pytorch/test/cpp/api/optim.cpp

#include <catch.hpp>

#include <torch/torch.h>

using namespace torch;
using namespace torch::nn;

bool test_optimizer_xor(Optimizer optim, std::shared_ptr<ContainerList> model) {
  float running_loss = 1;
  int epoch = 0;
  while (running_loss > 0.1) {
    int64_t bs = 4;
    auto inp = at::CPU(at::kFloat).tensor({bs, 2});
    auto lab = at::CPU(at::kFloat).tensor({bs});
    for (size_t i = 0; i < bs; i++) {
      const int64_t a = std::rand() % 2;
      const int64_t b = std::rand() % 2;
      const int64_t c = static_cast<uint64_t>(a) ^ static_cast<uint64_t>(b);
      inp[i][0] = a;
      inp[i][1] = b;
      lab[i] = c;
    }
    // forward
    auto input = Var(inp);
    auto target = Var(lab, false);

    std::function<at::Scalar()> closure = [&]() -> at::Scalar {
      optim->zero_grad();
      auto x = input;
      for (auto& layer : *model)
        x = layer->forward({x})[0].sigmoid_();
      Variable loss = at::binary_cross_entropy(x, target);
      backward(loss);
      return at::Scalar(loss.data());
    };

    at::Scalar loss = optim->step(closure);

    running_loss = running_loss * 0.99 + loss.toFloat() * 0.01;
    if (epoch > 3000) {
      return false;
    }
    epoch++;
  }
  return true;
}

TEST_CASE("optim") {
  std::srand(0);
  setSeed(0);
  auto model = std::make_shared<ContainerList>();
  model->append(Linear(2, 8).build());
  model->append(Linear(8, 1).build());

  SECTION("lbfgs") {
    auto optim = LBFGS(model, 5e-2).max_iter(5).make();
    REQUIRE(test_optimizer_xor(optim, model));
  }

  SECTION("sgd") {
    auto optim =
        SGD(model, 1e-1).momentum(0.9).nesterov().weight_decay(1e-6).make();
    REQUIRE(test_optimizer_xor(optim, model));
  }

  SECTION("adagrad") {
    auto optim = Adagrad(model, 1.0).weight_decay(1e-6).lr_decay(1e-3).make();
    REQUIRE(test_optimizer_xor(optim, model));
  }

  SECTION("rmsprop_simple") {
    auto optim = RMSprop(model, 1e-1).centered().make();
    REQUIRE(test_optimizer_xor(optim, model));
  }

  SECTION("rmsprop") {
    auto optim = RMSprop(model, 1e-1).momentum(0.9).weight_decay(1e-6).make();
    REQUIRE(test_optimizer_xor(optim, model));
  }

  /*
  // This test appears to be flaky, see
  https://github.com/pytorch/pytorch/issues/7288 SECTION("adam") { auto optim =
  Adam(model, 1.0).weight_decay(1e-6).make(); REQUIRE(test_optimizer_xor(optim,
  model));
  }
  */

  SECTION("amsgrad") {
    auto optim = Adam(model, 0.1).weight_decay(1e-6).amsgrad().make();
    REQUIRE(test_optimizer_xor(optim, model));
  }
}