Formatting

src/onnx/parse_fastgelu.cpp

@@ -17,19 +17,18 @@ struct parse_fastgelu : op_parser<parse_fastgelu>
                           onnx_parser::node_info info,
                           const std::vector<instruction_ref>& args) const
     {
-        if (args.size() != 1)
-            MIGRAPHX_THROW("FastGelu: too many arguments. Expected 1; got " + std::to_string(args.size()));
-        
+        if(args.size() != 1)
+            MIGRAPHX_THROW("FastGelu: too many arguments. Expected 1; got " +
+                           std::to_string(args.size()));
 
         // silu approximation
-        auto x = args.front();
-        auto x_type = x->get_shape().type();
-        auto lit = info.add_literal(literal{shape{x_type, {1}}, {1.702f}});
+        auto x       = args.front();
+        auto x_type  = x->get_shape().type();
+        auto lit     = info.add_literal(literal{shape{x_type, {1}}, {1.702f}});
         auto sigmoid = info.add_broadcastable_binary_op("mul", lit, x);
-        sigmoid = info.add_instruction(make_op("sigmoid"), sigmoid);
+        sigmoid      = info.add_instruction(make_op("sigmoid"), sigmoid);
         return info.add_instruction(make_op("mul"), sigmoid, x);
 
-
         // tanh approximation
         /* auto x = args.front();
         auto x_type = x->get_shape().type();
@@ -48,13 +47,12 @@ struct parse_fastgelu : op_parser<parse_fastgelu>
 
         return info.add_instruction(make_op("mul"), x, tanh); */
 
-
         // tanh approximation with pow
         /* auto x = args.front();
         auto x_type = x->get_shape().type();
         auto three = info.add_literal(literal{shape{x_type, {1}}, {3}});
-        three = info.add_instruction(make_op("multibroadcast", {{"out_lens", x->get_shape().lens()}}), three);
-        auto x3 = info.add_instruction(make_op("pow"), x, three);
+        three = info.add_instruction(make_op("multibroadcast", {{"out_lens",
+        x->get_shape().lens()}}), three); auto x3 = info.add_instruction(make_op("pow"), x, three);
         auto magic_number = info.add_literal(literal{shape{x_type, {1}}, {0.044715f}});
         x3 = info.add_broadcastable_binary_op("mul", magic_number, x3);
         auto product = info.add_instruction(make_op("add"), x, x3);

src/onnx/parse_gelu.cpp

@@ -17,18 +17,19 @@ struct parse_gelu : op_parser<parse_gelu>
                           onnx_parser::node_info info,
                           const std::vector<instruction_ref>& args) const
     {
-        if (args.size() != 1)
-            MIGRAPHX_THROW("Gelu: too many arguments. Expected 1; got " + std::to_string(args.size()));
-        
-        auto x = args.front();
-        auto x_type = x->get_shape().type();
+        if(args.size() != 1)
+            MIGRAPHX_THROW("Gelu: too many arguments. Expected 1; got " +
+                           std::to_string(args.size()));
+
+        auto x        = args.front();
+        auto x_type   = x->get_shape().type();
         auto root_inv = info.add_literal(literal{shape{x_type, {1}}, {1.0f / std::sqrt(2.0f)}});
-        auto product = info.add_broadcastable_binary_op("mul", x, root_inv);
-        auto erf = info.add_instruction(make_op("erf"), product);
-        auto one = info.add_literal(literal{shape{x_type, {1}}, {1.0f}});
-        erf = info.add_broadcastable_binary_op("add", one, erf);
-        auto half = info.add_literal(literal{shape{x_type, {1}}, {0.5f}});
-        erf = info.add_broadcastable_binary_op("mul", half, erf);
+        auto product  = info.add_broadcastable_binary_op("mul", x, root_inv);
+        auto erf      = info.add_instruction(make_op("erf"), product);
+        auto one      = info.add_literal(literal{shape{x_type, {1}}, {1.0f}});
+        erf           = info.add_broadcastable_binary_op("add", one, erf);
+        auto half     = info.add_literal(literal{shape{x_type, {1}}, {0.5f}});
+        erf           = info.add_broadcastable_binary_op("mul", half, erf);
 
         return info.add_instruction(make_op("mul"), x, erf);
     }

test/onnx/gen_onnx.py

@@ -1671,11 +1671,7 @@ def gelu_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [16, 384, 3072])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [16, 384, 3072])
 
-    node = onnx.helper.make_node(
-        'Gelu',
-        inputs=['x'],
-        outputs=['y']
-    )
+    node = onnx.helper.make_node('Gelu', inputs=['x'], outputs=['y'])
 
     return ([node], [x], [y])
 
@@ -1685,11 +1681,7 @@ def fastgelu_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [16, 384, 3072])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [16, 384, 3072])
 
-    node = onnx.helper.make_node(
-        'FastGelu',
-        inputs=['x'],
-        outputs=['y']
-    )
+    node = onnx.helper.make_node('FastGelu', inputs=['x'], outputs=['y'])
 
     return ([node], [x], [y])