RustPython/Lib/profiling/tracing/__init__.py

"""Tracing profiler for Python.

This module provides deterministic profiling of Python programs by tracing
every function call and return.
"""

__all__ = ("run", "runctx", "Profile")

import _lsprof
import importlib.machinery
import importlib.util
import io
from profiling.tracing._utils import _Utils

# ____________________________________________________________
# Simple interface

def run(statement, filename=None, sort=-1):
    """Run statement under profiler optionally saving results in filename

    This function takes a single argument that can be passed to the
    "exec" statement, and an optional file name.  In all cases this
    routine attempts to "exec" its first argument and gather profiling
    statistics from the execution. If no file name is present, then this
    function automatically prints a simple profiling report, sorted by the
    standard name string (file/line/function-name) that is presented in
    each line.
    """
    return _Utils(Profile).run(statement, filename, sort)

def runctx(statement, globals, locals, filename=None, sort=-1):
    """Run statement under profiler, supplying your own globals and locals,
    optionally saving results in filename.

    statement and filename have the same semantics as profile.run
    """
    return _Utils(Profile).runctx(statement, globals, locals,
                                             filename, sort)

# ____________________________________________________________

class Profile(_lsprof.Profiler):
    """Profile(timer=None, timeunit=None, subcalls=True, builtins=True)

    Builds a profiler object using the specified timer function.
    The default timer is a fast built-in one based on real time.
    For custom timer functions returning integers, timeunit can
    be a float specifying a scale (i.e. how long each integer unit
    is, in seconds).
    """

    # Most of the functionality is in the base class.
    # This subclass only adds convenient and backward-compatible methods.

    def print_stats(self, sort=-1):
        import pstats
        if not isinstance(sort, tuple):
            sort = (sort,)
        pstats.Stats(self).strip_dirs().sort_stats(*sort).print_stats()

    def dump_stats(self, file):
        import marshal
        with open(file, 'wb') as f:
            self.create_stats()
            marshal.dump(self.stats, f)

    def create_stats(self):
        self.disable()
        self.snapshot_stats()

    def snapshot_stats(self):
        entries = self.getstats()
        self.stats = {}
        callersdicts = {}
        # call information
        for entry in entries:
            func = label(entry.code)
            nc = entry.callcount         # ncalls column of pstats (before '/')
            cc = nc - entry.reccallcount # ncalls column of pstats (after '/')
            tt = entry.inlinetime        # tottime column of pstats
            ct = entry.totaltime         # cumtime column of pstats
            callers = {}
            callersdicts[id(entry.code)] = callers
            self.stats[func] = cc, nc, tt, ct, callers
        # subcall information
        for entry in entries:
            if entry.calls:
                func = label(entry.code)
                for subentry in entry.calls:
                    try:
                        callers = callersdicts[id(subentry.code)]
                    except KeyError:
                        continue
                    nc = subentry.callcount
                    cc = nc - subentry.reccallcount
                    tt = subentry.inlinetime
                    ct = subentry.totaltime
                    if func in callers:
                        prev = callers[func]
                        nc += prev[0]
                        cc += prev[1]
                        tt += prev[2]
                        ct += prev[3]
                    callers[func] = nc, cc, tt, ct

    # The following two methods can be called by clients to use
    # a profiler to profile a statement, given as a string.

    def run(self, cmd):
        import __main__
        dict = __main__.__dict__
        return self.runctx(cmd, dict, dict)

    def runctx(self, cmd, globals, locals):
        self.enable()
        try:
            exec(cmd, globals, locals)
        finally:
            self.disable()
        return self

    # This method is more useful to profile a single function call.
    def runcall(self, func, /, *args, **kw):
        self.enable()
        try:
            return func(*args, **kw)
        finally:
            self.disable()

    def __enter__(self):
        self.enable()
        return self

    def __exit__(self, *exc_info):
        self.disable()

# ____________________________________________________________

def label(code):
    if isinstance(code, str):
        return ('~', 0, code)    # built-in functions ('~' sorts at the end)
    else:
        return (code.co_filename, code.co_firstlineno, code.co_name)

# ____________________________________________________________

def main():
    import os
    import sys
    import runpy
    import pstats
    from optparse import OptionParser
    usage = "cProfile.py [-o output_file_path] [-s sort] [-m module | scriptfile] [arg] ..."
    parser = OptionParser(usage=usage)
    parser.allow_interspersed_args = False
    parser.add_option('-o', '--outfile', dest="outfile",
        help="Save stats to <outfile>", default=None)
    parser.add_option('-s', '--sort', dest="sort",
        help="Sort order when printing to stdout, based on pstats.Stats class",
        default=2,
        choices=sorted(pstats.Stats.sort_arg_dict_default))
    parser.add_option('-m', dest="module", action="store_true",
        help="Profile a library module", default=False)

    if not sys.argv[1:]:
        parser.print_usage()
        sys.exit(2)

    (options, args) = parser.parse_args()
    sys.argv[:] = args

    # The script that we're profiling may chdir, so capture the absolute path
    # to the output file at startup.
    if options.outfile is not None:
        options.outfile = os.path.abspath(options.outfile)

    if len(args) > 0:
        if options.module:
            code = "run_module(modname, run_name='__main__')"
            globs = {
                'run_module': runpy.run_module,
                'modname': args[0]
            }
        else:
            progname = args[0]
            sys.path.insert(0, os.path.dirname(progname))
            with io.open_code(progname) as fp:
                code = compile(fp.read(), progname, 'exec', module='__main__')
            spec = importlib.machinery.ModuleSpec(name='__main__', loader=None,
                                                  origin=progname)
            module = importlib.util.module_from_spec(spec)
            # Set __main__ so that importing __main__ in the profiled code will
            # return the same namespace that the code is executing under.
            sys.modules['__main__'] = module
            # Ensure that we're using the same __dict__ instance as the module
            # for the global variables so that updates to globals are reflected
            # in the module's namespace.
            globs = module.__dict__
            globs.update({
                # Set __spec__ to None so the profiled program behaves like a
                # script run directly (gh-140729).
                '__spec__': None,
                '__file__': spec.origin,
                '__name__': spec.name,
                '__package__': None,
            })

        try:
            runctx(code, globs, None, options.outfile, options.sort)
        except BrokenPipeError as exc:
            # Prevent "Exception ignored" during interpreter shutdown.
            sys.stdout = None
            sys.exit(exc.errno)
    else:
        parser.print_usage()
    return parser

# When invoked as main program, invoke the profiler on a script
if __name__ == '__main__':
    main()