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test_flatbuffer.cpp
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#include <test/cpp/jit/test_utils.h>
#include <gtest/gtest.h>
#include <c10/core/TensorOptions.h>
#include <torch/csrc/autograd/generated/variable_factories.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/resolver.h>
#include <torch/csrc/jit/mobile/compatibility/backport.h>
#include <torch/csrc/jit/mobile/compatibility/backport_manager.h>
#include <torch/csrc/jit/mobile/compatibility/model_compatibility.h>
#include <torch/csrc/jit/mobile/compatibility/runtime_compatibility.h>
#include <torch/csrc/jit/mobile/flatbuffer_loader.h>
#include <torch/csrc/jit/mobile/import.h>
#include <torch/csrc/jit/mobile/interpreter.h>
#include <torch/csrc/jit/mobile/module.h>
#include <torch/csrc/jit/mobile/parse_bytecode.h>
#include <torch/csrc/jit/mobile/parse_operators.h>
#include <torch/csrc/jit/serialization/export.h>
#include <torch/csrc/jit/serialization/export_bytecode.h>
#include <torch/csrc/jit/serialization/flatbuffer_serializer.h>
#include <torch/csrc/jit/serialization/flatbuffer_serializer_jit.h>
#include <torch/csrc/jit/serialization/import.h>
#include <torch/custom_class.h>
#include <torch/torch.h>
#include <caffe2/serialize/versions.h>
#include <torch/csrc/jit/serialization/import_export_functions.h>
#include <unordered_set>
// Tests go in torch::jit
namespace torch {
namespace jit {
mobile::Module parse_mobile_module(
void* data,
size_t,
bool should_copy_tensor_memory = false) {
auto* flatbuffer_module = mobile::serialization::GetMutableModule(data);
return initialize_mobile_module(
flatbuffer_module, c10::nullopt, should_copy_tensor_memory);
}
TEST(FlatbufferTest, UpsampleNearest2d) {
Module m("m");
m.define(R"(
def forward(self, input: Tensor, scale:float):
return torch.upsample_nearest2d(input, [1, 1], float(scale), float(scale))
)");
std::vector<IValue> inputs;
inputs.emplace_back(torch::rand({1, 3, 128, 128}));
inputs.emplace_back(at::Scalar(2.0));
auto ref = m.forward(inputs);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
res = bc.forward(inputs);
auto resd = res.toTensor();
auto refd = ref.toTensor();
ASSERT_TRUE(resd.equal(refd));
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
auto res2 = bc2.forward(inputs);
auto resd2 = res2.toTensor();
ASSERT_TRUE(resd2.equal(refd));
}
TEST(FlatbufferTest, UpsampleNearest2dWithCopyTensorMemory) {
Module m("m");
m.define(R"(
def forward(self, input: Tensor, scale:float):
return torch.upsample_nearest2d(input, [1, 1], float(scale), float(scale))
)");
std::vector<IValue> inputs;
inputs.emplace_back(torch::rand({1, 3, 128, 128}));
inputs.emplace_back(at::Scalar(2.0));
auto ref = m.forward(inputs);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
res = bc.forward(inputs);
auto resd = res.toTensor();
auto refd = ref.toTensor();
ASSERT_TRUE(resd.equal(refd));
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size(), true);
buff = flatbuffers::DetachedBuffer();
auto res2 = bc2.forward(inputs);
auto resd2 = res2.toTensor();
ASSERT_TRUE(resd2.equal(refd));
}
TEST(FlatbufferTest, CheckAttrAccess) {
Module m("m");
m.register_attribute("mobile_optimized", BoolType::get(), true);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
bool mobile_optimized = bc.attr("mobile_optimized", false).toBool();
AT_ASSERT(mobile_optimized);
m.setattr("mobile_optimized", false);
bc = jitModuleToMobile(m, options);
mobile_optimized = bc.attr("mobile_optimized", false).toBool();
AT_ASSERT(!mobile_optimized);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
auto mobile_optimized2 = bc2.attr("mobile_optimized", false).toBool();
AT_ASSERT(!mobile_optimized2);
}
TEST(FlatbufferTest, MethodInvocation) { // NOLINT (use =delete in gtest)
