clang format

src/quantization.cpp

@@ -14,7 +14,8 @@ instruction_ref insert_quant_ins(program& prog,
                                  instruction_ref& ins,
                                  shape::type_t type,
                                  std::unordered_map<instruction_ref, instruction_ref>& map_ins,
-                            float scale = 1.0f, float shift = 0.0f)
+                                 float scale = 1.0f,
+                                 float shift = 0.0f)
 {
     if(map_ins.count(ins) > 0)
     {
@@ -110,7 +111,7 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
 {
     // For now, we only support the int8 quantization of gemm and convolution
     std::vector<std::string> op_names = {"dot", "convolution"};
-    if (!std::all_of(ins_names.begin(), ins_names.end(), [&](auto name) {
+    if(!std::all_of(ins_names.begin(), ins_names.end(), [&](auto name) {
            return std::find(op_names.begin(), op_names.end(), name);
        }))
     {
@@ -145,14 +146,15 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
             // operation, if it has 3 inputs, then the last one should
             // be converted to int32_type
             shape::type_t quant_type = shape::int8_type;
-            if (ins->name() == "dot" and inputs.size() == 3 and input == inputs.back())
+            if(ins->name() == "dot" and inputs.size() == 3 and input == inputs.back())
             {
                 quant_type = shape::int32_type;
             }
 
             auto param = int8_param[param_index++];
             auto s     = input->get_shape();
-            if(s.type() == shape::float_type || s.type() == shape::double_type || s.type() == shape::int32_type)
+            if(s.type() == shape::float_type || s.type() == shape::double_type ||
+               s.type() == shape::int32_type)
             {
                 // if the input is a convert operator, uses its input
                 // as its current input
@@ -160,19 +162,20 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
                 if(input->name() == "convert")
                 {
                     auto tmp_ins = input->inputs().front();
-                    if (tmp_ins->get_shape().type() == quant_type)
+                    if(tmp_ins->get_shape().type() == quant_type)
                     {
                         quant_input = input->inputs().front();
                     }
                     else
                     {
-                        quant_input = insert_quant_ins(prog, input, quant_type, map_quant_ins, param.first, param.second);                        
+                        quant_input = insert_quant_ins(
+                            prog, input, quant_type, map_quant_ins, param.first, param.second);
                     }
-                    
                 }
                 else
                 {
-                    quant_input = insert_quant_ins(prog, input, quant_type, map_quant_ins, param.first, param.second);
+                    quant_input = insert_quant_ins(
+                        prog, input, quant_type, map_quant_ins, param.first, param.second);
                 }
                 converted_inputs.push_back(quant_input);
             }
@@ -210,9 +213,7 @@ void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
 
         prog.replace_instruction(ins, op, converted_inputs);
     }
-
 }
 
-
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/convert.cpp

@@ -13,9 +13,7 @@ shape hip_convert::compute_shape(std::vector<shape> inputs) const
     return op.compute_shape(inputs);
 }
 
-argument hip_convert::compute(context& ctx,
-                             const shape&,
-                             const std::vector<argument>& args) const
+argument hip_convert::compute(context& ctx, const shape&, const std::vector<argument>& args) const
 {
     device::convert(ctx.get_stream().get(), args[1], args[0], op.scale, op.shift);
     return args[1];