aider/benchmark/benchmark.py at main · tapas4java/aider · GitHub

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#!/usr/bin/env python

import datetime

import json

import os

import random

import re

import shutil

import subprocess

import time

from collections import defaultdict

from json.decoder import JSONDecodeError

from pathlib import Path

from types import SimpleNamespace

from typing import List

import git

import lox

import matplotlib.pyplot as plt

import numpy as np

import openai

import pandas as pd

import prompts

import typer

from imgcat import imgcat

from rich.console import Console

from aider import models

from aider.coders import Coder

from aider.dump import dump # noqa: F401

from aider.io import InputOutput

BENCHMARK_DNAME = Path(os.environ["AIDER_BENCHMARK_DIR"])

ORIGINAL_DNAME = BENCHMARK_DNAME / "exercism-python"

app = typer.Typer(add_completion=False, pretty_exceptions_enable=False)

def show_stats(dirnames, graphs):

raw_rows = []

for dirname in dirnames:

row = summarize_results(dirname)

raw_rows.append(row)

# return

repeats = []

seen = dict()

rows = []

for row in raw_rows:

if not row:

continue

if row.model == "gpt-3.5-turbo":

row.model = "gpt-3.5-turbo-0613"

if row.model == "gpt-4":

row.model = "gpt-4-0613"

if row.edit_format == "diff-func-string":

row.edit_format = "diff-func"

if (

row.model == "gpt-3.5-turbo-0613"

and row.edit_format == "whole"

and "repeat" not in row.dir_name

# remember this row, so we can update it with the repeat_avg

repeat_row = len(rows)

gpt35 = "gpt-3.5-turbo"

gpt4 = "gpt-4"

if row.model.startswith(gpt35):

row.model = gpt35 + "\n" + row.model[len(gpt35) :]

elif row.model.startswith(gpt4):

row.model = gpt4 + "\n" + row.model[len(gpt4) :]

if row.model == "gpt-4\n-1106-preview":

row.model += "\n(preliminary)"

if row.completed_tests < 133:

print(f"Warning: {row.dir_name} is incomplete: {row.completed_tests}")

if "repeat" in row.dir_name:

repeats.append(vars(row))

continue

kind = (row.model, row.edit_format)

if kind in seen:

dump(row.dir_name)

dump(seen[kind])

return

seen[kind] = row.dir_name

rows.append(vars(row))

if repeats:

extra = rows[repeat_row]

dump(extra)

repeats.append(extra)

repeats = pd.DataFrame.from_records(repeats)

repeat_max = repeats["pass_rate_2"].max()

repeat_min = repeats["pass_rate_2"].min()

repeat_avg = repeats["pass_rate_2"].mean()

repeat_lo = repeat_avg - repeat_min

repeat_hi = repeat_max - repeat_avg

dump(repeat_max)

dump(repeat_min)

dump(repeat_avg)

# use the average in the main bar

rows[repeat_row]["pass_rate_2"] = repeat_avg

else:

repeat_hi = repeat_lo = repeat_avg = None

df = pd.DataFrame.from_records(rows)

df.sort_values(by=["model", "edit_format"], inplace=True)

# dump(df)

if graphs:

plot_timing(df)

plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg)

def plot_timing(df):

"""plot a graph showing the average duration of each (model, edit_format)"""

plt.rcParams["hatch.linewidth"] = 0.5

plt.rcParams["hatch.color"] = "#444444"

from matplotlib import rc

rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"], "size": 10})

fig, ax = plt.subplots(figsize=(6, 4))

ax.grid(axis="y", zorder=0, lw=0.2)

zorder = 1

grouped = df.groupby(["model", "edit_format"])["avg_duration"].mean().unstack()

num_models, num_formats = grouped.shape

pos = np.array(range(num_models))

width = 0.8 / num_formats

formats = grouped.columns

models = grouped.index

for i, fmt in enumerate(formats):

edge = dict(edgecolor="#ffffff", linewidth=1.5)

color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"

hatch = "////" if "func" in fmt else ""

rects = ax.bar(

pos + i * width,

grouped[fmt],

width * 0.95,

label=fmt,

color=color,

hatch=hatch,

zorder=zorder + 1,

**edge,

)