const std::vector<std::string> test_programs{
// test invoking a method with default parameter
R"(
def test_func(self, x, b : int = 4):
return self.foo + x + b
)",
// inner method call with default parameter (gets inlined)
R"(
def add_with_default_arg(self, x, b : int = 4):
return self.foo + x + b
def test_func(self, x):
return self.add_with_default_arg(x) # invoke method w/ default arg
)",
// simple method call
R"(
def test_func(self, x):
b = 4
return self.foo + x + b
)",
};
for (const auto& test_program : test_programs) {
Module m("m");
m.register_parameter("foo", torch::ones({}), false);
m.define(test_program);
const int fortyTwo = 42; // (keep linter happy)
auto minput = fortyTwo * torch::ones({});
auto ref = m.run_method("test_func", minput);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
const auto& test_func = bc.get_method("test_func");
IValue res;
for (int i = 0; i < 3; ++i) {
res = test_func({minput});
}
auto resd = res.toTensor().item<float>();
auto refd = ref.toTensor().item<float>();
AT_ASSERT(resd == refd);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
const auto& test_func2 = bc2.get_method("test_func");
IValue res2;
for (int i = 0; i < 3; ++i) {
res2 = test_func2({minput});
}
auto resd2 = res2.toTensor().item<float>();
AT_ASSERT(resd2 == refd);
}
}
#if defined(ENABLE_FLATBUFFER) && !defined(FB_XPLAT_BUILD)
TEST(FlatbufferTest, FlatbufferBackPortTest) {
Module m("m");
m.define(R"(
def forward(self, input: Tensor, scale:float):
return torch.upsample_nearest2d(input, [1, 1], float(scale), float(scale))
)");
std::stringstream ss;
m._save_for_mobile(ss, {}, false, true);
std::stringstream oss;
bool backPortSuccess = _backport_for_mobile(ss, oss, 5);
ASSERT_TRUE(backPortSuccess);
}
#endif // defined(ENABLE_FLATBUFFER) && !defined(FB_XPLAT_BUILD)
TEST(FlatbufferTest, ExtraFiles) {
const auto script = R"JIT(
def forward(self):
x = torch.rand(5, 5)
x = x.mm(x)
return x
)JIT";
auto module =
std::make_shared<Module>("Module", std::make_shared<CompilationUnit>());
module->define(script);
std::ostringstream oss;
std::unordered_map<std::string, std::string> extra_files;
extra_files["metadata.json"] = "abc";
extra_files["mobile_info.json"] = "{\"key\": 23}";
std::unordered_map<std::string, std::string> loaded_extra_files;
#if defined ENABLE_FLATBUFFER
std::stringstream ss;
module->_save_for_mobile(ss, extra_files, true, /*use_flatbuffer=*/true);
loaded_extra_files["metadata.json"] = "";
auto mobile_module = _load_for_mobile(ss, c10::nullopt, loaded_extra_files);
ASSERT_EQ(loaded_extra_files["metadata.json"], "abc");
ASSERT_EQ(loaded_extra_files["mobile_info.json"], "{\"key\": 23}");
// load it twice using the same stream
auto mobile_module2 = _load_for_mobile(ss, c10::nullopt, loaded_extra_files);
#else
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(*module, options);
auto buff = save_mobile_module_to_bytes(bc, extra_files);
loaded_extra_files["metadata.json"] = "";
auto* flatbuffer_module =
mobile::serialization::GetMutableModule(buff.data());
parseExtraFiles(flatbuffer_module, loaded_extra_files);
#endif
ASSERT_EQ(loaded_extra_files["metadata.json"], "abc");
ASSERT_EQ(loaded_extra_files["mobile_info.json"], "{\"key\": 23}");
}
TEST(FlatbufferTest, Conv) {
auto s = std::getenv("PYTORCH_TEST_WITH_TSAN");
if (s && strcmp(s, "1") == 0)
return;
std::vector<torch::jit::IValue> inputs;
Module m("m");
m.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
m.register_parameter("bias", torch::ones({20}), false);
m.define(R"(
def forward(self, input):
return torch._convolution(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
)");
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,modernize-use-emplace)
inputs.push_back(torch::ones({1, 1, 28, 28}));
auto outputref = m.forward(inputs).toTensor();
