pytorch/test/cpp/jit
Nikolay Korovaiko 000739c31a Function calls for fallback paths (#43274)
Summary:
This PR adds API to package unoptimized/fallback blocks as function calls. It's mainly meant to be used by TensorExpressionsFuser and SpecializeAutogradZero passes as both specialize the original graph but would also like to provide a fallback path in case the assumptions under which the graph was specialized do not hold for some inputs.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43274

Reviewed By: malfet

Differential Revision: D23406961

Pulled By: Krovatkin

fbshipit-source-id: ef21fc9ad886953461b09418d02c75c58375490c
2020-08-28 23:31:02 -07:00
..
__init__.py
CMakeLists.txt
gtest.cpp
README.md
test_alias_analysis.cpp
test_argument_spec.cpp
test_autodiff.cpp
test_backend.cpp
test_base.cpp
test_base.h
test_class_import.cpp
test_class_parser.cpp
test_class_type.cpp
test_cleanup_passes.cpp
test_code_template.cpp
test_constant_pooling.cpp
test_create_autodiff_subgraphs.cpp
test_custom_class.cpp [FX] Native callables in FX lowering (#43426) 2020-08-27 00:00:03 -07:00
test_custom_operators.cpp Operator generator based on templated selective build. (#43456) 2020-08-27 07:26:07 -07:00
test_dce.cpp
test_fuser.cpp
test_gpu.cpp [NVFuser] Enable E2E BCast-PWise-Reduction fusions (#43129) 2020-08-18 09:10:08 -07:00
test_graph_executor.cpp
test_inliner.cpp
test_interface.cpp
test_interpreter.cpp Add prim::TypeCheck operation (#43026) 2020-08-21 20:03:24 -07:00
test_ir.cpp
test_irparser.cpp [JIT] IRParser: store list attributes as generic ivalue lists. (#43785) 2020-08-28 13:27:28 -07:00
test_jit_type.cpp
test_lite_interpreter.cpp add training mode to mobile::Module (#42880) 2020-08-17 00:20:03 -07:00
test_lite_trainer.cpp Add lite SequentialSampler to torch mobile (#43299) 2020-08-24 09:45:24 -07:00
test_misc.cpp Function calls for fallback paths (#43274) 2020-08-28 23:31:02 -07:00
test_mobile_type_parser.cpp
test_module_api.cpp [jit] make clone works for interface type (#42121) 2020-07-31 10:24:27 -07:00
test_peephole_optimize.cpp
test_qualified_name.cpp
test_save_load.cpp
test_schema_matching.cpp
test_subgraph_matcher.cpp
test_subgraph_rewriter.cpp
test_subgraph_utils.cpp [JIT] Subgraph utils: add an optional vmap argument to the API to allow retrieving value mappings. (#43235) 2020-08-25 18:13:29 -07:00
test_utils.cpp
test_utils.h
tests.h Function calls for fallback paths (#43274) 2020-08-28 23:31:02 -07:00
tests_setup.py
torch_python_test.cpp

JIT C++ Tests

How to add 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.

Here is an example test file you can copy-paste.

#include <test/cpp/jit/test_base.h>

// Tests go in torch::jit
namespace torch {
namespace jit {

// 1. Test cases are void() functions.
// 2. They start with the prefix `test`
void testCaseOne() {
    // ...
}

void testCaseTwo() {
    // ...
}
}
}

Then, register your test in tests.h:

// Add to TH_FORALL_TESTS_CUDA instead for CUDA-requiring tests
#define TH_FORALL_TESTS(_)             \
  _(ADFormulas)                        \
  _(Attributes)                        \
  ...
  _(CaseOne)  // note that the `test` prefix is omitted.
  _(CaseTwo)

We glob all the test files together in CMakeLists.txt so that you don't have to edit it every time you add a test. Unfortunately, this means that in order to get the build to pick up your new test file, you need to re-run cmake:

python setup.py build --cmake

Why do we have two different test runners?

We have two different ways of running our cpp tests:

  1. With gtest, from a standalone binary.
  2. With Python, from TestJit.test_cpp and TestJit.test_cpp_cuda (in test/test_jit.py)

We want both because we need to test things from a pure-C++ environment and with all our various Python patch-points enabled.

How do I run the tests?

The following commands assume you are in PyTorch root.

  1. With gtest:
    # (re)build the test binary
    ninja build/bin/test_jit
    # run
    build/bin/test_jit --gtest_filter='glob_style_filter*'
    
  2. With Python:
    python test/test_jit.py TestJit.test_cpp TestJit.test_cpp_cuda