clang format

src/include/migraphx/op/convert.hpp

@@ -41,7 +41,7 @@ struct convert : unary<convert>
     }
 
     convert(shape::type_t t) : target_type{t} {}
-    convert(shape::type_t t, float sle, float sft) : target_type{t}, scale {sle}, shift{sft} {}
+    convert(shape::type_t t, float sle, float sft) : target_type{t}, scale{sle}, shift{sft} {}
     convert() {}
 };
 

src/quantization.cpp

@@ -215,14 +215,15 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
         // used as scale and shift(.0f), which will generate results diffrent from
         // the original results. To adjust the output to be "correct(approximatly
         // equal)", we need additional calculation for the adjustment
-        if (ins->name() == "dot")
+        if(ins->name() == "dot")
         {
             auto dot_op         = any_cast<op::dot>(ins->get_operator());
-            int32_t quant_alpha = static_cast<int32_t>(dot_op.alpha / (int8_param[0].first * int8_param[1].first) + 0.5f);
+            int32_t quant_alpha = static_cast<int32_t>(
+                dot_op.alpha / (int8_param[0].first * int8_param[1].first) + 0.5f);
             int32_t quant_beta = static_cast<int32_t>(dot_op.beta + 0.5f);
             prog.replace_instruction(ins, op::quant_dot{quant_alpha, quant_beta}, converted_inputs);
         }
-        else if (ins->name() == "convolution")
+        else if(ins->name() == "convolution")
         {
             // Current MIOpen convolution does not support alpha and beta,
             // so we need a separate multiply to adjust the output
@@ -234,7 +235,10 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
             auto group         = conv_op.group;
             auto adjust_factor = 1.0 / int8_param[0].first * int8_param[1].first;
 
-            auto conv_res = prog.insert_instruction(ins, op::quant_convolution{padding, stride, dilation, padding_mode, group}, converted_inputs);
+            auto conv_res = prog.insert_instruction(
+                ins,
+                op::quant_convolution{padding, stride, dilation, padding_mode, group},
+                converted_inputs);
             auto conv_lens = conv_res->get_shape().lens();
             auto fl        = prog.add_literal(literal(adjust_factor));
             auto adj_fact  = prog.insert_instruction(ins, op::multibroadcast{conv_lens}, fl);