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921 lines (809 loc) · 34.4 KB
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# Copyright (c) 2020, 2026, Oracle and/or its affiliates. All rights reserved.
# DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
#
# The Universal Permissive License (UPL), Version 1.0
#
# Subject to the condition set forth below, permission is hereby granted to any
# person obtaining a copy of this software, associated documentation and/or
# data (collectively the "Software"), free of charge and under any and all
# copyright rights in the Software, and any and all patent rights owned or
# freely licensable by each licensor hereunder covering either (i) the
# unmodified Software as contributed to or provided by such licensor, or (ii)
# the Larger Works (as defined below), to deal in both
#
# (a) the Software, and
#
# (b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
# one is included with the Software each a "Larger Work" to which the Software
# is contributed by such licensors),
#
# without restriction, including without limitation the rights to copy, create
# derivative works of, display, perform, and distribute the Software and make,
# use, sell, offer for sale, import, export, have made, and have sold the
# Software and the Larger Work(s), and to sublicense the foregoing rights on
# either these or other terms.
#
# This license is subject to the following condition:
#
# The above copyright notice and either this complete permission notice or at a
# minimum a reference to the UPL must be included in all copies or substantial
# portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import glob
import json
import math
import os
import re
import shutil
import sys
import tempfile
from os.path import join, abspath
import mx
import mx_benchmark
SUITE = None
python_vm_registry = None
# By default we disabled some benchmarks, both because some don't run and
# because we want to reduce the total runtime of the suites.
DEFAULT_NUMPY_BENCHMARKS = [
"bench_app",
"bench_core",
# "bench_function_base",
"bench_indexing",
# "bench_io",
"bench_linalg",
# "bench_ma",
# "bench_random",
"bench_reduce",
# "bench_shape_base",
# "bench_ufunc",
]
SKIPPED_NUMPY_BENCHMARKS = [
"bench_core.CountNonzero.time_count_nonzero(1, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero(2, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero(3, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero(1, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero(2, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero(3, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_axis(1, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_axis(2, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_axis(3, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_axis(1, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_axis(2, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_axis(3, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_multi_axis(1, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_multi_axis(2, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_multi_axis(3, 1000000, <class 'str'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_multi_axis(1, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_multi_axis(2, 1000000, <class 'object'>)", # Times out
"bench_core.CountNonzero.time_count_nonzero_multi_axis(3, 1000000, <class 'object'>)", # Times out
"bench_linalg.LinalgSmallArrays.time_det_small_array", # TODO fails with numpy.linalg.LinAlgError
"bench_indexing.IndexingSeparate.time_mmap_fancy_indexing", # Hangs in periodic job GR-73912
"bench_indexing.IndexingStructured0D.time_array_slice", # Hangs in periodic job GR-73912
]
