mirror of
https://github.com/pytorch/pytorch.git
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dd277e9086
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/27382 Test Plan: Imported from OSS Differential Revision: D17766735 Pulled By: pbelevich fbshipit-source-id: c7a66daeb17550eb9a5d26944427723d4ebdc6c8
423 lines
12 KiB
C++
423 lines
12 KiB
C++
#include <gtest/gtest.h>
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#include <torch/torch.h>
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#include <algorithm>
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#include <memory>
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#include <vector>
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#include <test/cpp/api/support.h>
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using namespace torch::nn;
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using namespace torch::test;
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struct SequentialTest : torch::test::SeedingFixture {};
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TEST_F(SequentialTest, CanContainThings) {
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Sequential sequential(Linear(3, 4), ReLU(), BatchNorm(3));
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}
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TEST_F(SequentialTest, ConstructsFromSharedPointer) {
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struct M : torch::nn::Module {
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explicit M(int value_) : value(value_) {}
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int value;
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int forward() {
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return value;
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}
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};
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Sequential sequential(
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std::make_shared<M>(1), std::make_shared<M>(2), std::make_shared<M>(3));
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ASSERT_EQ(sequential->size(), 3);
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Sequential sequential_named(modules_ordered_dict({
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{"m1", std::make_shared<M>(1)},
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{std::string("m2"), std::make_shared<M>(2)},
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{"m3", std::make_shared<M>(3)}
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}));
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ASSERT_EQ(sequential->size(), 3);
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}
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TEST_F(SequentialTest, ConstructsFromConcreteType) {
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static int copy_count;
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struct M : torch::nn::Module {
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explicit M(int value_) : value(value_) {}
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M(const M& other) : torch::nn::Module(other) {
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copy_count++;
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}
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int value;
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int forward() {
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return value;
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}
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};
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copy_count = 0;
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Sequential sequential(M(1), M(2), M(3));
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ASSERT_EQ(sequential->size(), 3);
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// NOTE: The current implementation expects each module to be copied exactly once,
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// which happens when the module is passed into `std::make_shared<T>()`.
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// TODO: Find a way to avoid copying, and then delete the copy constructor of `M`.
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ASSERT_EQ(copy_count, 3);
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copy_count = 0;
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Sequential sequential_named(modules_ordered_dict({
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{"m1", M(1)},
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{std::string("m2"), M(2)},
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{"m3", M(3)}
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}));
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ASSERT_EQ(sequential->size(), 3);
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ASSERT_EQ(copy_count, 3);
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}
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TEST_F(SequentialTest, ConstructsFromModuleHolder) {
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struct MImpl : torch::nn::Module {
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explicit MImpl(int value_) : value(value_) {}
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int forward() {
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return value;
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}
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int value;
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};
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struct M : torch::nn::ModuleHolder<MImpl> {
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using torch::nn::ModuleHolder<MImpl>::ModuleHolder;
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using torch::nn::ModuleHolder<MImpl>::get;
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};
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Sequential sequential(M(1), M(2), M(3));
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ASSERT_EQ(sequential->size(), 3);
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Sequential sequential_named(modules_ordered_dict({
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{"m1", M(1)},
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{std::string("m2"), M(2)},
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{"m3", M(3)}
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}));
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ASSERT_EQ(sequential->size(), 3);
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}
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TEST_F(SequentialTest, PushBackAddsAnElement) {
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struct M : torch::nn::Module {
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explicit M(int value_) : value(value_) {}
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int forward() {
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return value;
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}
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int value;
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};
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// Test unnamed submodules
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Sequential sequential;
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ASSERT_EQ(sequential->size(), 0);
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ASSERT_TRUE(sequential->is_empty());
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sequential->push_back(Linear(3, 4));
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ASSERT_EQ(sequential->size(), 1);
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sequential->push_back(std::make_shared<M>(1));
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ASSERT_EQ(sequential->size(), 2);
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sequential->push_back(M(2));
