pytorch/test/cpp/jit
Zino Benaissa 690946c49d Generalize constant_table from tensor only to ivalue (#40718)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40718

Currently only constant except tensor must be inlined during serialization.
Tensor are stored in the contant table. This patch generalizes this capability
to any IValue. This is particularly useful for non ASCII string literal that
cannot be inlined.

Test Plan: Imported from OSS

Differential Revision: D22298169

Pulled By: bzinodev

fbshipit-source-id: 88cc59af9cc45e426ca8002175593b9e431f4bac
2020-07-09 09:09:40 -07:00
..
__init__.py
CMakeLists.txt [JIT] Move TestBackend to test directory (#40840) 2020-07-07 21:00:38 -07:00
gtest.cpp
README.md
test_alias_analysis.cpp Unify boxed function signature between jit and c10 (#37034) 2020-06-29 19:24:26 -07:00
test_argument_spec.cpp
test_autodiff.cpp
test_backend.cpp Revert D22418731: [JIT] Add out-of-source-tree to_backend tests 2020-07-08 13:11:45 -07:00
test_base.cpp Unify boxed function signature between jit and c10 (#37034) 2020-06-29 19:24:26 -07:00
test_base.h
test_class_import.cpp Generalize constant_table from tensor only to ivalue (#40718) 2020-07-09 09:09:40 -07:00
test_class_parser.cpp
test_class_type.cpp
test_code_template.cpp
test_constant_pooling.cpp
test_create_autodiff_subgraphs.cpp
test_custom_class.cpp
test_custom_operators.cpp Unify boxed function signature between jit and c10 (#37034) 2020-06-29 19:24:26 -07:00
test_dce.cpp
test_fuser.cpp
test_gpu.cpp [nvFuser] Working towards reductions, codegen improvements (#40864) 2020-07-06 14:52:49 -07:00
test_graph_executor.cpp
test_inliner.cpp
test_interface.cpp Generalize constant_table from tensor only to ivalue (#40718) 2020-07-09 09:09:40 -07:00
test_interpreter.cpp
test_ir.cpp
test_irparser.cpp [JIT] IRParser: properly parse negative numbers. (#39981) 2020-06-15 12:28:41 -07:00
test_jit_type.cpp [JIT] Add Type::repr_str to return human-readable str (#39544) 2020-06-10 12:01:24 -07:00
test_lite_interpreter.cpp s/torch::jit::class_/torch::class_/ (#40795) 2020-07-06 15:53:33 -07:00
test_misc.cpp Add callback with TLS state API in futures (#40326) 2020-07-08 23:25:35 -07:00
test_mobile_type_parser.cpp [JIT] Add Type::repr_str to return human-readable str (#39544) 2020-06-10 12:01:24 -07:00
test_module_api.cpp
test_peephole_optimize.cpp
test_qualified_name.cpp
test_save_load.cpp Adds dynamic versioning pattern (#40279) 2020-06-24 12:52:50 -07:00
test_schema_matching.cpp Unify boxed function signature between jit and c10 (#37034) 2020-06-29 19:24:26 -07:00
test_subgraph_matcher.cpp [jit][subgraph_matcher] Enable regex matching for string attributes of node (#39454) 2020-06-05 23:03:38 -07:00
test_subgraph_rewriter.cpp [jit][subgraph_rewriter] Support list of filters (#39867) 2020-06-12 08:24:49 -07:00
test_subgraph_utils.cpp
test_utils.cpp
test_utils.h
tests.h Add callback with TLS state API in futures (#40326) 2020-07-08 23:25:35 -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