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 3; ++i) {
res = bc.get_method("forward")(inputs);
}
auto output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(
outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
for (int i = 0; i < 3; ++i) {
res = bc2.get_method("forward")(inputs);
}
output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(
outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
}
TEST(FlatbufferTest, ConvWithCopyTensorMemory) {
auto s = std::getenv("PYTORCH_TEST_WITH_TSAN");
if (s && strcmp(s, "1") == 0)
return;
std::vector<torch::jit::IValue> inputs;
Module m("m");
m.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
m.register_parameter("bias", torch::ones({20}), false);
m.define(R"(
def forward(self, input):
return torch._convolution(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
)");
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,modernize-use-emplace)
inputs.push_back(torch::ones({1, 1, 28, 28}));
auto outputref = m.forward(inputs).toTensor();
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 3; ++i) {
res = bc.get_method("forward")(inputs);
}
auto output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(
outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size(), true);
buff = flatbuffers::DetachedBuffer();
for (int i = 0; i < 3; ++i) {
res = bc2.get_method("forward")(inputs);
}
output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(
outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
}
TEST(FlatbufferTest, Inline) {
Module m("m");
m.define(R"JIT(
def foo1(self, x):
return x + 1
def foo2(self, x):
return self.foo1(x) + 2
def foo3(self, x):
return self.foo2(x) + 3
)JIT");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
std::vector<torch::jit::IValue> inputs({torch::ones({})});
auto output = bc.get_method("foo3")(inputs);
AT_ASSERT(output.toTensor().item<float>() == 7.0);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
std::vector<torch::jit::IValue> inputs2({torch::ones({})});
output = bc2.get_method("foo3")(inputs2);
AT_ASSERT(output.toTensor().item<float>() == 7.0);
}
TEST(FlatbufferTest, InlineWithCopyTensorMemory) {
Module m("m");
m.define(R"JIT(
def foo1(self, x):
return x + 1
def foo2(self, x):
return self.foo1(x) + 2
def foo3(self, x):
return self.foo2(x) + 3
)JIT");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
std::vector<torch::jit::IValue> inputs({torch::ones({})});
auto output = bc.get_method("foo3")(inputs);
AT_ASSERT(output.toTensor().item<float>() == 7.0);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size(), true);
buff = flatbuffers::DetachedBuffer();
std::vector<torch::jit::IValue> inputs2({torch::ones({})});
output = bc2.get_method("foo3")(inputs2);
AT_ASSERT(output.toTensor().item<float>() == 7.0);
}
TEST(FlatbufferTest, Tuple) {
Module m("m");
m.define(R"JIT(
def foo(self, x):
return (1, 2, x + 3)
def forward(self, x):
tuple = self.foo(x)
return tuple
)JIT");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
std::vector<torch::jit::IValue> inputs({torch::ones({})});
auto output = bc.get_method("forward")(inputs);
AT_ASSERT(output.toTupleRef().elements()[1].toInt() == 2);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
output = bc2.get_method("forward")(inputs);
AT_ASSERT(output.toTuple()->elements()[1].toInt() == 2);
}
TEST(FlatbufferTest, Dict) {
Module m("m");
m.define(R"JIT(
def foo(self, x):
return {"result": x + 1}
def forward(self, x):
d = self.foo(x)
return d
)JIT");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
std::vector<torch::jit::IValue> inputs({torch::ones({})});
auto output = bc.get_method("forward")(inputs);
AT_ASSERT(output.toGenericDict().at("result").toTensor().item().toInt() == 2);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
