def main(args): logging = config.get_logging(args.log_name) logging.info(args) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = torch.cuda.device_count() tokenizer = BertTokenizer.build_tokenizer(args) # train_data_iter = MSmarco_iterator(args, tokenizer, batch_size=args.train_batch_size, world_size=n_gpu, accumulation_steps=args.gradient_accumulation_steps, name="msmarco_train.pk") dev_data_iter = MSmarco_iterator(args, tokenizer, batch_size=args.valid_batch_size, world_size=n_gpu, name="msmarco_dev.pk") logging.info("| dev batch data size {}".format(len(dev_data_iter))) # num_train_steps = (96032//2//2)+(data_size-96032)//2 missing_keys = [] unexpected_keys = [] error_msgs = [] pre_dir = args.pre_dir config_file = os.path.join(pre_dir, CONFIG_NAME) bert_config = BertConfig.from_json_file(config_file) model = MSmarco(bert_config) logging.info("| load model from {}".format(args.path)) state_dict = torch.load(args.path, map_location=torch.device('cpu')) metadata = getattr(state_dict, '_metadata', None) # state_dict = state_dict.copy() # if metadata is not None: # state_dict._metadata = metadata def load(module, prefix=''): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) for name, child in module._modules.items(): if child is not None: load(child, prefix + name + '.') load(model, prefix='module.') if len(missing_keys) > 0: # logger.info("Weights of {} not initialized from pretrained model: {}".format( # model.__class__.__name__, missing_keys)) print("| Weights of {} not initialized from pretrained model: {}".format( model.__class__.__name__, missing_keys)) if len(unexpected_keys) > 0: # logger.info("Weights from pretrained model not used in {}: {}".format( # model.__class__.__name__, unexpected_keys)) print("Weights from pretrained model not used in {}: {}".format( model.__class__.__name__, unexpected_keys)) # model._load_from_state_dict(state_dict, prefix="module.") model.to(device) if n_gpu > 1: model = torch.nn.DataParallel(model) # save_checkpoint(args, model, epochs) validation(args, model, dev_data_iter, n_gpu, 0, 0, logging)
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--bert_model", default=None, type=str, required=True, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model checkpoints will be written.") ## Other parameters parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json") parser.add_argument("--predict_file", default=None, type=str, help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json") parser.add_argument("--max_seq_length", default=384, type=int, help="The maximum total input sequence length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded.") parser.add_argument("--doc_stride", default=128, type=int, help="When splitting up a long document into chunks, how much stride to take between chunks.") parser.add_argument("--max_query_length", default=64, type=int, help="The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") parser.add_argument("--do_train", default=False, action='store_true', help="Whether to run training.") parser.add_argument("--do_predict", default=False, action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--predict_batch_size", default=8, type=int, help="Total batch size for predictions.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument("--warmup_proportion", default=0.1, type=float, help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% " "of training.") parser.add_argument("--n_best_size", default=20, type=int, help="The total number of n-best predictions to generate in the nbest_predictions.json " "output file.") parser.add_argument("--max_answer_length", default=30, type=int, help="The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another.") parser.add_argument("--verbose_logging", default=False, action='store_true', help="If true, all of the warnings related to data processing will be printed. " "A number of warnings are expected for a normal SQuAD evaluation.") parser.add_argument("--no_cuda", default=False, action='store_true', help="Whether not to use CUDA when available") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--gradient_accumulation_steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--optimize_on_cpu', default=False, action='store_true', help="Whether to perform optimization and keep the optimizer averages on CPU") parser.add_argument('--fp16', default=False, action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument('--loss_scale', type=float, default=128, help='Loss scaling, positive power of 2 values can improve fp16 convergence.') parser.add_argument('--do_lower_case', default=False, action='store_true', help='whether case sensitive') parser.add_argument('--do-test', default=False, action='store_true', help='if test ,train and dev data will be small') parser.add_argument("--pre-dir", type=str, help="where the pretrained checkpoint") args = parser.parse_args() print(args) # local_rand 多节点训练 if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') if args.fp16: logger.info("16-bits training currently not supported in distributed training") args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496) # logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits trainiing: {}".format( # device, n_gpu, bool(args.local_rank != -1), args.fp16)) # gradient_accumulation_steps == freq_update if args.