SKIPPED_NUMPY_BENCHMARK_PATTERNS = [
# These patterns match ASV's expanded benchmark names. They keep the small
# cases while dropping large Cartesian-product outliers that dominate the
# periodic job runtime.
r"bench_core\.CorrConv\.time_(?:correlate|convolve)\((?:100000,|[^)]*, (?:1000|10000), )",
r"bench_core\.CountNonzero\.time_count_nonzero(?:_axis|_multi_axis)?\([1-3], (?:10000|1000000), ",
r"bench_core\.Nonzero\.time_nonzero(?:_sparse|_dense)?\([^)]*, \((?:1000000,|1000, 1000)\)\)",
r"bench_core\.StatsMethods\.time_(?:max|mean|min|prod|std|sum|var)\('[^']+', 10000\)",
r"bench_indexing\.Indexing\.time_op\('(?:complex64|complex128|object|O,i)'",
r"bench_indexing\.Indexing\.time_op\('[^']+', 'indexes_rand_'",
r"bench_indexing\.Indexing\.time_op\('[^']+', '[^']+', 'np\.ix_\(I, I\)'",
r"bench_indexing\.IndexingWith1DArr\.time_(?:getitem|setitem)_ordered\(\((?:1000, 2|2, 1000, 1|1000, 3)\), ",
r"bench_linalg\.LinAlgTransposeVdot\.time_(?:transpose|vdot)\(\(64, 64\), ",
r"bench_linalg\.MatmulStrided\.time_matmul\('matmul_m(?:150|400)_",
]
DEFAULT_PANDAS_BENCHMARKS = [
"reshape",
"replace"
]
SKIPPED_PANDAS_BENCHMARKS = [
"replace.ReplaceDict.time_replace_series", # Times out
"replace.ReplaceList.time_replace_list", # OOM, WIP msimacek
"replace.ReplaceList.time_replace_list_one_match", # OOM, WIP msimacek
"reshape.Crosstab.time_crosstab_normalize_margins", # Times out
"reshape.Cut.peakmem_cut_interval", # Times out
"reshape.Cut.time_cut_interval", # Times out
"reshape.GetDummies.time_get_dummies_1d_sparse", # Times out
"reshape.PivotTable.time_pivot_table_categorical", # Hangs in periodic job GR-74045
"reshape.PivotTable.time_pivot_table_margins", # Times out
"reshape.WideToLong.time_wide_to_long_big", # Times out
"reshape.Cut.time_qcut_datetime", # Transient failure GR-61245, exit code -11
"reshape.Explode.time_explode", # Transient failure GR-61245, exit code -11
]
SETUPTOOLS_PIN = "77.0.1"
def add_cpython_build_env(env=None):
if python3_home := os.environ.get("PYTHON3_HOME"):
checkout_include = join(python3_home, "Include")
installed_includes = sorted(glob.glob(join(python3_home, os.pardir, "include", "python*")))
include_dir = next(
(path for path in [checkout_include, *installed_includes] if os.path.exists(join(path, "Python.h"))),
None,
)
if include_dir:
env = env.copy() if env is not None else os.environ.copy()
include_dirs = [include_dir]
if include_dir == checkout_include:
include_dirs.append(python3_home)
python_includes = os.pathsep.join(include_dirs)
include_flags = " ".join(f"-I{path}" for path in python_includes.split(os.pathsep))
env["CPATH"] = python_includes + (os.pathsep + env["CPATH"] if env.get("CPATH") else "")
for key in ["CFLAGS", "CPPFLAGS", "CXXFLAGS"]:
env[key] = include_flags + (" " + env[key] if env.get(key) else "")
lib_dir = join(python3_home, os.pardir, "lib")
if not os.path.isdir(lib_dir):
lib_dir = python3_home
env["LIBRARY_PATH"] = lib_dir + (os.pathsep + env["LIBRARY_PATH"] if env.get("LIBRARY_PATH") else "")
return env
DEFAULT_PYPERFORMANCE_BENCHMARKS = [
# "2to3",
# "chameleon",
"chaos",
# "crypto_pyaes",
# "django_template",
# "dulwich_log",
"fannkuch",
"float",
"go",
"hexiom",
# "html5lib",
"json_dumps",
"json_loads",
# "logging",
# "mako",
"meteor_contest",
"nbody",
"nqueens",
"pathlib",
"pickle",
"pickle_dict",
"pickle_list",
"pickle_pure_python",
"pidigits",
"pyflate",
"regex_compile",
"regex_dna",
"regex_effbot",
"regex_v8",
"richards",
"scimark",
"spectral_norm",
# "sqlalchemy_declarative",
# "sqlalchemy_imperative",
# "sqlite_synth",
# "sympy",
"telco",
# "tornado_http",
"unpack_sequence",
"unpickle",
"unpickle_list",
# "unpickle_pure_python",
# "xml_etree",
]
DEFAULT_PYPY_BENCHMARKS = [
"ai",
# "bm_chameleon",
# "bm_dulwich_log",
"bm_mako",