ax.bar_label(rects, padding=4, labels=[f"{v:.1f}s" for v in grouped[fmt]], size=6)

ax.set_xticks([p + 0.5 * width for p in pos])

ax.set_xticklabels(models)

ax.set_ylabel("Average GPT response time\nper exercise (sec)")

ax.set_title("GPT Code Editing Speed\n(time per coding task)")

ax.legend(

title="Edit Format",

loc="upper left",

)

ax.set_ylim(top=max(grouped.max()) * 1.1) # Set y-axis limit to 10% more than the max value

plt.tight_layout()

plt.savefig("tmp_timing.svg")

imgcat(fig)

def plot_outcomes(df, repeats, repeat_hi, repeat_lo, repeat_avg):

tries = [df.groupby(["model", "edit_format"])["pass_rate_2"].mean()]

if True:

tries += [df.groupby(["model", "edit_format"])["pass_rate_1"].mean()]

plt.rcParams["hatch.linewidth"] = 0.5

plt.rcParams["hatch.color"] = "#444444"

from matplotlib import rc

rc("font", **{"family": "sans-serif", "sans-serif": ["Helvetica"], "size": 10})

fig, ax = plt.subplots(figsize=(6, 4))

ax.grid(axis="y", zorder=0, lw=0.2)

zorder = 1

for grouped in tries:

zorder += 1

df = grouped.unstack()

num_models, num_formats = df.shape

pos = np.array(range(num_models))

width = 0.8 / num_formats

formats = df.columns

models = df.index

for i, fmt in enumerate(formats):

if zorder > 1:

edge = dict(

edgecolor="#ffffff",

linewidth=1.5,

)

else:

edge = dict()

if zorder == 2:

edge["label"] = fmt

color = "#b3e6a8" if "diff" in fmt else "#b3d1e6"

hatch = "////" if "func" in fmt else ""

rects = ax.bar(

pos + i * width,

df[fmt],

width * 0.95,

color=color,

hatch=hatch,

zorder=zorder,

**edge,

)

if zorder == 2:

ax.bar_label(rects, padding=4, labels=[f"{v:.0f}%" for v in df[fmt]], size=6)

if len(repeats):

ax.errorbar(

1.4,

repeat_avg,

yerr=[[repeat_lo], [repeat_hi]],

fmt="none",

zorder=5,

capsize=2.5,

elinewidth=1,

markeredgewidth=1,

)

ax.set_xticks([p + 0.5 * width for p in pos])

ax.set_xticklabels(models)

top = 95

ax.annotate(

"First attempt,\nbased on\nnatural language\ninstructions",

xy=(2.20, 41),

xytext=(2, top),

horizontalalignment="center",

verticalalignment="top",

arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},

)

ax.annotate(

"Second attempt,\nincluding unit test\nerror output",

xy=(2.55, 56),

xytext=(3.5, top),

horizontalalignment="center",

verticalalignment="top",

arrowprops={"arrowstyle": "->", "connectionstyle": "arc3,rad=0.3"},

)

ax.set_ylabel("Percent of exercises completed successfully")

# ax.set_xlabel("Model")

ax.set_title("GPT Code Editing Skill\n(percent coding tasks correct)")

ax.legend(

title="Edit Format",

loc="upper left",

# bbox_to_anchor=(0.95, 0.95),

)

ax.set_ylim(top=100)

plt.tight_layout()

plt.savefig("tmp.svg")

imgcat(fig)

# df.to_csv("tmp.benchmarks.csv")

def resolve_dirname(dirname, use_single_prior, make_new):

if len(dirname.parts) > 1:

return dirname

priors = list(BENCHMARK_DNAME.glob(f"*--{dirname}"))

if len(priors) == 1 and use_single_prior:

dirname = priors[0].name

print(f"Using pre-existing {dirname}")

elif len(priors):

if not make_new:

print(f"Prior runs of {dirname} exist, use --new or name one explicitly")

print()

for prior in priors:

print(prior)

return

if not re.match(r"\d\d\d\d-\d\d-\d\d-", str(dirname)):

now = datetime.datetime.now()

now = now.strftime("%Y-%m-%d-%H-%M-%S--")