output = bc2.get_method("forward")(inputs);
AT_ASSERT(output.toGenericDict().at("result").toTensor().item().toInt() == 2);
}
TEST(FlatbufferTest, Prim) {
Module m("m");
m.define(R"JIT(
def forward(self, x):
return int(x)
)JIT");
std::vector<IValue> inputs;
auto minput = 3.5 * torch::ones({});
inputs.emplace_back(minput);
auto ref = m.run_method("forward", minput);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 3; ++i) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto bcinputs = inputs;
res = bc.get_method("forward")(bcinputs);
}
auto resi = res.toInt();
auto refi = ref.toInt();
AT_ASSERT(resi == refi);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
for (int i = 0; i < 3; ++i) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto bcinputs = inputs;
res = bc2.get_method("forward")(bcinputs);
}
auto resi2 = res.toInt();
AT_ASSERT(resi2 == refi);
}
TEST(FlatbufferTest, PrimScalar) {
Module m("m");
m.define(R"JIT(
def forward(self, x):
return int(x.item())
)JIT");
std::vector<IValue> inputs;
auto minput = 3.5 * torch::ones({});
inputs.emplace_back(minput);
auto ref = m.run_method("forward", minput);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 3; ++i) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto bcinputs = inputs;
res = bc.get_method("forward")(bcinputs);
}
auto resi = res.toInt();
auto refi = ref.toInt();
AT_ASSERT(resi == refi);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
for (int i = 0; i < 3; ++i) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto bcinputs = inputs;
res = bc2.get_method("forward")(bcinputs);
}
auto resi2 = res.toInt();
AT_ASSERT(resi2 == refi);
}
TEST(FlatbufferTest, WrongMethodName) {
Module m("m");
m.register_parameter("foo", torch::ones({}), false);
m.define(R"(
def add(self, x):
b = 4
return self.foo + x + b
)");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
std::vector<IValue> inputs;
auto minput = 5 * torch::ones({});
inputs.emplace_back(minput);
ASSERT_THROWS_WITH_MESSAGE(
bc.get_method("forward")(inputs), "is not defined");
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
ASSERT_THROWS_WITH_MESSAGE(
bc2.get_method("forward")(inputs), "is not defined");
}
TEST(FlatbufferTest, SetState) {
Module m("m");
m.register_parameter("foo", torch::ones({}), false);
m.define(R"(
def __getstate__(self):
return self.foo
def __setstate__(self, a):
self.foo = a
def forward(self, x):
b = 4
return self.foo + x + b
)");
std::vector<IValue> inputs;
auto minput = 5 * torch::ones({});
inputs.emplace_back(minput);
std::stringstream ms;
m.save(ms);
auto loaded_m = load(ms);
auto ref = loaded_m.run_method("forward", minput);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 3; ++i) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto bcinputs = inputs;
res = bc.get_method("forward")(bcinputs);
}
auto resd = res.toTensor().item<float>();
auto refd = ref.toTensor().item<float>();
AT_ASSERT(resd == refd);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
for (int i = 0; i < 3; ++i) {
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto bcinputs = inputs;
res = bc2.get_method("forward")(bcinputs);
}
auto resd2 = res.toTensor().item<float>();
AT_ASSERT(resd2 == refd);
}
class TorchBindFlatbufferTestStruct : public torch::jit::CustomClassHolder {
public:
std::string get(at::Tensor t) {
std::stringstream ss;
ss << "Hello! Your tensor has ";
ss << t.numel();
ss << " elements!";
return ss.str();
}
};
namespace {
struct ClassNamespaceValue : public SugaredValue {
explicit ClassNamespaceValue(c10::QualifiedName name)
: basename_(std::move(name)) {}
std::shared_ptr<SugaredValue> attr(
const SourceRange& loc,
GraphFunction& m,
const std::string& name) override {
const auto fullName = c10::QualifiedName(basename_, name);
// Check to see if it is a custom class.