"bm_mdp",
"chaos",
# "cpython_doc",
"crypto_pyaes",
"deltablue",
"django",
"eparse",
"fannkuch",
"float",
"genshi_text",
"genshi_xml",
"go",
"hexiom2",
"html5lib",
"json_bench",
"meteor-contest",
"nbody_modified",
"nqueens",
"pidigits",
"pyflate-fast",
"pyxl_bench",
"raytrace-simple",
"richards",
"scimark_fft",
"scimark_lu",
"scimark_montecarlo",
"scimark_sor",
"scimark_sparsematmult",
"spectral-norm",
"spitfire2",
"spitfire_cstringio2",
# "sqlalchemy_declarative",
# "sqlalchemy_imperative",
# "sqlitesynth",
# "sympy_expand",
# "sympy_integrate",
# "sympy_str",
# "sympy_sum",
# "telco",
# "twisted_names",
# "twisted_pb",
# "twisted_tcp",
]
def create_asv_benchmark_selection(benchmarks, skipped=(), skipped_patterns=()):
regex = '|'.join(benchmarks)
negative_lookaheads = [re.escape(skip) + (r'\b' if not skip.endswith(')') else '') for skip in skipped]
negative_lookaheads.extend(skipped_patterns)
if not negative_lookaheads:
return regex
return '^(?!' + '|'.join(negative_lookaheads) + ')(' + regex + ')'
def patch_asv_for_cpython_312(workdir, vm_venv):
pattern = join(workdir, vm_venv, "lib", "python*", "site-packages", "asv", "plugins", "virtualenv.py")
candidates = glob.glob(pattern)
if not candidates:
mx.abort(f"Could not find ASV virtualenv plugin to patch: {pattern}")
if len(candidates) != 1:
mx.abort(f"Found multiple ASV virtualenv plugins to patch: {candidates}")
virtualenv_py = candidates[0]
with open(virtualenv_py) as f:
content = f.read()
patched_import = "from packaging.version import parse as LooseVersion"
if patched_import in content:
mx.log(f"ASV virtualenv plugin already patched: {virtualenv_py}")
return
distutils_import = "from distutils.version import LooseVersion"
if distutils_import not in content:
mx.abort(f"Unexpected ASV virtualenv plugin contents, cannot patch: {virtualenv_py}")
content = content.replace(
distutils_import,
"try:\n"
" from packaging.version import parse as LooseVersion\n"
"except Exception:\n"
" from distutils.version import LooseVersion",
1,
)
with open(virtualenv_py, "w") as f:
f.write(content)
mx.log(f"Patched ASV virtualenv plugin for CPython 3.12+: {virtualenv_py}")
class PyPerfJsonRule(mx_benchmark.Rule):
"""Parses a JSON file produced by PyPerf and creates a measurement result."""
def __init__(self, filenames: str, suiteName: str):
self.filenames = filenames.split(",")
self.suiteName = suiteName
def parse(self, text: str) -> list:
r = []
for filename in self.filenames:
self._parse_file(r, filename)
return r
def _parse_file(self, r: list, filename: str):
with open(self._prepend_working_dir(filename)) as fp:
js = json.load(fp)
benchmarks = js["benchmarks"]
for benchmark in benchmarks:
name = benchmark.get("metadata", js["metadata"])["name"]
unit = benchmark.get("metadata", {}).get("unit") or js["metadata"]["unit"]
unit = {
"second": "s",
"byte": "B",
}[unit]
metric = {
"s": "time",
"B": "max-rss",
}[unit]
for run in benchmark["runs"]:
if values := run.get("values", None):
if metric == "time":
warmups = run.get("warmups", [])
for idx, warmup in enumerate(warmups):
r.append(
{
"bench-suite": self.suiteName,
"benchmark": name,
"metric.name": "warmup",
"metric.unit": unit,
"metric.score-function": "id",
"metric.better": "lower",
"metric.type": "numeric",
"metric.iteration": idx,
"metric.value": warmup[1], # 0 is inner_loop count
}
)
for value in values:
r.append(
{
"bench-suite": self.suiteName,
"benchmark": name,
"metric.name": metric,
"metric.unit": unit,
"metric.score-function": "id",
"metric.better": "lower",
"metric.type": "numeric",
"metric.iteration": 0,
"metric.value": value,
}
)
class AsvJsonRule(mx_benchmark.Rule):
"""Parses a JSON file produced by ASV (airspeed-velocity) and creates a measurement result."""