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ASSERT_EQ(sequential->size(), 3);
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// Mix named and unnamed submodules
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Sequential sequential_named;
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ASSERT_EQ(sequential_named->size(), 0);
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ASSERT_TRUE(sequential_named->is_empty());
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sequential_named->push_back(Linear(3, 4));
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ASSERT_EQ(sequential_named->size(), 1);
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ASSERT_EQ(sequential_named->named_children()[0].key(), "0");
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sequential_named->push_back(std::string("linear2"), Linear(3, 4));
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ASSERT_EQ(sequential_named->size(), 2);
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ASSERT_EQ(sequential_named->named_children()[1].key(), "linear2");
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sequential_named->push_back("shared_m1", std::make_shared<M>(1));
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ASSERT_EQ(sequential_named->size(), 3);
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ASSERT_EQ(sequential_named->named_children()[2].key(), "shared_m1");
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sequential_named->push_back(std::make_shared<M>(1));
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ASSERT_EQ(sequential_named->size(), 4);
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ASSERT_EQ(sequential_named->named_children()[3].key(), "3");
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sequential_named->push_back(M(1));
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ASSERT_EQ(sequential_named->size(), 5);
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ASSERT_EQ(sequential_named->named_children()[4].key(), "4");
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sequential_named->push_back(std::string("m2"), M(1));
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ASSERT_EQ(sequential_named->size(), 6);
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ASSERT_EQ(sequential_named->named_children()[5].key(), "m2");
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}
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TEST_F(SequentialTest, AccessWithAt) {
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struct M : torch::nn::Module {
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explicit M(int value_) : value(value_) {}
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int forward() {
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return value;
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}
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int value;
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};
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std::vector<std::shared_ptr<M>> modules = {
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std::make_shared<M>(1), std::make_shared<M>(2), std::make_shared<M>(3)};
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Sequential sequential;
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for (auto& module : modules) {
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sequential->push_back(module);
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}
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ASSERT_EQ(sequential->size(), 3);
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// returns the correct module for a given index
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for (size_t i = 0; i < modules.size(); ++i) {
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ASSERT_EQ(&sequential->at<M>(i), modules[i].get());
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}
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// throws for a bad index
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ASSERT_THROWS_WITH(
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sequential->at<M>(modules.size() + 1), "Index out of range");
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ASSERT_THROWS_WITH(
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sequential->at<M>(modules.size() + 1000000), "Index out of range");
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}
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TEST_F(SequentialTest, AccessWithPtr) {
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struct M : torch::nn::Module {
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explicit M(int value_) : value(value_) {}
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int forward() {
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return value;
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}
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int value;
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};
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std::vector<std::shared_ptr<M>> modules = {
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std::make_shared<M>(1), std::make_shared<M>(2), std::make_shared<M>(3)};
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Sequential sequential;
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for (auto& module : modules) {
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sequential->push_back(module);
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}
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ASSERT_EQ(sequential->size(), 3);
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// returns the correct module for a given index
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for (size_t i = 0; i < modules.size(); ++i) {
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ASSERT_EQ(sequential->ptr(i).get(), modules[i].get());
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ASSERT_EQ(sequential[i].get(), modules[i].get());
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ASSERT_EQ(sequential->ptr<M>(i).get(), modules[i].get());
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}
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// throws for a bad index
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ASSERT_THROWS_WITH(sequential->ptr(modules.size() + 1), "Index out of range");
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ASSERT_THROWS_WITH(
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sequential->ptr(modules.size() + 1000000), "Index out of range");
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}
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TEST_F(SequentialTest, CallingForwardOnEmptySequentialIsDisallowed) {
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Sequential empty;
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ASSERT_THROWS_WITH(
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empty->forward<int>(), "Cannot call forward() on an empty Sequential");
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}
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TEST_F(SequentialTest, CallingForwardChainsCorrectly) {
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struct MockModule : torch::nn::Module {
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explicit MockModule(int value) : expected(value) {}
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int expected;
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int forward(int value) {
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assert(value == expected);
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return value + 1;
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}
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};
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Sequential sequential(MockModule{1}, MockModule{2}, MockModule{3});
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ASSERT_EQ(sequential->forward<int>(1), 4);
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}
