refactor: apply linting

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

core/conversion/conversion.cpp

@@ -105,7 +105,8 @@ void AddLayer(ConversionCtx* ctx, const torch::jit::Node* n) {
       // Node input has not been converted yet or is a prim op
       TORCHTRT_THROW_ERROR(
           "Unable to retrieve all node inputs for node: "
-          << util::node_info(n) << " (ctx.AddLayer)\nSpecifically failed to retrieve value for input: %" << input->debugName());
+          << util::node_info(n) << " (ctx.AddLayer)\nSpecifically failed to retrieve value for input: %"
+          << input->debugName());
     }
   }
 
@@ -541,8 +542,9 @@ bool VerifyConverterSupportForBlock(const torch::jit::Block* b, bool suppress_er
     }
 
     if (!suppress_errors) {
-      unsupported_msg << "You can either implement converters for these ops in your application or request implementation"
-                    << std::endl;
+      unsupported_msg
+          << "You can either implement converters for these ops in your application or request implementation"
+          << std::endl;
       unsupported_msg << "https://www.github.com/nvidia/Torch-TensorRT/issues" << std::endl;
       unsupported_msg << std::endl << "In Module:" << std::endl;
 

core/conversion/evaluators/aten.cpp

@@ -103,7 +103,7 @@ DEFINE_GENERIC_TWO_INPUT_EVALUATOR(
 DEFINE_ARITHMATIC_TWO_INPUT_EVALUATOR(
     pow,
     "aten::pow",
-    pow(a,b),
+    pow(a, b),
     std::set<std::string>({
         "aten::pow.int(int a, int b) -> (float)",
         "aten::pow.float(float a, float b) -> (float)",
@@ -114,7 +114,7 @@ DEFINE_ARITHMATIC_TWO_INPUT_EVALUATOR(
 DEFINE_TWO_INPUT_SIMPLE_EVALUATOR(
     and,
     "aten::__and__",
-    a && b,
+    a&& b,
     bool,
     std::set<std::string>({"aten::__and__(int a, int b) -> (bool)", "aten::__and__.bool(bool a, bool b) -> (bool)"}));
 DEFINE_TWO_INPUT_SIMPLE_EVALUATOR(or, "aten::__or__", a || b, bool, {"aten::__or__(int a, int b) -> (bool)"});

core/conversion/evaluators/eval_macros.h

@@ -77,7 +77,7 @@
        },                                                                                              \
        EvalOptions().validSchemas(schemas)});
 
-#define DEFINE_ARITHMATIC_TWO_INPUT_EVALUATOR(name, node_kind, operation, schemas)                        \
+#define DEFINE_ARITHMATIC_TWO_INPUT_EVALUATOR(name, node_kind, operation, schemas)                     \
   auto name##_registrations TORCHTRT_UNUSED = RegisterNodeEvaluators().evaluator(                      \
       {c10::Symbol::fromQualString(node_kind),                                                         \
        [](const torch::jit::Node* n, kwargs& args) -> c10::optional<torch::jit::IValue> {              \