refactor: Update example names

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

Author: narendasan

.bazelrc

@@ -33,4 +33,10 @@ build:python --define=target_lang=python
 
 build:pre_cxx11_abi --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"
 build:pre_cxx11_abi --linkopt="-D_GLIBCXX_USE_CXX11_ABI=0"
-build:pre_cxx11_abi --define=abi=pre_cxx11_abi
+build:pre_cxx11_abi --define=abi=pre_cxx11_abi
+
+build:ci_testing --define=torchtrt_src=pre_built
+build:use_precompiled_torchtrt --define=torchtrt_src=pre_built
+
+test:ci_testing --define=torchtrt_src=pre_built
+test:use_precompiled_torchtrt --define=torchtrt_src=pre_built

docker/dist-test.sh

@@ -5,4 +5,5 @@ pip3 install timm --trusted-host pypi.org --trusted-host pypi.python.org --trust
 cd tests/modules && python3 ./hub.py
 cd ../..
 
-bazel test //tests:tests //tests:python_api_tests --compilation_mode=opt --jobs=4
+bazel test //tests:tests //tests:python_api_tests --compilation_mode=opt --jobs=4 --define=torchtrt_src=prebuilt
+

examples/benchmark/BUILD

@@ -7,7 +7,7 @@ cc_binary(
         "timer.h",
     ],
     deps = [
-        "//cpp:trtorch",
+        "//cpp:torch_tensorrt",
         "@libtorch",
         "@libtorch//:caffe2",
     ],

examples/benchmark/README.md

@@ -1,12 +1,12 @@
 # Benchmarking
 
-This is a quick benchmarking application for TRTorch. It lets you run supported TorchScript modules both in JIT and TRT and returns the average runtime and throughput.
+This is a quick benchmarking application for Torch-TensorRT. It lets you run supported TorchScript modules both in JIT and TRT and returns the average runtime and throughput.
 
 ## Compilation / Usage
 
 Run with bazel:
 
