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optim.cpp
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optim.cpp
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#include <gtest/gtest.h>
#include <c10/util/irange.h>
#include <torch/torch.h>
#include <test/cpp/api/optim_baseline.h>
#include <test/cpp/api/support.h>
#include <cmath>
#include <cstdlib>
#include <functional>
#include <iostream>
#include <memory>
#include <random>
#include <vector>
using namespace torch::nn;
using namespace torch::optim;
template <typename OptimizerClass, typename Options>
bool test_optimizer_xor(Options options) {
torch::manual_seed(0);
Sequential model(
Linear(2, 8),
Functional(torch::sigmoid),
Linear(8, 1),
Functional(torch::sigmoid));
const int64_t kBatchSize = 200;
const int64_t kMaximumNumberOfEpochs = 3000;
OptimizerClass optimizer(model->parameters(), options);
float running_loss = 1;
int epoch = 0;
while (running_loss > 0.1) {
auto inputs = torch::empty({kBatchSize, 2});
auto labels = torch::empty({kBatchSize});
for (const auto i : c10::irange(kBatchSize)) {
inputs[i] = torch::randint(2, {2}, torch::kInt64);
labels[i] = inputs[i][0].item<int64_t>() ^ inputs[i][1].item<int64_t>();
}
inputs.set_requires_grad(true);
auto step = [&](OptimizerClass& optimizer,
Sequential model,
torch::Tensor inputs,
torch::Tensor labels) {
auto closure = [&]() {
optimizer.zero_grad();
auto x = model->forward(inputs);
auto loss = torch::binary_cross_entropy(x, labels);
loss.backward();
return loss;
};
return optimizer.step(closure);
};
torch::Tensor loss = step(optimizer, model, inputs, labels);
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,cppcoreguidelines-avoid-magic-numbers,bugprone-narrowing-conversions)
running_loss = running_loss * 0.99 + loss.item<float>() * 0.01;
if (epoch > kMaximumNumberOfEpochs) {
std::cout << "Loss is too high after epoch " << epoch << ": "
<< running_loss << std::endl;
return false;
}
epoch++;
}
return true;
}
template <typename Parameters>
void assign_parameter(
const Parameters& parameters,
const char* name,
torch::Tensor new_tensor) {
auto parameter = parameters[name];
parameter.set_requires_grad(false);
parameter.flatten().copy_(new_tensor);
parameter.set_requires_grad(true);
}
template <typename OptimizerClass, typename Options>
void check_exact_values(
Options options,
std::vector<std::vector<torch::Tensor>> expected_parameters) {
const size_t kIterations = 1001;
const size_t kSampleEvery = 100;
torch::manual_seed(0);
Sequential model(
Linear(2, 3),
Functional(torch::sigmoid),
Linear(3, 1),
Functional(torch::sigmoid));
model->to(torch::kFloat64);
// Use exact input values because matching random values is hard.
auto parameters = model->named_parameters();
assign_parameter(
parameters,
"0.weight",
torch::tensor(
{-0.2109, -0.4976, -0.1413, -0.3420, -0.2524, 0.6976},
torch::kFloat64));
assign_parameter(
parameters,
"0.bias",
torch::tensor({-0.1085, -0.2979, 0.6892}, torch::kFloat64));
assign_parameter(
parameters,
"2.weight",
torch::tensor({-0.0508, -0.3941, -0.2843}, torch::kFloat64));
assign_parameter(
parameters, "2.bias", torch::tensor({-0.0711}, torch::kFloat64));
auto optimizer = OptimizerClass(parameters.values(), options);
torch::Tensor input =
torch::tensor({0.1, 0.2, 0.3, 0.4, 0.5, 0.6}, torch::kFloat64)
.reshape({3, 2});
for (const auto i : c10::irange(kIterations)) {