def __init__(self, filename: str, suiteName: str):
self.filename = filename
self.suiteName = suiteName
def parse(self, text: str) -> list:
import itertools
r = []
with open(self._prepend_working_dir(self.filename)) as fp:
js = json.load(fp)
columns = js["result_columns"] # type: list[str]
peak_idx = columns.index("result")
param_idx = columns.index("params")
try:
samples_idx = columns.index("samples")
except ValueError:
samples_idx = -1
for benchmark, result in js["results"].items():
param_combinations = itertools.product(*result[param_idx])
for run_idx, params in enumerate(param_combinations):
peak_values = result[peak_idx]
if not peak_values:
continue
value = peak_values[run_idx]
if not value or math.isnan(value):
continue
r.append(
{
"bench-suite": self.suiteName,
"benchmark": benchmark,
"metric.name": "time",
"metric.unit": "s",
"metric.score-function": "id",
"metric.better": "lower",
"metric.type": "numeric",
"metric.iteration": 0,
"metric.value": value,
"config.run-flags": " ".join(params),
}
)
# It may be that the samples are missing; so omit this step
if 0 <= samples_idx < len(result):
for iteration, value in enumerate(result[samples_idx][run_idx]):
r.append(
{
"bench-suite": self.suiteName,
"benchmark": benchmark,
"metric.name": "warmup",
"metric.unit": "s",
"metric.score-function": "id",
"metric.better": "lower",
"metric.type": "numeric",
"metric.iteration": iteration,
"metric.value": value,
"config.run-flags": " ".join(params),
}
)
return r
class PyPyJsonRule(mx_benchmark.Rule, mx_benchmark.AveragingBenchmarkMixin):
"""Parses a JSON file produced by the Unladen Swallow or PyPy benchmark harness and creates a measurement result."""
def __init__(self, filename: str, suiteName: str):
self.filename = filename
self.suiteName = suiteName
def parse(self, text: str) -> list:
r = []
with open(self._prepend_working_dir(self.filename)) as fp:
js = json.load(fp)
for result in js["results"]:
name = result[0]
if result[1] == "RawResult":
values = result[2]["base_times"]
elif result[1] == "SimpleComparisonResult":
values = [result[2]["base_time"]]
else:
mx.warn(f"No data found for {name} with {result[1]}")
continue
for iteration, value in enumerate(values):
r.append(
{
"bench-suite": self.suiteName,
"benchmark": name,
"metric.name": "warmup",
"metric.unit": "s",
"metric.score-function": "id",
"metric.better": "lower",
"metric.type": "numeric",
"metric.iteration": iteration,
"metric.value": value,
}
)
self.addAverageAcrossLatestResults(r)
return r
class WildcardList:
"""It is not easy to track for external suites which benchmarks are
available, so we just return a wildcard list and assume the caller knows
what they want to run"""
def __init__(self, benchmarks=None):
self.benchmarks = benchmarks
def __contains__(self, x):
return True
def __iter__(self):
if not self.benchmarks:
mx.abort(
"Cannot iterate over benchmark names in foreign benchmark suites. "
+ "Leave off the benchmark name part to run all, or name the benchmarks yourself."
