| #include <ATen/Parallel.h> |
| #include <gtest/gtest.h> |
| |
| #include <c10/util/irange.h> |
| #include <torch/csrc/deploy/deploy.h> |
| #include <torch/script.h> |
| #include <torch/torch.h> |
| |
| #include <future> |
| #include <iostream> |
| #include <string> |
| |
| int main(int argc, char* argv[]) { |
| ::testing::InitGoogleTest(&argc, argv); |
| int rc = RUN_ALL_TESTS(); |
| return rc; |
| } |
| |
| void compare_torchpy_jit(const char* model_filename, const char* jit_filename) { |
| // Test |
| torch::deploy::InterpreterManager m(1); |
| torch::deploy::Package p = m.load_package(model_filename); |
| auto model = p.load_pickle("model", "model.pkl"); |
| at::IValue eg; |
| { |
| auto I = p.acquire_session(); |
| eg = I.self.attr("load_pickle")({"model", "example.pkl"}).toIValue(); |
| } |
| |
| at::Tensor output = model(eg.toTuple()->elements()).toTensor(); |
| |
| // Reference |
| auto ref_model = torch::jit::load(jit_filename); |
| at::Tensor ref_output = |
| ref_model.forward(eg.toTuple()->elements()).toTensor(); |
| |
| ASSERT_TRUE(ref_output.allclose(output, 1e-03, 1e-05)); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| const char* simple = "torch/csrc/deploy/example/generated/simple"; |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| const char* simple_jit = "torch/csrc/deploy/example/generated/simple_jit"; |
| |
| const char* path(const char* envname, const char* path) { |
| const char* e = getenv(envname); |
| return e ? e : path; |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, LoadLibrary) { |
| torch::deploy::InterpreterManager m(1); |
| torch::deploy::Package p = m.load_package( |
| path("LOAD_LIBRARY", "torch/csrc/deploy/example/generated/load_library")); |
| auto model = p.load_pickle("fn", "fn.pkl"); |
| model({}); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, SimpleModel) { |
| compare_torchpy_jit(path("SIMPLE", simple), path("SIMPLE_JIT", simple_jit)); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, ResNet) { |
| compare_torchpy_jit( |
| path("RESNET", "torch/csrc/deploy/example/generated/resnet"), |
| path("RESNET_JIT", "torch/csrc/deploy/example/generated/resnet_jit")); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, Movable) { |
| torch::deploy::InterpreterManager m(1); |
| torch::deploy::ReplicatedObj obj; |
| { |
| auto I = m.acquire_one(); |
| auto model = |
| I.global("torch.nn", "Module")(std::vector<torch::deploy::Obj>()); |
| obj = I.create_movable(model); |
| } |
| obj.acquire_session(); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, MultiSerialSimpleModel) { |
| torch::deploy::InterpreterManager manager(3); |
| torch::deploy::Package p = manager.load_package(path("SIMPLE", simple)); |
| auto model = p.load_pickle("model", "model.pkl"); |
| auto ref_model = torch::jit::load(path("SIMPLE_JIT", simple_jit)); |
| |
| auto input = torch::ones({10, 20}); |
| size_t ninterp = 3; |
| std::vector<at::Tensor> outputs; |
| |
| // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores) |
| for (const auto i : c10::irange(ninterp)) { |
| outputs.push_back(model({input.alias()}).toTensor()); |
| } |
| |
| // Generate reference |
| auto ref_output = ref_model.forward({input.alias()}).toTensor(); |
| |
| // Compare all to reference |
| for (const auto i : c10::irange(ninterp)) { |
| ASSERT_TRUE(ref_output.equal(outputs[i])); |
| } |
| |
| // test kwargs api with args |
| std::vector<c10::IValue> args; |
| args.emplace_back(input); |
| std::unordered_map<std::string, c10::IValue> kwargs_empty; |
| auto jit_output_args = model.call_kwargs(args, kwargs_empty).toTensor(); |
| ASSERT_TRUE(ref_output.equal(jit_output_args)); |
| |
| // and with kwargs only |
| std::unordered_map<std::string, c10::IValue> kwargs; |
| kwargs["input"] = input; |
| auto jit_output_kwargs = model.call_kwargs(kwargs).toTensor(); |
| ASSERT_TRUE(ref_output.equal(jit_output_kwargs)); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, ThreadedSimpleModel) { |
| size_t nthreads = 3; |
| torch::deploy::InterpreterManager manager(nthreads); |
| |
| torch::deploy::Package p = manager.load_package(path("SIMPLE", simple)); |
| auto model = p.load_pickle("model", "model.pkl"); |
| auto ref_model = torch::jit::load(path("SIMPLE_JIT", simple_jit)); |
| |
| auto input = torch::ones({10, 20}); |
| |
| std::vector<at::Tensor> outputs; |
| |
| std::vector<std::future<at::Tensor>> futures; |
| // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores) |
