             extend_op<cpu_batch_norm_inference, op::batch_norm_inference>();
-        apply_map["lrn"]           = extend_op<cpu_lrn, op::lrn>();
-        apply_map["contiguous"]    = extend_op<cpu_contiguous, op::contiguous>();
-        apply_map["pad"]           = extend_op<cpu_pad, op::pad>();
-        apply_map["concat"]        = extend_op<cpu_concat, op::concat>();
-        apply_map["gather"]        = extend_op<cpu_gather, op::gather>();
-        apply_map["convert"] = extend_op<cpu_convert, op::convert>();
-        apply_map["logsoftmax"]    = extend_op<cpu_logsoftmax, op::logsoftmax>();
-        apply_map["leaky_relu"]    = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>();
-        apply_map["elu"]           = extend_op<cpu_unary<elu_op>, op::elu>();
-        apply_map["identity"]      = simple_op<cpu_unary<identity_op>>();
-        apply_map["abs"]           = simple_op<cpu_unary<abs_op>>();
-        apply_map["sinh"]          = simple_op<cpu_unary<sinh_op>>();
-        apply_map["cosh"]          = simple_op<cpu_unary<cosh_op>>();
-        apply_map["tanh"]          = simple_op<cpu_unary<tanh_op>>();
-        apply_map["sigmoid"]       = simple_op<cpu_unary<sigmoid_op>>();
-        apply_map["exp"]           = simple_op<cpu_unary<exp_op>>();
-        apply_map["log"]           = simple_op<cpu_unary<log_op>>();
-        apply_map["neg"]           = simple_op<cpu_unary<neg_op>>();
-        apply_map["sin"]           = simple_op<cpu_unary<sin_op>>();
-        apply_map["cos"]           = simple_op<cpu_unary<cos_op>>();
-        apply_map["tan"]           = simple_op<cpu_unary<tan_op>>();
-        apply_map["asin"]          = simple_op<cpu_unary<asin_op>>();
-        apply_map["acos"]          = simple_op<cpu_unary<acos_op>>();
-        apply_map["atan"]          = simple_op<cpu_unary<atan_op>>();
-        apply_map["relu"]          = simple_op<cpu_unary<relu_op>>();
-        apply_map["add"]           = simple_op<cpu_binary<add_op>>();
-        apply_map["sub"]           = simple_op<cpu_binary<sub_op>>();
-        apply_map["mul"]           = simple_op<cpu_binary<mul_op>>();
-        apply_map["div"]           = simple_op<cpu_binary<div_op>>();
-        apply_map["max"]           = simple_op<cpu_binary<max_op>>();
-        apply_map["min"]           = simple_op<cpu_binary<min_op>>();
+        apply_map["lrn"]        = extend_op<cpu_lrn, op::lrn>();
+        apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>();
+        apply_map["pad"]        = extend_op<cpu_pad, op::pad>();
+        apply_map["concat"]     = extend_op<cpu_concat, op::concat>();
+        apply_map["gather"]     = extend_op<cpu_gather, op::gather>();
+        apply_map["convert"]    = extend_op<cpu_convert, op::convert>();
+        apply_map["logsoftmax"] = extend_op<cpu_logsoftmax, op::logsoftmax>();
+        apply_map["leaky_relu"] = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>();
+        apply_map["elu"]        = extend_op<cpu_unary<elu_op>, op::elu>();
+        apply_map["identity"]   = simple_op<cpu_unary<identity_op>>();
+        apply_map["abs"]        = simple_op<cpu_unary<abs_op>>();
+        apply_map["sinh"]       = simple_op<cpu_unary<sinh_op>>();
+        apply_map["cosh"]       = simple_op<cpu_unary<cosh_op>>();
+        apply_map["tanh"]       = simple_op<cpu_unary<tanh_op>>();
+        apply_map["sigmoid"]    = simple_op<cpu_unary<sigmoid_op>>();
+        apply_map["exp"]        = simple_op<cpu_unary<exp_op>>();
+        apply_map["log"]        = simple_op<cpu_unary<log_op>>();
+        apply_map["neg"]        = simple_op<cpu_unary<neg_op>>();
+        apply_map["sin"]        = simple_op<cpu_unary<sin_op>>();
+        apply_map["cos"]        = simple_op<cpu_unary<cos_op>>();
+        apply_map["tan"]        = simple_op<cpu_unary<tan_op>>();
+        apply_map["asin"]       = simple_op<cpu_unary<asin_op>>();
+        apply_map["acos"]       = simple_op<cpu_unary<acos_op>>();
+        apply_map["atan"]       = simple_op<cpu_unary<atan_op>>();
+        apply_map["relu"]       = simple_op<cpu_unary<relu_op>>();
+        apply_map["add"]        = simple_op<cpu_binary<add_op>>();
+        apply_map["sub"]        = simple_op<cpu_binary<sub_op>>();
+        apply_map["mul"]        = simple_op<cpu_binary<mul_op>>();
+        apply_map["div"]        = simple_op<cpu_binary<div_op>>();
+        apply_map["max"]        = simple_op<cpu_binary<max_op>>();
+        apply_map["min"]        = simple_op<cpu_binary<min_op>>();
 
         apply_map["softmax"] = simple_op<softmax2d>();
     }

src/targets/gpu/convert.cpp

@@ -14,8 +14,8 @@ shape hip_convert::compute_shape(std::vector<shape> inputs) const
 }
 
 argument hip_convert::compute(context& ctx,
-                                    const shape& output_shape,
-                                    const std::vector<argument>& args) const
+                              const shape& output_shape,
+                              const std::vector<argument>& args) const
 {
     return device::convert(ctx.get_stream().get(), args[1], args[0]);
 }

src/targets/gpu/device/convert.cpp

@@ -6,8 +6,7 @@ inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 namespace device {
 
-argument
-convert(hipStream_t stream, const argument& result, const argument& arg)
+argument convert(hipStream_t stream, const argument& result, const argument& arg)
 {
     result.visit([&](auto output) {
         arg.visit([&](auto input) {

src/targets/gpu/include/migraphx/gpu/device/convert.hpp

@@ -11,8 +11,7 @@ inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 namespace device {
 
-argument
-convert(hipStream_t stream, const argument& result, const argument& arg);
+argument convert(hipStream_t stream, const argument& result, const argument& arg);
 
 } // namespace device
 } // namespace gpu