pytorch/test/cpp/api/modules.cpp

#include <gtest/gtest.h>

#include <torch/nn/module.h>
#include <torch/nn/modules/batchnorm.h>
#include <torch/nn/modules/conv.h>
#include <torch/nn/modules/dropout.h>
#include <torch/nn/modules/embedding.h>
#include <torch/nn/modules/functional.h>
#include <torch/nn/modules/linear.h>
#include <torch/types.h>
#include <torch/utils.h>

#include <test/cpp/api/support.h>

using namespace torch::nn;
using namespace torch::test;

class TestModel : public torch::nn::Module {
 public:
  TestModel()
      : l1(register_module("l1", Linear(10, 3))),
        l2(register_module("l2", Linear(3, 5))),
        l3(register_module("l3", Linear(5, 100))) {}

  Linear l1, l2, l3;
};

class NestedModel : public torch::nn::Module {
 public:
  NestedModel()
      : param_(register_parameter("param", torch::empty({3, 2, 21}))),
        l1(register_module("l1", Linear(5, 20))),
        t(register_module("test", std::make_shared<TestModel>())) {}

  torch::Tensor param_;
  Linear l1;
  std::shared_ptr<TestModel> t;
};

struct ModulesTest : torch::test::SeedingFixture {};

TEST_F(ModulesTest, Conv1d) {
  Conv1d model(Conv1dOptions(3, 2, 3).stride(2));
  auto x = torch::randn({2, 3, 5}, torch::requires_grad());
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 3);
  ASSERT_EQ(s.ndimension(), 0);
  for (auto i = 0; i < 3; i++) {
    ASSERT_EQ(y.size(i), 2);
  }

  ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3);
}

TEST_F(ModulesTest, Conv2dEven) {
  Conv2d model(Conv2dOptions(3, 2, 3).stride(2));
  auto x = torch::randn({2, 3, 5, 5}, torch::requires_grad());
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 4);
  ASSERT_EQ(s.ndimension(), 0);
  for (auto i = 0; i < 4; i++) {
    ASSERT_EQ(y.size(i), 2);
  }

  ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 3);
}

TEST_F(ModulesTest, Conv2dUneven) {
  Conv2d model(Conv2dOptions(3, 2, {3, 2}).stride({2, 2}));
  auto x = torch::randn({2, 3, 5, 4}, torch::requires_grad());
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 4);
  ASSERT_EQ(s.ndimension(), 0);
  for (auto i = 0; i < 4; i++) {
    ASSERT_EQ(y.size(i), 2);
  }

  ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 2);
}

TEST_F(ModulesTest, Conv3d) {
  Conv3d model(Conv3dOptions(3, 2, 3).stride(2));
  auto x = torch::randn({2, 3, 5, 5, 5}, torch::requires_grad());
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 5);
  ASSERT_EQ(s.ndimension(), 0);
  for (auto i = 0; i < 5; i++) {
    ASSERT_EQ(y.size(i), 2);
  }

  ASSERT_TRUE(model->weight.grad().numel() == 3 * 2 * 3 * 3 * 3);
}

TEST_F(ModulesTest, Linear) {
  Linear model(5, 2);
  auto x = torch::randn({10, 5}, torch::requires_grad());
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 2);
  ASSERT_EQ(s.ndimension(), 0);
  ASSERT_EQ(y.size(0), 10);
  ASSERT_EQ(y.size(1), 2);

  ASSERT_EQ(model->weight.grad().numel(), 2 * 5);
}

TEST_F(ModulesTest, SimpleContainer) {
  auto model = std::make_shared<SimpleContainer>();
  auto l1 = model->add(Linear(10, 3), "l1");
  auto l2 = model->add(Linear(3, 5), "l2");
  auto l3 = model->add(Linear(5, 100), "l3");

  auto x = torch::randn({1000, 10}, torch::requires_grad());
  x = l1->forward(x).clamp_min(0);
  x = l2->forward(x).clamp_min(0);
  x = l3->forward(x).clamp_min(0);

  x.backward();
  ASSERT_EQ(x.ndimension(), 2);
  ASSERT_EQ(x.size(0), 1000);
  ASSERT_EQ(x.size(1), 100);
  ASSERT_EQ(x.min().item<float>(), 0);
}

