cpython/Python/perf_trampoline.c at main · grantramsay/cpython · GitHub

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Perf trampoline instrumentation

===============================

This file contains instrumentation to allow to associate

calls to the CPython eval loop back to the names of the Python

functions and filename being executed.

Many native performance profilers like the Linux perf tools are

only available to 'see' the C stack when sampling from the profiled

process. This means that if we have the following python code:

import time

def foo(n):

# Some CPU intensive code

def bar(n):

foo(n)

def baz(n):

bar(n)

baz(10000000)

A performance profiler that is only able to see native frames will

produce the following backtrace when sampling from foo():

_PyEval_EvalFrameDefault -----> Evaluation frame of foo()

_PyEval_Vector

_PyFunction_Vectorcall

PyObject_Vectorcall

call_function

_PyEval_EvalFrameDefault ------> Evaluation frame of bar()

_PyEval_EvalFrame

_PyEval_Vector

_PyFunction_Vectorcall

PyObject_Vectorcall

call_function

_PyEval_EvalFrameDefault -------> Evaluation frame of baz()

_PyEval_EvalFrame

_PyEval_Vector

_PyFunction_Vectorcall

PyObject_Vectorcall

call_function

...

Py_RunMain

Because the profiler is only able to see the native frames and the native

function that runs the evaluation loop is the same (_PyEval_EvalFrameDefault)

then the profiler and any reporter generated by it will not be able to

associate the names of the Python functions and the filenames associated with

those calls, rendering the results useless in the Python world.

To fix this problem, we introduce the concept of a trampoline frame. A

trampoline frame is a piece of code that is unique per Python code object that

is executed before entering the CPython eval loop. This piece of code just

calls the original Python evaluation function (_PyEval_EvalFrameDefault) and

forwards all the arguments received. In this way, when a profiler samples

frames from the previous example it will see;

_PyEval_EvalFrameDefault -----> Evaluation frame of foo()

[Jit compiled code 3]

_PyEval_Vector

_PyFunction_Vectorcall

PyObject_Vectorcall

call_function

_PyEval_EvalFrameDefault ------> Evaluation frame of bar()

[Jit compiled code 2]

_PyEval_EvalFrame

_PyEval_Vector

_PyFunction_Vectorcall

PyObject_Vectorcall

call_function

_PyEval_EvalFrameDefault -------> Evaluation frame of baz()

[Jit compiled code 1]

_PyEval_EvalFrame

_PyEval_Vector

_PyFunction_Vectorcall

PyObject_Vectorcall

call_function

...

Py_RunMain

When we generate every unique copy of the trampoline (what here we called "[Jit

compiled code N]") we write the relationship between the compiled code and the

Python function that is associated with it. Every profiler requires this

information in a different format. For example, the Linux "perf" profiler

requires a file in "/tmp/perf-PID.map" (name and location not configurable)

with the following format:

If this file is available when "perf" generates reports, it will automatically

associate every trampoline with the Python function that it is associated with

allowing it to generate reports that include Python information. These reports

then can also be filtered in a way that *only* Python information appears.

Notice that for this to work, there must be a unique copied of the trampoline

per Python code object even if the code in the trampoline is the same. To

achieve this we have a assembly template in Objects/asm_trampiline.S that is

compiled into the Python executable/shared library. This template generates a

symbol that maps the start of the assembly code and another that marks the end

of the assembly code for the trampoline. Then, every time we need a unique

trampoline for a Python code object, we copy the assembly code into a mmaped

area that has executable permissions and we return the start of that area as

our trampoline function.

Asking for a mmap-ed memory area for trampoline is very wasteful so we

allocate big arenas of memory in a single mmap call, we populate the entire

arena with copies of the trampoline (this allows us to now have to invalidate

the icache for the instructions in the page) and then we return the next

available chunk every time someone asks for a new trampoline. We keep a linked

list of arenas in case the current memory arena is exhausted and another one is

needed.

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 (note that as trampilines are dynamically generated there won't be

any DWARF information available for them).

