fix: Address review comments, fix failing tests due to bool mishandling

Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>

core/lowering/lowering.cpp

@@ -24,7 +24,7 @@ void LowerBlock(torch::jit::Block* b) {
   DropUnusedNodes(b);
 }
 
-void LowerGraph(std::shared_ptr<torch::jit::Graph>& g, bool disable_cse) {
+void LowerGraph(std::shared_ptr<torch::jit::Graph>& g, LowerInfo lower_info) {
   passes::UnpackHardSwish(g);
   torch::jit::EliminateRedundantGuards(g);
   torch::jit::RemoveListMutation(g);
@@ -42,7 +42,7 @@ void LowerGraph(std::shared_ptr<torch::jit::Graph>& g, bool disable_cse) {
   passes::Conv3DToConvolution(g);
   passes::FuseAddMMBranches(g);
   passes::RemoveBNDimCheck(g);
-  if (!disable_cse) {
+  if (!lower_info.disable_cse) {
     torch::jit::EliminateCommonSubexpression(g);
   }
   // torch::jit::UnrollLoops(g);
@@ -72,25 +72,19 @@ std::pair<std::shared_ptr<torch::jit::Graph>, std::vector<torch::jit::IValue>> L
   auto g = lowered_mod.get_method(method_name).graph();
   LOG_GRAPH(*g);
 
+  LOG_GRAPH("LibTorch Lowering");
+  auto graph_and_ivalues = torch::jit::LowerGraph(*g, lowered_mod._ivalue());
+
   // Go through TRTorch Lowering to reformat graph to be conversion friendly
-  // and also segment for accelerators and executors (TRT-DLA, TRT-GPU, PYT)
+  // and also segment for accelerators and executors (TRT-DLA, TRT-GPU  , PYT)
   // unfreeze_module is used to not perform constant folding on weights in the network.
   // In quantization aware trained (QAT) models, weights are passed through quantize and
   // dequantize nodes which should not be folded. So unfreeze_module is set to True for QAT models.
-  if (!lower_info.unfreeze_module) {
-    LOG_GRAPH("TRTorch Graph Lowering");
-    lowering::LowerGraph(g, false);
-  }
+  LOG_GRAPH("TRTorch Graph Lowering");
+  lowering::LowerGraph(graph_and_ivalues.first, lower_info);
 
-  LOG_GRAPH("LibTorch Lowering");
-  auto graph_and_ivalues = torch::jit::LowerGraph(*g, lowered_mod._ivalue());
-
-  if (lower_info.unfreeze_module) {
-    LOG_GRAPH("TRTorch Graph Lowering");
-    lowering::LowerGraph(graph_and_ivalues.first, true);
-  }
   // Is this necessary?
-  lowering::LowerBlock(g->block());
+  // lowering::LowerBlock(g->block());
 
   return graph_and_ivalues;
 }

core/lowering/lowering.h

@@ -8,11 +8,17 @@ namespace lowering {
 
 struct LowerInfo {
   // Internal flag to ensure torch.jit.Module does not get freezed in lowering.cpp. This is required for QAT models.
-  bool unfreeze_module;
+  bool unfreeze_module = false;
+  // CommonSubexpressionElimination removes duplicate expressions which are used frequently in the graph.
+  // for eg:  CSE replaces similar value-d stride nodes of multiple conv layers in a network with a single stride node.
+  // In QAT models, if two conv layers are consuming same input, there is a QDQ node for each input of the conv.
+  // Since these QDQ nodes will be identical as they share same input, one of them is eliminated due to CSE lowering
+  // pass. Disable this in order to not disturb TensorRT's QAT optimizations.
+  bool disable_cse = false;
 };
 
 void LowerBlock(torch::jit::Block* b);
-void LowerGraph(std::shared_ptr<torch::jit::Graph>& g, bool disable_cse = false);
+void LowerGraph(std::shared_ptr<torch::jit::Graph>& g, LowerInfo lower_info);
 torch::jit::Module LowerModule(const torch::jit::script::Module& mod);
 std::pair<std::shared_ptr<torch::jit::Graph>, std::vector<torch::jit::IValue>> Lower(
     const torch::jit::script::Module& mod,

cpp/api/src/compile_spec.cpp

@@ -409,6 +409,7 @@ core::CompileSpec to_internal_compile_spec(CompileSpec external) {
       internal.convert_info.engine_settings.calibrator = external.ptq_calibrator;
     } else {
       internal.lower_info.unfreeze_module = true;
+      internal.lower_info.disable_cse = true;
       internal.convert_info.engine_settings.calibrator = nullptr;
     }
   } else {

py/trtorch/csrc/tensorrt_backend.cpp

@@ -27,12 +27,12 @@ c10::impl::GenericDict TensorRTBackend::compile(c10::IValue mod_val, c10::impl::
   mod = core::lowering::LowerModule(mod);
 
   auto spec = c10::impl::toTypedDict<std::string, at::IValue>(method_compile_spec);
-  core::CompileSpec cfg({});
+  lowering::LowerInfo lower_info;
   for (auto it = spec.begin(), end = spec.end(); it != end; ++it) {
     const auto& method_name = it->key();
     auto method = mod.get_method(method_name);
     auto graph = method.graph();
-    core::lowering::LowerGraph(graph, cfg.lower_info);
+    core::lowering::LowerGraph(graph, lower_info);
   }
 
   auto handles = c10::impl::GenericDict(

py/trtorch/csrc/tensorrt_classes.cpp

@@ -188,6 +188,7 @@ core::CompileSpec CompileSpec::toInternalCompileSpec() {
     if (info.convert_info.engine_settings.enabled_precisions.find(nvinfer1::DataType::kINT8) !=
         info.convert_info.engine_settings.enabled_precisions.end()) {
       info.lower_info.unfreeze_module = true;
+      info.lower_info.disable_cse = true;
     }
   }
   info.convert_info.engine_settings.sparse_weights = sparse_weights;