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TEST_F(SequentialTest, CallingForwardWithTheWrongReturnTypeThrows) {
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struct M : public torch::nn::Module {
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int forward() {
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return 5;
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}
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};
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Sequential sequential(M{});
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ASSERT_EQ(sequential->forward<int>(), 5);
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ASSERT_THROWS_WITH(
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sequential->forward<float>(),
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"The type of the return value is int, but you asked for type float");
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}
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TEST_F(SequentialTest, TheReturnTypeOfForwardDefaultsToTensor) {
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struct M : public torch::nn::Module {
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torch::Tensor forward(torch::Tensor v) {
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return v;
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}
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};
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Sequential sequential(M{});
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auto variable = torch::ones({3, 3}, torch::requires_grad());
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ASSERT_TRUE(sequential->forward(variable).equal(variable));
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}
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TEST_F(SequentialTest, ForwardReturnsTheLastValue) {
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torch::manual_seed(0);
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Sequential sequential(Linear(10, 3), Linear(3, 5), Linear(5, 100));
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auto x = torch::randn({1000, 10}, torch::requires_grad());
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auto y = sequential->forward(x);
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ASSERT_EQ(y.ndimension(), 2);
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ASSERT_EQ(y.size(0), 1000);
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ASSERT_EQ(y.size(1), 100);
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}
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TEST_F(SequentialTest, SanityCheckForHoldingStandardModules) {
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Sequential sequential(
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Linear(10, 3),
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Conv2d(1, 2, 3),
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Dropout(0.5),
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BatchNorm(5),
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Embedding(4, 10),
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LSTM(4, 5));
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}
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TEST_F(SequentialTest, ExtendPushesModulesFromOtherSequential) {
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struct A : torch::nn::Module {
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int forward(int x) {
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return x;
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}
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};
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struct B : torch::nn::Module {
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int forward(int x) {
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return x;
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}
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};
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struct C : torch::nn::Module {
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int forward(int x) {
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return x;
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}
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};
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struct D : torch::nn::Module {
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int forward(int x) {
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return x;
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}
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};
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Sequential a(A{}, B{});
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Sequential b(C{}, D{});
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a->extend(*b);
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ASSERT_EQ(a->size(), 4);
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ASSERT_TRUE(a[0]->as<A>());
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ASSERT_TRUE(a[1]->as<B>());
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ASSERT_TRUE(a[2]->as<C>());
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ASSERT_TRUE(a[3]->as<D>());
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ASSERT_EQ(b->size(), 2);
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ASSERT_TRUE(b[0]->as<C>());
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ASSERT_TRUE(b[1]->as<D>());
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std::vector<std::shared_ptr<A>> c = {std::make_shared<A>(),
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std::make_shared<A>()};
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b->extend(c);
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ASSERT_EQ(b->size(), 4);
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ASSERT_TRUE(b[0]->as<C>());
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ASSERT_TRUE(b[1]->as<D>());
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ASSERT_TRUE(b[2]->as<A>());
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ASSERT_TRUE(b[3]->as<A>());
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}
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TEST_F(SequentialTest, HasReferenceSemantics) {
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Sequential first(Linear(2, 3), Linear(4, 4), Linear(4, 5));
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Sequential second(first);
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ASSERT_EQ(first.get(), second.get());
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ASSERT_EQ(first->size(), second->size());
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ASSERT_TRUE(std::equal(
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first->begin(),
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first->end(),
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second->begin(),
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[](const AnyModule& first, const AnyModule& second) {
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return &first == &second;
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}));
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}
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TEST_F(SequentialTest, IsCloneable) {
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Sequential sequential(Linear(3, 4), Functional(torch::relu), BatchNorm(3));
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Sequential clone =
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std::dynamic_pointer_cast<SequentialImpl>(sequential->clone());
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ASSERT_EQ(sequential->size(), clone->size());
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for (size_t i = 0; i < sequential->size(); ++i) {
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// The modules should be the same kind (type).