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format( args.gradient_accumulation_steps)) # 缩小了batch args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps) print("| gpu count : {}".format(n_gpu)) print("| train batch size in each gpu : {}".format(args.train_batch_size)) print("| biuid tokenizer and model in : {}".format(args.pre_dir)) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_predict: raise ValueError("At least one of `do_train` or `do_predict` must be True.") if args.do_train: if not args.train_file: raise ValueError( "If `do_train` is True, then `train_file` must be specified.") if args.do_predict: if not args.predict_file: raise ValueError( "If `do_predict` is True, then `predict_file` must be specified.") if os.path.exists(args.output_dir) and os.listdir(args.output_dir): raise ValueError("Output directory () already exists and is not empty.") os.makedirs(args.output_dir, exist_ok=True) tokenizer = BertTokenizer.build_tokenizer(args) train_examples = None # 一共需要更新多少次 num_train_steps = None if args.do_train: # 加载训练的数据 # 如果测试的话和可以截断这个 train_examples = read_squad_examples( input_file=args.train_file, is_training=True) if args.do_test: train_examples = train_examples[:1000] num_train_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) # Prepare model # model = BertForQuestionAnswering.from_pretrained(args.bert_model, # cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank)) model = BertForQuestionAnswering.build_model(args) if args.fp16: model.half() model.to(device) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer if args.fp16: param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \ for n, param in model.named_parameters()] elif args.optimize_on_cpu: param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \ for n, param in model.named_parameters()] else: # 获得所有的参数,包括名字 param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] # for n,v in param_optimizer: # print("| name is {}\n".format(n)) # # print(oo) # 吧模型的参数分为两个组 # 第一组是包括 no_decay = ['bias', 'gamma', 'beta'] 关键字的, ---》 'weight_decay_rate': 0.0 # 第二组是没有包括关键字 ---》 'weight_decay_rate': 0.01 optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} ] # 优化器 optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_steps) global_step = 0 if args.do_train: train_features = convert_examples_to_features( examples=train_examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, # token之后的最大长度。default -》 384 doc_stride=args.doc_stride, # 分块后后每个快的长度 128 max_query_length=args.max_query_length, # query token之后的最大长度 default -》 64 is_training=True) # logger.info("| orig train data = %d", len(train_examples)) # logger.info("| features train data = %d", len(train_features)) # logger.info("| Batch size = %d", args.train_batch_size) # logger.info("| Num steps = %d", num_train_steps) print("| train data count {}, batch size {}, num steps {}".format(len(train_features), args.train_batch_size, num_train_steps)) # 统一的长度,全部被pad到相同的长度 all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) # 一个pad的mask,有效的input为1,mask就为0 all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) # 表示句子的顺序,0,1 --- pad == 0 all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) # target all_start_positions = torch.tensor([f.start_position for f in train_features], dtype=torch.long) all_end_positions = torch.tensor([f.end_position for f in train_features], dtype=torch.long) # 第0维就是数据的index,传入的数据需要保持他们的第一维的size相同 train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions) if args.local_rank == -1: # go here # 返回一个随机的index,这个index是包括所有的数据 train_sampler = RandomSampler(train_data) else: ## edit 2 : 搞错了分支了 # 如果数据是拍过序的话,那么有一些gpu的数据的长度会很小,会导致效率和其他的问题???? # 由于这个数据集的操作的特殊性,每一条的数据都是相同的。所以没有影响。 # 返回一个iter,对每一个rank都返回一个片段。[len*rand,len+len*rand] # 返回的是一个index print("| in distributedSample") train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): if n_gpu == 1: batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self input_ids, input_mask, segment_ids, start_positions, end_positions = batch loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions) # print("| loss is {}".format(loss)) # print("| loss size is {}".format(loss.size())) # print(oo) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.fp16 and args.loss_scale != 1.0: # rescale loss for fp16 training # see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html loss = loss * args.loss_scale if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps loss.backward() if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16 or args.optimize_on_cpu: if args.fp16 and args.loss_scale != 1.0: # scale down gradients for fp16 training for param in model.parameters(): if param.grad is not None: param.grad.data = param.grad.data / args.loss_scale is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True) if