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[autotvm] fix typos in comment #4591

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Merged
zhiics merged 1 commit into from
Dec 27, 2019
Merged

[autotvm] fix typos in comment #4591

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2 changes: 1 addition & 1 deletion python/tvm/autotvm/database.py
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Expand Up @@ -156,7 +156,7 @@ def filter(self, func):
Examples
--------
get records for a target
>>> db.filter(lambda inp, resulst: "cuda" in inp.target.keys)
>>> db.filter(lambda inp, results: "cuda" in inp.target.keys)
get records with errors
>>> db.filter(lambda inp, results: any(r.error_no != 0 for r in results))
"""
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2 changes: 1 addition & 1 deletion python/tvm/autotvm/tophub.py
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Expand Up @@ -223,7 +223,7 @@ def load_reference_log(backend, model, workload_name, template_key):
if model == inp.target.model:
find = True
break
# if device model is not find, use the device model with the most tuned worklaods
# if device model is not find, use the device model with the most tuned workloads
if not find and counts:
model = max(counts.items(), key=lambda k: k[1])[0]

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2 changes: 1 addition & 1 deletion python/tvm/autotvm/tuner/xgboost_cost_model.py
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Expand Up @@ -51,7 +51,7 @@ class XGBoostCostModel(CostModel):
'itervar' is more accurate but 'knob' is much faster.
There are some constraints on 'itervar', if you meet
problems with feature extraction when using 'itervar',
you can swith to 'knob'.
you can switch to 'knob'.

For cross-shape tuning (e.g. many convolutions with different shapes),
'itervar' and 'curve' has better transferability,
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2 changes: 1 addition & 1 deletion python/tvm/autotvm/tuner/xgboost_tuner.py
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Expand Up @@ -40,7 +40,7 @@ class XGBTuner(ModelBasedTuner):
'itervar' is more accurate but 'knob' is much faster.
There are some constraints on 'itervar', if you meet
problems with feature extraction when using 'itervar',
you can swith to 'knob'.
you can switch to 'knob'.

For cross-shape tuning (e.g. many convolutions with different shapes),
'itervar' and 'curve' has better transferability,
Expand Down