optimizer.zero_grad();
auto output = model->forward(input);
auto loss = output.sum();
loss.backward();
auto closure = []() { return torch::tensor({10}); };
optimizer.step(closure);
if (i % kSampleEvery == 0) {
ASSERT_TRUE(
expected_parameters.at(i / kSampleEvery).size() == parameters.size());
for (const auto p : c10::irange(parameters.size())) {
ASSERT_TRUE(parameters[p]->defined());
// Always compare using double dtype, regardless of the original dtype
// of the tensors
auto computed = parameters[p]->flatten().to(torch::kFloat64);
auto expected =
expected_parameters.at(i / kSampleEvery).at(p).to(torch::kFloat64);
if (!computed.allclose(expected, /*rtol=*/1e-3, /*atol=*/5e-4)) {
std::cout << "Iteration " << i << ": " << computed
<< " != " << expected << " (parameter " << p << ")"
<< std::endl;
ASSERT_TRUE(false);
}
}
}
}
}
TEST(OptimTest, OptimizerAccessors) {
auto options = AdagradOptions(1.0);
std::vector<torch::Tensor> params;
for (const auto i : c10::irange(3)) {
(void)i; // Suppress unused variable warning
params.push_back(torch::randn(10));
}
auto optimizer = Adagrad(params, options);
// test for defaults() method with non-const reference
auto& options_ = static_cast<AdagradOptions&>(optimizer.defaults());
ASSERT_TRUE(options == options_);
// test for param_groups() with non-const reference return
auto& params_groups = optimizer.param_groups();
// NOLINTNEXTLINE(modernize-use-emplace)
params_groups.push_back(OptimizerParamGroup(params));
auto& params_1 = params_groups[1].params();
for (const auto i : c10::irange(params_1.size())) {
torch::equal(params[i], params_1[i]);
}
// test for add_param_group() when one or more params existing in another
// param_group are passed in the new param group to be added
ASSERT_THROWS_WITH(
optimizer.add_param_group(OptimizerParamGroup(params)),
"some parameters appear in more than one parameter group");
// test for state() with non-const reference return
auto& state_ = static_cast<AdagradParamState&>(
*(optimizer.state()[params_1[0].unsafeGetTensorImpl()]));
state_.step(state_.step() + 1);
const auto& optimizer_ = Adagrad(params, options);
optimizer_.defaults();
// test for param_groups() with const reference return
(void)optimizer_.param_groups();
// test for state() with const reference return
optimizer_.state();
}
#define OLD_INTERFACE_WARNING_CHECK(func) \
{ \
torch::test::WarningCapture warnings; \
func; \
ASSERT_EQ( \
torch::test::count_substr_occurrences( \
warnings.str(), "will be removed"), \
1); \
}
struct MyOptimizerOptions
: public OptimizerCloneableOptions<MyOptimizerOptions> {
MyOptimizerOptions(double lr = 1.0) : lr_(lr){};
TORCH_ARG(double, lr) = 1.0;
};
TEST(OptimTest, OldInterface) {
struct MyOptimizer : Optimizer {
using Optimizer::Optimizer;
torch::Tensor step(LossClosure closure = nullptr) override {
return {};
}
explicit MyOptimizer(
std::vector<at::Tensor> params,
MyOptimizerOptions defaults = {})
: // NOLINTNEXTLINE(performance-move-const-arg)
Optimizer(
{std::move(OptimizerParamGroup(params))},
std::make_unique<MyOptimizerOptions>(defaults)) {}
};
std::vector<torch::Tensor> parameters = {
torch::ones({2, 3}), torch::zeros({2, 3}), torch::rand({2, 3})};
{
MyOptimizer optimizer(parameters);
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
size_t size;
OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
ASSERT_EQ(size, parameters.size());
}
{