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ASSERT_EQ(sequential[i]->name(), clone[i]->name());
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// But not pointer-equal (distinct objects).
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ASSERT_NE(sequential[i], clone[i]);
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}
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// Verify that the clone is deep, i.e. parameters of modules are cloned too.
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torch::NoGradGuard no_grad;
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auto params1 = sequential->named_parameters();
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auto params2 = clone->named_parameters();
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ASSERT_EQ(params1.size(), params2.size());
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for (auto& param : params1) {
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ASSERT_FALSE(pointer_equal(param.value(), params2[param.key()]));
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ASSERT_EQ(param->device(), params2[param.key()].device());
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ASSERT_TRUE(param->allclose(params2[param.key()]));
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param->add_(2);
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}
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for (auto& param : params1) {
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ASSERT_FALSE(param->allclose(params2[param.key()]));
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}
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}
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TEST_F(SequentialTest, RegistersElementsAsSubmodules) {
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Sequential sequential(Linear(10, 3), Conv2d(1, 2, 3), FeatureDropout(0.5));
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auto modules = sequential->children();
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ASSERT_TRUE(modules[0]->as<Linear>());
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ASSERT_TRUE(modules[1]->as<Conv2d>());
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ASSERT_TRUE(modules[2]->as<FeatureDropout>());
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}
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TEST_F(SequentialTest, CloneToDevice_CUDA) {
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Sequential sequential(Linear(3, 4), Functional(torch::relu), BatchNorm(3));
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torch::Device device(torch::kCUDA, 0);
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Sequential clone =
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std::dynamic_pointer_cast<SequentialImpl>(sequential->clone(device));
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for (const auto& p : clone->parameters()) {
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ASSERT_EQ(p.device(), device);
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}
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for (const auto& b : clone->buffers()) {
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ASSERT_EQ(b.device(), device);
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}
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}
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TEST_F(SequentialTest, PrettyPrintSequential) {
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Sequential sequential(
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Linear(10, 3),
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Conv2d(1, 2, 3),
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Dropout(0.5),
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BatchNorm(5),
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Embedding(4, 10),
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LSTM(4, 5));
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ASSERT_EQ(
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c10::str(sequential),
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"torch::nn::Sequential(\n"
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" (0): torch::nn::Linear(in_features=10, out_features=3, bias=true)\n"
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" (1): torch::nn::Conv2d(input_channels=1, output_channels=2, kernel_size=[3, 3], stride=[1, 1])\n"
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" (2): torch::nn::Dropout(rate=0.5)\n"
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" (3): torch::nn::BatchNorm(features=5, eps=1e-05, momentum=0.1, affine=true, stateful=true)\n"
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" (4): torch::nn::Embedding(num_embeddings=4, embedding_dim=10)\n"
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" (5): torch::nn::LSTM(input_size=4, hidden_size=5, layers=1, dropout=0)\n"
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")");
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Sequential sequential_named(modules_ordered_dict({
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{"linear", Linear(10, 3)},
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{"conv2d", Conv2d(1, 2, 3)},
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{"dropout", Dropout(0.5)},
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{"batchnorm", BatchNorm(5)},
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{"embedding", Embedding(4, 10)},
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{"lstm", LSTM(4, 5)}
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}));
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ASSERT_EQ(
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c10::str(sequential_named),
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"torch::nn::Sequential(\n"
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" (linear): torch::nn::Linear(in_features=10, out_features=3, bias=true)\n"
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" (conv2d): torch::nn::Conv2d(input_channels=1, output_channels=2, kernel_size=[3, 3], stride=[1, 1])\n"
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" (dropout): torch::nn::Dropout(rate=0.5)\n"
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" (batchnorm): torch::nn::BatchNorm(features=5, eps=1e-05, momentum=0.1, affine=true, stateful=true)\n"
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" (embedding): torch::nn::Embedding(num_embeddings=4, embedding_dim=10)\n"
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" (lstm): torch::nn::LSTM(input_size=4, hidden_size=5, layers=1, dropout=0)\n"
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")");
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}
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