if (auto custom_class = getCustomClass(fullName.qualifiedName())) {
return std::make_shared<ClassValue>(custom_class);
}
// If it's not a custom class, assume it's another namespace
// NOLINTNEXTLINE(performance-move-const-arg)
return std::make_shared<ClassNamespaceValue>(std::move(fullName));
}
std::string kind() const override {
return "Class Namespace";
}
private:
c10::QualifiedName basename_;
};
struct TestModuleResolver : public Resolver {
std::shared_ptr<SugaredValue> resolveValue(
const std::string& name,
GraphFunction& m,
const SourceRange& loc) override {
if (name == "torch") {
return std::make_shared<BuiltinModule>("aten");
} else if (name == "__torch__") {
return std::make_shared<ClassNamespaceValue>(c10::QualifiedName(name));
}
return nullptr;
}
TypePtr resolveType(const std::string& name, const SourceRange& loc)
override {
return nullptr;
}
};
} // namespace
TEST(FlatbufferTest, BuiltinClass) {
script::Module m("m");
auto cls = getCustomClass(
"__torch__.torch.classes._TorchScriptTesting._FlatbufferTest");
TORCH_INTERNAL_ASSERT(cls);
c10::intrusive_ptr<torch::CustomClassHolder> obj_holder;
m.register_attribute("my_obj", cls, IValue::make_capsule(obj_holder));
m.register_parameter("foo", torch::ones({}), false);
m.define(
R"(
def __getstate__(self):
return 1
def __setstate__(self, a):
self.my_obj = __torch__.torch.classes._TorchScriptTesting._FlatbufferTest()
def forward(self, x) -> str:
return self.my_obj.get(x)
)",
std::make_shared<TestModuleResolver>());
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
std::string expected = "Hello! Your tensor has 12 elements!";
auto res =
bc2.get_method("forward")(std::vector<IValue>{torch::zeros({3, 4})});
const auto& str2 = res.toStringRef();
AT_ASSERT(str2 == expected);
}
TEST(FlatbufferTest, BuiltinFunction) {
script::Module m("m");
auto custom_class_obj = make_custom_class<TorchBindFlatbufferTestStruct>();
m.register_attribute("my_obj", custom_class_obj.type(), custom_class_obj);
m.define(R"(
def forward(self, x) -> str:
return self.my_obj.get(x)
)");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
auto res =
bc.get_method("forward")(std::vector<IValue>{torch::zeros({3, 4})});
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
auto str = res.toStringRef();
std::string expected = "Hello! Your tensor has 12 elements!";
AT_ASSERT(str == expected);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
res = bc2.get_method("forward")(std::vector<IValue>{torch::zeros({3, 4})});
// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
str = res.toStringRef();
AT_ASSERT(str == expected);
}
TEST(FlatbufferTest, Eval) {
std::vector<torch::jit::IValue> inputs;
Module m("m");
m.define(R"(
def __init__(self, x):
self.training = True
def forward(self, input):
return torch.dropout(input, 1.0, self.training)
)");
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,modernize-use-emplace)
inputs.push_back(torch::ones({1, 1, 28, 28}));
m.eval();
auto outputref = m.forward(inputs).toTensor();
// save m in training mode to make sure that mobile eval() will correctly
// change back to eval mode
m.train();
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
bc.eval();
IValue res;
for (int i = 0; i < 3; ++i) {
res = bc.get_method("forward")(inputs);
}
auto output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(
outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
bc2.eval();
for (int i = 0; i < 3; ++i) {
res = bc2.get_method("forward")(inputs);
}
output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(
