utils/scale-test

#!/usr/bin/env python
#
# -*- python -*-
#
# Runs a .gyb scale-testing file repeatedly through swiftc while varying a
# scaling variable 'N', collects json stats from the compiler, transforms the
# problem to log-space and runs a linear regression to estimate the exponent on
# the stat's growth curve relative to N.
#
# The estimate will be more accurate as N increases, so if you get a
# not-terribly-convincing estimate, try increasing --begin and --end to larger
# values.
#

from __future__ import print_function

import argparse
import json
import math
import os
import os.path
import shutil
import subprocess
import sys
import tempfile

import gyb


def find_which(p):
    for d in os.environ["PATH"].split(os.pathsep):
        full = os.path.join(d, p)
        if os.path.isfile(full) and os.access(full, os.X_OK):
            return full
    return p


# Evidently the debug-symbol reader in dtrace is sufficiently slow and/or buggy
# that attempting to inject probes into a binary w/ debuginfo is asking for a
# failed run (possibly racing with probe insertion, or probing the stabs
# entries, see rdar://problem/7037927 or rdar://problem/11490861 respectively),
# so we sniff the presence of debug symbols here.
def has_debuginfo(swiftc):
    swiftc = find_which(swiftc)
    for line in subprocess.check_output(
            ["dwarfdump", "--file-stats", swiftc]).splitlines():
        if '%' not in line:
            continue
        fields = line.split()
        if fields[8] != '0.00%' or fields[10] != '0.00%':
            return True
    return False


def write_input_file(args, ast, d, n):
    fname = "in%d.swift" % n
    pathname = os.path.join(d, fname)
    with open(pathname, 'w+') as f:
        f.write(gyb.execute_template(ast, '', N=n))
    return fname


def run_once_with_primary(args, ast, rng, primary_idx):
    r = {}
    try:
        if args.tmpdir is not None and not os.path.exists(args.tmpdir):
            os.makedirs(args.tmpdir, 0700)
        d = tempfile.mkdtemp(dir=args.tmpdir)
        inputs = [write_input_file(args, ast, d, i) for i in rng]
        primary = inputs[primary_idx]
        ofile = "out.o"

        mode = "-c"
        if args.typecheck:
            mode = "-typecheck"

        focus = ["-primary-file", primary]
        if args.whole_module_optimization:
            focus = ['-whole-module-optimization']

        opts = []
        if args.optimize:
            opts = ['-O']
        elif args.optimize_none:
            opts = ['-Onone']
        elif args.optimize_unchecked:
            opts = ['-Ounchecked']

        extra = args.Xfrontend[:]
        if args.debuginfo:
            extra.append('-g')

        command = [args.swiftc_binary,
                   "-frontend", mode,
                   "-o", ofile] + opts + focus + extra + inputs

        if args.trace:
            print("running: " + " ".join(command))

        if args.dtrace:
            trace = "trace.txt"
            script = ("pid$target:swiftc:*%s*:entry { @[probefunc] = count() }"
                      % args.select)
            subprocess.check_call(
                ["sudo", "dtrace", "-q",
                 "-o", trace,
                 "-b", "256",
                 "-n", script,
                 "-c", " ".join(command)], cwd=d)
            r = {fields[0]: int(fields[1]) for fields in
                 [line.split() for line in open(os.path.join(d, trace))]
                 if len(fields) == 2}
        else:
            if args.debug:
                command = ["lldb", "--"] + command
            stats = "stats.json"
            argv = command + ["-Xllvm", "-stats",
                              "-Xllvm", "-stats-json",
                              "-Xllvm", "-info-output-file=" + stats]
            subprocess.check_call(argv, cwd=d)
            with open(os.path.join(d, stats)) as f:
                r = json.load(f)
    finally:
        shutil.rmtree(d)

    return {k: v for (k, v) in r.items() if args.select in k}


def run_once(args, ast, rng):
    if args.sum_multi:
        cumulative = {}
        for i in range(len(rng)):
            tmp = run_once_with_primary(args, ast, rng, i)
            for (k, v) in tmp.items():
                if k in cumulative:
                    cumulative[k] += v
                else:
                    cumulative[k] = v
        return cumulative
    else:
        return run_once_with_primary(args, ast, rng, -1)


def run_many(args):

    if args.dtrace and has_debuginfo(args.swiftc_binary):
        print("")
        print("**************************************************")
        print("")
        print("dtrace is unreliable on binaries w/ debug symbols")
        print("please run 'strip -S %s'" % args.swiftc_binary)
        print("or pass a different --swiftc-binary")
        print("")
        print("**************************************************")
        print("")
        exit(1)

    ast = gyb.parse_template(args.file.name, args.file.read())
    rng = range(args.begin, args.end, args.step)
    if args.multi_file or args.sum_multi:
        return (rng, [run_once(args, ast, range(i)) for i in rng])
    else:
        return (rng, [run_once(args, ast, [r]) for r in rng])


def is_small(x):
    return abs(x) < 1e-9


def linear_regression(x, y):
    # By the book: https://en.wikipedia.org/wiki/Simple_linear_regression
    n = float(len(x))
    assert n == len(y)
    if n == 0:
        return 0, 0
    sum_x = sum(x)
    sum_y = sum(y)
    sum_prod = sum(a * b for a, b in zip(x, y))
    sum_x_sq = sum(a ** 2 for a in x)
    sum_y_sq = sum(b ** 2 for b in y)
    mean_x = sum_x / n
    mean_y = sum_y / n
    mean_prod = sum_prod / n
    mean_x_sq = sum_x_sq / n
    mean_y_sq = sum_y_sq / n
    covar_xy = mean_prod - mean_x * mean_y
    var_x = mean_x_sq - mean_x**2
    var_y = mean_y_sq - mean_y**2
    slope = covar_xy / var_x
    inter = mean_y - slope * mean_x

