chore: apply linting

Signed-off-by: Bo Wang <[email protected]>

Author: bowang007

core/compiler.cpp

@@ -176,7 +176,6 @@ void AddSegmentedBlockToGraph(
   return;
 }
 
-
 torch::jit::script::Module CompileGraphWithFallback(const torch::jit::script::Module& mod, CompileSpec cfg) {
   // TODO: Should be doing a functional transform but need PR #31978
   // [jit] More robust mangling

core/partitioning/SegmentedBlock.h

@@ -3,9 +3,9 @@
 #include <vector>
 
 #include "NvInfer.h"
-#include "torch/csrc/jit/ir/ir.h"
 #include "core/ir/ir.h"
 #include "core/partitioning/PartitionInfo.h"
+#include "torch/csrc/jit/ir/ir.h"
 
 namespace trtorch {
 namespace core {

core/partitioning/shape_analysis.cpp

@@ -19,7 +19,6 @@ std::vector<torch::jit::IValue> generateRandomInputs(std::vector<ir::InputRange>
   return random_inputs;
 }
 
-
 void getSegmentsOutputByRunning(
     SegmentedBlock& seg_block,
     std::unordered_map<torch::jit::Value*, torch::jit::IValue>& ivalues_maps) {

tests/core/partitioning/test_fallback_graph_output.cpp

@@ -57,7 +57,6 @@ TEST(Partitioning, ComputeMobileNetFallbackGraphCorrectly) {
   cfg.partition_info.enabled = true;
   cfg.partition_info.forced_fallback_operators.push_back("aten::hardtanh");
 
-
   auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
   auto trt_mod = trtorch::core::CompileGraph(mod, cfg);
   auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();

tests/core/partitioning/test_shape_analysis.cpp

@@ -2,9 +2,8 @@
 #include "core/partitioning/partitioning.h"
 #include "core/util/trt_util.h"
 #include "gtest/gtest.h"
-#include "torch/script.h"
 #include "torch/csrc/jit/ir/irparser.h"
-
+#include "torch/script.h"
 
 bool checkSegmentedBlockInputShape(
     std::vector<trtorch::core::partitioning::SegmentedBlock>& segmented_blocks,
@@ -62,8 +61,11 @@ TEST(Partitioning, InferSequentialModelSegmentedBlockShapeCorrectly) {
 
   std::vector<trtorch::core::partitioning::SegmentedBlock> segmented_blocks =
       trtorch::core::partitioning::Partition(g, input_ranges, partition_info);
-  ASSERT_TRUE(checkSegmentedBlockInputShape(segmented_blocks, {{{3, 3, 16, 16}, {32, 3, 3, 3}, {32}, {16, 32, 3, 3}, {16}},
-                                                               {{3, 16, 16, 16}}, {{3, 16, 16, 16}, {8, 16, 3, 3}, {8}}}));
+  ASSERT_TRUE(checkSegmentedBlockInputShape(
+      segmented_blocks,
+      {{{3, 3, 16, 16}, {32, 3, 3, 3}, {32}, {16, 32, 3, 3}, {16}},
+       {{3, 16, 16, 16}},
+       {{3, 16, 16, 16}, {8, 16, 3, 3}, {8}}}));
 }
 
 TEST(Partitioning, InferBranchModelSegmentedBlockShapeCorrectly) {
@@ -99,10 +101,11 @@ TEST(Partitioning, InferBranchModelSegmentedBlockShapeCorrectly) {
   input_ranges.push_back(trtorch::core::ir::InputRange({16, 32, 3, 3}));
   input_ranges.push_back(trtorch::core::ir::InputRange({16}));
 
-
-std::vector<trtorch::core::partitioning::SegmentedBlock> segmented_blocks =
+  std::vector<trtorch::core::partitioning::SegmentedBlock> segmented_blocks =
       trtorch::core::partitioning::Partition(g, input_ranges, partition_info);
   ASSERT_TRUE(checkSegmentedBlockInputShape(
-      segmented_blocks, {{{3, 3, 16, 16}, {32, 3, 3, 3}, {32}, {16, 32, 3, 3}, {16}},
-                         {{3, 32, 16, 16}}, {{3, 32, 16, 16},  {16, 32, 3, 3}, {16}, {3, 16, 16, 16}}}));
+      segmented_blocks,
+      {{{3, 3, 16, 16}, {32, 3, 3, 3}, {32}, {16, 32, 3, 3}, {16}},
+       {{3, 32, 16, 16}},
+       {{3, 32, 16, 16}, {16, 32, 3, 3}, {16}, {3, 16, 16, 16}}}));
 }