refactor!: Changing the C++ api to be snake case

BREAKING CHANGE: This changes the C++ API ::ts
APIs to be snake case and for CompileModules to
become just compile

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

Author: narendasan

.gitignore

@@ -56,4 +56,5 @@ examples/int8/ptq/ptq
 examples/int8/qat/qat
 examples/int8/training/vgg16/data/*
 examples/int8/datasets/data/*
-env/**/*
+env/**/*
+bazel-Torch-TensorRT-Preview

README.md

@@ -14,7 +14,7 @@ More Information / System Architecture:
 
 ## Building a docker container for Torch-TensorRT Preview
 
-We provide a `Dockerfile` in `docker/` directory. We build `Torch-TensorRT` on top of a `Pytorch NGC container` which provide basic dependencies (like CUDA, CUDNN, CUBLAS, TensorRT, Pytorch and others) The dependency libraries in the container can be found in the <a href="https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html">release notes</a>.  
+We provide a `Dockerfile` in `docker/` directory. We build `Torch-TensorRT` on top of a `Pytorch NGC container` which provide basic dependencies (like CUDA, CUDNN, CUBLAS, TensorRT, Pytorch and others) The dependency libraries in the container can be found in the <a href="https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html">release notes</a>.
 
 Please follow this instruction to build a Docker container.
 
@@ -41,7 +41,7 @@ auto compile_settings = torch_tensorrt::ts::CompileSpec({input});
 // FP16 execution
 compile_settings.enabled_precisions = {torch::kHalf};
 // Compile module
-auto trt_mod = torch_tensorrt::ts::CompileModule(ts_mod, compile_settings);
+auto trt_mod = torch_tensorrt::ts::compile(ts_mod, compile_settings);
 // Run like normal
 auto results = trt_mod.forward({in_tensor});
 // Save module for later