)
else:
return iter(self.benchmarks)
class PySuite(mx_benchmark.TemporaryWorkdirMixin, mx_benchmark.VmBenchmarkSuite):
pass
class PyPerformanceSuite(PySuite):
VERSION = "1.14.0"
def name(self):
return "pyperformance-suite"
def group(self):
return "Graal"
def subgroup(self):
return "graalpython"
def benchmarkList(self, bmSuiteArgs):
return WildcardList(DEFAULT_PYPERFORMANCE_BENCHMARKS)
def rules(self, output, benchmarks, bmSuiteArgs):
return [PyPerfJsonRule(output, self.name())]
def createVmCommandLineArgs(self, benchmarks, runArgs):
return []
def get_vm_registry(self):
return python_vm_registry
def _vmRun(self, vm, workdir, command, benchmarks, bmSuiteArgs):
workdir = abspath(workdir)
vm_venv = f"{self.name()}-{vm.name()}-{vm.config_name()}"
_, _, vm_dims = vm.run(workdir, ["--version"])
if not hasattr(self, "prepared"):
self.prepared = True
vm.run(workdir, ["-m", "venv", join(workdir, vm_venv)])
mx.run(
[
join(vm_venv, "bin", "pip"),
"install",
f"pyperformance=={self.VERSION}",
],
cwd=workdir,
)
if benchmarks:
bms = ["-b", ",".join(benchmarks)]
else:
bms = ["-b", ",".join(DEFAULT_PYPERFORMANCE_BENCHMARKS)]
json_file = f"{vm_venv}.json"
retcode = mx.run(
[
join(vm_venv, "bin", "pyperformance"),
"run",
"--inherit-environ",
"PIP_INDEX_URL,PIP_EXTRA_INDEX_URL,PIP_TRUSTED_HOST,PIP_TIMEOUT,PIP_RETRIES,LD_LIBRARY_PATH,LIBRARY_PATH,CPATH,PATH,PYPY_GC_MAX,JAVA_OPTS,GRAAL_PYTHON_ARGS,GRAAL_PYTHON_VM_ARGS",
"-o",
json_file,
*bms,
],
cwd=workdir,
nonZeroIsFatal=False,
)
mx.log(f"Return code of benchmark harness: {retcode}")
# run again in single shot mode for memory measurements
json_file_memory = f"{vm_venv}_memory.json"
retcode = mx.run(
[
join(vm_venv, "bin", "pyperformance"),
"run",
"--debug-single-value",
"--track-memory",
"--inherit-environ",
"PIP_INDEX_URL,PIP_EXTRA_INDEX_URL,PIP_TRUSTED_HOST,PIP_TIMEOUT,PIP_RETRIES,LD_LIBRARY_PATH,LIBRARY_PATH,CPATH,PATH,PYPY_GC_MAX,JAVA_OPTS,GRAAL_PYTHON_ARGS,GRAAL_PYTHON_VM_ARGS",
"-o",
json_file_memory,
*bms,
],
cwd=workdir,
nonZeroIsFatal=False,
)
mx.log(f"Return code of benchmark harness: {retcode}")
shutil.copy(join(workdir, json_file), join(SUITE.dir, "raw_results.json"))
shutil.copy(join(workdir, json_file_memory), join(SUITE.dir, "raw_results_memory.json"))
return retcode, ",".join([join(workdir, json_file), join(workdir, json_file_memory)]), vm_dims
class PyPySuite(PySuite):
VERSION = "0324a252cf1a"
def name(self):
return "pypy-suite"
def group(self):
return "Graal"
def subgroup(self):
return "graalpython"
def benchmarkList(self, bmSuiteArgs):
return WildcardList(DEFAULT_PYPY_BENCHMARKS)
def rules(self, output, benchmarks, bmSuiteArgs):
return [PyPyJsonRule(output, self.name())]
def createVmCommandLineArgs(self, benchmarks, runArgs):
return []
def get_vm_registry(self):
return python_vm_registry
def _vmRun(self, vm, workdir, command, benchmarks, bmSuiteArgs):
workdir = abspath(workdir)
vm_venv = f"{self.name()}-{vm.name()}-{vm.config_name()}"
_, _, vm_dims = vm.run(workdir, ["--version"])
if not hasattr(self, "prepared"):
self.prepared = True