#include "Python.h"

#include "pycore_ceval.h"

#include "pycore_frame.h"

#include "pycore_interp.h"

#ifdef PY_HAVE_PERF_TRAMPOLINE

#include <fcntl.h>

#include <stdio.h>

#include <stdlib.h>

#include <sys/mman.h>

#include <sys/types.h>

#include <unistd.h>

#if defined(__arm__) || defined(__arm64__) || defined(__aarch64__)

#define PY_HAVE_INVALIDATE_ICACHE

#if defined(__clang__) || defined(__GNUC__)

extern void __clear_cache(void *, void*);

#endif

static void invalidate_icache(char* begin, char*end) {

#if defined(__clang__) || defined(__GNUC__)

return __clear_cache(begin, end);

#else

return;

#endif

}

#endif

/* The function pointer is passed as last argument. The other three arguments

* are passed in the same order as the function requires. This results in

* shorter, more efficient ASM code for trampoline.

typedef PyObject *(*py_evaluator)(PyThreadState *, _PyInterpreterFrame *,

int throwflag);

typedef PyObject *(*py_trampoline)(PyThreadState *, _PyInterpreterFrame *, int,

py_evaluator);

extern void *_Py_trampoline_func_start; // Start of the template of the

// assembly trampoline

extern void *

_Py_trampoline_func_end; // End of the template of the assembly trampoline

struct code_arena_st {

char *start_addr; // Start of the memory arena

char *current_addr; // Address of the current trampoline within the arena

size_t size; // Size of the memory arena

size_t size_left; // Remaining size of the memory arena

size_t code_size; // Size of the code of every trampoline in the arena

struct code_arena_st

*prev; // Pointer to the arena or NULL if this is the first arena.

};

typedef struct code_arena_st code_arena_t;

typedef struct trampoline_api_st trampoline_api_t;

#define perf_status _PyRuntime.ceval.perf.status

#define extra_code_index _PyRuntime.ceval.perf.extra_code_index

#define perf_code_arena _PyRuntime.ceval.perf.code_arena

#define trampoline_api _PyRuntime.ceval.perf.trampoline_api

#define perf_map_file _PyRuntime.ceval.perf.map_file

static void *

perf_map_get_file(void)

{

if (perf_map_file) {

return perf_map_file;

}

char filename[100];

pid_t pid = getpid();

// Location and file name of perf map is hard-coded in perf tool.

// Use exclusive create flag wit nofollow to prevent symlink attacks.

int flags = O_WRONLY | O_CREAT | O_EXCL | O_NOFOLLOW | O_CLOEXEC;

snprintf(filename, sizeof(filename) - 1, "/tmp/perf-%jd.map",

(intmax_t)pid);

int fd = open(filename, flags, 0600);

if (fd == -1) {

perf_status = PERF_STATUS_FAILED;

PyErr_SetFromErrnoWithFilename(PyExc_OSError, filename);

return NULL;

}

perf_map_file = fdopen(fd, "w");

if (!perf_map_file) {

perf_status = PERF_STATUS_FAILED;

PyErr_SetFromErrnoWithFilename(PyExc_OSError, filename);

close(fd);

return NULL;

}

return perf_map_file;

}

static int

perf_map_close(void *state)

{

FILE *fp = (FILE *)state;

int ret = 0;

if (fp) {

ret = fclose(fp);

}

perf_map_file = NULL;

perf_status = PERF_STATUS_NO_INIT;

return ret;

}

static void

perf_map_write_entry(void *state, const void *code_addr,

unsigned int code_size, PyCodeObject *co)

{

assert(state != NULL);

FILE *method_file = (FILE *)state;

const char *entry = PyUnicode_AsUTF8(co->co_qualname);

if (entry == NULL) {

_PyErr_WriteUnraisableMsg("Failed to get qualname from code object",

NULL);

return;

}

const char *filename = PyUnicode_AsUTF8(co->co_filename);

if (filename == NULL) {

_PyErr_WriteUnraisableMsg("Failed to get filename from code object",

NULL);

return;

}

fprintf(method_file, "%p %x py::%s:%s\n", code_addr, code_size, entry,

filename);

fflush(method_file);