core/partitioning/README.md

@@ -62,6 +62,6 @@ torchtrt::ts::CompileSpec cfg(input_sizes);
 cfg.torch_fallback = torchtrt::CompileSpec::TorchFallback(true);
 cfg.torch_fallback.min_block_size = 2;
 cfg.torch_fallback.forced_fallback_ops.push_back("aten::relu");
-auto trt_mod = torchtrt::ts::CompileModule(mod, cfg);
+auto trt_mod = torchtrt::ts::compile(mod, cfg);
 auto out = trt_mod.forward({in});
 ```

cpp/bin/torchtrtc/main.cpp

@@ -600,7 +600,7 @@ int main(int argc, char** argv) {
   // Instead of compiling, just embed engine in a PyTorch module
   if (embed_engine) {
     std::string serialized_engine = read_buf(real_input_path);
-    auto trt_mod = torchtrt::ts::EmbedEngineInNewModule(serialized_engine, compile_settings.device);
+    auto trt_mod = torchtrt::ts::embed_engine_in_new_module(serialized_engine, compile_settings.device);
     trt_mod.save(real_output_path);
     return 0;
   }
@@ -622,12 +622,12 @@ int main(int argc, char** argv) {
   }
 
   if (save_engine) {
-    auto engine = torchtrt::ts::ConvertMethodToTRTEngine(mod, "forward", compile_settings);
+    auto engine = torchtrt::ts::convert_method_to_trt_engine(mod, "forward", compile_settings);
     std::ofstream out(real_output_path);
     out << engine;
     out.close();
   } else {
-    auto trt_mod = torchtrt::ts::CompileModule(mod, compile_settings);
+    auto trt_mod = torchtrt::ts::compile(mod, compile_settings);
 
     if (!no_threshold_check &&
         (compile_settings.enabled_precisions.size() == 1 &&

cpp/include/torch_tensorrt/torch_tensorrt.h

@@ -701,7 +701,7 @@ struct TORCHTRT_API CompileSpec {
  *
  * @returns bool: Method is supported by Torch-TensorRT.TorchScript
  */
-TORCHTRT_API bool CheckMethodOperatorSupport(const torch::jit::Module& module, std::string method_name);
+TORCHTRT_API bool check_method_operator_support(const torch::jit::Module& module, std::string method_name);
 
 /**
  * @brief Compile a TorchScript module for NVIDIA GPUs using TensorRT
@@ -717,7 +717,7 @@ TORCHTRT_API bool CheckMethodOperatorSupport(const torch::jit::Module& module, s
  *
  * @return: A new module trageting a TensorRT engine
  */
-TORCHTRT_API torch::jit::Module CompileModule(const torch::jit::Module& module, CompileSpec info);
+TORCHTRT_API torch::jit::Module compile(const torch::jit::Module& module, CompileSpec info);
 
 /**
  * @brief Compile a TorchScript method for NVIDIA GPUs using TensorRT
@@ -733,7 +733,7 @@ TORCHTRT_API torch::jit::Module CompileModule(const torch::jit::Module& module,
  * @return: std::string: Serialized TensorRT engine equivilant to the method
  * graph
  */
-TORCHTRT_API std::string ConvertMethodToTRTEngine(
+TORCHTRT_API std::string convert_method_to_trt_engine(
     const torch::jit::Module& module,
     std::string method_name,
     CompileSpec info);
@@ -751,6 +751,6 @@ TORCHTRT_API std::string ConvertMethodToTRTEngine(
  *
  * @return: A new module trageting a TensorRT engine
  */
-TORCHTRT_API torch::jit::Module EmbedEngineInNewModule(const std::string& engine, Device device);
+TORCHTRT_API torch::jit::Module embed_engine_in_new_module(const std::string& engine, Device device);
 } // namespace torchscript
 } // namespace torch_tensorrt

cpp/src/torch_tensorrt.cpp

@@ -12,11 +12,11 @@ namespace torchscript {
 // Defined in compile_spec.cpp
 torch_tensorrt::core::CompileSpec to_internal_compile_spec(CompileSpec external);
 
-bool CheckMethodOperatorSupport(const torch::jit::script::Module& module, std::string method_name) {
+bool check_method_operator_support(const torch::jit::script::Module& module, std::string method_name) {
   return torch_tensorrt::core::CheckMethodOperatorSupport(module, method_name);
 }
 
-std::string ConvertMethodToTRTEngine(
+std::string convert_method_to_trt_engine(
     const torch::jit::script::Module& module,
     std::string method_name,
     CompileSpec info) {
@@ -26,14 +26,14 @@ std::string ConvertMethodToTRTEngine(
   return torch_tensorrt::core::ConvertGraphToTRTEngine(module, method_name, to_internal_compile_spec(info));
 }
 
-torch::jit::script::Module CompileModule(const torch::jit::script::Module& module, CompileSpec info) {
+torch::jit::script::Module compile(const torch::jit::script::Module& module, CompileSpec info) {
   LOG_DEBUG(get_build_info());
   // Want to export a much simpler (non TRT header dependent) API so doing the
   // type conversion here
   return torch_tensorrt::core::CompileGraph(module, to_internal_compile_spec(info));
 }
 
-torch::jit::Module EmbedEngineInNewModule(const std::string& engine, Device device) {
+torch::jit::Module embed_engine_in_new_module(const std::string& engine, Device device) {
   return torch_tensorrt::core::EmbedEngineInNewModule(engine, to_internal_cuda_device(device));
 }
 

examples/benchmark/main.cpp

@@ -127,11 +127,11 @@ int main(int argc, const char* argv[]) {
   compile_spec.enabled_precisions.insert(torch::kF16);
 #endif
 
-  auto trt_mod = torch_tensorrt::ts::CompileModule(mod, compile_spec);
+  auto trt_mod = torch_tensorrt::ts::compile(mod, compile_spec);
 
 #ifdef SAVE_ENGINE
   std::cout << "Compiling graph to save as TRT engine (/tmp/engine_converted_from_jit.trt)" << std::endl;
-  auto engine = torch_tensorrt::ts::ConvertMethodToTRTEngine(mod, "forward", compile_spec);
+  auto engine = torch_tensorrt::ts::convert_method_to_trt_engine(mod, "forward", compile_spec);
   std::ofstream out("/tmp/engine_converted_from_jit.trt");
   out << engine;
   out.close();

examples/int8/ptq/main.cpp

@@ -56,14 +56,14 @@ torch::jit::Module compile_int8_model(const std::string& data_dir, torch::jit::M
 
 #ifdef SAVE_ENGINE
   std::cout << "Compiling graph to save as TRT engine (/tmp/engine_converted_from_jit.trt)" << std::endl;
-  auto engine = torch_tensorrt::ts::ConvertMethodToTRTEngine(mod, "forward", compile_spec);
+  auto engine = torch_tensorrt::ts::convert_method_to_trt_engine(mod, "forward", compile_spec);
   std::ofstream out("/tmp/int8_engine_converted_from_jit.trt");
   out << engine;
   out.close();
 #endif
 
   std::cout << "Compiling and quantizing module" << std::endl;
-  auto trt_mod = torch_tensorrt::ts::CompileModule(mod, compile_spec);
+  auto trt_mod = torch_tensorrt::ts::compile(mod, compile_spec);
   return std::move(trt_mod);
 }
 

examples/int8/qat/main.cpp

@@ -40,14 +40,14 @@ torch::jit::Module compile_int8_qat_model(const std::string& data_dir, torch::ji
 
 #ifdef SAVE_ENGINE
   std::cout << "Compiling graph to save as TRT engine (/tmp/engine_converted_from_jit.trt)" << std::endl;
-  auto engine = torch_tensorrt::ts::ConvertMethodToTRTEngine(mod, "forward", compile_spec);
+  auto engine = torch_tensorrt::ts::convert_method_to_trt_engine(mod, "forward", compile_spec);
   std::ofstream out("/tmp/int8_engine_converted_from_jit.trt");
   out << engine;
   out.close();
 #endif
 
   std::cout << "Compiling and quantizing module" << std::endl;
-  auto trt_mod = torch_tensorrt::ts::CompileModule(mod, compile_spec);
+  auto trt_mod = torch_tensorrt::ts::compile(mod, compile_spec);
   return std::move(trt_mod);
 }
 

tests/accuracy/test_dla_fp16_accuracy.cpp

@@ -34,7 +34,7 @@ TEST_P(AccuracyTests, DLAFP16AccuracyIsClose) {
   compile_spec.device.allow_gpu_fallback = true;
   compile_spec.workspace_size = 1 << 28;
 
-  auto trt_mod = torch_tensorrt::ts::CompileModule(mod, compile_spec);
+  auto trt_mod = torch_tensorrt::ts::compile(mod, compile_spec);
 
   torch::Tensor trt_correct = torch::zeros({1}, {torch::kCUDA}), trt_total = torch::zeros({1}, {torch::kCUDA});
   for (auto batch : *eval_dataloader) {