#Python auto tune software mod

PassContext ( opt_level = 3 ): lib = relay. apply_history_best ( log_file ): print ( 'Compile.' ) with tvm. get ( 'nn.conv2d' ),) ) # run tuning tasks print ( 'Tuning.' ) tune_tasks ( tasks, ** tuning_opt ) # compile kernels with history best records with autotvm. extract_from_program ( mod, target = target, params = params, ops = ( relay. remove ( tmp_log_file )ДЇinally, we launch tuning jobs and evaluate the end-to-end performance.Дef tune_and_evaluate ( tuning_opt ): # extract workloads from relay program print ( 'Extract tasks.' ) mod, params, input_shape, _ = get_network ( network, batch_size = 1 ) tasks = autotvm. pick_best ( tmp_log_file, log_filename ) os. tune ( n_trial = tsk_trial, early_stopping = early_stopping, measure_option = measure_option, callbacks =, ) # pick best records to a cache file autotvm. load_from_file ( tmp_log_file )) # process tuning tsk_trial = min ( n_trial, len ( tsk. remove ( tmp_log_file ) for i, tsk in enumerate ( reversed ( tasks )): prefix = ' ' % ( i + 1, len ( tasks )) # create tuner if tuner = 'xgb' or tuner = 'xgb-rank' : tuner_obj = XGBTuner ( tsk, loss_type = 'rank' ) elif tuner = 'xgb_knob' : tuner_obj = XGBTuner ( tsk, loss_type = 'rank', feature_type = 'knob' ) elif tuner = 'xgb_itervar' : tuner_obj = XGBTuner ( tsk, loss_type = 'rank', feature_type = 'itervar' ) elif tuner = 'xgb_curve' : tuner_obj = XGBTuner ( tsk, loss_type = 'rank', feature_type = 'curve' ) elif tuner = 'ga' : tuner_obj = GATuner ( tsk, pop_size = 50 ) elif tuner = 'random' : tuner_obj = RandomTuner ( tsk ) elif tuner = 'gridsearch' : tuner_obj = GridSearchTuner ( tsk ) else : raise ValueError ( 'Invalid tuner: ' + tuner ) if use_transfer_learning : if os. def tune_tasks ( tasks, measure_option, tuner = 'xgb', n_trial = 1000, early_stopping = None, log_filename = 'tuning.log', use_transfer_learning = True, ): # create tmp log file tmp_log_file = log_filename + '.tmp' if os. # You can skip the implementation of this function for this tutorial. from_expr ( net ) else : raise ValueError ( 'Unsupported network: ' + name ) return mod, params, input_shape, output_shape

#Python auto tune software mod

from_mxnet ( block, shape =, dtype = dtype ) net = mod net = relay. get_workload ( batch_size = batch_size, dtype = dtype ) elif name = 'mxnet' : # an example for mxnet model from _zoo.vision import get_model block = get_model ( 'resnet18_v1', pretrained = True ) mod, params = relay. get_workload ( batch_size = batch_size, version = '1.1', dtype = dtype ) elif name = 'inception_v3' : input_shape = ( batch_size, 3, 299, 299 ) mod, params = relay. get_workload ( batch_size = batch_size ) elif name = 'squeezenet_v1.1' : mod, params = relay. get_workload ( num_layers = n_layer, batch_size = batch_size, dtype = dtype ) elif name = 'mobilenet' : mod, params = relay. get_workload ( num_layers = n_layer, batch_size = batch_size, dtype = dtype ) elif 'vgg' in name : n_layer = int ( name. Use AutoScheduler for Template-Free SchedulingДef get_network ( name, batch_size ): '''Get the symbol definition and random weight of a network''' input_shape = ( batch_size, 3, 224, 224 ) output_shape = ( batch_size, 1000 ) if 'resnet' in name : n_layer = int ( name.Auto-tuning a Convolutional Network for Mobile GPU.Auto-tuning a Convolutional Network for ARM CPU.Auto-tuning a Convolutional Network for x86 CPU.Auto-tuning a Convolutional Network for NVIDIA GPU.Tuning High Performance Convolution on NVIDIA GPUs.Work With Tensor Expression and Schedules.