}

_PyPerf_Callbacks _Py_perfmap_callbacks = {

&perf_map_get_file,

&perf_map_write_entry,

&perf_map_close

};

static int

new_code_arena(void)

{

// non-trivial programs typically need 64 to 256 kiB.

size_t mem_size = 4096 * 16;

assert(mem_size % sysconf(_SC_PAGESIZE) == 0);

char *memory =

mmap(NULL, // address

mem_size, PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANONYMOUS,

-1, // fd (not used here)

0); // offset (not used here)

if (!memory) {

PyErr_SetFromErrno(PyExc_OSError);

_PyErr_WriteUnraisableMsg(

"Failed to create new mmap for perf trampoline", NULL);

perf_status = PERF_STATUS_FAILED;

return -1;

}

void *start = &_Py_trampoline_func_start;

void *end = &_Py_trampoline_func_end;

size_t code_size = end - start;

// TODO: Check the effect of alignment of the code chunks. Initial investigation

// showed that this has no effect on performance in x86-64 or aarch64 and the current

// version has the advantage that the unwinder in GDB can unwind across JIT-ed code.

// We should check the values in the future and see if there is a

// measurable performance improvement by rounding trampolines up to 32-bit

// or 64-bit alignment.

size_t n_copies = mem_size / code_size;

for (size_t i = 0; i < n_copies; i++) {

memcpy(memory + i * code_size, start, code_size * sizeof(char));

}

// Some systems may prevent us from creating executable code on the fly.

int res = mprotect(memory, mem_size, PROT_READ | PROT_EXEC);

if (res == -1) {

PyErr_SetFromErrno(PyExc_OSError);

munmap(memory, mem_size);

_PyErr_WriteUnraisableMsg(

"Failed to set mmap for perf trampoline to PROT_READ | PROT_EXEC",

NULL);

return -1;

}

#ifdef PY_HAVE_INVALIDATE_ICACHE

// Before the JIT can run a block of code that has been emitted it must invalidate

// the instruction cache on some platforms like arm and aarch64.

invalidate_icache(memory, memory + mem_size);

#endif

code_arena_t *new_arena = PyMem_RawCalloc(1, sizeof(code_arena_t));

if (new_arena == NULL) {

PyErr_NoMemory();

munmap(memory, mem_size);

_PyErr_WriteUnraisableMsg("Failed to allocate new code arena struct",

NULL);

return -1;

}

new_arena->start_addr = memory;

new_arena->current_addr = memory;

new_arena->size = mem_size;

new_arena->size_left = mem_size;

new_arena->code_size = code_size;

new_arena->prev = perf_code_arena;

perf_code_arena = new_arena;

return 0;

}

static void

free_code_arenas(void)

{

code_arena_t *cur = perf_code_arena;

code_arena_t *prev;

perf_code_arena = NULL; // invalid static pointer

while (cur) {

munmap(cur->start_addr, cur->size);

prev = cur->prev;

PyMem_RawFree(cur);

cur = prev;

}

static inline py_trampoline

code_arena_new_code(code_arena_t *code_arena)

{

py_trampoline trampoline = (py_trampoline)code_arena->current_addr;

code_arena->size_left -= code_arena->code_size;

code_arena->current_addr += code_arena->code_size;

return trampoline;

}

static inline py_trampoline

compile_trampoline(void)

{

if ((perf_code_arena == NULL) ||

(perf_code_arena->size_left <= perf_code_arena->code_size)) {

if (new_code_arena() < 0) {

return NULL;

}

assert(perf_code_arena->size_left <= perf_code_arena->size);

return code_arena_new_code(perf_code_arena);

}

static PyObject *

py_trampoline_evaluator(PyThreadState *ts, _PyInterpreterFrame *frame,

int throw)

{

if (perf_status == PERF_STATUS_FAILED ||

perf_status == PERF_STATUS_NO_INIT) {

goto default_eval;

}

PyCodeObject *co = frame->f_code;

py_trampoline f = NULL;

assert(extra_code_index != -1);

int ret = _PyCode_GetExtra((PyObject *)co, extra_code_index, (void **)&f);

if (ret != 0 || f == NULL) {

// This is the first time we see this code object so we need

// to compile a trampoline for it.