dirname = now + dirname.name

dirname = BENCHMARK_DNAME / dirname

return dirname

@app.command()

def main(

dirnames: List[str] = typer.Argument(..., help="Directory names"),

graphs: bool = typer.Option(False, "--graphs", help="Generate graphs"),

model: str = typer.Option("gpt-3.5-turbo", "--model", "-m", help="Model name"),

edit_format: str = typer.Option(None, "--edit-format", "-e", help="Edit format"),

keywords: str = typer.Option(

None, "--keywords", "-k", help="Only run tests that contain keywords (comma sep)"

clean: bool = typer.Option(

False, "--clean", "-c", help="Discard the existing testdir and make a clean copy"

cont: bool = typer.Option(False, "--cont", help="Continue the (single) matching testdir"),

make_new: bool = typer.Option(False, "--new", "-n", help="Make a new dated testdir"),

no_unit_tests: bool = typer.Option(False, "--no-unit-tests", help="Do not run unit tests"),

no_aider: bool = typer.Option(False, "--no-aider", help="Do not run aider"),

verbose: bool = typer.Option(False, "--verbose", "-v", help="Verbose output"),

stats_only: bool = typer.Option(

False, "--stats", "-s", help="Do not run tests, just collect stats on completed tests"

diffs_only: bool = typer.Option(False, "--diffs", help="Just diff the provided stats dirs"),

tries: int = typer.Option(2, "--tries", "-r", help="Number of tries for running tests"),

threads: int = typer.Option(1, "--threads", "-t", help="Number of threads to run in parallel"),

num_tests: int = typer.Option(-1, "--num-tests", "-n", help="Number of tests to run"),

repo = git.Repo(search_parent_directories=True)

commit_hash = repo.head.object.hexsha[:7]

if repo.is_dirty():

commit_hash += "-dirty"

if len(dirnames) > 1 and not (stats_only or diffs_only):

print("Only provide 1 dirname unless running with --stats or --diffs")

return 1

updated_dirnames = []

for dirname in dirnames:

dirname = Path(dirname)

dirname = resolve_dirname(dirname, stats_only or cont, make_new)

if not dirname:

return 1

updated_dirnames.append(dirname)

if stats_only:

return show_stats(updated_dirnames, graphs)

if diffs_only:

return show_diffs(updated_dirnames)

assert len(updated_dirnames) == 1, updated_dirnames

dirname = updated_dirnames[0]

if "AIDER_DOCKER" not in os.environ:

print("Warning: benchmarking runs unvetted code from GPT, run in a docker container")

return

assert BENCHMARK_DNAME.exists() and BENCHMARK_DNAME.is_dir(), BENCHMARK_DNAME

assert ORIGINAL_DNAME.exists() and ORIGINAL_DNAME.is_dir(), ORIGINAL_DNAME

if clean and dirname.exists():

print("Cleaning up and replacing", dirname)

dir_files = set(fn.name for fn in dirname.glob("*"))

original_files = set(fn.name for fn in ORIGINAL_DNAME.glob("*"))

if dir_files != original_files:

print("ERROR: will not delete dir that does not look like original tests", dirname)

return

dest = dirname.parent / "OLD" / dirname.name

if dest.exists():

old_now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")

dest = dirname.parent / "OLD" / (old_now + dirname.name)

dirname.rename(dest)

if not dirname.exists():

print(f"Copying {ORIGINAL_DNAME} -> {dirname} ...")

shutil.copytree(ORIGINAL_DNAME, dirname)

print("...done")

test_dnames = sorted(os.listdir(dirname))

if keywords:

keywords = keywords.split(",")

test_dnames = [dn for dn in test_dnames for keyword in keywords if keyword in dn]

random.shuffle(test_dnames)

if num_tests > 0:

test_dnames = test_dnames[:num_tests]

if threads == 1:

all_results = []

for testname in test_dnames:

results = run_test(

dirname / testname,

model,

edit_format,

tries,

no_unit_tests,

no_aider,

verbose,

commit_hash,

)

all_results.append(results)

summarize_results(dirname)

else:

run_test_threaded = lox.thread(threads)(run_test)

for testname in test_dnames:

