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History

ydwu4 c77352b5cc Add torch._library.register_fake_class to fakify torchBind class (#122622 ) This PR only adds abstract class registration logic without touching existing tests so they still trace with real script object. The added tests are only for registration APIs and test error messages. Our design is that the abstract implementation should be in Python. This is much better in terms of usability. But this also has implications for custom op that takes script object as input, which is detailed later in this stack. Pull Request resolved: https://github.com/pytorch/pytorch/pull/122622 Approved by: https://github.com/zou3519 ghstack dependencies: #122619, #122620, #122621		2024-04-02 23:52:17 +00:00
..
upgrader_models
__init__.py
CMakeLists.txt
README.md
script_module_v4.ptl
script_module_v5.ptl
script_module_v6.ptl
source_range_test.cpp
test_add_if_then_else.cpp
test_alias_analysis.cpp
test_argument_spec.cpp
test_autodiff.cpp
test_backend_compiler_lib.cpp
test_backend_compiler_preprocess.cpp
test_backend_lib.cpp
test_backend.cpp
test_class_import.cpp
test_class_parser.cpp
test_class_type.cpp
test_cleanup_passes.cpp
test_code_template.cpp
test_concat_opt.cpp
test_constant_pooling.cpp
test_create_autodiff_subgraphs.cpp
test_cs_debug_info_serialization.cpp
test_custom_class_registrations.cpp	Add torch._library.register_fake_class to fakify torchBind class (#122622 )	2024-04-02 23:52:17 +00:00
test_custom_class_registrations.h
test_custom_class.cpp
test_custom_operators.cpp
test_dce.cpp
test_exception.cpp
test_file_format.cpp
test_flatbuffer.cpp
test_fuser.cpp
test_graph_executor.cpp
test_graph_iterator.cpp
test_inliner.cpp
test_interface.cpp
test_interpreter_async.pt
test_interpreter.cpp
test_ir.cpp
test_irparser.cpp
test_jit_logging_levels.cpp
test_jit_type.cpp
test_lite_interpreter_direct.cpp
test_lite_interpreter.cpp
test_lite_trainer.cpp
test_load_upgraders.cpp
test_memory_dag.cpp
test_misc.cpp
test_mobile_type_parser.cpp
test_module_api.cpp
test_op_replacement.cpp
test_peephole_optimize.cpp
test_qualified_name.cpp
test_save_load.cpp
test_schema_info.cpp
test_schema_matching.cpp
test_script_profile.cpp
test_shape_analysis.cpp
test_stack_opt.cpp
test_subgraph_matcher.cpp
test_subgraph_rewriter.cpp
test_subgraph_utils.cpp
test_union.cpp
test_upgrader_utils.cpp
test_utils.cpp
test_utils.h
tests_setup.py
torch_python_test.cpp

README.md

JIT C++ Tests

Adding a new test

First, create a new test file. Test files should have be placed in this directory, with a name that starts with test_, like test_foo.cpp.

In general a single test suite

Add your test file to the JIT_TEST_SRCS list in test/cpp/jit/CMakeLists.txt.

A test file may look like:

#include <gtest/gtest.h>

using namespace ::torch::jit

TEST(FooTest, BarBaz) {
   // ...
}

// Append '_CUDA' to the test case name will automatically filter it out if CUDA
// is not compiled.
TEST(FooTest, NeedsAGpu_CUDA) {
   // ...
}

// Similarly, if only one GPU is detected, tests with `_MultiCUDA` at the end
// will not be run.
TEST(FooTest, NeedsMultipleGpus_MultiCUDA) {
   // ...
}

Building and running the tests

The following commands assume you are in PyTorch root.

# ... Build PyTorch from source, e.g.
python setup.py develop
# (re)build just the binary
ninja -C build bin/test_jit
# run tests
build/bin/test_jit --gtest_filter='glob_style_filter*'