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android / platform / external / pytorch / 978304fc9cc40e6717f82d959969a5bd5dd645f4 / . / test / cpp / api / modules.cpp
blob: cdf4f0ea0deb07e347cfa0a0203f734a81e05b25 [file] [log] [blame]
#include <gtest/gtest.h>
#include <c10/util/irange.h>
#include <torch/torch.h>
#include <test/cpp/api/support.h>
#include <random>
#include <torch/nn/options/activation.h>
#include <torch/nn/functional/activation.h>
#include <torch/expanding_array.h>
#include <limits>
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(1).bias(false));
model->weight.set_data(torch::arange(18, torch::dtype(torch::kFloat)).reshape({2, 3, 3}));
auto x = torch::arange(30, torch::dtype(torch::kFloat).requires_grad(true)).reshape({2, 3, 5});
auto y = model(x);
auto expected = torch::tensor({{{ 312., 348., 384.},
{ 798., 915., 1032.}},
{{ 852., 888., 924.},
{2553., 2670., 2787.}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3);
}
TEST_F(ModulesTest, Conv1dSameStrided) {
auto options = Conv1dOptions(3, 2, 3);
options.stride(1).padding(torch::kSame);
Conv1d model_valid(options);
ASSERT_THROWS_WITH(
[&]{ Conv1d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
}
TEST_F(ModulesTest, Conv2dEven) {
Conv2d model(Conv2dOptions(3, 2, 3).stride(1).bias(false));
model->weight.set_data(torch::arange(54, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 3}));
auto x = torch::arange(75, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 5});
auto y = model(x);
auto expected = torch::tensor({{{{15219., 15570., 15921.},
{16974., 17325., 17676.},
{18729., 19080., 19431.}},
{{37818., 38898., 39978.},
{43218., 44298., 45378.},
{48618., 49698., 50778.}}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 3);
}
TEST_F(ModulesTest, Conv2dUneven) {
Conv2d model(Conv2dOptions(3, 2, {3, 2}).stride({1, 1}).bias(false));
model->weight.set_data(torch::arange(36, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 2}));
auto x = torch::arange(60, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 4});
auto y = model(x);
auto expected = torch::tensor({{{{ 5289., 5442., 5595.},
{ 5901., 6054., 6207.},
{ 6513., 6666., 6819.}},
{{13227., 13704., 14181.},
{15135., 15612., 16089.},
{17043., 17520., 17997.}}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 2);
}
TEST_F(ModulesTest, Conv2dSameStrided) {
auto options = Conv2dOptions(3, 2, {3, 4});
options.stride(1).padding(torch::kSame);
Conv2d model_valid(options);
ASSERT_THROWS_WITH(
[&]{ Conv2d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
ASSERT_THROWS_WITH(
[&]{ Conv2d model_invalid(options.stride({1, 2})); }(),
"padding='same' is not supported for strided convolutions");
}
TEST_F(ModulesTest, Conv3d) {
Conv3d model(Conv3dOptions(3, 2, 3).stride(1).bias(false));
model->weight.set_data(torch::arange(162, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 3, 3}));
auto x = torch::arange(375, torch::dtype(torch::kFloat).requires_grad(true)).reshape({1, 3, 5, 5, 5});
auto y = model(x);
auto expected = torch::tensor({{{{{ 700704., 703944., 707184.},
{ 716904., 720144., 723384.},
{ 733104., 736344., 739584.}},
{{ 781704., 784944., 788184.},
{ 797904., 801144., 804384.},
{ 814104., 817344., 820584.}},
{{ 862704., 865944., 869184.},
{ 878904., 882144., 885384.},
{ 895104., 898344., 901584.}}},
{{{1724220., 1734021., 1743822.},
{1773225., 1783026., 1792827.},
{1822230., 1832031., 1841832.}},
{{1969245., 1979046., 1988847.},
{2018250., 2028051., 2037852.},
{2067255., 2077056., 2086857.}},
{{2214270., 2224071., 2233872.},
{2263275., 2273076., 2282877.},
{2312280., 2322081., 2331882.}}}}}, torch::kFloat);
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(model->weight.grad().numel() == 3 * 2 * 3 * 3 * 3);
}
TEST_F(ModulesTest, Conv3dSameStrided) {
auto options = Conv3dOptions(3, 2, {3, 4, 5});
options.stride(1).padding(torch::kSame);
Conv3d model_valid(options);
ASSERT_THROWS_WITH(
[&]{ Conv3d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
ASSERT_THROWS_WITH(
[&]{ Conv3d model_invalid(options.stride({1, 2, 1})); }(),
"padding='same' is not supported for strided convolutions");
}
TEST_F(ModulesTest, ConvTranspose1d) {
ConvTranspose1d model(ConvTranspose1dOptions(3, 2, 3).stride(1).bias(false));
model->weight.set_data(torch::arange(18.).view({2, 3, 3}));
auto x = torch::arange(20.).reshape({2, 2, 5});
auto y = model(x);
auto expected = torch::tensor({{{ 45., 104., 179., 212., 245., 188., 107.},
{ 60., 140., 242., 293., 344., 260., 146.},
{ 75., 176., 305., 374., 443., 332., 185.}},
{{ 135., 304., 509., 542., 575., 428., 237.},
{ 210., 460., 752., 803., 854., 620., 336.},
{ 285., 616., 995., 1064., 1133., 812., 435.}}});
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3);
}
TEST_F(ModulesTest, ConvTranspose2dEven) {
ConvTranspose2d model(ConvTranspose2dOptions(3, 2, 3).stride(1).bias(false));
model->weight.set_data(torch::arange(54.).view({2, 3, 3, 3}));
auto x = torch::arange(50.).view({1, 2, 5, 5});
auto y = model(x);
auto expected = torch::tensor({{{{ 675., 1402., 2183., 2270., 2357., 1634., 849.},
{ 1560., 3240., 5044., 5236., 5428., 3760., 1952.},
{ 2685., 5574., 8673., 8988., 9303., 6438., 3339.},
{ 3180., 6594., 10248., 10563., 10878., 7518., 3894.},
{ 3675., 7614., 11823., 12138., 12453., 8598., 4449.},
{ 2820., 5832., 9040., 9268., 9496., 6544., 3380.},
{ 1605., 3314., 5129., 5252., 5375., 3698., 1907.}},
{{ 900., 1870., 2912., 3053., 3194., 2210., 1146.},
{ 2100., 4356., 6772., 7072., 7372., 5092., 2636.},
{ 3630., 7518., 11670., 12147., 12624., 8706., 4500.},
{ 4395., 9078., 14055., 14532., 15009., 10326., 5325.},
{ 5160., 10638., 16440., 16917., 17394., 11946., 6150.},
{ 3900., 8028., 12388., 12724., 13060., 8956., 4604.},
{ 2190., 4502., 6938., 7115., 7292., 4994., 2564.}},
{{ 1125., 2338., 3641., 3836., 4031., 2786., 1443.},
{ 2640., 5472., 8500., 8908., 9316., 6424., 3320.},
{ 4575., 9462., 14667., 15306., 15945., 10974., 5661.},
{ 5610., 11562., 17862., 18501., 19140., 13134., 6756.},
{ 6645., 13662., 21057., 21696., 22335., 15294., 7851.},
{ 4980., 10224., 15736., 16180., 16624., 11368., 5828.},
{ 2775., 5690., 8747., 8978., 9209., 6290., 3221.}}}});
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 3);
}
TEST_F(ModulesTest, ConvTranspose2dUneven) {
ConvTranspose2d model(ConvTranspose2dOptions(3, 2, {3, 2}).stride({1, 1}).bias(false));
model->weight.set_data(torch::arange(36.).view({2, 3, 3, 2}));
auto x = torch::arange(40.).view({1, 2, 5, 4});
auto y = model(x);
auto expected = torch::tensor({{{{ 360., 758., 796., 834., 440.},
{ 832., 1752., 1836., 1920., 1012.},
{1432., 3014., 3152., 3290., 1732.},
{1696., 3566., 3704., 3842., 2020.},
{1960., 4118., 4256., 4394., 2308.},
{1504., 3152., 3252., 3352., 1756.},
{ 856., 1790., 1844., 1898., 992.}},
{{ 480., 1010., 1072., 1134., 596.},
{1120., 2352., 2484., 2616., 1372.},
{1936., 4058., 4268., 4478., 2344.},
{2344., 4898., 5108., 5318., 2776.},
{2752., 5738., 5948., 6158., 3208.},
{2080., 4328., 4476., 4624., 2404.},
{1168., 2426., 2504., 2582., 1340.}},
{{ 600., 1262., 1348., 1434., 752.},
{1408., 2952., 3132., 3312., 1732.},
{2440., 5102., 5384., 5666., 2956.},
{2992., 6230., 6512., 6794., 3532.},
{3544., 7358., 7640., 7922., 4108.},
{2656., 5504., 5700., 5896., 3052.},
{1480., 3062., 3164., 3266., 1688.}}}});
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 2);
}
TEST_F(ModulesTest, ConvTranspose3d) {
ConvTranspose3d model(ConvTranspose3dOptions(2, 2, 2).stride(1).bias(false));
model->weight.set_data(torch::arange(32.).reshape({2, 2, 2, 2, 2}));
auto x = torch::arange(16.).reshape({1, 2, 2, 2, 2});
auto y = model(x);
auto expected = torch::tensor({{{{{ 128., 280., 154.},
{ 304., 664., 364.},
{ 184., 400., 218.}},
{{ 352., 768., 420.},
{ 832., 1808., 984.},
{ 496., 1072., 580.}},
{{ 256., 552., 298.},
{ 592., 1272., 684.},
{ 344., 736., 394.}}},
{{{ 192., 424., 234.},
{ 464., 1016., 556.},
{ 280., 608., 330.}},
{{ 544., 1184., 644.},
{1280., 2768., 1496.},
{ 752., 1616., 868.}},
{{ 384., 824., 442.},
{ 880., 1880., 1004.},
{ 504., 1072., 570.}}}}});
ASSERT_TRUE(torch::allclose(y, expected));
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(model->weight.grad().numel() == 2 * 2 * 2 * 2 * 2);
}
TEST_F(ModulesTest, MaxPool1d) {
MaxPool1d model(MaxPool1dOptions(3).stride(2));
auto x = torch::ones({1, 1, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1 ,2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
}
TEST_F(ModulesTest, MaxPool1dReturnIndices) {
MaxPool1d model(MaxPool1dOptions(3).stride(2));
auto x = torch::ones({1, 1, 5}, torch::requires_grad());
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
ASSERT_EQ(y.dim(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1 ,2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
ASSERT_TRUE(torch::allclose(indices, torch::tensor({{{0, 2}}}, torch::kLong)));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({1, 1, 2}));
}
TEST_F(ModulesTest, MaxPool2dEven) {
MaxPool2d model(MaxPool2dOptions(3).stride(2));
auto x = torch::ones({2, 5, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2 ,2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(ModulesTest, MaxPool2dUneven) {
MaxPool2d model(MaxPool2dOptions({3, 2}).stride({2, 2}));
auto x = torch::ones({2, 5, 4}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(ModulesTest, MaxPool2dReturnIndices) {
MaxPool2d model(MaxPool2dOptions(3).stride(2));
auto x = torch::ones({2, 5, 5}, torch::requires_grad());
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
ASSERT_EQ(y.dim(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2 ,2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
ASSERT_TRUE(torch::allclose(
indices,
torch::tensor({{{ 0, 2},
{10, 12}},
{{ 0, 2},
{10, 12}}}, torch::kLong)));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(ModulesTest, MaxPool3d) {
MaxPool3d model(MaxPool3dOptions(3).stride(2));
auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(ModulesTest, MaxPool3dReturnIndices) {
MaxPool3d model(MaxPool3dOptions(3).stride(2));
auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
ASSERT_EQ(y.dim(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
ASSERT_TRUE(torch::allclose(
indices,
torch::tensor({{{{ 0, 2},
{10, 12}},
{{50, 52},
{60, 62}}},
{{{ 0, 2},
{10, 12}},
{{50, 52},
{60, 62}}}}, torch::kLong)));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(ModulesTest, AvgPool1d) {
AvgPool1d model(AvgPool1dOptions(3).stride(2));
auto x = torch::ones({1, 1, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({1, 1, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 2}));
}
TEST_F(ModulesTest, AvgPool2dEven) {
AvgPool2d model(AvgPool2dOptions(3).stride(2));
auto x = torch::ones({2, 5, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(ModulesTest, AvgPool2dUneven) {
AvgPool2d model(AvgPool2dOptions({3, 2}).stride({2, 2}));
auto x = torch::ones({2, 5, 4}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(ModulesTest, AvgPool3d) {
AvgPool3d model(AvgPool3dOptions(3).stride(2));
auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(ModulesTest, FractionalMaxPool2d) {
FractionalMaxPool2d model(FractionalMaxPool2dOptions(3).output_size(2));
auto x = torch::ones({2, 5, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(ModulesTest, FractionalMaxPool2dReturnIndices) {
FractionalMaxPool2d model(FractionalMaxPool2dOptions(3).output_size(2));
auto x = torch::ones({2, 5, 5}, torch::requires_grad());
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
ASSERT_EQ(y.dim(), 3);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2}));
ASSERT_TRUE(torch::allclose(
indices,
torch::tensor({{{ 0, 2},
{10, 12}},
{{ 0, 2},
{10, 12}}})));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2}));
}
TEST_F(ModulesTest, FractionalMaxPool3d) {
FractionalMaxPool3d model(FractionalMaxPool3dOptions(3).output_size(2));
auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(ModulesTest, FractionalMaxPool3dReturnIndices) {
FractionalMaxPool3d model(FractionalMaxPool3dOptions(3).output_size(2));
auto x = torch::ones({2, 5, 5, 5}, torch::requires_grad());
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
ASSERT_EQ(y.dim(), 4);
ASSERT_TRUE(torch::allclose(y, torch::ones({2, 2, 2, 2})));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
ASSERT_TRUE(torch::allclose(
indices,
torch::tensor({{{{ 0, 2},
{10, 12}},
{{50, 52},
{60, 62}}},
{{{ 0, 2},
{10, 12}},
{{50, 52},
{60, 62}}}})));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 2, 2, 2}));
}
TEST_F(ModulesTest, LPPool1d) {
int norm_type = 2;
int stride = 2;
int kernel_size = 3;
LPPool1d model(LPPool1dOptions(norm_type, kernel_size).stride(stride));
auto x = torch::ones({1, 1, 5});
auto y = model(x);
auto expected = (torch::pow(torch::tensor({{{1, 1}}}, torch::kFloat), norm_type) * kernel_size).pow(1. / norm_type);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, expected));
ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
}
TEST_F(ModulesTest, LPPool2d) {
int norm_type = 2;
int stride = 2;
std::vector<int64_t> kernel_size({2, 3});
LPPool2d model(LPPool2dOptions(norm_type, kernel_size).stride(stride));
auto x = torch::ones({1, 2, 5});
auto y = model(x);
auto expected = (torch::pow(torch::tensor({{{1, 1}}}, torch::kFloat), norm_type) * (kernel_size[0] * kernel_size[1])).pow(1. / norm_type);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, expected));
ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 2}));
}
TEST_F(ModulesTest, Identity) {
Identity identity;
auto input = torch::tensor({{1, 3, 4}, {2, 3, 4}}, torch::dtype(torch::kFloat).requires_grad(true));
auto output = identity->forward(input);
auto expected = torch::tensor({{1, 3, 4}, {2, 3, 4}}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(torch::equal(output, expected));
ASSERT_TRUE(torch::equal(input.grad(), torch::ones_like(input)));
}
TEST_F(ModulesTest, Flatten) {
Flatten flatten;
auto input = torch::tensor({{1, 3, 4}, {2, 5, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
auto output = flatten->forward(input);
auto expected = torch::tensor({{1, 3, 4}, {2, 5, 6}}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(torch::equal(output, expected));
ASSERT_TRUE(torch::equal(input.grad(), torch::ones_like(input)));
// Testing with optional arguments start_dim and end_dim
Flatten flatten_optional_dims(FlattenOptions().start_dim(2).end_dim(3));
input = torch::tensor({
{{{1, 2}, {3, 4}}, {{5, 6}, {7, 8}}},
{{{9, 10}, {11, 12}}, {{13, 14}, {15, 16}}}
}, torch::dtype(torch::kFloat).requires_grad(true)); // Tensor with sizes (2, 2, 2, 2)
output = flatten_optional_dims->forward(input);
expected = torch::tensor({
{{1, 2, 3, 4}, {5, 6, 7, 8}},
{{9, 10, 11, 12}, {13, 14, 15, 16}}
}, torch::kFloat); // Tensor with sizes (2, 2, 4)
s = output.sum();
s.backward();
ASSERT_TRUE(torch::equal(output, expected));
ASSERT_TRUE(torch::equal(input.grad(), torch::ones_like(input)));
}
TEST_F(ModulesTest, Unflatten) {
// Non-named tensor
Unflatten unflatten(UnflattenOptions(0, {2, 2}));
auto output = unflatten->forward(torch::tensor({1, 2, 3, 4}));
auto expected = torch::tensor({{1, 2}, {3, 4}});
ASSERT_TRUE(torch::equal(output, expected));
// Named tensor
auto make_dimnames = [](std::vector<std::string> names) {
std::vector<torch::Dimname> dimnames;
// NOLINTNEXTLINE(performance-for-range-copy)
for (auto name : names) {
// NOLINTNEXTLINE(performance-inefficient-vector-operation)
dimnames.push_back(
torch::Dimname::fromSymbol(torch::Symbol::dimname(name)));
}
return dimnames;
};
unflatten = Unflatten(UnflattenOptions(
"B",
{std::pair<std::string, int64_t>{"B1", 2},
std::pair<std::string, int64_t>{"B2", 2}}));
output = unflatten->forward(
torch::tensor({{1, 2, 3, 4}}).refine_names(make_dimnames({"A", "B"})));
expected = torch::tensor({{{1, 2}, {3, 4}}})
.refine_names(make_dimnames({"A", "B1", "B2"}));
ASSERT_TRUE(torch::equal(output, expected));
}
TEST_F(ModulesTest, AdaptiveMaxPool1d) {
AdaptiveMaxPool1d model(3);
auto x = torch::tensor({{{1, 2, 3, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({{{2, 4, 5}}}, torch::kFloat)));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
}
TEST_F(ModulesTest, AdaptiveMaxPool1dReturnIndices) {
AdaptiveMaxPool1d model(3);
auto x = torch::tensor({{{1, 2, 3, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
ASSERT_EQ(y.dim(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({{{2, 4, 5}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
ASSERT_TRUE(torch::allclose(indices, torch::tensor({{{1, 3, 4}}}, torch::kLong)));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({1, 1, 3}));
}
TEST_F(ModulesTest, AdaptiveMaxPool2dEven) {
AdaptiveMaxPool2d model(3);
auto x = torch::arange(0., 50);
x.resize_({2, 5, 5}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{6, 8, 9},
{16, 18, 19},
{21, 23, 24}},
{{31, 33, 34},
{41, 43, 44},
{46, 48, 49}},
}, torch::kFloat)));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
}
TEST_F(ModulesTest, AdaptiveMaxPool2dUneven) {
AdaptiveMaxPool2d model(AdaptiveMaxPool2dOptions({3, 2}));
auto x = torch::arange(0., 40);
x.resize_({2, 5, 4}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{5, 7},
{13, 15},
{17, 19}},
{{25, 27},
{33, 35},
{37, 39}},
}, torch::kFloat)));