| for (const auto i : c10::irange(nthreads)) { |
| futures.push_back(std::async(std::launch::async, [&model]() { |
| auto input = torch::ones({10, 20}); |
| for (const auto i : c10::irange(100)) { |
| model({input.alias()}).toTensor(); |
| } |
| auto result = model({input.alias()}).toTensor(); |
| return result; |
| })); |
| } |
| for (const auto i : c10::irange(nthreads)) { |
| outputs.push_back(futures[i].get()); |
| } |
| |
| // Generate reference |
| auto ref_output = ref_model.forward({input.alias()}).toTensor(); |
| |
| // Compare all to reference |
| for (const auto i : c10::irange(nthreads)) { |
| ASSERT_TRUE(ref_output.equal(outputs[i])); |
| } |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, ThrowsSafely) { |
| // See explanation in deploy.h |
| torch::deploy::InterpreterManager manager(3); |
| // NOLINTNEXTLINE(hicpp-avoid-goto,cppcoreguidelines-avoid-goto) |
| EXPECT_THROW(manager.load_package("some garbage path"), c10::Error); |
| |
| torch::deploy::Package p = manager.load_package(path("SIMPLE", simple)); |
| // NOLINTNEXTLINE(hicpp-avoid-goto,cppcoreguidelines-avoid-goto) |
| EXPECT_THROW(p.load_pickle("some other", "garbage path"), c10::Error); |
| |
| auto model = p.load_pickle("model", "model.pkl"); |
| // NOLINTNEXTLINE(hicpp-avoid-goto,cppcoreguidelines-avoid-goto) |
| EXPECT_THROW(model(at::IValue("unexpected input")), c10::Error); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, AcquireMultipleSessionsInTheSamePackage) { |
| torch::deploy::InterpreterManager m(1); |
| |
| torch::deploy::Package p = m.load_package(path("SIMPLE", simple)); |
| auto I = p.acquire_session(); |
| |
| auto I1 = p.acquire_session(); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, AcquireMultipleSessionsInDifferentPackages) { |
| torch::deploy::InterpreterManager m(1); |
| |
| torch::deploy::Package p = m.load_package(path("SIMPLE", simple)); |
| auto I = p.acquire_session(); |
| |
| torch::deploy::Package p1 = m.load_package( |
| path("RESNET", "torch/csrc/deploy/example/generated/resnet")); |
| auto I1 = p1.acquire_session(); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, TensorSharingNotAllowed) { |
| size_t nthreads = 2; |
| torch::deploy::InterpreterManager m(nthreads); |
| // generate a tensor from one interpreter |
| auto I0 = m.all_instances()[0].acquire_session(); |
| auto I1 = m.all_instances()[1].acquire_session(); |
| auto obj = I0.global("torch", "empty")({I0.from_ivalue(2)}); |
| auto t = obj.toIValue().toTensor(); |
| // try to feed it to the other interpreter, should error |
| // NOLINTNEXTLINE(hicpp-avoid-goto,cppcoreguidelines-avoid-goto) |
| ASSERT_THROW(I1.global("torch", "sigmoid")({t}), c10::Error); |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, TaggingRace) { |
| // At time of writing, this takes about 7s to run on DEBUG=1. I think |
| // this is OK, but feel free to fiddle with the knobs here to reduce the |
| // runtime |
| constexpr int64_t trials = 4; |
| constexpr int64_t nthreads = 16; |
| torch::deploy::InterpreterManager m(nthreads); |
| // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores) |
| for (const auto n : c10::irange(trials)) { |
| at::Tensor t = torch::empty(2); |
| std::atomic<int64_t> success(0); |
| std::atomic<int64_t> failed(0); |
| at::parallel_for(0, nthreads, 1, [&](int64_t begin, int64_t end) { |
| for (const auto i : c10::irange(begin, end)) { |
| auto I = m.all_instances()[i].acquire_session(); |
| try { |
| I.from_ivalue(t); |
| success++; |
| } catch (const c10::Error& e) { |
| failed++; |
| } |
| } |
| }); |
| ASSERT_EQ(success, 1); |
| ASSERT_EQ(failed, nthreads - 1); |
| } |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, DisarmHook) { |
| at::Tensor t = torch::empty(2); |
| { |
| torch::deploy::InterpreterManager m(1); |
| auto I = m.acquire_one(); |
| I.from_ivalue(t); |
| } // unload the old interpreter |
| torch::deploy::InterpreterManager m(1); |
| auto I = m.acquire_one(); |
| // NOLINTNEXTLINE(hicpp-avoid-goto,cppcoreguidelines-avoid-goto) |
| ASSERT_THROW(I.from_ivalue(t), c10::Error); // NOT a segfault |
| } |
| |
| // NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables) |
| TEST(TorchpyTest, RegisterModule) { |
| torch::deploy::InterpreterManager m(2); |
| m.register_module_source("foomodule", "def add1(x): return x + 1\n"); |
| for (const auto& interp : m.all_instances()) { |
| auto I = interp.acquire_session(); |
| AT_ASSERT(3 == I.global("foomodule", "add1")({2}).toIValue().toInt()); |
| } |
| } |