if artifact := os.environ.get("PYPY_BENCHMARKS_DIR"):
shutil.copytree(artifact, join(workdir, "benchmarks"))
else:
mx.warn("PYPY_BENCHMARKS_DIR is not set, cloning repository")
mx.run(
["hg", "clone", "https://foss.heptapod.net/pypy/benchmarks"],
cwd=workdir,
)
mx.run(
["hg", "up", "-C", self.VERSION], cwd=join(workdir, "benchmarks")
)
# workaround for pypy's benchmarks script issues
with open(join(workdir, "benchmarks", "nullpython.py")) as f:
content = f.read()
content = content.replace("/usr/bin/python", "/usr/bin/env python")
with open(join(workdir, "benchmarks", "nullpython.py"), "w") as f:
f.write(content)
with open(join(workdir, "benchmarks", "benchmarks.py")) as f:
content = f.read()
content = content.replace(
'float(line.split(b" ")[0])', "float(line.split()[0])"
)
with open(join(workdir, "benchmarks", "benchmarks.py"), "w") as f:
f.write(content)
vm.run(workdir, ["-m", "venv", join(workdir, vm_venv)])
json_file = f"{vm_venv}.json"
if benchmarks:
bms = ["-b", ",".join(benchmarks)]
else:
bms = ["-b", ",".join(DEFAULT_PYPY_BENCHMARKS)]
retcode = mx.run(
[
sys.executable,
join(workdir, "benchmarks", "run_local.py"),
f"{vm_venv}/bin/python",
"-o",
join(workdir, json_file),
*bms,
],
cwd=workdir,
nonZeroIsFatal=False,
)
shutil.copy(join(workdir, json_file), join(SUITE.dir, "raw_results.json"))
mx.log(f"Return code of benchmark harness: {retcode}")
return retcode, join(workdir, json_file), vm_dims
class NumPySuite(PySuite):
VERSION = "2.4.4"
BENCHMARK_REQ = [
"asv==0.5.1",
"asv-runner==0.2.1",
f"setuptools=={SETUPTOOLS_PIN}",
"distlib==0.3.6",
"filelock==3.8.0",
"platformdirs==2.5.2",
"six==1.16.0",
"virtualenv==20.16.3",
"packaging==24.0",
f"numpy=={VERSION}",
]
def name(self):
return "numpy-suite"
def group(self):
return "Graal"
def subgroup(self):
return "graalpython"
def benchmarkList(self, bmSuiteArgs):
return WildcardList(DEFAULT_NUMPY_BENCHMARKS)
def rules(self, output, benchmarks, bmSuiteArgs):
return [AsvJsonRule(output, self.name())]
def createVmCommandLineArgs(self, benchmarks, runArgs):
return []
def get_vm_registry(self):
return python_vm_registry
def _vmRun(self, vm, workdir, command, benchmarks, bmSuiteArgs):
workdir = abspath(workdir)
benchdir = join(workdir, "numpy", "benchmarks")
vm_venv = f"{self.name()}-{vm.name()}-{vm.config_name()}"
_, _, vm_dims = vm.run(workdir, ["--version"])
if not hasattr(self, "prepared"):
self.prepared = True
npdir = join(workdir, "numpy")
if artifact := os.environ.get("NUMPY_BENCHMARKS_DIR"):
shutil.copytree(artifact, npdir)
else:
mx.warn("NUMPY_BENCHMARKS_DIR is not set, cloning numpy repository")
repo_url = os.environ.get("NUMPY_REPO_URL", "https://github.com/numpy/numpy.git")
mx.run(
[
"git",
"clone",
"--depth",
"1",
repo_url,
"--branch",
f"v{self.VERSION}",
"--single-branch",
],
cwd=workdir,
)
shutil.rmtree(join(npdir, ".git"))
mx.run(["git", "init", "."], cwd=npdir)
mx.run(["git", "config", "user.email", "you@example.com"], cwd=npdir)
mx.run(["git", "config", "user.name", "YourName"], cwd=npdir)
