@icemelon9 This suggestion is more about infrastructure so that we're not
required to keep track of individual log files and how they were produced. We
need this to decide whether or not we can skip a task based on existing results.
@comaniac @kevinthesun I've updated the PR to include more concretely the ideas
being discussed. I think an auto-tuning 'job' is distinct from a task as I am
using it to refer to a series of tasks tuned sequentially (eg. tuning a network
would be a 'job'). A JSON file containing all of the jobs is produced which
contains information such as the start/finish time of the job, target/platform
parameters and importantly the optimal configs for each task in the job. In
principle this would allow you to 'revert' an auto-tuning job from the config
library if you discovered you'd done something invalid during a job (I've done
this a few times...) Keeping the entire history of a job can be controlled by a
flag.
I'm hacking one of the tutorial scripts to use the config library mechanism
instead, `tune_with_config_library.py`. For convenience, here's the current
tuning loop:
```
def tune_kernels(tasks,
config_library,
measure_option,
tuner='gridsearch',
early_stopping=None,
log_filename='tuning.log'):
with config_library.tune(target):
for i, tsk in enumerate(tasks):
prefix = "[Task %2d/%2d] " % (i+1, len(tasks))
# converting conv2d tasks to conv2d_NCHWc tasks
op_name = tsk.workload[0]
if op_name == 'conv2d':
func_create = 'topi_x86_conv2d_NCHWc'
elif op_name == 'depthwise_conv2d_nchw':
func_create = 'topi_x86_depthwise_conv2d_NCHWc_from_nchw'
else:
raise ValueError("Tuning {} is not supported on
x86".format(op_name))
task = autotvm.task.create(func_create, args=tsk.args,
target=target, template_key='direct')
task.workload = tsk.workload
# create tuner
if tuner == 'xgb' or tuner == 'xgb-rank':
tuner_obj = XGBTuner(task, loss_type='rank')
elif tuner == 'ga':
tuner_obj = GATuner(task, pop_size=50)
elif tuner == 'random':
tuner_obj = RandomTuner(task)
elif tuner == 'gridsearch':
tuner_obj = GridSearchTuner(task)
else:
raise ValueError("Invalid tuner: " + tuner)
# do tuning
n_trial=10
tuner_obj.tune(
n_trial=n_trial,
early_stopping=early_stopping,
measure_option=measure_option,
config_library=config_library,
callbacks=[autotvm.callback.progress_bar(n_trial,
prefix=prefix)],
)
```
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