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 2}));
}
TEST_F(ModulesTest, AdaptiveMaxPool2dReturnIndicesEven) {
AdaptiveMaxPool2d model(3);
auto x = torch::arange(0., 50);
x.resize_({2, 5, 5}).set_requires_grad(true);
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{6, 8, 9},
{16, 18, 19},
{21, 23, 24}},
{{31, 33, 34},
{41, 43, 44},
{46, 48, 49}},
}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
ASSERT_EQ(indices.ndimension(), 3);
ASSERT_TRUE(torch::allclose(indices, torch::tensor({
{{6, 8, 9},
{16, 18, 19},
{21, 23, 24}},
{{6, 8, 9},
{16, 18, 19},
{21, 23, 24}},
}, torch::kLong)));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 3, 3}));
}
TEST_F(ModulesTest, AdaptiveMaxPool2dReturnIndicesUneven) {
AdaptiveMaxPool2d model(AdaptiveMaxPool2dOptions({3, 2}));
auto x = torch::arange(0., 40);
x.resize_({2, 5, 4}).set_requires_grad(true);
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{5, 7},
{13, 15},
{17, 19}},
{{25, 27},
{33, 35},
{37, 39}},
}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 2}));
ASSERT_EQ(indices.ndimension(), 3);
ASSERT_TRUE(torch::allclose(indices, torch::tensor({
{{5, 7},
{13, 15},
{17, 19}},
{{5, 7},
{13, 15},
{17, 19}},
}, torch::kLong)));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({2, 3, 2}));
}
TEST_F(ModulesTest, AdaptiveMaxPool3d) {
AdaptiveMaxPool3d model(3);
auto x = torch::arange(0., 64);
x.resize_({1, 4, 4, 4}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{21, 22, 23},
{25, 26, 27},
{29, 30, 31}},
{{37, 38, 39},
{41, 42, 43},
{45, 46, 47}},
{{53, 54, 55},
{57, 58, 59},
{61, 62, 63}},
}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
}
TEST_F(ModulesTest, AdaptiveMaxPool3dReturnIndices) {
AdaptiveMaxPool3d model(3);
auto x = torch::arange(0., 64);
x.resize_({1, 4, 4, 4}).set_requires_grad(true);
torch::Tensor y, indices;
std::tie(y, indices) = model->forward_with_indices(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{21, 22, 23},
{25, 26, 27},
{29, 30, 31}},
{{37, 38, 39},
{41, 42, 43},
{45, 46, 47}},
{{53, 54, 55},
{57, 58, 59},
{61, 62, 63}},
}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
ASSERT_EQ(indices.ndimension(), 4);
ASSERT_TRUE(torch::allclose(indices, torch::tensor({
{{21, 22, 23},
{25, 26, 27},
{29, 30, 31}},
{{37, 38, 39},
{41, 42, 43},
{45, 46, 47}},
{{53, 54, 55},
{57, 58, 59},
{61, 62, 63}},
}, torch::kLong)));
ASSERT_EQ(indices.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
}
TEST_F(ModulesTest, AdaptiveAvgPool1d) {
AdaptiveAvgPool1d model(3);
auto x = torch::tensor({{{1, 2, 3, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({{{1.5, 3.0, 4.5}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3}));
}
TEST_F(ModulesTest, AdaptiveAvgPool2dEven) {
AdaptiveAvgPool2d model(3);
auto x = torch::arange(0., 50);
x.resize_({2, 5, 5}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{ 3.0, 4.5, 6.0},
{10.5, 12.0, 13.5},
{18.0, 19.5, 21.0}},
{{28.0, 29.5, 31.0},
{35.5, 37.0, 38.5},
{43.0, 44.5, 46.0}},
}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 3}));
}
TEST_F(ModulesTest, AdaptiveAvgPool2dUneven) {
AdaptiveAvgPool2d model(AdaptiveAvgPool2dOptions({3, 2}));
auto x = torch::arange(0., 40);
x.resize_({2, 5, 4}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{2.5, 4.5},
{8.5, 10.5},
{14.5, 16.5}},
{{22.5, 24.5},
{28.5, 30.5},
{34.5, 36.5}},
}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 3, 2}));
}
TEST_F(ModulesTest, AdaptiveAvgPool3d) {
AdaptiveAvgPool3d model(3);
auto x = torch::arange(0., 64);
x.resize_({1, 4, 4, 4}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 4);
ASSERT_TRUE(torch::allclose(y, torch::tensor({
{{10.5, 11.5, 12.5},
{14.5, 15.5, 16.5},
{18.5, 19.5, 20.5}},
{{26.5, 27.5, 28.5},
{30.5, 31.5, 32.5},
{34.5, 35.5, 36.5}},
{{42.5, 43.5, 44.5},
{46.5, 47.5, 48.5},
{50.5, 51.5, 52.5}},
}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 3, 3, 3}));
}
TEST_F(ModulesTest, MaxUnpool1d) {
auto indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
auto x = torch::tensor({{{2, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto model = MaxUnpool1d{3};
auto y = model->forward(x, indices);
ASSERT_EQ(y.dim(), 3);
ASSERT_TRUE(torch::allclose(y,
torch::tensor({{{0, 2, 0, 4, 5, 0, 0, 0, 0}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 9}));
indices = torch::tensor({{{1, 3, 4}}}, torch::kLong);
x = torch::tensor({{{2, 4, 5}}}, torch::dtype(torch::kFloat).requires_grad(true));
model = MaxUnpool1d{MaxUnpool1dOptions(3).stride(2).padding(1)};
y = model->forward(x, indices, std::vector<int64_t>({1, 1, 5}));
ASSERT_EQ(y.dim(), 3);
ASSERT_TRUE(torch::allclose(y,
torch::tensor({{{0, 2, 0, 4, 5}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 5}));
}
TEST_F(ModulesTest, MaxPool1d_MaxUnpool1d) {
MaxPool1d pool {MaxPool1dOptions(2).stride(2)};
MaxUnpool1d unpool {MaxUnpool1dOptions(2).stride(2)};
auto input = torch::tensor({{{1, 2, 3, 4, 5, 6, 7, 8}}}, torch::kFloat);
torch::Tensor output, indices;
std::tie(output, indices) = pool->forward_with_indices(input);
ASSERT_TRUE(torch::allclose(
unpool(output, indices),
torch::tensor({{{0, 2, 0, 4, 0, 6, 0, 8}}} , torch::kFloat)));
// Example showcasing the use of output_size
input = torch::tensor({{{1, 2, 3, 4, 5, 6, 7, 8, 9}}}, torch::kFloat);
std::tie(output, indices) = pool->forward_with_indices(input);
ASSERT_TRUE(torch::allclose(
unpool(output, indices, input.sizes().vec()),
torch::tensor({{{0, 2, 0, 4, 0, 6, 0, 8, 0}}} , torch::kFloat)));
ASSERT_TRUE(torch::allclose(
unpool(output, indices),
torch::tensor({{{0, 2, 0, 4, 0, 6, 0, 8}}} , torch::kFloat)));
}
TEST_F(ModulesTest, MaxUnpool2d) {
auto indices = torch::tensor({
{{{ 6, 8, 9},
{16, 18, 19},
{21, 23, 24}}},
{{{ 6, 8, 9},
{16, 18, 19},
{21, 23, 24}}}}, torch::kLong);
auto x = torch::tensor({
{{{ 6, 8, 9},
{16, 18, 19},
{21, 23, 24}}},
{{{31, 33, 34},
{41, 43, 44},
{46, 48, 49}}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto model = MaxUnpool2d{MaxUnpool2dOptions(3).stride(2).padding(1)};
auto y = model->forward(x, indices);
ASSERT_EQ(y.dim(), 4);
ASSERT_TRUE(torch::allclose(y, torch::tensor(
{{{{ 0, 0, 0, 0, 0},
{ 0, 6, 0, 8, 9},
{ 0, 0, 0, 0, 0},
{ 0, 16, 0, 18, 19},
{ 0, 21, 0, 23, 24}}},
{{{ 0, 0, 0, 0, 0},
{ 0, 31, 0, 33, 34},
{ 0, 0, 0, 0, 0},
{ 0, 41, 0, 43, 44},
{ 0, 46, 0, 48, 49}}}} , torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({2, 1, 5, 5}));
}
TEST_F(ModulesTest, MaxPool2d_MaxUnpool2d) {
MaxPool2d pool {MaxPool2dOptions(2).stride(2)};
MaxUnpool2d unpool {MaxUnpool2dOptions(2).stride(2)};
auto input = torch::tensor({{{{ 1, 2, 3, 4},
{ 5, 6, 7, 8},
{ 9, 10, 11, 12},
{13, 14, 15, 16}}}}, torch::kFloat);
torch::Tensor output, indices;
std::tie(output, indices) = pool->forward_with_indices(input);
ASSERT_TRUE(torch::allclose(
unpool(output, indices),
torch::tensor({{{{ 0, 0, 0, 0},
{ 0, 6, 0, 8},
{ 0, 0, 0, 0},
{ 0, 14, 0, 16}}}} , torch::kFloat)));
ASSERT_TRUE(torch::allclose(
unpool(output, indices, std::vector<int64_t>{1, 1, 5, 5}),
torch::tensor({{{{ 0, 0, 0, 0, 0},
{ 6, 0, 8, 0, 0},
{ 0, 0, 0, 14, 0},
{ 16, 0, 0, 0, 0},
{ 0, 0, 0, 0, 0}}}}, torch::kFloat)));
}
TEST_F(ModulesTest, MaxUnpool3d) {
auto indices = torch::tensor({{{{{26}}}}}, torch::kLong);
auto x = torch::tensor({{{{{26}}}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto model = MaxUnpool3d{3};
auto y = model->forward(x, indices);
ASSERT_EQ(y.dim(), 5);
ASSERT_TRUE(torch::allclose(y, torch::tensor(
{{{{{ 0, 0, 0},
{ 0, 0, 0},
{ 0, 0, 0}},
{{ 0, 0, 0},
{ 0, 0, 0},
{ 0, 0, 0}},
{{ 0, 0, 0},
{ 0, 0, 0},
{ 0, 0, 26}}}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 3, 3, 3}));
}
TEST_F(ModulesTest, MaxUnpool3dOutputSize) {
auto indices = torch::tensor(
{{{{{21, 23},
{29, 31}},
{{53, 55},
{61, 63}}}}}, torch::kLong);
auto x = torch::tensor(
{{{{{21, 23},
{29, 31}},
{{53, 55},
{61, 63}}}}}, torch::dtype(torch::kFloat).requires_grad(true));
auto model = MaxUnpool3d{MaxUnpool3dOptions(3).stride(2).padding(1)};
auto y = model->forward(x, indices, std::vector<int64_t>({1, 1, 4, 4, 4}));
ASSERT_EQ(y.dim(), 5);
ASSERT_TRUE(torch::allclose(y, torch::tensor(
{{{{{ 0, 0, 0, 0},
{ 0, 0, 0, 0},
{ 0, 0, 0, 0},
{ 0, 0, 0, 0}},
{{ 0, 0, 0, 0},
{ 0, 21, 0, 23},
{ 0, 0, 0, 0},
{ 0, 29, 0, 31}},
{{ 0, 0, 0, 0},
{ 0, 0, 0, 0},
{ 0, 0, 0, 0},
{ 0, 0, 0, 0}},
{{ 0, 0, 0, 0},
{ 0, 53, 0, 55},
{ 0, 0, 0, 0},
{ 0, 61, 0, 63}}}}}, torch::kFloat)));
ASSERT_EQ(y.sizes(), std::vector<int64_t>({1, 1, 4, 4, 4}));
}
TEST_F(ModulesTest, MaxPool3d_MaxUnpool3d) {
MaxPool3d pool {MaxPool3dOptions(3).stride(2)};
MaxUnpool3d unpool {MaxUnpool3dOptions(3).stride(2)};
auto input = torch::randn({20, 16, 51, 33, 15});
torch::Tensor output, indices;
std::tie(output, indices) = pool->forward_with_indices(input);
auto unpooled_output = unpool(output, indices);
ASSERT_EQ(unpooled_output.sizes(), std::vector<int64_t>({20, 16, 51, 33, 15}));
}
TEST_F(ModulesTest, Linear) {
{
Linear model(5, 2);
auto x = torch::randn({10, 5}, torch::requires_grad());
auto y = model(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);
auto y_exp = torch::addmm(model->bias, x, model->weight.t());
ASSERT_TRUE(torch::allclose(y, y_exp));
}
{
Linear model(LinearOptions(5, 2).bias(false));
auto x = torch::randn({10, 5}, torch::requires_grad());
auto y = model(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);
auto y_exp = torch::mm(x, model->weight.t());
ASSERT_TRUE(torch::allclose(y, y_exp));
}
}
TEST_F(ModulesTest, LocalResponseNorm) {
{
LocalResponseNorm model(LocalResponseNormOptions(2));
const auto x = torch::arange(100., 136, torch::requires_grad()).reshape({2, 3, 3, 2});
auto y = model(x);
const auto y_exp = torch::tensor(
{{{{73.7788, 74.1462},
{74.5031, 74.8572},
{75.2010, 75.5420}},
{{61.6057, 61.7227},
{61.8347, 61.9418},
{62.0441, 62.1418}},
{{62.2349, 62.3235},
{62.4077, 62.4877},
{62.5635, 62.6353}}},
{{{79.3915, 79.6491},
{79.8978, 80.1446},
{80.3827, 80.6190}},
{{63.0317, 63.0742},
{63.1135, 63.1496},
{63.1826, 63.2126}},
{{63.2396, 63.2637},
{63.2850, 63.3036},
{63.3195, 63.3328}}}},
torch::kFloat
);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 4);
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.sizes(), x.sizes());
ASSERT_TRUE(torch::allclose(y, y_exp, 1e-4, 1e-7));
}
}
TEST_F(ModulesTest, LayerNorm) {
LayerNorm model(LayerNormOptions({2, 2}).eps(2e-5));
auto x = torch::randn({2, 2}, torch::requires_grad());
auto y = model(x);
auto y_exp = torch::layer_norm(x, {2, 2}, model->weight, model->bias, 2e-5);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 2);
ASSERT_EQ(s.ndimension(), 0);
for (const auto i : c10::irange(2)) {
ASSERT_EQ(y.size(i), 2);
}
ASSERT_EQ(model->weight.grad().numel(), 2 * 2);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, GroupNorm) {
GroupNorm model(GroupNormOptions(2, 2).eps(2e-5));
auto x = torch::randn({2, 2}, torch::requires_grad());
auto y = model(x);
auto y_exp = torch::group_norm(x, 2, model->weight, model->bias, 2e-5);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(y.ndimension(), 2);
ASSERT_EQ(s.ndimension(), 0);
for (const auto i : c10::irange(2)) {
ASSERT_EQ(y.size(i), 2);
}
ASSERT_EQ(model->weight.grad().numel(), 2);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, Bilinear) {
Bilinear model(5, 3, 2);
auto x1 = torch::randn({10, 5}, torch::requires_grad());
auto x2 = torch::randn({10, 3}, torch::requires_grad());
auto y = model(x1, x2);
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 * 3);
}
TEST_F(ModulesTest, Fold) {
{
Fold model(FoldOptions({3, 2}, {2, 2}));
auto input = torch::ones({1, 3 * 2 * 2, 2}, torch::requires_grad());
auto output = model(input);
auto expected = torch::tensor(
{{{{1.0, 1.0}, {2.0, 2.0}, {1.0, 1.0}},
{{1.0, 1.0}, {2.0, 2.0}, {1.0, 1.0}},
{{1.0, 1.0}, {2.0, 2.0}, {1.0, 1.0}}}},
torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 3, 3, 2}));
ASSERT_TRUE(output.allclose(expected));
}
{
// input wrong dimension
Fold model(FoldOptions({8, 8}, {3, 3}));
ASSERT_THROWS_WITH(
model(torch::randn({1, 3, 16, 16})),
"Input Error: Only unbatched (2D) or batched (3D) input Tensors are supported (got 4D)");
}
}
TEST_F(ModulesTest, Unfold) {
{
Unfold model(UnfoldOptions({2, 2}).padding(1).stride(2));
auto input = torch::arange(2., 14, torch::requires_grad()).view({1, 2, 2, 3});
auto output = model(input);
auto expected = torch::tensor(
{{{0.0, 0.0, 0.0, 6.0},
{0.0, 0.0, 5.0, 7.0},
{0.0, 3.0, 0.0, 0.0},
{2.0, 4.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 12.0},
{0.0, 0.0, 11.0, 13.0},
{0.0, 9.0, 0.0, 0.0},
{8.0, 10.0, 0.0, 0.0}}},
torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 8, 4}));
ASSERT_TRUE(output.allclose(expected));
}
{
// input wrong dimension
Unfold model(UnfoldOptions({2, 4}));
ASSERT_THROWS_WITH(
model(torch::randn({1, 5, 2})),
"Input Error: Only 4D input Tensors are supported (got 3D)");
}
{
// calculated output shape is too small
Unfold model(UnfoldOptions({2, 3}));
ASSERT_THROWS_WITH(
model(torch::randn({1, 2, 2, 2})),
"Given input with spatial size (2, 2), kernel_size=(2, 3), "
"dilation=(1, 1), padding=(0, 0), calculated shape of the array of "
"sliding blocks as (1, 0), which is too small (non-positive).");
}
}
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(x).clamp_min(0);
x = l2(x).clamp_min(0);
x = l3(x).clamp_min(0);
x.backward(torch::ones_like(x));
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(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(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, EmbeddingFromPretrained) {
auto weight = torch::tensor({{1., 2.3, 3.}, {4., 5.1, 6.3}});
Embedding embedding = torch::nn::Embedding::from_pretrained(weight);
auto input = torch::tensor({1}, torch::kLong);
ASSERT_TRUE(torch::allclose(embedding(input), torch::tensor({4.0000, 5.1000, 6.3000})));
}
TEST_F(ModulesTest, EmbeddingBagFromPretrained) {
auto weight = torch::tensor({{1., 2.3, 3.}, {4., 5.1, 6.3}});
EmbeddingBag embeddingbag = torch::nn::EmbeddingBag::from_pretrained(weight);
auto input = torch::zeros({{1, 2}}, torch::kLong);
input[0] = torch::tensor({1, 0});
ASSERT_TRUE(torch::allclose(embeddingbag(input), torch::tensor({2.5000, 3.7000, 4.6500})));
}
TEST_F(ModulesTest, AlphaDropout) {
AlphaDropout alpha_dropout(0.5);
torch::Tensor x = torch::ones(100, torch::requires_grad());
torch::Tensor y = alpha_dropout(x);
y.backward(torch::ones_like(y));
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>(), 40); // Probably
alpha_dropout->eval();
y = alpha_dropout(x);
ASSERT_EQ(y.sum().item<float>(), 100);
}
TEST_F(ModulesTest, FeatureAlphaDropout) {
FeatureAlphaDropout feature_alpha_dropout(0.5);
torch::Tensor x = torch::ones({10, 10}, torch::requires_grad());
torch::Tensor y = feature_alpha_dropout(x);
y.backward(torch::ones_like(y));
ASSERT_EQ(y.ndimension(), 2);
ASSERT_EQ(y.size(0), 10);
ASSERT_EQ(y.size(1), 10);
ASSERT_LT(y.sum().item<float>(), 130); // Probably
ASSERT_GT(y.sum().item<float>(), 40); // Probably
feature_alpha_dropout->eval();
y = feature_alpha_dropout(x);
ASSERT_EQ(y.sum().item<float>(), 100);
}
TEST_F(ModulesTest, Dropout) {
for (const auto inplace : {false, true}) {
Dropout dropout(DropoutOptions(0.5).inplace(inplace));
torch::Tensor x = torch::ones(100);
if (!inplace) {
x.requires_grad_(true);
}
torch::Tensor y = dropout(x);
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
if (inplace) {
ASSERT_TRUE(y.allclose(x));
} else {
y.backward(torch::ones_like(y));
}
dropout->eval();
y = dropout(torch::ones(100));
ASSERT_EQ(y.sum().item<float>(), 100);
}
}
TEST_F(ModulesTest, Dropout2d) {
auto p = 0.5;
for (const auto inplace : {false, true}) {
Dropout2d dropout(Dropout2dOptions(p).inplace(inplace));
torch::Tensor x = torch::empty({50, 50, 2, 2}).fill_(1 - p);
if (!inplace) {
x.requires_grad_(true);
}
torch::Tensor y = dropout(x);
ASSERT_EQ(y.ndimension(), 4);
ASSERT_EQ(y.size(0), 50);
ASSERT_EQ(y.size(1), 50);
ASSERT_EQ(y.size(2), 2);
ASSERT_EQ(y.size(3), 2);
ASSERT_LT((y.mean() - (1 - p)).abs().item<float>(), 0.05);
if (inplace) {
ASSERT_TRUE(y.allclose(x));
} else {
y.backward(torch::ones_like(y));
}
dropout->eval();
y = dropout(torch::ones({2, 2, 10, 10}));
ASSERT_EQ(y.sum().item<float>(), 400);
}
}
TEST_F(ModulesTest, Dropout3d) {
for (const auto inplace : {false, true}) {
auto p = 0.5;
Dropout3d dropout(Dropout3dOptions(p).inplace(inplace));
torch::Tensor x = torch::empty({50, 50, 2, 2, 2}).fill_(1 - p);
if (!inplace) {
x.requires_grad_(true);
}
torch::Tensor y = dropout(x);
ASSERT_EQ(y.ndimension(), 5);
ASSERT_EQ(y.size(0), 50);
ASSERT_EQ(y.size(1), 50);
ASSERT_EQ(y.size(2), 2);
ASSERT_EQ(y.size(3), 2);
ASSERT_EQ(y.size(4), 2);
ASSERT_LT((y.mean() - (1 - p)).abs().item<float>(), 0.05);
if (inplace) {
ASSERT_TRUE(y.allclose(x));
} else {
y.backward(torch::ones_like(y));
}
dropout->eval();
y = dropout(torch::ones({4, 4, 5, 5}));
ASSERT_EQ(y.sum().item<float>(), 400);
}
}
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(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, BatchNorm1dStateful) {
BatchNorm1d bn(5);
ASSERT_TRUE(bn->options.track_running_stats());
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_var.defined());
ASSERT_EQ(bn->running_var.dim(), 1);
ASSERT_EQ(bn->running_var.size(0), 5);
ASSERT_TRUE(bn->num_batches_tracked.defined());
ASSERT_EQ(bn->num_batches_tracked.dim(), 0);
