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Shivam Raikundalia 3ebbeb75fd [Profiler] Make Kineto traces export ns granularity for finer timestamps (#122425 ) (#123650 ) Summary: Kineto traces use microsecond level granularity because of chrome tracing defaults to that precision. Fix by adding preprocessor flag to TARGETS and BUCK files. Also remove any unnecessary ns to us conversions made in the profiler itself. This diff contains profiler changes only. Libkineto changes found in D54964435. Test Plan: Check JSON and chrome tracing to make sure values are as expected. Tracing with flags enabled should have ns precision. Tracings without flags should be same as master. Zoomer: https://www.internalfb.com/intern/zoomer/?profiling_run_fbid=796886748550189 Ran key_averages() to make sure FunctionEvent code working as expected: -- ------------ ------------ Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls ProfilerStep* 0.74% 3.976ms 64.40% 346.613ms 69.323ms 0.000us 0.00% 61.710ms 12.342ms 5 Optimizer.zero_grad#SGD.zero_grad 0.76% 4.109ms 0.76% 4.109ms 821.743us 0.000us 0.00% 0.000us 0.000us 5 ## forward ## 6.89% 37.057ms 27.19% 146.320ms 29.264ms 0.000us 0.00% 58.708ms 11.742ms 5 aten::conv2d 0.22% 1.176ms 7.74% 41.658ms 157.199us 0.000us 0.00% 27.550ms 103.962us 265 aten::convolution 0.79% 4.273ms 7.52% 40.482ms 152.762us 0.000us 0.00% 27.550ms 103.962us 265 aten::_convolution 0.69% 3.688ms 6.73% 36.209ms 136.637us 0.000us 0.00% 27.550ms 103.962us 265 aten::cudnn_convolution 6.04% 32.520ms 6.04% 32.520ms 122.719us 27.550ms 8.44% 27.550ms 103.962us 265 aten::add_ 2.42% 13.045ms 2.42% 13.045ms 30.694us 12.700ms 3.89% 12.700ms 29.882us 425 aten::batch_norm 0.19% 1.027ms 8.12% 43.717ms 164.971us 0.000us 0.00% 16.744ms 63.185us 265 aten::_batch_norm_impl_index 0.31% 1.646ms 7.93% 42.691ms 161.096us 0.000us 0.00% 16.744ms 63.185us 265 ------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ Differential Revision: D55925068 Pull Request resolved: https://github.com/pytorch/pytorch/pull/123650 Approved by: https://github.com/aaronenyeshi		2024-04-11 04:29:20 +00:00
..
upgrader_models
__init__.py
CMakeLists.txt	[Profiler] Make Kineto traces export ns granularity for finer timestamps (#122425 ) (#123650 )	2024-04-11 04:29:20 +00:00
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	[jit] AliasDB type hash - don't always return 0 (#121874 )	2024-03-14 23:16:08 +00:00
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	Add torch._library.register_fake_class to fakify torchBind class (#122622 )	2024-04-02 23:52:17 +00:00
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	Fix C++20 build (#112333 )	2024-02-13 05:10:19 +00:00
test_misc.cpp	Check QNNPACK support for the platform before running test (#119139 )	2024-02-12 20:21:07 +00:00
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	UFMT formatting on test/autograd test/ao test/cpp test/backends (#123369 )	2024-04-05 18:51:38 +00:00
torch_python_test.cpp

README.md

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*'