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https://github.com/ARM-software/workload-automation.git
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workloads/geekbench: Add support for Geekbench command-line build
Add Geekbench command-line build workload for Android targets. Geekbench apks allow to user to run the tests altogether. Using the command-line, a single test or multiple tests can be specified. Signed-off-by: Elif Topuz <elif.topuz@arm.com>
This commit is contained in:
Elif Topuz
Marc Bonnici
parent
b03f28d1d5
commit
0732fa9cf0
@ -1,4 +1,4 @@
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# Copyright 2013-2018 ARM Limited
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# Copyright 2013-2025 ARM Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -20,10 +20,11 @@ import tempfile
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import json
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from collections import defaultdict
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from wa import ApkUiautoWorkload, Parameter
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from wa import Workload, ApkUiautoWorkload, Parameter
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from wa.framework.exception import ConfigError, WorkloadError
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from wa.utils.misc import capitalize
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from wa.utils.types import version_tuple
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from wa.utils.types import version_tuple, list_or_integer
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from wa.utils.exec_control import once
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class Geekbench(ApkUiautoWorkload):
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@ -370,3 +371,233 @@ class GeekbenchCorproate(Geekbench): # pylint: disable=too-many-ancestors
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def namemify(basename, i):
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return basename + (' {}'.format(i) if i else '')
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class GeekbenchCmdline(Workload):
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name = "geekbench_cli"
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description = "Workload for running command line version Geekbench"
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gb6_workloads = {
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# Single-Core and Multi-Core
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101: 'File Compression',
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102: 'Navigation',
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103: 'HTML5 Browser',
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104: 'PDF Renderer',
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105: 'Photo Library',
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201: 'Clang',
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202: 'Text Processing',
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203: 'Asset Compression',
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301: 'Object Detection',
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402: 'Object Remover',
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403: 'HDR',
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404: 'Photo Filter',
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501: 'Ray Tracer',
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502: 'Structure from Motion',
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# OpenCL and Vulkan
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303: 'Face Detection',
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406: 'Edge Detection',
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407: 'Gaussian Blur',
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503: 'Feature Matching',
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504: 'Stereo Matching',
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601: 'Particle Physics',
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# Single-Core, Multi-Core, OpenCL, and Vulkan
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302: 'Background Blur',
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401: 'Horizon Detection',
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}
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gb5_workloads = {
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# Single-Core and Multi-Core
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101: 'AES-XTS',
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201: 'Text Compression',
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202: 'Image Compression',
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203: 'Navigation',
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204: 'HTML5',
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205: 'SQLite',
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206: 'PDF Rendering',
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207: 'Text Rendering',
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208: 'Clang',
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209: 'Camera',
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301: 'N-Body Physics',
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302: 'Rigid Body Physics',
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307: 'Image Inpainting',
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308: 'HDR',
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309: 'Ray Tracing',
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310: 'Structure from Motion',
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312: 'Speech Recognition',
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313: 'Machine Learning',
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# OpenCL and Vulkan
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220: 'Sobel',
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221: 'Canny',
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222: 'Stereo Matching',
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230: 'Histogram Equalization',
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304: 'Depth of Field',
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311: 'Feature Matching',
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320: 'Particle Physics',
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321: 'SFFT',
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# Single-Core, Multi-Core, OpenCL, and Vulkan
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303: 'Gaussian Blur',
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305: 'Face Detection',
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306: 'Horizon Detection',
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}
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binary_name = 'geekbench_aarch64'
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allowed_extensions = ['json', 'csv', 'xml', 'html', 'text']
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parameters = [
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Parameter('cpumask', kind=str, default='',
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description='CPU mask used by taskset.'),
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Parameter('section', kind=int, default=1, allowed_values=[1, 4, 9],
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description="""Run the specified sections. It should be 1 for CPU benchmarks,
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4 for OpenCL benchmarks and 9 for Vulkan benchmarks."""),
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Parameter('upload', kind=bool, default=False,
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description='Upload results to Geekbench Browser'),
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Parameter('is_single_core', kind=bool, default=True,
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description='Run workload in single-core or multi-core mode.'),
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Parameter('workload', kind=list_or_integer, default=301,
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description='Specify workload to run'),
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Parameter('iterations', kind=int, default=5,
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description='Number of iterations'),
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Parameter('workload_gap', kind=int, default=2000,
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description='N milliseconds gap between workloads'),
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Parameter('output_file', kind=str, default='gb_cli.json',
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description=f"""Specify the name of the output results file.
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If it is not specified, the output file will be generated as a JSON file.
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It can be {', '.join(allowed_extensions)} files."""),
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Parameter('timeout', kind=int, default=2000,
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description='The test timeout in ms. It should be long for 1000 iterations.'),
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Parameter('version', kind=str, default='6.3.0',
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description='Specifies which version of the Geekbench should run.'),
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]
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def __init__(self, target, **kwargs):
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super(GeekbenchCmdline, self).__init__(target, **kwargs)
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self.target_result_json = None
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self.host_result_json = None
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self.workloads = self.gb6_workloads
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self.params = ''
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self.output = ''
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self.target_exec_directory = ''
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self.tar_file_src = ''
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self.tar_file_dst = ''
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self.file_exists = False
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def init_resources(self, context):
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"""
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Retrieves necessary files to run the benchmark in TAR format.
