pytorch/test/cpp/jit
Han Qi b34b192d6b Reland "Make debug_pkl smaller by only emitting unique traces." (#73368)
Summary:
## Original commit message:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73368

debug_pkl file inside of pytorch's .pt file consists of a list of SourceRanges. Each SourceRange points to a Source which is a stack track, filename, and start, end numbers. Those are emitted in debug_pkl file as strings.
Since many SourceRange shares the same source, the string for trace can be deduped.
The newer format saves a set of unique traces in a tuple, then each SourceRange will save the offset of it's trace w.r.t. position in that tuple. (i.e. manually applying dictionary compression).
The above helps with smaller file size. On loading, if we copy each trace to Source as string the runtime memory would still blowup.
To mitigate this, we use SourceView directly instead of source which will take the reference of string inside of Deserializer and make that into string_view. This is safe because Deserializer is hold by Unpickler by shared_ptr, and Unpickler is also hold by shared_ptr by another Source object. That Source object will be alive during the model construction.

Test Plan:
## Original Test plan
unit test

Took original file (312271638_930.predictor.disagg.local); loaded with `torch.jit.load` save again with `torch.jit.save`. Unzip both, look at contents:
```
[qihan@devvm5585.vll0 ~]$ du archive -h
4.0K    archive/xl_model_weights
3.7M    archive/extra
8.0K    archive/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K    archive/code/__torch__/caffe2/torch/fb/model_transform
8.0K    archive/code/__torch__/caffe2/torch/fb
8.0K    archive/code/__torch__/caffe2/torch
8.0K    archive/code/__torch__/caffe2
20M     archive/code/__torch__/torch/fx/graph_module
20M     archive/code/__torch__/torch/fx
8.0K    archive/code/__torch__/torch/classes
20M     archive/code/__torch__/torch
20M     archive/code/__torch__
20M     archive/code
2.7M    archive/constants
35M     archive
[qihan@devvm5585.vll0 ~]$ du resaved -h
4.0K    resaved/extra
8.0K    resaved/code/__torch__/caffe2/torch/fb/model_transform/splitting
8.0K    resaved/code/__torch__/caffe2/torch/fb/model_transform
8.0K    resaved/code/__torch__/caffe2/torch/fb
8.0K    resaved/code/__torch__/caffe2/torch
8.0K    resaved/code/__torch__/caffe2
1.3M    resaved/code/__torch__/torch/fx/graph_module
1.3M    resaved/code/__torch__/torch/fx
8.0K    resaved/code/__torch__/torch/classes
1.4M    resaved/code/__torch__/torch
1.4M    resaved/code/__torch__
1.4M    resaved/code
2.7M    resaved/constants
13M     resaved
[qihan@devvm5585.vll0 ~]$
```
## Additional test:
`buck test mode/dev-tsan //caffe2/benchmarks/static_runtime:static_runtime_cpptest -- --exact 'caffe2/benchmarks/static_runtime:static_runtime_cpptest - StaticRuntime.to'` passes

 test jest.fbios.startup_cold_start.local.simulator f333356873 -

Differential Revision: D35196883

Pull Request resolved: https://github.com/pytorch/pytorch/pull/74869
Approved by: https://github.com/gmagogsfm
2022-04-18 22:34:21 +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.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_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*'