is_nan: logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling") args.loss_scale = args.loss_scale / 2 model.zero_grad() continue optimizer.step() copy_optimizer_params_to_model(model.named_parameters(), param_optimizer) else: optimizer.step() # optimizer.step() model.zero_grad() global_step += 1 if args.do_predict: eval_examples = read_squad_examples( input_file=args.predict_file, is_training=False) if args.do_test: eval_examples = eval_examples[:1000] eval_features = convert_examples_to_features( examples=eval_examples, tokenizer=tokenizer, max_seq_length=args.max_seq_length, doc_stride=args.doc_stride, max_query_length=args.max_query_length, is_training=False) # logger.info("| Running predictions *****") # logger.info("| orig dev data = %d", len(eval_examples)) # logger.info("| split dev data = %d", len(eval_features)) # logger.info("| dev batch = %d", args.predict_batch_size) print("\n| dev data count {}, batch size {}".format(len(eval_features), args.predict_batch_size)) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index) if args.local_rank == -1: eval_sampler = SequentialSampler(eval_data) else: eval_sampler = DistributedSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size) model.eval() all_results = [] # logger.info("Start evaluating") for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask) for i, example_index in enumerate(example_indices): start_logits = batch_start_logits[i].detach().cpu().tolist() end_logits = batch_end_logits[i].detach().cpu().tolist() eval_feature = eval_features[example_index.item()] unique_id = int(eval_feature.unique_id) all_results.append(RawResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits)) output_prediction_file = os.path.join(args.output_dir, "predictions.json") output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json") write_predictions(eval_examples, eval_features, all_results, args.n_best_size, args.max_answer_length, args.do_lower_case, output_prediction_file, output_nbest_file, args.verbose_logging)
def main(): parser = argparse.ArgumentParser() parser.add_argument("--save-dir", default="checkpoints", type=str, help="path to save checkpoints") ## Other parameters parser.add_argument("--data", default="data", type=str, help="MSmarco train and dev data") parser.add_argument("--origin-data", default="data", type=str, help="MSmarco train and dev data, will be tokenizer") parser.add_argument("--path", default="data", type=str, help="path(s) to model file(s), colon separated") parser.add_argument("--save", default="checkpoints/MSmarco", type=str, help="path(s) to model file(s), colon separated") parser.add_argument("--pre-dir", type=str, help="where the pretrained checkpoint") parser.add_argument("--log-name", type=str, help="where logfile") parser.add_argument( "--max-passage-tokens", default=200, type=int, help= "The maximum total input passage length after WordPiece tokenization. Sequences " "longer than this will be truncated, and sequences shorter than this will be padded." ) parser.add_argument( "--max-query-tokens", default=50, type=int, help= "The maximum number of tokens for the question. Questions longer than this will " "be truncated to this length.") parser.add_argument( '--gradient-accumulation-steps', type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass." ) parser.add_argument("--train-batch-size", default=2, type=int, help="Total batch size for training.") parser.add_argument("--predict-batch-size", default=1, type=int, help="Total batch size for predictions.") parser.add_argument("--lr", default=6.25e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num-train-epochs", default=3, type=int, help="Total number of training epochs to perform.") parser.add_argument( "--warmup-proportion", default=0.1, type=float, help= "Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% " "of training.") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--do-lower-case', default=False, action='store_true', help='whether case sensitive') parser.add_argument('--threshold', type=int, default=0.36) parser.add_argument('--logfile', type=str, default="logfile.log") parser.add_argument('--validate-updates', type=int, default=30000, metavar='N', help='validate every N updates') parser.add_argument('--loss-interval', type=int, default=5000, metavar='N', help='validate every N updates') args = parser.parse_args() # global logger # logger = logging.getLogger(args.log_name) # logger.error("| f**k logger") # first make corpus # tokenizer = BertTokenizer.build_tokenizer(args) # make_msmarco(args, tokenizer) print(args) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = torch.cuda.device_count() print("| gpu count : {}".format(n_gpu)) print("| train batch size in each gpu : {}".format(args.train_batch_size)) print("| biuid tokenizer and model in : {}".format(args.pre_dir)) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) tokenizer = BertTokenizer.build_tokenizer(args) train_data_iter = MSmarco_iterator( args, tokenizer, batch_size=args.train_batch_size, world_size=n_gpu, accumulation_steps=args.gradient_accumulation_steps, name="msmarco_train.pk") dev_data_iter = MSmarco_iterator(args, tokenizer, batch_size=args.train_batch_size, world_size=n_gpu, name="msmarco_dev.pk") gradient_accumulation_steps = args.gradient_accumulation_steps data_size = len(train_data_iter) num_train_steps = args.num_train_epochs * data_size print("| load dataset {}".format(data_size)) model = ParallelMSmarco.build_model(args) cls_criterion = nn.KLDivLoss() model.to(device) if n_gpu > 1: # model = torch.nn.DataParallel(model) model = DataParallelModel(model) cls_criterion = DataParallelCriterion(cls_criterion) param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0 }] optimizer = BertAdam(optimizer_grouped_parameters, lr=args.lr, warmup=args.warmup_proportion, t_total=num_train_steps) global_update = 0 for epochs in range(args.num_train_epochs): total_loss = 0 for step, batch in enumerate( tqdm(train_data_iter, desc="Train Iteration")): for key in batch.keys(): batch[key].to(device) targets = batch["targets"] batch.pop("targets") model.train() loss_logits = model(**batch) # pdb.set_trace() loss = cls_criterion(loss_logits, targets) if n_gpu > 1: loss = loss.sum() if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps # loss.backward() if (step + 1) % args.gradient_accumulation_steps == 0: optimizer.step() model.zero_grad() global_update += 1 # print("| loss {}".format(loss.size())) # optimizer.step() # model.zero_grad() # global_update += 1 if global_update > 0 and global_update % args.validate_updates == 0: validation(args, model, cls_criterion, dev_data_iter, n_gpu, epochs, global_update) if global_update > 0 and global_update % args.loss_interval == 0: logging.info( "TRAIN ::Epoch {} updates {}, train loss {}".format( epochs, global_update, loss.item())) save_checkpoint(args, model, epochs) validation(args, model, cls_criterion, dev_data_iter, n_gpu, epochs, global_update)
def main(args): logging = config.get_logging(args.log_name) logging.info("##"*20) logging.info("##"*20) logging.info("##"*20) logging.info(args) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = torch.cuda.device_count() logging.info("| question first :: {}".format(args.question_first)) logging.info("| gpu count : {}".format(n_gpu)) logging.info("| train batch size in each gpu : {}".format(args.train_batch_size)) logging.info("| biuid tokenizer and model in : {}".format(args.pre_dir)) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) tokenizer = BertTokenizer.build_tokenizer(args) train_data_iter = MSmarco_iterator(args, tokenizer, batch_size=args.train_batch_size, world_size=n_gpu, accumulation_steps=args.gradient_accumulation_steps, name="msmarco_train.pk") dev_data_iter = MSmarco_iterator(args, tokenizer, batch_size=args.valid_batch_size, world_size=n_gpu, name="msmarco_dev.pk") data_size = len(train_data_iter) gradient_accumulation_steps = args.gradient_accumulation_steps num_train_steps = args.num_train_epochs*data_size//gradient_accumulation_steps # logging.info("| load dataset {}".format(data_size)) logging.info("| train data size {}".format(len(train_data_iter)*n_gpu*args.train_batch_size)) logging.info("| dev data size {}".format(len(dev_data_iter)*n_gpu*args.valid_batch_size)) logging.info("| train batch data size {}".format(len(train_data_iter))) logging.info("| dev batch data size {}".format(len(dev_data_iter))) logging.info("| update in each train data {}".format(data_size//gradient_accumulation_steps)) logging.info("| total update {}".format(num_train_steps)) # num_train_steps = (96032//2//2)+(data_size-96032)//2 model = MSmarco.build_model(args) model.to(device) if n_gpu > 1: model = torch.nn.DataParallel(model) param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'gamma', 'beta', 'layer_norm'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0} ] optimizer = BertAdam(optimizer_grouped_parameters, lr=args.lr, warmup=args.warmup_proportion, t_total=num_train_steps) logging.info("| init lr is {}".format(optimizer.get_lr())) global_update = 0 for epochs in range(args.num_train_epochs): total_loss = 0 merge_batch = [] # count = 0 for step, batch in enumerate(tqdm(train_data_iter, desc="Train Iteration")): model.train() # if step < 96032: # merge_batch.append(batch) # if len(merge_batch) == 2: # batch = merger_tensor(merge_batch) # merge_batch = [] # else: # continue if n_gpu==1: for key in batch.keys(): batch[key]=batch[key].to(device) loss = model(**batch) # count += 1 # pdb.set_trace() if n_gpu > 1: loss = loss.mean() if args.gradient_accumulation_steps > 1: loss = loss/args.gradient_accumulation_steps loss.backward() if (step+1) % args.gradient_accumulation_steps == 0: optimizer.step() model.zero_grad() global_update += 1 if global_update % args.validate_updates==0: validation(args, model, dev_data_iter, n_gpu, epochs, global_update, logging) if (step+1) % args.loss_interval==0: logging.info("TRAIN ::Epoch {} updates {}, train loss {}".format(epochs, global_update, loss.item())) # save_checkpoint(args, model, epochs) validation(args, model, dev_data_iter, n_gpu, epochs, global_update, logging)