Add working code for quickstart example (#190)

Summary:
Pull Request resolved: #190

Adds working code for our quickstart example, and integrates into CI to ensure that it actually works.

Test Plan: Imported from OSS

Reviewed By: d4l3k

Differential Revision: D40645620

Pulled By: PaliC

fbshipit-source-id: 259784746a5a683204fc0deff55335540c26639f

Author: PaliC

.github/workflows/runtime_tests.yaml

@@ -39,6 +39,7 @@ jobs:
       - name: Examples
         run: |
           docker run --rm multipy bash -c "if [[ ${{ matrix.python-minor-version }} -lt 8 ]]; then source ~/venvs/multipy/bin/activate; fi && examples/build/hello_world_example"
+          docker run --rm multipy bash -c "if [[ ${{ matrix.python-minor-version }} -lt 8 ]]; then source ~/venvs/multipy/bin/activate; fi && python3 examples/quickstart/gen_package.py && ./examples/build/quickstart my_package.pt"
 
       - name: Compat Tests
         run: |

.gitignore

@@ -9,4 +9,5 @@ multipy/runtime/interpreter/cpython/**
 **/CMakeFiles/**
 multipy/runtime/interpreter/frozen/**
 multipy/runtime/example/generated/
+examples/**/*.pt
 *.egg-info

examples/CMakeLists.txt

@@ -18,5 +18,8 @@ set_target_properties(multipy_internal
 caffe2_interface_library(multipy_internal multipy)
 
 # build our examples
-add_executable(hello_world_example hello_world_example.cpp)
+add_executable(hello_world_example hello_world/hello_world_example.cpp)
 target_link_libraries(hello_world_example PUBLIC "-Wl,--no-as-needed -rdynamic" dl pthread util multipy c10 torch_cpu)
+
+add_executable(quickstart quickstart/quickstart.cpp)
+target_link_libraries(quickstart PUBLIC "-Wl,--no-as-needed -rdynamic" dl pthread util multipy c10 torch_cpu)

examples/hello_world_example.cpp renamed to examples/hello_world/hello_world_example.cpp

@@ -1,3 +1,7 @@
+/* Basic example of a single interpreter from `torch::deploy`
+   to invoke python methods directly. Here we specifically
+   invoke `print` to print out `Hello World`.
+*/
 #include <multipy/runtime/deploy.h>
 #include <multipy/runtime/path_environment.h>
 #include <torch/script.h>

examples/quickstart/gen_package.py

@@ -0,0 +1,17 @@
+# An example usage of `torch.package` which is
+# used for our quickstart example.
+
+import torchvision
+from torch.package import PackageExporter
+
+# Instantiate some model
+model = torchvision.models.resnet.resnet18()
+
+# Package and export it.
+with PackageExporter("my_package.pt") as e:
+    e.intern("torchvision.**")
+    e.extern("numpy.**")
+    e.extern("sys")
+    e.extern("PIL.*")
+    e.extern("typing_extensions")
+    e.save_pickle("model", "model.pkl", model)

examples/quickstart/quickstart.cpp

@@ -0,0 +1,47 @@
+/* quickstart.cpp highlights some of the most basic concepts of `torch::deploy`.
+Specifically loading a pytorch model which was serialized by `torch.package`
+and running it.
+
+In order to run this file, one needs to provide an archive produced by
+`torch.package`. The one used in our example is created by `gen_package.py`
+which produces my_package.pt. This program takes the path to the archive as
+an argument.
+*/
+
+#include <multipy/runtime/deploy.h>
+#include <multipy/runtime/path_environment.h>
+#include <torch/script.h>
+#include <torch/torch.h>
+
+#include <iostream>
+#include <memory>
+
+int main(int argc, const char* argv[]) {
+  if (argc != 2) {
+    std::cerr << "usage: example-app <path-to-exported-script-module>\n";
+    return -1;
+  }
+
+  // Start an interpreter manager governing 4 embedded interpreters.
+  torch::deploy::InterpreterManager manager(4);
+  torch::deploy::ReplicatedObj model;
+  try {
+    // Load the model from the multipy.package.
+    torch::deploy::Package package = manager.loadPackage(argv[1]);
+    model = package.loadPickle("model", "model.pkl");
+  } catch (const c10::Error& e) {
+    std::cerr << "error loading the model\n";
+    std::cerr << e.msg();
+    return -1;
+  }
+
+  // Create a vector of inputs.
+  std::vector<torch::jit::IValue> inputs;
+  inputs.push_back(torch::ones({1, 3, 224, 224}));
+
+  // Execute the model and turn its output into a tensor.
+  at::Tensor output = model(inputs).toTensor();
+  std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << '\n';
+
+  std::cout << "ok\n";
+}