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How to use prune method in molecule
Best Python code snippet using molecule_python
ranked_structures_pruner.py
Source:ranked_structures_pruner.py 
...36 # The "leader" is the first weights-tensor in the list37 return self.params_names[0]38 def is_supported(self, param_name):39 return param_name in self.params_names40 def fraction_to_prune(self, param_name):41 return self.desired_sparsity42 def set_param_mask(self, param, param_name, zeros_mask_dict, meta):43 if not self.is_supported(param_name):44 return45 fraction_to_prune = self.fraction_to_prune(param_name)46 try:47 model = meta['model']48 except TypeError:49 model = None50 return self.prune_to_target_sparsity(param, param_name, zeros_mask_dict, fraction_to_prune, model)51 def prune_to_target_sparsity(self, param, param_name, zeros_mask_dict, target_sparsity, model):52 if not self.is_supported(param_name):53 return54 binary_map = None55 if self.group_dependency == "Leader":56 if target_sparsity != self.last_target_sparsity:57 # Each time we change the target sparsity we need to compute and cache the leader's binary-map.58 # We don't have control over the order that this function is invoked, so the only indication that59 # we need to compute a new leader binary-map is the change of the target_sparsity....
prune_main.py
Source:prune_main.py
1import numpy as np2import argparse3import torch4from .prune_base import prune_parse_arguments as prune_base_parse_arguments5from .admm import ADMM, prune_parse_arguments as admm_prune_parse_arguments6from .retrain import Retrain, prune_parse_arguments as retrain_parse_arguments7from .admm import admm_adjust_learning_rate8from .multi_level_admm import MultiLevelADMM9from .multi_level_retrain import MultiLevelRetrain10from .utils_pr import prune_parse_arguments as utils_prune_parse_arguments11prune_algo = None12retrain = None13def main_prune_parse_arguments(parser):14 parser.add_argument('--sp-store-weights', type=str,15 help="store the final weights, "16 "maybe used by the next stage pruning")17 parser.add_argument("--sp-lars", action="store_true",18 help="enable LARS learning rate scheduler")19 parser.add_argument('--sp-lars-trust-coef', type=float, default=0.001,20 help="LARS trust coefficient")21def prune_parse_arguments(parser):22 main_prune_parse_arguments(parser)23 prune_base_parse_arguments(parser)24 admm_prune_parse_arguments(parser)25 utils_prune_parse_arguments(parser)26 retrain_parse_arguments(parser)27def prune_init(args, model, logger=None, fixed_model=None, pre_defined_mask=None):28 global prune_algo, retrain29 if args.sp_admm_multi:30 prune_algo = MultiLevelADMM(args, model, logger)31 return32 if args.sp_retrain:33 if args.sp_prune_before_retrain:34 # For prune before retrain, we need to also set sp-admm-sparsity-type in the command line35 # We need to set sp_admm_update_epoch for ADMM, so set it to 1.36 args.sp_admm_update_epoch = 137 prune_algo = ADMM(args, model, logger, False)38 prune_algo.prune_harden()39 prune_algo = None40 retrain = Retrain(args, model, logger, pre_defined_mask)41 retrain.fix_layer_weight_save()42 return43 if args.sp_retrain_multi:44 prune_algo = None45 retrain = MultiLevelRetrain(args, model, logger)46 if args.sp_admm:47 prune_algo = ADMM(args, model, logger)48 return49 if args.sp_subset_progressive:50 prune_algo = ADMM(args, model, logger)51 retrain = Retrain(args, model, logger)52 return53def prune_update(epoch=0, batch_idx=0):54 if prune_algo != None:55 return prune_algo.prune_update(epoch, batch_idx)56 elif retrain != None:57 return retrain.update_mask(epoch)58def prune_update_grad(opt):59 if retrain != None:60 return retrain.update_grad(opt)61def prune_fix_layer_restore():62 if prune_algo != None:63 return64 if retrain != None:65 return retrain.fix_layer_weight_restore()66def prune_generate_small_resnet_model(mode, ratio1, ratio2):67 pass68def prune_update_loss(loss):69 if prune_algo == None:70 return loss71 return prune_algo.prune_update_loss(loss)72def prune_update_combined_loss(loss):73 if prune_algo == None:74 return loss, loss, loss75 return prune_algo.prune_update_combined_loss(loss)76def prune_harden():77 if prune_algo == None:78 return None79 return prune_algo.prune_harden()80def prune_apply_masks():81 if prune_algo:82 prune_algo.apply_masks()83 if retrain:84 retrain.apply_masks()85 else:86 return87 assert(False)88def prune_apply_masks_on_grads():89 if prune_algo:90 prune_algo.apply_masks_on_grads()91 if retrain:92 retrain.apply_masks_on_grads()93 else:94 return95 assert(False)96def