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25261a4776
pytorch/test/cpp/api/tensor.cpp
Edward Yang 25261a4776 Merge Tensor and Variable. (#28620) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28620

All Tensors are Variables now, they just happen to have requires_grad=False. Tensors ALWAYS have `VariableTensorId` in their type set.

When constructing this patch, I had to make decisions about what I would fix in this patch, and what I would leave for follow up PRs. Here is the cleanup that happens in this patch:

- The `is_variable` property is removed from TensorOptions. I removed this immediately because unlike Tensor::is_variable, TensorOptions::is_variable doesn't respect our VariableTensorId thread-local state. This means that there were a bunch of places where TensorOptions::is_variable was false, which is obviously bogus in the world when tensor and variable are merged. Instead of keeping the method as a function that always returns true, I just opted to remove it entirely (it's not public API.) All places we set `is_variable` are deleted.
  - Knock on effect: there is no longer a separate DeprecatedTypeProperties for the variable and non-variable versions of type.
  - Knock on effect: instead of asserting on TensorOptions::is_variable, instead we just test `at::impl::variable_is_excluded()`
- There is now only one copy of the cuDNN RNN dropout cache, not two (I'm not sure why we had two to begin with)

Some cleanup that doesn't happen in this patch:
- Eliminating unnecessary uses of `make_variable`
- Eliminating `Tensor::is_variable`

The most subtle part of this patch is retaining tracing behavior: the fact that everything is a Variable means that more code gets routed to VariableType than before; this can change traces. I identified two places where we didn't appropriately turn off VariableType, mostly factory functions:

- `torch.tensor` must turn off VariableType before invoking `at::empty` to construct the tensor, as it subsequently does direct data access
- `tensor_slow` (invoked when you pass a Python scalar to a tensor argument) must turn off VariableType before calling `scalar_to_tensor` so the scalar gets traced as constant, rather than as a call to `scalar_to_tensor`.

Honestly, these are all giant hacks, and should be replaced with a more specialized guard that just toggles tracing.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Reviewed By: dreiss

Differential Revision: D18171156

Pulled By: ezyang

fbshipit-source-id: 5b6a045beba37492647e350190f495114e86504d
2019-11-04 14:59:57 -08:00

