Add sample rt app

Signed-off-by: Dheeraj Peri <[email protected]>

Author: peri044

examples/sample_rt_app/BUILD

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+package(default_visibility = ["//visibility:public"])
+
+# trtorch is the dowloaded tar file
+# It has include, lib, bin directory and LICENSE file
+cc_library(
+    name = "trtorch_runtime",
+    srcs = ["trtorch/lib/libtrtorchrt.so", "trtorch/lib/libtrtorch_plugins.so"],
+    hdrs = ["trtorch/include/trtorch/core/runtime/runtime.h"], # "trtorch/include/trtorch/trtorch.h", "trtorch/include/trtorch/macros.h"],
+    includes = ["trtorch/include/trtorch"],
+    #include_prefix="trtorch/include",
+)
+
+cc_binary(
+    name = "samplertapp",
+    srcs = [
+        "main.cpp"
+    ],
+    includes = ["trtorch/include", "trtorch/include/trtorch"],
+    deps = [
+        ":trtorch_runtime",
+        "@libtorch//:libtorch",
+        "@libtorch//:caffe2",
+        "@tensorrt//:nvinfer",
+    ],
+)

examples/sample_rt_app/main.cpp

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+// #include "examples/sample_rt_app/trtorch/include/trtorch/core/runtime/runtime.h"
+#include "trtorch/core/runtime/runtime.h"
+#include <iostream>
+#include <fstream>
+#include <memory>
+#include <sstream>
+#include <vector>
+#include "torch/script.h"
+#include "trtorch/include/trtorch/trtorch.h"
+
+// Load the TRT engine from engine_path
+std::vector<char> loadEngine(std::string engine_path){
+  std::ifstream engineFile(engine_path, std::ios::binary);
+  if (!engineFile)
+  {
+      std::cerr << "Error opening TensorRT Engine file at : " << engine_path << std::endl;
+  }
+
+  engineFile.seekg(0, engineFile.end);
+  long int fsize = engineFile.tellg();
+  engineFile.seekg(0, engineFile.beg);
+
+  std::vector<char> engineData(fsize);
+  engineFile.read(engineData.data(), fsize);
+  if (!engineFile)
+  {
+      std::cerr << "Error loading engine from: " << engine_path << std::endl;
+  }
+
+  return engineData;
+}
+
+int main(int argc, const char* argv[]) {
+  if (argc < 2) {
+    std::cerr
+        << "usage: samplertapp <path-to-pre-built-trt-engine>\n";
+    return -1;
+  }
+
+  std::string engine_path = argv[1];
+  auto engineData = loadEngine(engine_path);
+
+  std::cout << "Running TRT engine" << std::endl;
+  auto engine_ptr = c10::make_intrusive<TRTEngine>("test_engine", engineData.data());
+  auto inputs = at::randint(-5, 5, {1, 3, 5, 5}, {at::kCUDA});
+  auto outputs = trtorch::core::runtime::execute_engine(inputs, engine_ptr);
+  std::cout << "TRT engine execution completed. " << std::endl;
+}

examples/sample_rt_app/network.py

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+import torch
+import torch.nn as nn
+
+# Create a sample network with a conv and gelu node.
+# Gelu layer in TRTorch is converted to CustomGeluPluginDynamic from TensorRT plugin registry.
+class ConvGelu(torch.nn.Module):
+    def __init__(self):
+        super(ConvGelu, self).__init__()
+        self.conv = nn.Conv2d(3, 32, 3, 1)
+        self.gelu = nn.GELU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.gelu(x)
+        return x
+
+def main():
+
+    model = ConvGelu().eval().cuda()
+    scripted_model = torch.jit.script(model)
+    # Save the torchscript model
+    torch.jit.save(scripted_model, 'conv_gelu.jit')
+    print("Generated conv_gelu.jit model.")
+
+if __name__ == "__main__":
+    main()