prune_retrain_show_masks(debug=False):97 if retrain == None:98 print("Retrain is None!")99 return100 retrain.show_masks(debug)101def prune_store_weights():102 model = None103 args = None104 logger = None105 if prune_algo :106 model = prune_algo.model107 args = prune_algo.args108 logger = prune_algo.logger109 elif retrain:110 model = retrain.model111 args = retrain.args112 logger = retrain.logger113 else:114 return115 filename = args.sp_store_weights116 if filename is None:117 return118 variables = {}119 if logger:120 p = logger.info121 else:122 p = print123 with torch.no_grad():124 p("Storing weights to {}".format(filename))125 torch.save(model.state_dict(), filename)126def prune_store_prune_params():127 if prune_algo == None:128 return129 return prune_algo.prune_store_params()130def prune_print_sparsity(model=None, logger=None, show_sparse_only=False, compressed_view=False):131 if model is None:132 if prune_algo:133 model = prune_algo.model134 elif retrain:135 model = retrain.model136 else:137 return138 if logger:139 p = logger.info140 elif prune_algo:141 p = prune_algo.logger.info142 elif retrain:143 p = retrain.logger.info144 else:145 p = print146 if show_sparse_only:147 print("The sparsity of all params (>0.01): num_nonzeros, total_num, sparsity")148 total_nz = 0149 total = 0150 for (name, W) in model.named_parameters():151 #print(name, W.shape)152 non_zeros = W.detach().cpu().numpy().astype(np.float32) != 0153 num_nonzeros = np.count_nonzero(non_zeros)154 total_num = non_zeros.size155 sparsity = 1 - (num_nonzeros * 1.0) / total_num156 if sparsity > 0.01:157 print("{}, {}, {}, {}, {}".format(name, non_zeros.shape, num_nonzeros, total_num, sparsity))158 total_nz += num_nonzeros159 total += total_num160 if total > 0:161 print("Overall sparsity for layers with sparsity >0.01: {}".format(1 - float(total_nz)/total))162 else:163 print("All layers are dense!")164 return165 if compressed_view is True:166 total_w_num = 0167 total_w_num_nz = 0168 for (name, W) in model.named_parameters():169 if "weight" in name:170 non_zeros = W.detach().cpu().numpy().astype(np.float32) != 0171 num_nonzeros = np.count_nonzero(non_zeros)172 total_w_num_nz += num_nonzeros173 total_num = non_zeros.size174 total_w_num += total_num175 sparsity = 1 - (total_w_num_nz * 1.0) / total_w_num176 print("The sparsity of all params with 'weights' in its name: num_nonzeros, total_num, sparsity")177 print("{}, {}, {}".format(total_w_num_nz, total_w_num, sparsity))178 return179 print("The sparsity of all parameters: name, num_nonzeros, total_num, shape, sparsity")180 for (name, W) in model.named_parameters():181 non_zeros = W.detach().cpu().numpy().astype(np.float32) != 0182 num_nonzeros = np.count_nonzero(non_zeros)183 total_num = non_zeros.size184 sparsity = 1 - (num_nonzeros * 1.0) / total_num185 print("{}: {}, {}, {}, [{}]".format(name, str(num_nonzeros), str(total_num), non_zeros.shape, str(sparsity)))186def prune_update_learning_rate(optimizer, epoch, args):187 if prune_algo == None:188 return None189 return admm_adjust_learning_rate(optimizer, epoch, args)190# do not use, will be deprecated191def prune_retrain_apply_masks():192 apply_masks()193def prune_generate_yaml(model, sparsity, yaml_filename=None):194 if yaml_filename is None:195 yaml_filename = 'sp_{}.yaml'.format(sparsity)196 with open(yaml_filename,'w') as f:197 f.write("prune_ratios: \n")198 num_w = 0199 for name, W in model.named_parameters():200 print(name, W.shape)201 num_w += W.detach().cpu().numpy().size202 if len(W.detach().cpu().numpy().shape) > 1:203 with open(yaml_filename,'a') as f:204 if 'module.' in name:205 f.write("{}: {}\n".format(name[7:], sparsity))206 else:207 f.write("{}: {}\n".format(name, sparsity))208 print("Yaml file {} generated".format(yaml_filename))209 print("Total number of parameters: {}".format(num_w))...
__init__.py
Source:__init__.py
1import os2import sys3# file_dir = os.path.dirname(__file__)4# sys.path.append(file_dir)5from .prune_main import prune_parse_arguments, \6 prune_init, \7 prune_update, \8 prune_update_grad, \9 prune_update_loss, \10 prune_update_combined_loss, \11 prune_harden, \12 prune_apply_masks, \13 prune_apply_masks_on_grads, \14 prune_store_prune_params,\15 prune_update_learning_rate, \16 prune_store_weights, \17 prune_generate_yaml, \18 prune_fix_layer_restore19 #prune_harden_first_before_retrain20# debug functions21from .prune_main import prune_print_sparsity, prune_retrain_show_masks22# will be deprecated functions23from .prune_main import prune_retrain_apply_masks...
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