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#include <gtest/gtest.h>
#include <test/cpp/api/support.h>
#include <torch/torch.h>
#include <cmath>
#include <cstddef>
#include <vector>
#include <test/cpp/common/support.h>
template <typename T>
bool exactly_equal(at::Tensor left, T right) {
return left.item<T>() == right;
}
template <typename T>
bool almost_equal(at::Tensor left, T right, T tolerance = 1e-4) {
return std::abs(left.item<T>() - right) < tolerance;
}
#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
ASSERT_TRUE( \
tensor.device().type() == at::Device((device_), (index_)).type()); \
ASSERT_TRUE( \
tensor.device().index() == at::Device((device_), (index_)).index()); \
ASSERT_EQ(tensor.dtype(), (type_)); \
ASSERT_TRUE(tensor.layout() == (layout_))
TEST(TensorTest, ToDtype) {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
tensor = tensor.to(at::kInt);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
tensor = tensor.to(at::kChar);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kChar, at::kStrided);
tensor = tensor.to(at::kDouble);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
tensor = tensor.to(at::TensorOptions(at::kInt));
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
tensor = tensor.to(at::TensorOptions(at::kChar));
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kChar, at::kStrided);
tensor = tensor.to(at::TensorOptions(at::kDouble));
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
}
TEST(TensorTest, ToTensorAndTensorAttributes) {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
auto other = at::empty({3, 4}, at::kInt);
tensor = tensor.to(other);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
other = at::empty({3, 4}, at::kDouble);
tensor = tensor.to(other.dtype());
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
tensor = tensor.to(other.device());
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
other = at::empty({3, 4}, at::kLong);
tensor = tensor.to(other.device(), other.dtype());
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kLong, at::kStrided);
other = at::empty({3, 4}, at::kInt);
tensor = tensor.to(other.options());
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
}
// Not currently supported.
// TEST(TensorTest, ToLayout) {
// auto tensor = at::empty({3, 4});
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
//
// tensor = tensor.to(at::kSparse);
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kSparse);
//
// tensor = tensor.to(at::kStrided);
// REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
// }
TEST(TensorTest, ToOptionsWithRequiresGrad) {
{
// Respects requires_grad
auto tensor = torch::empty({3, 4}, at::requires_grad());
ASSERT_TRUE(tensor.requires_grad());
tensor = tensor.to(at::kDouble);
ASSERT_TRUE(tensor.requires_grad());
// Throws if requires_grad is set in TensorOptions
ASSERT_THROW(
tensor.to(at::TensorOptions().requires_grad(true)), c10::Error);
ASSERT_THROW(
tensor.to(at::TensorOptions().requires_grad(false)), c10::Error);
}
{
auto tensor = torch::empty({3, 4});
ASSERT_FALSE(tensor.requires_grad());
// Respects requires_grad
tensor = tensor.to(at::kDouble);
ASSERT_FALSE(tensor.requires_grad());
// Throws if requires_grad is set in TensorOptions
ASSERT_THROW(
tensor.to(at::TensorOptions().requires_grad(true)), c10::Error);
ASSERT_THROW(
tensor.to(at::TensorOptions().requires_grad(false)), c10::Error);
}
}
TEST(TensorTest, ToDoesNotCopyWhenOptionsAreAllTheSame) {
{
auto tensor = at::empty({3, 4}, at::kFloat);
auto hopefully_not_copy = tensor.to(at::kFloat);
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
}
{
auto tensor = at::empty({3, 4}, at::kFloat);
auto hopefully_not_copy = tensor.to(tensor.options());
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
}
{
auto tensor = at::empty({3, 4}, at::kFloat);
auto hopefully_not_copy = tensor.to(tensor.dtype());
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
}
{
auto tensor = at::empty({3, 4}, at::kFloat);
auto hopefully_not_copy = tensor.to(tensor.device());
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
}
{
auto tensor = at::empty({3, 4}, at::kFloat);
auto hopefully_not_copy = tensor.to(tensor);
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
}
}
TEST(TensorTest, AtTensorCtorScalar) {
auto tensor = at::tensor(123);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_EQ(tensor[0].item<int32_t>(), 123);
tensor = at::tensor(123.456f);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kFloat);