std::vector<at::Tensor> params;
MyOptimizer optimizer(params);
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
size_t size;
OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
ASSERT_EQ(size, 0);
OLD_INTERFACE_WARNING_CHECK(optimizer.add_parameters(parameters));
OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
ASSERT_EQ(size, parameters.size());
std::vector<torch::Tensor> params_;
OLD_INTERFACE_WARNING_CHECK(params_ = optimizer.parameters());
for (const auto p : c10::irange(size)) {
ASSERT_TRUE(params_[p].allclose(parameters[p]));
}
}
{
Linear linear(3, 4);
MyOptimizer optimizer(linear->parameters());
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
size_t size;
OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
ASSERT_EQ(size, linear->parameters().size());
}
}
TEST(OptimTest, XORConvergence_SGD) {
ASSERT_TRUE(test_optimizer_xor<SGD>(
SGDOptions(0.1).momentum(0.9).nesterov(true).weight_decay(1e-6)));
}
TEST(OptimTest, XORConvergence_LBFGS) {
ASSERT_TRUE(test_optimizer_xor<LBFGS>(LBFGSOptions(1.0)));
ASSERT_TRUE(test_optimizer_xor<LBFGS>(
LBFGSOptions(1.0).line_search_fn("strong_wolfe")));
}
TEST(OptimTest, XORConvergence_Adagrad) {
ASSERT_TRUE(test_optimizer_xor<Adagrad>(
AdagradOptions(1.0).weight_decay(1e-6).lr_decay(1e-3)));
}
TEST(OptimTest, XORConvergence_RMSprop) {
ASSERT_TRUE(test_optimizer_xor<RMSprop>(RMSpropOptions(0.1).centered(true)));
}
TEST(OptimTest, XORConvergence_RMSpropWithMomentum) {
ASSERT_TRUE(test_optimizer_xor<RMSprop>(
RMSpropOptions(0.1).momentum(0.9).weight_decay(1e-6)));
}
TEST(OptimTest, XORConvergence_Adam) {
ASSERT_TRUE(test_optimizer_xor<Adam>(AdamOptions(0.1).weight_decay(1e-6)));
}
TEST(OptimTest, XORConvergence_AdamWithAmsgrad) {
ASSERT_TRUE(test_optimizer_xor<Adam>(
AdamOptions(0.1).weight_decay(1e-6).amsgrad(true)));
}
TEST(OptimTest, ProducesPyTorchValues_Adam) {
check_exact_values<Adam>(AdamOptions(1.0), expected_parameters::Adam());
}
TEST(OptimTest, ProducesPyTorchValues_AdamWithWeightDecay) {
check_exact_values<Adam>(
AdamOptions(1.0).weight_decay(1e-2),
expected_parameters::Adam_with_weight_decay());
}
TEST(OptimTest, ProducesPyTorchValues_AdamWithWeightDecayAndAMSGrad) {
check_exact_values<Adam>(
AdamOptions(1.0).weight_decay(1e-6).amsgrad(true),
expected_parameters::Adam_with_weight_decay_and_amsgrad());
}
TEST(OptimTest, XORConvergence_AdamW) {
ASSERT_TRUE(test_optimizer_xor<AdamW>(AdamWOptions(0.1)));
}
TEST(OptimTest, XORConvergence_AdamWWithAmsgrad) {
ASSERT_TRUE(test_optimizer_xor<AdamW>(AdamWOptions(0.1).amsgrad(true)));
}
TEST(OptimTest, ProducesPyTorchValues_AdamW) {
check_exact_values<AdamW>(AdamWOptions(1.0), expected_parameters::AdamW());
}
TEST(OptimTest, ProducesPyTorchValues_AdamWWithoutWeightDecay) {
check_exact_values<AdamW>(
AdamWOptions(1.0).weight_decay(0),
expected_parameters::AdamW_without_weight_decay());
}
TEST(OptimTest, ProducesPyTorchValues_AdamWWithAMSGrad) {
check_exact_values<AdamW>(
AdamWOptions(1.0).amsgrad(true),
expected_parameters::AdamW_with_amsgrad());
}
TEST(OptimTest, ProducesPyTorchValues_Adagrad) {
check_exact_values<Adagrad>(
AdagradOptions(1.0), expected_parameters::Adagrad());
}
TEST(OptimTest, ProducesPyTorchValues_AdagradWithWeightDecay) {
check_exact_values<Adagrad>(
AdagradOptions(1.0).weight_decay(1e-2),
expected_parameters::Adagrad_with_weight_decay());
}
TEST(OptimTest, ProducesPyTorchValues_AdagradWithWeightDecayAndLRDecay) {
