# This script runs a set of small benchmarks to help identify scaling
# bottlenecks in the free-threaded interpreter. The benchmarks consist
# of patterns that ought to scale well, but haven't in the past. This is
# typically due to reference count contention or lock contention.
#
# This is not intended to be a general multithreading benchmark suite, nor
# are the benchmarks intended to be representative of real-world workloads.
#
# On Linux, to avoid confounding hardware effects, the script attempts to:
# * Use a single CPU socket (to avoid NUMA effects)
# * Use distinct physical cores (to avoid hyperthreading/SMT effects)
# * Use "performance" cores (Intel, ARM) on CPUs that have performance and
#   efficiency cores
#
# It also helps to disable dynamic frequency scaling (i.e., "Turbo Boost")
#
# Intel:
# > echo "1" | sudo tee /sys/devices/system/cpu/intel_pstate/no_turbo
#
# AMD:
# > echo "0" | sudo tee /sys/devices/system/cpu/cpufreq/boost
#

import copy
import math
import os
import queue
import sys
import threading
import time
from collections import namedtuple
from dataclasses import dataclass
from operator import methodcaller
from typing import NamedTuple

# The iterations in individual benchmarks are scaled by this factor.
WORK_SCALE = 100

ALL_BENCHMARKS = {}

threads = []
in_queues = []
out_queues = []


def register_benchmark(func):
    ALL_BENCHMARKS[func.__name__] = func
    return func

@register_benchmark
def object_cfunction():
    accu = 0
    tab = [1] * 100
    for i in range(1000 * WORK_SCALE):
        tab.pop(0)
        tab.append(i)
        accu += tab[50]
    return accu

@register_benchmark
def cmodule_function():
    N = 1000 * WORK_SCALE
    for i in range(N):
        math.cos(i / N)

@register_benchmark
def object_lookup_special():
    # round() uses `_PyObject_LookupSpecial()` internally.
    N = 1000 * WORK_SCALE
    for i in range(N):
        round(i / N)

class MyContextManager:
    def __enter__(self):
        pass
    def __exit__(self, exc_type, exc_value, traceback):
        pass

@register_benchmark
def context_manager():
    N = 1000 * WORK_SCALE
    for i in range(N):
        with MyContextManager():
            pass

@register_benchmark
def mult_constant():
    x = 1.0
    for i in range(3000 * WORK_SCALE):
        x *= 1.01

def simple_gen():
    for i in range(10):
        yield i

@register_benchmark
def generator():
    accu = 0
    for i in range(100 * WORK_SCALE):
        for v in simple_gen():
            accu += v
    return accu

class Counter:
    def __init__(self):
        self.i = 0

    def next_number(self):
        self.i += 1
        return self.i

@register_benchmark
def pymethod():
    c = Counter()
    for i in range(1000 * WORK_SCALE):
        c.next_number()
    return c.i

def next_number(i):
    return i + 1

@register_benchmark
def pyfunction():
    accu = 0
    for i in range(1000 * WORK_SCALE):
        accu = next_number(i)
    return accu

def double(x):
    return x + x

module = sys.modules[__name__]

@register_benchmark
def module_function():
    total = 0
    for i in range(1000 * WORK_SCALE):
        total += module.double(i)
    return total

class MyObject:
    pass

@register_benchmark
def load_string_const():
    accu = 0
    for i in range(1000 * WORK_SCALE):
        if i == 'a string':
            accu += 7
        else:
            accu += 1
    return accu

@register_benchmark
def load_tuple_const():
    accu = 0
    for i in range(1000 * WORK_SCALE):
        if i == (1, 2):
            accu += 7
        else:
            accu += 1
    return accu

@register_benchmark
def create_pyobject():
    for i in range(1000 * WORK_SCALE):
        o = MyObject()

@register_benchmark
def create_closure():
    for i in range(1000 * WORK_SCALE):
        def foo(x):
            return x
        foo(i)

@register_benchmark
def create_dict():
    for i in range(1000 * WORK_SCALE):
        d = {
            "key": "value",
        }

if hasattr(__builtins__, "frozendict"):
    @register_benchmark
    def create_frozendict():
        for i in range(1000 * WORK_SCALE):
            d = frozendict(key="value")

thread_local = threading.local()

@register_benchmark
def thread_local_read():
    tmp = thread_local
    tmp.x = 10
    for i in range(500 * WORK_SCALE):
        _ = tmp.x
        _ = tmp.x
        _ = tmp.x
        _ = tmp.x
        _ = tmp.x

class MyClass:
    __slots__ = ()

    def func(self):
        pass

@register_benchmark
def method_caller():
    mc = methodcaller("func")
    obj = MyClass()
    for i in range(1000 * WORK_SCALE):
        mc(obj)

@dataclass
class MyDataClass:
    x: int
    y: int
    z: int

@register_benchmark
def instantiate_dataclass():
    for _ in range(1000 * WORK_SCALE):
        obj = MyDataClass(x=1, y=2, z=3)

MyNamedTuple = namedtuple("MyNamedTuple", ["x", "y", "z"])

@register_benchmark
def instantiate_namedtuple():
    for _ in range(1000 * WORK_SCALE):
        obj = MyNamedTuple(x=1, y=2, z=3)


class MyTypingNamedTuple(NamedTuple):
    x: int
    y: int
    z: int

@register_benchmark
def instantiate_typing_namedtuple():
    for _ in range(1000 * WORK_SCALE):
        obj = MyTypingNamedTuple(x=1, y=2, z=3)