ASSERT_TRUE(almost_equal(tensor[0], 123.456f));
tensor = at::tensor(123.456);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 123.456));
tensor = at::tensor(123, at::dtype(at::kFloat)) + 0.5;
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.dtype(), at::kFloat);
ASSERT_TRUE(almost_equal(tensor[0], 123.5));
}
TEST(TensorTest, AtTensorCtorSingleDim) {
auto tensor = at::tensor({1, 2, 3});
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = at::tensor(std::vector<int>({1, 2, 3}));
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = at::tensor({1.5, 2.25, 3.125});
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 1.5));
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
tensor = at::tensor({1.1, 2.2, 3.3}, at::dtype(at::kInt));
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_EQ(tensor.layout(), at::kStrided);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = at::tensor(std::vector<double>({1.5, 2.25, 3.125}));
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 1.5));
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
std::vector<int> v = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
tensor = at::tensor(v);
ASSERT_EQ(tensor.numel(), v.size());
ASSERT_EQ(tensor.dtype(), at::kInt);
for (size_t i = 0; i < v.size(); ++i) {
ASSERT_TRUE(exactly_equal(tensor[i], v.at(i)));
}
std::vector<double> w = {1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9, 10.0};
tensor = at::tensor(w);
ASSERT_EQ(tensor.numel(), w.size());
ASSERT_EQ(tensor.dtype(), at::kDouble);
for (size_t i = 0; i < w.size(); ++i) {
ASSERT_TRUE(almost_equal(tensor[i], w.at(i)));
}
}
TEST(TensorTest, TorchTensorCtorScalar) {
auto tensor = torch::tensor(123);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({}));
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_EQ(tensor.item<int32_t>(), 123);
tensor = torch::tensor(123.456f);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({}));
ASSERT_EQ(tensor.dtype(), at::kFloat);
ASSERT_TRUE(almost_equal(tensor, 123.456f));
tensor = torch::tensor(123.456);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({}));
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor, 123.456));
tensor = torch::tensor({123.456});
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1}));
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 123.456));
tensor = torch::tensor(true);
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({}));
ASSERT_EQ(tensor.dtype(), at::kBool);
ASSERT_TRUE(exactly_equal(tensor, true));
tensor = torch::tensor({true});
ASSERT_EQ(tensor.numel(), 1);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1}));
ASSERT_EQ(tensor.dtype(), at::kBool);
ASSERT_TRUE(exactly_equal(tensor[0], true));
}
TEST(TensorTest, TorchTensorCtorSingleDim) {
auto tensor = torch::tensor({1, 2, 3});
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = torch::tensor(at::ArrayRef<int>({1, 2, 3}));
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = torch::tensor(std::vector<int>({1, 2, 3}));
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
ASSERT_EQ(tensor.dtype(), at::kInt);
ASSERT_TRUE(exactly_equal(tensor[0], 1));
ASSERT_TRUE(exactly_equal(tensor[1], 2));
ASSERT_TRUE(exactly_equal(tensor[2], 3));
tensor = torch::tensor({1.5, 2.25, 3.125});
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 1.5));
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
tensor = torch::tensor(at::ArrayRef<double>({1.5, 2.25, 3.125}));
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 1.5));
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
tensor = torch::tensor(std::vector<double>({1.5, 2.25, 3.125}));
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
ASSERT_EQ(tensor.dtype(), at::kDouble);
ASSERT_TRUE(almost_equal(tensor[0], 1.5));
ASSERT_TRUE(almost_equal(tensor[1], 2.25));
ASSERT_TRUE(almost_equal(tensor[2], 3.125));
tensor = torch::tensor({true, false, true});
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
ASSERT_EQ(tensor.dtype(), at::kBool);
ASSERT_TRUE(exactly_equal(tensor[0], true));
ASSERT_TRUE(exactly_equal(tensor[1], false));
ASSERT_TRUE(exactly_equal(tensor[2], true));
tensor = torch::tensor(at::ArrayRef<bool>({true, false, true}));
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({3}));
ASSERT_EQ(tensor.dtype(), at::kBool);
ASSERT_TRUE(exactly_equal(tensor[0], true));
ASSERT_TRUE(exactly_equal(tensor[1], false));
ASSERT_TRUE(exactly_equal(tensor[2], true));
}
TEST(TensorTest, TorchTensorCtorMultiDim) {