run_test_threaded.scatter(

dirname / testname,

model,

edit_format,

tries,

no_unit_tests,

no_aider,

verbose,

commit_hash,

)

all_results = run_test_threaded.gather(tqdm=True)

print()

summarize_results(dirname)

return 0

def show_diffs(dirnames):

dirnames = sorted(dirnames)

all_results = dict((dirname, load_results(dirname)) for dirname in dirnames)

testcases = set()

for results in all_results.values():

testcases.update(result["testcase"] for result in results)

testcases = sorted(testcases)

unchanged = set()

for testcase in testcases:

all_outcomes = []

for dirname in dirnames:

results = all_results[dirname]

result = [r for r in results if r["testcase"] == testcase][0]

outcomes = tuple(result["tests_outcomes"])

all_outcomes.append(True in outcomes)

if len(set(all_outcomes)) == 1:

unchanged.add(testcase)

continue

print()

print(testcase)

for outcome, dirname in zip(all_outcomes, dirnames):

print(outcome, f"{dirname}/{testcase}/.aider.chat.history.md")

changed = set(testcases) - unchanged

print()

print("changed:", len(changed), ",".join(sorted(changed)))

print("unchanged:", len(unchanged), ",".join(sorted(unchanged)))

def load_results(dirname):

dirname = Path(dirname)

all_results = [json.loads(fname.read_text()) for fname in dirname.glob("*/.aider.results.json")]

return all_results

def summarize_results(dirname):

all_results = load_results(dirname)

res = SimpleNamespace()

res.total_tests = len(list(Path(dirname).glob("*")))

try:

tries = max(len(results["tests_outcomes"]) for results in all_results if results)

except ValueError:

tries = 0

res.dir_name = str(dirname)

passed_tests = [0] * tries

res.completed_tests = 0

res.duration = 0

res.cost = 0

res.error_outputs = 0

res.user_asks = 0

res.test_timeouts = 0

res.exhausted_context_windows = 0

variants = defaultdict(set)

for results in all_results:

if not results:

continue

res.completed_tests += 1

passed = results["tests_outcomes"][-1]

if passed:

for i in range(len(results["tests_outcomes"]) - 1, tries):

passed_tests[i] += 1

res.cost += results["cost"]

res.duration += results["duration"]

res.test_timeouts += results.get("test_timeouts", 0)

res.error_outputs += results.get("num_error_outputs", 0)

res.user_asks += results.get("num_user_asks", 0)

res.exhausted_context_windows += results.get("num_exhausted_context_windows", 0)

for key in "model edit_format commit_hash".split():

val = results.get(key)

variants[key].add(val)

if not res.completed_tests:

return

console = Console(highlight=False)

console.rule(title=str(dirname))

console.print(f"test-cases: {res.completed_tests}")

for key, val in variants.items():

if len(val) > 1:

style = "red"

else:

style = None

val = ", ".join(map(str, val))

setattr(res, key, val)

console.print(f"{key}: {val}", style=style)

print("num_error_outputs:", res.error_outputs)

print("num_user_asks:", res.user_asks)

style = "red" if res.exhausted_context_windows else None

console.print("num_exhausted_context_windows", res.exhausted_context_windows, style=style)

style = "red" if res.test_timeouts else None

console.print("test_timeouts:", res.test_timeouts, style=style)

console.print()

for i in range(tries):

pass_rate = 100 * passed_tests[i] / res.completed_tests

console.print(f"{pass_rate:.1f}% correct after try {i}")

setattr(res, f"pass_rate_{i+1}", pass_rate)

console.print()

res.avg_duration = res.duration / res.completed_tests

console.print(f"duration: {res.avg_duration:.1f} sec/test-case")

res.avg_cost = res.cost / res.completed_tests

projected_cost = res.avg_cost * res.total_tests

console.print(

f"costs: ${res.avg_cost:.4f}/test-case, ${res.cost:.2f} total,"

f" ${projected_cost:.2f} projected"

)

console.rule()

