test_backend_lib.cpp

#include <torch/csrc/jit/backends/backend.h>
#include <torch/csrc/jit/backends/backend_debug_handler.h>
#include <torch/csrc/jit/backends/backend_preprocess.h>

namespace torch {
namespace jit {
// This test JIT backend is intended to do the minimal amount of work
// necessary to test that the JIT backend registration endpoints and
// code generation are working correctly. It is not intended to
// produce numerically correct results.
template <bool isAvailable>
class TestBackend : public PyTorchBackendInterface {
 public:
  // Constructor.
  // NOLINTNEXTLINE(modernize-use-equals-default)
  explicit TestBackend() {}
  // NOLINTNEXTLINE(modernize-use-override)
  virtual ~TestBackend() = default;

  bool is_available() override {
    return isAvailable;
  }

  c10::impl::GenericDict compile(
      c10::IValue processed,
      c10::impl::GenericDict method_compile_spec) override {
    auto spec =
        c10::impl::toTypedDict<std::string, at::IValue>(method_compile_spec);

    // Return the same string as a value for every key in method_compile_spec.
    auto handles = c10::Dict<std::string, std::string>();
    for (const auto& it : spec) {
      handles.insert(it.key(), it.key());
    }
    return c10::impl::toGenericDict(handles);
  }
  c10::impl::GenericList execute(
      c10::IValue handle,
      c10::impl::GenericList inputs) override {
    TORCH_INTERNAL_ASSERT(handle.isString());
    TORCH_INTERNAL_ASSERT(inputs.size() > 0);

    c10::List<at::Tensor> output_list;

    // Implement simple accumulator and negative accumulator (?) ops. Return one
    // or both of them depending on the handle to make sure multiple outputs are
    // handled.
    c10::IValue value = inputs[0];
    at::Tensor accum = value.toTensor();
    accum = accum.clone();
    at::Tensor sub_accum = value.toTensor();
    sub_accum = sub_accum.clone();

    for (size_t i = 1, e = inputs.size(); i < e; ++i) {
      value = inputs[i];
      accum.add_(value.toTensor(), 1.0);
      sub_accum.sub_(value.toTensor(), 1.0);
    }

    if (handle.toStringRef() == "accum") {
      output_list.emplace_back(accum);
    } else if (handle.toStringRef() == "sub_accum") {
      output_list.emplace_back(sub_accum);
    } else if (handle.toStringRef() == "forward") {
      output_list.emplace_back(accum);
      output_list.emplace_back(sub_accum);
    }

    return c10::impl::toList(output_list);
  }
};

namespace {
c10::IValue preprocess(
    const Module& mod,
    const c10::Dict<IValue, IValue>& method_compile_spec,
    const BackendDebugHandleGenerator& generate_debug_handles) {
  return mod._ivalue();
}

constexpr auto backend_name = "test_backend";
static auto cls_available =
    torch::jit::backend<TestBackend<true>>(backend_name);
static auto pre_reg = backend_preprocess_register(backend_name, preprocess);

constexpr auto backend_unavailable_name = "test_backend_unavailable";
static auto cls_unavailable =
    torch::jit::backend<TestBackend<false>>(backend_unavailable_name);
static auto pre_reg_unavailable =
    backend_preprocess_register(backend_unavailable_name, preprocess);

} // namespace
} // namespace jit
} // namespace torch