| #include "gtest/gtest.h" |
| |
| #include "ATen/ATen.h" |
| #include "ATen/core/Reduction.h" |
| |
| // for TH compat test only... |
| struct THFloatTensor; |
| extern "C" THFloatTensor * THFloatTensor_newWithSize2d(size_t a, size_t b); |
| extern "C" void THFloatTensor_fill(THFloatTensor *, float v); |
| |
| #include <iostream> |
| #include <chrono> |
| #include <string.h> |
| #include <sstream> |
| #include "test_seed.h" |
| |
| #define ASSERT_EQ_RESOLVED(X, Y) \ |
| { \ |
| bool isEQ = X == Y; \ |
| ASSERT_TRUE(isEQ); \ |
| } |
| |
| using namespace at; |
| |
| void TestResize(Type& type) { |
| auto a = at::empty({0}, type.options()); |
| a.resize_({3, 4}); |
| ASSERT_EQ_RESOLVED(a.numel(), 12); |
| a.resize_({5, 7}); |
| ASSERT_EQ_RESOLVED(a.numel(), 35); |
| } |
| |
| void TestOnesAndDot(Type& type) { |
| Tensor b0 = ones({1, 1}, type); |
| ASSERT_EQ_RESOLVED((b0 + b0).sum().item<double>(), 2); |
| |
| Tensor b1 = ones({1, 2}, type); |
| ASSERT_EQ_RESOLVED((b1 + b1).sum().item<double>(), 4); |
| |
| Tensor b = ones({3, 4}, type); |
| ASSERT_EQ_RESOLVED((b + b).sum().item<double>(), 24); |
| ASSERT_EQ_RESOLVED(b.numel(), 12); |
| ASSERT_EQ_RESOLVED(b.view(-1).dot(b.view(-1)).item<double>(), 12); |
| } |
| |
| void TestSort(Type& type) { |
| Tensor b = rand({3, 4}, type); |
| |
| auto z = b.sort(1); |
| auto z_sorted = std::get<0>(z); |
| |
| bool isLT = z_sorted[0][0].item<float>() < z_sorted[0][1].item<float>(); |
| ASSERT_TRUE(isLT); |
| } |
| |
| void TestRandperm(Type& type) { |
| if (type.backend() != Backend::CUDA) { |
| Tensor b = randperm(15, type); |
| Tensor rv, ri; |
| std::tie(rv, ri) = sort(b, 0); |
| bool isLE = (rv[0].item<float>() <= rv[1].item<float>()); |
| ASSERT_TRUE(isLE); |
| } |
| } |
| |
| void SendContext() { |
| std::stringstream ss; |
| ss << "context: " << std::hex << (int64_t)&globalContext() << std::endl; |
| } |
| |
| void TestAdd(Type& type) { |
| Tensor a = rand({3, 4}, type); |
| Tensor b = rand({3, 4}, type); |
| Tensor c = add(a, add(a, b)); |
| // TODO:0-dim Tensor d(3.f); |
| Scalar d = 3.f; |
| ASSERT_TRUE(add(c, d).allclose(a + a + b + d)); |
| } |
| |
| void TestLoadsOfAdds(Type& type) { |
| auto begin = std::chrono::high_resolution_clock::now(); |
| Tensor d = ones({3, 4}, type); |
| Tensor r = zeros({3, 4}, type); |
| for (auto i = 0; i < 100000; i++) { |
| add_out(r, r, d); |
| } |
| auto end = std::chrono::high_resolution_clock::now(); |
| // TODO TEST PERF? |
| std::cout << std::dec << " " |
| << std::chrono::duration_cast<std::chrono::milliseconds>( |
| end - begin) |
| .count() |
| << " ms" << std::endl; |
| ASSERT_EQ_RESOLVED(norm(100000 * d).item<double>(), norm(r).item<double>()); |
| } |
| |
| void TestLoadOfAddsWithCopy(Type& type) { |
| auto begin = std::chrono::high_resolution_clock::now(); |
| Tensor d = ones({3, 4}, type); |
| Tensor r = zeros({3, 4}, type); |
| for (auto i = 0; i < 100000; i++) { |
| r = add(r, d); |
| } |
| auto end = std::chrono::high_resolution_clock::now(); |
| // TODO TEST PERF? |
| std::cout << std::dec << " " |
| << std::chrono::duration_cast<std::chrono::milliseconds>( |
| end - begin) |
| .count() |