outputref[0][0][0][0].item<int>() == output[0][0][0][0].item<int>());
}
TEST(FlatbufferTest, FindWrongMethodName) {
Module m("m");
m.register_parameter("foo", torch::ones({}), false);
m.define(R"(
def add(self, x):
b = 4
return self.foo + x + b
)");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
ASSERT_TRUE(bc.find_method("forward") == c10::nullopt);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
ASSERT_TRUE(bc2.find_method("forward") == c10::nullopt);
}
TEST(FlatbufferTest, FindAndRunMethod) {
Module m("m");
m.register_parameter("foo", torch::ones({}), false);
m.define(R"(
def add_it(self, x):
b = 4
return self.foo + x + b
)");
std::vector<IValue> inputs;
auto minput = 5 * torch::ones({});
inputs.emplace_back(minput);
auto ref = m.get_method("add_it")(inputs);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 3; ++i) {
auto bcinputs = inputs;
auto method = bc.find_method("add_it");
AT_ASSERT(method != c10::nullopt);
res = (*method)(std::move(bcinputs));
}
auto resd = res.toTensor().item<float>();
auto refd = ref.toTensor().item<float>();
AT_ASSERT(resd == refd);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
for (int i = 0; i < 3; ++i) {
auto bcinputs = inputs;
auto method = bc2.find_method("add_it");
AT_ASSERT(method != c10::nullopt);
res = (*method)(std::move(bcinputs));
}
resd = res.toTensor().item<float>();
AT_ASSERT(resd == refd);
}
TEST(FlatbufferTest, RunMethodVariadic) {
Module m("m");
m.register_parameter("foo", torch::ones({}), false);
m.define(R"(
def add_three(self, x, y):
return self.foo + x + y
)");
std::vector<IValue> inputs;
auto inputx = 5 * torch::ones({});
auto inputy = 4 * torch::ones({});
auto ref = m.run_method("add_three", inputx, inputy);
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res = bc.run_method("add_three", inputx, inputy);
auto resd = res.toTensor().item<float>();
auto refd = ref.toTensor().item<float>();
AT_ASSERT(resd == refd);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
res = bc.run_method("add_three", inputx, inputy);
resd = res.toTensor().item<float>();
AT_ASSERT(resd == refd);
}
TEST(FlatbufferTest, DuplicateSetState) {
Module m("M");
m.register_parameter("foo", torch::ones({}), false);
m.define(R"(
def __getstate__(self):
return self.foo + self.foo
def __setstate__(self, a):
self.foo = a
def forward(self, x):
b = 4
return self.foo + x + b
)");
Module b("B");
b.register_module("M0", m);
b.register_module("M1", m);
b.define(R"(
def forward(self, x):
return self.M0.forward(x) + self.M1.forward(x)
)");
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
const auto methods = bc.get_methods();
const size_t expected_n = 3;
ASSERT_EQ(methods.size(), expected_n);
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
const auto methods2 = bc.get_methods();
ASSERT_EQ(methods2.size(), expected_n);
}
TEST(FlatbufferTest, OpNameExportFetchRootOperators) {
torch::jit::Module m("m");
m.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
m.register_parameter("bias", torch::ones({20}), false);
m.define(R"(
def forward(self, input):
x1 = torch.zeros(2, 2)
x2 = torch.empty_like(torch.empty(2, 2))
x3 = torch._convolution(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
return (x1, x2, x3)
)");
m.eval();
CompilationOptions options;
mobile::Module ptl_model = jitModuleToMobile(m, options);
std::set<std::string> operator_names =
torch::jit::mobile::_export_operator_list(ptl_model);
std::set<std::string> expected_operator_names = {
"aten::_convolution",
"aten::empty.memory_format",
"aten::empty_like",
"aten::zeros",
};
EXPECT_EQ(operator_names, expected_operator_names)
