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699 lines (585 loc) · 21 KB
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from __future__ import annotations
import argparse
from collections import defaultdict
import dataclasses
import datetime
import json
from pathlib import Path
import re
import tempfile
from typing import Callable, Iterable, Sequence
from matplotlib import pyplot as plt
import matplotlib
import numpy as np
import rich_argparse
from scour import scour
matplotlib.use("agg")
from . import config as mconfig
from . import flags as mflags
from . import result
from . import runners as mrunners
from . import util
from .util import PathLike
INTERPRETER_HEAVY = {
"chaos",
"coroutines",
"deepcopy",
"deltablue",
"generators",
"go",
"hexiom",
"logging",
"nbody",
"pickle_pure_python",
"pprint",
"raytrace",
"richards",
"richards_super",
"sqlglot_parse",
"tomli_loads",
"unpack_sequence",
"unpickle_pure_python",
}
def savefig(output_filename: PathLike, **kwargs):
class Options:
quiet = True
remove_descriptive_elements = True
strip_comments = True
indent_type = "none"
strip_ids = True
shorten_ids = True
digits = 3
output_filename = Path(output_filename)
plt.savefig(output_filename, **kwargs)
plt.close("all")
if output_filename.suffix == ".svg":
with tempfile.NamedTemporaryFile(
dir=output_filename.parent, delete=False
) as tmp:
with open(output_filename) as fd:
scour.start(Options(), fd, tmp)
output_filename.unlink()
Path(tmp.name).rename(output_filename)
def plot_diff_pair(ax, data):
if not len(data):
return []
all_data = []
violins = []
colors = []
for i, (name, values, _mean) in enumerate(data):
if values is not None:
idx = np.round(np.linspace(0, len(values) - 1, 100)).astype(int)
violins.append(values[idx])
all_data.extend(values)
if name in INTERPRETER_HEAVY:
colors.append("red")
else:
colors.append("C0")
else:
violins.append([1.0])
all_data.extend([1.0])
colors.append("C0")
ax.text(1.01, i + 1, "insignificant")
violins.append(all_data)
violin = ax.violinplot(
violins,
vert=False,
showmeans=True,
showmedians=False,
widths=1.0,
quantiles=[[0.1, 0.9]] * len(violins),
)
violin["cquantiles"].set_linestyle(":")
for body, color in zip(violin["bodies"], colors):
body.set_facecolor(color)
for i, values in enumerate(violins):
if not np.all(values == [1.0]):
mean = np.mean(values)
ax.text(mean, i + 1.3, f"{mean:.04f}", size=8)
return all_data
def formatter(val, pos):
return f"{val:.02f}×"
def plot_diff(
combined_data: result.CombinedData,
output_filename: PathLike,
title: str,
differences: tuple[str, str],
) -> None:
_, axs = plt.subplots(
figsize=(8, 2 + len(combined_data) * 0.3), layout="constrained"
)
plt.axvline(1.0)
plot_diff_pair(axs, combined_data)
names = [x[0] for x in combined_data]
names.append("ALL")
axs.set_yticks(np.arange(len(names)) + 1, names)
axs.set_ylim(0, len(names) + 1)
axs.tick_params(axis="x", bottom=True, top=True, labelbottom=True, labeltop=True)
axs.xaxis.set_major_formatter(formatter)
xlim = axs.get_xlim()
if xlim[0] > 0.75 and xlim[1] < 1.25:
axs.set_xlim(0.75, 1.25)
axs.annotate(
f"{differences[1]} ⟶",
xy=(1.0, 1.0),
xycoords=("data", "axes fraction"),
xytext=(10, 0),
textcoords="offset pixels",
)
axs.annotate(
f"⟵ {differences[0]}",
xy=(1.0, 1.0),
xycoords=("data", "axes fraction"),
xytext=(-10, 0),
textcoords="offset pixels",
horizontalalignment="right",
)
axs.grid()
axs.set_title(title)
savefig(output_filename)
def get_micro_version(version: str) -> str:
micro = version.split(".")[-1].replace("+", "")
if match := re.match(r"[0-9]+([a-z]+.+)", micro):
micro = match.groups()[0]
return micro
def annotate_y_axis(ax, differences: tuple[str, str]):
ax.yaxis.set_major_formatter(formatter)
ax.grid()