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WA will look for `gb_cli_artifacts_<version>.tar` file to deploy them to the
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working directory. If there is no specified version, it will look for version
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6.3.0 by default.
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"""
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self.deployable_assets = [''.join(['gb_cli_artifacts', '_', self.version, '.tar'])]
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# Create an executables directory
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self.target_exec_directory = self.target.path.join(self.target.executables_directory, f'gb_cli-{self.version}')
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self.target.execute("mkdir -p {}".format(self.target_exec_directory))
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# Source and Destination paths for the artifacts tar file
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self.tar_file_src = self.target.path.join(self.target.working_directory, self.deployable_assets[0])
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self.tar_file_dst = self.target.path.join(self.target_exec_directory, self.deployable_assets[0])
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# Check the tar file if it already exists
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if self.target.file_exists(self.tar_file_dst):
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self.file_exists = True
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else:
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# Get the assets file
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super(GeekbenchCmdline, self).init_resources(context)
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@once
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def initialize(self, context):
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if self.version[0] == '5':
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self.workloads = self.gb5_workloads
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# If the tar file does not exist in the target, deploy the assets
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if not self.file_exists:
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super(GeekbenchCmdline, self).initialize(context)
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# Move the tar file to the executables directory
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self.target.execute(
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'{} mv {} {}'.format(
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self.target.busybox, self.tar_file_src, self.tar_file_dst))
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# Extract the tar file
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self.target.execute(
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'{} tar -xf {} -C {}'.format(
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self.target.busybox, self.tar_file_dst, self.target_exec_directory))
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def setup(self, context):
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super(GeekbenchCmdline, self).setup(context)
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self.params = ''
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self.params += '--section {} '.format(self.section)
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if self.section == 1:
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self.params += '--single-core ' if self.is_single_core else '--multi-core '
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self.params += '--upload ' if self.upload else '--no-upload '
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known_workloads = '\n'.join("{}: {}".format(k, v) for k, v in self.workloads.items())
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if any([t not in self.workloads.keys() for t in self.workload]):
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msg = 'Unknown workload(s) specified. Known workloads: {}'
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raise ValueError(msg.format(known_workloads))
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self.params += '--workload {} '.format(''.join("{},".format(i) for i in self.workload))
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if self.iterations:
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self.params += '--iterations {} '.format(self.iterations)
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if self.workload_gap:
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self.params += '--workload-gap {} '.format(self.workload_gap)
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extension = os.path.splitext(self.output_file)[1][1:]
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if self.output_file and extension not in self.allowed_extensions:
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msg = f"No allowed extension specified. Allowed extensions: {', '.join(self.allowed_extensions)}"
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raise ValueError(msg)
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elif self.output_file:
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# Output results file with the given name and extension
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self.target_result_json = os.path.join(self.target_exec_directory, self.output_file)
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self.params += '--export-{} {}'.format(extension, self.target_result_json)
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self.host_result_json = os.path.join(context.output_directory, self.output_file)
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else:
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# The output file is not specified
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self.target_result_json = os.path.join(self.target_exec_directory, self.output_file)
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self.params += '--save {}'.format(self.target_result_json)
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self.host_result_json = os.path.join(context.output_directory, self.output_file)
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def run(self, context):
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super(GeekbenchCmdline, self).run(context)
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taskset = f"taskset {self.cpumask}" if self.cpumask else ""
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binary = self.target.path.join(self.target_exec_directory, self.binary_name)
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cmd = '{} {} {}'.format(taskset, binary, self.params)
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try:
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self.output = self.target.execute(cmd, timeout=self.timeout, as_root=True)
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except KeyboardInterrupt:
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self.target.killall(self.binary_name)
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raise
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def update_output(self, context):
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super(GeekbenchCmdline, self).update_output(context)
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if not self.output:
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return
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for workload in self.workload:
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scores = []
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matches = re.findall(self.workloads[workload] + '(.+\d)', self.output)
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for match in matches:
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scores.append(int(re.search(r'\d+', match).group(0)))
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if self.section == 4:
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context.add_metric("OpenCL Score " + self.workloads[workload], scores[0])
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elif self.section == 9:
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context.add_metric("Vulkan Score " + self.workloads[workload], scores[0])
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else:
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context.add_metric("Single-Core Score " + self.workloads[workload], scores[0])
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if not self.is_single_core:
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context.add_metric("Multi-Core Score " + self.workloads[workload], scores[1])
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def extract_results(self, context):
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# Extract results on the target
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super(GeekbenchCmdline, self).extract_results(context)
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self.target.pull(self.target_result_json, self.host_result_json)
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context.add_artifact('GeekbenchCmdline_results', self.host_result_json, kind='raw')
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@once
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def finalize(self, context):
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if self.cleanup_assets:
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self.target.remove(self.target_exec_directory)
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