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, BatchNorm1dStateless) {
BatchNorm1d bn(BatchNorm1dOptions(5).track_running_stats(false).affine(false));
ASSERT_FALSE(bn->running_mean.defined());
ASSERT_FALSE(bn->running_var.defined());
ASSERT_FALSE(bn->num_batches_tracked.defined());
ASSERT_FALSE(bn->weight.defined());
ASSERT_FALSE(bn->bias.defined());
}
TEST_F(ModulesTest, BatchNorm1d) {
BatchNorm1d bn(5);
bn->eval();
auto input = torch::arange(2. * 5 * 2).view({2, 5, 2}).requires_grad_();
auto output = bn->forward(input);
auto expected = torch::tensor({{{ 0.0000, 1.0000},
{ 2.0000, 3.0000},
{ 4.0000, 5.0000},
{ 6.0000, 7.0000},
{ 8.0000, 9.0000}},
{{10.0000, 10.9999},
{11.9999, 12.9999},
{13.9999, 14.9999},
{15.9999, 16.9999},
{17.9999, 18.9999}}});
ASSERT_TRUE(output.allclose(expected));
auto s = output.sum();
s.backward();
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, BatchNorm2dStateful) {
BatchNorm2d bn(5);
ASSERT_TRUE(bn->options.track_running_stats());
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_var.defined());
ASSERT_EQ(bn->running_var.dim(), 1);
ASSERT_EQ(bn->running_var.size(0), 5);
ASSERT_TRUE(bn->num_batches_tracked.defined());
ASSERT_EQ(bn->num_batches_tracked.dim(), 0);
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, BatchNorm2dStateless) {
BatchNorm2d bn(BatchNorm2dOptions(5).track_running_stats(false).affine(false));
ASSERT_FALSE(bn->running_mean.defined());
ASSERT_FALSE(bn->running_var.defined());
ASSERT_FALSE(bn->num_batches_tracked.defined());
ASSERT_FALSE(bn->weight.defined());
ASSERT_FALSE(bn->bias.defined());
}
TEST_F(ModulesTest, BatchNorm2d) {
BatchNorm2d bn(5);
bn->eval();
auto input = torch::arange(2. * 5 * 2 * 2).view({2, 5, 2, 2}).requires_grad_();
auto output = bn->forward(input);
auto expected = torch::tensor({{{{ 0.0000, 1.0000},
{ 2.0000, 3.0000}},
{{ 4.0000, 5.0000},
{ 6.0000, 7.0000}},
{{ 8.0000, 9.0000},
{10.0000, 10.9999}},
{{11.9999, 12.9999},
{13.9999, 14.9999}},
{{15.9999, 16.9999},
{17.9999, 18.9999}}},
{{{19.9999, 20.9999},
{21.9999, 22.9999}},
{{23.9999, 24.9999},
{25.9999, 26.9999}},
{{27.9999, 28.9999},
{29.9998, 30.9998}},
{{31.9998, 32.9998},
{33.9998, 34.9998}},
{{35.9998, 36.9998},
{37.9998, 38.9998}}}});
ASSERT_TRUE(output.allclose(expected));
auto s = output.sum();
s.backward();
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, BatchNorm3dStateful) {
BatchNorm3d bn(5);
ASSERT_TRUE(bn->options.track_running_stats());
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_var.defined());
ASSERT_EQ(bn->running_var.dim(), 1);
ASSERT_EQ(bn->running_var.size(0), 5);
ASSERT_TRUE(bn->num_batches_tracked.defined());
ASSERT_EQ(bn->num_batches_tracked.dim(), 0);
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, BatchNorm3dStateless) {
BatchNorm3d bn(BatchNorm3dOptions(5).track_running_stats(false).affine(false));
ASSERT_FALSE(bn->running_mean.defined());
ASSERT_FALSE(bn->running_var.defined());
ASSERT_FALSE(bn->num_batches_tracked.defined());
ASSERT_FALSE(bn->weight.defined());
ASSERT_FALSE(bn->bias.defined());
}
TEST_F(ModulesTest, BatchNorm3d) {
BatchNorm3d bn(5);
bn->eval();
auto input = torch::arange(2. * 5 * 2 * 2 * 2).view({2, 5, 2, 2, 2}).requires_grad_();
auto output = bn->forward(input);
auto expected = torch::tensor({{{{{ 0.0000, 1.0000},
{ 2.0000, 3.0000}},
{{ 4.0000, 5.0000},
{ 6.0000, 7.0000}}},
{{{ 8.0000, 9.0000},
{10.0000, 10.9999}},
{{11.9999, 12.9999},
{13.9999, 14.9999}}},
{{{15.9999, 16.9999},
{17.9999, 18.9999}},
{{19.9999, 20.9999},
{21.9999, 22.9999}}},
{{{23.9999, 24.9999},
{25.9999, 26.9999}},
{{27.9999, 28.9999},
{29.9998, 30.9998}}},
{{{31.9998, 32.9998},
{33.9998, 34.9998}},
{{35.9998, 36.9998},
{37.9998, 38.9998}}}},
{{{{39.9998, 40.9998},
{41.9998, 42.9998}},
{{43.9998, 44.9998},
{45.9998, 46.9998}}},
{{{47.9998, 48.9998},
{49.9997, 50.9997}},
{{51.9997, 52.9997},
{53.9997, 54.9997}}},
{{{55.9997, 56.9997},
{57.9997, 58.9997}},
{{59.9997, 60.9997},
{61.9997, 62.9997}}},
{{{63.9997, 64.9997},
{65.9997, 66.9997}},
{{67.9997, 68.9997},
{69.9996, 70.9996}}},
{{{71.9996, 72.9996},
{73.9996, 74.9996}},
{{75.9996, 76.9996},
{77.9996, 78.9996}}}}});
ASSERT_TRUE(output.allclose(expected));
auto s = output.sum();
s.backward();
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, InstanceNorm1dStateful) {
InstanceNorm1d instance_norm(InstanceNorm1dOptions(5).track_running_stats(true).affine(true));
ASSERT_TRUE(instance_norm->options.track_running_stats());
ASSERT_TRUE(instance_norm->running_mean.defined());
ASSERT_EQ(instance_norm->running_mean.dim(), 1);
ASSERT_EQ(instance_norm->running_mean.size(0), 5);
ASSERT_TRUE(instance_norm->running_var.defined());
ASSERT_EQ(instance_norm->running_var.dim(), 1);
ASSERT_EQ(instance_norm->running_var.size(0), 5);
ASSERT_TRUE(instance_norm->num_batches_tracked.defined());
ASSERT_EQ(instance_norm->num_batches_tracked.dim(), 0);
ASSERT_TRUE(instance_norm->options.affine());
ASSERT_TRUE(instance_norm->weight.defined());
ASSERT_EQ(instance_norm->weight.dim(), 1);
ASSERT_EQ(instance_norm->weight.size(0), 5);
ASSERT_TRUE(instance_norm->bias.defined());
ASSERT_EQ(instance_norm->bias.dim(), 1);
ASSERT_EQ(instance_norm->bias.size(0), 5);
}
TEST_F(ModulesTest, InstanceNorm1dStateless) {
InstanceNorm1d instance_norm(InstanceNorm1dOptions(5).track_running_stats(false).affine(false));
ASSERT_FALSE(instance_norm->running_mean.defined());
ASSERT_FALSE(instance_norm->running_var.defined());
ASSERT_FALSE(instance_norm->num_batches_tracked.defined());
ASSERT_FALSE(instance_norm->weight.defined());
ASSERT_FALSE(instance_norm->bias.defined());
}
TEST_F(ModulesTest, InstanceNorm1d) {
InstanceNorm1d instance_norm(5);
instance_norm->eval();
auto input = torch::arange(2. * 5 * 2).view({2, 5, 2}).requires_grad_();
auto output = instance_norm->forward(input);
auto expected = torch::tensor({{{-1.0000, 1.0000},
{-1.0000, 1.0000},
{-1.0000, 1.0000},
{-1.0000, 1.0000},
{-1.0000, 1.0000}},
{{-1.0000, 1.0000},
{-1.0000, 1.0000},
{-1.0000, 1.0000},
{-1.0000, 1.0000},
{-1.0000, 1.0000}}});
ASSERT_TRUE(output.allclose(expected, 1e-3));
auto s = output.sum();
s.backward();
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, InstanceNorm2dStateful) {
InstanceNorm2d instance_norm(InstanceNorm2dOptions(5).track_running_stats(true).affine(true));
ASSERT_TRUE(instance_norm->options.track_running_stats());
ASSERT_TRUE(instance_norm->running_mean.defined());
ASSERT_EQ(instance_norm->running_mean.dim(), 1);
ASSERT_EQ(instance_norm->running_mean.size(0), 5);
ASSERT_TRUE(instance_norm->running_var.defined());
ASSERT_EQ(instance_norm->running_var.dim(), 1);
ASSERT_EQ(instance_norm->running_var.size(0), 5);
ASSERT_TRUE(instance_norm->num_batches_tracked.defined());
ASSERT_EQ(instance_norm->num_batches_tracked.dim(), 0);
ASSERT_TRUE(instance_norm->options.affine());
ASSERT_TRUE(instance_norm->weight.defined());
ASSERT_EQ(instance_norm->weight.dim(), 1);
ASSERT_EQ(instance_norm->weight.size(0), 5);
ASSERT_TRUE(instance_norm->bias.defined());
ASSERT_EQ(instance_norm->bias.dim(), 1);
ASSERT_EQ(instance_norm->bias.size(0), 5);
}
TEST_F(ModulesTest, InstanceNorm2dStateless) {
InstanceNorm2d instance_norm(InstanceNorm2dOptions(5).track_running_stats(false).affine(false));
ASSERT_FALSE(instance_norm->running_mean.defined());
ASSERT_FALSE(instance_norm->running_var.defined());
ASSERT_FALSE(instance_norm->num_batches_tracked.defined());
ASSERT_FALSE(instance_norm->weight.defined());
ASSERT_FALSE(instance_norm->bias.defined());
}
TEST_F(ModulesTest, InstanceNorm2d) {
InstanceNorm2d instance_norm(5);
instance_norm->eval();
auto input = torch::arange(2. * 5 * 2 * 2).view({2, 5, 2, 2}).requires_grad_();
auto output = instance_norm->forward(input);
auto expected = torch::tensor({{{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}}},
{{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}},
{{-1.3416, -0.4472},
{ 0.4472, 1.3416}}}});
ASSERT_TRUE(output.allclose(expected, 1e-3));
auto s = output.sum();
s.backward();
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, InstanceNorm3dStateful) {
InstanceNorm3d instance_norm(InstanceNorm3dOptions(5).track_running_stats(true).affine(true));
ASSERT_TRUE(instance_norm->options.track_running_stats());
ASSERT_TRUE(instance_norm->running_mean.defined());
ASSERT_EQ(instance_norm->running_mean.dim(), 1);
ASSERT_EQ(instance_norm->running_mean.size(0), 5);
ASSERT_TRUE(instance_norm->running_var.defined());
ASSERT_EQ(instance_norm->running_var.dim(), 1);
ASSERT_EQ(instance_norm->running_var.size(0), 5);
ASSERT_TRUE(instance_norm->num_batches_tracked.defined());
ASSERT_EQ(instance_norm->num_batches_tracked.dim(), 0);
ASSERT_TRUE(instance_norm->options.affine());
ASSERT_TRUE(instance_norm->weight.defined());
ASSERT_EQ(instance_norm->weight.dim(), 1);
ASSERT_EQ(instance_norm->weight.size(0), 5);
ASSERT_TRUE(instance_norm->bias.defined());
ASSERT_EQ(instance_norm->bias.dim(), 1);
ASSERT_EQ(instance_norm->bias.size(0), 5);
}
TEST_F(ModulesTest, InstanceNorm3dStateless) {
InstanceNorm3d instance_norm(InstanceNorm3dOptions(5).track_running_stats(false).affine(false));
ASSERT_FALSE(instance_norm->running_mean.defined());
ASSERT_FALSE(instance_norm->running_var.defined());
ASSERT_FALSE(instance_norm->num_batches_tracked.defined());
ASSERT_FALSE(instance_norm->weight.defined());
ASSERT_FALSE(instance_norm->bias.defined());
}
TEST_F(ModulesTest, InstanceNorm3d) {
InstanceNorm3d instance_norm(5);
instance_norm->eval();
auto input = torch::arange(2. * 5 * 2 * 2 * 2).view({2, 5, 2, 2, 2}).requires_grad_();
auto output = instance_norm->forward(input);
auto expected = torch::tensor({{{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}}},
{{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}},
{{{-1.5275, -1.0911},
{-0.6547, -0.2182}},
{{ 0.2182, 0.6547},
{ 1.0911, 1.5275}}}}});
ASSERT_TRUE(output.allclose(expected, 1e-3));
auto s = output.sum();
s.backward();
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
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(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(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, L1Loss) {
L1Loss loss;
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = loss->forward(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), std::vector<int64_t>());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, MSELoss) {
MSELoss loss;
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = loss->forward(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), torch::IntArrayRef());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, BCELoss) {
BCELoss loss;
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = loss->forward(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), torch::IntArrayRef());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, KLDivLoss) {
KLDivLoss loss;
auto input = torch::randn({5,6}, torch::requires_grad());
auto target = torch::empty({5,6}).random_(2);
auto output = loss->forward(torch::sigmoid(input), target);
auto s = output.sum();
s.backward();
ASSERT_EQ(output.sizes(), torch::IntArrayRef());
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, HingeEmbeddingLoss) {
HingeEmbeddingLoss loss(HingeEmbeddingLossOptions().margin(2));
auto input = torch::tensor({{2, 22, 4}, {20, 10, 0}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{2, 6, 4}, {1, 10, 0}}, torch::kFloat);
auto output = loss->forward(input, target);
auto expected = torch::tensor({10}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, MultiMarginLoss) {
auto weight = torch::tensor({0.3, 0.3, 0.4}, torch::kFloat);
MultiMarginLoss loss(MultiMarginLossOptions().margin(2).weight(weight));
auto input = torch::tensor({{0.2, 0.2, 0.6}, {0.1, 0.8, 0.1}, {0.9, 0.09, 0.01}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({2, 1, 0}, torch::kLong);
auto output = loss->forward(input, target);
auto expected = torch::tensor({0.305556}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, CosineEmbeddingLoss) {
CosineEmbeddingLoss cos(CosineEmbeddingLossOptions().margin(0.5));
auto input1 = torch::tensor({{2, 3, 4}, {6, 2, 4}}, torch::dtype(torch::kFloat).requires_grad(true));
auto input2 = torch::tensor({{2, 3, 5}, {9, 12, 0}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({1, -1});
auto output = cos(input1, input2, target);
auto expected = torch::tensor({0.1004}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-4));
ASSERT_EQ(input1.sizes(), input1.grad().sizes());
ASSERT_EQ(input2.sizes(), input2.grad().sizes());
}
TEST_F(ModulesTest, SmoothL1LossDefaultOptions) {
SmoothL1Loss loss;
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output = loss(input, target);
auto expected = torch::tensor(0.0233335, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, HuberLossDefaultOptions) {
HuberLoss loss;
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output = loss(input, target);
auto expected = torch::tensor(0.0233335, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, MultiLabelMarginLossDefaultOptions) {
MultiLabelMarginLoss loss;
auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
auto output = loss->forward(input, target);
auto expected = torch::tensor({0.8500}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, SmoothL1LossNoReduction) {
SmoothL1Loss loss(/*reduction=*/torch::kNone);
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output = loss(input, target);
auto expected = torch::tensor({0.005, 0.02, 0.045}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, HuberLossNoReduction) {
HuberLoss loss(/*reduction=*/torch::kNone);
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output = loss(input, target);
auto expected = torch::tensor({0.005, 0.02, 0.045}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, MultiLabelMarginLossNoReduction) {
MultiLabelMarginLoss loss(torch::kNone);
auto input = torch::tensor({{0.1, 0.2, 0.4, 0.8}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{3, 0, -1, 1}}, torch::kLong);
auto output = loss->forward(input, target);
auto expected = torch::tensor({0.8500}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, SmoothL1LossBeta) {
auto options = SmoothL1LossOptions().beta(0.2);
SmoothL1Loss loss(options);
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output = loss(input, target);
auto expected = torch::tensor(0.108333, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, HuberLossDelta) {
auto options = HuberLossOptions().delta(0.2);
HuberLoss loss(options);
auto input = torch::tensor({0.1, 1.2, 4.7}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0., 1., 5.}, torch::kFloat);
auto output = loss(input, target);
auto expected = torch::tensor(0.0216666, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, TripletMarginLoss) {
TripletMarginLoss loss(TripletMarginLossOptions().margin(1.0));
auto anchor = torch::tensor({{3., 3.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto positive = torch::tensor({{2., 2.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto negative = torch::tensor({{0., 0.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto output = loss->forward(anchor, positive, negative);
auto expected = torch::tensor({0.}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
}
TEST_F(ModulesTest, TripletMarginWithDistanceLossDefaultParity) {
// Check that if we use torch::pairwise_distance with the default
// TripletMarginLoss options as our distance function, the outputs
// are equal (i.e., equal under defaults).
std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
reductions = {torch::kSum, torch::kMean, torch::kNone};
std::vector<float> margins = {0.5, 1.0, 1.5};
std::vector<bool> swaps = {true, false};
for (auto& reduction : reductions) {
for (auto& margin : margins) {
for (const auto swap : swaps) {
auto anchor =
torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto positive =
torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto negative =
torch::randn({100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto basicOptions = TripletMarginLossOptions()
.reduction(reduction)
.margin(margin)
.swap(swap);
auto distanceOptions =
TripletMarginWithDistanceLossOptions()
.reduction(reduction)
.margin(margin)
.swap(swap);
TripletMarginLoss basicLoss(basicOptions);
TripletMarginWithDistanceLoss distanceLoss(distanceOptions);
auto basicOutput = basicLoss->forward(anchor, positive, negative);
auto distanceOutput = distanceLoss->forward(anchor, positive, negative);
auto basicOperatorOutput = basicLoss(anchor, positive, negative);
auto distanceOperatorOutput = distanceLoss(anchor, positive, negative);
ASSERT_TRUE(distanceOutput.allclose(basicOutput, 1e-6, 1e-6));
ASSERT_TRUE(distanceOperatorOutput.allclose(distanceOutput, 1e-6, 1e-6));
ASSERT_TRUE(distanceOperatorOutput.allclose(basicOperatorOutput, 1e-6, 1e-6));
// handle for torch::kNone reduction
auto sum = distanceOutput.sum();
sum.backward();
ASSERT_EQ(anchor.sizes(), anchor.grad().sizes());
ASSERT_EQ(positive.sizes(), positive.grad().sizes());
ASSERT_EQ(negative.sizes(), negative.grad().sizes());
}
}
}
}
TEST_F(ModulesTest, TripletMarginWithDistanceLossFunctionalParity) {
// Check for parity between F::triplet_margin_with_distance_loss and
// TripletMarginWithDistanceLoss.
