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This refactor was prompted by challenges handling mixed int/float operations in C++. A previous version of this patch added overloads for each permutation of int/float and was unwieldy https://github.com/pytorch/pytorch/pull/87722/ This PR takes a different approach. The general outline of the patch is to combine the C++ types SymIntNode and SymFloatNode into a single type, SymNode. This is type erased; we no longer know statically at C++ if we have an int/float and have to test it with the is_int()/is_float() virtual methods. This has a number of knock on effects. - We no longer have C++ classes to bind to Python. Instead, we take an entirely new approach to our Python API, where we have a SymInt/SymFloat class defined entirely in Python, which hold a SymNode (which corresponds to the C++ SymNode). However, SymNode is not pybind11-bound; instead, it lives as-is in Python, and is wrapped into C++ SymNode using PythonSymNode when it goes into C++. This implies a userland rename. In principle, it is also possible for the canonical implementation of SymNode to be written in C++, and then bound to Python with pybind11 (we have this code, although it is commented out.) However, I did not implement this as we currently have no C++ implementations of SymNode. Because we do return SymInt/SymFloat from C++ bindings, the C++ binding code needs to know how to find these classes. Currently, this is done just by manually importing torch and getting the attributes. - Because SymInt/SymFloat are easy Python wrappers, __sym_dispatch__ now takes SymInt/SymFloat, rather than SymNode, bringing it in line with how __torch_dispatch__ works. Some miscellaneous improvements: - SymInt now has a constructor that takes SymNode. Note that this constructor is ambiguous if you pass in a subclass of SymNode, so an explicit downcast is necessary. This means toSymFloat/toSymInt are no more. This is a mild optimization as it means rvalue reference works automatically. - We uniformly use the caster for c10::SymInt/SymFloat, rather than going the long way via the SymIntNode/SymFloatNode. - Removed some unnecessary toSymInt/toSymFloat calls in normalize_* functions, pretty sure this doesn't do anything. - guard_int is now a free function, since to guard on an int you cannot assume the method exists. A function can handle both int and SymInt inputs. - We clean up the magic method definition code for SymInt/SymFloat/SymNode. ONLY the user classes (SymInt/SymFloat) get magic methods; SymNode gets plain methods; this is to help avoid confusion between the two types. Signed-off-by: Edward Z. Yang <ezyang@fb.com> cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 Pull Request resolved: https://github.com/pytorch/pytorch/pull/87817 Approved by: https://github.com/albanD, https://github.com/anjali411 |
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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.cpp | ||
| test_backend_compiler_lib.cpp | ||
| test_backend_compiler_preprocess.cpp | ||
| test_backend_lib.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.cpp | ||
| test_custom_class_registrations.cpp | ||
| test_custom_class_registrations.h | ||
| 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.cpp | ||
| test_interpreter_async.pt | ||
| test_ir.cpp | ||
| test_irparser.cpp | ||
| test_jit_logging_levels.cpp | ||
| test_jit_type.cpp | ||
| test_lite_interpreter.cpp | ||
| test_lite_interpreter_direct.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*'