mx.run(["git", "commit", "--allow-empty", "-m", "init"], cwd=npdir)
mx.run(["git", "branch", f"v{self.VERSION}"], cwd=npdir)
mx.run(["git", "branch", "main"], cwd=npdir, nonZeroIsFatal=False)
mx.run(["git", "branch", "master"], cwd=npdir, nonZeroIsFatal=False)
vm.run(workdir, ["-m", "venv", join(workdir, vm_venv)])
pip = join(workdir, vm_venv, "bin", "pip")
env = add_cpython_build_env() if vm.name() == "cpython" else None
mx.run([pip, "install", *self.BENCHMARK_REQ], cwd=workdir, env=env)
if vm.name() == "cpython":
patch_asv_for_cpython_312(workdir, vm_venv)
mx.run(
[join(workdir, vm_venv, "bin", "asv"), "machine", "--yes"], cwd=benchdir
)
if not benchmarks:
benchmarks = DEFAULT_NUMPY_BENCHMARKS
retcode = mx.run(
[
join(workdir, vm_venv, "bin", "asv"),
"run",
"--strict",
"--record-samples",
"-e",
"--python=same",
"--set-commit-hash",
f"v{self.VERSION}",
"-b", create_asv_benchmark_selection(
benchmarks, skipped=SKIPPED_NUMPY_BENCHMARKS, skipped_patterns=SKIPPED_NUMPY_BENCHMARK_PATTERNS
),
],
cwd=benchdir,
nonZeroIsFatal=False,
)
json_file = glob.glob(join(benchdir, "results", "*", "*numpy*.json"))
mx.log(f"Return code of benchmark harness: {retcode}")
if json_file:
json_file = json_file[0]
shutil.copy(json_file, join(SUITE.dir, "raw_results.json"))
return retcode, json_file, vm_dims
else:
return -1, "", vm_dims
class PandasSuite(PySuite):
VERSION = "3.0.2"
VERSION_TAG = "v" + VERSION
BENCHMARK_REQ = [
"asv==0.5.1",
f"setuptools=={SETUPTOOLS_PIN}",
"distlib==0.3.6",
"filelock==3.8.0",
"platformdirs==2.5.2",
"six==1.16.0",
"virtualenv==20.16.3",
"packaging==24.0",
"jinja2",
"tzdata==2026.2",
f"numpy=={NumPySuite.VERSION}",
f"pandas=={VERSION}",
]
def name(self):
return "pandas-suite"
def group(self):
return "Graal"
def subgroup(self):
return "graalpython"
def benchmarkList(self, bmSuiteArgs):
return WildcardList([
"reshape",
"replace",
])
def rules(self, output, benchmarks, bmSuiteArgs):
return [AsvJsonRule(output, self.name())]
def createVmCommandLineArgs(self, benchmarks, runArgs):
return []
def get_vm_registry(self):
return python_vm_registry
def _vmRun(self, vm, workdir, command, benchmarks, bmSuiteArgs):
workdir = abspath(workdir)
benchdir = join(workdir, "pandas", "asv_bench")
vm_venv = f"{self.name()}-{vm.name()}-{vm.config_name()}"
_, _, vm_dims = vm.run(workdir, ["--version"])
if not hasattr(self, "prepared"):
self.prepared = True
npdir = join(workdir, "pandas")
if artifact := os.environ.get("PANDAS_BENCHMARKS_DIR"):
shutil.copytree(artifact, npdir)
else:
mx.warn("PANDAS_BENCHMARKS_DIR is not set, cloning pandas repository")
repo_url = os.environ.get("PANDAS_REPO_URL", "https://github.com/pandas-dev/pandas.git")
mx.log("Cloning Pandas from " + repo_url)
mx.run(
[
"git",
"clone",
"--depth",
"1",
repo_url,
"--branch",
self.VERSION_TAG,
"--single-branch",
],
cwd=workdir,
)
shutil.rmtree(join(npdir, ".git"))
pandas_benchmarks_dir = join(npdir, "asv_bench", "benchmarks")
accepted = ["__init__", "pandas_vb_common"] + list(self.benchmarkList([]))
removed_files = []
for f in os.listdir(pandas_benchmarks_dir):