auto pairwise_distance = [&](const torch::Tensor& x, const torch::Tensor& y) {
return torch::pairwise_distance(x, y);
};
auto cosine_distance = [&](const torch::Tensor& x,
const torch::Tensor& y) {
return 1.0 - torch::cosine_similarity(x, y);
};
std::vector<TripletMarginWithDistanceLossOptions::distance_function_t>
distance_functions = {pairwise_distance, cosine_distance};
std::vector<TripletMarginWithDistanceLossOptions::reduction_t>
reductions = {torch::kSum, torch::kMean, torch::kNone};
std::vector<float> margins = {0.5, 1.0, 1.5};
std::vector<bool> swaps = {true, false};
for (auto& function : distance_functions) {
for (auto& reduction : reductions) {
for (auto& margin : margins) {
for (const auto swap : swaps) {
auto moduleOptions =
TripletMarginWithDistanceLossOptions()
.distance_function(function)
.reduction(reduction)
.margin(margin)
.swap(swap);
auto functionOptions =
torch::nn::functional::TripletMarginWithDistanceLossFuncOptions()
.distance_function(function)
.reduction(reduction)
.margin(margin)
.swap(swap);
auto anchor = torch::randn(
{100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto positive = torch::randn(
{100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
auto negative = torch::randn(
{100, 128}, torch::dtype(torch::kFloat).requires_grad(true));
TripletMarginWithDistanceLoss distanceLoss(moduleOptions);
auto moduleOutput = distanceLoss->forward(anchor, positive, negative);
auto moduleOperatorOutput = distanceLoss(anchor, positive, negative);
auto functionOutput = torch::nn::functional::triplet_margin_with_distance_loss(
anchor, positive, negative, functionOptions);
ASSERT_TRUE(moduleOutput.allclose(functionOutput, 1e-6, 1e-6));
ASSERT_TRUE(moduleOperatorOutput.allclose(functionOutput, 1e-6, 1e-6));
}
}
}
}
}
TEST_F(ModulesTest, NLLLoss) {
NLLLoss loss;
auto input = torch::tensor({{-0.1315, -3.1315, -2.5315},
{-3.7038, -0.1038, -2.6038},
{-2.3422, -1.3422, -0.4422}},
torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({1, 0, 2}, torch::kLong);
auto output = loss->forward(input, target);
auto expected = torch::tensor(2.4258, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_TRUE(
NLLLoss(NLLLossOptions().ignore_index(-100).reduction(torch::kMean))
->forward(input, target).allclose(expected, 1e-04));
}
TEST_F(ModulesTest, CrossEntropyLoss) {
CrossEntropyLoss loss;
auto input = torch::tensor({{3., 3.}, {2., 2.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({0, 1}, torch::kLong);
auto output = loss->forward(input, target);
auto expected = torch::tensor(0.6931, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_EQ(input.sizes(), input.grad().sizes());
ASSERT_TRUE(
CrossEntropyLoss(CrossEntropyLossOptions().ignore_index(-100).reduction(torch::kMean))
->forward(input, target).allclose(expected, 1e-04));
// label smoothing with class indices
loss = CrossEntropyLoss(CrossEntropyLossOptions().label_smoothing(0.15).reduction(torch::kMean));
input = torch::tensor({{3., 1.}, {1., 2.}}, torch::dtype(torch::kFloat).requires_grad(true));
target = torch::tensor({0, 1}, torch::kLong);
output = loss->forward(input, target);
expected = torch::tensor(0.3326, torch::kFloat);
s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_EQ(input.sizes(), input.grad().sizes());
// label smoothing with with target probabilities
loss = CrossEntropyLoss(CrossEntropyLossOptions().label_smoothing(0.2).reduction(torch::kMean));
input = torch::tensor({{3., 1.}, {1., 2.}}, torch::dtype(torch::kFloat).requires_grad(true));
target = torch::tensor({{0.8, 0.2}, {0.1, 0.9}}, torch::kFloat);
output = loss->forward(input, target);
expected = torch::tensor(0.5701, torch::kFloat);
s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, CosineSimilarity) {
CosineSimilarity cos(CosineSimilarityOptions().dim(1));
auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
auto output = cos->forward(input1, input2);
auto expected = torch::tensor({0.8078, 0.8721}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected, 1e-04));
ASSERT_EQ(input1.sizes(), input1.grad().sizes());
}
TEST_F(ModulesTest, SoftMarginLossDefaultOptions) {
SoftMarginLoss loss;
auto input = torch::tensor({2., 4., 1., 3.}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
auto output = loss->forward(input, target);
auto expected = torch::tensor({1.3767317}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, MultiLabelSoftMarginLossDefaultOptions) {
MultiLabelSoftMarginLoss loss;
auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
auto output = loss->forward(input, target);
auto expected = torch::tensor({0.7608436}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, SoftMarginLossNoReduction) {
SoftMarginLoss loss(torch::kNone);
auto input = torch::tensor({2., 4., 1., 3.}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({-1., 1., 1., -1.}, torch::kFloat);
auto output = loss->forward(input, target);
auto expected = torch::tensor({2.1269281, 0.01814993, 0.3132617, 3.0485873}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, MultiLabelSoftMarginLossWeightedNoReduction) {
auto input = torch::tensor({{0., 2., 2., 0.}, {2., 1., 0., 1.}}, torch::dtype(torch::kFloat).requires_grad(true));
auto target = torch::tensor({{0., 0., 1., 0.}, {1., 0., 1., 1.}}, torch::kFloat);
auto weight = torch::tensor({0.1, 0.6, 0.4, 0.8}, torch::kFloat);
auto options = MultiLabelSoftMarginLossOptions().reduction(torch::kNone).weight(weight);
MultiLabelSoftMarginLoss loss = MultiLabelSoftMarginLoss(options);
auto output = loss->forward(input, target);
auto expected = torch::tensor({0.4876902, 0.3321295}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input.sizes(), input.grad().sizes());
}
TEST_F(ModulesTest, PairwiseDistance) {
PairwiseDistance dist(PairwiseDistanceOptions().p(1));
auto input1 = torch::tensor({{1, 2, 3}, {4, 5, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
auto input2 = torch::tensor({{1, 8, 3}, {2, 1, 6}}, torch::dtype(torch::kFloat).requires_grad(true));
auto output = dist->forward(input1, input2);
auto expected = torch::tensor({6, 6}, torch::kFloat);
auto s = output.sum();
s.backward();
ASSERT_TRUE(output.allclose(expected));
ASSERT_EQ(input1.sizes(), input1.grad().sizes());
}
TEST_F(ModulesTest, ELU) {
const auto size = 3;
for (const auto alpha : {0.0, 0.42, 1.0, 4.2, 42.42}) {
for (const auto inplace : {false, true}) {
ELU model {ELUOptions().alpha(alpha).inplace(inplace)};
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size});
if (!inplace) {
x.requires_grad_(true);
}
auto x_orig = x.clone();
auto y = model(x);
torch::Tensor s = y.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = torch::max(torch::zeros_like(x_orig), x_orig) +
torch::min(torch::zeros_like(x_orig), alpha * (torch::exp(x_orig) - 1.0));
ASSERT_TRUE(torch::allclose(y, y_exp));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y_exp));
} else {
s.backward();
}
}
}
}
TEST_F(ModulesTest, SELU) {
for (const auto inplace : {false, true}) {
SELU model(inplace);
auto input = torch::randn({5, 5});
if (!inplace) {
input.requires_grad_(true);
}
auto input_orig = input.clone();
auto output = model->forward(input);
const double scale = 1.0507009873554804934193349852946;
const double alpha = 1.6732632423543772848170429916717;
auto zero = torch::zeros_like(input);
auto expected = scale *
(torch::max(zero, input_orig) +
torch::min(zero, alpha * (torch::exp(input_orig) - 1)));
auto s = output.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
if (inplace) {
ASSERT_TRUE(input.allclose(expected));
} else {
s.backward();
}
}
}
TEST_F(ModulesTest, Hardshrink) {
const auto size = 3;
for (const auto lambda : {-4.2, -1.0, -0.42, 0.0, 0.42, 1.0, 4.2, 42.42}) {
Hardshrink model {HardshrinkOptions().lambda(lambda)};
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = (x.abs() > lambda) * x;
ASSERT_TRUE(torch::allclose(y, y_exp));
}
}
TEST_F(ModulesTest, Hardtanh) {
const auto size = 3;
for (const auto min_val : {-4.2, -1.0, -0.42, 0.0}) {
for (const auto max_val : {0.42, 1.0, 4.2}) {
for (const auto inplace : {false, true}) {
Hardtanh model {HardtanhOptions().min_val(min_val).max_val(max_val).inplace(inplace)};
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size});
if (!inplace) {
x.requires_grad_(true);
}
auto x_orig = x.clone();
auto y = model(x);
torch::Tensor s = y.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = (x_orig < min_val) * min_val +
((x_orig >= min_val) * (x_orig <= max_val)) * x_orig +
(x_orig > max_val) * max_val;
ASSERT_TRUE(torch::allclose(y, y_exp));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y_exp));
} else {
s.backward();
}
}
}
}
}
TEST_F(ModulesTest, HardtanhMinValGEMaxVal) {
ASSERT_THROWS_WITH(Hardtanh{HardtanhOptions().min_val(0.42).max_val(0.42)},
"max_val must be greater than min_val");
ASSERT_THROWS_WITH(Hardtanh{HardtanhOptions().min_val(0.42).max_val(-0.42)},
"max_val must be greater than min_val");
Hardtanh ht {HardtanhOptions().min_val(-0.42).max_val(0.42)};
ht->options.min_val(0.42);
ASSERT_THROWS_WITH(ht->reset(), "max_val must be greater than min_val");
ht->options.max_val(-0.42);
ASSERT_THROWS_WITH(ht->reset(), "max_val must be greater than min_val");
}
TEST_F(ModulesTest, LeakyReLU) {
const auto size = 3;
for (const auto inplace : {false, true}) {
for (const auto negative_slope : {0.0, 0.42, 1.0}) {
for (const auto type : {torch::kFloat, torch::kBFloat16}) {
LeakyReLU model {LeakyReLUOptions().negative_slope(negative_slope).inplace(inplace)};
auto x = torch::linspace(-10.0, 10.0, size * size * size).to(type);
x.resize_({size, size, size});
if (!inplace) {
x.requires_grad_(true);
}
auto x_orig = x.clone();
auto y = model(x);
torch::Tensor s = y.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = (x_orig < 0) * x_orig * negative_slope + (x_orig >= 0) * x_orig;
ASSERT_TRUE(torch::allclose(y, y_exp));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y_exp));
} else {
s.backward();
}
}
}
}
}
TEST_F(ModulesTest, LogSigmoid) {
const auto size = 3;
LogSigmoid model;
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = torch::log(torch::ones_like(x)/(torch::ones_like(x) + torch::exp(torch::neg(x))));
ASSERT_TRUE(torch::allclose(y, y_exp, 1e-4, 1e-7));
}
TEST_F(ModulesTest, Softmax) {
Softmax m(/*dim=*/1);
auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
auto output = m(input);
auto sum = torch::sum(torch::exp(input), 1);
for (const auto i : c10::irange(2)) {
auto expected = torch::exp(input[i]) / sum[i];
ASSERT_TRUE(torch::allclose(output[i], expected));
}
}
TEST_F(ModulesTest, Softmin) {
Softmin m(/*dim=*/1);
auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
auto output = m(input);
auto sum = torch::sum(torch::exp(-input), 1);
for (const auto i : c10::irange(2)) {
auto expected = torch::exp(-input[i]) / sum[i];
ASSERT_TRUE(torch::allclose(output[i], expected));
}
}
TEST_F(ModulesTest, LogSoftmax) {
LogSoftmax m(/*dim=*/1);
auto input = torch::arange(10, torch::kFloat).reshape({2, 5});
auto output = m(input);
auto sum = torch::sum(torch::exp(input), 1);
for (const auto i : c10::irange(2)) {
auto expected = torch::log(torch::exp(input[i]) / sum[i]);
ASSERT_TRUE(torch::allclose(output[i], expected));
}
}
TEST_F(ModulesTest, AdaptiveLogSoftmaxWithLoss) {
{
// log_probs actually returns log_proba
AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 4, {2}).div_value(2.));
auto x = torch::randn({4, 8});
auto logprob_out = asfm->log_prob(x);
ASSERT_TRUE(torch::allclose(torch::exp(logprob_out).data().sum(1), torch::ones(4)));
}
{
// test predict
AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 10, {4, 8}).div_value(2.).head_bias(true));
auto x = torch::randn({64, 8});
auto logprob_out = asfm->log_prob(x);
auto predict_out = asfm->predict(x);
ASSERT_TRUE(torch::allclose(predict_out, logprob_out.argmax(1)));
}
{
// cluster sizes
AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(16, 20, {4, 10, 15}).div_value(2.));
auto x = torch::arange(100, 132, torch::kFloat).reshape({2, 16});
auto y = torch::tensor({0, 17}, torch::kLong);
auto asm_out = asfm(x, y);
ASSERT_EQ(asm_out.output.sizes(), std::vector<int64_t>({2}));
}
{
// forward returns the same thing as log_probs
AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 4, {2}).div_value(2.));
auto x = torch::randn({4, 8});
auto logprob_out = asfm->log_prob(x);
NLLLoss nll_loss;
for (const auto v : c10::irange(4)) {
auto y = torch::full({4}, v, torch::kLong);
auto asm_out = asfm(x, y);
auto out = asm_out.output;
auto loss = torch::tensor(asm_out.loss, torch::kFloat);
auto expected = nll_loss->forward(logprob_out, y);
ASSERT_TRUE(torch::allclose(loss, expected));
ASSERT_TRUE(torch::allclose(out, logprob_out.gather(1, y.unsqueeze(1)).squeeze()));
}
}
{
// test no batch dim
AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(16, 20, {4, 10, 15}).div_value(2.));
auto x = torch::randn({1, 16});
auto y = torch::tensor({17});
auto x2 = x.squeeze(0);
auto y2 = y.squeeze(0);
ASSERT_TRUE(torch::allclose(asfm(x, y).output.squeeze(0), asfm(x2, y2).output));
}
}
TEST_F(ModulesTest, Softmax2d) {
Softmax2d m;
auto input = torch::arange(24, torch::kFloat).reshape({1, 2, 3, 4});
auto output = m(input);
auto sum = torch::sum(torch::exp(input), 1);
for (const auto i : c10::irange(1)) {
for (const auto j : c10::irange(2)) {
for (const auto k : c10::irange(3)) {
for (const auto l : c10::irange(4)) {
auto expected = torch::exp(input[i][j][k][l]) / sum[i][k][l];
ASSERT_TRUE(torch::allclose(output[i][j][k][l], expected));
}
}
}
}
}
TEST_F(ModulesTest, PReLU) {
const auto num_parameters = 42;
const auto init = 0.42;
PReLU model {PReLUOptions().num_parameters(num_parameters).init(init)};
ASSERT_EQ(model->weight.sizes(), std::vector<int64_t>({num_parameters}));
ASSERT_TRUE(torch::allclose(model->weight,
torch::full(num_parameters, init)));
const auto x = torch::rand({100, num_parameters}) * 200 - 100;
const auto y = model(x);
const auto s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), x.ndimension());
ASSERT_EQ(y.sizes(), x.sizes());
const auto y_exp = (x < 0) * model->weight * x + (x >= 0) * x;
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, ReLU) {
for (const auto inplace : {false, true}) {
const auto size = 3;
ReLU model(inplace);
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size});
if (!inplace) {
x.requires_grad_(true);
}
auto x_orig = x.clone();
auto y = model(x);
torch::Tensor s = y.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = (x_orig < 0) * 0 + (x_orig >= 0) * x_orig;
ASSERT_TRUE(torch::allclose(y, y_exp));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y_exp));
} else {
s.backward();
}
}
}
TEST_F(ModulesTest, ReLU6) {
for (const auto inplace : {false, true}) {
const auto size = 3;
ReLU6 model(inplace);
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size});
if (!inplace) {
x.requires_grad_(true);
}
auto x_orig = x.clone();
auto y = model(x);
torch::Tensor s = y.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = (x_orig < 0) * 0 + ((x_orig >= 0) * (x_orig <= 6)) * x_orig + (x_orig > 6) * 6;
ASSERT_TRUE(torch::allclose(y, y_exp));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y_exp));
} else {
s.backward();
}
}
}
TEST_F(ModulesTest, RReLU) {
const auto size = 3;
for (const auto lower : {0.01, 0.1, 0.2}) {
for (const auto upper : {0.3, 0.4, 0.5}) {
for (const auto inplace : {false, true}) {
for (const auto type : {torch::kFloat, torch::kBFloat16}) {
RReLU model {RReLUOptions().lower(lower).upper(upper).inplace(inplace)};
auto x = torch::linspace(-10.0, 10.0, size * size * size).to(type);
x.resize_({size, size, size});
if (!inplace) {
x.requires_grad_(true);
}
auto x_orig = x.clone();
auto y = model(x);
torch::Tensor s = y.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto z = ((x_orig >= 0) * (x_orig == y) +
(x_orig < 0) * (y >= x_orig * upper) * (y <= lower * x_orig)) * 1.0;
ASSERT_TRUE(torch::allclose(z, torch::ones_like(z)));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y));
} else {
s.backward();
}
}
}
}
}
}
TEST_F(ModulesTest, CELU) {
const auto size = 3;
for (const auto inplace : {false, true}) {
for (const auto alpha : {0.42, 1.0, 4.2, 42.42}) {
CELU model {CELUOptions().alpha(alpha).inplace(inplace)};
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size});
if (!inplace) {
x.requires_grad_(true);
}
auto x_orig = x.clone();
auto y = model(x);
torch::Tensor s = y.sum();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = torch::max(torch::zeros_like(x_orig), x_orig) +
torch::min(torch::zeros_like(x_orig), alpha * (torch::exp(x_orig / alpha) - 1.0));
ASSERT_TRUE(torch::allclose(y, y_exp));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y_exp));
} else {
s.backward();
}
}
}
}
TEST_F(ModulesTest, GLU) {
int64_t dim = 1;
GLU model(dim);
auto input = torch::randn({4, 2}, torch::requires_grad());
auto output = model->forward(input);
auto input_size = input.sizes()[dim] / 2;
auto first_half = input.narrow(dim, 0, input_size);
auto second_half = input.narrow(dim, input_size, input_size);
auto expected = first_half * torch::sigmoid(second_half);
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
GLU model_default_options;
ASSERT_TRUE(model_default_options->forward(input).allclose(expected));
}
TEST_F(ModulesTest, GELU) {
GELU model(GELUOptions().approximate("none"));
const auto x = torch::linspace(-3.0, 3.0, 100);
const auto y_exp = x * 0.5 * (1.0 + torch::erf(x / std::sqrt(2.0)));
const auto y = model(x);
ASSERT_TRUE(torch::allclose(y, y_exp, 1.4e-06, 1e-05));
}
TEST_F(ModulesTest, TanhGELU) {
GELU model(GELUOptions().approximate("tanh"));
const auto x = torch::linspace(-3.0, 3.0, 100);
const auto inner = std::sqrt(2 / M_PI) * (x + 0.044715 * x.pow(3.0));
const auto y_exp = 0.5 * x * (1.0 + inner.tanh());
const auto y = model(x);
ASSERT_TRUE(torch::allclose(y, y_exp, 1.4e-06, 1e-05));
}
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
TEST_F(ModulesTest, Mish) {
Mish model;
auto x = torch::randn(100) * 10;
auto y_exp = x * x.exp().log1p().tanh();
auto y = model(x);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, Sigmoid) {
Sigmoid model;
auto x = torch::randn(100) * 10;