    # Compute the correlation coefficient aka r^2, to compare goodness-of-fit.
    if is_small(var_y):
        # all of the outputs are the same, so this is a perfect fit
        assert is_small(covar_xy)
        cor_coeff_sq = 1.0
    elif is_small(var_x):
        # all of the inputs are the same, and the outputs are different, so
        # this is a completely imperfect fit
        assert is_small(covar_xy)
        cor_coeff_sq = 0.0
    else:
        cor_coeff_sq = covar_xy**2 / (var_x * var_y)

    return slope, inter, cor_coeff_sq


# Y = a * X^b, returns a, b, R^2
def fit_polynomial_model(x, y):
    # transform into linear regression via log(Y) = b*log(X) + log(a)
    log_x = [math.log(val) for val in x]
    log_y = [math.log(val) for val in y]

    b, log_a, r2 = linear_regression(log_x, log_y)
    return b, math.exp(log_a), r2


# Y = a * b^X, returns a, b, R^2
def fit_exponential_model(x, y):
    # transform into linear regression via log(Y) = log(b) * X + log(a)
    log_y = [math.log(val) for val in y]

    log_b, log_a, r2 = linear_regression(x, log_y)
    return math.exp(log_b), math.exp(log_a), r2


def report(args, rng, runs):
    bad = False
    keys = set.intersection(*[set(j.keys()) for j in runs])
    if len(keys) == 0:
        print("No data found")
        if len(args.select) != 0:
            "(perhaps try a different --select?)"
        return True
    rows = []
    for k in keys:
        vals = [r[k] for r in runs]
        bounded = [max(v, 1) for v in vals]
        p_b, p_a, p_r2 = fit_polynomial_model(rng, bounded)
        e_b, e_a, e_r2 = fit_exponential_model(rng, bounded)
        if p_r2 >= e_r2:
            # polynomial is best
            p_b = 0 if is_small(p_b) else p_b
            rows.append((False, p_b, k, vals))
        else:
            # exponential is best
            rows.append((True, e_b, k, vals))
    # Exponential fits always go after polynomial fits.
    rows.sort()
    for (is_exp, b, k, vals) in rows:
        # same threshold for both the polynomial exponent or the exponential
        # base.
        if is_exp:
            this_is_bad = b >= args.exponential_threshold
            formatted = '%1.1f^n' % b
        else:
            this_is_bad = b >= args.polynomial_threshold
            formatted = 'n^%1.1f' % b

        if this_is_bad:
            bad = True
        if not args.quiet or this_is_bad:
            print("O(%s) : %s" % (formatted, k))
            if args.values:
                print("                = ", vals)
    return bad


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        'file', type=argparse.FileType(),
        help='Path to GYB template file (defaults to stdin)', nargs='?',
        default=sys.stdin)
    parser.add_argument(
        '--values', action='store_true',
        default=False, help='print stat values')
    parser.add_argument(
        '--trace', action='store_true',
        default=False, help='trace compiler invocations')
    parser.add_argument(
        '--quiet', action='store_true',
        default=False, help='only print superlinear stats')
    parser.add_argument(
        '--polynomial-threshold', type=float,
        default=1.2,
        help='minimum exponent for polynomial fit to consider "bad scaling"')
    parser.add_argument(
        '--exponential-threshold', type=float,
        default=1.2,
        help='minimum base for exponential fit to consider "bad scaling"')
    parser.add_argument(
        '-typecheck', '--typecheck', action='store_true',
        default=False, help='only run compiler with -typecheck')
    parser.add_argument(
        '-g', '--debuginfo', action='store_true',
        default=False, help='run compiler with -g')
    parser.add_argument(
        '-wmo', '--whole-module-optimization', action='store_true',
        default=False, help='run compiler with -whole-module-optimization')
    parser.add_argument(
        '--dtrace', action='store_true',
        default=False, help='use dtrace to sample all functions')
    parser.add_argument(
        '-Xfrontend', action='append',
        default=[], help='pass additional args to frontend jobs')
    parser.add_argument(
        '--begin', type=int,
        default=10, help='first value for N')
    parser.add_argument(
        '--end', type=int,
        default=100, help='last value for N')
    parser.add_argument(
        '--step', type=int,
        default=10, help='step value for N')
    parser.add_argument(
        '--swiftc-binary',
        default="swiftc", help='swift binary to execute')
    parser.add_argument(
        '--tmpdir', type=str,
        default=None, help='directory to create tempfiles in')
    parser.add_argument(
        '--select',
        default="", help='substring of counters/symbols to limit attention to')
    parser.add_argument(
        '--debug', action='store_true',
        default=False, help='invoke lldb on each scale test')

    group = parser.add_mutually_exclusive_group()
    group.add_argument(
        '-O', '--optimize', action='store_true',
        default=False, help='run compiler with -O')
    group.add_argument(
        '-Onone', '--optimize-none', action='store_true',
        default=False, help='run compiler with -Onone')
    group.add_argument(
        '-Ounchecked', '--optimize-unchecked', action='store_true',
        default=False, help='run compiler with -Ounchecked')

    group = parser.add_mutually_exclusive_group()
    group.add_argument(
        '--multi-file', action='store_true',
        default=False, help='vary number of input files as well')
    group.add_argument(
        '--sum-multi', action='store_true',
        default=False, help='simulate a multi-primary run and sum stats')

    args = parser.parse_args(sys.argv[1:])
    (rng, runs) = run_many(args)
    if report(args, rng, runs):
        exit(1)
    exit(0)


if __name__ == '__main__':
    main()