-> Note: Make sure libtorch and TensorRT are in your LD_LIBRARY_PATH before running, if you need a location you can `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:[WORKSPACE ROOT]/bazel-TRTorch/external/libtorch/lib:[WORKSPACE ROOT]/bazel-TRTorch/external/tensorrt/lib`
+> Note: Make sure libtorch and TensorRT are in your LD_LIBRARY_PATH before running, if you need a location you can `export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:[WORKSPACE ROOT]/bazel-Torch-TensorRT/external/libtorch/lib:[WORKSPACE ROOT]/bazel-Torch-TensorRT/external/tensorrt/lib`
 
 ``` sh
 bazel run //cpp/benchmark --cxxopt="-DNDEBUG" --cxxopt="-DJIT" --cxxopt="-DTRT" -- [PATH TO JIT MODULE FILE] [INPUT SIZE (explicit batch)]
@@ -20,7 +20,7 @@ bazel run //cpp/benchmark  --cxxopt="-DNDEBUG" --cxxopt="-DJIT" --cxxopt="-DTRT"
 
 ### Options
 
-You can run a module with JIT or TRT via TRTorch in either FP32 or FP16. These options are controlled by preprocessor directives.
+You can run a module with JIT or TRT via Torch-TensorRT in either FP32 or FP16. These options are controlled by preprocessor directives.
 
 - To enable JIT profiling, add the argument `--cxxopt="-DJIT"`
 

examples/benchmark/main.cpp

@@ -4,7 +4,7 @@
 #include "torch/script.h"
 
 #include "timer.h"
-#include "trtorch/trtorch.h"
+#include "torch_tensorrt/torch_tensorrt.h"
 
 #include <iostream>
 #include <memory>
@@ -120,18 +120,18 @@ int main(int argc, const char* argv[]) {
   at::globalContext().setBenchmarkCuDNN(true);
 
 #ifdef TRT
-  auto compile_spec = trtorch::CompileSpec(dims);
+  auto compile_spec = torch_tensorrt::ts::CompileSpec(dims);
   compile_spec.workspace_size = 1 << 20;
 
 #ifdef HALF
   compile_spec.enabled_precisions.insert(torch::kF16);
 #endif
 
-  auto trt_mod = trtorch::CompileGraph(mod, compile_spec);
+  auto trt_mod = torch_tensorrt::ts::CompileModule(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 = trtorch::ConvertGraphToTRTEngine(mod, "forward", compile_spec);
+  auto engine = torch_tensorrt::ts::ConvertMethodToTRTEngine(mod, "forward", compile_spec);
   std::ofstream out("/tmp/engine_converted_from_jit.trt");
   out << engine;
   out.close();

examples/custom_converters/README.md

@@ -31,11 +31,11 @@ Once we are clear about these rules and writing patterns, we can create a sepera
 
 namespace my_custom_converters {
 
-auto actelu = trtorch::core::conversion::converters::RegisterNodeConversionPatterns().pattern(
+auto actelu = torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns().pattern(
     {"aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> (Tensor)",
-     [](trtorch::core::conversion::ConversionCtx* ctx,
+     [](torch_tensorrt::core::conversion::ConversionCtx* ctx,
         const torch::jit::Node* n,
-        trtorch::core::conversion::converters::args& args) -> bool {
+        torch_tensorrt::core::conversion::converters::args& args) -> bool {
        auto in = args[0].ITensorOrFreeze(ctx);
        auto alpha = args[1].unwrapToDouble();
 
@@ -45,7 +45,7 @@ auto actelu = trtorch::core::conversion::converters::RegisterNodeConversionPatte
        }
 
        new_layer->setAlpha(alpha);
-       new_layer->setName(trtorch::core::util::node_info(n).c_str());
+       new_layer->setName(torch_tensorrt::core::util::node_info(n).c_str());
        ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
 
        return true;
@@ -65,18 +65,18 @@ from setuptools import setup, Extension
 from torch.utils import cpp_extension
 
 
-# library_dirs should point to the libtrtorch.so, include_dirs should point to the dir that include the headers
+# library_dirs should point to the libtorch_tensorrt.so, include_dirs should point to the dir that include the headers
 # 1) download the latest package from https://github.com/NVIDIA/TRTorch/releases/
-# 2) Extract the file from downloaded package, we will get the "trtorch" directory
-# 3) Set trtorch_path to that directory
-trtorch_path = <PATH TO TRTORCH>
+# 2) Extract the file from downloaded package, we will get the "torch_tensorrt" directory
+# 3) Set torch_tensorrt_path to that directory
+torch_tensorrt_path = <PATH TO TRTORCH>
 
 
 ext_modules = [
     cpp_extension.CUDAExtension('elu_converter', ['./csrc/elu_converter.cpp'],
-                                library_dirs=[(trtorch_path + "/lib/")],
-                                libraries=["trtorch"],
-                                include_dirs=[trtorch_path + "/include/trtorch/"])
+                                library_dirs=[(torch_tensorrt_path + "/lib/")],
+                                libraries=["torch_tensorrt"],
+                                include_dirs=[torch_tensorrt_path + "/include/torch_tensorrt/"])
 ]
 
 setup(
@@ -88,7 +88,7 @@ setup(
 Make sure to include the path for header files in `include_dirs` and the path
 for dependent libraries in `library_dirs`. Generally speaking, you should download
 the latest package from [here](https://github.com/NVIDIA/TRTorch/releases), extract
-the files, and the set the `trtorch_path` to it. You could also add other compilation
+the files, and the set the `torch_tensorrt_path` to it. You could also add other compilation
 flags in cpp_extension if you need. Then, run above python scripts as:
 ```shell
 python3 setup.py install --user
@@ -104,7 +104,7 @@ We use `torch.ops.load_library` to load `.so`. For example, we could load the EL