ylim = ax.get_ylim()
if ylim[0] > 0.9 and ylim[1] < 1.3:
ax.set_ylim([0.9, 1.3])
ax.legend(loc="upper left")
ax.annotate(
f"{differences[1]} ⟶",
xy=(0.0, 1.0),
xycoords=("axes fraction", "data"),
xytext=(10, 10),
textcoords="offset pixels",
rotation=90,
clip_on=True,
)
ax.annotate(
f"⟵ {differences[0]}",
xy=(0.0, 1.0),
xycoords=("axes fraction", "data"),
xytext=(10, -10),
textcoords="offset pixels",
rotation=90,
clip_on=True,
verticalalignment="top",
)
def add_axvline(ax, dt: datetime.datetime, name: str):
ax.axvline(dt)
ax.annotate(
name,
xy=(dt, 0.9),
xycoords=("data", "axes fraction"),
xytext=(10, 0),
textcoords="offset pixels",
rotation=90,
)
@dataclasses.dataclass
class LongitudinalPlotConfig:
@dataclasses.dataclass
class Subplot:
base: str
version: str
flags: list[str] = dataclasses.field(default_factory=list)
runners: list[str] = dataclasses.field(default_factory=list)
def __post_init__(self):
if not util.valid_version(self.base):
raise RuntimeError(f"Invalid base '{self.base}' in `longitudinal_plot`")
if (
not util.valid_version(self.version)
or len(self.version.split(".")) != 2
):
raise RuntimeError(
f"Invalid version '{self.version}' in `longitudinal_plot`"
)
self.flags = mflags.normalize_flags(self.flags)
subplots: list[Subplot] = dataclasses.field(default_factory=list)
def __post_init__(self):
self.subplots = [
self.Subplot(**subplot) # pyright: ignore[reportCallIssue]
for subplot in self.subplots
]
def longitudinal_plot(
results: Iterable[result.Result],
output_filename: PathLike,
getter: Callable[[result.BenchmarkComparison], float | None] = lambda r: (
r.geometric_mean_float
),
differences: tuple[str, str] = ("slower", "faster"),
title="Performance improvement by configuration",
):
cfg = mconfig.get_config()
if cfg.longitudinal_plot is None or not cfg.longitudinal_plot.subplots:
print("No longitudinal plot config found. Skipping.")
return
all_cfg = cfg.longitudinal_plot.subplots
def get_comparison_value(ref, r, base):
key = ",".join((str(ref.filename)[8:], str(r.filename)[8:], base))
if key in data:
return data[key]
else:
value = getter(result.BenchmarkComparison(ref, r, base))
data[key] = value
return value
output_filename = Path(output_filename)
data_cache = output_filename.with_suffix(".json")
if data_cache.is_file():
with data_cache.open() as fd:
data = json.load(fd)
else:
data = {}
axs: Sequence[matplotlib.Axes] # pyright: ignore
fig, _axs = plt.subplots(
len(all_cfg),
1,
figsize=(10, 5 * len(all_cfg)),
layout="constrained",
) # type: ignore
if len(all_cfg) == 1:
axs = [_axs]
else:
axs = _axs
results = [r for r in results if r.fork == "python"]
runners = cfg.runners.values()
for subcfg, ax in zip(all_cfg, axs):
version = [int(x) for x in subcfg.version.split(".")]
ver_results = [
r for r in results if list(r.parsed_version.release[0:2]) == version
]
if subcfg.runners:
cfg_runners = [r for r in runners if r.nickname in subcfg.runners]
else:
cfg_runners = runners
if len(subcfg.flags):
titleflags = f" ({','.join(subcfg.flags)})"
else:
titleflags = ""
subtitle = f"Python {subcfg.version}.x{titleflags} vs. {subcfg.base}"
ax.set_title(subtitle)
first_runner = True
for runner in cfg_runners:
assert runner.plot is not None # typing
runner_results = [
r
for r in ver_results
if r.nickname == runner.nickname and r.flags == subcfg.flags
]
for r in results:
if (
r.nickname == runner.nickname
and r.version == subcfg.base
and r.flags == []
):
ref = r
break
else:
continue
runner_results.sort(
key=lambda x: datetime.datetime.fromisoformat(x.commit_datetime)
)
dates = [
datetime.datetime.fromisoformat(x.commit_datetime)
for x in runner_results
]
changes = [
get_comparison_value(ref, r, subcfg.base) for r in runner_results
]
if any(x is not None for x in changes):