@register_benchmark
def super_call():
    # TODO: super() on the same class from multiple threads still doesn't
    # scale well, so use a class per-thread here for now.
    class Base:
        def method(self):
            return 1

    class Derived(Base):
        def method(self):
            return super().method()

    obj = Derived()
    for _ in range(1000 * WORK_SCALE):
        obj.method()


@register_benchmark
def deepcopy():
    x = {'list': [1, 2], 'tuple': (1, None)}
    for i in range(40 * WORK_SCALE):
        copy.deepcopy(x)


def bench_one_thread(func):
    t0 = time.perf_counter_ns()
    func()
    t1 = time.perf_counter_ns()
    return t1 - t0


def bench_parallel(func):
    t0 = time.perf_counter_ns()
    for inq in in_queues:
        inq.put(func)
    for outq in out_queues:
        outq.get()
    t1 = time.perf_counter_ns()
    return t1 - t0


def benchmark(func):
    delta_one_thread = bench_one_thread(func)
    delta_many_threads = bench_parallel(func)

    speedup = delta_one_thread * len(threads) / delta_many_threads
    if speedup >= 1:
        factor = speedup
        direction = "faster"
    else:
        factor = 1 / speedup
        direction = "slower"

    use_color = hasattr(sys.stdout, 'isatty') and sys.stdout.isatty()
    color = reset_color = ""
    if use_color:
        if speedup <= 1.1:
            color = "\x1b[31m"  # red
        elif speedup < len(threads)/2:
            color = "\x1b[33m"  # yellow
        reset_color = "\x1b[0m"

    print(f"{color}{func.__name__:<25} {round(factor, 1):>4}x {direction}{reset_color}")

def determine_num_threads_and_affinity():
    if sys.platform != "linux":
        return [None] * os.cpu_count()

    # Try to use `lscpu -p` on Linux
    import subprocess
    try:
        output = subprocess.check_output(["lscpu", "-p=cpu,node,core,MAXMHZ"],
                                         text=True, env={"LC_NUMERIC": "C"})
    except (FileNotFoundError, subprocess.CalledProcessError):
        return [None] * os.cpu_count()

    table = []
    for line in output.splitlines():
        if line.startswith("#"):
            continue
        cpu, node, core, maxhz = line.split(",")
        if maxhz == "":
            maxhz = "0"
        table.append((int(cpu), int(node), int(core), float(maxhz)))

    cpus = []
    cores = set()
    max_mhz_all = max(row[3] for row in table)
    for cpu, node, core, maxmhz in table:
        # Choose only CPUs on the same node, unique cores, and try to avoid
        # "efficiency" cores.
        if node == 0 and core not in cores and maxmhz == max_mhz_all:
            cpus.append(cpu)
            cores.add(core)
    return cpus


def thread_run(cpu, in_queue, out_queue):
    if cpu is not None and hasattr(os, "sched_setaffinity"):
        # Set the affinity for the current thread
        os.sched_setaffinity(0, (cpu,))

    while True:
        func = in_queue.get()
        if func is None:
            break
        func()
        out_queue.put(None)


def initialize_threads(opts):
    if opts.threads == -1:
        cpus = determine_num_threads_and_affinity()
    else:
        cpus = [None] * opts.threads  # don't set affinity

    print(f"Running benchmarks with {len(cpus)} threads")
    for cpu in cpus:
        inq = queue.Queue()
        outq = queue.Queue()
        in_queues.append(inq)
        out_queues.append(outq)
        t = threading.Thread(target=thread_run, args=(cpu, inq, outq), daemon=True)
        threads.append(t)
        t.start()


def main(opts):
    global WORK_SCALE
    if not hasattr(sys, "_is_gil_enabled") or sys._is_gil_enabled():
        sys.stderr.write("expected to be run with the  GIL disabled\n")

    benchmark_names = opts.benchmarks
    if benchmark_names:
        for name in benchmark_names:
            if name not in ALL_BENCHMARKS:
                sys.stderr.write(f"Unknown benchmark: {name}\n")
                sys.exit(1)
    else:
        benchmark_names = ALL_BENCHMARKS.keys()

    WORK_SCALE = opts.scale

    if not opts.baseline_only:
        initialize_threads(opts)

    do_bench = not opts.baseline_only and not opts.parallel_only
    for name in benchmark_names:
        func = ALL_BENCHMARKS[name]
        if do_bench:
            benchmark(func)
            continue

        if opts.parallel_only:
            delta_ns = bench_parallel(func)
        else:
            delta_ns = bench_one_thread(func)

        time_ms = delta_ns / 1_000_000
        print(f"{func.__name__:<18} {time_ms:.1f} ms")


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("-t", "--threads", type=int, default=-1,
                        help="number of threads to use")
    parser.add_argument("--scale", type=int, default=100,
                        help="work scale factor for the benchmark (default=100)")
    parser.add_argument("--baseline-only", default=False, action="store_true",
                        help="only run the baseline benchmarks (single thread)")
    parser.add_argument("--parallel-only", default=False, action="store_true",
                        help="only run the parallel benchmark (many threads)")
    parser.add_argument("benchmarks", nargs="*",
                        help="benchmarks to run")
    options = parser.parse_args()
    main(options)