| << " ms" << std::endl; |
| ASSERT_EQ_RESOLVED(norm(100000 * d).item<double>(), norm(r).item<double>()); |
| } |
| |
| void TestIsContiguous(Type& type) { |
| Tensor a = rand({3, 4}, type); |
| ASSERT_TRUE(a.is_contiguous()); |
| a = a.transpose(0, 1); |
| ASSERT_FALSE(a.is_contiguous()); |
| } |
| |
| void TestPermute(Type& type) { |
| Tensor a = rand({3, 4, 5}, type); |
| Tensor b = a.permute({1, 2, 0}); |
| ASSERT_TRUE(b.sizes().equals({4, 5, 3})); |
| ASSERT_TRUE(b.strides().equals({5, 1, 20})); |
| } |
| |
| void TestMm(Type& type) { |
| Tensor a = rand({3, 4}, type); |
| Tensor b = rand({4}, type); |
| Tensor c = mv(a, b); |
| ASSERT_TRUE(c.equal(addmv(zeros({3}, type), a, b, 0, 1))); |
| } |
| |
| void TestSqueeze(Type& type) { |
| Tensor a = rand({2, 1}, type); |
| Tensor b = squeeze(a); |
| ASSERT_EQ_RESOLVED(b.dim(), 1); |
| a = rand({1}, type); |
| b = squeeze(a); |
| // TODO 0-dim squeeze |
| ASSERT_TRUE(a[0].equal(b)); |
| } |
| |
| void TestCopy(Type& type) { |
| Tensor a = zeros({4, 3}, type); |
| Tensor e = rand({4, 3}, type); |
| a.copy_(e); |
| ASSERT_TRUE(a.equal(e)); |
| } |
| |
| void TestCopyBroadcasting(Type& type) { |
| Tensor a = zeros({4, 3}, type); |
| Tensor e = rand({3}, type); |
| a.copy_(e); |
| for (int i = 0; i < 4; ++i) { |
| ASSERT_TRUE(a[i].equal(e)); |
| } |
| } |
| void TestAbsValue(Type& type) { |
| Tensor r = at::abs(type.scalarTensor(-3)); |
| ASSERT_EQ_RESOLVED(r.item<int32_t>(), 3); |
| } |
| /* |
| TODO(zach): operator overloads |
| #if 0 |
| { |
| std::cout << "eq (value):" << std::endl; |
| Tensor a = Tensor(10.f); |
| std::cout << (a == 11_i64) << " -- should be 0" << std::endl; |
| std::cout << (a == 10_i64) << " -- should be 1" << std::endl; |
| std::cout << (a == 10.) << " -- should be 1" << std::endl; |
| } |
| #endif |
| */ |
| |
| void TestAddingAValueWithScalar(Type& type) { |
| Tensor a = rand({4, 3}, type); |
| ASSERT_TRUE((ones({4, 3}, type) + a).equal(add(a, 1))); |
| } |
| |
| void TestSelect(Type& type) { |
| Tensor a = rand({3, 7}, type); |
| auto a_13 = select(a, 1, 3); |
| auto a_13_02 = select(select(a, 1, 3), 0, 2); |
| ASSERT_TRUE(a[0][3].equal(a_13[0])); |
| ASSERT_TRUE(a[2][3].equal(a_13_02)); |
| } |
| |
| void TestZeroDim(Type& type) { |
| Tensor a = type.scalarTensor(4); // rand(type, {1}); |
| |
| Tensor b = rand({3, 4}, type); |
| ASSERT_EQ_RESOLVED((a + a).dim(), 0); |
| ASSERT_EQ_RESOLVED((1 + a).dim(), 0); |
| ASSERT_EQ_RESOLVED((b + a).dim(), 2); |
| ASSERT_EQ_RESOLVED((a + b).dim(), 2); |
| auto c = rand({3, 4}, type); |
| ASSERT_EQ_RESOLVED(c[1][2].dim(), 0); |
| |
| auto f = rand({3, 4}, type); |
| f[2] = zeros({4}, type); |
| f[1][0] = -1; |
| ASSERT_EQ_RESOLVED(f[2][0].item<double>(), 0); |
| } |
| |
| void TestTensorFromTH() { |
| int a = 4; |
| THFloatTensor* t = THFloatTensor_newWithSize2d(a, a); |
| THFloatTensor_fill(t, a); |
| Tensor tt = CPU(kFloat).unsafeTensorFromTH(t, false); |
| ASSERT_NO_THROW(tt); |
| } |
| |
| void TestToCFloat() { |
| Tensor a = zeros({3, 4}); |
| Tensor b = ones({3, 7}); |
| Tensor c = cat({a, b}, 1); |
| ASSERT_EQ_RESOLVED(c.size(1), 11); |
| |
| Tensor e = rand({}); |
