Formatting

src/onnx/parse_attention.cpp

@@ -28,9 +28,9 @@ struct parse_attention : op_parser<parse_attention>
             num_heads = info.attributes.at("num_heads").i();
 
         // input shape: (batch_size, sequence_length, input_hidden_size)
-        auto input_lens        = input->get_shape().lens();
-        auto batch_size        = input_lens.at(0);
-        auto sequence_length   = input_lens.at(1);
+        auto input_lens      = input->get_shape().lens();
+        auto batch_size      = input_lens.at(0);
+        auto sequence_length = input_lens.at(1);
 
         // bias shape= (3 * hidden_size)
         auto bias_lens   = bias->get_shape().lens();

src/onnx/parse_layernorm.cpp

@@ -19,11 +19,11 @@ struct parse_layernorm : op_parser<parse_layernorm>
                           const std::vector<instruction_ref>& args) const
     {
         // un-fuse layernorm op so migraphx can handle fusion instead
-        
-        auto x        = args.front(); 
-        auto x_type   = x->get_shape().type();
-        auto weights        = args.at(1); 
-        auto bias        = args.at(2); 
+
+        auto x       = args.front();
+        auto x_type  = x->get_shape().type();
+        auto weights = args.at(1);
+        auto bias    = args.at(2);
 
         float epsilon = 1e-12f;
         int64_t axis  = -1;
@@ -35,23 +35,23 @@ struct parse_layernorm : op_parser<parse_layernorm>
         {
             epsilon = parser.parse_value(info.attributes.at("axis")).at<int64_t>();
         }
-    
-        auto epsilon_lit  = info.add_literal(literal{shape{x_type, {1}}, {epsilon}});
-        auto exponent = info.add_literal(literal{shape{x_type, {1}}, {2.0}});
-        auto dims = x->get_shape().lens();
+
+        auto epsilon_lit = info.add_literal(literal{shape{x_type, {1}}, {epsilon}});
+        auto exponent    = info.add_literal(literal{shape{x_type, {1}}, {2.0}});
+        auto dims        = x->get_shape().lens();
 
         auto mean = info.add_instruction(migraphx::make_op("reduce_mean", {{"axes", {axis}}}), x);
         auto mean_mbcast =
             info.add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", dims}}), mean);
-        auto sub = info.add_instruction(migraphx::make_op("sub"), x, mean_mbcast);
-        auto exponent_mbcast =
-            info.add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", dims}}), exponent);
+        auto sub             = info.add_instruction(migraphx::make_op("sub"), x, mean_mbcast);
+        auto exponent_mbcast = info.add_instruction(
+            migraphx::make_op("multibroadcast", {{"out_lens", dims}}), exponent);
         auto pow = info.add_instruction(migraphx::make_op("pow"), sub, exponent_mbcast);
         auto var = info.add_instruction(migraphx::make_op("reduce_mean", {{"axes", {axis}}}), pow);
         auto add_epsilon = info.add_broadcastable_binary_op("add", var, epsilon_lit);
-        auto sqrt = info.add_instruction(migraphx::make_op("sqrt"), add_epsilon);
-        auto div = info.add_broadcastable_binary_op("div", sub, sqrt);
-        auto mul = info.add_broadcastable_binary_op("mul", div, weights);
+        auto sqrt        = info.add_instruction(migraphx::make_op("sqrt"), add_epsilon);
+        auto div         = info.add_broadcastable_binary_op("div", sub, sqrt);
+        auto mul         = info.add_broadcastable_binary_op("mul", div, weights);
 
         return info.add_broadcastable_binary_op("add", mul, bias);
     }