@comaniac I've done some refactoring to disentangle 'TuningJob' from the
ConfigLibrary. The tuning loop now looks like this:
```
def tune_kernels(tasks,
n_trial,
config_library,
measure_option,
log_filename='tuning.log'):
# Create a tuning job and point it at a config library
job = TuningJob(
log_filename,
target,
config_library=config_library,
)
# Use the tuning job during the tuning loop
with job:
for i, tsk in enumerate(tasks):
prefix = "[Task %2d/%2d] " % (i+1, len(tasks))
# Convert conv2d tasks to conv2d_NCHWc tasks
task = autotvm.task.create("topi_x86_conv2d_NCHWc", args=tsk.args,
target=target, template_key='direct')
task.workload = tsk.workload
# Create tuner
tuner_obj = GridSearchTuner(task)
# Do tuning - the tuner will skip tasks which have already been
tuned
# in the config library
tuner_obj.tune(
n_trial=n_trial,
early_stopping=n_trial,
measure_option=measure_option,
callbacks=[autotvm.callback.progress_bar(n_trial,
prefix=prefix)],
)
```
Using 'with job' puts the job into the global tuning scope. The job will then
automatically register it's own callback to the tuners and a new tuner method
`load_library` is called if the TuningJob has a ConfigLibrary attached. This is
where the resume logic can be implemented. Currently I have only implemented
the basic logic to skip completed tasks, but it should be possible to implement
more advanced resume logic fairly easily as you have access to the full
ConfigLibrary.
If you don't specify a ConfigLibrary with a job, it will just log all the
results to the specified log file.
Config files are indexed within the library by target, so to use configs from
the library you can simply do `with config_library.load(target):`. This just
returns ApplyHistoryBest, it doesn't implement a new DispatchContext.
I've updated my PR (https://github.com/apache/incubator-tvm/pull/4151)
accordingly. Note that the PR does not include every feature discussed here but
is intended as initial infrastructure on top of which more advanced features
can be developed.
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