{
auto tensor = torch::tensor({{1, 2}});
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 2}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 3, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{true, false}});
ASSERT_EQ(tensor.dtype(), torch::kBool);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 2}));
auto expected = torch::empty(tensor.sizes(), torch::kBool);
expected[0][0] = true;
expected[0][1] = false;
ASSERT_TRUE(torch::equal(tensor, expected));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{1.0, 2.0}});
ASSERT_EQ(tensor.dtype(), torch::kDouble);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 2}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 3, torch::kDouble).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{1, 2}}, torch::dtype(torch::kInt));
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 2}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 3, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{1}, {2}});
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({2, 1}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 3, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{true}, {false}});
ASSERT_EQ(tensor.dtype(), torch::kBool);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({2, 1}));
auto expected = torch::empty(tensor.sizes(), torch::kBool);
expected[0][0] = true;
expected[1][0] = false;
ASSERT_TRUE(torch::equal(tensor, expected));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{1, 2}}});
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 2}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 3, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{1}, {2}}});
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 2, 1}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 3, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{1, 2}, {3, 4}});
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({2, 2}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 5, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{1, 2}, {3, 4}}, torch::dtype(torch::kFloat).requires_grad(true));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({2, 2}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 5, torch::kFloat).view(tensor.sizes())));
ASSERT_TRUE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{{{{{{1.0, 2.0, 3.0}}}}}, {{{{{4.0, 5.0, 6.0}}}}}, {{{{{7.0, 8.0, 9.0}}}}}}}});
ASSERT_EQ(tensor.dtype(), torch::kDouble);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 3, 1, 1, 1, 1, 3}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 10, torch::kDouble).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
ASSERT_THROWS_WITH(torch::tensor({{{2, 3, 4}, {{5, 6}, {7}}}}),
"Expected all sub-lists to have sizes: 2 (e.g. {5, 6}), but got sub-list {7} with sizes: 1");
}
{
ASSERT_THROWS_WITH(torch::tensor({{{1, 2.0}, {1, 2.0}}}),
"Expected all elements of the tensor to have the same scalar type: Int, but got element of scalar type: Double");
}
{
ASSERT_THROWS_WITH(torch::tensor({{{true, 2.0, 3}, {true, 2.0, 3}}}),
"Expected all elements of the tensor to have the same scalar type: Bool, but got element of scalar type: Double");
}
{
ASSERT_THROWS_WITH(torch::tensor({{{true}, {2}}}),
"Expected all elements of the tensor to have the same scalar type: Bool, but got element of scalar type: Int");
}
{
ASSERT_THROWS_WITH(torch::tensor({{{true, 2}}}),
"Expected all elements of the tensor to have the same scalar type: Bool, but got element of scalar type: Int");
}
{
auto tensor = torch::tensor({{{{{{{{{{1}}}}}}}}}});
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 1, 1, 1, 1, 1, 1, 1, 1}));
ASSERT_TRUE(torch::allclose(tensor, torch::full({1}, 1, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{{{{{{{{1, 2}}}}}}}}}});
ASSERT_EQ(tensor.dtype(), torch::kInt);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 1, 1, 1, 1, 1, 1, 1, 2}));
ASSERT_TRUE(torch::allclose(tensor, torch::arange(1, 3, torch::kInt).view(tensor.sizes())));
ASSERT_FALSE(tensor.requires_grad());
}
}
TEST(TensorTest, TorchTensorCtorMultiDim_CUDA) {
{
auto tensor = torch::tensor(
{{{{{{{{1.0, 2.0, 3.0}}}}}, {{{{{4.0, 5.0, 6.0}}}}}, {{{{{7.0, 8.0, 9.0}}}}}}}},
torch::dtype(torch::kDouble).device(torch::kCUDA));
ASSERT_TRUE(tensor.device().is_cuda());
ASSERT_EQ(tensor.dtype(), torch::kDouble);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 3, 1, 1, 1, 1, 3}));
ASSERT_TRUE(torch::allclose(
tensor,
