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Syncing nvfuser devel branch to upstream master. https://github.com/csarofeen/pytorch/ Code changes includes: - codegen improvements: 1. double support in expression evaluator - bug fixes: 1. dropout fix - rework RNG to support broadcasted dropout (Fixes #82784) 2. expand fix - Patch expand+reduction, expand+view, rework view analysis and guard - scheduler: 1. manual transpose schedule example 2. WIP transpose scheduler Commits that's in this PR from the devel branch: ``` b7435afcd22c917713c2f41a7237bc26e1183f14 Transpose scheduler, step 1 (#1854) 8a45dbf72034684eb8e18b1835b533e90b68f184 Add an example on how to manually schedule transpose (#1889) 83dbf56a9554b2efbd5416461d938fff477b0b27 Patch dropout fix (#1898) 69d3519a532250719b1aa8341b50e067b181b42d Expand+Reduction, Expand+View support, rework View analysis and guards (#1883) 15091c488e96343bdc49e3990acbf238a3b3da51 Rework RNG to correctly support broadcasted dropout (#1888) aafe2d048aaac596e503596a41303423619f3954 Make ExpressionEvaluator support Double (#1885) ``` RUN_TORCHBENCH: nvfuser Differential Revision: [D38657074](https://our.internmc.facebook.com/intern/diff/D38657074) Pull Request resolved: https://github.com/pytorch/pytorch/pull/83239 Approved by: https://github.com/davidberard98 |
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| .. | ||
| 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 | ||
| 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 | ||
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*'