ax.plot(
dates,
changes,
color=runner.plot.color,
linestyle=runner.plot.style,
marker=runner.plot.marker,
markersize=5,
label=runner.plot.name,
alpha=0.9,
)
if first_runner:
annotations = set()
for r, date, change in zip(runner_results, dates, changes):
micro = get_micro_version(r.version)
if micro not in annotations and not r.version.endswith("+"):
annotations.add(micro)
text = ax.annotate(
micro,
xy=(date, change),
xycoords="data",
xytext=(-3, 15),
textcoords="offset points",
rotation=90,
arrowprops=dict(arrowstyle="-", connectionstyle="arc"),
)
text.set_color("#888")
text.set_size(8)
text.arrow_patch.set_color("#888")
first_runner = False
annotate_y_axis(ax, differences)
fig.suptitle(title)
savefig(output_filename, dpi=150)
with data_cache.open("w") as fd:
json.dump(data, fd, indent=2)
def _standardize_xlims(axs: Sequence[matplotlib.Axes]) -> None: # pyright: ignore
if not len(axs):
return
minx = None
maxx = None
for ax in axs:
if not ax.has_data():
continue
data = ax.get_lines()[0].get_data()[0]
if len(data) > 1:
xlim = ax.get_xlim()
if minx is None or maxx is None:
minx, maxx = xlim
else:
minx = min(minx, xlim[0])
maxx = max(maxx, xlim[1])
for ax in axs:
if ax.has_data():
ax.set_xlim((minx, maxx))
@dataclasses.dataclass
class FlagEffectPlotConfig:
@dataclasses.dataclass
class Subplot:
name: str
version: str
head_flags: list[str] = dataclasses.field(default_factory=list)
base_flags: list[str] = dataclasses.field(default_factory=list)
runner_map: dict[str, str] = dataclasses.field(default_factory=dict)
runners: list[str] = dataclasses.field(default_factory=list)
def __post_init__(self):
if (
not util.valid_version(self.version)
or len(self.version.split(".")) != 2
):
raise RuntimeError(
f"Invalid version '{self.version}' in `flag_effect_plot`"
)
self.head_flags = mflags.normalize_flags(self.head_flags)
self.base_flags = mflags.normalize_flags(self.base_flags)
subplots: list[Subplot] = dataclasses.field(default_factory=list)
def __post_init__(self):
self.subplots = [
self.Subplot(**subplot) # pyright: ignore[reportCallIssue]
for subplot in self.subplots
]
def flag_effect_plot(
results: Iterable[result.Result],
output_filename: PathLike,
getter: Callable[[result.BenchmarkComparison], float | None] = lambda r: (
r.geometric_mean_float
),
differences: tuple[str, str] = ("slower", "faster"),
title="Performance improvement by configuration",
):
output_filename = Path(output_filename)
# We don't need to track the performance of the Tier 2 configuration
cfg = mconfig.get_config()
if cfg.flag_effect_plot is None or not cfg.flag_effect_plot.subplots:
print("No flag effect plot config found. Skipping.")
return
subplots = cfg.flag_effect_plot.subplots
def get_comparison_value(ref, r, force_valid):
key = ",".join((str(ref.filename)[8:], str(r.filename)[8:]))
if key in data:
return data[key]
else:
value = getter(
result.BenchmarkComparison(ref, r, "default", force_valid=force_valid)
)
data[key] = value
return value
data_cache = output_filename.with_suffix(".json")
if data_cache.is_file():
with data_cache.open() as fd:
data = json.load(fd)
else:
data = {}
axs: Sequence[matplotlib.Axes] # pyright: ignore
fig, _axs = plt.subplots(
len(subplots), 1, figsize=(10, 5 * len(subplots)), layout="constrained"
) # type: ignore
if len(subplots) == 1:
axs = [_axs]
else:
axs = _axs
results = [r for r in results if r.fork == "python"]
commits: dict[str, dict[tuple[str, ...], dict[str, result.Result]]] = {}
for r in results:
commits.setdefault(r.nickname, {}).setdefault(tuple(r.flags), {})[
r.cpython_hash
] = r
for subplot, ax in zip(subplots, axs):
ax.set_title(f"Effect of {subplot.name}")
version = tuple(int(x) for x in subplot.version.split("."))
