pytorch/test/cpp/api/misc.cpp

#include <gtest/gtest.h>

#include <torch/nn/init.h>
#include <torch/nn/modules/linear.h>
#include <torch/tensor.h>
#include <torch/utils.h>

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

TEST(NoGradTest, SetsGradModeCorrectly) {
  torch::manual_seed(0);
  torch::NoGradGuard guard;
  torch::nn::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_FALSE(model->parameters()["weight"].grad().defined());
}

struct AutogradTest : torch::test::SeedingFixture {
  AutogradTest() {
    x = torch::randn({3, 3}, torch::requires_grad());
    y = torch::randn({3, 3});
    z = x * y;
  }
  torch::Tensor x, y, z;
};

TEST_F(AutogradTest, CanTakeDerivatives) {
  z.backward();
  ASSERT_TRUE(x.grad().allclose(y));
}

TEST_F(AutogradTest, CanTakeDerivativesOfZeroDimTensors) {
  z.sum().backward();
  ASSERT_TRUE(x.grad().allclose(y));
}

TEST_F(AutogradTest, CanPassCustomGradientInputs) {
  z.sum().backward(torch::ones({}) * 2);
  ASSERT_TRUE(x.grad().allclose(y * 2));
}

TEST(NNInitTest, CanInitializeTensorThatRequiresGrad) {
  auto tensor = torch::empty({3, 4}, torch::requires_grad());
  ASSERT_THROWS_WITH(
      tensor.fill_(1),
      "a leaf Variable that requires grad "
      "has been used in an in-place operation");
  ASSERT_EQ(torch::nn::init::ones_(tensor).sum().toCInt(), 12);
}