torch::arange(1, 10, torch::kDouble).view(tensor.sizes()).to(torch::kCUDA)));
ASSERT_FALSE(tensor.requires_grad());
}
}
TEST(TensorTest, TorchTensorCtorZeroSizedDim) {
{
auto tensor = torch::tensor({});
ASSERT_EQ(tensor.numel(), 0);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({0}));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{}, {}});
ASSERT_EQ(tensor.numel(), 0);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({2, 0}));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{}, {}}});
ASSERT_EQ(tensor.numel(), 0);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 2, 0}));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{}}});
ASSERT_EQ(tensor.numel(), 0);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 0}));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{{{{{{}}}}}}}});
ASSERT_EQ(tensor.numel(), 0);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 1, 1, 1, 1, 1, 0}));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{{{{{{}}}}, {{{{}}}}}}}});
ASSERT_EQ(tensor.numel(), 0);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 1, 2, 1, 1, 1, 0}));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_FALSE(tensor.requires_grad());
}
{
auto tensor = torch::tensor({{{{{{{{{{}}}}}}}}}});
ASSERT_EQ(tensor.numel(), 0);
ASSERT_EQ(tensor.sizes(), std::vector<int64_t>({1, 1, 1, 1, 1, 1, 1, 1, 1, 0}));
ASSERT_EQ(tensor.dtype(), torch::kFloat);
ASSERT_FALSE(tensor.requires_grad());
}
}
TEST(TensorTest, TorchTensorCtorPreservesInitListDtype) {
ASSERT_EQ(torch::tensor({1, 2, 3}).dtype(), torch::kInt);
ASSERT_EQ(torch::tensor({{1, 2, 3}}).dtype(), torch::kInt);
ASSERT_EQ(torch::tensor({1., 2., 3.}).dtype(), torch::kDouble);
ASSERT_EQ(torch::tensor({{1., 2., 3.}}).dtype(), torch::kDouble);
ASSERT_EQ(torch::tensor({1, 2, 3}, torch::TensorOptions()).dtype(), torch::kInt);
ASSERT_EQ(torch::tensor({{1, 2, 3}}, torch::TensorOptions()).dtype(), torch::kInt);
ASSERT_EQ(torch::tensor({1., 2., 3.}, torch::TensorOptions()).dtype(), torch::kDouble);
ASSERT_EQ(torch::tensor({{1., 2., 3.}}, torch::TensorOptions()).dtype(), torch::kDouble);
}
TEST(TensorTest, PrettyPrintTensorDataContainer) {
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer(1.1)),
"1.1");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer({1.1, 2.2})),
"{1.1, 2.2}");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer({{1, 2}, {3, 4}})),
"{{1, 2}, {3, 4}}");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer({{{{{{{{1.1, 2.2, 3.3}}}}}, {{{{{4.4, 5.5, 6.6}}}}}, {{{{{7.7, 8.8, 9.9}}}}}}}})),
"{{{{{{{{1.1, 2.2, 3.3}}}}}, {{{{{4.4, 5.5, 6.6}}}}}, {{{{{7.7, 8.8, 9.9}}}}}}}}");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer({{{{{{{{{{1}}}}}}}}}})),
"{{{{{{{{{{1}}}}}}}}}}");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer({{{{{{{{{{}}}}}}}}}})),
"{{{{{{{{{{}}}}}}}}}}");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer({{{{{{{{{{1, 2}}}}}}}}}})),
"{{{{{{{{{{1, 2}}}}}}}}}}");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer(at::ArrayRef<double>({1.1, 2.2}))),
"{1.1, 2.2}");
}
{
ASSERT_EQ(
c10::str(torch::detail::TensorDataContainer(std::vector<double>({1.1, 2.2}))),
"{1.1, 2.2}");
}
}
TEST(TensorTest, TensorDataContainerCallingAccessorOfWrongType) {
{
ASSERT_THROWS_WITH(
torch::detail::TensorDataContainer(1.1).init_list(),
"Can only call `init_list()` on a TensorDataContainer that has `is_init_list() == true`");
ASSERT_THROWS_WITH(
torch::detail::TensorDataContainer(1.1).tensor(),
"Can only call `tensor()` on a TensorDataContainer that has `is_tensor() == true`");
}
{
ASSERT_THROWS_WITH(
torch::detail::TensorDataContainer({1.1, 2.2}).scalar(),
"Can only call `scalar()` on a TensorDataContainer that has `is_scalar() == true`");
ASSERT_THROWS_WITH(
torch::detail::TensorDataContainer({1.1, 2.2}).tensor(),
"Can only call `tensor()` on a TensorDataContainer that has `is_tensor() == true`");
}
{
ASSERT_THROWS_WITH(
torch::detail::TensorDataContainer(at::ArrayRef<double>({1.1, 2.2})).scalar(),
"Can only call `scalar()` on a TensorDataContainer that has `is_scalar() == true`");
ASSERT_THROWS_WITH(
torch::detail::TensorDataContainer(at::ArrayRef<double>({1.1, 2.2})).init_list(),
"Can only call `init_list()` on a TensorDataContainer that has `is_init_list() == true`");
}
}
TEST(TensorTest, FromBlob) {
std::vector<double> v = {1.0, 2.0, 3.0};
auto tensor = torch::from_blob(
v.data(), v.size(), torch::dtype(torch::kFloat64).requires_grad(true));
ASSERT_TRUE(tensor.is_variable());