assert len(version) == 2, (
"Version config in {subplot.name} should only be major.minor"
)
for runner in cfg.runners.values():
assert runner.plot is not None
if subplot.runners and runner.nickname not in subplot.runners:
continue
runner_is_mapped = runner.nickname in subplot.runner_map
if subplot.runner_map and not runner_is_mapped:
continue
head_results = commits.get(runner.nickname, {}).get(
tuple(subplot.head_flags), {}
)
base_results = commits.get(
subplot.runner_map.get(runner.nickname, runner.nickname), {}
).get(tuple(subplot.base_flags), {})
line = []
for cpython_hash, r in head_results.items():
if cpython_hash in base_results:
if r.parsed_version.release[0:2] != version:
continue
line.append(
(
r.commit_datetime,
get_comparison_value(
base_results[cpython_hash], r, runner_is_mapped
),
)
)
line.sort(key=lambda x: datetime.datetime.fromisoformat(x[0]))
dates = [datetime.datetime.fromisoformat(x[0]) for x in line]
changes = [x[1] for x in line]
if any(x is not None for x in changes):
ax.plot(
dates,
changes,
color=runner.plot.color,
linestyle=runner.plot.style,
marker=runner.plot.marker,
markersize=5,
label=runner.plot.name,
alpha=0.9,
)
annotate_y_axis(ax, differences)
fig.suptitle(title)
_standardize_xlims(axs)
savefig(output_filename, dpi=150)
with data_cache.open("w") as fd:
json.dump(data, fd, indent=2)
@dataclasses.dataclass
class BenchmarkLongitudinalPlotConfig:
base: str
version: str
runners: list[str] = dataclasses.field(default_factory=list)
head_flags: list[str] = dataclasses.field(default_factory=list)
base_flags: list[str] = dataclasses.field(default_factory=list)
def __post_init__(self):
if not util.valid_version(self.base):
raise RuntimeError(
f"Invalid base '{self.base}' in `benchmark_longitudinal_plot`"
)
if not util.valid_version(self.version) or len(self.version.split(".")) != 2:
raise RuntimeError(
f"Invalid version '{self.version}' in `benchmark_longitudinal_plot`"
)
self.head_flags = mflags.normalize_flags(self.head_flags)
self.base_flags = mflags.normalize_flags(self.base_flags)
def benchmark_longitudinal_plot(
results: Iterable[result.Result], output_filename: PathLike
):
cfg = mconfig.get_config()
if cfg.benchmark_longitudinal_plot is None:
print("No benchmark longitudinal plot config found. Skipping.")
return
cfg = cfg.benchmark_longitudinal_plot
output_filename = Path(output_filename)
cache_filename = output_filename.with_suffix(".json")
if cache_filename.is_file():
with cache_filename.open() as fd:
cache = json.load(fd)
else:
cache = {}
results = [r for r in results if r.fork == "python" and r.nickname in cfg.runners]
base = None
for r in results:
if r.version == cfg.base and r.flags == cfg.base_flags:
base = r
break
else:
raise ValueError(f"Base version {cfg.base} not found")
results = [
r
for r in results
if r.version.startswith(cfg.version) and r.flags == cfg.head_flags
]
by_benchmark = defaultdict(lambda: defaultdict(list))
for r in results:
if r.filename.name not in cache:
comparison = result.BenchmarkComparison(base, r, "")
timing = comparison.get_timing_diff()
for name, _diff, mean in timing:
# Don't include insignificant results
if mean > 0.0:
value = [r.commit_date, mean, r.cpython_hash]
if r.filename.name not in cache:
cache[r.filename.name] = {}
cache[r.filename.name][name] = value
for name, value in cache[r.filename.name].items():
by_benchmark[name][r.nickname].append(value)
with cache_filename.open("w") as fd:
json.dump(cache, fd, indent=2)
# Exclude any benchmarks where we don't have enough data to make a
# meaningful plot
by_benchmark = {
k: v for k, v in by_benchmark.items() if any(len(x) > 2 for x in v.values())
}
fig, axs = plt.subplots(
len(by_benchmark),
1,
figsize=(10, len(by_benchmark)),
layout="constrained",
)
if len(by_benchmark) == 1:
axs = [axs]
plt.suptitle(f"Performance change by benchmark on {cfg.version} vs. {cfg.base}")
first = True
for (benchmark, runners), ax in zip(sorted(by_benchmark.items()), axs):
for runner_name, timings in runners.items():
runner = mrunners.get_runner_by_nickname(runner_name)
assert runner.plot is not None # typing
timings.sort(key=lambda x: datetime.datetime.fromisoformat(x[0]))
dates = [datetime.datetime.fromisoformat(x[0]) for x in timings]
ax.plot(
dates,
[x[1] for x in timings],
label=runner.plot.name,
color=runner.plot.color,
linestyle=runner.plot.style,
marker=runner.plot.marker,
markersize=2,
)
ax.set_xticks([])
ax.set_ylabel(benchmark, rotation=0, horizontalalignment="right")
ax.yaxis.set_major_formatter(formatter)
for spine in ax.spines.values():
spine.set_visible(False)
ax.grid(True, axis="y")
ax.axhline(1.0, color="#666", linestyle="-")
ax.set_facecolor("#f0f0f0")
if first:
ax.legend(loc="upper left")
first = False
savefig(output_filename, dpi=150)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
"Compare two benchmark .json files",
formatter_class=rich_argparse.ArgumentDefaultsRichHelpFormatter,
)
parser.add_argument("ref", help="The reference .json file")
parser.add_argument("head", help="The head .json file")
parser.add_argument("output", help="Output filename")
args = parser.parse_args()
ref = result.Result.from_filename(Path(args.ref))
head = result.Result.from_filename(Path(args.head))
compare = result.BenchmarkComparison(ref, head, "base")
compare.write_timing_plot(Path(args.output))