# print(json.dumps(vars(res), indent=4, sort_keys=True))

return res

def run_test(

testdir, model_name, edit_format, tries, no_unit_tests, no_aider, verbose, commit_hash

if not os.path.isdir(testdir):

print("Not a dir:", testdir)

return

testdir = Path(testdir)

history_fname = testdir / ".aider.chat.history.md"

results_fname = testdir / ".aider.results.json"

if results_fname.exists():

try:

res = json.loads(results_fname.read_text())

return res

except JSONDecodeError:

print(f"{results_fname} failed to parse, skipping")

return

fnames = []

for fname in testdir.glob("*"):

if "test" not in fname.name and fname.is_file() and fname.name[0] != ".":

fnames.append(fname)

# restore the original file, in case we interrupted a prev run

# after it had saved changes

original_fname = ORIGINAL_DNAME / testdir.name / fname.name

shutil.copy(original_fname, fname)

file_list = " ".join(fname.name for fname in fnames)

instructions = ""

introduction = testdir / ".docs/introduction.md"

if introduction.exists():

instructions += introduction.read_text()

instructions += (testdir / ".docs/instructions.md").read_text()

instructions_append = testdir / ".docs/instructions.append.md"

if instructions_append.exists():

instructions += instructions_append.read_text()

instructions += prompts.instructions_addendum.format(file_list=file_list)

io = InputOutput(

pretty=True,

yes=False,

chat_history_file=history_fname,

)

main_model = models.Model.create(model_name)

edit_format = edit_format or main_model.edit_format

dump(main_model)

dump(edit_format)

show_fnames = ",".join(map(str, fnames))

print("fnames:", show_fnames)

openai.api_key = os.environ["OPENAI_API_KEY"]

coder = Coder.create(

main_model,

edit_format,

io,

fnames=fnames,

use_git=False,

stream=False,

pretty=False,

verbose=verbose,

)

timeouts = 0

dur = 0

test_outcomes = []

for i in range(tries):

start = time.time()

if not no_aider:

coder.run(with_message=instructions)

dur += time.time() - start

if coder.last_keyboard_interrupt:

raise KeyboardInterrupt

if no_unit_tests:

break

try:

errors = run_unit_tests(testdir, history_fname)

except subprocess.TimeoutExpired:

errors = "Tests timed out!"

timeouts += 1

if errors:

test_outcomes.append(False)

else:

test_outcomes.append(True)

break

errors = errors.splitlines()

print(errors[-1])

errors = errors[:50]

errors = "\n".join(errors)

instructions = errors

instructions += prompts.test_failures.format(file_list=file_list)

results = dict(

testdir=str(testdir),

testcase=testdir.name,

model=main_model.name,

edit_format=edit_format,

tests_outcomes=test_outcomes,

cost=coder.total_cost,

duration=dur,

test_timeouts=timeouts,

commit_hash=commit_hash,

num_error_outputs=io.num_error_outputs,

num_user_asks=io.num_user_asks,

num_exhausted_context_windows=coder.num_exhausted_context_windows,

chat_hashes=list(

zip(

coder.chat_completion_call_hashes,

coder.chat_completion_response_hashes,

)

dump(results)

results_fname.write_text(json.dumps(results, indent=4))

return results

def run_unit_tests(testdir, history_fname):

command = [

"python",

"-m",

"unittest",

"discover",

"-s",

str(testdir),

"-t",

str(testdir),

"-p",

"*_test.py",

]

print(" ".join(command))

timeout = 60

result = subprocess.run(

command,

stdout=subprocess.PIPE,

stderr=subprocess.STDOUT,

text=True,

timeout=timeout,

)

success = result.returncode == 0

res = result.stdout

res = cleanup_test_output(res, testdir)

with history_fname.open("a") as fh:

fh.write(f"```\n{res}\n```")

if not success:

print(f"Tests failed: {testdir}")

return res

def cleanup_test_output(output, testdir):

# remove timing info, to avoid randomizing the response to GPT

res = re.sub(

r"^Ran \d+ tests in \d+\.\d+s$",

"",

output,

flags=re.MULTILINE,

)

res = re.sub(

r"^====*$",

"====",

res,

flags=re.MULTILINE,

)

res = re.sub(

r"^----*$",

"----",

res,

flags=re.MULTILINE,

)

res = res.replace(str(testdir), str(testdir.name))

return res

if __name__ == "__main__":

app()

aider/benchmark/benchmark.py at main · tapas4java/aider · GitHub

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