formatting

src/include/migraphx/op/unsqueeze.hpp

@@ -29,9 +29,9 @@ struct unsqueeze
     std::string name() const { return "unsqueeze"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        auto input_shape     = inputs[0];
-        auto type            = input_shape.type();
-        auto old_lens        = input_shape.lens();
+        auto input_shape = inputs[0];
+        auto type        = input_shape.type();
+        auto old_lens    = input_shape.lens();
 
         if(input_shape.scalar())
             return shape{type, old_lens};

src/onnx/onnx.cpp

@@ -1375,14 +1375,17 @@ struct onnx_parser
             case onnx::TensorProto::FLOAT: return create_literal(shape::float_type, dims, s.data());
             case onnx::TensorProto::UINT8: throw std::runtime_error("");
             case onnx::TensorProto::INT8: return create_literal(shape::int32_type, dims, s.data());
-            case onnx::TensorProto::UINT16: return create_literal(shape::int32_type, dims, s.data());
+            case onnx::TensorProto::UINT16:
+                return create_literal(shape::int32_type, dims, s.data());
             case onnx::TensorProto::INT16: return create_literal(shape::int32_type, dims, s.data());
             case onnx::TensorProto::INT32: return create_literal(shape::int32_type, dims, s.data());
             case onnx::TensorProto::INT64: return create_literal(shape::int64_type, dims, s.data());
             case onnx::TensorProto::STRING: throw std::runtime_error("");
             case onnx::TensorProto::BOOL: return create_literal(shape::int32_type, dims, s.data());
-            case onnx::TensorProto::FLOAT16: return create_literal(shape::half_type, dims, s.data());
-            case onnx::TensorProto::DOUBLE: return create_literal(shape::double_type, dims, s.data());
+            case onnx::TensorProto::FLOAT16:
+                return create_literal(shape::half_type, dims, s.data());
+            case onnx::TensorProto::DOUBLE:
+                return create_literal(shape::double_type, dims, s.data());
             case onnx::TensorProto::UINT32: throw std::runtime_error("");
             case onnx::TensorProto::UINT64: throw std::runtime_error("");
             case onnx::TensorProto::COMPLEX64: throw std::runtime_error("");
@@ -1394,21 +1397,28 @@ struct onnx_parser
         {
         case onnx::TensorProto::UNDEFINED: throw std::runtime_error("");
         case onnx::TensorProto::FLOAT:
-            return create_literal(shape::float_type, dims, t.float_data().begin(), t.float_data().end());
+            return create_literal(
+                shape::float_type, dims, t.float_data().begin(), t.float_data().end());
         case onnx::TensorProto::UINT8: throw std::runtime_error("");
         case onnx::TensorProto::INT8:
-            return create_literal(shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
+            return create_literal(
+                shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
         case onnx::TensorProto::UINT16:
-            return create_literal(shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
+            return create_literal(
+                shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
         case onnx::TensorProto::INT16:
-            return create_literal(shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
+            return create_literal(
+                shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
         case onnx::TensorProto::INT32:
-            return create_literal(shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
+            return create_literal(
+                shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
         case onnx::TensorProto::INT64:
-            return create_literal(shape::int64_type, dims, t.int64_data().begin(), t.int64_data().end());
+            return create_literal(
+                shape::int64_type, dims, t.int64_data().begin(), t.int64_data().end());
         case onnx::TensorProto::STRING: throw std::runtime_error("");
         case onnx::TensorProto::BOOL:
-            return create_literal(shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
+            return create_literal(
+                shape::int32_type, dims, t.int32_data().begin(), t.int32_data().end());
         case onnx::TensorProto::FLOAT16:
         {
             std::vector<uint16_t> data_uint16(t.int32_data().begin(), t.int32_data().end());
@@ -1430,7 +1440,8 @@ struct onnx_parser
         MIGRAPHX_THROW("Invalid tensor type");
     }
 
-    static literal create_literal(shape::type_t shape_type, std::vector<size_t> dims, const char* data)
+    static literal
+    create_literal(shape::type_t shape_type, std::vector<size_t> dims, const char* data)
     {
         if(dims.empty())
             return literal{{shape_type, {1}, {0}}, data};
@@ -1438,14 +1449,14 @@ struct onnx_parser
     }
 
     template <class Iterator>
-    static literal create_literal(shape::type_t shape_type, std::vector<size_t> dims, Iterator start, Iterator end)
+    static literal
+    create_literal(shape::type_t shape_type, std::vector<size_t> dims, Iterator start, Iterator end)
     {
         if(dims.empty())
             return literal{{shape_type, {1}, {0}}, start, end};
         return literal{{shape_type, dims}, start, end};
     }
 
-
     static shape parse_type(const onnx::TypeProto& t)
     {
         shape::type_t shape_type{};

src/tf/tf.cpp

@@ -736,7 +736,8 @@ struct tf_parser
         {
         case tensorflow::DataType::DT_INVALID: throw std::runtime_error("");
         case tensorflow::DataType::DT_FLOAT:
-            return create_literal(shape::float_type, dims, get_data_vals(t.float_val(), shape_size));
+            return create_literal(
+                shape::float_type, dims, get_data_vals(t.float_val(), shape_size));
         case tensorflow::DataType::DT_UINT8: throw std::runtime_error("");
         case tensorflow::DataType::DT_INT8:
             return create_literal(shape::int32_type, dims, get_data_vals(t.int_val(), shape_size));
@@ -747,7 +748,8 @@ struct tf_parser
         case tensorflow::DataType::DT_INT32:
             return create_literal(shape::int32_type, dims, get_data_vals(t.int_val(), shape_size));
         case tensorflow::DataType::DT_INT64:
-            return create_literal(shape::int64_type, dims, get_data_vals(t.int64_val(), shape_size));
+            return create_literal(
+                shape::int64_type, dims, get_data_vals(t.int64_val(), shape_size));
         case tensorflow::DataType::DT_STRING: throw std::runtime_error("");
         case tensorflow::DataType::DT_BOOL:
             return create_literal(shape::int32_type, dims, get_data_vals(t.bool_val(), shape_size));
@@ -834,13 +836,13 @@ struct tf_parser
     }
 
     template <class T>
-    static literal create_literal(shape::type_t shape_type, std::vector<size_t> dims, std::vector<T> data)
+    static literal
+    create_literal(shape::type_t shape_type, std::vector<size_t> dims, std::vector<T> data)
     {
         if(dims.empty() or (dims.size() == 1 and dims.front() == 1))
             return literal{{shape_type, {1}, {0}}, data};
         return literal{{shape_type, dims}, data};
     }
-
 };
 
 program parse_tf(const std::string& name, bool is_nhwc)

test/onnx/onnx_test.cpp

@@ -699,8 +699,8 @@ TEST_CASE(add_scalar_test)
 {
     migraphx::program p;
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}});
-    auto l1 =
-        p.add_literal(migraphx::literal{migraphx::shape{migraphx::shape::float_type, {1}, {0}}, {1}});
+    auto l1 = p.add_literal(
+        migraphx::literal{migraphx::shape{migraphx::shape::float_type, {1}, {0}}, {1}});
     auto m0 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l0);
     auto m1 = p.add_instruction(migraphx::op::multibroadcast{{2, 3, 4, 5}}, l1);
     p.add_instruction(migraphx::op::add{}, m0, m1);