auto y_exp = 1 / (1 + torch::exp(-x));
auto y = model(x);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, PixelShuffle) {
PixelShuffle module(/*upscale_factor=*/2);
auto x = torch::tensor(
{{{{-17, 19}, {-1, 2}},
{{7, 14}, {-3, 1}},
{{0, -2}, {-12, 14}},
{{-15, 0}, {-3, 9}}}}, torch::kFloat);
auto y_exp = torch::tensor(
{{{{-17, 7, 19, 14},
{0, -15, -2, 0},
{-1, -3, 2, 1},
{-12, -3, 14, 9}}}}, torch::kFloat);
auto y = module(x);
ASSERT_EQ(y.ndimension(), 4);
ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 1, 4, 4}));
ASSERT_TRUE(y.allclose(y_exp));
}
TEST_F(ModulesTest, PixelUnshuffle) {
PixelUnshuffle module(/*downscale_factor=*/2);
auto x = torch::tensor(
{{{{-17, 7, 19, 14}, {0, -15, -2, 0}, {-1, -3, 2, 1}, {-12, -3, 14, 9}}}},
torch::kFloat);
auto y_exp = torch::tensor(
{{{{-17, 19}, {-1, 2}},
{{7, 14}, {-3, 1}},
{{0, -2}, {-12, 14}},
{{-15, 0}, {-3, 9}}}},
torch::kFloat);
auto y = module(x);
ASSERT_EQ(y.ndimension(), 4);
ASSERT_EQ(y.sizes(), torch::IntArrayRef({1, 4, 2, 2}));
ASSERT_TRUE(y.allclose(y_exp));
}
TEST_F(ModulesTest, Softplus) {
const auto size = 3;
for (const auto beta : {0.5, 1.0, 2.0}) {
for (const auto threshold : {1.0, 3.0, 5.0}) {
Softplus model {SoftplusOptions().beta(beta).threshold(threshold)};
auto x = torch::linspace(-3.0, 3.0, 61);
x.resize_({size, size, size});
auto y_exp =
(x <= threshold) * torch::log(1 + torch::exp(x * beta)) / beta +
(x > threshold) * x;
auto y = model(x);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
ASSERT_TRUE(torch::allclose(y, y_exp));
}
}
}
TEST_F(ModulesTest, Softshrink) {
const auto size = 3;
for (const auto lambda : {0.0, 0.42, 1.0, 4.2, 42.42}) {
Softshrink model {/*lambda=*/lambda};
auto x = torch::linspace(-10.0, 10.0, size * size * size);
x.resize_({size, size, size}).set_requires_grad(true);
auto y = model(x);
torch::Tensor s = y.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
auto y_exp = (x < -lambda) * (x + lambda) + (x > lambda) * (x - lambda);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
}
TEST_F(ModulesTest, Softsign) {
Softsign model;
auto x = torch::randn(100) * 10;
auto y_exp = x / (1 + x.abs());
auto y = model(x);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, Tanh) {
Tanh model;
auto x = torch::randn(100) * 10;
auto y_exp = (x.exp() - (-x).exp()) / (x.exp() + (-x).exp());
auto y = model(x);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, Tanhshrink) {
Tanhshrink model;
auto x = torch::randn(100) * 10;
auto y_exp = x - x.tanh();
auto y = model(x);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
TEST_F(ModulesTest, Threshold) {
const auto size = 3;
for (const auto threshold : {0.5, 1.0, 2.0}) {
for (const auto value : {0.5, 1.0, 2.0}) {
for (const auto inplace : {false, true}) {
Threshold model {ThresholdOptions(threshold, value).inplace(inplace)};
auto x = torch::linspace(-3.0, 3.0, 61);
x.resize_({size, size, size});
auto x_orig = x.clone();
auto y_exp = (x_orig <= threshold) * value + (x_orig > threshold) * x_orig;
auto y = model(x);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), std::vector<int64_t>({size, size, size}));
ASSERT_TRUE(torch::allclose(y, y_exp));
if (inplace) {
ASSERT_TRUE(torch::allclose(x, y_exp));
}
}
}
}
}
TEST_F(ModulesTest, Upsampling1D) {
{
Upsample model(UpsampleOptions()
.size(std::vector<int64_t>({4}))
.mode(torch::kNearest));
auto input = torch::ones({1, 1, 2}, torch::requires_grad());
auto output = model->forward(input);
auto expected = torch::ones({1, 1, 4});
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
}
{
for (const auto align_corners : {true, false}) {
// test float scale factor up & down sampling
for (const auto scale_factor : {0.5, 1.5, 2.0}) {
Upsample model(UpsampleOptions()
.scale_factor(std::vector<double>({scale_factor}))
.mode(torch::kLinear)
.align_corners(align_corners));
auto input = torch::ones({1, 1, 2}, torch::requires_grad());
auto output = model->forward(input);
auto expected_size =
static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
auto expected = torch::ones({1, 1, expected_size});
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
}
}
}
{
// linear (1D) upsampling spatial invariance
Upsample model(UpsampleOptions()
.scale_factor(std::vector<double>({3}))
.mode(torch::kLinear)
.align_corners(false));
auto input = torch::zeros({1, 1, 9});
input.narrow(2, 0, 4).normal_();
auto output = model->forward(input);
auto expected = model->forward(input.narrow(2, 0, 5));
ASSERT_TRUE(torch::allclose(output.narrow(2, 0, 15), expected));
}
}
TEST_F(ModulesTest, Upsampling2D) {
{
Upsample model(UpsampleOptions()
.size(std::vector<int64_t>({4, 4}))
.mode(torch::kNearest));
auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
auto output = model->forward(input);
auto expected = torch::ones({1, 1, 4, 4});
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
}
{
for (const auto align_corners : {true, false}) {
// test float scale factor up & down sampling
for (const auto scale_factor : {0.5, 1.5, 2.0}) {
Upsample model(UpsampleOptions()
.scale_factor(std::vector<double>({scale_factor, scale_factor}))
.mode(torch::kBilinear)
.align_corners(align_corners));
auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
auto output = model->forward(input);
auto expected_size =
static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
auto expected = torch::ones({1, 1, expected_size, expected_size});
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
}
}
}
{
for (const auto align_corners : {true, false}) {
// test float scale factor up & down sampling
for (const auto scale_factor : {0.5, 1.5, 2.0}) {
Upsample model(UpsampleOptions()
.scale_factor(std::vector<double>({scale_factor, scale_factor}))
.mode(torch::kBicubic)
.align_corners(align_corners));
auto input = torch::ones({1, 1, 2, 2}, torch::requires_grad());
auto output = model->forward(input);
auto expected_size =
static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
auto expected = torch::ones({1, 1, expected_size, expected_size});
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
}
}
}
}
TEST_F(ModulesTest, Upsampling3D) {
{
Upsample model(UpsampleOptions()
.size(std::vector<int64_t>({4, 4, 4}))
.mode(torch::kNearest));
auto input = torch::ones({1, 1, 2, 2, 2}, torch::requires_grad());
auto output = model->forward(input);
auto expected = torch::ones({1, 1, 4, 4, 4});
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
}
{
for (const auto align_corners : {true, false}) {
// test float scale factor up & down sampling
for (const auto scale_factor : {0.5, 1.5, 2.0}) {
Upsample model(
UpsampleOptions()
.scale_factor(std::vector<double>({scale_factor, scale_factor, scale_factor}))
.mode(torch::kTrilinear)
.align_corners(align_corners));
auto input = torch::ones({1, 1, 2, 2, 2}, torch::requires_grad());
auto output = model->forward(input);
auto expected_size =
static_cast<int64_t>(std::floor(input.size(-1) * scale_factor));
auto expected =
torch::ones({1, 1, expected_size, expected_size, expected_size});
auto s = output.sum();
s.backward();
ASSERT_EQ(s.ndimension(), 0);
ASSERT_TRUE(output.allclose(expected));
}
}
}
}
TEST_F(ModulesTest, CTCLoss) {
CTCLoss loss {CTCLossOptions().reduction(torch::kNone)};
const auto target_lengths = torch::tensor({0, 0, 0});
const auto input_lengths = torch::tensor({50, 50, 50});
const auto targets =
torch::randint(1, 15, at::IntArrayRef({0}), torch::kLong);
const auto log_probs =
torch::randn({50, 3, 15}, torch::kDouble).log_softmax(2);
const auto output =
loss->forward(log_probs, targets, input_lengths, target_lengths);
ASSERT_TRUE(output.ge(0).all().item<bool>());
ASSERT_TRUE(torch::allclose(
-log_probs.sum(0).slice(1, 0, 1).view_as(output), output));
}
TEST_F(ModulesTest, PoissonNLLLoss) {
const auto input = torch::tensor({0.5, 1.5, 2.5});
const auto target = torch::tensor({1., 2., 3.});
const auto component_wise_loss = torch::exp(input) - target * input;
{
PoissonNLLLoss loss {PoissonNLLLossOptions().reduction(torch::kNone)};
ASSERT_TRUE(torch::allclose(
component_wise_loss,
loss->forward(input, target)
));
}
{
PoissonNLLLoss loss {PoissonNLLLossOptions().reduction(torch::kSum)};
ASSERT_TRUE(torch::allclose(
torch::sum(component_wise_loss),
loss->forward(input, target)
));
}
{
PoissonNLLLoss loss {PoissonNLLLossOptions().reduction(torch::kMean)};
ASSERT_TRUE(torch::allclose(
torch::mean(component_wise_loss),
loss->forward(input, target)
));
}
}
TEST_F(ModulesTest, MarginRankingLoss) {
{
MarginRankingLoss loss;
const auto input1 = torch::randn(15) * 10;
const auto input2 = torch::randn(15) * 10;
const auto target = torch::randn(15).sign();
ASSERT_TRUE(torch::allclose(
loss->forward(input1, input2, target),
(-target * (input1 - input2)).clamp(0).mean()
));
}
{
MarginRankingLoss loss {MarginRankingLossOptions().margin(0.5).reduction(torch::kSum)};
const auto input1 = torch::randn(15) * 10;
const auto input2 = torch::randn(15) * 10;
const auto target = torch::randn(15).sign();
const auto margin = 0.5;
ASSERT_TRUE(torch::allclose(
loss->forward(input1, input2, target),
(-target * (input1 - input2) + margin).clamp(0).sum()
));
}
{
MarginRankingLoss loss {MarginRankingLossOptions().margin(0.5).reduction(torch::kMean)};
const auto input1 = torch::randn(15) * 10;
const auto input2 = torch::randn(15) * 10;
const auto target = torch::randn(15).sign();
const auto margin = 0.5;
ASSERT_TRUE(torch::allclose(
loss->forward(input1, input2, target),
(-target * (input1 - input2) + margin).clamp(0).mean()
));
}
}
TEST_F(ModulesTest, BCEWithLogitsLoss) {
{ // test BCE with logits raises if target and input are different size
{
const auto target = torch::rand(5);
const auto input = torch::rand({5, 1});
ASSERT_THROWS_WITH(
BCEWithLogitsLoss()(input, target),
"must be the same as input size"
);
}
{
const auto target = torch::rand({5, 1});
const auto input = torch::rand(5);
ASSERT_THROWS_WITH(
BCEWithLogitsLoss()(input, target),
"must be the same as input size"
);
}
}
{ // test BCE with logits gives same result as sigmoid and bce loss
auto sigmoid = Sigmoid();
auto target = torch::rand({64, 4});
auto output = torch::rand({64, 4}) - 0.5;
ASSERT_TRUE(torch::allclose(
BCEWithLogitsLoss()(output, target),
BCELoss()(sigmoid(output), target)
));
auto weight = torch::rand(4);
ASSERT_TRUE(torch::allclose(
BCEWithLogitsLoss(
BCEWithLogitsLossOptions().weight(weight)
)(output, target),
BCELoss(
BCELossOptions().weight(weight)
)(sigmoid(output), target)
));
target = torch::zeros({4, 1}, torch::kFloat);
output = torch::empty({4, 1}, torch::kFloat).fill_(-100);
ASSERT_TRUE(torch::allclose(
BCEWithLogitsLoss()(output, target),
BCELoss()(sigmoid(output), target)
));
ASSERT_TRUE(torch::allclose(
BCEWithLogitsLoss(
BCEWithLogitsLossOptions().reduction(torch::kNone)
)(output, target),
BCELoss(
BCELossOptions().reduction(torch::kNone)
)(sigmoid(output), target)
));
weight = torch::rand({1}, torch::kFloat);
ASSERT_TRUE(torch::allclose(
BCEWithLogitsLoss(
BCEWithLogitsLossOptions().weight(weight)
)(output, target),
BCELoss(
BCELossOptions().weight(weight)
)(sigmoid(output), target)
));
}
{ // test BCE with logits has correct grad at zero
const auto output = torch::zeros({3, 1}, torch::requires_grad());
const auto target = torch::zeros({3, 1});
BCEWithLogitsLoss(BCEWithLogitsLossOptions()
.reduction(torch::kSum))(output, target).backward();
const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
ASSERT_TRUE(torch::allclose(output.grad(), expected_grad));
}
{ // test BCE with logits broadcasts weights
const auto target = torch::rand({16, 4});
const auto output = torch::rand({16, 4}) - 0.5;
auto weight = torch::rand(4);
auto out1 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().weight(weight)
)(output, target);
weight = weight.expand({16, 4}).contiguous();
auto out2 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().weight(weight)
)(output, target);
ASSERT_TRUE(torch::allclose(out1, out2));
weight = torch::rand({16, 1});
out1 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().weight(weight)
)(output, target);
weight = weight.expand({16, 4}).contiguous();
out2 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().weight(weight)
)(output, target);
ASSERT_TRUE(torch::allclose(out1, out2));
}
{ // test BCE with logits ones in pos weights are the same as none
const auto target = torch::rand({64, 4});
const auto output = torch::rand({64, 4}) - 0.5;
const auto pos_weight = torch::ones({64, 4});
ASSERT_TRUE(torch::allclose(
BCEWithLogitsLoss()(output, target),
BCEWithLogitsLoss(
BCEWithLogitsLossOptions().pos_weight(pos_weight)
)(output, target)
));
}
{ // test BCE with logits broadcasts pos weights
const auto target = torch::rand({64, 4});
const auto output = torch::rand({64, 4}) - 0.5;
const auto pos_weight = torch::rand(4);
const auto out1 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().pos_weight(pos_weight)
)(output, target);
const auto pos_weight1 = pos_weight.expand({1, 4});
const auto out2 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().pos_weight(pos_weight)
)(output, target);
const auto pos_weight2 = pos_weight.expand({64, 4});
const auto out3 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().pos_weight(pos_weight)
)(output, target);
ASSERT_TRUE(torch::allclose(out1, out2));
ASSERT_TRUE(torch::allclose(out1, out3));
}
{ // test BCE with logits with pos weight has correct grad at zero
const auto output = torch::zeros({3, 1}, torch::requires_grad());
const auto target = torch::zeros({3, 1});
const auto pos_weight = torch::ones({3, 1});
BCEWithLogitsLoss(
BCEWithLogitsLossOptions().pos_weight(pos_weight).reduction(torch::kSum)
)(output, target).backward();
const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
const auto grad = output.grad();
ASSERT_TRUE(torch::allclose(grad, expected_grad));
}
{ // test BCE with logits stability
const auto output = torch::tensor({0., -120.});
const auto target = torch::tensor({0., 1.});
const auto pos_weight = torch::tensor({1., 1.});
const auto out1 = BCEWithLogitsLoss()(output, target);
ASSERT_TRUE(torch::isfinite(out1).all().item<bool>());
const auto out2 = BCEWithLogitsLoss(
BCEWithLogitsLossOptions().pos_weight(pos_weight)
)(output, target);
ASSERT_TRUE(torch::isfinite(out2).all().item<bool>());
}
}
namespace detail {
namespace F = torch::nn::functional;
torch::Tensor _batchmatmul(const torch::Tensor& a, const torch::Tensor& b) {
TORCH_INTERNAL_ASSERT(a.size(0) == b.size(0));
TORCH_INTERNAL_ASSERT(a.size(1) == b.size(1));
auto retval = torch::zeros({a.size(0), a.size(1), a.size(2), b.size(3)}, torch::kFloat32);
for (const auto i : c10::irange(a.size(0))) {
for (const auto j : c10::irange(a.size(1))) {
retval[i][j] = torch::matmul(a[i][j], b[i][j]);
}
}
return retval;
}
torch::Tensor _softmax(const torch::Tensor& x) {
auto output = torch::zeros(x.sizes());
for (const auto i : c10::irange(x.size(0))) {
for (const auto j : c10::irange(x.size(1))) {
for (const auto k : c10::irange(x.size(2))) {
const auto& x_curr = x[i][j][k];
const auto e_x = torch::exp(x_curr - torch::max(x_curr));
output[i][j][k] = e_x / torch::sum(e_x);
}
}
}
return output;
}
std::tuple<torch::Tensor, torch::Tensor> _scaled_dot_attn_ref(
const torch::Tensor& Q, const torch::Tensor& K, const torch::Tensor& V,
at::IntArrayRef dims, const torch::Tensor& unseen_mask = {},
const torch::Tensor& key_padding_mask = {},
bool average_attn_weights = true) {
auto QKT = _batchmatmul(
Q,
K.permute({0, 1, 3, 2}) / std::sqrt(dims[3])
);
const auto b1 = QKT.size(0);
const auto b2 = QKT.size(1);
const auto s1 = QKT.size(2);
const auto s2 = QKT.size(3);
if (unseen_mask.defined() || key_padding_mask.defined()) {
for (const auto i : c10::irange(b1)) {
for (const auto j : c10::irange(b2)) {
for (const auto m : c10::irange(s1)) {
for (const auto n : c10::irange(s2)) {
if (unseen_mask.defined() && unseen_mask[m][n].item<double>() == 0) {
QKT[i][j][m][n] = -std::numeric_limits<double>::infinity();
}
if (key_padding_mask.defined() && key_padding_mask[i][n].item<double>() != 0) {
QKT[i][j][m][n] = -std::numeric_limits<double>::infinity();
}
}
}
}
}
}
auto reference = _softmax(QKT);
auto ref_attn_weight = reference;
if (average_attn_weights) {
// NOLINTNEXTLINE(bugprone-argument-comment)
ref_attn_weight = torch::sum(ref_attn_weight, /*axis=*/1) / b2;
}
reference = _batchmatmul(reference, V);
return std::tie(reference, ref_attn_weight);
}
torch::Tensor _split_heads_ref(const torch::Tensor& X, at::IntArrayRef dims, int nheads, int d_head) {
auto X_split = X.reshape({dims[0], dims[1], nheads, d_head});
auto X_split_transposed = X_split.permute({0, 2, 1, 3});
return X_split_transposed.reshape({dims[0], nheads, dims[1], d_head});
}
torch::Tensor _combine_heads_ref(const torch::Tensor& X, at::IntArrayRef dims, int nheads, int d_head) {
auto X_transposed = X.permute({0, 2, 1, 3});
auto reference = X_transposed.reshape({dims[0], dims[1], nheads * d_head});
return reference;
}
torch::Tensor _fc(torch::Tensor X, torch::Tensor X_weight, torch::Tensor X_bias) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto X_fc_b = X_bias;
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto X_fc_w = X_weight;
return torch::matmul(X, torch::t(X_fc_w)) + X_fc_b;
}
void _multihead_attn_test_helper(bool add_key_padding_mask = false,
bool add_bias_kv = false, bool add_zero_attn = false,
bool saved_kv = false, bool same_embed_dim = false,
bool average_attn_weights = true) {
std::random_device device;
std::mt19937 generator(device());
std::uniform_int_distribution<int> d_2_10(2, 10);
std::uniform_int_distribution<int> d_3_10(3, 10);
bool registration_checked = false;
for (const auto i : c10::irange(100)) {
(void)i; // Suppress unused variable warning
const auto batch_sz = d_2_10(generator);
const auto seq_len = d_2_10(generator);
const auto d_head = d_3_10(generator);
const auto nheads = d_3_10(generator);
const auto d_model = d_head * nheads;
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int kv_dim;
if (same_embed_dim) {
kv_dim = d_model;
} else {
std::uniform_int_distribution<int> d(5, 20);
kv_dim = d(generator);
while (kv_dim == d_model) {
