Files
pytorch/test/cpp/jit/torch_python_test.cpp
Scott Wolchok 82f7f8d471 [PyTorch] Adopt IValue::toTupleRef() where obvious (#65505)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65505

Generated with

`fastmod -m 'toTuple\(\)(\s*)->' 'toTupleRef()${1}.'`

, followed by

`fastmod '(std::move\(.*)toTupleRef\(\).' '${1}toTuple()->'`

to unbreak 2 callsites.
ghstack-source-id: 142065835

Test Plan: CI

Reviewed By: gchanan

Differential Revision: D31131025

fbshipit-source-id: 54457ae5bbeb38db9c7f196d469b98521c3d3f34
2021-11-02 10:22:18 -07:00

87 lines
2.2 KiB
C++

#include <ATen/core/ivalue.h>
#include <c10/util/Exception.h>
#include <torch/csrc/WindowsTorchApiMacro.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/script.h>
namespace torch {
namespace jit {
#ifdef _MSC_VER
#define JIT_TEST_API
#else
#define JIT_TEST_API TORCH_API
#endif
namespace {
bool isSandcastle() {
return (
(std::getenv("SANDCASTLE")) ||
(std::getenv("TW_JOB_USER") &&
std::string(std::getenv("TW_JOB_USER")) == "sandcastle"));
}
void testEvalModeForLoadedModule() {
if (isSandcastle())
return; // The module file to load is not generated in Sandcastle
std::string module_path = "dropout_model.pt";
torch::jit::Module module = torch::jit::load(module_path);
AT_ASSERT(module.attr("dropout").toModule().is_training());
module.eval();
AT_ASSERT(!module.attr("dropout").toModule().is_training());
module.train();
AT_ASSERT(module.attr("dropout").toModule().is_training());
}
void testSerializationInterop() {
if (isSandcastle()) {
// The module file to load is not generated in Sandcastle
return;
}
// This should be generated by `test/cpp/jit/tests_setup.py`
std::ifstream input_stream("ivalue.pt");
std::vector<char> input;
input.insert(
input.begin(),
std::istream_iterator<char>(input_stream),
std::istream_iterator<char>());
IValue ivalue = pickle_load(input);
auto elements = ivalue.toTupleRef().elements();
auto ones = torch::ones({2, 2});
AT_ASSERT(ones.equal(elements.at(0).toTensor()));
// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
auto twos = torch::ones({3, 5}) * 2;
AT_ASSERT(twos.equal(elements.at(1).toTensor()));
}
void testTorchSaveError() {
if (isSandcastle()) {
// The file to load is not generated in Sandcastle
return;
}
// This should be generated by `test/cpp/jit/tests_setup.py`
bool passed = true;
try {
torch::jit::load("eager_value.pt");
passed = false;
} catch (const std::exception& c) {
}
// Ensure torch::jit::load did not run
AT_ASSERT(passed);
}
} // namespace
JIT_TEST_API void runJITCPPTests() {
// TODO: this test never ran before and is broken.
// testSerializationInterop();
testEvalModeForLoadedModule();
testTorchSaveError();
}
} // namespace jit
} // namespace torch