comaniac commented on a change in pull request #7376:
URL: https://github.com/apache/tvm/pull/7376#discussion_r571141012
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File path: python/tvm/auto_scheduler/dispatcher.py
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@@ -301,6 +306,85 @@ def update(self, target, workload_key, state):
entry[workload_args] = (state, 1)
+class ApplyHistoryBestOrSample(ApplyHistoryBest):
+ """
+ Apply the history best config, or sample a valid schedule if no config is
found.
+
+ Parameters
+ ----------
+ records : str or iterator of (auto_scheduler.measure.MeasureInput,\
+ auto_scheduler.measure.MeasureResult)
+ Collection of tuning records.
+ If is str, then it should be the filename of a records log file.
+ Each row of this file is an encoded record pair. Otherwise, it is an
iterator.
+ sample_simple_workloads: bool
+ When False, sampling will not apply to simple workloads (w/o
reduction).
+ cost_model_file: str
+ The filename of the pre-trained XGBoost cost model. If not present,
then random
+ model will be used.
+ """
+
+ def __init__(self, records, sample_simple_workloads=False,
cost_model_file=None):
+ self.sample_simple_workloads = sample_simple_workloads
+ self.log_dir = tempdir()
+ if cost_model_file is None:
+ self.cost_model = RandomModel()
+ else:
+ self.cost_model = XGBModel(num_warmup_sample=1,
model_file=cost_model_file)
Review comment:
Ah I see. It does confuse and we should improve it later.
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