check_exact_values<Adagrad>(
AdagradOptions(1.0).weight_decay(1e-6).lr_decay(1e-3),
expected_parameters::Adagrad_with_weight_decay_and_lr_decay());
}
TEST(OptimTest, ProducesPyTorchValues_RMSprop) {
check_exact_values<RMSprop>(
RMSpropOptions(0.1), expected_parameters::RMSprop());
}
TEST(OptimTest, ProducesPyTorchValues_RMSpropWithWeightDecay) {
check_exact_values<RMSprop>(
RMSpropOptions(0.1).weight_decay(1e-2),
expected_parameters::RMSprop_with_weight_decay());
}
TEST(OptimTest, ProducesPyTorchValues_RMSpropWithWeightDecayAndCentered) {
check_exact_values<RMSprop>(
RMSpropOptions(0.1).weight_decay(1e-6).centered(true),
expected_parameters::RMSprop_with_weight_decay_and_centered());
}
TEST(
OptimTest,
ProducesPyTorchValues_RMSpropWithWeightDecayAndCenteredAndMomentum) {
check_exact_values<RMSprop>(
RMSpropOptions(0.1).weight_decay(1e-6).centered(true).momentum(0.9),
expected_parameters::
RMSprop_with_weight_decay_and_centered_and_momentum());
}
TEST(OptimTest, ProducesPyTorchValues_SGD) {
check_exact_values<SGD>(SGDOptions(0.1), expected_parameters::SGD());
}
TEST(OptimTest, ProducesPyTorchValues_SGDWithWeightDecay) {
check_exact_values<SGD>(
SGDOptions(0.1).weight_decay(1e-2),
expected_parameters::SGD_with_weight_decay());
}
TEST(OptimTest, ProducesPyTorchValues_SGDWithWeightDecayAndMomentum) {
check_exact_values<SGD>(
SGDOptions(0.1).weight_decay(1e-2).momentum(0.9),
expected_parameters::SGD_with_weight_decay_and_momentum());
}
TEST(OptimTest, ProducesPyTorchValues_SGDWithWeightDecayAndNesterovMomentum) {
check_exact_values<SGD>(
SGDOptions(0.1).weight_decay(1e-6).momentum(0.9).nesterov(true),
expected_parameters::SGD_with_weight_decay_and_nesterov_momentum());
}
TEST(OptimTest, ProducesPyTorchValues_LBFGS) {
check_exact_values<LBFGS>(LBFGSOptions(1.0), expected_parameters::LBFGS());
}
TEST(OptimTest, ProducesPyTorchValues_LBFGS_with_line_search) {
check_exact_values<LBFGS>(
LBFGSOptions(1.0).line_search_fn("strong_wolfe"),
expected_parameters::LBFGS_with_line_search());
}
TEST(OptimTest, ZeroGrad) {
torch::manual_seed(0);
Linear model(2, 8);
SGD optimizer(model->parameters(), 0.1);
for (const auto& parameter : model->parameters()) {
ASSERT_FALSE(parameter.grad().defined());
}
auto output = model->forward(torch::ones({5, 2}));
auto loss = output.sum();
loss.backward();
for (const auto& parameter : model->parameters()) {
ASSERT_TRUE(parameter.grad().defined());
ASSERT_GT(parameter.grad().sum().item<float>(), 0);
}
optimizer.zero_grad();
for (const auto& parameter : model->parameters()) {
ASSERT_FALSE(parameter.grad().defined());
}
}
TEST(OptimTest, ExternalVectorOfParameters) {
torch::manual_seed(0);
std::vector<torch::Tensor> parameters = {
torch::randn({2, 2}), torch::randn({3, 3}), torch::randn({4, 4})};
std::vector<torch::Tensor> original_parameters = {
parameters[0].clone(), parameters[1].clone(), parameters[2].clone()};
// Set all gradients to one
for (auto& parameter : parameters) {
parameter.mutable_grad() = torch::ones_like(parameter);
}
SGD optimizer(parameters, 1.0);
optimizer.step();
ASSERT_TRUE(parameters[0].allclose(original_parameters[0] - 1.0));
ASSERT_TRUE(parameters[1].allclose(original_parameters[1] - 1.0));
ASSERT_TRUE(parameters[2].allclose(original_parameters[2] - 1.0));
}
TEST(OptimTest, AddParameter_LBFGS) {
torch::manual_seed(0);