<< "Expected the root operator lists to be the same";
auto buff = save_mobile_module_to_bytes(ptl_model);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
operator_names = torch::jit::mobile::_export_operator_list(bc2);
EXPECT_EQ(operator_names, expected_operator_names)
<< "Expected the root operator lists to be the same";
}
TEST(FlatbufferTest, DefaultArgsConv) {
auto s = std::getenv("PYTORCH_TEST_WITH_TSAN");
if (s && strcmp(s, "1") == 0)
return;
std::vector<torch::jit::IValue> inputs;
Module m("m");
m.register_parameter("weight", torch::ones({20, 1, 5, 5}), false);
m.register_parameter("bias", torch::ones({20}), false);
m.define(R"(
def forward(self, input):
return torch.conv2d(input, self.weight, self.bias, [1, 1], [0, 0], [1, 1], 1)
)");
inputs.emplace_back(torch::ones({1, 1, 28, 28}));
auto outputref = m.forward(inputs).toTensor();
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 1; ++i) {
res = bc.get_method("forward")(inputs);
}
auto output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(output.equal(outputref));
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
for (int i = 0; i < 1; ++i) {
res = bc2.get_method("forward")(inputs);
}
output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(output.equal(outputref));
}
namespace {
void testLiteModuleCompareResultTensors(
Module& m,
const std::vector<torch::jit::IValue>& inputs,
const std::string& method_name = "forward") {
auto outputref = m.get_method(method_name)(inputs).toTensor();
CompilationOptions options;
mobile::Module bc = jitModuleToMobile(m, options);
IValue res;
for (int i = 0; i < 3; ++i) {
res = bc.get_method(method_name)(inputs);
}
auto output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(output.equal(outputref));
auto buff = save_mobile_module_to_bytes(bc);
mobile::Module bc2 = parse_mobile_module(buff.data(), buff.size());
for (int i = 0; i < 3; ++i) {
res = bc2.get_method(method_name)(inputs);
}
output = res.toTensor();
AT_ASSERT(outputref.dim() == output.dim());
AT_ASSERT(output.equal(outputref));
}
static void testDefaultArgsPinv(int num_args) {
Module m("m");
if (num_args == 1) {
m.define(R"(
def forward(self, input):
return torch.linalg_pinv(input)
)");
} else if (num_args == 2) {
m.define(R"(
def forward(self, input):
return torch.linalg_pinv(input, 1e-5)
)");
} else if (num_args == 3) {
m.define(R"(
def forward(self, input):
return torch.linalg_pinv(input, 1e-5, True)
)");
}
std::vector<torch::jit::IValue> inputs;
const int N = 28;
auto input = torch::range(1, N * N, 1);
input[0] = 1; // a more stable matrix
input = input.view({N, N});
inputs.emplace_back(input);
testLiteModuleCompareResultTensors(m, inputs);
}
} // namespace
#if !defined FB_XPLAT_BUILD
TEST(FlatbufferTest, DefaultArgsPinv) {
// Test with different number of specified arguments.
// Arguments not specified take default value.
for (int num_args = 1; num_args <= 3; ++num_args) {
testDefaultArgsPinv(num_args);
}
// bytecode with one specified argument:
// (6,
// ('__torch__.m.forward',
// (('instructions',
// (('STOREN', 1, 2),
// ('DROPR', 1, 0),
// ('MOVE', 2, 0),
// ('OP', 0, 0),
// ('RET', 0, 0))),
// ('operators', (('aten::linalg_pinv', '', 1),)),
// ('constants', (False, 1e-15)), # default constants are not
// used
// ('types', ()),
// ('register_size', 2)),
// (('arguments',
// ((('name', 'self'), ('type', '__torch__.m'), ('default_value',
// None)),
// (('name', 'input'), ('type', 'Tensor'), ('default_value',
// None)))),
// ('returns',
// ((('name', ''), ('type', 'Tensor'), ('default_value',