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pythongh-96143: Allow Linux perf profiler to see Python calls (python…
…GH-96123)⚠️ ⚠️ Note for reviewers, hackers and fellow systems/low-level/compiler engineers⚠️ ⚠️ If you have a lot of experience with this kind of shenanigans and want to improve the **first** version, **please make a PR against my branch** or **reach out by email** or **suggest code changes directly on GitHub**. If you have any **refinements or optimizations** please, wait until the first version is merged before starting hacking or proposing those so we can keep this PR productive.
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.. highlight:: shell-session | ||
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.. _perf_profiling: | ||
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============================================== | ||
Python support for the Linux ``perf`` profiler | ||
============================================== | ||
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:author: Pablo Galindo | ||
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The Linux ``perf`` profiler is a very powerful tool that allows you to profile and | ||
obtain information about the performance of your application. ``perf`` also has | ||
a very vibrant ecosystem of tools that aid with the analysis of the data that it | ||
produces. | ||
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The main problem with using the ``perf`` profiler with Python applications is that | ||
``perf`` only allows to get information about native symbols, this is, the names of | ||
the functions and procedures written in C. This means that the names and file names | ||
of the Python functions in your code will not appear in the output of the ``perf``. | ||
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Since Python 3.12, the interpreter can run in a special mode that allows Python | ||
functions to appear in the output of the ``perf`` profiler. When this mode is | ||
enabled, the interpreter will interpose a small piece of code compiled on the | ||
fly before the execution of every Python function and it will teach ``perf`` the | ||
relationship between this piece of code and the associated Python function using | ||
`perf map files`_. | ||
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.. warning:: | ||
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Support for the ``perf`` profiler is only currently available for Linux on | ||
selected architectures. Check the output of the configure build step or | ||
check the output of ``python -m sysconfig | grep HAVE_PERF_TRAMPOLINE`` | ||
to see if your system is supported. | ||
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For example, consider the following script: | ||
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.. code-block:: python | ||
def foo(n): | ||
result = 0 | ||
for _ in range(n): | ||
result += 1 | ||
return result | ||
def bar(n): | ||
foo(n) | ||
def baz(n): | ||
bar(n) | ||
if __name__ == "__main__": | ||
baz(1000000) | ||
We can run perf to sample CPU stack traces at 9999 Hertz: | ||
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$ perf record -F 9999 -g -o perf.data python my_script.py | ||
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Then we can use perf report to analyze the data: | ||
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.. code-block:: shell-session | ||
$ perf report --stdio -n -g | ||
# Children Self Samples Command Shared Object Symbol | ||
# ........ ........ ............ .......... .................. .......................................... | ||
# | ||
91.08% 0.00% 0 python.exe python.exe [.] _start | ||
| | ||
---_start | ||
| | ||
--90.71%--__libc_start_main | ||
Py_BytesMain | ||
| | ||
|--56.88%--pymain_run_python.constprop.0 | ||
| | | ||
| |--56.13%--_PyRun_AnyFileObject | ||
| | _PyRun_SimpleFileObject | ||
| | | | ||
| | |--55.02%--run_mod | ||
| | | | | ||
| | | --54.65%--PyEval_EvalCode | ||
| | | _PyEval_EvalFrameDefault | ||
| | | PyObject_Vectorcall | ||
| | | _PyEval_Vector | ||
| | | _PyEval_EvalFrameDefault | ||
| | | PyObject_Vectorcall | ||
| | | _PyEval_Vector | ||
| | | _PyEval_EvalFrameDefault | ||
| | | PyObject_Vectorcall | ||
| | | _PyEval_Vector | ||
| | | | | ||
| | | |--51.67%--_PyEval_EvalFrameDefault | ||
| | | | | | ||
| | | | |--11.52%--_PyLong_Add | ||
| | | | | | | ||
| | | | | |--2.97%--_PyObject_Malloc | ||
... | ||
As you can see here, the Python functions are not shown in the output, only ``_Py_Eval_EvalFrameDefault`` appears | ||
(the function that evaluates the Python bytecode) shows up. Unfortunately that's not very useful because all Python | ||
functions use the same C function to evaluate bytecode so we cannot know which Python function corresponds to which | ||
bytecode-evaluating function. | ||
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Instead, if we run the same experiment with perf support activated we get: | ||
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.. code-block:: shell-session | ||
$ perf report --stdio -n -g | ||
# Children Self Samples Command Shared Object Symbol | ||
# ........ ........ ............ .......... .................. ..................................................................... | ||
# | ||
90.58% 0.36% 1 python.exe python.exe [.] _start | ||
| | ||
---_start | ||
| | ||
--89.86%--__libc_start_main | ||
Py_BytesMain | ||
| | ||
|--55.43%--pymain_run_python.constprop.0 | ||
| | | ||
| |--54.71%--_PyRun_AnyFileObject | ||
| | _PyRun_SimpleFileObject | ||
| | | | ||
| | |--53.62%--run_mod | ||
| | | | | ||
| | | --53.26%--PyEval_EvalCode | ||
| | | py::<module>:/src/script.py | ||
| | | _PyEval_EvalFrameDefault | ||
| | | PyObject_Vectorcall | ||
| | | _PyEval_Vector | ||
| | | py::baz:/src/script.py | ||
| | | _PyEval_EvalFrameDefault | ||
| | | PyObject_Vectorcall | ||
| | | _PyEval_Vector | ||
| | | py::bar:/src/script.py | ||
| | | _PyEval_EvalFrameDefault | ||
| | | PyObject_Vectorcall | ||
| | | _PyEval_Vector | ||
| | | py::foo:/src/script.py | ||
| | | | | ||
| | | |--51.81%--_PyEval_EvalFrameDefault | ||
| | | | | | ||
| | | | |--13.77%--_PyLong_Add | ||
| | | | | | | ||
| | | | | |--3.26%--_PyObject_Malloc | ||
Enabling perf profiling mode | ||
---------------------------- | ||
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There are two main ways to activate the perf profiling mode. If you want it to be | ||
active since the start of the Python interpreter, you can use the `-Xperf` option: | ||
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$ python -Xperf my_script.py | ||
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There is also support for dynamically activating and deactivating the perf | ||
profiling mode by using the APIs in the :mod:`sys` module: | ||
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.. code-block:: python | ||
import sys | ||
sys.activate_stack_trampoline("perf") | ||
# Run some code with Perf profiling active | ||
sys.deactivate_stack_trampoline() | ||
# Perf profiling is not active anymore | ||
These APIs can be handy if you want to activate/deactivate profiling mode in | ||
response to a signal or other communication mechanism with your process. | ||
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Now we can analyze the data with ``perf report``: | ||
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$ perf report -g -i perf.data | ||
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How to obtain the best results | ||
------------------------------- | ||
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For the best results, Python should be compiled with | ||
``CFLAGS="-fno-omit-frame-pointer -mno-omit-leaf-frame-pointer"`` as this allows | ||
profilers to unwind using only the frame pointer and not on DWARF debug | ||
information. This is because as the code that is interposed to allow perf | ||
support is dynamically generated it doesn't have any DWARF debugging information | ||
available. | ||
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You can check if you system has been compiled with this flag by running: | ||
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$ python -m sysconfig | grep 'no-omit-frame-pointer' | ||
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If you don't see any output it means that your interpreter has not been compiled with | ||
frame pointers and therefore it may not be able to show Python functions in the output | ||
of ``perf``. | ||
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.. _perf map files: https://github.com/torvalds/linux/blob/0513e464f9007b70b96740271a948ca5ab6e7dd7/tools/perf/Documentation/jit-interface.txt |
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