From 201d305f763013fc04dedfc37eb8dc44ce3b7569 Mon Sep 17 00:00:00 2001 From: Wang Yucheng Date: Sat, 28 Dec 2019 03:22:42 +0800 Subject: [PATCH] [autotvm] fix typos in comment (#4591) --- python/tvm/autotvm/database.py | 2 +- python/tvm/autotvm/tophub.py | 2 +- python/tvm/autotvm/tuner/xgboost_cost_model.py | 2 +- python/tvm/autotvm/tuner/xgboost_tuner.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/python/tvm/autotvm/database.py b/python/tvm/autotvm/database.py index f820c1234832..07f3766acb1d 100644 --- a/python/tvm/autotvm/database.py +++ b/python/tvm/autotvm/database.py @@ -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)) """ diff --git a/python/tvm/autotvm/tophub.py b/python/tvm/autotvm/tophub.py index 98369e27a4e8..ebfe6ee3a354 100644 --- a/python/tvm/autotvm/tophub.py +++ b/python/tvm/autotvm/tophub.py @@ -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] diff --git a/python/tvm/autotvm/tuner/xgboost_cost_model.py b/python/tvm/autotvm/tuner/xgboost_cost_model.py index 265365144639..34f4c03e224b 100644 --- a/python/tvm/autotvm/tuner/xgboost_cost_model.py +++ b/python/tvm/autotvm/tuner/xgboost_cost_model.py @@ -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, diff --git a/python/tvm/autotvm/tuner/xgboost_tuner.py b/python/tvm/autotvm/tuner/xgboost_tuner.py index a7498c3b6309..2ebea86d8e3e 100644 --- a/python/tvm/autotvm/tuner/xgboost_tuner.py +++ b/python/tvm/autotvm/tuner/xgboost_tuner.py @@ -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,