kv_dim = d(generator);
}
}
std::vector<int64_t> dims {batch_sz, seq_len, kv_dim};
torch::Tensor saved_k;
torch::Tensor saved_k_tensor;
torch::Tensor saved_v;
torch::Tensor saved_v_tensor;
if (saved_kv) {
saved_k = torch::rand({batch_sz * nheads, seq_len, d_head});
saved_k_tensor = saved_k;
saved_v = torch::rand({batch_sz * nheads, seq_len, d_head});
saved_v_tensor = saved_v;
}
torch::Tensor key_padding_mask;
torch::Tensor key_padding_mask_tensor;
if (add_key_padding_mask) {
const auto seq_mask = torch::randint(0, 2, {1, seq_len});
key_padding_mask = seq_mask.repeat({batch_sz, 1}) == 1;
key_padding_mask_tensor = key_padding_mask;
}
const auto decoder_state = torch::rand({batch_sz, d_model});
const torch::Tensor K = torch::rand(dims);
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
const torch::Tensor V = K;
const torch::Tensor Q = decoder_state.clone().resize_({batch_sz, 1, d_model});
auto attn_mask = torch::randint(0, 2, {1, seq_len});
const torch::Tensor attn_mask_tensor = attn_mask.clone();
attn_mask_tensor.masked_fill_(attn_mask_tensor == 0, -std::numeric_limits<double>::infinity());
attn_mask_tensor.masked_fill_(attn_mask_tensor > 0, double(0.0));
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
const torch::Tensor decoder_state_tensor = decoder_state;
const torch::Tensor source_hid_tensor = K.transpose(0, 1);
const auto options = MultiheadAttentionOptions(d_model, nheads)
.add_bias_kv(add_bias_kv)
.add_zero_attn(add_zero_attn)
.kdim(kv_dim)
.vdim(kv_dim);
const auto multihead_attn_module = MultiheadAttention(options);
if (!registration_checked) {
// make sure parameters are all registered correctly
auto named_parameters = multihead_attn_module->named_parameters();
if (same_embed_dim) {
ASSERT_TRUE(named_parameters.contains("in_proj_weight"));
}
else {
ASSERT_TRUE(named_parameters.contains("q_proj_weight"));
ASSERT_TRUE(named_parameters.contains("k_proj_weight"));
ASSERT_TRUE(named_parameters.contains("v_proj_weight"));
}
if (add_bias_kv) {
ASSERT_TRUE(named_parameters.contains("bias_k"));
ASSERT_TRUE(named_parameters.contains("bias_v"));
}
// make sure sub modules are all registered correctly
auto submodules = multihead_attn_module->named_children();
ASSERT_TRUE(submodules.contains("out_proj"));
registration_checked = true;
}
torch::Tensor bias_k;
torch::Tensor bias_v;
if (add_bias_kv) {
bias_k = multihead_attn_module->bias_k.detach();
bias_v = multihead_attn_module->bias_v.detach();
} else {
bias_k.reset();
bias_v.reset();
}
torch::Tensor _Q = decoder_state_tensor.unsqueeze(1).transpose(0, 1);
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
torch::Tensor _V = source_hid_tensor;
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
torch::Tensor _K = source_hid_tensor;
torch::Tensor result;
torch::Tensor result_weight;
if (multihead_attn_module->_qkv_same_embed_dim) {
std::tie(result, result_weight) = F::multi_head_attention_forward(
_Q, _K, _V,
F::MultiheadAttentionForwardFuncOptions(
/*embed_dim_to_check=*/d_model,
/*num_heads=*/nheads,
/*in_proj_weight=*/multihead_attn_module->in_proj_weight,
/*in_proj_bias=*/multihead_attn_module->in_proj_bias,
/*bias_k=*/multihead_attn_module->bias_k,
/*bias_v=*/multihead_attn_module->bias_v,
/*add_zero_attn=*/multihead_attn_module->options.add_zero_attn(),
/*dropout_p=*/multihead_attn_module->options.dropout(),
/*out_proj_weight=*/multihead_attn_module->out_proj->weight,
/*out_proj_bias=*/multihead_attn_module->out_proj->bias
)
.training(multihead_attn_module->is_training())
.key_padding_mask(key_padding_mask_tensor)
.need_weights(true)
.attn_mask(attn_mask_tensor)
.static_k(saved_k_tensor)
.static_v(saved_v_tensor)
.average_attn_weights(average_attn_weights)
);
} else {
std::tie(result, result_weight) = F::multi_head_attention_forward(
_Q, _K, _V,
F::MultiheadAttentionForwardFuncOptions(
/*embed_dim_to_check=*/d_model,
/*num_heads=*/nheads,
/*in_proj_weight=*/{},
/*in_proj_bias=*/multihead_attn_module->in_proj_bias,
/*bias_k=*/multihead_attn_module->bias_k,
/*bias_v=*/multihead_attn_module->bias_v,
/*add_zero_attn=*/multihead_attn_module->options.add_zero_attn(),
/*dropout_p=*/multihead_attn_module->options.dropout(),
/*out_proj_weight=*/multihead_attn_module->out_proj->weight,
/*out_proj_bias=*/multihead_attn_module->out_proj->bias
)
.training(multihead_attn_module->is_training())
.key_padding_mask(key_padding_mask_tensor)
.need_weights(true)
.attn_mask(attn_mask_tensor)
.use_separate_proj_weight(true)
.q_proj_weight(multihead_attn_module->q_proj_weight)
.k_proj_weight(multihead_attn_module->k_proj_weight)
.v_proj_weight(multihead_attn_module->v_proj_weight)
.static_k(saved_k_tensor)
.static_v(saved_v_tensor)
.average_attn_weights(average_attn_weights)
);
}
result = result.squeeze(0).detach();
torch::Tensor q_proj_weight;
torch::Tensor k_proj_weight;
torch::Tensor v_proj_weight;
if (multihead_attn_module->_qkv_same_embed_dim) {
q_proj_weight = multihead_attn_module->in_proj_weight.slice(/*dim=*/0, 0, d_model);
k_proj_weight = multihead_attn_module->in_proj_weight.slice(/*dim=*/0, d_model, (d_model * 2));
v_proj_weight = multihead_attn_module->in_proj_weight.slice(/*dim=*/0, (d_model * 2));
} else {
q_proj_weight = multihead_attn_module->q_proj_weight;
k_proj_weight = multihead_attn_module->k_proj_weight;
v_proj_weight = multihead_attn_module->v_proj_weight;
}
auto Q_fc = _fc(Q, q_proj_weight, multihead_attn_module->in_proj_bias.slice(/*dim=*/0, 0, d_model));
auto K_fc = _fc(K, k_proj_weight, multihead_attn_module->in_proj_bias.slice(/*dim=*/0, d_model, (d_model * 2)));
auto V_fc = _fc(V, v_proj_weight, multihead_attn_module->in_proj_bias.slice(/*dim=*/0, (d_model * 2)));
if (add_bias_kv) {
K_fc = torch::cat({K_fc, bias_k.repeat({K_fc.size(0) / bias_k.size(0), 1, 1}/*, axis=0*/)}, /*dim=*/1);
V_fc = torch::cat({V_fc, bias_v.repeat({V_fc.size(0) / bias_v.size(0), 1, 1}/*, axis=0*/)}, /*dim=*/1);
if (attn_mask.defined()) {
attn_mask = torch::cat({attn_mask, torch::ones({1, 1})}, /*dim=*/1);
}
if (key_padding_mask.defined()) {
key_padding_mask = torch::cat({key_padding_mask, torch::full({batch_sz, 1}, false, torch::kBool)}, /*dim=*/1);
}
dims[1] += 1;
}
const auto Q_split = _split_heads_ref(
Q_fc, {batch_sz, 1, d_model}, nheads, d_head
);
torch::Tensor K_split;
if (saved_k.defined()) {
K_split = saved_k.reshape({dims[0], nheads, dims[1], d_head});
} else {
K_split = _split_heads_ref(K_fc, dims, nheads, d_head);
}
torch::Tensor V_split;
if (saved_v.defined()) {
V_split = saved_v.reshape({dims[0], nheads, dims[1], d_head});
} else {
V_split = _split_heads_ref(V_fc, dims, nheads, d_head);
}
if (add_zero_attn) {
dims[1] += 1;
K_split = torch::cat({
K_split,
torch::zeros({K_split.size(0), K_split.size(1), 1, K_split.size(3)})
}, /*dim=*/2);
V_split = torch::cat({
V_split,
torch::zeros({V_split.size(0), V_split.size(1), 1, V_split.size(3)})
}, /*dim=*/2);
if (attn_mask.defined()) {
attn_mask = torch::cat({attn_mask, torch::ones({1, 1})}, /*dim=*/1);
}
if (key_padding_mask.defined()) {
key_padding_mask = torch::cat({key_padding_mask, torch::full({batch_sz, 1}, false, torch::kBool)}, /*dim=*/1);
}
}
torch::Tensor attn_heads;
torch::Tensor ref_attn_weight;
std::tie(attn_heads, ref_attn_weight) = _scaled_dot_attn_ref(
Q_split,
K_split,
V_split,
Q_split.sizes(),
attn_mask,
key_padding_mask,
average_attn_weights
);
const auto combined_attn_heads = _combine_heads_ref(attn_heads, {batch_sz, 1}, nheads, d_head);
auto reference = _fc(combined_attn_heads, multihead_attn_module->out_proj->weight, multihead_attn_module->out_proj->bias);
// NOLINTNEXTLINE(bugprone-argument-comment)
reference = torch::squeeze(reference, /*axis=*/1);
// result = reference
ASSERT_EQ(result.sizes(), std::vector<int64_t>({batch_sz, d_model}));
ASSERT_TRUE(torch::allclose(result, reference, 1e-5, 1e-5, /*equal_nan=*/true));
// result_weight = ref_attn_weight
result_weight = result_weight.detach();
ASSERT_EQ(result_weight.sizes(), ref_attn_weight.sizes());
ASSERT_TRUE(torch::allclose(result_weight, ref_attn_weight, 1e-5, 1e-5, /*equal_nan=*/true));
}
}
}
TEST_F(ModulesTest, MultiheadAttention) {
using namespace ::detail;
for (auto average_attn_weights : {false, true}) {
// test_multihead_attn_add_zero_attn
_multihead_attn_test_helper(
/*add_key_padding_mask=*/false,
/*add_bias_kv=*/false,
/*add_zero_attn=*/true,
/*saved_kv=*/false,
/*same_embed_dim=*/false,
/*average_attn_weights=*/average_attn_weights
);
// test_multihead_attn_add_bias_kv
_multihead_attn_test_helper(
/*add_key_padding_mask=*/false,
/*add_bias_kv=*/true,
/*add_zero_attn=*/false,
/*saved_kv=*/false,
/*same_embed_dim=*/false,
/*average_attn_weights=*/average_attn_weights
);
// test_multihead_attn_no_masking():
_multihead_attn_test_helper();
// test_multihead_attn_key_padding_mask
_multihead_attn_test_helper(
/*add_key_padding_mask=*/true,
/*add_bias_kv=*/false,
/*add_zero_attn=*/false,
/*saved_kv=*/false,
/*same_embed_dim=*/false,
/*average_attn_weights=*/average_attn_weights
);
// test_multihead_attn_saved_kv
_multihead_attn_test_helper(
/*add_key_padding_mask=*/false,
/*add_bias_kv=*/false,
/*add_zero_attn=*/false,
/*saved_kv=*/true,
/*same_embed_dim=*/false,
/*average_attn_weights=*/average_attn_weights
);
// test_multihead_attn_add_bias_kv_zero_attn
_multihead_attn_test_helper(
/*add_key_padding_mask=*/true,
/*add_bias_kv=*/true,
/*add_zero_attn=*/true,
/*saved_kv=*/false,
/*same_embed_dim=*/false,
/*average_attn_weights=*/average_attn_weights
);
// test_multihead_attn_all_arguments1
_multihead_attn_test_helper(
/*add_key_padding_mask=*/true,
/*add_bias_kv=*/false,
/*add_zero_attn=*/true,
/*saved_kv=*/true,
/*same_embed_dim=*/false,
/*average_attn_weights=*/average_attn_weights
);
ASSERT_THROWS_WITH(
// test_multihead_attn_all_arguments2
_multihead_attn_test_helper(
/*add_key_padding_mask=*/true,
/*add_bias_kv=*/true,
/*add_zero_attn=*/true,
/*saved_kv=*/true,
/*same_embed_dim=*/false,
/*average_attn_weights=*/average_attn_weights
),
"bias cannot be added to static key"
);
// test_multihead_attn_all_arguments3
_multihead_attn_test_helper(
/*add_key_padding_mask=*/true,
/*add_bias_kv=*/false,
/*add_zero_attn=*/true,
/*saved_kv=*/true,
/*same_embed_dim=*/true,
/*average_attn_weights=*/average_attn_weights
);
}
}
TEST_F(ModulesTest, PrettyPrintIdentity) {
ASSERT_EQ(c10::str(Identity()), "torch::nn::Identity()");
}
TEST_F(ModulesTest, PrettyPrintFlatten) {
ASSERT_EQ(c10::str(Flatten()),
"torch::nn::Flatten(start_dim=1, end_dim=-1)");
ASSERT_EQ(c10::str(Flatten(FlattenOptions().start_dim(2).end_dim(4))),
"torch::nn::Flatten(start_dim=2, end_dim=4)");
}
TEST_F(ModulesTest, PrettyPrintUnflatten) {
ASSERT_EQ(
c10::str(Unflatten(UnflattenOptions(0, {2, 2}))),
"torch::nn::Unflatten(dim=0, unflattened_size={2, 2})");
ASSERT_EQ(
c10::str(Unflatten(UnflattenOptions(
"B",
{std::pair<std::string, int64_t>{"B1", 2},
std::pair<std::string, int64_t>{"B2", 2}}))),
"torch::nn::Unflatten(dim=\"B\", unflattened_size={{\"B1\", 2}, {\"B2\", 2}})");
}
TEST_F(ModulesTest, ReflectionPad1d) {
{
ReflectionPad1d m(ReflectionPad1dOptions(2));
auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
auto output = m(input);
auto expected = torch::tensor({{{2., 1., 0., 1., 2., 3., 2., 1.},
{6., 5., 4., 5., 6., 7., 6., 5.}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ReflectionPad1d m(ReflectionPad1dOptions({3, 1}));
auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
auto output = m(input);
auto expected = torch::tensor({{{3., 2., 1., 0., 1., 2., 3., 2.},
{7., 6., 5., 4., 5., 6., 7., 6.}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ReflectionPad2d) {
{
ReflectionPad2d m(ReflectionPad2dOptions(2));
auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
auto output = m(input);
auto expected = torch::tensor({{{{8., 7., 6., 7., 8., 7., 6.},
{5., 4., 3., 4., 5., 4., 3.},
{2., 1., 0., 1., 2., 1., 0.},
{5., 4., 3., 4., 5., 4., 3.},
{8., 7., 6., 7., 8., 7., 6.},
{5., 4., 3., 4., 5., 4., 3.},
{2., 1., 0., 1., 2., 1., 0.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ReflectionPad2d m(ReflectionPad2dOptions({1, 1, 2, 0}));
auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
auto output = m(input);
auto expected = torch::tensor({{{{7., 6., 7., 8., 7.},
{4., 3., 4., 5., 4.},
{1., 0., 1., 2., 1.},
{4., 3., 4., 5., 4.},
{7., 6., 7., 8., 7.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ReflectionPad3d) {
{
ReflectionPad3d m(ReflectionPad3dOptions(1));
auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{{{7., 6., 7., 6.},
{5., 4., 5., 4.},
{7., 6., 7., 6.},
{5., 4., 5., 4.}},
{{3., 2., 3., 2.},
{1., 0., 1., 0.},
{3., 2., 3., 2.},
{1., 0., 1., 0.}},
{{7., 6., 7., 6.},
{5., 4., 5., 4.},
{7., 6., 7., 6.},
{5., 4., 5., 4.}},
{{3., 2., 3., 2.},
{1., 0., 1., 0.},
{3., 2., 3., 2.},
{1., 0., 1., 0.}}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ReflectionPad3d m(ReflectionPad3dOptions({0, 1, 1, 0, 1, 2}));
auto input = torch::arange(16, torch::kFloat).reshape({1, 1, 4, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{{{6., 7., 6.},
{4., 5., 4.},
{6., 7., 6.}},
{{2., 3., 2.},
{0., 1., 0.},
{2., 3., 2.}},
{{6., 7., 6.},
{4., 5., 4.},
{6., 7., 6.}},
{{10., 11., 10.},
{8., 9., 8.},
{10., 11., 10.}},
{{14., 15., 14.},
{12., 13., 12.},
{14., 15., 14.}},
{{10., 11., 10.},
{8., 9., 8.},
{10., 11., 10.}},
{{6., 7., 6.},
{4., 5., 4.},
{6., 7., 6.}}}}}, torch::kFloat);
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 1, 7, 3, 3}));
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ReplicationPad1d) {
{
ReplicationPad1d m(ReplicationPad1dOptions(2));
auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
auto output = m(input);
auto expected = torch::tensor({{{0., 0., 0., 1., 2., 3., 3., 3.},
{4., 4., 4., 5., 6., 7., 7., 7.}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ReplicationPad1d m(ReplicationPad1dOptions({3, 1}));
auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
auto output = m(input);
auto expected = torch::tensor({{{0., 0., 0., 0., 1., 2., 3., 3.},
{4., 4., 4., 4., 5., 6., 7., 7.}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ReplicationPad2d) {
{
ReplicationPad2d m(ReplicationPad2dOptions(2));
auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
auto output = m(input);
auto expected = torch::tensor({{{{0., 0., 0., 1., 2., 2., 2.},
{0., 0., 0., 1., 2., 2., 2.},
{0., 0., 0., 1., 2., 2., 2.},
{3., 3., 3., 4., 5., 5., 5.},
{6., 6., 6., 7., 8., 8., 8.},
{6., 6., 6., 7., 8., 8., 8.},
{6., 6., 6., 7., 8., 8., 8.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ReplicationPad2d m(ReplicationPad2dOptions({1, 1, 2, 0}));
auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
auto output = m(input);
auto expected = torch::tensor({{{{0., 0., 1., 2., 2.},
{0., 0., 1., 2., 2.},
{0., 0., 1., 2., 2.},
{3., 3., 4., 5., 5.},
{6., 6., 7., 8., 8.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ReplicationPad3d) {
{
ReplicationPad3d m(ReplicationPad3dOptions(1));
auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{{{0., 0., 1., 1.},
{0., 0., 1., 1.},
{2., 2., 3., 3.},
{2., 2., 3., 3.}},
{{0., 0., 1., 1.},
{0., 0., 1., 1.},
{2., 2., 3., 3.},
{2., 2., 3., 3.}},
{{4., 4., 5., 5.},
{4., 4., 5., 5.},
{6., 6., 7., 7.},
{6., 6., 7., 7.}},
{{4., 4., 5., 5.},
{4., 4., 5., 5.},
{6., 6., 7., 7.},
{6., 6., 7., 7.}}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ReplicationPad3d m(ReplicationPad3dOptions({1, 2, 1, 2, 1, 2}));
auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{{{0., 0., 1., 1., 1.},
{0., 0., 1., 1., 1.},
{2., 2., 3., 3., 3.},
{2., 2., 3., 3., 3.},
{2., 2., 3., 3., 3.}},
{{0., 0., 1., 1., 1.},
{0., 0., 1., 1., 1.},
{2., 2., 3., 3., 3.},
{2., 2., 3., 3., 3.},
{2., 2., 3., 3., 3.}},
{{4., 4., 5., 5., 5.},
{4., 4., 5., 5., 5.},
{6., 6., 7., 7., 7.},
{6., 6., 7., 7., 7.},
{6., 6., 7., 7., 7.}},
{{4., 4., 5., 5., 5.},
{4., 4., 5., 5., 5.},
{6., 6., 7., 7., 7.},
{6., 6., 7., 7., 7.},
{6., 6., 7., 7., 7.}},
{{4., 4., 5., 5., 5.},
{4., 4., 5., 5., 5.},
{6., 6., 7., 7., 7.},
{6., 6., 7., 7., 7.},
{6., 6., 7., 7., 7.}}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ZeroPad2d) {
{
ZeroPad2d m(ZeroPad2dOptions(2));
auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
auto output = m(input);
auto expected = torch::tensor({{{{0., 0., 0., 0., 0., 0., 0.},
{0., 0., 0., 0., 0., 0., 0.},
{0., 0., 0., 1., 2., 0., 0.},
{0., 0., 3., 4., 5., 0., 0.},
{0., 0., 6., 7., 8., 0., 0.},
{0., 0., 0., 0., 0., 0., 0.},
{0., 0., 0., 0., 0., 0., 0.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ZeroPad2d m(ZeroPad2dOptions({1, 1, 2, 0}));
auto input = torch::arange(9, torch::kFloat).reshape({1, 1, 3, 3});
auto output = m(input);
auto expected = torch::tensor({{{{0., 0., 0., 0., 0.},
{0., 0., 0., 0., 0.},
{0., 0., 1., 2., 0.},
{0., 3., 4., 5., 0.},
{0., 6., 7., 8., 0.}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ConstantPad1d) {
{
ConstantPad1d m(ConstantPad1dOptions(2, 3.5));
auto input = torch::arange(8, torch::kFloat).reshape({1, 2, 4});
auto output = m(input);
auto expected = torch::tensor({{{3.5000, 3.5000, 0.0000, 1.0000, 2.0000, 3.0000, 3.5000, 3.5000},
{3.5000, 3.5000, 4.0000, 5.0000, 6.0000, 7.0000, 3.5000, 3.5000}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ConstantPad1d m(ConstantPad1dOptions({3, 1}, 3.5));
auto input = torch::arange(6, torch::kFloat).reshape({1, 2, 3});
auto output = m(input);
auto expected = torch::tensor({{{3.5000, 3.5000, 3.5000, 0.0000, 1.0000, 2.0000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.0000, 4.0000, 5.0000, 3.5000}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ConstantPad2d) {
{
ConstantPad2d m(ConstantPad2dOptions(2, 3.5));
auto input = torch::arange(4, torch::kFloat).reshape({1, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 0.0000, 1.0000, 3.5000, 3.5000},
{3.5000, 3.5000, 2.0000, 3.0000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ConstantPad2d m(ConstantPad2dOptions({3, 0, 2, 1}, 3.5));
auto input = torch::arange(4, torch::kFloat).reshape({1, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 0.0000, 1.0000},
{3.5000, 3.5000, 3.5000, 2.0000, 3.0000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, ConstantPad3d) {