TEST_F(ModulesTest, EmbeddingBasic) {
  const int64_t dict_size = 10;
  Embedding model(dict_size, 2);
  ASSERT_TRUE(model->named_parameters().contains("weight"));
  ASSERT_EQ(model->weight.ndimension(), 2);
  ASSERT_EQ(model->weight.size(0), dict_size);
  ASSERT_EQ(model->weight.size(1), 2);

  // Cannot get gradients to change indices (input) - only for embedding
  // params
  auto x = torch::full({10}, dict_size - 1, torch::kInt64);
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 2);
  ASSERT_EQ(s.ndimension(), 0);
  ASSERT_EQ(y.size(0), 10);
  ASSERT_EQ(y.size(1), 2);

  ASSERT_EQ(model->weight.grad().numel(), 2 * dict_size);
}

TEST_F(ModulesTest, EmbeddingList) {
  Embedding model(6, 4);
  auto x = torch::full({2, 3}, 5, torch::kInt64);
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 3);
  ASSERT_EQ(y.size(0), 2);
  ASSERT_EQ(y.size(1), 3);
  ASSERT_EQ(y.size(2), 4);
}

TEST_F(ModulesTest, Dropout) {
  Dropout dropout(0.5);
  torch::Tensor x = torch::ones(100, torch::requires_grad());
  torch::Tensor y = dropout->forward(x);

  y.backward();
  ASSERT_EQ(y.ndimension(), 1);
  ASSERT_EQ(y.size(0), 100);
  ASSERT_LT(y.sum().item<float>(), 130); // Probably
  ASSERT_GT(y.sum().item<float>(), 70); // Probably

  dropout->eval();
  y = dropout->forward(x);
  ASSERT_EQ(y.sum().item<float>(), 100);
}

TEST_F(ModulesTest, Parameters) {
  auto model = std::make_shared<NestedModel>();
  auto parameters = model->named_parameters();
  ASSERT_EQ(parameters["param"].size(0), 3);
  ASSERT_EQ(parameters["param"].size(1), 2);
  ASSERT_EQ(parameters["param"].size(2), 21);
  ASSERT_EQ(parameters["l1.bias"].size(0), 20);
  ASSERT_EQ(parameters["l1.weight"].size(0), 20);
  ASSERT_EQ(parameters["l1.weight"].size(1), 5);
  ASSERT_EQ(parameters["test.l1.bias"].size(0), 3);
  ASSERT_EQ(parameters["test.l1.weight"].size(0), 3);
  ASSERT_EQ(parameters["test.l1.weight"].size(1), 10);
  ASSERT_EQ(parameters["test.l2.bias"].size(0), 5);
  ASSERT_EQ(parameters["test.l2.weight"].size(0), 5);
  ASSERT_EQ(parameters["test.l2.weight"].size(1), 3);
  ASSERT_EQ(parameters["test.l3.bias"].size(0), 100);
  ASSERT_EQ(parameters["test.l3.weight"].size(0), 100);
  ASSERT_EQ(parameters["test.l3.weight"].size(1), 5);
}

TEST_F(ModulesTest, FunctionalCallsSuppliedFunction) {
  bool was_called = false;
  auto functional = Functional([&was_called](torch::Tensor input) {
    was_called = true;
    return input;
  });
  auto output = functional->forward(torch::ones(5, torch::requires_grad()));
  ASSERT_TRUE(was_called);
  ASSERT_TRUE(output.equal(torch::ones(5, torch::requires_grad())));

  was_called = false;
  // Use the call operator overload here.
  output = functional(torch::ones(5, torch::requires_grad()));
  ASSERT_TRUE(was_called);
  ASSERT_TRUE(output.equal(torch::ones(5, torch::requires_grad())));
}