ASSERT_TRUE(tensor.requires_grad());
ASSERT_EQ(tensor.dtype(), torch::kFloat64);
ASSERT_EQ(tensor.numel(), 3);
ASSERT_EQ(tensor[0].item<double>(), 1);
ASSERT_EQ(tensor[1].item<double>(), 2);
ASSERT_EQ(tensor[2].item<double>(), 3);
}
TEST(TensorTest, FromBlobUsesDeleter) {
bool called = false;
{
std::vector<int32_t> v = {1, 2, 3};
auto tensor = torch::from_blob(
v.data(),
v.size(),
/*deleter=*/[&called](void* data) { called = true; },
torch::kInt32);
}
ASSERT_TRUE(called);
}
TEST(TensorTest, FromBlobWithStrides) {
// clang-format off
std::vector<int32_t> v = {
1, 2, 3,
4, 5, 6,
7, 8, 9
};
// clang-format on
auto tensor = torch::from_blob(
v.data(),
/*sizes=*/{3, 3},
/*strides=*/{1, 3},
torch::kInt32);
ASSERT_TRUE(tensor.is_variable());
ASSERT_EQ(tensor.dtype(), torch::kInt32);
ASSERT_EQ(tensor.numel(), 9);
const std::vector<int64_t> expected_strides = {1, 3};
ASSERT_EQ(tensor.strides(), expected_strides);
for (int64_t i = 0; i < tensor.size(0); ++i) {
for (int64_t j = 0; j < tensor.size(1); ++j) {
// NOTE: This is column major because the strides are swapped.
EXPECT_EQ(tensor[i][j].item<int32_t>(), 1 + (j * tensor.size(1)) + i);
}
}
}
TEST(TensorTest, Item) {
{
torch::Tensor tensor = torch::tensor(3.14);
torch::Scalar scalar = tensor.item();
ASSERT_NEAR(scalar.to<float>(), 3.14, 1e-5);
}
{
torch::Tensor tensor = torch::tensor(123);
torch::Scalar scalar = tensor.item();
ASSERT_EQ(scalar.to<int>(), 123);
}
}
TEST(TensorTest, Item_CUDA) {
{
torch::Tensor tensor = torch::tensor(3.14, torch::kCUDA);
torch::Scalar scalar = tensor.item();
ASSERT_NEAR(scalar.to<float>(), 3.14, 1e-5);
}
{
torch::Tensor tensor = torch::tensor(123, torch::kCUDA);
torch::Scalar scalar = tensor.item();
ASSERT_EQ(scalar.to<int>(), 123);
}
}
TEST(TensorTest, DataPtr) {
auto tensor = at::empty({3, 4}, at::kFloat);
auto tensor_not_copy = tensor.to(tensor.options());
ASSERT_EQ(tensor_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
ASSERT_EQ(tensor_not_copy.data_ptr(), tensor.data_ptr());
}
TEST(TensorTest, Data) {
const auto tensor = torch::rand({3, 3});
ASSERT_TRUE(torch::equal(tensor, tensor.data()));
}
TEST(TensorTest, BackwardAndGrad) {
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = x * x;
y.backward();
ASSERT_EQ(x.grad().item<float>(), 10.0);
}
TEST(TensorTest, BackwardCreatesOnesGrad) {
const auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
x.backward();
ASSERT_TRUE(torch::equal(x.grad(),
torch::ones_like(x)));
}
TEST(TensorTest, BackwardNonScalarOutputs) {
auto x = torch::randn({5, 5}, torch::requires_grad());
auto y = x * x;
ASSERT_THROWS_WITH(y.backward(),
"grad can be implicitly created only for scalar outputs");
}
TEST(TensorTest, IsLeaf) {
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = x * x;
ASSERT_TRUE(x.is_leaf());
ASSERT_FALSE(y.is_leaf());
}
TEST(TensorTest, OutputNr) {
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = x * x;
ASSERT_EQ(x.output_nr(), 0);
ASSERT_EQ(y.output_nr(), 0);
}
TEST(TensorTest, Version) {
auto x = torch::ones(3);
ASSERT_EQ(x._version(), 0);
x.mul_(2);
ASSERT_EQ(x._version(), 1);
x.add_(1);
ASSERT_EQ(x._version(), 2);
}
TEST(TensorTest, Detach) {
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = x * x;
const auto y_detached = y.detach();
ASSERT_FALSE(y.is_leaf());
ASSERT_TRUE(y_detached.is_leaf());
ASSERT_FALSE(y_detached.requires_grad());
}
TEST(TensorTest, DetachInplace) {
auto x = torch::tensor({5}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = x * x;
auto y_detached = y.detach_();
ASSERT_TRUE(y.is_leaf());
ASSERT_FALSE(y.requires_grad());
ASSERT_TRUE(y_detached.is_leaf());
ASSERT_FALSE(y_detached.requires_grad());
}
TEST(TensorTest, SetData) {
auto x = torch::randn({5});
auto y = torch::randn({5});
ASSERT_FALSE(torch::equal(x, y));
ASSERT_NE(x.data_ptr<float>(), y.data_ptr<float>());
x.set_data(y);
ASSERT_TRUE(torch::equal(x, y));
ASSERT_EQ(x.data_ptr<float>(), y.data_ptr<float>());
}
TEST(TensorTest, RequiresGradInplace) {
auto x = torch::tensor({5.0});
x.requires_grad_(true);
ASSERT_TRUE(x.requires_grad());
auto y = x * x;
ASSERT_THROWS_WITH(y.requires_grad_(false),
"you can only change requires_grad flags of leaf variables.");
x.requires_grad_(false);
ASSERT_FALSE(x.requires_grad());
const auto int_tensor = torch::tensor({5}, at::TensorOptions().dtype(torch::kInt));
ASSERT_THROWS_WITH(int_tensor.requires_grad_(true),
"Only Tensors of floating point dtype can require gradients");
}
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