{
ConstantPad3d m(ConstantPad3dOptions(1, 3.5));
auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{{{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000}},
{{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 0.0000, 1.0000, 3.5000},
{3.5000, 2.0000, 3.0000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000}},
{{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 4.0000, 5.0000, 3.5000},
{3.5000, 6.0000, 7.0000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000}},
{{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000}}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
{
ConstantPad3d m(ConstantPad3dOptions({1, 2, 1, 2, 1, 2}, 3.5));
auto input = torch::arange(8, torch::kFloat).reshape({1, 1, 2, 2, 2});
auto output = m(input);
auto expected = torch::tensor({{{{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 0.0000, 1.0000, 3.5000, 3.5000},
{3.5000, 2.0000, 3.0000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 4.0000, 5.0000, 3.5000, 3.5000},
{3.5000, 6.0000, 7.0000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000}},
{{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000},
{3.5000, 3.5000, 3.5000, 3.5000, 3.5000}}}}}, torch::kFloat);
ASSERT_TRUE(output.allclose(expected));
}
}
TEST_F(ModulesTest, CrossMapLRN2d) {
/// size 3, default options
auto input = torch::arange(9, torch::kFloat32).view({1, 1, 3, 3}).requires_grad_(true);
auto expected = torch::tensor({{{{0.00000000, 0.99997497, 1.99980010},
{2.99932500, 3.99840070, 4.99687700},
{5.99460600, 6.99143740, 7.98722360}}}}, torch::kFloat32);
auto grad_expected = torch::tensor({{{{1.00000000, 0.99992496, 0.99970007},
{0.99932520, 0.99880093, 0.99812720},
{0.99730474, 0.99633380, 0.99521490}}}}, torch::kFloat32);
auto crossmaplrn2d = CrossMapLRN2d(3);
auto output = crossmaplrn2d(input);
output.sum().backward();
ASSERT_TRUE(input.grad().allclose(grad_expected));
ASSERT_TRUE(output.allclose(expected));
/// size change
crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(4).alpha(1e-4).beta(0.75).k(1));
output = crossmaplrn2d(input);
expected = torch::tensor({{{{0.00000000, 0.99998120, 1.99985000},
{2.99949400, 3.99880050, 4.99765800},
{5.99595300, 6.99357600, 7.99041300}}}}, torch::kFloat32);
ASSERT_TRUE(output.allclose(expected));
/// alpha change
crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-3).beta(0.75).k(1));
output = crossmaplrn2d(input);
expected = torch::tensor({{{{0.00000000, 0.99975010, 1.99800230},
{2.99326750, 3.98407440, 4.96897600},
{5.94656100, 6.91545720, 7.87434340}}}}, torch::kFloat32);
ASSERT_TRUE(output.allclose(expected));
/// beta change
crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-4).beta(0.95).k(1));
output = crossmaplrn2d(input);
expected = torch::tensor({{{{0.00000000, 0.99996830, 1.99974680},
{2.99914500, 3.99797440, 4.99604460},
{5.99316840, 6.98915600, 7.98382000}}}}, torch::kFloat32);
ASSERT_TRUE(output.allclose(expected));
/// k change
crossmaplrn2d = CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-4).beta(0.75).k(2));
output = crossmaplrn2d(input);
expected = torch::tensor({{{{0.00000000, 0.59459610, 1.18914770},
{1.78361000, 2.37793870, 2.97208900},
{3.56601700, 4.15967700, 4.75302650}}}}, torch::kFloat32);
ASSERT_TRUE(output.allclose(expected));
}
TEST_F(ModulesTest, RNNCell) {
torch::manual_seed(0);
auto rnn = RNNCell(1, 2);
auto input = torch::randn({3, 1});
auto hx = torch::randn({3, 2});
auto output = rnn(input, hx);
auto expected = torch::tensor({{-0.5078, 0.4380},
{-0.7215, 0.2969},
{-0.1304, 0.0653}});
ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
output = rnn(input);
expected = torch::tensor({{-0.0775, 0.6688},
{-0.0734, 0.4759},
{-0.0725, 0.4225}});
ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
}
TEST_F(ModulesTest, LSTMCell) {
torch::manual_seed(0);
auto rnn = LSTMCell(1, 2);
auto input = torch::randn({3, 1});
auto hx = torch::randn({3, 2});
auto cx = torch::randn({3, 2});
auto output = rnn(input, std::make_tuple(hx, cx));
auto output_hx = std::get<0>(output);
auto output_cx = std::get<1>(output);
auto expected_hx = torch::tensor({{-0.2462, 0.0810},
{-0.2206, 0.1867},
{-0.0146, 0.0429}});
auto expected_cx = torch::tensor({{-0.4480, 0.1071},
{-0.6245, 0.2687},
{-0.0322, 0.0518}});
ASSERT_TRUE(torch::allclose(output_hx, expected_hx, 1e-05, 2e-04));
ASSERT_TRUE(torch::allclose(output_cx, expected_cx, 1e-05, 2e-04));
output = rnn(input);
output_hx = std::get<0>(output);
output_cx = std::get<1>(output);
expected_hx = torch::tensor({{-0.1331, 0.1634},
{-0.1494, 0.2869},
{-0.1428, 0.2263}});
expected_cx = torch::tensor({{-0.2679, 0.2180},
{-0.3049, 0.3493},
{-0.2896, 0.2853}});
ASSERT_TRUE(torch::allclose(output_hx, expected_hx, 1e-05, 2e-04));
ASSERT_TRUE(torch::allclose(output_cx, expected_cx, 1e-05, 2e-04));
}
TEST_F(ModulesTest, GRUCell) {
torch::manual_seed(0);
auto rnn = GRUCell(1, 2);
auto input = torch::randn({3, 1});
auto hx = torch::randn({3, 2});
auto output = rnn(input, hx);
auto expected = torch::tensor({{ 1.0243, 0.3227},
{-0.5659, 0.0330},
{-0.4030, -0.2800}});
ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
output = rnn(input);
expected = torch::tensor({{-0.0085, 0.1095},
{-0.1291, 0.2675},
{-0.1339, 0.2725}});
ASSERT_TRUE(torch::allclose(output, expected, 1e-05, 2e-04));
}
TEST_F(ModulesTest, PrettyPrintLinear) {
ASSERT_EQ(
c10::str(Linear(3, 4)), "torch::nn::Linear(in_features=3, out_features=4, bias=true)");
}
TEST_F(ModulesTest, PrettyPrintBilinear) {
ASSERT_EQ(
c10::str(Bilinear(3, 2, 4)), "torch::nn::Bilinear(in1_features=3, in2_features=2, out_features=4, bias=true)");
ASSERT_EQ(
c10::str(Bilinear(BilinearOptions(3, 2, 4).bias(false))), "torch::nn::Bilinear(in1_features=3, in2_features=2, out_features=4, bias=false)");
}
TEST_F(ModulesTest, PrettyPrintConv) {
ASSERT_EQ(
c10::str(Conv1d(3, 4, 5)),
"torch::nn::Conv1d(3, 4, kernel_size=5, stride=1)");
ASSERT_EQ(
c10::str(Conv2d(3, 4, 5)),
"torch::nn::Conv2d(3, 4, kernel_size=[5, 5], stride=[1, 1])");
ASSERT_EQ(
c10::str(Conv2d(Conv2dOptions(3, 4, 5).stride(2))),
"torch::nn::Conv2d(3, 4, kernel_size=[5, 5], stride=[2, 2])");
{
const auto options =
Conv2dOptions(3, 4, std::vector<int64_t>{5, 6}).stride({1, 2});
ASSERT_EQ(
c10::str(Conv2d(options)),
"torch::nn::Conv2d(3, 4, kernel_size=[5, 6], stride=[1, 2])");
}
ASSERT_EQ(
c10::str(Conv3d(4, 4, std::vector<int64_t>{5, 6, 7})),
"torch::nn::Conv3d(4, 4, kernel_size=[5, 6, 7], stride=[1, 1, 1])");
{
const auto options =
Conv3dOptions(4, 4, std::vector<int64_t>{5, 6, 7})
.stride({1, 2, 3})
.padding(1)
.dilation(0)
.groups(2)
.bias(false)
.padding_mode(torch::kCircular);
ASSERT_EQ(
c10::str(
Conv3d(options)),
"torch::nn::Conv3d("
"4, "
"4, "
"kernel_size=[5, 6, 7], "
"stride=[1, 2, 3], "
"padding=[1, 1, 1], "
"dilation=[0, 0, 0], "
"groups=2, "
"bias=false, "
"padding_mode=kCircular)");
}
}
TEST_F(ModulesTest, PrettyPrintConvTranspose) {
ASSERT_EQ(
c10::str(ConvTranspose1d(3, 4, 5)),
"torch::nn::ConvTranspose1d(3, 4, kernel_size=5, stride=1)");
ASSERT_EQ(
c10::str(ConvTranspose2d(3, 4, 5)),
"torch::nn::ConvTranspose2d(3, 4, kernel_size=[5, 5], stride=[1, 1])");
ASSERT_EQ(
c10::str(ConvTranspose2d(ConvTranspose2dOptions(3, 4, 5).stride(2))),
"torch::nn::ConvTranspose2d(3, 4, kernel_size=[5, 5], stride=[2, 2])");
{
const auto options =
ConvTranspose2dOptions(3, 4, std::vector<int64_t>{5, 6}).stride({1, 2});
ASSERT_EQ(
c10::str(ConvTranspose2d(options)),
"torch::nn::ConvTranspose2d(3, 4, kernel_size=[5, 6], stride=[1, 2])");
}
ASSERT_EQ(
c10::str(ConvTranspose3d(4, 4, std::vector<int64_t>{5, 6, 7})),
"torch::nn::ConvTranspose3d(4, 4, kernel_size=[5, 6, 7], stride=[1, 1, 1])");
{
const auto options =
ConvTranspose3dOptions(4, 4, std::vector<int64_t>{5, 6, 7})
.stride({1, 2, 3})
.padding(1)
.dilation(0)
.groups(2)
.bias(false)
.padding_mode(torch::kCircular);
ASSERT_EQ(
c10::str(
ConvTranspose3d(options)),
"torch::nn::ConvTranspose3d("
"4, "
"4, "
"kernel_size=[5, 6, 7], "
"stride=[1, 2, 3], "
"padding=[1, 1, 1], "
"dilation=[0, 0, 0], "
"groups=2, "
"bias=false, "
"padding_mode=kCircular)");
}
}
TEST_F(ModulesTest, PrettyPrintUpsample) {
ASSERT_EQ(
c10::str(Upsample(UpsampleOptions().size(std::vector<int64_t>({2, 4, 4})))),
"torch::nn::Upsample(size=[2, 4, 4], mode=kNearest)");
ASSERT_EQ(
c10::str(Upsample(UpsampleOptions().scale_factor(std::vector<double>({0.5, 1.5})).mode(torch::kBilinear))),
"torch::nn::Upsample(scale_factor=[0.5, 1.5], mode=kBilinear)");
}
TEST_F(ModulesTest, PrettyPrintFold) {
ASSERT_EQ(
c10::str(Fold(FoldOptions({2, 2}, {5, 5}))),
"torch::nn::Fold(output_size=[2, 2], kernel_size=[5, 5], dilation=[1, 1], padding=[0, 0], stride=[1, 1])");
ASSERT_EQ(
c10::str(Fold(FoldOptions({8, 8}, {3, 3}).dilation(2).padding({2, 1}).stride(2))),
"torch::nn::Fold(output_size=[8, 8], kernel_size=[3, 3], dilation=[2, 2], padding=[2, 1], stride=[2, 2])");
}
TEST_F(ModulesTest, PrettyPrintUnfold) {
ASSERT_EQ(
c10::str(Unfold(torch::IntArrayRef({2, 4}))),
"torch::nn::Unfold(kernel_size=[2, 4], dilation=[1, 1], padding=[0, 0], stride=[1, 1])");
ASSERT_EQ(
c10::str(Unfold(UnfoldOptions({2, 4}).dilation(2).padding({2, 1}).stride(2))),
"torch::nn::Unfold(kernel_size=[2, 4], dilation=[2, 2], padding=[2, 1], stride=[2, 2])");
}
TEST_F(ModulesTest, PrettyPrintMaxPool) {
ASSERT_EQ(
c10::str(MaxPool1d(5)),
"torch::nn::MaxPool1d(kernel_size=5, stride=5, padding=0, dilation=1, ceil_mode=false)");
ASSERT_EQ(
c10::str(MaxPool2d(5)),
"torch::nn::MaxPool2d(kernel_size=[5, 5], stride=[5, 5], padding=[0, 0], dilation=[1, 1], ceil_mode=false)");
ASSERT_EQ(
c10::str(MaxPool2d(MaxPool2dOptions(5).stride(2))),
"torch::nn::MaxPool2d(kernel_size=[5, 5], stride=[2, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=false)");
ASSERT_EQ(
c10::str(MaxPool3d(5)),
"torch::nn::MaxPool3d(kernel_size=[5, 5, 5], stride=[5, 5, 5], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=false)");
ASSERT_EQ(
c10::str(MaxPool3d(MaxPool3dOptions(5).stride(2))),
"torch::nn::MaxPool3d(kernel_size=[5, 5, 5], stride=[2, 2, 2], padding=[0, 0, 0], dilation=[1, 1, 1], ceil_mode=false)");
const auto options =
MaxPool2dOptions(std::vector<int64_t>{5, 6}).stride({1, 2});
ASSERT_EQ(
c10::str(MaxPool2d(options)),
"torch::nn::MaxPool2d(kernel_size=[5, 6], stride=[1, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=false)");
}
TEST_F(ModulesTest, PrettyPrintAvgPool) {
ASSERT_EQ(
c10::str(AvgPool1d(5)),
"torch::nn::AvgPool1d(kernel_size=5, stride=5, padding=0)");
ASSERT_EQ(
c10::str(AvgPool2d(5)),
"torch::nn::AvgPool2d(kernel_size=[5, 5], stride=[5, 5], padding=[0, 0])");
ASSERT_EQ(
c10::str(AvgPool2d(AvgPool2dOptions(5).stride(2))),
"torch::nn::AvgPool2d(kernel_size=[5, 5], stride=[2, 2], padding=[0, 0])");
ASSERT_EQ(
c10::str(AvgPool3d(5)),
"torch::nn::AvgPool3d(kernel_size=[5, 5, 5], stride=[5, 5, 5], padding=[0, 0, 0])");
ASSERT_EQ(
c10::str(AvgPool3d(AvgPool3dOptions(5).stride(2))),
"torch::nn::AvgPool3d(kernel_size=[5, 5, 5], stride=[2, 2, 2], padding=[0, 0, 0])");
const auto options =
AvgPool2dOptions(std::vector<int64_t>{5, 6}).stride({1, 2});
ASSERT_EQ(
c10::str(AvgPool2d(options)),
"torch::nn::AvgPool2d(kernel_size=[5, 6], stride=[1, 2], padding=[0, 0])");
}
TEST_F(ModulesTest, PrettyPrinFractionalMaxPool) {
ASSERT_EQ(
c10::str(FractionalMaxPool2d(FractionalMaxPool2dOptions(5).output_size(1))),
"torch::nn::FractionalMaxPool2d()");
ASSERT_EQ(
c10::str(FractionalMaxPool3d(FractionalMaxPool3dOptions(5).output_size(1))),
"torch::nn::FractionalMaxPool3d()");
}
TEST_F(ModulesTest, PrettyPrintLPPool) {
ASSERT_EQ(
c10::str(LPPool1d(2, 5)),
"torch::nn::LPPool1d(norm_type=2, kernel_size=5, stride=5, ceil_mode=false)");
ASSERT_EQ(
c10::str(LPPool1d(LPPool1dOptions(1, 2).stride(5).ceil_mode(true))),
"torch::nn::LPPool1d(norm_type=1, kernel_size=2, stride=5, ceil_mode=true)");
ASSERT_EQ(
c10::str(LPPool2d(2, std::vector<int64_t>({1, 2}))),
"torch::nn::LPPool2d(norm_type=2, kernel_size=[1, 2], stride=[1, 2], ceil_mode=false)");
ASSERT_EQ(
c10::str(LPPool2d(LPPool2dOptions(1, std::vector<int64_t>({3, 4})).stride({5, 6}).ceil_mode(true))),
"torch::nn::LPPool2d(norm_type=1, kernel_size=[3, 4], stride=[5, 6], ceil_mode=true)");
}
TEST_F(ModulesTest, PrettyPrintAdaptiveMaxPool) {
ASSERT_EQ(
c10::str(AdaptiveMaxPool1d(5)),
"torch::nn::AdaptiveMaxPool1d(output_size=5)");
const auto options = AdaptiveMaxPool1dOptions(3);
ASSERT_EQ(
c10::str(AdaptiveMaxPool1d(options)),
"torch::nn::AdaptiveMaxPool1d(output_size=3)");
ASSERT_EQ(
c10::str(AdaptiveMaxPool2d(5)),
"torch::nn::AdaptiveMaxPool2d(output_size=[5, 5])");
ASSERT_EQ(
c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({5, 6}))),
"torch::nn::AdaptiveMaxPool2d(output_size=[5, 6])");
ASSERT_EQ(
c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({5, c10::nullopt}))),
"torch::nn::AdaptiveMaxPool2d(output_size=[5, None])");
ASSERT_EQ(
c10::str(AdaptiveMaxPool2d(AdaptiveMaxPool2dOptions({c10::nullopt, c10::nullopt}))),
"torch::nn::AdaptiveMaxPool2d(output_size=[None, None])");
ASSERT_EQ(
c10::str(AdaptiveMaxPool3d(5)),
"torch::nn::AdaptiveMaxPool3d(output_size=[5, 5, 5])");
ASSERT_EQ(
c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({5, 6, 7}))),
"torch::nn::AdaptiveMaxPool3d(output_size=[5, 6, 7])");
ASSERT_EQ(
c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({5, c10::nullopt, 7}))),
"torch::nn::AdaptiveMaxPool3d(output_size=[5, None, 7])");
ASSERT_EQ(
c10::str(AdaptiveMaxPool3d(AdaptiveMaxPool3dOptions({c10::nullopt, c10::nullopt, c10::nullopt}))),
"torch::nn::AdaptiveMaxPool3d(output_size=[None, None, None])");
}
TEST_F(ModulesTest, PrettyPrintAdaptiveAvgPool) {
ASSERT_EQ(
c10::str(AdaptiveAvgPool1d(5)),
"torch::nn::AdaptiveAvgPool1d(output_size=5)");
ASSERT_EQ(
c10::str(AdaptiveAvgPool2d(5)),
"torch::nn::AdaptiveAvgPool2d(output_size=[5, 5])");
ASSERT_EQ(
c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({5, 6}))),
"torch::nn::AdaptiveAvgPool2d(output_size=[5, 6])");
ASSERT_EQ(
c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({5, c10::nullopt}))),
"torch::nn::AdaptiveAvgPool2d(output_size=[5, None])");
ASSERT_EQ(
c10::str(AdaptiveAvgPool2d(AdaptiveAvgPool2dOptions({c10::nullopt, c10::nullopt}))),
"torch::nn::AdaptiveAvgPool2d(output_size=[None, None])");
ASSERT_EQ(
c10::str(AdaptiveAvgPool3d(5)),
"torch::nn::AdaptiveAvgPool3d(output_size=[5, 5, 5])");
ASSERT_EQ(
c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({5, 6, 7}))),
"torch::nn::AdaptiveAvgPool3d(output_size=[5, 6, 7])");
ASSERT_EQ(
c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({5, c10::nullopt, 7}))),
"torch::nn::AdaptiveAvgPool3d(output_size=[5, None, 7])");
ASSERT_EQ(
c10::str(AdaptiveAvgPool3d(AdaptiveAvgPool3dOptions({c10::nullopt, c10::nullopt, c10::nullopt}))),
"torch::nn::AdaptiveAvgPool3d(output_size=[None, None, None])");
}
TEST_F(ModulesTest, PrettyPrintMaxUnpool) {
ASSERT_EQ(
c10::str(MaxUnpool1d(5)),
"torch::nn::MaxUnpool1d(kernel_size=5, stride=5, padding=0)");
ASSERT_EQ(
c10::str(MaxUnpool1d(MaxUnpool1dOptions(5).stride(3).padding(1))),
"torch::nn::MaxUnpool1d(kernel_size=5, stride=3, padding=1)");
ASSERT_EQ(
c10::str(MaxUnpool2d(5)),
"torch::nn::MaxUnpool2d(kernel_size=[5, 5], stride=[5, 5], padding=[0, 0])");
ASSERT_EQ(
c10::str(MaxUnpool2d(std::vector<int64_t>{5, 6})),
"torch::nn::MaxUnpool2d(kernel_size=[5, 6], stride=[5, 6], padding=[0, 0])");
ASSERT_EQ(
c10::str(MaxUnpool2d(MaxUnpool2dOptions(std::vector<int64_t>{5, 6}).stride({3, 4}).padding({1, 2}))),
"torch::nn::MaxUnpool2d(kernel_size=[5, 6], stride=[3, 4], padding=[1, 2])");
}
TEST_F(ModulesTest, PrettyPrintDropout) {
ASSERT_EQ(c10::str(Dropout()), "torch::nn::Dropout(p=0.5, inplace=false)");
ASSERT_EQ(c10::str(Dropout(0.42)), "torch::nn::Dropout(p=0.42, inplace=false)");
ASSERT_EQ(c10::str(Dropout(DropoutOptions().p(0.42).inplace(true))), "torch::nn::Dropout(p=0.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintDropout2d) {
ASSERT_EQ(c10::str(Dropout2d()), "torch::nn::Dropout2d(p=0.5, inplace=false)");
ASSERT_EQ(c10::str(Dropout2d(0.42)), "torch::nn::Dropout2d(p=0.42, inplace=false)");
ASSERT_EQ(c10::str(Dropout2d(Dropout2dOptions().p(0.42).inplace(true))), "torch::nn::Dropout2d(p=0.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintDropout3d) {
ASSERT_EQ(c10::str(Dropout3d()), "torch::nn::Dropout3d(p=0.5, inplace=false)");
ASSERT_EQ(c10::str(Dropout3d(0.42)), "torch::nn::Dropout3d(p=0.42, inplace=false)");
ASSERT_EQ(c10::str(Dropout3d(Dropout3dOptions().p(0.42).inplace(true))), "torch::nn::Dropout3d(p=0.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintFunctional) {
ASSERT_EQ(c10::str(Functional(torch::relu)), "torch::nn::Functional()");
}
TEST_F(ModulesTest, PrettyPrintBatchNorm1d) {
ASSERT_EQ(
c10::str(BatchNorm1d(
BatchNorm1dOptions(4).eps(0.5).momentum(0.1).affine(false)
.track_running_stats(true))),
"torch::nn::BatchNorm1d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
}
TEST_F(ModulesTest, PrettyPrintBatchNorm2d) {
ASSERT_EQ(
c10::str(BatchNorm2d(
BatchNorm2dOptions(4).eps(0.5).momentum(0.1).affine(false)
.track_running_stats(true))),
"torch::nn::BatchNorm2d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
}
TEST_F(ModulesTest, PrettyPrintBatchNorm3d) {
ASSERT_EQ(
c10::str(BatchNorm3d(
BatchNorm3dOptions(4).eps(0.5).momentum(0.1).affine(false)
.track_running_stats(true))),
"torch::nn::BatchNorm3d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
}
TEST_F(ModulesTest, PrettyPrintInstanceNorm1d) {
ASSERT_EQ(
c10::str(InstanceNorm1d(
InstanceNorm1dOptions(4).eps(0.5).momentum(0.1).affine(false)
.track_running_stats(true))),
"torch::nn::InstanceNorm1d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
}
TEST_F(ModulesTest, PrettyPrintInstanceNorm2d) {
ASSERT_EQ(
c10::str(InstanceNorm2d(
InstanceNorm2dOptions(4).eps(0.5).momentum(0.1).affine(false)
.track_running_stats(true))),
"torch::nn::InstanceNorm2d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
}
TEST_F(ModulesTest, PrettyPrintInstanceNorm3d) {
ASSERT_EQ(
c10::str(InstanceNorm3d(
InstanceNorm3dOptions(4).eps(0.5).momentum(0.1).affine(false)
.track_running_stats(true))),
"torch::nn::InstanceNorm3d(4, eps=0.5, momentum=0.1, affine=false, track_running_stats=true)");