TEST_F(ModulesTest, FunctionalWithTorchFunction) {
  auto functional = Functional(torch::relu);
  ASSERT_EQ(functional(torch::ones({})).item<float>(), 1);
  ASSERT_EQ(functional(torch::ones({})).item<float>(), 1);
  ASSERT_EQ(functional(torch::ones({}) * -1).item<float>(), 0);
}

TEST_F(ModulesTest, FunctionalArgumentBinding) {
  auto functional =
      Functional(torch::elu, /*alpha=*/1, /*scale=*/0, /*input_scale=*/1);
  ASSERT_EQ(functional(torch::ones({})).item<float>(), 0);
}

TEST_F(ModulesTest, BatchNormStateful) {
  BatchNorm bn(5);

  // Is stateful by default.
  ASSERT_TRUE(bn->options.stateful());

  ASSERT_TRUE(bn->running_mean.defined());
  ASSERT_EQ(bn->running_mean.dim(), 1);
  ASSERT_EQ(bn->running_mean.size(0), 5);

  ASSERT_TRUE(bn->running_variance.defined());
  ASSERT_EQ(bn->running_variance.dim(), 1);
  ASSERT_EQ(bn->running_variance.size(0), 5);

  // Is affine by default.
  ASSERT_TRUE(bn->options.affine());

  ASSERT_TRUE(bn->weight.defined());
  ASSERT_EQ(bn->weight.dim(), 1);
  ASSERT_EQ(bn->weight.size(0), 5);

  ASSERT_TRUE(bn->bias.defined());
  ASSERT_EQ(bn->bias.dim(), 1);
  ASSERT_EQ(bn->bias.size(0), 5);
}
TEST_F(ModulesTest, BatchNormStateless) {
  BatchNorm bn(BatchNormOptions(5).stateful(false).affine(false));

  ASSERT_FALSE(bn->running_mean.defined());
  ASSERT_FALSE(bn->running_variance.defined());
  ASSERT_FALSE(bn->weight.defined());
  ASSERT_FALSE(bn->bias.defined());

  ASSERT_THROWS_WITH(
      bn->forward(torch::ones({2, 5})),
      "Calling BatchNorm::forward is only permitted "
      "when the 'stateful' option is true (was false). "
      "Use BatchNorm::pure_forward instead.");
}

TEST_F(ModulesTest, BatchNormPureForward) {
  BatchNorm bn(BatchNormOptions(5).affine(false));
  bn->eval();

  // Want to make sure we use the supplied values in `pure_forward` even if
  // we are stateful.
  auto input = torch::randn({2, 5});
  auto mean = torch::randn(5);
  auto variance = torch::rand(5);
  auto output = bn->pure_forward(input, mean, variance);
  auto expected = (input - mean) / torch::sqrt(variance + bn->options.eps());
  ASSERT_TRUE(output.allclose(expected));
}

TEST_F(ModulesTest, Linear_CUDA) {
  Linear model(5, 2);
  model->to(torch::kCUDA);
  auto x =
      torch::randn({10, 5}, torch::device(torch::kCUDA).requires_grad(true));
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 2);
  ASSERT_EQ(s.ndimension(), 0);
  ASSERT_EQ(y.size(0), 10);
  ASSERT_EQ(y.size(1), 2);

  ASSERT_EQ(model->weight.grad().numel(), 2 * 5);
}

TEST_F(ModulesTest, Linear2_CUDA) {
  Linear model(5, 2);
  model->to(torch::kCUDA);
  model->to(torch::kCPU);
  auto x = torch::randn({10, 5}, torch::requires_grad());
  auto y = model->forward(x);
  torch::Tensor s = y.sum();

  s.backward();
  ASSERT_EQ(y.ndimension(), 2);
  ASSERT_EQ(s.ndimension(), 0);
  ASSERT_EQ(y.size(0), 10);
  ASSERT_EQ(y.size(1), 2);

  ASSERT_EQ(model->weight.grad().numel(), 2 * 5);
}