std::vector<torch::Tensor> parameters = {torch::randn({5, 5})};
std::vector<torch::Tensor> original_parameters = {parameters[0].clone()};
// Set all gradients to one
for (auto& parameter : parameters) {
parameter.mutable_grad() = torch::ones_like(parameter);
}
LBFGS optimizer(std::vector<torch::Tensor>{}, 1.0);
OLD_INTERFACE_WARNING_CHECK(optimizer.add_parameters(parameters));
optimizer.step([]() { return torch::tensor(1); });
// REQUIRE this doesn't throw
}
// Check whether the learning rate of the parameter groups in the optimizer are
// the same as the expected learning rates given in the epoch:learning rate map
void check_lr_change(
Optimizer& optimizer,
LRScheduler& lr_scheduler,
std::map<unsigned, double> expected_epoch_lrs) {
// Find maximum epoch in map
unsigned kIterations = std::max_element(
expected_epoch_lrs.begin(),
expected_epoch_lrs.end(),
[](const std::pair<unsigned, double>& a,
const std::pair<unsigned, double>& b) -> bool {
return a.second > b.second;
})
->first;
for (unsigned i = 0; i <= kIterations; i++) {
const auto epoch_iter = expected_epoch_lrs.find(i);
if (epoch_iter != expected_epoch_lrs.end()) {
// Compare the similarity of the two floating point learning rates
ASSERT_TRUE(
fabs(
epoch_iter->second -
optimizer.param_groups()[0].options().get_lr()) <
std::numeric_limits<double>::epsilon());
}
optimizer.step();
lr_scheduler.step();
}
}
// Very similar to check_lr_change, but for ReduceLROnPlateauScheduler
// which does not inherit from LRScheduler and requires a metrics
// input to step().
void check_lr_change_for_reduce_on_plateau(
Optimizer& optimizer,
ReduceLROnPlateauScheduler& lr_scheduler,
std::map<unsigned, double> expected_epoch_lrs) {
// Find maximum epoch in map
unsigned kIterations = std::max_element(
expected_epoch_lrs.begin(),
expected_epoch_lrs.end(),
[](const std::pair<unsigned, double>& a,
const std::pair<unsigned, double>& b) -> bool {
return a.second > b.second;
})
->first;
for (unsigned i = 0; i <= kIterations; i++) {
const auto epoch_iter = expected_epoch_lrs.find(i);
if (epoch_iter != expected_epoch_lrs.end()) {
// Compare the similarity of the two floating point learning rates
ASSERT_TRUE(
fabs(
epoch_iter->second -
optimizer.param_groups()[0].options().get_lr()) <
std::numeric_limits<double>::epsilon());
}
optimizer.step();
lr_scheduler.step(5.0);
}
}
TEST(OptimTest, CheckLRChange_StepLR_Adam) {
torch::Tensor parameters = torch::zeros({1});
auto optimizer = Adam({parameters}, AdamOptions().lr(1e-3));
const unsigned step_size = 20;
const double gamma = 0.5;
StepLR step_lr_scheduler(optimizer, step_size, gamma);
// The learning rate should have halved at epoch 20
const std::map<unsigned, double> expected_epoch_lrs = {{1, 1e-3}, {25, 5e-4}};
check_lr_change(optimizer, step_lr_scheduler, expected_epoch_lrs);
}
TEST(OptimTest, CheckLRChange_ReduceLROnPlateau_Adam) {
torch::Tensor parameters = torch::zeros({1});
auto optimizer = Adam({parameters}, AdamOptions().lr(1e-3));
const float factor = 0.5;
const int patience = 20;
ReduceLROnPlateauScheduler reduce_lr_on_plateau_scheduler(
optimizer,
ReduceLROnPlateauScheduler::SchedulerMode::min,
factor,
patience);
// The learning rate should have halved at epoch 20
const std::map<unsigned, double> expected_epoch_lrs = {{1, 1e-3}, {25, 5e-4}};
check_lr_change_for_reduce_on_plateau(
optimizer, reduce_lr_on_plateau_scheduler, expected_epoch_lrs);
}