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new a6cbe0d13e [python][docs] fix docstring / comment typos (#11608)
a6cbe0d13e is described below
commit a6cbe0d13eacbdcb6471caade4baa4b02926a490
Author: Christian Convey <[email protected]>
AuthorDate: Thu Jun 23 13:41:59 2022 -0400
[python][docs] fix docstring / comment typos (#11608)
---
python/tvm/auto_scheduler/cost_model/xgb_model.py | 10 +++++-----
python/tvm/auto_scheduler/task_scheduler.py | 12 ++++++------
2 files changed, 11 insertions(+), 11 deletions(-)
diff --git a/python/tvm/auto_scheduler/cost_model/xgb_model.py
b/python/tvm/auto_scheduler/cost_model/xgb_model.py
index 3cf65954be..a4e39b9061 100644
--- a/python/tvm/auto_scheduler/cost_model/xgb_model.py
+++ b/python/tvm/auto_scheduler/cost_model/xgb_model.py
@@ -98,8 +98,8 @@ class XGBModel(PythonBasedModel):
The random seed
model_file: Optional[str]
If is not None, save model to this file after every update.
- adapative_training: bool = False
- Whether to use adapatie training, which reduces the training frequency
when there are
+ adaptive_training: bool = False
+ Whether to use adaptive training, which reduces the training frequency
when there are
too many logs.
"""
@@ -109,7 +109,7 @@ class XGBModel(PythonBasedModel):
num_warmup_sample=100,
seed=None,
model_file=None,
- adapative_training=False,
+ adaptive_training=False,
):
global xgb
try:
@@ -141,7 +141,7 @@ class XGBModel(PythonBasedModel):
self.num_warmup_sample = num_warmup_sample
self.verbose_eval = verbose_eval
self.model_file = model_file
- self.adapative_training = adapative_training
+ self.adaptive_training = adaptive_training
super().__init__()
@@ -169,7 +169,7 @@ class XGBModel(PythonBasedModel):
self.results.extend(results)
if (
- self.adapative_training
+ self.adaptive_training
and len(self.inputs) - self.last_train_length <
self.last_train_length / 5
):
# Set a training threshold related to `last_train_length` to
reduce the training
diff --git a/python/tvm/auto_scheduler/task_scheduler.py
b/python/tvm/auto_scheduler/task_scheduler.py
index 762c507359..c23c9b3c0c 100644
--- a/python/tvm/auto_scheduler/task_scheduler.py
+++ b/python/tvm/auto_scheduler/task_scheduler.py
@@ -47,7 +47,7 @@ def make_search_policies(
verbose,
load_model_file=None,
load_log_file=None,
- adapative_training=False,
+ adaptive_training=False,
):
"""Make a list of search policies for a list of search tasks.
It creates one policy per task.
@@ -71,7 +71,7 @@ def make_search_policies(
load_log_file: Optional[str]
Load measurement records from this file. If it is not None, the status
of the
task scheduler, search policies and cost models will be restored
according to this file.
- adapative_training: bool = False
+ adaptive_training: bool = False
Option used by XGBModel to reduce the model training frequency when
there're too
many logs.
@@ -89,7 +89,7 @@ def make_search_policies(
cost_model = XGBModel(
num_warmup_sample=len(tasks) * num_measures_per_round,
model_file=load_model_file,
- adapative_training=adapative_training,
+ adaptive_training=adaptive_training,
)
if load_model_file and os.path.isfile(load_model_file):
logger.info("TaskScheduler: Load pretrained model...")
@@ -283,7 +283,7 @@ class TaskScheduler:
tune_option,
search_policy="default",
search_policy_params=None,
- adapative_training=False,
+ adaptive_training=False,
per_task_early_stopping=None,
):
"""Tune a batch of tasks together.
@@ -300,7 +300,7 @@ class TaskScheduler:
"sketch.random" for SketchPolicy + RandomModel.
search_policy_params : Optional[Dict[str, Any]]
The parameters of the search policy
- adapative_training : bool = False
+ adaptive_training : bool = False
Option used by XGBModel to reduce the model training frequency
when there're
too many logs.
per_task_early_stopping : Optional[int]
@@ -347,7 +347,7 @@ class TaskScheduler:
tune_option.verbose,
self.load_model_file,
self.load_log_file,
- adapative_training,
+ adaptive_training,
)
# do a round robin first to warm up