| ASSERT_EQ_RESOLVED(*e.data<float>(), e.sum().item<float>()); |
| } |
| void TestToString() { |
| Tensor b = ones({3, 7}) * .0000001f; |
| std::stringstream s; |
| s << b << "\n"; |
| std::string expect = "1e-07 *"; |
| ASSERT_EQ_RESOLVED(s.str().substr(0, expect.size()), expect); |
| } |
| |
| void TestIndexingByScalar() { |
| Tensor tensor = arange(0, 10, kInt); |
| Tensor one = ones({}, kInt); |
| for (int64_t i = 0; i < tensor.numel(); ++i) { |
| ASSERT_TRUE(tensor[i].equal(one * i)); |
| } |
| for (size_t i = 0; i < static_cast<uint64_t>(tensor.numel()); ++i) { |
| ASSERT_TRUE(tensor[i].equal(one * static_cast<int64_t>(i))); |
| } |
| for (int i = 0; i < tensor.numel(); ++i) { |
| ASSERT_TRUE(tensor[i].equal(one * i)); |
| } |
| for (int16_t i = 0; i < tensor.numel(); ++i) { |
| ASSERT_TRUE(tensor[i].equal(one * i)); |
| } |
| for (int8_t i = 0; i < tensor.numel(); ++i) { |
| ASSERT_TRUE(tensor[i].equal(one * i)); |
| } |
| // Throw StartsWith("Can only index tensors with integral scalars") |
| ASSERT_ANY_THROW(tensor[Scalar(3.14)].equal(one)); |
| } |
| |
| void TestIndexingByZerodimTensor() { |
| Tensor tensor = arange(0, 10, kInt); |
| Tensor one = ones({}, kInt); |
| for (int i = 0; i < tensor.numel(); ++i) { |
| ASSERT_TRUE(tensor[one * i].equal(one * i)); |
| } |
| // Throw StartsWith( |
| // "Can only index tensors with integral scalars") |
| ASSERT_ANY_THROW(tensor[ones({}) * 3.14].equal(one)); |
| // Throw StartsWith("Can only index with tensors that are defined") |
| ASSERT_ANY_THROW(tensor[Tensor()].equal(one)); |
| // Throw StartsWith("Can only index with tensors that are scalars (zero-dim)") |
| ASSERT_ANY_THROW(tensor[ones({2, 3, 4}, kInt)].equal(one)); |
| } |
| void TestIndexingMixedDevice(Type& type) { |
| Tensor tensor = randn({20, 20}, type); |
| Tensor index = arange(10, kLong).cpu(); |
| Tensor result = tensor.index({index}); |
| ASSERT_TRUE(result[0].equal(tensor[0])); |
| } |
| void TestDispatch() { |
| Tensor tensor = randn({20, 20}); |
| Tensor other = randn({20, 20}); |
| auto result = tensor.m(relu).m(mse_loss, other, Reduction::Mean); |
| ASSERT_TRUE(result.allclose(mse_loss(relu(tensor), other))); |
| } |
| |
| void TestCore() { |
| int i = CoreTest(); |
| ASSERT_EQ_RESOLVED(i + 1, CoreTest()); |
| } |
| |
| void test(Type& type) { |
| TestResize(type); |
| TestOnesAndDot(type); |
| |
| TestSort(type); |
| TestRandperm(type); |
| TestAdd(type); |
| TestLoadsOfAdds(type); |
| TestLoadOfAddsWithCopy(type); |
| TestIsContiguous(type); |
| TestPermute(type); |
| TestMm(type); |
| TestSqueeze(type); |
| TestCopy(type); |
| TestCopyBroadcasting(type); |
| TestAbsValue(type); |
| TestAddingAValueWithScalar(type); |
| TestSelect(type); |
| TestZeroDim(type); |
| TestTensorFromTH(); |
| TestToCFloat(); |
| TestToString(); |
| TestIndexingByScalar(); |
| TestIndexingByZerodimTensor(); |
| TestIndexingMixedDevice(type); |
| TestDispatch(); |
| TestCore(); |
| } |
| |
| TEST(BasicTest, BasicTestCPU) { |
| manual_seed(123, at::kCPU); |
| |
| test(CPU(kFloat)); |
| } |
| |
| TEST(BasicTest, BasicTestCUDA) { |
| manual_seed(123, at::kCUDA); |
| |
| if (at::hasCUDA()) { |
| test(CUDA(kFloat)); |
| } |
| } |