}
TEST_F(ModulesTest, PrettyPrintLayerNorm) {
ASSERT_EQ(
c10::str(LayerNorm(LayerNormOptions({2, 2}))),
"torch::nn::LayerNorm([2, 2], eps=1e-05, elementwise_affine=true)");
ASSERT_EQ(
c10::str(LayerNorm(LayerNormOptions({2, 2}).elementwise_affine(false).eps(2e-5))),
"torch::nn::LayerNorm([2, 2], eps=2e-05, elementwise_affine=false)");
}
TEST_F(ModulesTest, PrettyPrintGroupNorm) {
ASSERT_EQ(
c10::str(GroupNorm(GroupNormOptions(2, 2))),
"torch::nn::GroupNorm(2, 2, eps=1e-05, affine=true)");
ASSERT_EQ(
c10::str(GroupNorm(GroupNormOptions(2, 2).eps(2e-5).affine(false))),
"torch::nn::GroupNorm(2, 2, eps=2e-05, affine=false)");
}
TEST_F(ModulesTest, PrettyPrintLocalResponseNorm) {
ASSERT_EQ(
c10::str(LocalResponseNorm(LocalResponseNormOptions(2))),
"torch::nn::LocalResponseNorm(2, alpha=0.0001, beta=0.75, k=1)");
ASSERT_EQ(
c10::str(LocalResponseNorm(LocalResponseNormOptions(2).alpha(0.0002).beta(0.85).k(2.))),
"torch::nn::LocalResponseNorm(2, alpha=0.0002, beta=0.85, k=2)");
}
TEST_F(ModulesTest, PrettyPrintEmbedding) {
ASSERT_EQ(
c10::str(Embedding(EmbeddingOptions(10, 2))),
"torch::nn::Embedding(num_embeddings=10, embedding_dim=2)");
ASSERT_EQ(
c10::str(Embedding(EmbeddingOptions(10, 2).padding_idx(3).max_norm(2))),
"torch::nn::Embedding(num_embeddings=10, embedding_dim=2, padding_idx=3, max_norm=2)");
ASSERT_EQ(
c10::str(Embedding(EmbeddingOptions(10, 2).padding_idx(3).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true))),
"torch::nn::Embedding(num_embeddings=10, embedding_dim=2, padding_idx=3, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true)");
}
TEST_F(ModulesTest, PrettyPrintEmbeddingBag) {
ASSERT_EQ(
c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2))),
"torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2)");
ASSERT_EQ(
c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2))),
"torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2)");
ASSERT_EQ(
c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true))),
"torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true)");
ASSERT_EQ(
c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true).mode(torch::kSum))),
"torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true, mode=kSum)");
ASSERT_EQ(
c10::str(EmbeddingBag(EmbeddingBagOptions(10, 2).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true).mode(torch::kSum).padding_idx(5))),
"torch::nn::EmbeddingBag(num_embeddings=10, embedding_dim=2, max_norm=2, norm_type=2.5, scale_grad_by_freq=true, sparse=true, mode=kSum, padding_idx=5)");
}
TEST_F(ModulesTest, PrettyPrintL1Loss) {
ASSERT_EQ(
c10::str(L1Loss()),
"torch::nn::L1Loss()");
}
TEST_F(ModulesTest, PrettyPrintKLDivLoss) {
ASSERT_EQ(
c10::str(KLDivLoss()),
"torch::nn::KLDivLoss()");
}
TEST_F(ModulesTest, PrettyPrintMSELoss) {
ASSERT_EQ(
c10::str(MSELoss()),
"torch::nn::MSELoss()");
}
TEST_F(ModulesTest, PrettyPrintBCELoss) {
ASSERT_EQ(
c10::str(BCELoss()),
"torch::nn::BCELoss()");
}
TEST_F(ModulesTest, PrettyPrintHingeEmbeddingLoss) {
ASSERT_EQ(
c10::str(HingeEmbeddingLoss(HingeEmbeddingLossOptions().margin(4))),
"torch::nn::HingeEmbeddingLoss(margin=4)");
}
TEST_F(ModulesTest, PrettyPrintCosineEmbeddingLoss) {
ASSERT_EQ(
c10::str(CosineEmbeddingLoss(CosineEmbeddingLossOptions().margin(0.25))),
"torch::nn::CosineEmbeddingLoss(margin=0.25)");
}
TEST_F(ModulesTest, PrettyPrintTripletMarginLoss) {
ASSERT_EQ(
c10::str(TripletMarginLoss(TripletMarginLossOptions().margin(3).p(2).eps(1e-06).swap(false))),
"torch::nn::TripletMarginLoss(margin=3, p=2, eps=1e-06, swap=false)");
}
TEST_F(ModulesTest, PrettyPrintTripletMarginWithDistanceLoss) {
auto distanceOptions = TripletMarginWithDistanceLossOptions()
.distance_function([&](const torch::Tensor& x,
const torch::Tensor& y) {
return torch::pairwise_distance(x, y, 2.0, 1e-6);
})
.margin(1.5)
.swap(true)
.reduction(torch::kMean);
ASSERT_EQ(
c10::str(TripletMarginWithDistanceLoss(distanceOptions)),
"torch::nn::TripletMarginWithDistanceLoss(margin=1.5, swap=true)");
}
TEST_F(ModulesTest, PrettyPrintNLLLoss) {
ASSERT_EQ(
c10::str(NLLLoss()), "torch::nn::NLLLoss()");
}
TEST_F(ModulesTest, PrettyPrinCrossEntropyLoss) {
ASSERT_EQ(
c10::str(CrossEntropyLoss()), "torch::nn::CrossEntropyLoss()");
}
TEST_F(ModulesTest, PrettyPrintMultiLabelMarginLoss) {
ASSERT_EQ(c10::str(MultiLabelMarginLoss()), "torch::nn::MultiLabelMarginLoss()");
}
TEST_F(ModulesTest, PrettyPrintMultiLabelSoftMarginLoss) {
ASSERT_EQ(c10::str(MultiLabelSoftMarginLoss()), "torch::nn::MultiLabelSoftMarginLoss()");
}
TEST_F(ModulesTest, PrettyPrintSoftMarginLoss) {
ASSERT_EQ(c10::str(SoftMarginLoss()), "torch::nn::SoftMarginLoss()");
}
TEST_F(ModulesTest, PrettyPrintCosineSimilarity) {
ASSERT_EQ(
c10::str(CosineSimilarity()),
"torch::nn::CosineSimilarity(dim=1, eps=1e-08)");
ASSERT_EQ(
c10::str(CosineSimilarity(CosineSimilarityOptions().dim(0).eps(0.5))),
"torch::nn::CosineSimilarity(dim=0, eps=0.5)");
}
TEST_F(ModulesTest, PrettyPrintPairwiseDistance) {
ASSERT_EQ(
c10::str(PairwiseDistance()),
"torch::nn::PairwiseDistance(p=2, eps=1e-06, keepdim=false)");
ASSERT_EQ(
c10::str(PairwiseDistance(PairwiseDistanceOptions().p(3).eps(0.5).keepdim(true))),
"torch::nn::PairwiseDistance(p=3, eps=0.5, keepdim=true)");
}
TEST_F(ModulesTest, PrettyPrintReflectionPad) {
ASSERT_EQ(
c10::str(ReflectionPad1d(ReflectionPad1dOptions(2))),
"torch::nn::ReflectionPad1d(padding=[2, 2])");
ASSERT_EQ(
c10::str(ReflectionPad1d(ReflectionPad1dOptions({3, 1}))),
"torch::nn::ReflectionPad1d(padding=[3, 1])");
ASSERT_EQ(
c10::str(ReflectionPad2d(ReflectionPad2dOptions(2))),
"torch::nn::ReflectionPad2d(padding=[2, 2, 2, 2])");
ASSERT_EQ(
c10::str(ReflectionPad2d(ReflectionPad2dOptions({1, 1, 2, 0}))),
"torch::nn::ReflectionPad2d(padding=[1, 1, 2, 0])");
}
TEST_F(ModulesTest, PrettyPrintReplicationPad) {
ASSERT_EQ(
c10::str(ReplicationPad1d(ReplicationPad1dOptions(2))),
"torch::nn::ReplicationPad1d(padding=[2, 2])");
ASSERT_EQ(
c10::str(ReplicationPad1d(ReplicationPad1dOptions({3, 1}))),
"torch::nn::ReplicationPad1d(padding=[3, 1])");
ASSERT_EQ(
c10::str(ReplicationPad2d(ReplicationPad2dOptions(2))),
"torch::nn::ReplicationPad2d(padding=[2, 2, 2, 2])");
ASSERT_EQ(
c10::str(ReplicationPad2d(ReplicationPad2dOptions({1, 1, 2, 0}))),
"torch::nn::ReplicationPad2d(padding=[1, 1, 2, 0])");
ASSERT_EQ(
c10::str(ReplicationPad3d(ReplicationPad3dOptions(1))),
"torch::nn::ReplicationPad3d(padding=[1, 1, 1, 1, 1, 1])");
ASSERT_EQ(
c10::str(ReplicationPad3d(ReplicationPad3dOptions({1, 2, 1, 2, 1, 2}))),
"torch::nn::ReplicationPad3d(padding=[1, 2, 1, 2, 1, 2])");
}
TEST_F(ModulesTest, PrettyPrintZeroPad2d) {
ASSERT_EQ(
c10::str(ZeroPad2d(ZeroPad2dOptions(2))),
"torch::nn::ZeroPad2d(padding=[2, 2, 2, 2])");
ASSERT_EQ(
c10::str(ZeroPad2d(ZeroPad2dOptions({1, 1, 2, 0}))),
"torch::nn::ZeroPad2d(padding=[1, 1, 2, 0])");
}
TEST_F(ModulesTest, PrettyPrintConstantPad) {
ASSERT_EQ(
c10::str(ConstantPad1d(ConstantPad1dOptions(2, 3.5))),
"torch::nn::ConstantPad1d(padding=[2, 2], value=3.5)");
ASSERT_EQ(
c10::str(ConstantPad1d(ConstantPad1dOptions({3, 1}, 3.5))),
"torch::nn::ConstantPad1d(padding=[3, 1], value=3.5)");
ASSERT_EQ(
c10::str(ConstantPad2d(ConstantPad2dOptions(2, 3.5))),
"torch::nn::ConstantPad2d(padding=[2, 2, 2, 2], value=3.5)");
ASSERT_EQ(
c10::str(ConstantPad2d(ConstantPad2dOptions({3, 0, 2, 1}, 3.5))),
"torch::nn::ConstantPad2d(padding=[3, 0, 2, 1], value=3.5)");
ASSERT_EQ(
c10::str(ConstantPad3d(ConstantPad3dOptions(1, 3.5))),
"torch::nn::ConstantPad3d(padding=[1, 1, 1, 1, 1, 1], value=3.5)");
ASSERT_EQ(
c10::str(ConstantPad3d(ConstantPad3dOptions({1, 2, 1, 2, 1, 2}, 3.5))),
"torch::nn::ConstantPad3d(padding=[1, 2, 1, 2, 1, 2], value=3.5)");
}
TEST_F(ModulesTest, PrettyPrintNestedModel) {
struct InnerTestModule : torch::nn::Module {
InnerTestModule()
: torch::nn::Module("InnerTestModule"),
fc(register_module("fc", torch::nn::Linear(3, 4))),
table(register_module("table", torch::nn::Embedding(10, 2))) {}
torch::nn::Linear fc;
torch::nn::Embedding table;
};
struct TestModule : torch::nn::Module {
TestModule()
: torch::nn::Module("TestModule"),
fc(register_module("fc", torch::nn::Linear(4, 5))),
table(register_module("table", torch::nn::Embedding(EmbeddingOptions(10, 2)))),
inner(register_module("inner", std::make_shared<InnerTestModule>())) {
}
torch::nn::Linear fc;
torch::nn::Embedding table;
std::shared_ptr<InnerTestModule> inner;
};
ASSERT_EQ(
c10::str(TestModule{}),
"TestModule(\n"
" (fc): torch::nn::Linear(in_features=4, out_features=5, bias=true)\n"
" (table): torch::nn::Embedding(num_embeddings=10, embedding_dim=2)\n"
" (inner): InnerTestModule(\n"
" (fc): torch::nn::Linear(in_features=3, out_features=4, bias=true)\n"
" (table): torch::nn::Embedding(num_embeddings=10, embedding_dim=2)\n"
" )\n"
")");
}
TEST_F(ModulesTest, PrettyPrintELU) {
ASSERT_EQ(c10::str(ELU()), "torch::nn::ELU(alpha=1)");
ASSERT_EQ(c10::str(ELU(ELUOptions().alpha(42.42).inplace(true))),
"torch::nn::ELU(alpha=42.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintSELU) {
ASSERT_EQ(c10::str(SELU()), "torch::nn::SELU()");
ASSERT_EQ(c10::str(SELU(SELUOptions().inplace(true))),
"torch::nn::SELU(inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintGLU) {
ASSERT_EQ(c10::str(GLU()), "torch::nn::GLU(dim=-1)");
ASSERT_EQ(c10::str(GLU(1)), "torch::nn::GLU(dim=1)");
}
TEST_F(ModulesTest, PrettyPrintHardshrink) {
ASSERT_EQ(c10::str(Hardshrink()), "torch::nn::Hardshrink(0.5)");
ASSERT_EQ(c10::str(Hardshrink(HardshrinkOptions().lambda(42.42))),
"torch::nn::Hardshrink(42.42)");
}
TEST_F(ModulesTest, PrettyPrintHardtanh) {
ASSERT_EQ(c10::str(Hardtanh()),
"torch::nn::Hardtanh(min_val=-1, max_val=1)");
ASSERT_EQ(c10::str(Hardtanh(
HardtanhOptions().min_val(-42.42).max_val(0.42).inplace(true))),
"torch::nn::Hardtanh(min_val=-42.42, max_val=0.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintLeakyReLU) {
ASSERT_EQ(c10::str(LeakyReLU()),
"torch::nn::LeakyReLU(negative_slope=0.01)");
ASSERT_EQ(c10::str(LeakyReLU(
LeakyReLUOptions().negative_slope(0.42).inplace(true))),
"torch::nn::LeakyReLU(negative_slope=0.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintLogSigmoid) {
ASSERT_EQ(c10::str(LogSigmoid()), "torch::nn::LogSigmoid()");
}
TEST_F(ModulesTest, PrettyPrintSoftmax) {
ASSERT_EQ(c10::str(Softmax(SoftmaxOptions(1))), "torch::nn::Softmax(dim=1)");
}
TEST_F(ModulesTest, PrettyPrintSoftmin) {
ASSERT_EQ(c10::str(Softmin(SoftminOptions(1))), "torch::nn::Softmin(dim=1)");
}
TEST_F(ModulesTest, PrettyPrintLogSoftmax) {
ASSERT_EQ(c10::str(LogSoftmax(LogSoftmaxOptions(1))),
"torch::nn::LogSoftmax(dim=1)");
}
TEST_F(ModulesTest, PrettyPrintSoftmax2d) {
ASSERT_EQ(c10::str(Softmax2d()), "torch::nn::Softmax2d()");
}
TEST_F(ModulesTest, PrettyPrintPReLU) {
ASSERT_EQ(c10::str(PReLU()), "torch::nn::PReLU(num_parameters=1)");
ASSERT_EQ(c10::str(PReLU(PReLUOptions().num_parameters(42))),
"torch::nn::PReLU(num_parameters=42)");
}
TEST_F(ModulesTest, PrettyPrintReLU) {
ASSERT_EQ(c10::str(ReLU()), "torch::nn::ReLU()");
ASSERT_EQ(c10::str(ReLU(ReLUOptions().inplace(true))),
"torch::nn::ReLU(inplace=true)");
ASSERT_EQ(c10::str(ReLU(/*inplace=*/true)),
"torch::nn::ReLU(inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintReLU6) {
ASSERT_EQ(c10::str(ReLU6()), "torch::nn::ReLU6()");
ASSERT_EQ(c10::str(ReLU6(ReLU6Options().inplace(true))),
"torch::nn::ReLU6(inplace=true)");
ASSERT_EQ(c10::str(ReLU6(/*inplace=*/true)),
"torch::nn::ReLU6(inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintRReLU) {
ASSERT_EQ(c10::str(RReLU()),
"torch::nn::RReLU(lower=0.125, upper=0.333333)");
ASSERT_EQ(c10::str(RReLU(
RReLUOptions().lower(0.24).upper(0.42).inplace(true))),
"torch::nn::RReLU(lower=0.24, upper=0.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintCELU) {
ASSERT_EQ(c10::str(CELU()), "torch::nn::CELU(alpha=1)");
ASSERT_EQ(c10::str(CELU(CELUOptions().alpha(42.42).inplace(true))),
"torch::nn::CELU(alpha=42.42, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintSigmoid) {
ASSERT_EQ(c10::str(Sigmoid()), "torch::nn::Sigmoid()");
}
TEST_F(ModulesTest, PrettyPrintPixelShuffle) {
ASSERT_EQ(c10::str(PixelShuffle(PixelShuffleOptions(5))),
"torch::nn::PixelShuffle(upscale_factor=5)");
}
TEST_F(ModulesTest, PrettyPrintPixelUnshuffle) {
ASSERT_EQ(
c10::str(PixelUnshuffle(PixelUnshuffleOptions(5))),
"torch::nn::PixelUnshuffle(downscale_factor=5)");
}
TEST_F(ModulesTest, PrettyPrintSoftplus) {
ASSERT_EQ(c10::str(Softplus()),
"torch::nn::Softplus(beta=1, threshold=20)");
ASSERT_EQ(c10::str(Softplus(
SoftplusOptions().beta(0.24).threshold(42.42))),
"torch::nn::Softplus(beta=0.24, threshold=42.42)");
}
TEST_F(ModulesTest, PrettyPrintSoftshrink) {
ASSERT_EQ(c10::str(Softshrink()), "torch::nn::Softshrink(0.5)");
ASSERT_EQ(c10::str(Softshrink(SoftshrinkOptions(42.42))),
"torch::nn::Softshrink(42.42)");
}
TEST_F(ModulesTest, PrettyPrintSoftsign) {
ASSERT_EQ(c10::str(Softsign()), "torch::nn::Softsign()");
}
TEST_F(ModulesTest, PrettyPrintTanh) {
ASSERT_EQ(c10::str(Tanh()), "torch::nn::Tanh()");
}
TEST_F(ModulesTest, PrettyPrintTanhshrink) {
ASSERT_EQ(c10::str(Tanhshrink()), "torch::nn::Tanhshrink()");
}
TEST_F(ModulesTest, PrettyPrintThreshold) {
ASSERT_EQ(c10::str(Threshold(24.24, 42.42)),
"torch::nn::Threshold(threshold=24.24, value=42.42)");
ASSERT_EQ(c10::str(Threshold(
ThresholdOptions(42.42, 24.24).inplace(true))),
"torch::nn::Threshold(threshold=42.42, value=24.24, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintCTCLoss) {
ASSERT_EQ(c10::str(CTCLoss()), "torch::nn::CTCLoss()");
ASSERT_EQ(c10::str(CTCLoss(
CTCLossOptions().blank(42).zero_infinity(false)
.reduction(torch::kSum))), "torch::nn::CTCLoss()");
}
TEST_F(ModulesTest, PrettyPrintPoissonNLLLoss) {
ASSERT_EQ(c10::str(PoissonNLLLoss()), "torch::nn::PoissonNLLLoss()");
ASSERT_EQ(c10::str(PoissonNLLLoss(
PoissonNLLLossOptions().log_input(false).full(true).eps(0.42)
.reduction(torch::kSum))),
"torch::nn::PoissonNLLLoss()");
}
TEST_F(ModulesTest, PrettyPrintMarginRankingLoss) {
ASSERT_EQ(c10::str(MarginRankingLoss()), "torch::nn::MarginRankingLoss()");
ASSERT_EQ(c10::str(MarginRankingLoss(
MarginRankingLossOptions().margin(0.5).reduction(torch::kSum))),
"torch::nn::MarginRankingLoss()");
}
TEST_F(ModulesTest, PrettyPrintCrossMapLRN2d) {
ASSERT_EQ(c10::str(CrossMapLRN2d(4)),
"torch::nn::CrossMapLRN2d(4, alpha=0.0001, beta=0.75, k=1)");
ASSERT_EQ(c10::str(CrossMapLRN2d(CrossMapLRN2dOptions(3).alpha(1e-5).beta(0.1).k(10))),
"torch::nn::CrossMapLRN2d(3, alpha=1e-05, beta=0.1, k=10)");
}
TEST_F(ModulesTest, PrettyPrintAlphaDropout) {
ASSERT_EQ(c10::str(AlphaDropout()),
"torch::nn::AlphaDropout(p=0.5, inplace=false)");
ASSERT_EQ(c10::str(AlphaDropout(AlphaDropoutOptions(0.2))),
"torch::nn::AlphaDropout(p=0.2, inplace=false)");
ASSERT_EQ(c10::str(AlphaDropout(AlphaDropoutOptions(0.2).inplace(true))),
"torch::nn::AlphaDropout(p=0.2, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintFeatureAlphaDropout) {
ASSERT_EQ(c10::str(FeatureAlphaDropout()),
"torch::nn::FeatureAlphaDropout(p=0.5, inplace=false)");
ASSERT_EQ(c10::str(FeatureAlphaDropout(FeatureAlphaDropoutOptions(0.2))),
"torch::nn::FeatureAlphaDropout(p=0.2, inplace=false)");
ASSERT_EQ(c10::str(FeatureAlphaDropout(FeatureAlphaDropoutOptions(0.2).inplace(true))),
"torch::nn::FeatureAlphaDropout(p=0.2, inplace=true)");
}
TEST_F(ModulesTest, PrettyPrintBCEWithLogitsLoss) {
ASSERT_EQ(c10::str(BCEWithLogitsLoss()), "torch::nn::BCEWithLogitsLoss()");
ASSERT_EQ(c10::str(BCEWithLogitsLoss(
BCEWithLogitsLossOptions()
.weight(torch::ones({3, 3}))
.pos_weight(torch::ones({3, 3}))
.reduction(torch::kSum))),
"torch::nn::BCEWithLogitsLoss()");
}
TEST_F(ModulesTest, PrettyPrintMultiheadAttention) {
ASSERT_EQ(c10::str(MultiheadAttention(20, 10)),
"torch::nn::MultiheadAttention(\n (out_proj): torch::nn::Linear(in_features=20, out_features=20, bias=true)\n)");
ASSERT_EQ(c10::str(MultiheadAttention(MultiheadAttentionOptions(20, 10).bias(false))),
"torch::nn::MultiheadAttention(\n (out_proj): torch::nn::Linear(in_features=20, out_features=20, bias=false)\n)");
}
TEST_F(ModulesTest, PrettyPrintRNNCell) {
ASSERT_EQ(c10::str(RNNCell(20, 10)),
"torch::nn::RNNCell(20, 10)");
ASSERT_EQ(c10::str(RNNCell(RNNCellOptions(20, 10).bias(false).nonlinearity(torch::kTanh))),
"torch::nn::RNNCell(20, 10, bias=false)");
ASSERT_EQ(c10::str(RNNCell(RNNCellOptions(20, 10).bias(false).nonlinearity(torch::kReLU))),
"torch::nn::RNNCell(20, 10, bias=false, nonlinearity=kReLU)");
}
TEST_F(ModulesTest, PrettyPrintLSTMCell) {
ASSERT_EQ(c10::str(LSTMCell(20, 10)),
"torch::nn::LSTMCell(20, 10)");
ASSERT_EQ(c10::str(LSTMCell(LSTMCellOptions(20, 10).bias(false))),
"torch::nn::LSTMCell(20, 10, bias=false)");
}
TEST_F(ModulesTest, PrettyPrintGRUCell) {
ASSERT_EQ(c10::str(GRUCell(20, 10)),
"torch::nn::GRUCell(20, 10)");
ASSERT_EQ(c10::str(GRUCell(GRUCellOptions(20, 10).bias(false))),
"torch::nn::GRUCell(20, 10, bias=false)");
}
TEST_F(ModulesTest, PrettyPrintAdaptiveLogSoftmaxWithLoss) {
{
AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 4, {2}).div_value(2.));
ASSERT_EQ(
c10::str(asfm),
"torch::nn::AdaptiveLogSoftmaxWithLoss(\n"
" (head): torch::nn::Linear(in_features=8, out_features=3, bias=false)\n"
" (tail): torch::nn::ModuleList(\n"
" (0): torch::nn::Sequential(\n"
" (0): torch::nn::Linear(in_features=8, out_features=4, bias=false)\n"
" (1): torch::nn::Linear(in_features=4, out_features=2, bias=false)\n"
" )\n"
" )\n"
")");
}
{
AdaptiveLogSoftmaxWithLoss asfm(AdaptiveLogSoftmaxWithLossOptions(8, 10, {4, 8}).div_value(2.).head_bias(true));
ASSERT_EQ(
c10::str(asfm),
"torch::nn::AdaptiveLogSoftmaxWithLoss(\n"
" (head): torch::nn::Linear(in_features=8, out_features=6, bias=true)\n"
" (tail): torch::nn::ModuleList(\n"
" (0): torch::nn::Sequential(\n"
" (0): torch::nn::Linear(in_features=8, out_features=4, bias=false)\n"
" (1): torch::nn::Linear(in_features=4, out_features=4, bias=false)\n"
" )\n"
" (1): torch::nn::Sequential(\n"
" (0): torch::nn::Linear(in_features=8, out_features=2, bias=false)\n"
" (1): torch::nn::Linear(in_features=2, out_features=2, bias=false)\n"
" )\n"
" )\n"
")");
}
}