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The following commit(s) were added to refs/heads/main by this push: new a6cbe0d13e [python][docs] fix docstring / comment typos (#11608) a6cbe0d13e is described below commit a6cbe0d13eacbdcb6471caade4baa4b02926a490 Author: Christian Convey <ccon...@octoml.ai> 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