# Remove any file or directory that is not a benchmark suite we want to run.
# Keep all files starting with "_"
if os.path.splitext(f)[0] not in accepted:
removed_files.append(f)
f_path = join(pandas_benchmarks_dir, f)
if os.path.isdir(f_path):
shutil.rmtree(f_path)
else:
os.remove(f_path)
mx.log("Removed Pandas benchmark files: " + repr(removed_files))
mx.run(["git", "init", "."], cwd=npdir)
mx.run(["git", "config", "user.email", "you@example.com"], cwd=npdir)
mx.run(["git", "config", "user.name", "YourName"], cwd=npdir)
mx.run(["git", "commit", "--allow-empty", "-m", "init"], cwd=npdir)
mx.run(["git", "branch", self.VERSION_TAG], cwd=npdir)
mx.run(["git", "branch", "main"], cwd=npdir, nonZeroIsFatal=False)
mx.run(["git", "branch", "master"], cwd=npdir, nonZeroIsFatal=False)
vm.run(workdir, ["-m", "venv", join(workdir, vm_venv)])
pip = join(workdir, vm_venv, "bin", "pip")
with tempfile.NamedTemporaryFile('w') as constraints:
constraints.write(f"setuptools=={SETUPTOOLS_PIN}\n")
constraints.flush()
env = os.environ.copy()
env['PIP_CONSTRAINT'] = constraints.name
if vm.name() == "cpython":
env = add_cpython_build_env(env)
mx.run([pip, "install", *self.BENCHMARK_REQ], cwd=workdir, env=env)
if vm.name() == "cpython":
patch_asv_for_cpython_312(workdir, vm_venv)
mx.run(
[join(workdir, vm_venv, "bin", "asv"), "machine", "--yes"], cwd=benchdir
)
if not benchmarks:
benchmarks = DEFAULT_PANDAS_BENCHMARKS
retcode = mx.run(
[
join(workdir, vm_venv, "bin", "asv"),
"run",
"--strict",
"--record-samples",
"-e",
"--python=same",
"--set-commit-hash",
self.VERSION_TAG,
"-b", create_asv_benchmark_selection(benchmarks, skipped=SKIPPED_PANDAS_BENCHMARKS),
],
cwd=benchdir,
nonZeroIsFatal=False,
)
json_file = glob.glob(join(benchdir, "results", "*", "*pandas*.json"))
mx.log(f"Return code of benchmark harness: {retcode}")
if json_file:
json_file = json_file[0]
shutil.copy(json_file, join(SUITE.dir, "raw_results.json"))
return retcode, json_file, vm_dims
else:
return -1, "", vm_dims
def register_python_benchmarks():
global python_vm_registry, SUITE
from mx_graalpython_benchmark import python_vm_registry as vm_registry
python_vm_registry = vm_registry
SUITE = mx.suite("graalpython")
mx_benchmark.add_bm_suite(PyPerformanceSuite())
mx_benchmark.add_bm_suite(PyPySuite())
mx_benchmark.add_bm_suite(NumPySuite())
mx_benchmark.add_bm_suite(PandasSuite())