refactor: Apply linting

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

Author: narendasan

core/conversion/conversion.cpp

@@ -539,7 +539,7 @@ bool VerifyConverterSupportForBlock(const torch::jit::Block* b, bool suppress_er
             auto loc = unsupported_node_locations.find(x.second);
             if (loc == unsupported_node_locations.end()) {
               unsupported_node_locations.insert({x.second, {torch_tensorrt::core::util::GetPyTorchSourceCode(n)}});
-            } else  {
+            } else {
               loc->second.insert(torch_tensorrt::core::util::GetPyTorchSourceCode(n));
             }
           }

core/lowering/register_trt_placeholder_ops.cpp

@@ -12,7 +12,7 @@ RegisterOperators trt_placeholder_ops_reg({
     /// to a TRT constant Tensor
     Operator(
         "trt::const(Tensor val) -> Tensor",
-        [](Stack& stack) {/*noop*/},
+        [](Stack& stack) { /*noop*/ },
         aliasAnalysisFromSchema()),
 });
 

cpp/include/torch_tensorrt/torch_tensorrt.h

@@ -262,7 +262,6 @@ enum class EngineCapability : int8_t {
   kDLA_STANDALONE,
 };
 
-
 /**
  * @brief TensorFormat is an enum class which defines the memeory layout used to store Tensor Data
  * */

py/setup.py

@@ -182,12 +182,19 @@ def run(self):
 class CleanCommand(Command):
     """Custom clean command to tidy up the project root."""
     PY_CLEAN_DIRS = [
-        './build', './dist', './torch_tensorrt/__pycache__', './torch_tensorrt/lib', './torch_tensorrt/include',
-        './torch_tensorrt/bin', './*.pyc', './*.tgz', './*.egg-info',
+        './build',
+        './dist',
+        './torch_tensorrt/__pycache__',
+        './torch_tensorrt/lib',
+        './torch_tensorrt/include',
+        './torch_tensorrt/bin',
+        './*.pyc',
+        './*.tgz',
+        './*.egg-info',
     ]
     PY_CLEAN_FILES = [
-        './torch_tensorrt/*.so', './torch_tensorrt/_version.py',
-        './torch_tensorrt/BUILD', './torch_tensorrt/WORKSPACE', './torch_tensorrt/LICENSE'
+        './torch_tensorrt/*.so', './torch_tensorrt/_version.py', './torch_tensorrt/BUILD', './torch_tensorrt/WORKSPACE',
+        './torch_tensorrt/LICENSE'
     ]
     description = "Command to tidy up the project root"
     user_options = []
@@ -220,7 +227,6 @@ def run(self):
                 os.remove(path)
 
 
-
 ext_modules = [
     cpp_extension.CUDAExtension(
         'torch_tensorrt._C', [
@@ -235,8 +241,7 @@ def run(self):
             dir_path + "torch_tensorrt/csrc", dir_path + "torch_tensorrt/include",
             dir_path + "/../bazel-TRTorch/external/tensorrt/include",
             dir_path + "/../bazel-Torch-TensorRT-Preview/external/tensorrt/include",
-            dir_path + "/../bazel-Torch-TensorRT/external/tensorrt/include",
-            dir_path + "/../"
+            dir_path + "/../bazel-Torch-TensorRT/external/tensorrt/include", dir_path + "/../"
         ],
         extra_compile_args=[
             "-Wno-deprecated",

py/torch_tensorrt/_compile.py

@@ -6,6 +6,7 @@
 from torch import fx
 from enum import Enum
 
+
 class _IRType(Enum):
     """Enum to set the minimum required logging level to print a message to stdout
     """

tests/core/lowering/test_conv1d_pass.cpp

@@ -35,7 +35,8 @@ TEST(LoweringPasses, Conv1dCorrectly) {
         %12 : Tensor = aten::_convolution(%0, %1, %2, %stride, %padding, %dilation, %3, %output_padding, %6, %3, %3, %3, %3)
         return (%12))IR";
 
-  torch_tensorrt::core::util::logging::get_logger().set_reportable_log_level(torch_tensorrt::core::util::logging::LogLevel::kGRAPH);
+  torch_tensorrt::core::util::logging::get_logger().set_reportable_log_level(
+      torch_tensorrt::core::util::logging::LogLevel::kGRAPH);
   auto sg = std::make_shared<torch::jit::Graph>();
   torch::jit::parseIR(source_graph, &*sg);
   torch_tensorrt::core::lowering::passes::Conv1DToConvolution(sg);
@@ -92,7 +93,8 @@ TEST(LoweringPasses, ConvTransposed1dCorrectly) {
         %12 : Tensor = aten::_convolution(%0, %1, %2, %stride, %padding, %dilation, %8, %output_padding, %5, %7, %7, %7, %7)
         return (%12))IR";
 
-  torch_tensorrt::core::util::logging::get_logger().set_reportable_log_level(torch_tensorrt::core::util::logging::LogLevel::kGRAPH);
+  torch_tensorrt::core::util::logging::get_logger().set_reportable_log_level(
+      torch_tensorrt::core::util::logging::LogLevel::kGRAPH);
   auto sg = std::make_shared<torch::jit::Graph>();
   torch::jit::parseIR(source_graph, &*sg);
   torch_tensorrt::core::lowering::passes::ConvTransposed1DToConvolution(sg);