py_trampoline new_trampoline = compile_trampoline();

if (new_trampoline == NULL) {

goto default_eval;

}

trampoline_api.write_state(trampoline_api.state, new_trampoline,

perf_code_arena->code_size, co);

_PyCode_SetExtra((PyObject *)co, extra_code_index,

(void *)new_trampoline);

f = new_trampoline;

}

assert(f != NULL);

return f(ts, frame, throw, _PyEval_EvalFrameDefault);

default_eval:

// Something failed, fall back to the default evaluator.

return _PyEval_EvalFrameDefault(ts, frame, throw);

}

#endif // PY_HAVE_PERF_TRAMPOLINE

int

_PyIsPerfTrampolineActive(void)

{

#ifdef PY_HAVE_PERF_TRAMPOLINE

PyThreadState *tstate = _PyThreadState_GET();

return tstate->interp->eval_frame == py_trampoline_evaluator;

#endif

return 0;

}

void

_PyPerfTrampoline_GetCallbacks(_PyPerf_Callbacks *callbacks)

{

if (callbacks == NULL) {

return;

}

#ifdef PY_HAVE_PERF_TRAMPOLINE

callbacks->init_state = trampoline_api.init_state;

callbacks->write_state = trampoline_api.write_state;

callbacks->free_state = trampoline_api.free_state;

#endif

return;

}

int

_PyPerfTrampoline_SetCallbacks(_PyPerf_Callbacks *callbacks)

{

if (callbacks == NULL) {

return -1;

}

#ifdef PY_HAVE_PERF_TRAMPOLINE

if (trampoline_api.state) {

_PyPerfTrampoline_Fini();

}

trampoline_api.init_state = callbacks->init_state;

trampoline_api.write_state = callbacks->write_state;

trampoline_api.free_state = callbacks->free_state;

trampoline_api.state = NULL;

perf_status = PERF_STATUS_OK;

#endif

return 0;

}

int

_PyPerfTrampoline_Init(int activate)

{

#ifdef PY_HAVE_PERF_TRAMPOLINE

PyThreadState *tstate = _PyThreadState_GET();

if (tstate->interp->eval_frame &&

tstate->interp->eval_frame != py_trampoline_evaluator) {

PyErr_SetString(PyExc_RuntimeError,

"Trampoline cannot be initialized as a custom eval "

"frame is already present");

return -1;

}

if (!activate) {

tstate->interp->eval_frame = NULL;

}

else {

tstate->interp->eval_frame = py_trampoline_evaluator;

if (new_code_arena() < 0) {

return -1;

}

if (trampoline_api.state == NULL) {

void *state = trampoline_api.init_state();

if (state == NULL) {

return -1;

}

trampoline_api.state = state;

}

extra_code_index = _PyEval_RequestCodeExtraIndex(NULL);

if (extra_code_index == -1) {

return -1;

}

perf_status = PERF_STATUS_OK;

}

#endif

return 0;

}

int

_PyPerfTrampoline_Fini(void)

{

#ifdef PY_HAVE_PERF_TRAMPOLINE

PyThreadState *tstate = _PyThreadState_GET();

if (tstate->interp->eval_frame == py_trampoline_evaluator) {

tstate->interp->eval_frame = NULL;

}

free_code_arenas();

if (trampoline_api.state != NULL) {

trampoline_api.free_state(trampoline_api.state);

trampoline_api.state = NULL;

}

extra_code_index = -1;

#endif

return 0;

}

PyStatus

_PyPerfTrampoline_AfterFork_Child(void)

{

#ifdef PY_HAVE_PERF_TRAMPOLINE

// Restart trampoline in file in child.

int was_active = _PyIsPerfTrampolineActive();

_PyPerfTrampoline_Fini();

if (was_active) {

_PyPerfTrampoline_Init(1);

}

#endif

return PyStatus_Ok();

}

cpython/Python/perf_trampoline.c at main · grantramsay/cpython · GitHub

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