 converter and use it in our application:
 ```python
 import torch
-import trtorch
+import torch_tensorrt
 
 # After "python3 setup install", you should find this .so file under generated "build" directory
 torch.ops.load_library('./elu_converter/build/lib.linux-x86_64-3.6/elu_converter.cpython-36m-x86_64-linux-gnu.so')
@@ -120,8 +120,8 @@ class Elu(torch.nn.Module):
         return self.elu(x)
 
 
-def cal_max_diff(pytorch_out, trtorch_out):
-    diff = torch.sub(pytorch_out, trtorch_out)
+def cal_max_diff(pytorch_out, torch_tensorrt_out):
+    diff = torch.sub(pytorch_out, torch_tensorrt_out)
     abs_diff = torch.abs(diff)
     max_diff = torch.max(abs_diff)
     print("Maximum differnce between TRTorch and PyTorch: \n", max_diff)
@@ -132,22 +132,22 @@ def main():
 
     scripted_model = torch.jit.script(model)
     compile_settings = {
-        "inputs": [trtorch.Input(
+        "inputs": [torch_tensorrt.Input(
             min_shape=[1024, 1, 32, 32],
             opt_shape=[1024, 1, 33, 33],
             max_shape=[1024, 1, 34, 34],
         }],
         "enabled_precisions": {torch.float, torch.half}  # Run with FP16
     }
-    trt_ts_module = trtorch.compile(scripted_model, compile_settings)
+    trt_ts_module = torch_tensorrt.compile(scripted_model, compile_settings)
     input_data = torch.randn((1024, 1, 32, 32))
     input_data = input_data.half().to("cuda")
     pytorch_out = model.forward(input_data)
 
-    trtorch_out = trt_ts_module(input_data)
+    torch_tensorrt_out = trt_ts_module(input_data)
     print('PyTorch output: \n', pytorch_out[0, :, :, 0])
-    print('TRTorch output: \n', trtorch_out[0, :, :, 0])
-    cal_max_diff(pytorch_out, trtorch_out)
+    print('TRTorch output: \n', torch_tensorrt_out[0, :, :, 0])
+    cal_max_diff(pytorch_out, torch_tensorrt_out)
 
 
 if __name__ == "__main__":

examples/custom_converters/elu_converter/csrc/elu_converter.cpp

@@ -3,11 +3,11 @@
 
 namespace my_custom_converters {
 
-auto actelu = trtorch::core::conversion::converters::RegisterNodeConversionPatterns().pattern(
+auto actelu = torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns().pattern(
     {"aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> (Tensor)",
-     [](trtorch::core::conversion::ConversionCtx* ctx,
+     [](torch_tensorrt::core::conversion::ConversionCtx* ctx,
         const torch::jit::Node* n,
-        trtorch::core::conversion::converters::args& args) -> bool {
+        torch_tensorrt::core::conversion::converters::args& args) -> bool {
        auto in = args[0].ITensorOrFreeze(ctx);
        auto alpha = args[1].unwrapToDouble();
 
@@ -17,7 +17,7 @@ auto actelu = trtorch::core::conversion::converters::RegisterNodeConversionPatte
        }
 
        new_layer->setAlpha(alpha);
-       new_layer->setName(trtorch::core::util::node_info(n).c_str());
+       new_layer->setName(torch_tensorrt::core::util::node_info(n).c_str());
        ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
 
        return true;

examples/custom_converters/elu_model.py

@@ -1,5 +1,5 @@
 import torch
-import trtorch
+import torch_tensorrt
 
 # After "python3 setup install", you should find this .so file under generated "build" directory
 torch.ops.load_library('./elu_converter/build/lib.linux-x86_64-3.6/elu_converter.cpython-36m-x86_64-linux-gnu.so')
@@ -15,34 +15,34 @@ def forward(self, x):
         return self.elu(x)
 
 
-def cal_max_diff(pytorch_out, trtorch_out):
-    diff = torch.sub(pytorch_out, trtorch_out)
+def cal_max_diff(pytorch_out, torch_tensorrt_out):
+    diff = torch.sub(pytorch_out, torch_tensorrt_out)
     abs_diff = torch.abs(diff)
     max_diff = torch.max(abs_diff)
-    print("Maximum differnce between TRTorch and PyTorch: \n", max_diff)
+    print("Maximum differnce between Torch-TensorRT and PyTorch: \n", max_diff)
 
 
 def main():
     model = Elu().eval()  #.cuda()
 
     scripted_model = torch.jit.script(model)
     compile_settings = {
-        "inputs": [trtorch.Input(
+        "inputs": [torch_tensorrt.Input(
             min_shape=[1024, 1, 32, 32],
             opt_shape=[1024, 1, 33, 33],
             max_shape=[1024, 1, 34, 34],
         )],
         "enabled_precisions": {torch.float, torch.half}  # Run with FP16
     }
-    trt_ts_module = trtorch.compile(scripted_model, compile_settings)
+    trt_ts_module = torch_tensorrt.ts.ompile(scripted_model, **compile_settings)
     input_data = torch.randn((1024, 1, 32, 32))
     input_data = input_data.half().to("cuda")
     pytorch_out = model.forward(input_data)
 
-    trtorch_out = trt_ts_module(input_data)
+    torch_tensorrt_out = trt_ts_module(input_data)
     print('PyTorch output: \n', pytorch_out[0, :, :, 0])
-    print('TRTorch output: \n', trtorch_out[0, :, :, 0])
-    cal_max_diff(pytorch_out, trtorch_out)
+    print('Torch-TensorRT output: \n', torch_tensorrt_out[0, :, :, 0])
+    cal_max_diff(pytorch_out, torch_tensorrt_out)
 
 
 if __name__ == "__main__":

examples/int8/benchmark/BUILD

@@ -10,7 +10,7 @@ cc_library(
         "benchmark.h",
     ],
     deps = [
-        "//cpp:trtorch",
+        "//cpp:torch_tensorrt",
         "@libtorch",
         "@libtorch//:caffe2",
     ],

examples/int8/benchmark/benchmark.cpp

@@ -3,7 +3,7 @@
 #include "cuda_runtime_api.h"
 #include "torch/script.h"
 #include "torch/torch.h"
-#include "trtorch/trtorch.h"
+#include "torch_tensorrt/torch_tensorrt.h"
 
 #include "timer.h"