import random
from itertools import izip_longest, groupby, chain
from wa.framework.configuration.core import (MetaConfiguration, RunConfiguration,
JobGenerator, Status, settings)
from wa.framework.configuration.parsers import ConfigParser
from wa.framework.configuration.plugin_cache import PluginCache
from wa.framework.exception import NotFoundError
from wa.framework.job import Job
from wa.utils import log
class CombinedConfig(object):
@staticmethod
def from_pod(pod):
instance = CombinedConfig()
instance.settings = MetaConfiguration.from_pod(pod.get('settings', {}))
instance.run_config = RunConfiguration.from_pod(pod.get('run_config', {}))
return instance
def __init__(self, settings=None, run_config=None):
self.settings = settings
self.run_config = run_config
def to_pod(self):
return {'settings': self.settings.to_pod(),
'run_config': self.run_config.to_pod()}
class ConfigManager(object):
"""
Represents run-time state of WA. Mostly used as a container for loaded
configuration and discovered plugins.
This exists outside of any command or run and is associated with the running
instance of wA itself.
"""
@property
def enabled_instruments(self):
return self.jobs_config.enabled_instruments
@property
def enabled_processors(self):
return self.jobs_config.enabled_processors
@property
def job_specs(self):
if not self._jobs_generated:
msg = 'Attempting to access job specs before '\
'jobs have been generated'
raise RuntimeError(msg)
return [j.spec for j in self._jobs]
@property
def jobs(self):
if not self._jobs_generated:
msg = 'Attempting to access jobs before '\
'they have been generated'
raise RuntimeError(msg)
return self._jobs
def __init__(self, settings=settings):
self.settings = settings
self.run_config = RunConfiguration()
self.plugin_cache = PluginCache()
self.jobs_config = JobGenerator(self.plugin_cache)
self.loaded_config_sources = []
self._config_parser = ConfigParser()
self._jobs = []
self._jobs_generated = False
self.agenda = None
def load_config_file(self, filepath):
self._config_parser.load_from_path(self, filepath)
self.loaded_config_sources.append(filepath)
def load_config(self, values, source, wrap_exceptions=True):
self._config_parser.load(self, values, source)
self.loaded_config_sources.append(source)
def get_plugin(self, name=None, kind=None, *args, **kwargs):
return self.plugin_cache.get_plugin(name, kind, *args, **kwargs)
def get_instruments(self, target):
instruments = []
for name in self.enabled_instruments:
try:
instruments.append(self.get_plugin(name, kind='instrument',
target=target))
except NotFoundError:
msg = 'Instrument "{}" not found'
raise NotFoundError(msg.format(name))
return instruments
def get_processors(self):
processors = []
for name in self.enabled_processors:
try:
proc = self.plugin_cache.get_plugin(name, kind='output_processor')
except NotFoundError:
msg = 'Output Processor "{}" not found'
raise NotFoundError(msg.format(name))
processors.append(proc)
return processors
def finalize(self):
if not self.agenda:
msg = 'Attempting to finalize config before agenda has been set'
raise RuntimeError(msg)
self.run_config.merge_device_config(self.plugin_cache)
return CombinedConfig(self.settings, self.run_config)
def generate_jobs(self, context):
job_specs = self.jobs_config.generate_job_specs(context.tm)
exec_order = self.run_config.execution_order
log.indent()
for spec, i in permute_iterations(job_specs, exec_order):
job = Job(spec, i, context)
job.load(context.tm.target)
self._jobs.append(job)
context.run_state.add_job(job)
log.dedent()
self._jobs_generated = True
def permute_by_job(specs):
"""
This is that "classic" implementation that executes all iterations of a
workload spec before proceeding onto the next spec.
"""
for spec in specs:
for i in range(1, spec.iterations + 1):
yield (spec, i)
def permute_by_iteration(specs):
"""
Runs the first iteration for all benchmarks first, before proceeding to the
next iteration, i.e. A1, B1, C1, A2, B2, C2... instead of A1, A1, B1, B2,
C1, C2...
If multiple sections where specified in the agenda, this will run all
sections for the first global spec first, followed by all sections for the
second spec, etc.
e.g. given sections X and Y, and global specs A and B, with 2 iterations,
this will run
X.A1, Y.A1, X.B1, Y.B1, X.A2, Y.A2, X.B2, Y.B2
"""
groups = [list(g) for k, g in groupby(specs, lambda s: s.workload_id)]
all_tuples = []
for spec in chain(*groups):
all_tuples.append([(spec, i + 1)
for i in xrange(spec.iterations)])
for t in chain(*map(list, izip_longest(*all_tuples))):
if t is not None:
yield t
def permute_by_section(specs):
"""
Runs the first iteration for all benchmarks first, before proceeding to the
next iteration, i.e. A1, B1, C1, A2, B2, C2... instead of A1, A1, B1, B2,
C1, C2...
If multiple sections where specified in the agenda, this will run all specs
for the first section followed by all specs for the seciod section, etc.
e.g. given sections X and Y, and global specs A and B, with 2 iterations,
this will run
X.A1, X.B1, Y.A1, Y.B1, X.A2, X.B2, Y.A2, Y.B2
"""
groups = [list(g) for k, g in groupby(specs, lambda s: s.section_id)]
all_tuples = []
for spec in chain(*groups):
all_tuples.append([(spec, i + 1)
for i in xrange(spec.iterations)])
for t in chain(*map(list, izip_longest(*all_tuples))):
if t is not None:
yield t
def permute_randomly(specs):
"""
This will generate a random permutation of specs/iteration tuples.
"""
result = []
for spec in specs:
for i in xrange(1, spec.iterations + 1):
result.append((spec, i))
random.shuffle(result)
for t in result:
yield t
permute_map = {
'by_iteration': permute_by_iteration,
'by_job': permute_by_job,
'by_section': permute_by_section,
'random': permute_randomly,
}
def permute_iterations(specs, exec_order):
if exec_order not in permute_map:
msg = 'Unknown execution order "{}"; must be in: {}'
raise ValueError(msg.format(exec_order, permute_map.keys()))
return permute_map[exec_order](specs)