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streamable.py
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streamable.py
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from dataclasses import dataclass
from enum import Enum
from time import monotonic
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
import click
from utils import rand_bytes, rand_full_block, rand_hash
from chia.types.blockchain_format.sized_bytes import bytes32
from chia.types.full_block import FullBlock
from chia.util.ints import uint8, uint64
from chia.util.streamable import Streamable, streamable
@dataclass(frozen=True)
@streamable
class BenchmarkInner(Streamable):
a: str
@dataclass(frozen=True)
@streamable
class BenchmarkMiddle(Streamable):
a: uint64
b: List[bytes32]
c: Tuple[str, bool, uint8, List[bytes]]
d: Tuple[BenchmarkInner, BenchmarkInner]
e: BenchmarkInner
@dataclass(frozen=True)
@streamable
class BenchmarkClass(Streamable):
a: Optional[BenchmarkMiddle]
b: Optional[BenchmarkMiddle]
c: BenchmarkMiddle
d: List[BenchmarkMiddle]
e: Tuple[BenchmarkMiddle, BenchmarkMiddle, BenchmarkMiddle]
def get_random_inner() -> BenchmarkInner:
return BenchmarkInner(rand_bytes(20).hex())
def get_random_middle() -> BenchmarkMiddle:
a: uint64 = uint64(10)
b: List[bytes32] = [rand_hash() for _ in range(a)]
c: Tuple[str, bool, uint8, List[bytes]] = ("benchmark", False, uint8(1), [rand_bytes(a) for _ in range(a)])
d: Tuple[BenchmarkInner, BenchmarkInner] = (get_random_inner(), get_random_inner())
e: BenchmarkInner = get_random_inner()
return BenchmarkMiddle(a, b, c, d, e)
def get_random_benchmark_object() -> BenchmarkClass:
a: Optional[BenchmarkMiddle] = None
b: Optional[BenchmarkMiddle] = get_random_middle()
c: BenchmarkMiddle = get_random_middle()
d: List[BenchmarkMiddle] = [get_random_middle() for _ in range(5)]
e: Tuple[BenchmarkMiddle, BenchmarkMiddle, BenchmarkMiddle] = (
get_random_middle(),
get_random_middle(),
get_random_middle(),
)
return BenchmarkClass(a, b, c, d, e)
def print_row(
runs: Union[str, int], iterations: Union[str, int], mode: str, duration: Union[str, int], end: str = "\n"
) -> None:
runs = "{0:<10}".format(f"{runs}")
iterations = "{0:<14}".format(f"{iterations}")
mode = "{0:<10}".format(f"{mode}")
duration = "{0:>13}".format(f"{duration}")
print(f"{runs} | {iterations} | {mode} | {duration}", end=end)
def benchmark_object_creation(iterations: int, class_generator: Callable[[], Any]) -> float:
start = monotonic()
obj = class_generator()
cls = type(obj)
for i in range(iterations):
cls(**obj.__dict__)
return monotonic() - start
def benchmark_conversion(
iterations: int,
class_generator: Callable[[], Any],
conversion_cb: Callable[[Any], Any],
preparation_cb: Optional[Callable[[Any], Any]] = None,
) -> float:
obj = class_generator()
start = monotonic()
prepared_data = obj
if preparation_cb is not None:
prepared_data = preparation_cb(obj)
for i in range(iterations):
conversion_cb(prepared_data)
return monotonic() - start
class Data(Enum):
all = 0
benchmark = 1
full_block = 2
class Mode(Enum):
all = 0
creation = 1
to_bytes = 2
from_bytes = 3
to_json = 4
from_json = 5
def to_bytes(obj: Any) -> bytes:
return bytes(obj)
@dataclass
class ModeParameter:
iterations: int
conversion_cb: Optional[Callable[[Any], Any]] = None
preparation_cb: Optional[Callable[[Any], Any]] = None
@dataclass
class BenchmarkParameter:
data_class: Type[Any]
object_creation_cb: Callable[[], Any]
mode_parameter: Dict[Mode, ModeParameter]
benchmark_parameter: Dict[Data, BenchmarkParameter] = {
Data.benchmark: BenchmarkParameter(
BenchmarkClass,
get_random_benchmark_object,
{
Mode.creation: ModeParameter(58000),
Mode.to_bytes: ModeParameter(2200, to_bytes),
Mode.from_bytes: ModeParameter(3600, BenchmarkClass.from_bytes, to_bytes),
Mode.to_json: ModeParameter(1100, BenchmarkClass.to_json_dict),
Mode.from_json: ModeParameter(930, BenchmarkClass.from_json_dict, BenchmarkClass.to_json_dict),
},
),
Data.full_block: BenchmarkParameter(
FullBlock,
rand_full_block,
{
Mode.creation: ModeParameter(43000),
Mode.to_bytes: ModeParameter(9650, to_bytes),
Mode.from_bytes: ModeParameter(365, FullBlock.from_bytes, to_bytes),
Mode.to_json: ModeParameter(2400, FullBlock.to_json_dict),
Mode.from_json: ModeParameter(335, FullBlock.from_json_dict, FullBlock.to_json_dict),
},
),
}
def run_benchmarks(data: Data, mode: Mode, runs: int, multiplier: int) -> None:
results: Dict[Data, Dict[Mode, List[int]]] = {}
for current_data, parameter in benchmark_parameter.items():
results[current_data] = {}
if data == Data.all or current_data == data:
print(f"\nRun {mode.name} benchmarks with the class: {parameter.data_class.__name__}")
print_row("runs", "iterations/run", "mode", "result [ms]")
for current_mode, mode_parameter in parameter.mode_parameter.items():
results[current_data][current_mode] = []
if mode == Mode.all or current_mode == mode:
duration: float
iterations: int = mode_parameter.iterations * multiplier
for _ in range(max(1, runs)):
if current_mode == Mode.creation:
duration = benchmark_object_creation(iterations, parameter.object_creation_cb)
else:
assert mode_parameter.conversion_cb is not None
duration = benchmark_conversion(
iterations,
parameter.object_creation_cb,
mode_parameter.conversion_cb,
mode_parameter.preparation_cb,
)
current_duration: int = int(duration * 1000)
results[current_data][current_mode].append(current_duration)
print_row("last", iterations, current_mode.name, current_duration, "\r")
average_duration: int = int(sum(results[current_data][current_mode]) / runs)
print_row(runs, iterations, current_mode.name, average_duration)
data_option_help: str = "|".join([d.name for d in Data])
mode_option_help: str = "|".join([m.name for m in Mode])
@click.command()
@click.option("-d", "--data", default=Data.all.name, help=data_option_help)
@click.option("-m", "--mode", default=Mode.all.name, help=mode_option_help)
@click.option("-r", "--runs", default=5, help="Number of benchmark runs to average results")
@click.option("-n", "--multiplier", default=1, help="Multiplier for iterations/run")
def run(data: str, mode: str, runs: int, multiplier: int) -> None:
try:
Data[data]
except Exception:
raise click.BadOptionUsage("data", f"{data} is not a valid data option. Select one from: " + data_option_help)
try:
Mode[mode]
except Exception:
raise click.BadOptionUsage("mode", f"{mode} is not a valid mode option. Select one from: " + mode_option_help)
run_benchmarks(Data[data], Mode[mode], runs, multiplier)
if __name__ == "__main__":
run() # pylint: disable = no-value-for-parameter